From fb329bf02569db4acb979eeb679988e48a7dbfbc Mon Sep 17 00:00:00 2001
From: Alistair Duffey <alistairduffey@gmail.com>
Date: Wed, 10 Apr 2024 11:58:39 +0100
Subject: [PATCH 01/65] Added sea_ice notebook

---
 projects/project-notebooks/SeaIce_2024.ipynb  | 3235 +++++++++++++++++
 .../Sea_ice_template_questions.svg            |    1 +
 2 files changed, 3236 insertions(+)
 create mode 100644 projects/project-notebooks/SeaIce_2024.ipynb
 create mode 100644 projects/template-images/Sea_ice_template_questions.svg

diff --git a/projects/project-notebooks/SeaIce_2024.ipynb b/projects/project-notebooks/SeaIce_2024.ipynb
new file mode 100644
index 000000000..9387adaaa
--- /dev/null
+++ b/projects/project-notebooks/SeaIce_2024.ipynb
@@ -0,0 +1,3235 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ada5523b",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/neuromatch/course-content-template/blob/main/projects/example_dataset/Example_based_on_CMA_structure.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/course-content-template/main/projects/example_dataset/Example_based_on_CMA_structure\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "045624d0",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "<h1>\n",
+    "Arctic Sea-ice Decline\n",
+    "</h1>\n",
+    "\n",
+    "\n",
+    "\n",
+    "__Content creators:__ Alistair Duffey, Michel Tsamados, Will Gregory\n",
+    "\n",
+    "__Content reviewers:__ Names & Surnames\n",
+    "\n",
+    "__Content editors:__ Konstantinos Tsafatinos\n",
+    "\n",
+    "__Production editors:__ Konstantinos Tsafatinos\n",
+    "\n",
+    "__Our 2024 Sponsors:__ Sponsors\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60881a20",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "The notebook should have the following outline:\n",
+    "\n",
+    "- Project background (video + template outline)\n",
+    "- Imports and functions common to multiple data sources\n",
+    "- Data source 1\n",
+    "- Data source background\n",
+    "- Imports and functions common to multiple datasets from the same data source\n",
+    "\n",
+    "- Dataset 1:\n",
+    "\n",
+    "    Dataset background (if needed)\n",
+    "    \n",
+    "    Imports and functions specific to the dataset\n",
+    "    \n",
+    "    Code to load the dataset\n",
+    "    \n",
+    "    Code to pre-process the dataset (if not taught and likely difficult for students)\n",
+    "    \n",
+    "    Code to visualise the data table shape/content\n",
+    "\n",
+    "...\n",
+    "- Dataset N:\n",
+    "    same as for Dataset 1\n",
+    "...\n",
+    "\n",
+    "- Further reading\n",
+    "\n",
+    "---\n",
+    "We encourage you to explain the key parts of your code through comments and text cells as you see fit.\n",
+    "\n",
+    "---\n",
+    "If the same or analogous dataset is covered in the curriculum tutorials, we can expect students to know how to load that data. In that case, please provide only the dataset background and refer students to the respective curriculum day while maintaining the structural hierarchy of the notebook.\n",
+    "\n",
+    "---\n",
+    "If you wish to include any plots in your template, those should follow the standard figure formatting. Please include the Figure settings cell from the tutorial notebook template. Let us know if you have any questions about this or are unsure how to proceed!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c08b96da",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "To include videos:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64da422f",
+   "metadata": {
+    "cellView": "form"
+   },
+   "outputs": [],
+   "source": [
+    "# @title Project Background\n",
+    "\n",
+    "from ipywidgets import widgets\n",
+    "from IPython.display import YouTubeVideo\n",
+    "from IPython.display import IFrame\n",
+    "from IPython.display import display\n",
+    "\n",
+    "\n",
+    "class PlayVideo(IFrame):\n",
+    "  def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
+    "    self.id = id\n",
+    "    if source == 'Bilibili':\n",
+    "      src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
+    "    elif source == 'Osf':\n",
+    "      src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
+    "    super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
+    "\n",
+    "\n",
+    "def display_videos(video_ids, W=400, H=300, fs=1):\n",
+    "  tab_contents = []\n",
+    "  for i, video_id in enumerate(video_ids):\n",
+    "    out = widgets.Output()\n",
+    "    with out:\n",
+    "      if video_ids[i][0] == 'Youtube':\n",
+    "        video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
+    "                             height=H, fs=fs, rel=0)\n",
+    "        print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
+    "      else:\n",
+    "        video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
+    "                          height=H, fs=fs, autoplay=False)\n",
+    "        if video_ids[i][0] == 'Bilibili':\n",
+    "          print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
+    "        elif video_ids[i][0] == 'Osf':\n",
+    "          print(f'Video available at https://osf.io/{video.id}')  \n",
+    "      display(video)\n",
+    "    tab_contents.append(out)\n",
+    "  return tab_contents\n",
+    "\n",
+    "\n",
+    "video_ids = [('Youtube', '<video_id_1>'), ('Bilibili', '<video_id_2>'), ('Osf', '<video_id_3>')]\n",
+    "tab_contents = display_videos(video_ids, W=854, H=480)\n",
+    "tabs = widgets.Tab()\n",
+    "tabs.children = tab_contents\n",
+    "for i in range(len(tab_contents)):\n",
+    "  tabs.set_title(i, video_ids[i][0])\n",
+    "display(tabs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9fb54e0",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "To include slides:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "b52fef8b",
+   "metadata": {
+    "cellView": "form"
+   },
+   "outputs": [],
+   "source": [
+    "# @title Tutorial slides\n",
+    "# @markdown These are the slides for the videos in all tutorials today\n",
+    "\n",
+    "from IPython.display import IFrame\n",
+    "#link_id = \"n7wdy\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63b697d5",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "In this project, you will be given the opportunity to explore data from climate models to examine the modelled change in sea ice coverage over time.\n",
+    "\n",
+    "The project aims to:\n",
+    "* Download, process and plot data showing modelled sea ice coverage over the historical period from a CMIP6 model.\n",
+    "* Calculate the total sea ice extent and assess it's rate of decline over the recent historical period, and projected into the future under a middle-of-the-road emissions scenario.\n",
+    "* Assess the dependence of future projections on emissions scenario, e.g. to assess whether any emissions scenario is sufficient to keep late-summer sea ice in the Arctic.\n",
+    "* Examine the spatial and seasonal variation of sea-ice and how this changes during its decline with warming.\n",
+    "\n",
+    "We also include a dataset of satellite observations, in case you would like to check the realism of the model's representaiton of sea ice in the recent historical period. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff7ae1c9",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "# Project Template\n",
+    "\n",
+    "<img src='../template-images/Sea_ice_template_questions.svg'/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51243f3",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "## Data Exploration Notebook\n",
+    "\n",
+    "## Project Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b68292ec",
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "# google colab installs\n",
+    "\n",
+    "# !mamaba install netCDF4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1e541a40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "\n",
+    "#Import only the libraries/objects that are necessary for more than one dataset.\n",
+    "#Dataset-specific imports should be in the respective notebook section.\n",
+    "\n",
+    "#If any external library has to be installed, !pip install library --quiet\n",
+    "#follow this order: numpy>matplotlib.\n",
+    "#import widgets in hidden Figure settings cell\n",
+    "\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import dask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "07545499",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# @title Figure settings\n",
+    "\n",
+    "#import ipywidgets as widgets       # interactive display\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "plt.style.use(\"https://raw.githubusercontent.com/ClimateMatchAcademy/course-content/main/cma.mplstyle\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee7c9ef5",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "## [datasource 1]: Climate model simulations of sea ice concentration\n",
+    "\n",
+    "Here we use output from CESM2-WACCM, the Community Earth System Model (version 2, CESM2), with the The Whole Atmosphere Community Climate Model (WACCM) as it's atmosphere component. \n",
+    "\n",
+    "We use the historical scenario, which runs with historical forcings 1850-2014. Note that this scenario is just one instance of internal variability under a world forced by historical GHGs. \n",
+    "\n",
+    "A note on the sea ice output for CMIP6 models: \n",
+    "\n",
+    "Sea ice component of models is generally output on the ocean grid, which is not normally a simple lat/lon grid, unlike many atmosphere model components. Here we use the variable 'siconca' which stands for 'sea ice concentration atmosphere' - this is the sea ice concentration regridded onto the model's atmosphere grid, and is somewhat easier to work with.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "80b20bd4-a095-4bef-9284-e896245e8df8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/users/a_duffey/.conda/envs/main/lib/python3.10/site-packages/xarray/conventions.py:516: SerializationWarning: variable 'siconca' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    },
+    {
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+       "}\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-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",
+       "  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",
+       "  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-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-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",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:    (time: 1980, lat: 192, lon: 288, nbnd: 2)\n",
+       "Coordinates:\n",
+       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
+       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
+       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
+       "Dimensions without coordinates: nbnd\n",
+       "Data variables:\n",
+       "    siconca    (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
+       "    time_bnds  (time, nbnd) object dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
+       "    lat_bnds   (lat, nbnd) float64 dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
+       "    lon_bnds   (lon, nbnd) float64 dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
+       "Attributes: (12/45)\n",
+       "    Conventions:            CF-1.7 CMIP-6.2\n",
+       "    activity_id:            CMIP\n",
+       "    branch_method:          standard\n",
+       "    branch_time_in_child:   674885.0\n",
+       "    branch_time_in_parent:  20075.0\n",
+       "    case_id:                4\n",
+       "    ...                     ...\n",
+       "    sub_experiment_id:      none\n",
+       "    table_id:               SImon\n",
+       "    tracking_id:            hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89\n",
+       "    variable_id:            siconca\n",
+       "    variant_info:           CMIP6 CESM2 hindcast (1850-2014) with high-top at...\n",
+       "    variant_label:          r1i1p1f1</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-370a4cef-676a-44f2-9336-76047eda225d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-370a4cef-676a-44f2-9336-76047eda225d' 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>: 1980</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1a4ac358-1748-4bb4-8860-d7be0ba94071' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a4ac358-1748-4bb4-8860-d7be0ba94071' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-d9d6f81c-1b57-4f9c-a0b7-97332dd35263' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d6f81c-1b57-4f9c-a0b7-97332dd35263' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c1e9b7d-ad58-42b0-94e1-4aa9a66d07c0' class='xr-var-data-in' type='checkbox'><label for='data-3c1e9b7d-ad58-42b0-94e1-4aa9a66d07c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
+       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
+       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
+       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
+       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
+       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
+       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
+       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
+       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
+       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
+       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
+       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
+       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
+       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
+       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
+       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
+       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
+       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
+       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
+       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
+       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
+       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
+       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
+       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
+       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
+       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
+       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
+       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
+       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
+       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
+       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
+       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</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'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-2104bea4-3266-4e82-ae39-6df0f15f9e07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2104bea4-3266-4e82-ae39-6df0f15f9e07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07efbccc-b568-4d6c-95d1-f516945b34fe' class='xr-var-data-in' type='checkbox'><label for='data-07efbccc-b568-4d6c-95d1-f516945b34fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</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'>1850-01-15 12:00:00 ... 2014-12-...</div><input id='attrs-dfc1d58c-ef17-472a-bee4-493b2862820a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfc1d58c-ef17-472a-bee4-493b2862820a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3caf291a-b5fc-4c93-904a-4c926a653d77' class='xr-var-data-in' type='checkbox'><label for='data-3caf291a-b5fc-4c93-904a-4c926a653d77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       ...,\n",
+       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2014, 12, 15, 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-af98ba17-1ba3-4af1-9b4d-6c74385f0357' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af98ba17-1ba3-4af1-9b4d-6c74385f0357' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>siconca</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=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-fa31497f-affc-4b24-8780-7182c8c2e393' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa31497f-affc-4b24-8780-7182c8c2e393' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-392008d9-7eff-4370-a4dc-751b0f2fb1c4' class='xr-var-data-in' type='checkbox'><label for='data-392008d9-7eff-4370-a4dc-751b0f2fb1c4' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>ICEFRAC</dd><dt><span>description :</span></dt><dd>Percentage of grid cell covered by sea ice</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>siconca</dd><dt><span>long_name :</span></dt><dd>Sea-Ice Area Percentage (Atmospheric Grid)</dd><dt><span>mipTable :</span></dt><dd>SImon</dd><dt><span>out_name :</span></dt><dd>siconca</dd><dt><span>prov :</span></dt><dd>SIMIP [seaicemon]</dd><dt><span>realm :</span></dt><dd>seaIce</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Sea-Ice Area Percentage (Atmospheric Grid)</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>variable_id :</span></dt><dd>siconca</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 417.66 MiB </td>\n",
+       "                        <td> 3.81 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1980, 192, 288) </td>\n",
+       "                        <td> (100, 100, 100) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 121 Tasks </td>\n",
+       "                        <td> 120 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"171\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"20\" x2=\"80\" y2=\"90\" />\n",
+       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"41\" />\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>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-71a9d479-3aa2-42a1-8d34-99aee3283334' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71a9d479-3aa2-42a1-8d34-99aee3283334' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36b15c4a-92a0-4fae-ac25-c9623da50949' class='xr-var-data-in' type='checkbox'><label for='data-36b15c4a-92a0-4fae-ac25-c9623da50949' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 30.94 kiB </td>\n",
+       "                        <td> 1.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1980, 2) </td>\n",
+       "                        <td> (100, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 21 Tasks </td>\n",
+       "                        <td> 20 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> object </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-90ef9d13-60dd-4b0f-932f-547389144329' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90ef9d13-60dd-4b0f-932f-547389144329' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a46c377-2b60-45f3-a03d-96bcc4d18ea4' class='xr-var-data-in' type='checkbox'><label for='data-4a46c377-2b60-45f3-a03d-96bcc4d18ea4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 3.00 kiB </td>\n",
+       "                        <td> 1.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (192, 2) </td>\n",
+       "                        <td> (100, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 3 Tasks </td>\n",
+       "                        <td> 2 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float64 </td>\n",
+       "                    <td> 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>lon_bnds</span></div><div class='xr-var-dims'>(lon, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-3f9f38d6-c160-4ad4-8971-dac8e1dec4c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f9f38d6-c160-4ad4-8971-dac8e1dec4c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-527c62b4-d6b2-4878-9d48-9a2767aa47f9' class='xr-var-data-in' type='checkbox'><label for='data-527c62b4-d6b2-4878-9d48-9a2767aa47f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 1.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (288, 2) </td>\n",
+       "                        <td> (100, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 4 Tasks </td>\n",
+       "                        <td> 3 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float64 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-35d9584f-1d6b-48a3-9d71-2beebb4f8a93' class='xr-section-summary-in' type='checkbox'  ><label for='section-35d9584f-1d6b-48a3-9d71-2beebb4f8a93' class='xr-section-summary' >Attributes: <span>(45)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>20075.0</dd><dt><span>case_id :</span></dt><dd>4</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BWHIST.f09_g17.CMIP6-historical-WACCM.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-05-02T15:33:18Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2-WACCM.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>native gx1v7 displaced pole grid (384x320 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>seaIce</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); atmosChem: WACCM (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb; landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC CHEM AER</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>SImon</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89</dd><dt><span>variable_id :</span></dt><dd>siconca</dd><dt><span>variant_info :</span></dt><dd>CMIP6 CESM2 hindcast (1850-2014) with high-top atmosphere (WACCM6) with interactive chemistry (TSMLT1), interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:    (time: 1980, lat: 192, lon: 288, nbnd: 2)\n",
+       "Coordinates:\n",
+       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
+       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
+       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
+       "Dimensions without coordinates: nbnd\n",
+       "Data variables:\n",
+       "    siconca    (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
+       "    time_bnds  (time, nbnd) object dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
+       "    lat_bnds   (lat, nbnd) float64 dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
+       "    lon_bnds   (lon, nbnd) float64 dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
+       "Attributes: (12/45)\n",
+       "    Conventions:            CF-1.7 CMIP-6.2\n",
+       "    activity_id:            CMIP\n",
+       "    branch_method:          standard\n",
+       "    branch_time_in_child:   674885.0\n",
+       "    branch_time_in_parent:  20075.0\n",
+       "    case_id:                4\n",
+       "    ...                     ...\n",
+       "    sub_experiment_id:      none\n",
+       "    table_id:               SImon\n",
+       "    tracking_id:            hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89\n",
+       "    variable_id:            siconca\n",
+       "    variant_info:           CMIP6 CESM2 hindcast (1850-2014) with high-top at...\n",
+       "    variant_label:          r1i1p1f1"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read in the historical scenario run data \n",
+    "# this is for one ensemble member - r1i1p1f1 \n",
+    "# and one scenario - 'historical' \n",
+    "directory_mod = '/badc/cmip6/data/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/SImon/siconca/gn/latest/'\n",
+    "files_mod = ['siconca_SImon_CESM2-WACCM_historical_r1i1p1f1_gn_185001-201412.nc']\n",
+    "\n",
+    "SI_ds = xr.open_mfdataset([directory_mod + file for file in files_mod],\n",
+    "                                chunks=100)          "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ce3a66c5-421c-4fb9-9253-ef156113ddf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/users/a_duffey/.conda/envs/main/lib/python3.10/site-packages/xarray/conventions.py:516: SerializationWarning: variable 'areacella' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
+      "  new_vars[k] = decode_cf_variable(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# also get the models grid areas as an input file:\n",
+    "areacella_file = '/badc/cmip6/data/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/fx/areacella/gn/latest/areacella_fx_CESM2-WACCM_historical_r1i1p1f1_gn.nc'\n",
+    "grid_cell_areas = xr.open_dataset(areacella_file)\n",
+    "grid_cell_areas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f3eed487-0116-4d52-b533-2c99cbb1f7cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# files for the SSP2-4.5 scenario, for the orange extension questions:\n",
+    "directory_cmip_data_ssp245 = '/badc/cmip6/data/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r1i1p1f1/SImon/siconca/gn/latest/'\n",
+    "\n",
+    "files_cmip_data_ssp245 = ['siconca_SImon_CESM2-WACCM_ssp245_r1i1p1f1_gn_201501-206412.nc',\n",
+    "                   'siconca_SImon_CESM2-WACCM_ssp245_r1i1p1f1_gn_206501-210012.nc']      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "e0b537fa-4a11-4c89-9c98-d73327cc229f",
+   "metadata": {},
+   "outputs": [
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+       "  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",
+       "  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-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-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",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:    (time: 1980, lat: 192, lon: 288, nbnd: 2)\n",
+       "Coordinates:\n",
+       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
+       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
+       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
+       "Dimensions without coordinates: nbnd\n",
+       "Data variables:\n",
+       "    siconca    (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
+       "    time_bnds  (time, nbnd) object dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
+       "    lat_bnds   (lat, nbnd) float64 dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
+       "    lon_bnds   (lon, nbnd) float64 dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
+       "Attributes: (12/45)\n",
+       "    Conventions:            CF-1.7 CMIP-6.2\n",
+       "    activity_id:            CMIP\n",
+       "    branch_method:          standard\n",
+       "    branch_time_in_child:   674885.0\n",
+       "    branch_time_in_parent:  20075.0\n",
+       "    case_id:                4\n",
+       "    ...                     ...\n",
+       "    sub_experiment_id:      none\n",
+       "    table_id:               SImon\n",
+       "    tracking_id:            hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89\n",
+       "    variable_id:            siconca\n",
+       "    variant_info:           CMIP6 CESM2 hindcast (1850-2014) with high-top at...\n",
+       "    variant_label:          r1i1p1f1</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-4c242a38-6df1-4862-b5bb-8d05b11d3f12' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4c242a38-6df1-4862-b5bb-8d05b11d3f12' 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>: 1980</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2f9c43f3-813b-47b5-a740-844d91e2ec62' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2f9c43f3-813b-47b5-a740-844d91e2ec62' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-e323c14f-8d48-43ed-8da5-21b94312232d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e323c14f-8d48-43ed-8da5-21b94312232d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a06b0f7-c19d-4f13-b5d2-2e4b5b68da05' class='xr-var-data-in' type='checkbox'><label for='data-5a06b0f7-c19d-4f13-b5d2-2e4b5b68da05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
+       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
+       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
+       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
+       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
+       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
+       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
+       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
+       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
+       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
+       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
+       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
+       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
+       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
+       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
+       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
+       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
+       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
+       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
+       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
+       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
+       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
+       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
+       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
+       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
+       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
+       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
+       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
+       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
+       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
+       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
+       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</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'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-68c02fa0-6df4-411c-a3f6-78b03c293d1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68c02fa0-6df4-411c-a3f6-78b03c293d1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c05b7a4-e489-47a6-a1a4-fff7df6e59e5' class='xr-var-data-in' type='checkbox'><label for='data-1c05b7a4-e489-47a6-a1a4-fff7df6e59e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</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'>1850-01-15 12:00:00 ... 2014-12-...</div><input id='attrs-1c7f3f19-2bf9-4369-9fd9-67b82065c523' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c7f3f19-2bf9-4369-9fd9-67b82065c523' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e87dfae9-d4ff-43da-a277-cb721261400f' class='xr-var-data-in' type='checkbox'><label for='data-e87dfae9-d4ff-43da-a277-cb721261400f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       ...,\n",
+       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2014, 12, 15, 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-d387521d-105b-4dd6-919b-97bf3f14d9b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d387521d-105b-4dd6-919b-97bf3f14d9b8' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>siconca</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=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-04d1c1fa-af30-46e4-b915-85df51558d58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04d1c1fa-af30-46e4-b915-85df51558d58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-721a8df2-b23a-454c-b0e0-1494e727d748' class='xr-var-data-in' type='checkbox'><label for='data-721a8df2-b23a-454c-b0e0-1494e727d748' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>ICEFRAC</dd><dt><span>description :</span></dt><dd>Percentage of grid cell covered by sea ice</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>siconca</dd><dt><span>long_name :</span></dt><dd>Sea-Ice Area Percentage (Atmospheric Grid)</dd><dt><span>mipTable :</span></dt><dd>SImon</dd><dt><span>out_name :</span></dt><dd>siconca</dd><dt><span>prov :</span></dt><dd>SIMIP [seaicemon]</dd><dt><span>realm :</span></dt><dd>seaIce</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Sea-Ice Area Percentage (Atmospheric Grid)</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>variable_id :</span></dt><dd>siconca</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 417.66 MiB </td>\n",
+       "                        <td> 3.81 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1980, 192, 288) </td>\n",
+       "                        <td> (100, 100, 100) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 121 Tasks </td>\n",
+       "                        <td> 120 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"171\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"50\" y1=\"0\" x2=\"121\" y2=\"70\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Text -->\n",
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+       "  <text x=\"35.294118\" y=\"93.719750\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,93.719750)\">1980</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-1e5229a1-a22b-4a80-a537-d3504356bbc6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e5229a1-a22b-4a80-a537-d3504356bbc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65b14fce-e00e-4a34-8e3b-3869812dc2b2' class='xr-var-data-in' type='checkbox'><label for='data-65b14fce-e00e-4a34-8e3b-3869812dc2b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 30.94 kiB </td>\n",
+       "                        <td> 1.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1980, 2) </td>\n",
+       "                        <td> (100, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 21 Tasks </td>\n",
+       "                        <td> 20 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> object </td>\n",
+       "                    <td> 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",
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+       "  <line x1=\"0\" y1=\"12\" x2=\"25\" y2=\"12\" />\n",
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+       "  <line x1=\"0\" y1=\"66\" x2=\"25\" y2=\"66\" />\n",
+       "  <line x1=\"0\" y1=\"72\" x2=\"25\" y2=\"72\" />\n",
+       "  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n",
+       "  <line x1=\"0\" y1=\"84\" x2=\"25\" y2=\"84\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"25\" y2=\"90\" />\n",
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+       "  <line x1=\"0\" y1=\"103\" x2=\"25\" y2=\"103\" />\n",
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+       "  <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",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">1980</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-9ed0c0a0-dde1-4865-8850-b1dd053a0378' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ed0c0a0-dde1-4865-8850-b1dd053a0378' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33add893-6a05-4811-af51-1a3a0aeb2a92' class='xr-var-data-in' type='checkbox'><label for='data-33add893-6a05-4811-af51-1a3a0aeb2a92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 3.00 kiB </td>\n",
+       "                        <td> 1.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (192, 2) </td>\n",
+       "                        <td> (100, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 3 Tasks </td>\n",
+       "                        <td> 2 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float64 </td>\n",
+       "                    <td> 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",
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+       "  <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",
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+       "  <!-- Text -->\n",
+       "  <text x=\"12.804567\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"45.609134\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.609134,60.000000)\">192</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-20cca63f-4987-4e1a-aa75-c35b92dc894f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20cca63f-4987-4e1a-aa75-c35b92dc894f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a13e8f02-3ee9-4798-92d8-e9fcc0714ed9' class='xr-var-data-in' type='checkbox'><label for='data-a13e8f02-3ee9-4798-92d8-e9fcc0714ed9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 1.56 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (288, 2) </td>\n",
+       "                        <td> (100, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 4 Tasks </td>\n",
+       "                        <td> 3 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float64 </td>\n",
+       "                    <td> 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",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8cf74968-975c-48c5-924d-12b73c2ccfe9' class='xr-section-summary-in' type='checkbox'  ><label for='section-8cf74968-975c-48c5-924d-12b73c2ccfe9' class='xr-section-summary' >Attributes: <span>(45)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>20075.0</dd><dt><span>case_id :</span></dt><dd>4</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BWHIST.f09_g17.CMIP6-historical-WACCM.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-05-02T15:33:18Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2-WACCM.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>native gx1v7 displaced pole grid (384x320 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>seaIce</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); atmosChem: WACCM (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb; landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC CHEM AER</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>SImon</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89</dd><dt><span>variable_id :</span></dt><dd>siconca</dd><dt><span>variant_info :</span></dt><dd>CMIP6 CESM2 hindcast (1850-2014) with high-top atmosphere (WACCM6) with interactive chemistry (TSMLT1), interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:    (time: 1980, lat: 192, lon: 288, nbnd: 2)\n",
+       "Coordinates:\n",
+       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
+       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
+       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
+       "Dimensions without coordinates: nbnd\n",
+       "Data variables:\n",
+       "    siconca    (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
+       "    time_bnds  (time, nbnd) object dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
+       "    lat_bnds   (lat, nbnd) float64 dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
+       "    lon_bnds   (lon, nbnd) float64 dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
+       "Attributes: (12/45)\n",
+       "    Conventions:            CF-1.7 CMIP-6.2\n",
+       "    activity_id:            CMIP\n",
+       "    branch_method:          standard\n",
+       "    branch_time_in_child:   674885.0\n",
+       "    branch_time_in_parent:  20075.0\n",
+       "    case_id:                4\n",
+       "    ...                     ...\n",
+       "    sub_experiment_id:      none\n",
+       "    table_id:               SImon\n",
+       "    tracking_id:            hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89\n",
+       "    variable_id:            siconca\n",
+       "    variant_info:           CMIP6 CESM2 hindcast (1850-2014) with high-top at...\n",
+       "    variant_label:          r1i1p1f1"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# we can take a look at our dataset shape by calling it: \n",
+    "SI_ds  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "59497066-568b-434e-9557-8fb8d9d5ad9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n",
+      "99.999916\n"
+     ]
+    }
+   ],
+   "source": [
+    "# we see that it contains the variable siconca\n",
+    "\n",
+    "# lets print the minimum and maximum of this variable to check its format:\n",
+    "print(SI_ds.siconca.min().values)\n",
+    "print(SI_ds.siconca.max().values)\n",
+    "# note that it is formattwd as a percentage not a fraction, ranging from 0 to 100. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae442fb9-ddb5-42c3-92ad-fb6f906f2d8c",
+   "metadata": {},
+   "source": [
+    "Great, now you are all set to use these model sea-ice data to address the questions you are interested in!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19e740fe-c069-4d24-b29d-a5936c6b0255",
+   "metadata": {},
+   "source": [
+    "# Solutions for the Green questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1826b08-3b1b-4eea-97e0-31ec140113a6",
+   "metadata": {},
+   "source": [
+    "## Q1. \n",
+    "Plot the seasonal mean (DJF, MAM, JJA, SON)\n",
+    "sea ice concentration fields from one CMIP6 ensemble member."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3a324ef6-0ee4-469e-b3ec-df9151850c6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy as cy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1677999d-e7fd-43eb-a55d-3a1ab8e16a50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '%')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# we'll do this for the preindustrial period:\n",
+    "timeperiod = [1850, 1900]\n",
+    "SI_ds_PI = SI_ds.sel(time=slice('1850', '1900'))\n",
+    "\n",
+    "# select northern hemisphere high latitudes:\n",
+    "SI_ds_PI = SI_ds_PI.sel(lat = slice(50, 90))\n",
+    "\n",
+    "# now use groupby function to get seasonal means\n",
+    "SI_ds_PI = SI_ds_PI.groupby(SI_ds_PI.time.dt.season).mean()\n",
+    "\n",
+    "\n",
+    "# now plot\n",
+    "#fig, axs = plt.subplots(2, 2, figsize=(8, 8))\n",
+    "fig, axs = plt.subplots(2, 2, figsize=(8, 8), \n",
+    "                    subplot_kw={'projection':ccrs.NorthPolarStereo(central_longitude=0.0), \n",
+    "                                'facecolor':'gray'})\n",
+    "i=0\n",
+    "for season in SI_ds_PI.season.values:\n",
+    "    ds = SI_ds_PI.isel(season=i)\n",
+    "    \n",
+    "    p = ds.siconca.plot.pcolormesh(ax=axs.flatten()[i],\n",
+    "                               levels=  np.arange(0, 110, 10),\n",
+    "                               cmap='Blues_r',\n",
+    "                               transform=ccrs.PlateCarree(), \n",
+    "                               x=\"lon\", y=\"lat\", \n",
+    "                               add_colorbar=False\n",
+    "                               )\n",
+    "    \n",
+    "    p.axes.set_extent([0, 360, 66, 90], ccrs.PlateCarree())\n",
+    "    axs.flatten()[i].gridlines()\n",
+    "    axs.flatten()[i].set_title(season)\n",
+    "    p.axes.coastlines()\n",
+    "    #p.axes.LAND()\n",
+    "    p.axes.add_feature(cy.feature.LAND, color='gray', zorder=1000)\n",
+    "    i = i+1\n",
+    "fig.subplots_adjust(right=0.85, top=0.95)\n",
+    "cbar_ax = fig.add_axes([0.9, 0.35, 0.05, 0.6])\n",
+    "cbar = fig.colorbar(p, cax=cbar_ax, orientation='vertical')\n",
+    "cbar.ax.set_ylabel('%', rotation=0, \n",
+    "                       verticalalignment='center', labelpad =0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa2cdf6f-a84c-42e9-902b-643c430e06b5",
+   "metadata": {},
+   "source": [
+    "## Q2. \n",
+    "For your CMIP6 model, compute the time series of mean sea ice extent over the period 1950-2014. Compute this for the months of March and September. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e47283b0-eeb9-47e9-8e39-69fbc7cb278c",
+   "metadata": {},
+   "source": [
+    "We define sea ice extent following the convention that a grid cell is 'sea ice' where the concentration is higher than 15%. \n",
+    "\n",
+    "Extent is then the sum of the grid cell areas with concetration above 15%. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "001c2472-f031-45a0-beef-a710d1adc768",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f89e2587df0>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "SI_ds_mar = SI_ds.where(SI_ds.time.dt.month == 3, drop=True)\n",
+    "SI_ds_sep = SI_ds.where(SI_ds.time.dt.month == 9, drop=True)\n",
+    "\n",
+    "# now define a function to convert concentration to extent:\n",
+    "def get_extent(ds):\n",
+    "    SI_binary = xr.where(ds.siconca>15, 1, 0)\n",
+    "    SI_cells_areas = SI_binary*grid_cell_areas\n",
+    "    # take only northern hemisphere:\n",
+    "    SI_cells_areas = SI_cells_areas.sel(lat=slice(0, 90))\n",
+    "    SI_extent_timeseries = SI_cells_areas.areacella.sum(dim=['lat', 'lon'])\n",
+    "    return SI_extent_timeseries\n",
+    "\n",
+    "SI_extent_mar = get_extent(SI_ds_mar)\n",
+    "SI_extent_sep = get_extent(SI_ds_sep)\n",
+    "\n",
+    "# quick plot to check:\n",
+    "plt.plot(SI_extent_mar.time.dt.year, SI_extent_mar, label='March')\n",
+    "plt.plot(SI_extent_sep.time.dt.year, SI_extent_sep, label='September')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88cb816c-aba8-4859-b195-6e405e4a36be",
+   "metadata": {},
+   "source": [
+    "Q3. Compute the linear trend in each sea ice extent time series between 1979-2014. Does the trend reflect a decline in sea ice extent within the model? Are these trends different between March and September? \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "60aa0476-e656-460a-b253-2971fd9d4b6d",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# we can use the scipy stats package to calculate linear trends and\n",
+    "# test for differences:\n",
+    "from scipy import stats\n",
+    "\n",
+    "# also define a conversion from m2 to million km2, to make our units more interpretable:\n",
+    "unit_conversion_m2_Mkm2 = 10**(-12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "5a5dc0a9-5bd1-4818-812c-4a70cb3e0dab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "march_test = SI_extent_mar.sel(time=slice('1979', '2014'))\n",
+    "linear_regression_march = stats.linregress(march_test.time.dt.year, unit_conversion_m2_Mkm2*march_test.values)\n",
+    "\n",
+    "september_test = SI_extent_sep.sel(time=slice('1979', '2014'))\n",
+    "linear_regression_september = stats.linregress(september_test.time.dt.year, unit_conversion_m2_Mkm2*september_test.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "49351d9a-69a8-4531-ba1a-aa7d2d51cafa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LinregressResult(slope=-0.026916248481935644, intercept=70.09838824698096, rvalue=-0.7085028184389062, pvalue=1.3324180848619235e-06, stderr=0.0045978893749957, intercept_stderr=9.179810401024811)\n",
+      "LinregressResult(slope=-0.10324355173521954, intercept=213.0497016999347, rvalue=-0.9053126699550053, pvalue=3.3882764111605495e-14, stderr=0.008307186154928226, intercept_stderr=16.58552167065367)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(linear_regression_march)\n",
+    "print(linear_regression_september)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "287978fa-34ee-4d45-8ca3-8e9c8be6f287",
+   "metadata": {},
+   "source": [
+    "Both months show a negative trend, but the trend is around 5 times larger in september in this model. September sea ice is declining at around 1M km2 per decade in this model. \n",
+    "\n",
+    "The code below shows that if this continues, this model sees sea-ice free period in late summer around 2060:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "633aac8f-107f-486c-8230-def833d71d44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2063.5642431822394\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_intercept_sep = -linear_regression_september.intercept/linear_regression_september.slope\n",
+    "print(x_intercept_sep)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c62a0f3e",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "# Further Reading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "378b7ff3",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "* On the CMIP6 projections for arctic sea ice, see Notz et al., 2020  https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019gl086749\n",
+    "* This paper by Steove & Notz, is a nice summary of the observational trends across all seasons, and how they compare to models' predictions: https://iopscience.iop.org/article/10.1088/1748-9326/aade56\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1fa5d34-263b-4182-ad58-a57122716944",
+   "metadata": {},
+   "source": [
+    "# Optional additional data source 2: Satellite observations\n",
+    "\n",
+    "National Snow & Ice Data (NSIDC) \n",
+    "Gridded Monthly Sea Ice Extent and Concentration, 1850 Onward, Version 2\n",
+    "https://doi.org/10.7265/jj4s-tq79\n",
+    "\n",
+    "From 1978, this is satellite passive microwave data. Prior to this, a range of sources are used (e.g. ship logs), but we will subset to only the satellite era here. \n",
+    "\n",
+    "The passive microwave observations are the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4. This data product uses a combination of two algorithms to generate the sea ice concentration; the NASA Team (NT) algorithm (Cavalieri et al. 1984), and the NASA Bootstrap (BT) algorithm (Comiso 1986). \n",
+    "\n",
+    "This version has been gridded onto a 1/4 degree latitude-longitude grid, and has been averaged to monthly resolution. It includes only latitudes North of 30°N (so is arctic-only)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "ee26e97b-14d9-4577-b3bc-b75fae4c471f",
+   "metadata": {},
+   "outputs": [
+    {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:          (latitude: 240, longitude: 1440, time: 2016)\n",
+       "Coordinates:\n",
+       "  * latitude         (latitude) float32 89.88 89.62 89.38 ... 30.62 30.38 30.12\n",
+       "  * longitude        (longitude) float32 0.125 0.375 0.625 ... 359.4 359.6 359.9\n",
+       "  * time             (time) object 1850-01-15 00:00:00 ... 2017-12-15 00:00:00\n",
+       "Data variables:\n",
+       "    seaice_conc      (time, latitude, longitude) uint8 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
+       "    seaice_source    (time, latitude, longitude) uint8 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
+       "    LandRegion_mask  (latitude, longitude) uint8 dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;\n",
+       "    Gridcell_Area    (latitude) float32 dask.array&lt;chunksize=(100,), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    version:       2.0\n",
+       "    release_date:  February 28, 2019\n",
+       "    Conventions:   CF-1.4\n",
+       "    citation:      https://doi.org/10.1111/j.1931-0846.2016.12195.x\n",
+       "    dataset_doi:   https://nsidc.org/data/g10010</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-5ea8bd87-44e8-4fa8-a4ed-a8af404920c6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5ea8bd87-44e8-4fa8-a4ed-a8af404920c6' 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'>latitude</span>: 240</li><li><span class='xr-has-index'>longitude</span>: 1440</li><li><span class='xr-has-index'>time</span>: 2016</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-592c11ec-a21d-472b-9556-45503b5063f7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-592c11ec-a21d-472b-9556-45503b5063f7' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>89.88 89.62 89.38 ... 30.38 30.12</div><input id='attrs-ec6ca268-74d8-45a0-83ea-c6a6f3288ec3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec6ca268-74d8-45a0-83ea-c6a6f3288ec3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1be8d7e-acc1-4ec5-9b21-a99e6d965ae9' class='xr-var-data-in' type='checkbox'><label for='data-d1be8d7e-acc1-4ec5-9b21-a99e6d965ae9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>valid_min :</span></dt><dd>30.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>array([89.875, 89.625, 89.375, ..., 30.625, 30.375, 30.125], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-3b923ac8-0471-4415-b9d7-526f6fb088c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b923ac8-0471-4415-b9d7-526f6fb088c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f4b62ab-5a2d-4a68-889c-b41754811d4d' class='xr-var-data-in' type='checkbox'><label for='data-5f4b62ab-5a2d-4a68-889c-b41754811d4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>360.0</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
+       "       3.59875e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-15 00:00:00 ... 2017-12-...</div><input id='attrs-9f8f4d0c-5e57-46ad-a983-fba6a2736d9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f8f4d0c-5e57-46ad-a983-fba6a2736d9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-224b4c53-c2b9-4bc0-9756-2f792206b6c9' class='xr-var-data-in' type='checkbox'><label for='data-224b4c53-c2b9-4bc0-9756-2f792206b6c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeJulian(1850, 1, 15, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeJulian(1850, 2, 15, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeJulian(1850, 3, 15, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       ...,\n",
+       "       cftime.DatetimeJulian(2017, 10, 15, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeJulian(2017, 11, 15, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeJulian(2017, 12, 15, 0, 0, 0, 0, has_year_zero=False)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9960d924-d7a1-40f1-8f84-395b88d66d9a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9960d924-d7a1-40f1-8f84-395b88d66d9a' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>seaice_conc</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-478771b6-e822-45d1-ac30-232c015955a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-478771b6-e822-45d1-ac30-232c015955a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62602096-d6e2-4022-bc5c-0a60316199c3' class='xr-var-data-in' type='checkbox'><label for='data-62602096-d6e2-4022-bc5c-0a60316199c3' 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>short_name :</span></dt><dd>Concentration</dd><dt><span>long_name :</span></dt><dd>Sea_Ice_Concentration</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Values are percent-concentration from 0 to 100.  Sentinel value of 120 in the concentration field indicates land</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 664.45 MiB </td>\n",
+       "                        <td> 0.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2016, 240, 1440) </td>\n",
+       "                        <td> (100, 100, 100) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 946 Tasks </td>\n",
+       "                        <td> 945 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> uint8 </td>\n",
+       "                    <td> numpy.ndarray </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>seaice_source</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-ae375d8f-a9a8-464c-8086-18566f4d6292' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae375d8f-a9a8-464c-8086-18566f4d6292' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88915b7d-c078-4077-af53-2d76314db7a9' class='xr-var-data-in' type='checkbox'><label for='data-88915b7d-c078-4077-af53-2d76314db7a9' 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>short_name :</span></dt><dd>Seaice Source</dd><dt><span>long_name :</span></dt><dd>Coded_Source_of_Sea_Ice_Concentration</dd><dt><span>standard_name :</span></dt><dd>source</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>name_source_00 :</span></dt><dd>Land</dd><dt><span>name_source_01 :</span></dt><dd>Satellite passive microwave</dd><dt><span>name_source_02 :</span></dt><dd>Danish Meteorological Institute</dd><dt><span>name_source_03 :</span></dt><dd>Dehn</dd><dt><span>name_source_04 :</span></dt><dd>NAVO yearbooks</dd><dt><span>name_source_05 :</span></dt><dd>AARI</dd><dt><span>name_source_06 :</span></dt><dd>Hill</dd><dt><span>name_source_07 :</span></dt><dd>Whaling Records - Complete Sea Ice</dd><dt><span>name_source_08 :</span></dt><dd>Whaling Records - Partial Sea Ice</dd><dt><span>name_source_09 :</span></dt><dd>Whaling Records - No Sea Ice</dd><dt><span>name_source_10 :</span></dt><dd>DMI yearbook narrative</dd><dt><span>name_source_11 :</span></dt><dd>ACSYS</dd><dt><span>name_source_12 :</span></dt><dd>Walsh and Johnson</dd><dt><span>name_source_13 :</span></dt><dd>JMA charts</dd><dt><span>name_source_14 :</span></dt><dd>Kelly ice extent grids</dd><dt><span>name_source_15 :</span></dt><dd>Landmask correction fill</dd><dt><span>name_source_16 :</span></dt><dd>Change-of-landmask ocean</dd><dt><span>name_source_17 :</span></dt><dd>Analog fill - spatial</dd><dt><span>name_source_18 :</span></dt><dd>Analog fill - temporal</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 664.45 MiB </td>\n",
+       "                        <td> 0.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2016, 240, 1440) </td>\n",
+       "                        <td> (100, 100, 100) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 946 Tasks </td>\n",
+       "                        <td> 945 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> uint8 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\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>LandRegion_mask</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</div><input id='attrs-b4f1ac6a-e40c-40e9-928f-cdfa83511acb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4f1ac6a-e40c-40e9-928f-cdfa83511acb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f07d0d34-30ea-436c-bd63-bc0690654672' class='xr-var-data-in' type='checkbox'><label for='data-f07d0d34-30ea-436c-bd63-bc0690654672' 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>short_name :</span></dt><dd>LandRegion Mask</dd><dt><span>long_name :</span></dt><dd>Land_and_Ocean_Region_Mask</dd><dt><span>standard_name :</span></dt><dd>source</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>name_regionmask_00 :</span></dt><dd>Ocean, no region specified</dd><dt><span>name_regionmask_01 :</span></dt><dd>Beaufort Sea</dd><dt><span>name_regionmask_02 :</span></dt><dd>Chukchi Sea</dd><dt><span>name_regionmask_03 :</span></dt><dd>East Siberian Sea</dd><dt><span>name_regionmask_04 :</span></dt><dd>Laptev Sea</dd><dt><span>name_regionmask_05 :</span></dt><dd>Kara Sea</dd><dt><span>name_regionmask_06 :</span></dt><dd>Barents Sea</dd><dt><span>name_regionmask_07 :</span></dt><dd>Greenland Sea</dd><dt><span>name_regionmask_08 :</span></dt><dd>Baffin Bay and Gulf of St Lawrence, on polar stereo grid</dd><dt><span>name_regionmask_09 :</span></dt><dd>Canadian Archipelago</dd><dt><span>name_regionmask_10 :</span></dt><dd>Hudson Bay</dd><dt><span>name_regionmask_11 :</span></dt><dd>Central Arctic</dd><dt><span>name_regionmask_12 :</span></dt><dd>Bering Sea</dd><dt><span>name_regionmask_13 :</span></dt><dd>Baltic Sea</dd><dt><span>name_regionmask_14 :</span></dt><dd>Sea of Okhotsk</dd><dt><span>name_regionmask_15 :</span></dt><dd>Yellow Sea, on polar stereo grid</dd><dt><span>name_regionmask_16 :</span></dt><dd>Cook Inlet, on polar stereo grid</dd><dt><span>name_regionmask_17 :</span></dt><dd>Yellow Sea, off polar stereo grid</dd><dt><span>name_regionmask_18 :</span></dt><dd>Baffin Bay and Gulf of St Lawrence, off polar stereo grid</dd><dt><span>name_regionmask_19 :</span></dt><dd>Cook Inlet, off polar stereo grid</dd><dt><span>name_regionmask_20 :</span></dt><dd>Land</dd><dt><span>name_regionmask_21 :</span></dt><dd>Unnamed ocean on polar stereo grid</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 337.50 kiB </td>\n",
+       "                        <td> 9.77 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (240, 1440) </td>\n",
+       "                        <td> (100, 100) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 46 Tasks </td>\n",
+       "                        <td> 45 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> uint8 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        <td>\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Gridcell_Area</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100,), meta=np.ndarray&gt;</div><input id='attrs-00febd06-dc4a-422e-a2a7-4f931119b918' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00febd06-dc4a-422e-a2a7-4f931119b918' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55d72adc-e116-4942-93c8-e3cdb44117be' class='xr-var-data-in' type='checkbox'><label for='data-55d72adc-e116-4942-93c8-e3cdb44117be' 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>short_name :</span></dt><dd>Grid_Cell_Area</dd><dt><span>long_name :</span></dt><dd>Grid_Cell_Area</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>units :</span></dt><dd>km^2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\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> 0.94 kiB </td>\n",
+       "                        <td> 400 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (240,) </td>\n",
+       "                        <td> (100,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 4 Tasks </td>\n",
+       "                        <td> 3 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-68905e9b-b5a8-424a-956d-702d3ca41358' class='xr-section-summary-in' type='checkbox'  checked><label for='section-68905e9b-b5a8-424a-956d-702d3ca41358' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>version :</span></dt><dd>2.0</dd><dt><span>release_date :</span></dt><dd>February 28, 2019</dd><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>citation :</span></dt><dd>https://doi.org/10.1111/j.1931-0846.2016.12195.x</dd><dt><span>dataset_doi :</span></dt><dd>https://nsidc.org/data/g10010</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:          (latitude: 240, longitude: 1440, time: 2016)\n",
+       "Coordinates:\n",
+       "  * latitude         (latitude) float32 89.88 89.62 89.38 ... 30.62 30.38 30.12\n",
+       "  * longitude        (longitude) float32 0.125 0.375 0.625 ... 359.4 359.6 359.9\n",
+       "  * time             (time) object 1850-01-15 00:00:00 ... 2017-12-15 00:00:00\n",
+       "Data variables:\n",
+       "    seaice_conc      (time, latitude, longitude) uint8 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
+       "    seaice_source    (time, latitude, longitude) uint8 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
+       "    LandRegion_mask  (latitude, longitude) uint8 dask.array<chunksize=(100, 100), meta=np.ndarray>\n",
+       "    Gridcell_Area    (latitude) float32 dask.array<chunksize=(100,), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    version:       2.0\n",
+       "    release_date:  February 28, 2019\n",
+       "    Conventions:   CF-1.4\n",
+       "    citation:      https://doi.org/10.1111/j.1931-0846.2016.12195.x\n",
+       "    dataset_doi:   https://nsidc.org/data/g10010"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Code to retrieve and load the data\n",
+    "Sea_ice_conc_obs_file = 'Data/G10010_sibt1850_v2.0.nc'\n",
+    "SI_obs_ds = xr.open_dataset(Sea_ice_conc_obs_file, chunks=100)\n",
+    "\n",
+    "# note the use of the chunks keyword. These data come on a high resolution grid, \n",
+    "# so are potentially too large to load into memory. Chunking (using dask) \n",
+    "# avoids this problem. We arbitrarily pick a chunk length of 100 along each dimension.     \n",
+    "\n",
+    "# we can print a useful summary of the data by calling it:\n",
+    "SI_obs_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "88016832-949b-47c9-9729-478e22716279",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code to preprocess data\n",
+    "# we will select only the satellite era observations:\n",
+    "SI_obs_ds = SI_obs_ds.where(SI_obs_ds.time.dt.year > 1978, drop=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf33e0f1-657a-40de-ba63-3df6fc4fd1d0",
+   "metadata": {},
+   "source": [
+    "We can now visualize the content of the dataset:\n",
+    "\n",
+    "Note that the sea ice concentration is (confusingly!) set to 120% over land. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "778eabdf-3594-476e-ae69-fda6d6d67001",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f89e0038910>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can select and plot the first month \n",
+    "# using .isel(time=0) and inbuilt xarray plotting,\n",
+    "# just to check the data looks reasonable:\n",
+    "SI_obs_ds.isel(time=0).seaice_conc.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64abc79c-689a-4e73-91c3-5fe86f7c7d6c",
+   "metadata": {},
+   "source": [
+    "Note that the dataset also includes variables for grid cell area. Grid cell area is in km2. \n",
+    "\n",
+    "We will need this to convert the spatial data into a timeseries of total Arctic sea ice area. \n",
+    "\n",
+    "The code below shows how this can be done:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "b7bf75ee-8e48-4e26-ac0d-200e940bd66e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f89dff311b0>]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# select only the ocean regions by ignoring any percentages over 100:\n",
+    "SI_obs_ds = SI_obs_ds.where(SI_obs_ds['seaice_conc'] < 101)\n",
+    "\n",
+    "# then multiple the ice fraction in each grid cell by the cell area:\n",
+    "# factor of 0.01 to convert from percentage to fraction\n",
+    "SI_obs_ds['seaice_area_km2'] = 0.01*SI_obs_ds['seaice_conc']*SI_obs_ds['Gridcell_Area']\n",
+    "\n",
+    "# finally, we can sum this sea-ice area variable over the spatial dimensions to get\n",
+    "# a timeseries of total Arctic sea ice area:\n",
+    "SI_total_area_obs = SI_obs_ds['seaice_area_km2'].sum(dim=['latitude', 'longitude'])\n",
+    "SI_total_area_obs.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fc76012-d7b8-4b5e-aa41-0facc05c0fa8",
+   "metadata": {},
+   "source": [
+    "Now you can add the observational decline of sea ice to your analysis if you wish!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc91b53b-1877-45f1-b9a1-e9fbf47b84b8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "include_colab_link": true,
+   "name": "Example_based_on_CMA_structure",
+   "toc_visible": true
+  },
+  "kernel": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "kernelspec": {
+   "display_name": "main",
+   "language": "python",
+   "name": "main"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/projects/template-images/Sea_ice_template_questions.svg b/projects/template-images/Sea_ice_template_questions.svg
new file mode 100644
index 000000000..8a08ccd50
--- /dev/null
+++ b/projects/template-images/Sea_ice_template_questions.svg
@@ -0,0 +1 @@
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\ No newline at end of file

From b4eaf5e4531acc68aedd7f51877b188b3740a92c Mon Sep 17 00:00:00 2001
From: wg4031 <wg4031@pu-h40v96gpd9.mynetworksettings.com>
Date: Wed, 20 Mar 2024 17:44:03 -0400
Subject: [PATCH 02/65] initial commit for precip notebook

---
 .../Regional_precipitation_2024.ipynb         | 491 ++++++++++++++++++
 .../Regional_precipitation_variability.ipynb  |   4 +-
 .../template-images/Precipitation_2024.svg    |   1 +
 3 files changed, 494 insertions(+), 2 deletions(-)
 create mode 100644 projects/project-notebooks/Regional_precipitation_2024.ipynb
 create mode 100644 projects/template-images/Precipitation_2024.svg

diff --git a/projects/project-notebooks/Regional_precipitation_2024.ipynb b/projects/project-notebooks/Regional_precipitation_2024.ipynb
new file mode 100644
index 000000000..70de33c04
--- /dev/null
+++ b/projects/project-notebooks/Regional_precipitation_2024.ipynb
@@ -0,0 +1,491 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ada5523b",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/neuromatch/course-content-template/blob/main/projects/example_dataset/Example_based_on_CMA_structure.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/course-content-template/main/projects/example_dataset/Example_based_on_CMA_structure\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "045624d0",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "<h1>\n",
+    "Regional Precipitation and Extreme events\n",
+    "</h1>\n",
+    "\n",
+    "\n",
+    "\n",
+    "__Content creators:__ Will Gregory & Laura Paccini\n",
+    "\n",
+    "__Content reviewers:__ Names & Surnames\n",
+    "\n",
+    "__Content editors:__ Konstantinos Tsafatinos\n",
+    "\n",
+    "__Production editors:__ Konstantinos Tsafatinos\n",
+    "\n",
+    "__Our 2024 Sponsors:__ Sponsors\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60881a20",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "The notebook should have the following outline:\n",
+    "\n",
+    "- Project background (video + template outline)\n",
+    "- Imports and functions common to multiple data sources\n",
+    "- Data source 1\n",
+    "- Data source background\n",
+    "- Imports and functions common to multiple datasets from the same data source\n",
+    "\n",
+    "- Dataset 1:\n",
+    "\n",
+    "    Dataset background (if needed)\n",
+    "    \n",
+    "    Imports and functions specific to the dataset\n",
+    "    \n",
+    "    Code to load the dataset\n",
+    "    \n",
+    "    Code to pre-process the dataset (if not taught and likely difficult for students)\n",
+    "    \n",
+    "    Code to visualise the data table shape/content\n",
+    "\n",
+    "...\n",
+    "- Dataset N:\n",
+    "    same as for Dataset 1\n",
+    "...\n",
+    "\n",
+    "- Further reading\n",
+    "\n",
+    "---\n",
+    "We encourage you to explain the key parts of your code through comments and text cells as you see fit.\n",
+    "\n",
+    "---\n",
+    "If the same or analogous dataset is covered in the curriculum tutorials, we can expect students to know how to load that data. In that case, please provide only the dataset background and refer students to the respective curriculum day while maintaining the structural hierarchy of the notebook.\n",
+    "\n",
+    "---\n",
+    "If you wish to include any plots in your template, those should follow the standard figure formatting. Please include the Figure settings cell from the tutorial notebook template. Let us know if you have any questions about this or are unsure how to proceed!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c08b96da",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "To include videos:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "64da422f",
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc75a50d6fd94a54812d7f38975591ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tab(children=(Output(), Output()), _titles={'0': 'Youtube', '1': 'Bilibili'})"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# @title Project Background\n",
+    "\n",
+    "from ipywidgets import widgets\n",
+    "from IPython.display import YouTubeVideo\n",
+    "from IPython.display import IFrame\n",
+    "from IPython.display import display\n",
+    "\n",
+    "\n",
+    "class PlayVideo(IFrame):\n",
+    "  def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
+    "    self.id = id\n",
+    "    if source == 'Bilibili':\n",
+    "      src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
+    "    elif source == 'Osf':\n",
+    "      src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
+    "    super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
+    "\n",
+    "\n",
+    "def display_videos(video_ids, W=400, H=300, fs=1):\n",
+    "  tab_contents = []\n",
+    "  for i, video_id in enumerate(video_ids):\n",
+    "    out = widgets.Output()\n",
+    "    with out:\n",
+    "      if video_ids[i][0] == 'Youtube':\n",
+    "        video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
+    "                             height=H, fs=fs, rel=0)\n",
+    "        print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
+    "      else:\n",
+    "        video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
+    "                          height=H, fs=fs, autoplay=False)\n",
+    "        if video_ids[i][0] == 'Bilibili':\n",
+    "          print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
+    "        elif video_ids[i][0] == 'Osf':\n",
+    "          print(f'Video available at https://osf.io/{video.id}')  \n",
+    "      display(video)\n",
+    "    tab_contents.append(out)\n",
+    "  return tab_contents\n",
+    "\n",
+    "\n",
+    "video_ids = [('Youtube', '<video_id_1>'), ('Bilibili', '<video_id_2>'), ('Osf', '<video_id_3>')]\n",
+    "tab_contents = display_videos(video_ids, W=854, H=480)\n",
+    "tabs = widgets.Tab()\n",
+    "tabs.children = tab_contents\n",
+    "for i in range(len(tab_contents)):\n",
+    "  tabs.set_title(i, video_ids[i][0])\n",
+    "display(tabs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9fb54e0",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "To include slides:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b52fef8b",
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "# @title Tutorial slides\n",
+    "# @markdown These are the slides for the videos in all tutorials today\n",
+    "\n",
+    "from IPython.display import IFrame\n",
+    "#link_id = \"n7wdy\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63b697d5",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "Provide a description of what students will do in the project"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff7ae1c9",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "# Project Template\n",
+    "\n",
+    "![Alt text](../template-images/Precipitation_2024.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51243f3",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "## Data Exploration Notebook\n",
+    "\n",
+    "## Project Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b68292ec",
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "# google colab installs\n",
+    "\n",
+    "# !mamaba install netCDF4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e541a40",
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "\n",
+    "#Import only the libraries/objects that are necessary for more than one dataset.\n",
+    "#Dataset-specific imports should be in the respective notebook section.\n",
+    "\n",
+    "#If any external library has to be installed, !pip install library --quiet\n",
+    "#follow this order: numpy>matplotlib.\n",
+    "#import widgets in hidden Figure settings cell\n",
+    "\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3692a9ca",
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "# @title Helper functions\n",
+    "\n",
+    "# If any helper functions you want to hide for clarity (that has been seen before\n",
+    "# or is simple/uniformative), add here\n",
+    "# If helper code depends on libraries that aren't used elsewhere,\n",
+    "# import those libaries here, rather than in the main import cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07545499",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# @title Figure settings\n",
+    "\n",
+    "import ipywidgets as widgets       # interactive display\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "plt.style.use(\"https://raw.githubusercontent.com/ClimateMatchAcademy/course-content/main/cma.mplstyle\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02a3777f",
+   "metadata": {},
+   "source": [
+    "# [datasource-1]\n",
+    "\n",
+    "Data source description. You may provide some background that motivated the generation of the associated datasets using hyperlinks to reference relevant papers. Please outline how the data has been collected or generated! Remember to provide credit to dataset authors in line with their copyright policy!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e9697620",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data source-specific imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a4660fdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data source-specific functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d4ab262",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "## [datasource-1]: Dataset 1: "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fa21050",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "Further background on the particular dataset if applicable. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2359308",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dataset-specific imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "396c635a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dataset-specific functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "92175f59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code to retrieve and load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "392c0e71",
+   "metadata": {},
+   "source": [
+    "Give advice on the required preprocessing. If applicable, refer to specific tutorials and let the students preprocess the data themselves. Otherwise, explain/illustrate the reasons for preprocessing and provide either the code itself (if students are likely to struggle on their own) or written guidance on the functions, data filtering thresholds, etc they should use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bae0683",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code to preprocess data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73f4f21b",
+   "metadata": {},
+   "source": [
+    "We can now visualize the content of the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75867210",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code to print the shape, array names, etc of the dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3543b122",
+   "metadata": {},
+   "source": [
+    "Consider adding some encouraging words along the lines of \"Now you are all set to address the questions you are interested in!\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9578b229",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "## [datasource 1]: Dataset 2:\n",
+    "\n",
+    "Further background on the particular datafile/variables if applicable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee7c9ef5",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "## [datasource 2]:\n",
+    "\n",
+    "Data source description. You may provide some background that motivated the generation of the associated datasets using hyperlinks to reference relevant papers. Please outline how the data has been collected or generated! Remember to provide credit to dataset authors in line with their copyright policy!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c62a0f3e",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "# Further Reading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "378b7ff3",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "Use bullet points to list further readings\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "include_colab_link": true,
+   "name": "Example_based_on_CMA_structure",
+   "toc_visible": true
+  },
+  "kernel": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/projects/project-notebooks/Regional_precipitation_variability.ipynb b/projects/project-notebooks/Regional_precipitation_variability.ipynb
index 41fddfdf0..958635b62 100644
--- a/projects/project-notebooks/Regional_precipitation_variability.ipynb
+++ b/projects/project-notebooks/Regional_precipitation_variability.ipynb
@@ -1,6 +1,6 @@
 {
  "cells": [
- {
+  {
    "cell_type": "markdown",
    "metadata": {
     "execution": {}
@@ -554,7 +554,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,
diff --git a/projects/template-images/Precipitation_2024.svg b/projects/template-images/Precipitation_2024.svg
new file mode 100644
index 000000000..c27d4db6a
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\ No newline at end of file

From d2e0dfbe1dc1c071677016f023e38223d715bdbd Mon Sep 17 00:00:00 2001
From: wg4031 <wg4031@pu-h40v96gpd9.mynetworksettings.com>
Date: Wed, 20 Mar 2024 17:46:24 -0400
Subject: [PATCH 03/65] initial commit for precip notebook

---
 projects/project-notebooks/Regional_precipitation_2024.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/projects/project-notebooks/Regional_precipitation_2024.ipynb b/projects/project-notebooks/Regional_precipitation_2024.ipynb
index 70de33c04..3039be1c0 100644
--- a/projects/project-notebooks/Regional_precipitation_2024.ipynb
+++ b/projects/project-notebooks/Regional_precipitation_2024.ipynb
@@ -204,7 +204,7 @@
    "source": [
     "# Project Template\n",
     "\n",
-    "![Alt text](../template-images/Precipitation_2024.svg)"
+    "![Alt text](https://github.com/William-gregory/climate-course-content/projects/template-images/Precipitation_2024.svg)"
    ]
   },
   {

From 66eb591f14d0caf9cf74218f33d3267649695f76 Mon Sep 17 00:00:00 2001
From: wg4031 <wg4031@pu-h40v96gpd9.mynetworksettings.com>
Date: Wed, 20 Mar 2024 17:50:33 -0400
Subject: [PATCH 04/65] add template question svg

---
 .../Regional_precipitation_2024.ipynb                | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/projects/project-notebooks/Regional_precipitation_2024.ipynb b/projects/project-notebooks/Regional_precipitation_2024.ipynb
index 3039be1c0..f587c54eb 100644
--- a/projects/project-notebooks/Regional_precipitation_2024.ipynb
+++ b/projects/project-notebooks/Regional_precipitation_2024.ipynb
@@ -251,9 +251,17 @@
     "#follow this order: numpy>matplotlib.\n",
     "#import widgets in hidden Figure settings cell\n",
     "\n",
-    "import random\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pooch\n",
+    "import os\n",
+    "import tempfile\n",
+    "import pandas as pd\n",
+    "import s3fs\n",
+    "import boto3\n",
+    "import botocore\n",
+    "import datetime"
    ]
   },
   {

From 09c0f1858e3017fab4bb84a09c5b97fe4705bf8a Mon Sep 17 00:00:00 2001
From: wg4031 <wg4031@pu-h40v96gpd9.mynetworksettings.com>
Date: Wed, 20 Mar 2024 17:53:28 -0400
Subject: [PATCH 05/65] try again with svg image

---
 projects/project-notebooks/Regional_precipitation_2024.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/projects/project-notebooks/Regional_precipitation_2024.ipynb b/projects/project-notebooks/Regional_precipitation_2024.ipynb
index f587c54eb..f9809f2c0 100644
--- a/projects/project-notebooks/Regional_precipitation_2024.ipynb
+++ b/projects/project-notebooks/Regional_precipitation_2024.ipynb
@@ -204,7 +204,7 @@
    "source": [
     "# Project Template\n",
     "\n",
-    "![Alt text](https://github.com/William-gregory/climate-course-content/projects/template-images/Precipitation_2024.svg)"
+    "<img src='../template-images/Precipitation_2024.svg'/>"
    ]
   },
   {

From 98241f5bb5ed40727f1e269a54dafe6ae86414cf Mon Sep 17 00:00:00 2001
From: wg4031 <wg4031@pu-h40v96gpd9.mynetworksettings.com>
Date: Wed, 20 Mar 2024 17:56:36 -0400
Subject: [PATCH 06/65] revised template questions

---
 projects/template-images/Precipitation_2024.svg | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/projects/template-images/Precipitation_2024.svg b/projects/template-images/Precipitation_2024.svg
index c27d4db6a..b054a1e7b 100644
--- a/projects/template-images/Precipitation_2024.svg
+++ b/projects/template-images/Precipitation_2024.svg
@@ -1 +1 @@
-<svg version="1.1" viewBox="0.0 0.0 960.0 540.0" fill="none" stroke="none" stroke-linecap="square" stroke-miterlimit="10" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><clipPath id="ge66283b09e_0_68.0"><path d="m0 0l960.0 0l0 540.0l-960.0 0l0 -540.0z" clip-rule="nonzero"/></clipPath><g clip-path="url(#ge66283b09e_0_68.0)"><path fill="#ffffff" d="m0 0l960.0 0l0 540.0l-960.0 0z" fill-rule="evenodd"/><path fill="#000000" fill-opacity="0.0" d="m19.657642 46.942257l919.46454 0l0 63.023624l-919.46454 0z" fill-rule="evenodd"/><path fill="#000000" d="m29.188892 67.46226l0 -8.015625l3.5 0q0.875 0 1.5625 0.171875q0.6875 0.171875 1.078125 0.625q0.40625 0.453125 0.40625 1.296875q0 0.546875 -0.25 0.96875q-0.234375 0.421875 -0.703125 0.6875q0.578125 0.203125 0.921875 0.640625q0.359375 0.421875 0.359375 1.15625q0 0.890625 -0.40625 1.4375q-0.40625 0.546875 -1.140625 0.796875q-0.71875 0.234375 -1.703125 0.234375l-3.625 0zm1.890625 -4.84375l1.671875 0q0.484375 0 0.75 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\ No newline at end of file

From cfc754ba7b6426b227a1f8496469bfd22addebf9 Mon Sep 17 00:00:00 2001
From: wg4031 <wg4031@pu-h40v96gpd9.mynetworksettings.com>
Date: Wed, 20 Mar 2024 18:04:00 -0400
Subject: [PATCH 07/65] tidy up notebook

---
 .../Regional_precipitation_2024.ipynb         | 72 ++++++++++---------
 1 file changed, 40 insertions(+), 32 deletions(-)

diff --git a/projects/project-notebooks/Regional_precipitation_2024.ipynb b/projects/project-notebooks/Regional_precipitation_2024.ipynb
index f9809f2c0..179bbd44d 100644
--- a/projects/project-notebooks/Regional_precipitation_2024.ipynb
+++ b/projects/project-notebooks/Regional_precipitation_2024.ipynb
@@ -117,44 +117,52 @@
     "from IPython.display import IFrame\n",
     "from IPython.display import display\n",
     "\n",
-    "\n",
     "class PlayVideo(IFrame):\n",
-    "  def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
-    "    self.id = id\n",
-    "    if source == 'Bilibili':\n",
-    "      src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
-    "    elif source == 'Osf':\n",
-    "      src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
-    "    super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
+    "    def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
+    "        self.id = id\n",
+    "        if source == \"Bilibili\":\n",
+    "            src = f\"https://player.bilibili.com/player.html?bvid={id}&page={page}\"\n",
+    "        elif source == \"Osf\":\n",
+    "            src = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render\"\n",
+    "        super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
     "\n",
     "\n",
     "def display_videos(video_ids, W=400, H=300, fs=1):\n",
-    "  tab_contents = []\n",
-    "  for i, video_id in enumerate(video_ids):\n",
-    "    out = widgets.Output()\n",
-    "    with out:\n",
-    "      if video_ids[i][0] == 'Youtube':\n",
-    "        video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
-    "                             height=H, fs=fs, rel=0)\n",
-    "        print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
-    "      else:\n",
-    "        video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
-    "                          height=H, fs=fs, autoplay=False)\n",
-    "        if video_ids[i][0] == 'Bilibili':\n",
-    "          print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
-    "        elif video_ids[i][0] == 'Osf':\n",
-    "          print(f'Video available at https://osf.io/{video.id}')  \n",
-    "      display(video)\n",
-    "    tab_contents.append(out)\n",
-    "  return tab_contents\n",
-    "\n",
-    "\n",
-    "video_ids = [('Youtube', '<video_id_1>'), ('Bilibili', '<video_id_2>'), ('Osf', '<video_id_3>')]\n",
-    "tab_contents = display_videos(video_ids, W=854, H=480)\n",
+    "    tab_contents = []\n",
+    "    for i, video_id in enumerate(video_ids):\n",
+    "        out = widgets.Output()\n",
+    "        with out:\n",
+    "            if video_ids[i][0] == \"Youtube\":\n",
+    "                video = YouTubeVideo(\n",
+    "                    id=video_ids[i][1], width=W, height=H, fs=fs, rel=0\n",
+    "                )\n",
+    "                print(f\"Video available at https://youtube.com/watch?v={video.id}\")\n",
+    "            else:\n",
+    "                video = PlayVideo(\n",
+    "                    id=video_ids[i][1],\n",
+    "                    source=video_ids[i][0],\n",
+    "                    width=W,\n",
+    "                    height=H,\n",
+    "                    fs=fs,\n",
+    "                    autoplay=False,\n",
+    "                )\n",
+    "                if video_ids[i][0] == \"Bilibili\":\n",
+    "                    print(\n",
+    "                        f\"Video available at https://www.bilibili.com/video/{video.id}\"\n",
+    "                    )\n",
+    "                elif video_ids[i][0] == \"Osf\":\n",
+    "                    print(f\"Video available at https://osf.io/{video.id}\")\n",
+    "            display(video)\n",
+    "        tab_contents.append(out)\n",
+    "    return tab_contents\n",
+    "\n",
+    "\n",
+    "video_ids = [('Youtube', '49XHRe61LI8'), ('Bilibili', 'BV1Au411L7fo')]\n",
+    "tab_contents = display_videos(video_ids, W=730, H=410)\n",
     "tabs = widgets.Tab()\n",
     "tabs.children = tab_contents\n",
     "for i in range(len(tab_contents)):\n",
-    "  tabs.set_title(i, video_ids[i][0])\n",
+    "    tabs.set_title(i, video_ids[i][0])\n",
     "display(tabs)"
    ]
   },
@@ -192,7 +200,7 @@
     "execution": {}
    },
    "source": [
-    "Provide a description of what students will do in the project"
+    "**In this project**, you will explore rain gauge and satellite data from CHIRPS and NOAA Terrestrial Climate Data Records NDVI datasets to extract rain estimates and land surface reflectance, respectively. This data will enable identification of extreme events in your region of interest. Besides investigating the relationships between these variables, you are encouraged to study the impact of extreme events on changes in vegetation."
    ]
   },
   {

From 45450eb05acd539543e3de70b3e7294e46ba9ffa Mon Sep 17 00:00:00 2001
From: wg4031 <wg4031@pu-h40v96gpd9.mynetworksettings.com>
Date: Wed, 20 Mar 2024 18:22:56 -0400
Subject: [PATCH 08/65] add data loaders to precip notebook

---
 .../Regional_precipitation_2024.ipynb         | 170 +++++++++++++-----
 1 file changed, 125 insertions(+), 45 deletions(-)

diff --git a/projects/project-notebooks/Regional_precipitation_2024.ipynb b/projects/project-notebooks/Regional_precipitation_2024.ipynb
index 179bbd44d..de499f25f 100644
--- a/projects/project-notebooks/Regional_precipitation_2024.ipynb
+++ b/projects/project-notebooks/Regional_precipitation_2024.ipynb
@@ -283,10 +283,16 @@
    "source": [
     "# @title Helper functions\n",
     "\n",
-    "# If any helper functions you want to hide for clarity (that has been seen before\n",
-    "# or is simple/uniformative), add here\n",
-    "# If helper code depends on libraries that aren't used elsewhere,\n",
-    "# import those libaries here, rather than in the main import cell"
+    "def pooch_load(filelocation=None,filename=None,processor=None):\n",
+    "    shared_location='/home/jovyan/shared/Data/Projects/Precipitation' # this is different for each day\n",
+    "    user_temp_cache=tempfile.gettempdir()\n",
+    "    \n",
+    "    if os.path.exists(os.path.join(shared_location,filename)):\n",
+    "        file = os.path.join(shared_location,filename)\n",
+    "    else:\n",
+    "        file = pooch.retrieve(filelocation,known_hash=None,fname=os.path.join(user_temp_cache,filename),processor=processor)\n",
+    "\n",
+    "    return file"
    ]
   },
   {
@@ -308,9 +314,33 @@
    "id": "02a3777f",
    "metadata": {},
    "source": [
-    "# [datasource-1]\n",
+    "## CHIRPS Version 2.0 Global Daily 0.25°\n",
+    "\n",
+    "The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a high-resolution precipitation dataset developed by the Climate Hazards Group at the University of California, Santa Barbara. It combines satellite-derived precipitation estimates with ground-based station data to provide gridded precipitation data at a quasi-global scale between 50°S-50°N. \n",
+    "\n",
+    "Read more about CHIRPS here:\n",
+    "\n",
+    "* [The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes](https://www.nature.com/articles/sdata201566)\n",
+    "\n",
+    "* [Climate Hazard Group CHG Wiki](https://wiki.chc.ucsb.edu/CHIRPS_FAQ)\n",
+    "\n",
+    "* [Data storage location](https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88190ac8",
+   "metadata": {},
+   "source": [
+    "### Indices for Extreme Events\n",
+    "The Expert Team on Climate Change Detection and Indices ([ETCCDI]( http://etccdi.pacificclimate.org/list_27_indices.shtml)) has defined various indices that focus on different aspects such as duration or intensity of extreme events. The following functions provide examples of how to compute indices for each category. You can modify these functions to suit your specific needs or create your own custom functions. Here are some tips you can use:\n",
+    "\n",
+    "- Most of the indices require daily data, so in order to select a specific season you can just use xarray to subset the data. Example:\n",
     "\n",
-    "Data source description. You may provide some background that motivated the generation of the associated datasets using hyperlinks to reference relevant papers. Please outline how the data has been collected or generated! Remember to provide credit to dataset authors in line with their copyright policy!\n"
+    "`daily_precip_DJF = data_chirps.sel(time=data_chirps['time.season']=='DJF')`\n",
+    "\n",
+    "- A common threshold for a wet event is precipitation greater than or equal to 1mm/day, while a dry (or non-precipitating) event is defined as precipitation less than 1mm/day.\n",
+    "- Some of the indices are based on percentiles. You can define a base period climatology to calculate percentile thresholds, such as the 5th, 10th, 90th, and 95th percentiles, to determine extreme events."
    ]
   },
   {
@@ -320,37 +350,93 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Data source-specific imports"
+    "def calculate_cdd_index(data):\n",
+    "    \"\"\"\n",
+    "    This function takes a daily precipitation dataset as input and calculates\n",
+    "    the Consecutive Dry Days (CDD) index, which represents the longest sequence\n",
+    "    of consecutive days with precipitation less than 1mm. The input data should\n",
+    "    be a DataArray with time coordinates, and the function returns a DataArray\n",
+    "    with the CDD values for each unique year in the input data.\n",
+    "    Parameters:\n",
+    "    ----------\n",
+    "      - data (xarray.DataArray): The input daily precipitation data should be\n",
+    "      a dataset (eg. for chirps_data the SataArray would be chirps_data.precip)\n",
+    "    Returns:\n",
+    "    -------\n",
+    "      - cdd (xarray.DataArray): The calculated CDD index\n",
+    "\n",
+    "    \"\"\"\n",
+    "    # create a boolean array for dry days (PR < 1mm)\n",
+    "    dry_days = data < 1\n",
+    "    # initialize CDD array\n",
+    "    cdd = np.zeros(len(data.groupby(\"time.year\")))\n",
+    "    # get unique years as a list\n",
+    "    unique_years = list(data.groupby(\"time.year\").groups.keys())\n",
+    "    # iterate for each day\n",
+    "    for i, year in enumerate(unique_years):\n",
+    "        consecutive_trues = []\n",
+    "        current_count = 0\n",
+    "        for day in dry_days.sel(time=data[\"time.year\"] == year).values:\n",
+    "            if day:\n",
+    "                current_count += 1\n",
+    "            else:\n",
+    "                if current_count > 0:\n",
+    "                    consecutive_trues.append(current_count)\n",
+    "                    current_count = 0\n",
+    "        if current_count > 0:\n",
+    "            consecutive_trues.append(current_count)\n",
+    "        # print(consecutive_trues)\n",
+    "        # CDD is the largest number of consecutive days\n",
+    "        cdd[i] = np.max(consecutive_trues)\n",
+    "    # transform to dataset\n",
+    "    cdd_da = xr.DataArray(cdd, coords={\"year\": unique_years}, dims=\"year\")\n",
+    "    return cdd_da"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "a4660fdc",
+   "execution_count": null,
+   "id": "196de237",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Data source-specific functions"
+    "# code to retrieve and load the data\n",
+    "\n",
+    "years=range(1981,2024) # the years you want. we want 1981 till 2023\n",
+    "file_paths=['https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.'+str(year)+'.days_p25.nc' for year in years] # the format of the files\n",
+    "filenames=['chirps-v2.0.'+str(year)+'.days_p25.nc' for year in years] # the format of the files\n",
+    "\n",
+    "downloaded_files=[ pooch_load(fpath,fname) for (fpath,fname) in zip(file_paths,filenames)] # download all of the files\n",
+    "\n",
+    "#### open data as xarray\n",
+    "chirps_data = xr.open_mfdataset(\n",
+    "    downloaded_files, combine=\"by_coords\"\n",
+    ")  # open the files as one dataset"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "1d4ab262",
-   "metadata": {
-    "execution": {}
-   },
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4c70cbc",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "## [datasource-1]: Dataset 1: "
+    "# code to print the shape, array names, etc of the dataset\n",
+    "chirps_data"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6fa21050",
+   "id": "1d4ab262",
    "metadata": {
     "execution": {}
    },
    "source": [
-    "Further background on the particular dataset if applicable. "
+    "## FAO Data: Cereal Production\n",
+    "\n",
+    "Cereal production is a crucial component of global agriculture and food security. The [Food and Agriculture Organization](https://www.fao.org/faostat/en/#data/QCL) collects and provides data on cereal production, which includes crops such as wheat, rice, maize, barley, oats, rye, sorghum, millet, and mixed grains. The data covers various indicators such as production quantity, area harvested, yield, and production value.\n",
+    "\n",
+    "The FAO also collects data on land under cereals production, which refers to the area of land that is being used to grow cereal crops. This information can be valuable for assessing the productivity and efficiency of cereal production systems in different regions, as well as identifying potential areas for improvement. Overall, the FAO's data on cereal production and land under cereals production is an important resource for policymakers, researchers, and other stakeholders who are interested in understanding global trends in agriculture and food security."
    ]
   },
   {
@@ -360,7 +446,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Dataset-specific imports"
+    "filename_cereal = 'data_cereal_land.csv'\n",
+    "url_cereal = 'https://raw.githubusercontent.com/Sshamekh/Heatwave/f85f43997e3d6ae61e5d729bf77cfcc188fbf2fd/data_cereal_land.csv'\n",
+    "ds_cereal_land = pd.read_csv(pooch_load(url_cereal,filename_cereal))\n",
+    "ds_cereal_land.head() "
    ]
   },
   {
@@ -370,7 +459,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Dataset-specific functions"
+    "# example\n",
+    "ds_cereal_land[(ds_cereal_land[\"Country Name\"] == \"Brazil\")].reset_index(\n",
+    "    drop=True\n",
+    ").iloc[0].transpose()"
    ]
   },
   {
@@ -427,30 +519,6 @@
     "Consider adding some encouraging words along the lines of \"Now you are all set to address the questions you are interested in!\""
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "9578b229",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "## [datasource 1]: Dataset 2:\n",
-    "\n",
-    "Further background on the particular datafile/variables if applicable."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ee7c9ef5",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "## [datasource 2]:\n",
-    "\n",
-    "Data source description. You may provide some background that motivated the generation of the associated datasets using hyperlinks to reference relevant papers. Please outline how the data has been collected or generated! Remember to provide credit to dataset authors in line with their copyright policy!"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "c62a0f3e",
@@ -468,8 +536,20 @@
     "execution": {}
    },
    "source": [
-    "Use bullet points to list further readings\n"
+    "- Anyamba, A. and Tucker, C.J., 2012. Historical perspective of AVHRR NDVI and vegetation drought monitoring. Remote sensing of drought: innovative monitoring approaches, 23, pp.20.[https://digitalcommons.unl.edu/nasapub/217/](https://digitalcommons.unl.edu/nasapub/217/)\n",
+    "- Zhang, X., Alexander, L., Hegerl, G.C., Jones, P., Tank, A.K., Peterson, T.C., Trewin, B. and Zwiers, F.W., 2011. Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdisciplinary Reviews: Climate Change, 2(6), pp.851-870.\n",
+    "- Schultz, P. A., and M. S. Halpert. \"Global correlation of temperature, NDVI and precipitation.\" Advances in Space Research 13.5 (1993): 277-280. \n",
+    "- Seneviratne, S.I. et al., 2021: Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V. et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1513–1766, https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/\n",
+    "- IPCC, 2021: Annex VI: Climatic Impact-driver and Extreme Indices [Gutiérrez J.M. et al.(eds.)]. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V. et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2205–2214, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_AnnexVI.pdf"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31380215",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 1c0c6e977d1b091c8c070f982f9efecaefd72ca3 Mon Sep 17 00:00:00 2001
From: wg4031 <wg4031@pu-h40v96gpd9.mynetworksettings.com>
Date: Wed, 20 Mar 2024 18:33:10 -0400
Subject: [PATCH 09/65] update FAO data description

---
 .../Regional_precipitation_2024.ipynb         | 50 ++++++-------------
 1 file changed, 15 insertions(+), 35 deletions(-)

diff --git a/projects/project-notebooks/Regional_precipitation_2024.ipynb b/projects/project-notebooks/Regional_precipitation_2024.ipynb
index de499f25f..ba0f05592 100644
--- a/projects/project-notebooks/Regional_precipitation_2024.ipynb
+++ b/projects/project-notebooks/Regional_precipitation_2024.ipynb
@@ -200,7 +200,7 @@
     "execution": {}
    },
    "source": [
-    "**In this project**, you will explore rain gauge and satellite data from CHIRPS and NOAA Terrestrial Climate Data Records NDVI datasets to extract rain estimates and land surface reflectance, respectively. This data will enable identification of extreme events in your region of interest. Besides investigating the relationships between these variables, you are encouraged to study the impact of extreme events on changes in vegetation."
+    "**In this project**, you will explore rain gauge and satellite data from the CHIRPS data set to extract rain estimates and land surface reflectance, respectively. These data will enable identification of extreme events in your region of interest. Besides investigating the relationships between these variables, you are encouraged to study the impact of extreme events on changes in vegetation."
    ]
   },
   {
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "88190ac8",
+   "id": "3f36c1fa",
    "metadata": {},
    "source": [
     "### Indices for Extreme Events\n",
@@ -396,7 +396,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "196de237",
+   "id": "ad439e00",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -414,10 +414,18 @@
     ")  # open the files as one dataset"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "660f8525",
+   "metadata": {},
+   "source": [
+    "We can now visualize the content of the dataset."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b4c70cbc",
+   "id": "114e4a72",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -436,7 +444,7 @@
     "\n",
     "Cereal production is a crucial component of global agriculture and food security. The [Food and Agriculture Organization](https://www.fao.org/faostat/en/#data/QCL) collects and provides data on cereal production, which includes crops such as wheat, rice, maize, barley, oats, rye, sorghum, millet, and mixed grains. The data covers various indicators such as production quantity, area harvested, yield, and production value.\n",
     "\n",
-    "The FAO also collects data on land under cereals production, which refers to the area of land that is being used to grow cereal crops. This information can be valuable for assessing the productivity and efficiency of cereal production systems in different regions, as well as identifying potential areas for improvement. Overall, the FAO's data on cereal production and land under cereals production is an important resource for policymakers, researchers, and other stakeholders who are interested in understanding global trends in agriculture and food security."
+    "The FAO also collects data on \"area harvested\", which refers to the area of land that is being used to grow cereal crops. This information can be valuable for assessing the productivity and efficiency of cereal production systems in different regions, as well as identifying potential areas for improvement. Overall, the FAO's data on cereal production and land under cereals production is an important resource for policymakers, researchers, and other stakeholders who are interested in understanding global trends in agriculture and food security."
    ]
   },
   {
@@ -465,34 +473,6 @@
     ").iloc[0].transpose()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "92175f59",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Code to retrieve and load the data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "392c0e71",
-   "metadata": {},
-   "source": [
-    "Give advice on the required preprocessing. If applicable, refer to specific tutorials and let the students preprocess the data themselves. Otherwise, explain/illustrate the reasons for preprocessing and provide either the code itself (if students are likely to struggle on their own) or written guidance on the functions, data filtering thresholds, etc they should use."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3bae0683",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Code to preprocess data\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "73f4f21b",
@@ -516,7 +496,7 @@
    "id": "3543b122",
    "metadata": {},
    "source": [
-    "Consider adding some encouraging words along the lines of \"Now you are all set to address the questions you are interested in!\""
+    "Now you are all set to address the questions you are interested in!"
    ]
   },
   {
@@ -546,7 +526,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31380215",
+   "id": "80a8bf0d",
    "metadata": {},
    "outputs": [],
    "source": []

From cc7724bdfcf26f689c6eba0af5700c539474beae Mon Sep 17 00:00:00 2001
From: wg4031 <wg4031@pu-h40v96gpd9.mynetworksettings.com>
Date: Wed, 27 Mar 2024 17:01:58 -0400
Subject: [PATCH 10/65] added OA materials

---
 .../2024_Ocean_acidification.ipynb            | 430 ++++++++++++++++++
 ...pynb => 2024_Regional_precipitation.ipynb} |   2 +-
 .../2024_OceanAcidification.svg               |   1 +
 ...tation_2024.svg => 2024_Precipitation.svg} |   0
 .../template-images/Template_questions.svg    |   1 +
 5 files changed, 433 insertions(+), 1 deletion(-)
 create mode 100644 projects/project-notebooks/2024_Ocean_acidification.ipynb
 rename projects/project-notebooks/{Regional_precipitation_2024.ipynb => 2024_Regional_precipitation.ipynb} (99%)
 create mode 100644 projects/template-images/2024_OceanAcidification.svg
 rename projects/template-images/{Precipitation_2024.svg => 2024_Precipitation.svg} (100%)
 create mode 100644 projects/template-images/Template_questions.svg

diff --git a/projects/project-notebooks/2024_Ocean_acidification.ipynb b/projects/project-notebooks/2024_Ocean_acidification.ipynb
new file mode 100644
index 000000000..7d37f7470
--- /dev/null
+++ b/projects/project-notebooks/2024_Ocean_acidification.ipynb
@@ -0,0 +1,430 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {},
+    "id": "x1WZhSN61YBE"
+   },
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ClimateMatchAcademy/course-content/blob/main/projects/project-notebooks/Ocean_acidification.ipynb) &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/ClimateMatchAcademy/course-content/main/projects/project-notebooks/Ocean_acidification.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "avjFjFXwWqyW"
+   },
+   "source": [
+    "# **Ocean Acidification**\n",
+    "\n",
+    "\n",
+    "**Content creators:** C. Gabriela Mayorga Adame, Lidia Krinova\n",
+    "\n",
+    "**Content reviewers:** Jenna Pearson, Abigail Bodner, Ohad Zivan, Chi Zhang\n",
+    "\n",
+    "**Content editors:** Zane Mitrevica, Natalie Steinemann, Ohad Zivan, Chi Zhang, Jenna Pearson\n",
+    "\n",
+    "**Production editors:** Wesley Banfield, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
+    "\n",
+    "**Our 2023 Sponsors:** NASA TOPS, Google DeepMind"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "umnBeSiJeqYo",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# @title Project Background\n",
+    "\n",
+    "from ipywidgets import widgets\n",
+    "from IPython.display import YouTubeVideo\n",
+    "from IPython.display import IFrame\n",
+    "from IPython.display import display\n",
+    "\n",
+    "\n",
+    "class PlayVideo(IFrame):\n",
+    "    def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
+    "        self.id = id\n",
+    "        if source == \"Bilibili\":\n",
+    "            src = f\"https://player.bilibili.com/player.html?bvid={id}&page={page}\"\n",
+    "        elif source == \"Osf\":\n",
+    "            src = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render\"\n",
+    "        super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
+    "\n",
+    "\n",
+    "def display_videos(video_ids, W=400, H=300, fs=1):\n",
+    "    tab_contents = []\n",
+    "    for i, video_id in enumerate(video_ids):\n",
+    "        out = widgets.Output()\n",
+    "        with out:\n",
+    "            if video_ids[i][0] == \"Youtube\":\n",
+    "                video = YouTubeVideo(\n",
+    "                    id=video_ids[i][1], width=W, height=H, fs=fs, rel=0\n",
+    "                )\n",
+    "                print(f\"Video available at https://youtube.com/watch?v={video.id}\")\n",
+    "            else:\n",
+    "                video = PlayVideo(\n",
+    "                    id=video_ids[i][1],\n",
+    "                    source=video_ids[i][0],\n",
+    "                    width=W,\n",
+    "                    height=H,\n",
+    "                    fs=fs,\n",
+    "                    autoplay=False,\n",
+    "                )\n",
+    "                if video_ids[i][0] == \"Bilibili\":\n",
+    "                    print(\n",
+    "                        f\"Video available at https://www.bilibili.com/video/{video.id}\"\n",
+    "                    )\n",
+    "                elif video_ids[i][0] == \"Osf\":\n",
+    "                    print(f\"Video available at https://osf.io/{video.id}\")\n",
+    "            display(video)\n",
+    "        tab_contents.append(out)\n",
+    "    return tab_contents\n",
+    "\n",
+    "\n",
+    "video_ids = [('Youtube', 'NAgrB8HxMMk'), ('Bilibili', 'BV1fM4y1x7g8')]\n",
+    "tab_contents = display_videos(video_ids, W=730, H=410)\n",
+    "tabs = widgets.Tab()\n",
+    "tabs.children = tab_contents\n",
+    "for i in range(len(tab_contents)):\n",
+    "    tabs.set_title(i, video_ids[i][0])\n",
+    "display(tabs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "TB8_jibV1YBM"
+   },
+   "outputs": [],
+   "source": [
+    "# @title Tutorial slides\n",
+    "# @markdown These are the slides for the videos in all tutorials today\n",
+    "from IPython.display import IFrame\n",
+    "link_id = \"n7wdy\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DZbqRlQKXhnc"
+   },
+   "source": [
+    "Human activities release CO2 into the atmosphere, which leads to atmospheric warming and climate change. A portion of this CO2 released by human activities is absorbed into the oceans, which has a direct, chemical effect on seawater, known as ocean acidification. When CO2 combines with water in the ocean it forms carbonic acid, which makes the ocean more acidic and can have negative impacts on certain marine ecosystems (e.g., reduce the ability of calcifying organisms to form their shells and skeletons). The degree of ocean acidification is often expressed in terms of the pH of seawater, which is the measure of acidity or alkalinity such that a pH below 7 is considered acidic, and a pH greater than 7 is considered alkaline, or basic. Additional background information on ocean acidification can be found [here](https://coastadapt.com.au/ocean-acidification-and-its-effects). In this project, you will explore spatial and temporal patterns of and relationships between pH, CO2, and temperature to assess changes in ocean acidification and the impact on marine ecosystems.\n",
+    "\n",
+    "**In this project**, you will analyse ocean model and observational data from global databases to extract variables like pH, CO<sub>2</sub>, and temperature, and to investigate ocean acidification process in your region of interest. This project will also be an opportunity to investigate the relationships between these variables as well as their impact on the marine ecosystems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "B2hG2V_2veFC"
+   },
+   "source": [
+    "# Project Template\n",
+    "\n",
+    "<img src='../template-images/2024_OceanAcidification.svg'/>\n",
+    "\n",
+    "*Note: The dashed boxes are socio-economic questions.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-uS_n9-3YJrZ"
+   },
+   "source": [
+    "# Data Exploration Notebook\n",
+    "## Project Setup\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "drh-swqCsb9k",
+    "tags": [
+     "colab"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# google colab installs\n",
+    "\n",
+    "# !mamaba install netCDF4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9nDg6MeVY1CX",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pooch\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import tempfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "8GNKCquN1YBV"
+   },
+   "outputs": [],
+   "source": [
+    "# helper functions\n",
+    "\n",
+    "def pooch_load(filelocation=None,filename=None,processor=None):\n",
+    "    shared_location='/home/jovyan/shared/Data/Projects/Ocean_Acidification' # this is different for each day\n",
+    "    user_temp_cache=tempfile.gettempdir()\n",
+    "\n",
+    "    if os.path.exists(os.path.join(shared_location,filename)):\n",
+    "        file = os.path.join(shared_location,filename)\n",
+    "    else:\n",
+    "        file = pooch.retrieve(filelocation,known_hash=None,fname=os.path.join(user_temp_cache,filename),processor=processor)\n",
+    "\n",
+    "    return file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "iYblWn8cZAGT"
+   },
+   "source": [
+    "## NOAA Ocean pH and Acidity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "W43m6TQBZrpe"
+   },
+   "source": [
+    "### Global surface ocean acidification indicators from 1750 to 2100 (NCEI Accession 0259391)\n",
+    "\n",
+    "This data package contains a hybrid surface ocean acidification (OA) data product that is produced based on three recent observational data products:\n",
+    "- Surface Ocean CO2 Atlas (SOCAT, version 2022)\n",
+    "- Global Ocean Data Analysis Product version 2 (GLODAPv2, version 2022)\n",
+    "- Coastal Ocean Data Analysis Product in North America (CODAP-NA, version 2021), and 14 Earth System Models from the sixth phase of the Coupled Model Intercomparison Project ([CMIP6](https://github.com/ClimateMatchAcademy/course-content/blob/main/tutorials/CMIP/CMIP_resource_bank.md)).\n",
+    "\n",
+    "The trajectories of ten OA indicators are included in this data product:\n",
+    "- Fugacity of carbon dioxide\n",
+    "- pH on Total Scale\n",
+    "- Total hydrogen ion content\n",
+    "- Free hydrogen ion content\n",
+    "- Carbonate ion content\n",
+    "- Aragonite saturation state\n",
+    "- Calcite saturation state\n",
+    "- Revelle Factor\n",
+    "- Total dissolved inorganic carbon content\n",
+    "- Total alkalinity content\n",
+    "\n",
+    "These OA trajectories are provided under preindustrial conditions, historical conditions, and future Shared Socioeconomic Pathways: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 from 1750 to 2100 on a global surface ocean grid. These OA trajectories are improved relative to previous OA data products with respect to data quantity, spatial and temporal coverage, diversity of the underlying data and model simulations, and the provided SSPs over the 21st century.\n",
+    "\n",
+    "**Citation:**\n",
+    "Jiang, L.-Q., Dunne, J., Carter, B. R., Tjiputra, J. F., Terhaar, J., Sharp, J. D., et al. (2023). Global surface ocean acidification indicators from 1750 to 2100. Journal of Advances in Modeling Earth Systems, 15, e2022MS003563. https://doi.org/10.1029/2022MS003563\n",
+    "\n",
+    "**Dataset**: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0259391.html\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xqiXjDKC9G2M"
+   },
+   "source": [
+    "We can load and visualize the **surface pH** as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "mZMo14eJs26r",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# code to retrieve and load the data\n",
+    "# url_SurfacepH= 'https://www.ncei.noaa.gov/data/oceans/ncei/ocads/data/0206289/Surface_pH_1770_2100/Surface_pH_1770_2000.nc' $ old CMIP5 dataset\n",
+    "filename_SurfacepH='pHT_median_historical.nc'\n",
+    "url_SurfacepH='https://www.ncei.noaa.gov/data/oceans/ncei/ocads/data/0259391/nc/median/pHT_median_historical.nc'\n",
+    "ds_pH = xr.open_dataset(pooch_load(url_SurfacepH,filename_SurfacepH))\n",
+    "ds_pH"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "S6fZBOG00xW6"
+   },
+   "source": [
+    "For those feeling adventurouts, there are also files of future projected changes under various scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, recall W2D1 tutorials):\n",
+    "*   pHT_median_ssp119.nc\n",
+    "*   pHT_median_ssp126.nc\n",
+    "*   pHT_median_ssp245.nc\n",
+    "*   pHT_median_ssp370.nc\n",
+    "*   pHT_median_ssp585.nc\n",
+    "\n",
+    "To load them, replace the filename in the path/filename line above. These data were calculated from CMIP6 models. To learn more about CMIP please see our [CMIP Resource Bank](https://github.com/ClimateMatchAcademy/course-content/blob/main/tutorials/CMIP/CMIP_resource_bank.md) and the [CMIP website](https://wcrp-cmip.org/).  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OcwO6XpY3rOS"
+   },
+   "source": [
+    "For the advanced questions in red you can use **sea surface temperature** and **CO<sub>2</sub> concentration** from [NOAA Global Monitoring Laboratory](https://gml.noaa.gov/ccgg/trends/gl_data.html)) which were previously introduced in the tutorials.\n",
+    "We can load and visualize this variables as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "rF3Z4CwmwE1K",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "filename_CO2= 'co2_mm_gl.csv'\n",
+    "url_CO2= 'https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_gl.csv'\n",
+    "ds_CO2 = pd.read_csv(pooch_load(url_CO2,filename_CO2),header=55)\n",
+    "\n",
+    "ds_CO2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ZkjKVTMN1YBc",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# from W1D3 tutorial 6 we have Sea Surface Temprature from 1981 to the present:\n",
+    "# download the monthly sea surface temperature data from NOAA Physical System\n",
+    "# Laboratory. The data is processed using the OISST SST Climate Data Records\n",
+    "# from the NOAA CDR program.\n",
+    "# the data downloading may take 2-3 minutes to complete.\n",
+    "\n",
+    "filename_sst='sst.mon.mean.nc'\n",
+    "url_sst = \"https://osf.io/6pgc2/download/\"\n",
+    "\n",
+    "ds_SST = xr.open_dataset(pooch_load(url_sst,filename_sst))\n",
+    "ds_SST"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tiOBsy9wF-59"
+   },
+   "source": [
+    "**Hints for socio-economic question (dashed boxes):**\n",
+    "\n",
+    "Use the attached image (Figure 4 in [Kroaker et al., 2013]( https://doi.org/10.1111/gcb.12179) research paper) and this [mapping tool](https://mapper.obis.org/). Search for species of interest on the mapping tool to see their spatial global distribution and changes over time. Be critical about what you see."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z9W0HPU01YBh"
+   },
+   "source": [
+    "\n",
+    "![effects of ocean acidification](https://onlinelibrary.wiley.com/cms/asset/e3670b99-729f-42e6-9f5a-b2a67d5702ca/gcb12179-fig-0004-m.png)\n",
+    "\n",
+    "Summary of effects of acidification among key taxonomic groups. Effects are represented as either mean percent (+) increase or percent (−) decrease in a given response."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eQkNW-g61YBi"
+   },
+   "source": [
+    "# Further Reading\n",
+    "\n",
+    "- Understanding Ocean Acidification\", NOAA (https://www.fisheries.noaa.gov/insight/understanding-ocean-acidification)\n",
+    "- \"Ocean acidification and its effects\", CoastAdapt (https://coastadapt.com.au/ocean-acidification-and-its-effects)\n",
+    "- \"Scientists Pinpoint How Ocean Acidification Weakens Coral Skeletons\", WHOI (https://www.whoi.edu/press-room/news-release/scientists-identify-how-ocean-acidification-weakens-coral-skeletons/)\n",
+    "- \"Ocean acidification and reefs\", Smithonian Tropical Research Institute (https://stri.si.edu/story/ocean-acidification-and-reefs)\n",
+    "- Ocean Acidification | Learn Science at Scitable https://www.nature.com/scitable/knowledge/library/ocean-acidification-25822734/\n",
+    "- Henry, J., J. Patterson, and L. Krimsky. 2020. “Ocean Acidification: Calcifying Marine Organisms: FA220, 3/2020”. EDIS 2020 (2):4. https://doi.org/10.32473/edis-fa220-2020.\n",
+    "- Lauderdale, J. M., S. Dutkiewicz, R. G. Williams, and M. J. Follows (2016), Quantifying the drivers of ocean-atmosphere CO2 fluxes, Global Biogeochem. Cycles, 30  https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016GB005400\n",
+    "- Increasingly Acidic Oceans Are Causing Fish to Behave Badly | Scientific American https://www.scientificamerican.com/article/increasingly-acidic-oceans-are-causing-fish-to-behave-badly/\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JsPWOtzV1YBj"
+   },
+   "source": [
+    "# **Resources**\n",
+    "\n",
+    "This tutorial uses data from the simulations conducted as part of the [CMIP6](https://wcrp-cmip.org/) multi-model ensemble.\n",
+    "\n",
+    "For examples on how to access and analyze data, please visit the [Pangeo Cloud CMIP6 Gallery](https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/index.html)\n",
+    "\n",
+    "For more information on what CMIP is and how to access the data, please see this [page](https://github.com/ClimateMatchAcademy/course-content/blob/main/tutorials/CMIP/CMIP_resource_bank.md)."
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": [
+    {
+     "file_id": "1UrzuI288izT-KofHfuuvFa1qDvOP0NyL",
+     "timestamp": 1711244888691
+    },
+    {
+     "file_id": "https://github.com/ClimateMatchAcademy/course-content/blob/main/projects/project-notebooks/Ocean_acidification.ipynb",
+     "timestamp": 1711242692952
+    }
+   ],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/projects/project-notebooks/Regional_precipitation_2024.ipynb b/projects/project-notebooks/2024_Regional_precipitation.ipynb
similarity index 99%
rename from projects/project-notebooks/Regional_precipitation_2024.ipynb
rename to projects/project-notebooks/2024_Regional_precipitation.ipynb
index ba0f05592..d67b4fb9b 100644
--- a/projects/project-notebooks/Regional_precipitation_2024.ipynb
+++ b/projects/project-notebooks/2024_Regional_precipitation.ipynb
@@ -212,7 +212,7 @@
    "source": [
     "# Project Template\n",
     "\n",
-    "<img src='../template-images/Precipitation_2024.svg'/>"
+    "<img src='../template-images/2024_Precipitation.svg'/>"
    ]
   },
   {
diff --git a/projects/template-images/2024_OceanAcidification.svg b/projects/template-images/2024_OceanAcidification.svg
new file mode 100644
index 000000000..f338af603
--- /dev/null
+++ b/projects/template-images/2024_OceanAcidification.svg
@@ -0,0 +1 @@
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From eabf9d77b8d9660a8aa5d589baf7aa11734b833f Mon Sep 17 00:00:00 2001
From: wg4031 <wg4031@pu-h40v96gpd9.mynetworksettings.com>
Date: Wed, 27 Mar 2024 17:19:49 -0400
Subject: [PATCH 11/65] updated Precip data with NDVI

---
 .../2024_Ocean_acidification.ipynb            |  8 +-
 .../2024_Regional_precipitation.ipynb         | 97 ++++++++++---------
 .../template-images/2024_Precipitation.svg    |  2 +-
 3 files changed, 58 insertions(+), 49 deletions(-)

diff --git a/projects/project-notebooks/2024_Ocean_acidification.ipynb b/projects/project-notebooks/2024_Ocean_acidification.ipynb
index 7d37f7470..9e41c0741 100644
--- a/projects/project-notebooks/2024_Ocean_acidification.ipynb
+++ b/projects/project-notebooks/2024_Ocean_acidification.ipynb
@@ -21,13 +21,13 @@
     "\n",
     "**Content creators:** C. Gabriela Mayorga Adame, Lidia Krinova\n",
     "\n",
-    "**Content reviewers:** Jenna Pearson, Abigail Bodner, Ohad Zivan, Chi Zhang\n",
+    "**Content reviewers:** \n",
     "\n",
-    "**Content editors:** Zane Mitrevica, Natalie Steinemann, Ohad Zivan, Chi Zhang, Jenna Pearson\n",
+    "**Content editors:** \n",
     "\n",
-    "**Production editors:** Wesley Banfield, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
+    "**Production editors:** \n",
     "\n",
-    "**Our 2023 Sponsors:** NASA TOPS, Google DeepMind"
+    "**Our 2023 Sponsors:** "
    ]
   },
   {
diff --git a/projects/project-notebooks/2024_Regional_precipitation.ipynb b/projects/project-notebooks/2024_Regional_precipitation.ipynb
index d67b4fb9b..38267848c 100644
--- a/projects/project-notebooks/2024_Regional_precipitation.ipynb
+++ b/projects/project-notebooks/2024_Regional_precipitation.ipynb
@@ -21,7 +21,7 @@
     "\n",
     "\n",
     "\n",
-    "__Content creators:__ Will Gregory & Laura Paccini\n",
+    "__Content creators:__ Laura Paccini & Will Gregory\n",
     "\n",
     "__Content reviewers:__ Names & Surnames\n",
     "\n",
@@ -32,49 +32,6 @@
     "__Our 2024 Sponsors:__ Sponsors\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "60881a20",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "The notebook should have the following outline:\n",
-    "\n",
-    "- Project background (video + template outline)\n",
-    "- Imports and functions common to multiple data sources\n",
-    "- Data source 1\n",
-    "- Data source background\n",
-    "- Imports and functions common to multiple datasets from the same data source\n",
-    "\n",
-    "- Dataset 1:\n",
-    "\n",
-    "    Dataset background (if needed)\n",
-    "    \n",
-    "    Imports and functions specific to the dataset\n",
-    "    \n",
-    "    Code to load the dataset\n",
-    "    \n",
-    "    Code to pre-process the dataset (if not taught and likely difficult for students)\n",
-    "    \n",
-    "    Code to visualise the data table shape/content\n",
-    "\n",
-    "...\n",
-    "- Dataset N:\n",
-    "    same as for Dataset 1\n",
-    "...\n",
-    "\n",
-    "- Further reading\n",
-    "\n",
-    "---\n",
-    "We encourage you to explain the key parts of your code through comments and text cells as you see fit.\n",
-    "\n",
-    "---\n",
-    "If the same or analogous dataset is covered in the curriculum tutorials, we can expect students to know how to load that data. In that case, please provide only the dataset background and refer students to the respective curriculum day while maintaining the structural hierarchy of the notebook.\n",
-    "\n",
-    "---\n",
-    "If you wish to include any plots in your template, those should follow the standard figure formatting. Please include the Figure settings cell from the tutorial notebook template. Let us know if you have any questions about this or are unsure how to proceed!"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "c08b96da",
@@ -433,6 +390,58 @@
     "chirps_data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c3fb296a",
+   "metadata": {},
+   "source": [
+    "## NOAA Fundamental Climate Data Records (FCDR) AVHRR Land Bundle - Normalized Difference Vegetation Index\n",
+    "\n",
+    "As we learned in the W1D3 tutorials, all the National Atmospheric and Oceanic Administration Climate Data Record (NOAA-CDR) datasets are available both at NOAA National Centers for Environmental Information (NCEI) and commercial cloud platforms. See the link [here](https://registry.opendata.aws/noaa-cdr-terrestrial/). We are accessing the data directly via the [Amazon Web Service (AWS) cloud storage space](https://noaa-cdr-ndvi-pds.s3.amazonaws.com/index.html).\n",
+    "\n",
+    "For this project we recommend using the Normalized Difference Vegetation Index (NDVI). It is one of the most commonly used remotely sensed indices. It measures the \"greeness\" of vegetation, and is useful in understanding vegetation density and assessing changes in plant health. For example, NDVI can be used to study the impacts of droughts, heatwaves, and insect infestations on plants covering Earth's surface. A good overview of this index from this particular sensor can be accessed [here](https://digitalcommons.unl.edu/nasapub/217/).\n",
+    "\n",
+    "Recall what we learned in W1D3 Tutorial 3, the data files on AWS are named systematically:\n",
+    "\n",
+    "> Sensor name: `AVHRR`  \n",
+    "> Product category: `Land`  \n",
+    "> Product version: `v005`  \n",
+    "> Product code: `AVH13C1`  \n",
+    "> Satellite platform: `NOAA-07`  \n",
+    "> Date of the data: `19810624`  \n",
+    "> Processing time: `c20170610041337` (*This will change for each file based on when the file was processed*)  \n",
+    "> File format: `.nc` (*netCDR-4 format*)\n",
+    "\n",
+    "In other words, if we are looking for the data of a specific day, we can easily locate where the file might be. \n",
+    "\n",
+    "For example, if we want to find the AVHRR data for the day of *2002-03-12 (or March 12, 2002)*, you can use:\n",
+    "\n",
+    "`s3://noaa-cdr-ndvi-pds/data/2002/AVHRR-Land_v005_AVH13C1_*_20020312_c*.nc`\n",
+    "\n",
+    "The reasaon that we put `*` in the above directory is because we are not sure about what satellite platform this data is from and when the data was processed. The `*` is called a **wildcard**, and is used because we want *all* the files that contain our specific criteria, but do not want to have to specify all the other pieces of the filename we are not sure about yet. It should return all the data satisfying that initial criteria and you can refine further once you see what is available. Essentially, this first step helps to narrow down the data search. You can then use this to create datasets from the timeframe of your choosing.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e66216a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we can use the data from W1D3 tutorial 3\n",
+    "# to access the NDVI data from AWS S3 bucket, we first need to connect to s3 bucket\n",
+    "\n",
+    "fs = s3fs.S3FileSystem(anon=True)\n",
+    "client = boto3.client('s3', config=botocore.client.Config(signature_version=botocore.UNSIGNED)) # initialize aws s3 bucket client\n",
+    "date_sel = datetime.datetime(2001,1,1,0) \n",
+    "file_location = fs.glob('s3://noaa-cdr-ndvi-pds/data/'+\n",
+    "                        date_sel.strftime('%Y')+'/AVHRR-Land_v005_AVH13C1_*_c*.nc') # the files we want for a whole year\n",
+    "files_for_pooch=[pooch_load('http://s3.amazonaws.com/'+file,file) for file in file_location]\n",
+    "\n",
+    "ds = xr.open_mfdataset(files_for_pooch, combine=\"by_coords\")  # open the file\n",
+    "ds"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1d4ab262",
diff --git a/projects/template-images/2024_Precipitation.svg b/projects/template-images/2024_Precipitation.svg
index b054a1e7b..5776551fd 100644
--- a/projects/template-images/2024_Precipitation.svg
+++ b/projects/template-images/2024_Precipitation.svg
@@ -1 +1 @@
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From 67426e88fe3ae3aff63023c592532414a4a92cec Mon Sep 17 00:00:00 2001
From: wg4031 <wg4031@pu-h40v96gpd9.mynetworksettings.com>
Date: Wed, 27 Mar 2024 18:13:41 -0400
Subject: [PATCH 12/65] update template slide

---
 projects/template-images/2024_Precipitation.svg | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/projects/template-images/2024_Precipitation.svg b/projects/template-images/2024_Precipitation.svg
index 5776551fd..6e26493f6 100644
--- a/projects/template-images/2024_Precipitation.svg
+++ b/projects/template-images/2024_Precipitation.svg
@@ -1 +1 @@
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From a5217f12840260ffbdbaed18e20849a25f1c4a58 Mon Sep 17 00:00:00 2001
From: William-gregory <william.gregory@outlook.com>
Date: Tue, 9 Apr 2024 11:04:57 -0400
Subject: [PATCH 13/65] add seaice project

---
 projects/project-notebooks/2024_SeaIce.ipynb | 408 +++++++++++++++++++
 projects/template-images/2024_SeaIce.svg     |   1 +
 2 files changed, 409 insertions(+)
 create mode 100644 projects/project-notebooks/2024_SeaIce.ipynb
 create mode 100644 projects/template-images/2024_SeaIce.svg

diff --git a/projects/project-notebooks/2024_SeaIce.ipynb b/projects/project-notebooks/2024_SeaIce.ipynb
new file mode 100644
index 000000000..9610ecb88
--- /dev/null
+++ b/projects/project-notebooks/2024_SeaIce.ipynb
@@ -0,0 +1,408 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ada5523b",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/neuromatch/course-content-template/blob/main/projects/example_dataset/Example_based_on_CMA_structure.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/course-content-template/main/projects/example_dataset/Example_based_on_CMA_structure\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "045624d0",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "<h1>\n",
+    "Sea ice changes\n",
+    "</h1>\n",
+    "\n",
+    "\n",
+    "\n",
+    "__Content creators:__ Will Gregory\n",
+    "\n",
+    "__Content reviewers:__ Names & Surnames\n",
+    "\n",
+    "__Content editors:__ Konstantinos Tsafatinos\n",
+    "\n",
+    "__Production editors:__ Konstantinos Tsafatinos\n",
+    "\n",
+    "__Our 2024 Sponsors:__ Sponsors\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c08b96da",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "To include videos:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "64da422f",
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc75a50d6fd94a54812d7f38975591ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tab(children=(Output(), Output()), _titles={'0': 'Youtube', '1': 'Bilibili'})"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# @title Project Background\n",
+    "\n",
+    "from ipywidgets import widgets\n",
+    "from IPython.display import YouTubeVideo\n",
+    "from IPython.display import IFrame\n",
+    "from IPython.display import display\n",
+    "\n",
+    "class PlayVideo(IFrame):\n",
+    "    def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
+    "        self.id = id\n",
+    "        if source == \"Bilibili\":\n",
+    "            src = f\"https://player.bilibili.com/player.html?bvid={id}&page={page}\"\n",
+    "        elif source == \"Osf\":\n",
+    "            src = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render\"\n",
+    "        super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
+    "\n",
+    "\n",
+    "def display_videos(video_ids, W=400, H=300, fs=1):\n",
+    "    tab_contents = []\n",
+    "    for i, video_id in enumerate(video_ids):\n",
+    "        out = widgets.Output()\n",
+    "        with out:\n",
+    "            if video_ids[i][0] == \"Youtube\":\n",
+    "                video = YouTubeVideo(\n",
+    "                    id=video_ids[i][1], width=W, height=H, fs=fs, rel=0\n",
+    "                )\n",
+    "                print(f\"Video available at https://youtube.com/watch?v={video.id}\")\n",
+    "            else:\n",
+    "                video = PlayVideo(\n",
+    "                    id=video_ids[i][1],\n",
+    "                    source=video_ids[i][0],\n",
+    "                    width=W,\n",
+    "                    height=H,\n",
+    "                    fs=fs,\n",
+    "                    autoplay=False,\n",
+    "                )\n",
+    "                if video_ids[i][0] == \"Bilibili\":\n",
+    "                    print(\n",
+    "                        f\"Video available at https://www.bilibili.com/video/{video.id}\"\n",
+    "                    )\n",
+    "                elif video_ids[i][0] == \"Osf\":\n",
+    "                    print(f\"Video available at https://osf.io/{video.id}\")\n",
+    "            display(video)\n",
+    "        tab_contents.append(out)\n",
+    "    return tab_contents\n",
+    "\n",
+    "\n",
+    "video_ids = [('Youtube', '49XHRe61LI8'), ('Bilibili', 'BV1Au411L7fo')]\n",
+    "tab_contents = display_videos(video_ids, W=730, H=410)\n",
+    "tabs = widgets.Tab()\n",
+    "tabs.children = tab_contents\n",
+    "for i in range(len(tab_contents)):\n",
+    "    tabs.set_title(i, video_ids[i][0])\n",
+    "display(tabs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9fb54e0",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "To include slides:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b52fef8b",
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "# @title Tutorial slides\n",
+    "# @markdown These are the slides for the videos in all tutorials today\n",
+    "\n",
+    "from IPython.display import IFrame\n",
+    "#link_id = \"n7wdy\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63b697d5",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "**In this project**, you will explore sea ice data from CMIP6. Specifically, you will investigate how the sea ice cover has changed over the last century, and how the ice cover will change into the future"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff7ae1c9",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "# Project Template\n",
+    "\n",
+    "<img src='../template-images/2024_SeaIce.svg'/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51243f3",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "## Data Exploration Notebook\n",
+    "\n",
+    "## Project Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b68292ec",
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "# google colab installs\n",
+    "\n",
+    "# !mamaba install netCDF4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e541a40",
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "\n",
+    "#Import only the libraries/objects that are necessary for more than one dataset.\n",
+    "#Dataset-specific imports should be in the respective notebook section.\n",
+    "\n",
+    "#If any external library has to be installed, !pip install library --quiet\n",
+    "#follow this order: numpy>matplotlib.\n",
+    "#import widgets in hidden Figure settings cell\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pooch\n",
+    "import os\n",
+    "import tempfile\n",
+    "import pandas as pd\n",
+    "import s3fs\n",
+    "import boto3\n",
+    "import botocore\n",
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3692a9ca",
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "# @title Helper functions\n",
+    "\n",
+    "def pooch_load(filelocation=None,filename=None,processor=None):\n",
+    "    shared_location='/home/jovyan/shared/Data/Projects/SeaIce' # this is different for each day\n",
+    "    user_temp_cache=tempfile.gettempdir()\n",
+    "    \n",
+    "    if os.path.exists(os.path.join(shared_location,filename)):\n",
+    "        file = os.path.join(shared_location,filename)\n",
+    "    else:\n",
+    "        file = pooch.retrieve(filelocation,known_hash=None,fname=os.path.join(user_temp_cache,filename),processor=processor)\n",
+    "\n",
+    "    return file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07545499",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# @title Figure settings\n",
+    "\n",
+    "import ipywidgets as widgets       # interactive display\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "plt.style.use(\"https://raw.githubusercontent.com/ClimateMatchAcademy/course-content/main/cma.mplstyle\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02a3777f",
+   "metadata": {},
+   "source": [
+    "## Met Office HadGCM-31 climate model\n",
+    "\n",
+    "The HadGCM-31 model is an IPPC-class climate model routinely used by the UK Met Office to make simulations of past and future climate. We will be looking specifically at sea ice changes in the Arctic from historical simulations over the period 1950-2014, as well as future changes under different forcing scenarios SSP126 and SSP245. We will be looking at sea ice concentration (siconc), to infer how the model has simulated changes in the Arctic sea ice cover under historical greenhouse gas forcing, and try and identify whether this particular model predicts an 'ice-free' Arctic in the future \n",
+    "\n",
+    "Read more about HadGEM here:\n",
+    "\n",
+    "* [HadGEM-GC31-LL](https://www.wdc-climate.de/ui/cmip6?input=CMIP6.HighResMIP.MOHC.HadGEM3-GC31-LL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad439e00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code to retrieve and load the data\n",
+    "historical_siconc = xr.open_dataset('Arctic_siconc_SImon_HadGEM3-GC31-LL_historical_r1i1p1f3_gn_195001-201412.nc')\n",
+    "future_ssp126_siconc = xr.open_dataset('Arctic_siconc_SImon_HadGEM3-GC31-LL_ssp126_r1i1p1f3_gn_201501-210012.nc')\n",
+    "future_ssp245_siconc = xr.open_dataset('Arctic_siconc_SImon_HadGEM3-GC31-LL_ssp245_r1i1p1f3_gn_201501-210012.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "660f8525",
+   "metadata": {},
+   "source": [
+    "We can now visualize the content of the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "114e4a72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code to print the shape, array names, etc of the dataset\n",
+    "historical_siconc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75867210",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code to print the shape, array names, etc of the dataset\n",
+    "future_ssp126_siconc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f876d8b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code to print the shape, array names, etc of the dataset\n",
+    "future_ssp245_siconc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3543b122",
+   "metadata": {},
+   "source": [
+    "Now you are all set to address the questions you are interested in!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c62a0f3e",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "# Further Reading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "378b7ff3",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "- Jahn , A. et al, 2024. Projections of an ice-free Arctic Ocean. Nat Rev Earth Environ 5, 164–176 [https://doi.org/10.1038/s43017-023-00515-9](https://doi.org/10.1038/s43017-023-00515-9)\n",
+    "- Notz, D. and SIMIP, 2020. Arctic sea ice in CMIP6. GRL, 47(10) https://doi.org/10.1029/2019GL086749\n",
+    "- Arthun et al. 2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80a8bf0d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "include_colab_link": true,
+   "name": "Example_based_on_CMA_structure",
+   "toc_visible": true
+  },
+  "kernel": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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From 0e9a0a2e07e7333f938e98a006a078a2dd33da26 Mon Sep 17 00:00:00 2001
From: William-gregory <william.gregory@outlook.com>
Date: Tue, 9 Apr 2024 11:11:51 -0400
Subject: [PATCH 14/65] added tripole description

---
 projects/project-notebooks/2024_SeaIce.ipynb | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/projects/project-notebooks/2024_SeaIce.ipynb b/projects/project-notebooks/2024_SeaIce.ipynb
index 9610ecb88..21f0038bb 100644
--- a/projects/project-notebooks/2024_SeaIce.ipynb
+++ b/projects/project-notebooks/2024_SeaIce.ipynb
@@ -273,7 +273,9 @@
    "source": [
     "## Met Office HadGCM-31 climate model\n",
     "\n",
-    "The HadGCM-31 model is an IPPC-class climate model routinely used by the UK Met Office to make simulations of past and future climate. We will be looking specifically at sea ice changes in the Arctic from historical simulations over the period 1950-2014, as well as future changes under different forcing scenarios SSP126 and SSP245. We will be looking at sea ice concentration (siconc), to infer how the model has simulated changes in the Arctic sea ice cover under historical greenhouse gas forcing, and try and identify whether this particular model predicts an 'ice-free' Arctic in the future \n",
+    "The HadGCM-31 model is an IPPC-class climate model routinely used by the UK Met Office to make simulations of past and future climate. We will be looking specifically at sea ice changes in the Arctic from historical simulations over the period 1950-2014, as well as future changes under different forcing scenarios SSP126 and SSP245. We will be looking at sea ice concentration (siconc), to infer how the model has simulated changes in the Arctic sea ice cover under historical greenhouse gas forcing, and try and identify whether this particular model predicts an 'ice-free' Arctic in the future.\n",
+    "\n",
+    "The HadGCM-31 model that you will be working with is a low-resolution model (1 degree grid). The model is configured on what is called a 'tripolar' grid. This means, rather than having the north pole at 90 degrees N, there are two poles in the Arctic which are centered over land (plus one in the Antarctic to make the tripole). Centering the poles in the Arctic over land make it easier for modelling, otherwise grid cells would become infinitely small at 90 degrees N. You may notice an artefact in the data associated with the tripolar grid - it will look like a straight line cutting across the Arctic.\n",
     "\n",
     "Read more about HadGEM here:\n",
     "\n",
@@ -326,7 +328,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f876d8b3",
+   "id": "332be1ef",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -361,7 +363,7 @@
    "source": [
     "- Jahn , A. et al, 2024. Projections of an ice-free Arctic Ocean. Nat Rev Earth Environ 5, 164–176 [https://doi.org/10.1038/s43017-023-00515-9](https://doi.org/10.1038/s43017-023-00515-9)\n",
     "- Notz, D. and SIMIP, 2020. Arctic sea ice in CMIP6. GRL, 47(10) https://doi.org/10.1029/2019GL086749\n",
-    "- Arthun et al. 2020"
+    "- Arthun et al, 2020. The Seasonal and Regional Transition to an Ice-Free Arctic. GRL, 48(1) https://doi.org/10.1029/2020GL090825"
    ]
   },
   {

From b480431bdd61c3bc8064d9690ca6fe621a0f7287 Mon Sep 17 00:00:00 2001
From: Will Gregory <William.gregory@outlook.com>
Date: Tue, 9 Apr 2024 16:59:35 -0400
Subject: [PATCH 15/65] Delete projects/project-notebooks/2024_SeaIce.ipynb

---
 projects/project-notebooks/2024_SeaIce.ipynb | 410 -------------------
 1 file changed, 410 deletions(-)
 delete mode 100644 projects/project-notebooks/2024_SeaIce.ipynb

diff --git a/projects/project-notebooks/2024_SeaIce.ipynb b/projects/project-notebooks/2024_SeaIce.ipynb
deleted file mode 100644
index 21f0038bb..000000000
--- a/projects/project-notebooks/2024_SeaIce.ipynb
+++ /dev/null
@@ -1,410 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ada5523b",
-   "metadata": {},
-   "source": [
-    "<a href=\"https://colab.research.google.com/github/neuromatch/course-content-template/blob/main/projects/example_dataset/Example_based_on_CMA_structure.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/course-content-template/main/projects/example_dataset/Example_based_on_CMA_structure\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "045624d0",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "<h1>\n",
-    "Sea ice changes\n",
-    "</h1>\n",
-    "\n",
-    "\n",
-    "\n",
-    "__Content creators:__ Will Gregory\n",
-    "\n",
-    "__Content reviewers:__ Names & Surnames\n",
-    "\n",
-    "__Content editors:__ Konstantinos Tsafatinos\n",
-    "\n",
-    "__Production editors:__ Konstantinos Tsafatinos\n",
-    "\n",
-    "__Our 2024 Sponsors:__ Sponsors\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c08b96da",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "To include videos:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "64da422f",
-   "metadata": {
-    "cellView": "form",
-    "execution": {}
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bc75a50d6fd94a54812d7f38975591ab",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Tab(children=(Output(), Output()), _titles={'0': 'Youtube', '1': 'Bilibili'})"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# @title Project Background\n",
-    "\n",
-    "from ipywidgets import widgets\n",
-    "from IPython.display import YouTubeVideo\n",
-    "from IPython.display import IFrame\n",
-    "from IPython.display import display\n",
-    "\n",
-    "class PlayVideo(IFrame):\n",
-    "    def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
-    "        self.id = id\n",
-    "        if source == \"Bilibili\":\n",
-    "            src = f\"https://player.bilibili.com/player.html?bvid={id}&page={page}\"\n",
-    "        elif source == \"Osf\":\n",
-    "            src = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render\"\n",
-    "        super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
-    "\n",
-    "\n",
-    "def display_videos(video_ids, W=400, H=300, fs=1):\n",
-    "    tab_contents = []\n",
-    "    for i, video_id in enumerate(video_ids):\n",
-    "        out = widgets.Output()\n",
-    "        with out:\n",
-    "            if video_ids[i][0] == \"Youtube\":\n",
-    "                video = YouTubeVideo(\n",
-    "                    id=video_ids[i][1], width=W, height=H, fs=fs, rel=0\n",
-    "                )\n",
-    "                print(f\"Video available at https://youtube.com/watch?v={video.id}\")\n",
-    "            else:\n",
-    "                video = PlayVideo(\n",
-    "                    id=video_ids[i][1],\n",
-    "                    source=video_ids[i][0],\n",
-    "                    width=W,\n",
-    "                    height=H,\n",
-    "                    fs=fs,\n",
-    "                    autoplay=False,\n",
-    "                )\n",
-    "                if video_ids[i][0] == \"Bilibili\":\n",
-    "                    print(\n",
-    "                        f\"Video available at https://www.bilibili.com/video/{video.id}\"\n",
-    "                    )\n",
-    "                elif video_ids[i][0] == \"Osf\":\n",
-    "                    print(f\"Video available at https://osf.io/{video.id}\")\n",
-    "            display(video)\n",
-    "        tab_contents.append(out)\n",
-    "    return tab_contents\n",
-    "\n",
-    "\n",
-    "video_ids = [('Youtube', '49XHRe61LI8'), ('Bilibili', 'BV1Au411L7fo')]\n",
-    "tab_contents = display_videos(video_ids, W=730, H=410)\n",
-    "tabs = widgets.Tab()\n",
-    "tabs.children = tab_contents\n",
-    "for i in range(len(tab_contents)):\n",
-    "    tabs.set_title(i, video_ids[i][0])\n",
-    "display(tabs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a9fb54e0",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "To include slides:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b52fef8b",
-   "metadata": {
-    "cellView": "form",
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# @title Tutorial slides\n",
-    "# @markdown These are the slides for the videos in all tutorials today\n",
-    "\n",
-    "from IPython.display import IFrame\n",
-    "#link_id = \"n7wdy\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "63b697d5",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "**In this project**, you will explore sea ice data from CMIP6. Specifically, you will investigate how the sea ice cover has changed over the last century, and how the ice cover will change into the future"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff7ae1c9",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "# Project Template\n",
-    "\n",
-    "<img src='../template-images/2024_SeaIce.svg'/>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f51243f3",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "## Data Exploration Notebook\n",
-    "\n",
-    "## Project Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b68292ec",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# google colab installs\n",
-    "\n",
-    "# !mamaba install netCDF4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1e541a40",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# Imports\n",
-    "\n",
-    "#Import only the libraries/objects that are necessary for more than one dataset.\n",
-    "#Dataset-specific imports should be in the respective notebook section.\n",
-    "\n",
-    "#If any external library has to be installed, !pip install library --quiet\n",
-    "#follow this order: numpy>matplotlib.\n",
-    "#import widgets in hidden Figure settings cell\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import xarray as xr\n",
-    "import pooch\n",
-    "import os\n",
-    "import tempfile\n",
-    "import pandas as pd\n",
-    "import s3fs\n",
-    "import boto3\n",
-    "import botocore\n",
-    "import datetime"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3692a9ca",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# @title Helper functions\n",
-    "\n",
-    "def pooch_load(filelocation=None,filename=None,processor=None):\n",
-    "    shared_location='/home/jovyan/shared/Data/Projects/SeaIce' # this is different for each day\n",
-    "    user_temp_cache=tempfile.gettempdir()\n",
-    "    \n",
-    "    if os.path.exists(os.path.join(shared_location,filename)):\n",
-    "        file = os.path.join(shared_location,filename)\n",
-    "    else:\n",
-    "        file = pooch.retrieve(filelocation,known_hash=None,fname=os.path.join(user_temp_cache,filename),processor=processor)\n",
-    "\n",
-    "    return file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "07545499",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# @title Figure settings\n",
-    "\n",
-    "import ipywidgets as widgets       # interactive display\n",
-    "%config InlineBackend.figure_format = 'retina'\n",
-    "plt.style.use(\"https://raw.githubusercontent.com/ClimateMatchAcademy/course-content/main/cma.mplstyle\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "02a3777f",
-   "metadata": {},
-   "source": [
-    "## Met Office HadGCM-31 climate model\n",
-    "\n",
-    "The HadGCM-31 model is an IPPC-class climate model routinely used by the UK Met Office to make simulations of past and future climate. We will be looking specifically at sea ice changes in the Arctic from historical simulations over the period 1950-2014, as well as future changes under different forcing scenarios SSP126 and SSP245. We will be looking at sea ice concentration (siconc), to infer how the model has simulated changes in the Arctic sea ice cover under historical greenhouse gas forcing, and try and identify whether this particular model predicts an 'ice-free' Arctic in the future.\n",
-    "\n",
-    "The HadGCM-31 model that you will be working with is a low-resolution model (1 degree grid). The model is configured on what is called a 'tripolar' grid. This means, rather than having the north pole at 90 degrees N, there are two poles in the Arctic which are centered over land (plus one in the Antarctic to make the tripole). Centering the poles in the Arctic over land make it easier for modelling, otherwise grid cells would become infinitely small at 90 degrees N. You may notice an artefact in the data associated with the tripolar grid - it will look like a straight line cutting across the Arctic.\n",
-    "\n",
-    "Read more about HadGEM here:\n",
-    "\n",
-    "* [HadGEM-GC31-LL](https://www.wdc-climate.de/ui/cmip6?input=CMIP6.HighResMIP.MOHC.HadGEM3-GC31-LL)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ad439e00",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# code to retrieve and load the data\n",
-    "historical_siconc = xr.open_dataset('Arctic_siconc_SImon_HadGEM3-GC31-LL_historical_r1i1p1f3_gn_195001-201412.nc')\n",
-    "future_ssp126_siconc = xr.open_dataset('Arctic_siconc_SImon_HadGEM3-GC31-LL_ssp126_r1i1p1f3_gn_201501-210012.nc')\n",
-    "future_ssp245_siconc = xr.open_dataset('Arctic_siconc_SImon_HadGEM3-GC31-LL_ssp245_r1i1p1f3_gn_201501-210012.nc')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "660f8525",
-   "metadata": {},
-   "source": [
-    "We can now visualize the content of the dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "114e4a72",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# code to print the shape, array names, etc of the dataset\n",
-    "historical_siconc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "75867210",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Code to print the shape, array names, etc of the dataset\n",
-    "future_ssp126_siconc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "332be1ef",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Code to print the shape, array names, etc of the dataset\n",
-    "future_ssp245_siconc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3543b122",
-   "metadata": {},
-   "source": [
-    "Now you are all set to address the questions you are interested in!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c62a0f3e",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "# Further Reading"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "378b7ff3",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "- Jahn , A. et al, 2024. Projections of an ice-free Arctic Ocean. Nat Rev Earth Environ 5, 164–176 [https://doi.org/10.1038/s43017-023-00515-9](https://doi.org/10.1038/s43017-023-00515-9)\n",
-    "- Notz, D. and SIMIP, 2020. Arctic sea ice in CMIP6. GRL, 47(10) https://doi.org/10.1029/2019GL086749\n",
-    "- Arthun et al, 2020. The Seasonal and Regional Transition to an Ice-Free Arctic. GRL, 48(1) https://doi.org/10.1029/2020GL090825"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "80a8bf0d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "include_colab_link": true,
-   "name": "Example_based_on_CMA_structure",
-   "toc_visible": true
-  },
-  "kernel": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 21732fa8f9fe90a58d2737f1a3aa0c80c2d81214 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Tue, 11 Jun 2024 01:28:40 +0200
Subject: [PATCH 16/65] Add edited template slide.

---
 projects/template-images/2024_Heatwaves.svg | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 projects/template-images/2024_Heatwaves.svg

diff --git a/projects/template-images/2024_Heatwaves.svg b/projects/template-images/2024_Heatwaves.svg
new file mode 100644
index 000000000..f0f238341
--- /dev/null
+++ b/projects/template-images/2024_Heatwaves.svg
@@ -0,0 +1 @@
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\ No newline at end of file

From 9ae6a0b6eecc4b3f3cf2b91ade85db3f4121a6e6 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Tue, 11 Jun 2024 01:30:50 +0200
Subject: [PATCH 17/65] Added .grib and .idx files that store climate data and
 meta data.

---
 .gitignore | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.gitignore b/.gitignore
index cbab5a5ed..404faae0a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,6 @@
 book/_build/
+*.grib
+*.idx
 *.nc
 *.csv
 .ipynb_checkpoints/

From 2d5db00eabf816d05b7833bb858f99a731a58d3e Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Tue, 11 Jun 2024 01:32:02 +0200
Subject: [PATCH 18/65] Slide widget simplifications, small text improvements.

---
 projects/keynote.ipynb | 36 +++++++++---------------------------
 1 file changed, 9 insertions(+), 27 deletions(-)

diff --git a/projects/keynote.ipynb b/projects/keynote.ipynb
index fc91edc44..3bae69ed4 100644
--- a/projects/keynote.ipynb
+++ b/projects/keynote.ipynb
@@ -35,9 +35,9 @@
     "execution": {}
    },
    "source": [
-    "Today marks the transition point from our study of the physical aspect of the climate system and climate change to the socio-economic effects of climate change as well as mitigation and adaptation strategies. You will start the day learning about individual and collective action.\n",
+    "Today marks the last day of an intense week, where we covered the study of the physical aspect of the climate system and climate change and its socio-economic effects as well as mitigation and adaptation strategies. You will start the day learning about individual and collective action.\n",
     "\n",
-    "What is it that we can expect from climate change in the future? How do do energy and food systems specifically contribute to climate change and what can we do to minimize this contribution? In his lecture, Dr Paul Behrens is talks about what choices we can make as individuals and as societies to improve the chances for a better future.\n",
+    "What is it that we can expect from climate change in the future? How do energy and food systems specifically contribute to climate change and what can we do to minimize this contribution? In his lecture, Dr. Paul Behrens talks about what choices we can make as individuals and as societies to improve the chances for a better future. \n",
     "You will then focus the rest of your day on advancing your research projects. There will be no tutorials."
    ]
   },
@@ -56,8 +56,7 @@
    "execution_count": null,
    "id": "9656d38e",
    "metadata": {
-    "cellView": "form",
-    "execution": {}
+    "cellView": "form"
    },
    "outputs": [],
    "source": [
@@ -124,34 +123,17 @@
    "execution_count": null,
    "id": "3961e4a9",
    "metadata": {
-    "cellView": "form",
-    "execution": {}
+    "cellView": "form"
    },
    "outputs": [],
    "source": [
-    "# @markdown\n",
-    "from ipywidgets import widgets\n",
+    "# @title Slides\n",
+    "# @markdown These are the slides for the videos in all tutorials today\n",
     "from IPython.display import IFrame\n",
-    "\n",
     "link_id = \"3tc2h\"\n",
-    "\n",
-    "download_link = f\"https://osf.io/download/{link_id}/\"\n",
-    "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
-    "# @markdown\n",
-    "out = widgets.Output()\n",
-    "with out:\n",
-    "    print(f\"If you want to download the slides: {download_link}\")\n",
-    "    display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
-    "display(out)"
+    "print(f\"If you want to download the slides: https://osf.io/download/{link_id}/\")\n",
+    "IFrame(src=f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\", width=854, height=480)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f88fae3",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -181,7 +163,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

From e6327a793e606fb31012fabf36d4f020d564f297 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Tue, 11 Jun 2024 01:34:14 +0200
Subject: [PATCH 19/65] Renamed according to 2024 convention.

---
 ...onal_precipitation.ipynb => Regional_precipitation_2024.ipynb} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/project-notebooks/{2024_Regional_precipitation.ipynb => Regional_precipitation_2024.ipynb} (100%)

diff --git a/projects/project-notebooks/2024_Regional_precipitation.ipynb b/projects/project-notebooks/Regional_precipitation_2024.ipynb
similarity index 100%
rename from projects/project-notebooks/2024_Regional_precipitation.ipynb
rename to projects/project-notebooks/Regional_precipitation_2024.ipynb

From d99396c276cbc44bae59e2d45defe3957a87523a Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Tue, 11 Jun 2024 01:51:07 +0200
Subject: [PATCH 20/65] Small prettifications.

---
 projects/template-images/2024_Heatwaves.svg | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/projects/template-images/2024_Heatwaves.svg b/projects/template-images/2024_Heatwaves.svg
index f0f238341..bc53c11fe 100644
--- a/projects/template-images/2024_Heatwaves.svg
+++ b/projects/template-images/2024_Heatwaves.svg
@@ -1 +1 @@
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\ No newline at end of file

From 6302dd4c7c2a6af7dd290c963ab2becd007ca4da Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Tue, 11 Jun 2024 01:52:55 +0200
Subject: [PATCH 21/65] Major update of Heatwaves project.

---
 .../Heatwaves_project_notebook_24.ipynb       | 2294 +++++++++++++++++
 1 file changed, 2294 insertions(+)
 create mode 100644 projects/project-notebooks/Heatwaves_project_notebook_24.ipynb

diff --git a/projects/project-notebooks/Heatwaves_project_notebook_24.ipynb b/projects/project-notebooks/Heatwaves_project_notebook_24.ipynb
new file mode 100644
index 000000000..96f9a914e
--- /dev/null
+++ b/projects/project-notebooks/Heatwaves_project_notebook_24.ipynb
@@ -0,0 +1,2294 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/projects/project-notebooks/Heatwaves_project_notebook_2024.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/projects/project-notebooks/Heatwaves_project_notebook_2024.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dbNfwh3vgEcU"
+   },
+   "source": [
+    "# Heatwaves\n",
+    "\n",
+    "**Content creators:** Wil Laura\n",
+    "\n",
+    "**Content reviewers:** Will Gregory, Paul Heubel, Laura Paccini, Jenna Pearson\n",
+    "\n",
+    "**Content editors:** Paul Heubel\n",
+    "\n",
+    "**Production editors:** Paul Heubel, Konstantine Tsafatinos\n",
+    "\n",
+    "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 621
+    },
+    "id": "mUt8EEPe0hKk",
+    "outputId": "3e1145b7-ff68-4475-d51c-88784416b5e8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78d2861846d84c73956fba516fcd182d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tab(children=(Output(), Output(), Output()), selected_index=0, titles=('Youtube', 'Bilibili', 'Osf'))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# @title Project Background\n",
+    "\n",
+    "from ipywidgets import widgets\n",
+    "from IPython.display import YouTubeVideo\n",
+    "from IPython.display import IFrame\n",
+    "from IPython.display import display\n",
+    "\n",
+    "\n",
+    "class PlayVideo(IFrame):\n",
+    "  def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
+    "    self.id = id\n",
+    "    if source == 'Bilibili':\n",
+    "      src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
+    "    elif source == 'Osf':\n",
+    "      src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
+    "    super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
+    "\n",
+    "\n",
+    "def display_videos(video_ids, W=400, H=300, fs=1):\n",
+    "  tab_contents = []\n",
+    "  for i, video_id in enumerate(video_ids):\n",
+    "    out = widgets.Output()\n",
+    "    with out:\n",
+    "      if video_ids[i][0] == 'Youtube':\n",
+    "        video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
+    "                             height=H, fs=fs, rel=0)\n",
+    "        print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
+    "      else:\n",
+    "        video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
+    "                          height=H, fs=fs, autoplay=False)\n",
+    "        if video_ids[i][0] == 'Bilibili':\n",
+    "          print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
+    "        elif video_ids[i][0] == 'Osf':\n",
+    "          print(f'Video available at https://osf.io/{video.id}')  \n",
+    "      display(video)\n",
+    "    tab_contents.append(out)\n",
+    "  return tab_contents\n",
+    "\n",
+    "video_ids = [('Youtube', 'aXBq-A8JsPE'), ('Bilibili', '<video_id_2>'), ('Osf', '<video_id_3>')]\n",
+    "tab_contents = display_videos(video_ids, W=854, H=480)\n",
+    "tabs = widgets.Tab()\n",
+    "tabs.children = tab_contents\n",
+    "for i in range(len(tab_contents)):\n",
+    "  tabs.set_title(i, video_ids[i][0])\n",
+    "display(tabs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 621
+    },
+    "id": "tX5Bh4Pivakh",
+    "outputId": "7036341a-1ed4-420a-b198-cecffce9cc01",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "If you want to download the slides: https://osf.io/download/zeyxn/\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"854\"\n",
+       "            height=\"480\"\n",
+       "            src=\"https://mfr.ca-1.osf.io/render?url=https://osf.io/zeyxn/?direct%26mode=render%26action=download%26mode=render\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x103fc6700>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# @title Slides\n",
+    "# @markdown These are the slides for the video introduction to the project\n",
+    "from IPython.display import IFrame\n",
+    "link_id = \"zeyxn\"\n",
+    "print(f\"If you want to download the slides: https://osf.io/download/{link_id}/\")\n",
+    "IFrame(src=f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\", width=854, height=480)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "o0arQA56IUtk"
+   },
+   "source": [
+    "**In this project**, you will look into the characterization of heatwaves using near-surface air temperature reanalysis data. Since we are talking about extreme events when the temperature exceeds a certain threshold for a continuous number of days, we will first analyze the global spatial and temporal distribution of air temperature. Next, we will calculate the number and timing of heatwaves for a local area, then focus on determining the percentage of a region under heat waves. Additionally, you will be able to explore its relationship with other climate drivers. Also, you are encouraged to analyze the health impact of heatwaves using an available mortality dataset. Finally, enjoy exploring the heatwaves!\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "I9_7XVXBgLf3"
+   },
+   "source": [
+    "# Project Template"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FT_VUme8iNod"
+   },
+   "source": [
+    "  <img src='../template-images/2024_Heatwaves.svg'>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "raQmOQkWigMy"
+   },
+   "source": [
+    "# Data Exploration Notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uuozLOtcjL5Q"
+   },
+   "source": [
+    "## Project Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# google colab installs\n",
+    "#!pip install pandas==1.5.3\n",
+    "\n",
+    "# !pip install cartopy --quiet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uYVofx_Ej0c5",
+    "outputId": "1506c899-d979-4b1e-aac4-1e12e2d2f6e1"
+   },
+   "outputs": [],
+   "source": [
+    "# import packages\n",
+    "#import xclim\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "#import matplotlib.dates as mdates\n",
+    "import cartopy.feature as cfeature\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "#from xclim.core.calendar import percentile_doy\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# @title Helper functions\n",
+    "import os\n",
+    "import pooch\n",
+    "import tempfile\n",
+    "\n",
+    "def pooch_load(filelocation=None, filename=None, processor=None):\n",
+    "    shared_location = \"/home/jovyan/shared/Data/projects/Heatwaves\"  # this is different for each day\n",
+    "    user_temp_cache = tempfile.gettempdir()\n",
+    "\n",
+    "    if os.path.exists(os.path.join(shared_location, filename)):\n",
+    "        file = os.path.join(shared_location, filename)\n",
+    "    else:\n",
+    "        file = pooch.retrieve(\n",
+    "            filelocation,\n",
+    "            known_hash=None,\n",
+    "            fname=os.path.join(user_temp_cache, filename),\n",
+    "            processor=processor,\n",
+    "        )\n",
+    "\n",
+    "    return file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# @title Figure settings\n",
+    "\n",
+    "import ipywidgets as widgets       # interactive display\n",
+    "\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "plt.style.use(\"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FICTmBLTkIPr"
+   },
+   "source": [
+    "## ECMWF Reanalysis v5 (ERA5): Air Temperature at 2m\n",
+    "\n",
+    "You will utilize the ERA5 dataset to examine temperature trends and heatwaves, applying the loading methods introduced in W1D1. Please, see the W1D2 course material for more information on reanalysis data. Besides, you can read more about ERA5 here: [Climate reanalysis](https://climate.copernicus.eu/climate-reanalysis).\n",
+    "\n",
+    "Specifically, in this project, you will focus on near-surface temperature, which refers to the temperature of air at $2 \\text{m}$ above the surface of land, sea, or inland waters, temperature with units of Kelvin $\\left(\\text{K}\\right)$.\n",
+    "\n",
+    "You will access the following subsampled data through the OSF cloud storage to simplify downloading. For the project, it is necessary to download data yourself when you are interested in exploring other regional subsets or variables. Please have a look at the [`get_ERA5_reanalysis_data.ipynb`](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D2_StateoftheClimateOceanandAtmosphereReanalysis/get_ERA5_reanalysis_data.ipynb) notebook, where we show how to use the Climate Data Store (CDS) API to get a subset of the huge ECMWF ERA5 Reanalysis data set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following, along with a small subsample file that was downloaded beforehand, we show how to load, explore, and visualize ERA5 data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "y_cz1nOyk7fe"
+   },
+   "outputs": [
+    {
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+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+       "  --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",
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+       "\n",
+       "html[theme=dark],\n",
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+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
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+       "  --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",
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+       "  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",
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+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
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+       "\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",
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+       ".xr-section-item input {\n",
+       "  display: none;\n",
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+       ".xr-section-summary-in:disabled + label:before {\n",
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+       ".xr-section-summary-in:checked + label:before {\n",
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+       ".xr-section-summary-in:checked + label > span {\n",
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+       "  color: var(--xr-font-color3);\n",
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+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
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+       "\n",
+       ".xr-array-data,\n",
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+       "  display: none;\n",
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+       "\n",
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+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 25MB\n",
+       "Dimensions:    (longitude: 41, latitude: 41, time: 3653)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
+       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0\n",
+       "  * time       (time) datetime64[ns] 29kB 1991-01-01 1991-01-02 ... 2000-12-31\n",
+       "Data variables:\n",
+       "    t2m        (time, latitude, longitude) float32 25MB ...\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      2024-02-14 07:40:23 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...</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-8b86f741-eddb-4860-961f-308eee45a2e4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8b86f741-eddb-4860-961f-308eee45a2e4' 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'>longitude</span>: 41</li><li><span class='xr-has-index'>latitude</span>: 41</li><li><span class='xr-has-index'>time</span>: 3653</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-af2c63ee-5859-4d16-9283-52811f95f78c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af2c63ee-5859-4d16-9283-52811f95f78c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>143.0 143.2 143.5 ... 152.8 153.0</div><input id='attrs-3d089d11-e546-46e3-9893-34b2b4748e93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d089d11-e546-46e3-9893-34b2b4748e93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b608161-9aaf-4390-b75e-7fc4d92187ee' class='xr-var-data-in' type='checkbox'><label for='data-5b608161-9aaf-4390-b75e-7fc4d92187ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([143.  , 143.25, 143.5 , 143.75, 144.  , 144.25, 144.5 , 144.75, 145.  ,\n",
+       "       145.25, 145.5 , 145.75, 146.  , 146.25, 146.5 , 146.75, 147.  , 147.25,\n",
+       "       147.5 , 147.75, 148.  , 148.25, 148.5 , 148.75, 149.  , 149.25, 149.5 ,\n",
+       "       149.75, 150.  , 150.25, 150.5 , 150.75, 151.  , 151.25, 151.5 , 151.75,\n",
+       "       152.  , 152.25, 152.5 , 152.75, 153.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 4.75 4.5 ... -4.5 -4.75 -5.0</div><input id='attrs-b23c0d42-1fa4-41a5-925f-0097bd406639' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b23c0d42-1fa4-41a5-925f-0097bd406639' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc53d2b4-d4fe-4e6e-b06e-ee39f817e98f' class='xr-var-data-in' type='checkbox'><label for='data-dc53d2b4-d4fe-4e6e-b06e-ee39f817e98f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 5.  ,  4.75,  4.5 ,  4.25,  4.  ,  3.75,  3.5 ,  3.25,  3.  ,  2.75,\n",
+       "        2.5 ,  2.25,  2.  ,  1.75,  1.5 ,  1.25,  1.  ,  0.75,  0.5 ,  0.25,\n",
+       "        0.  , -0.25, -0.5 , -0.75, -1.  , -1.25, -1.5 , -1.75, -2.  , -2.25,\n",
+       "       -2.5 , -2.75, -3.  , -3.25, -3.5 , -3.75, -4.  , -4.25, -4.5 , -4.75,\n",
+       "       -5.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1991-01-01 ... 2000-12-31</div><input id='attrs-2e400ee5-ef3e-4fb7-a340-7fa5c4135437' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e400ee5-ef3e-4fb7-a340-7fa5c4135437' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06eb4ce3-8ed3-45a1-a110-fdf8b1687616' class='xr-var-data-in' type='checkbox'><label for='data-06eb4ce3-8ed3-45a1-a110-fdf8b1687616' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1991-01-03T00:00:00.000000000&#x27;, ..., &#x27;2000-12-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-12-30T00:00:00.000000000&#x27;, &#x27;2000-12-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a07546be-afac-49b7-8c1d-631e10afa25f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a07546be-afac-49b7-8c1d-631e10afa25f' 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>t2m</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a8a981bc-83e8-437d-9ad7-5a30e4657687' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8a981bc-83e8-437d-9ad7-5a30e4657687' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52203164-ca8f-4cf9-b2b7-af327d62e0cb' class='xr-var-data-in' type='checkbox'><label for='data-52203164-ca8f-4cf9-b2b7-af327d62e0cb' 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>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><pre>[6140693 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-84c38f25-a102-4400-83c8-bafd41780b77' class='xr-section-summary-in' type='checkbox'  ><label for='section-84c38f25-a102-4400-83c8-bafd41780b77' 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>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-51257331-d851-43a5-9e0a-7df8257eec6d' class='xr-index-data-in' type='checkbox'/><label for='index-51257331-d851-43a5-9e0a-7df8257eec6d' 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([ 143.0, 143.25,  143.5, 143.75,  144.0, 144.25,  144.5, 144.75,  145.0,\n",
+       "       145.25,  145.5, 145.75,  146.0, 146.25,  146.5, 146.75,  147.0, 147.25,\n",
+       "        147.5, 147.75,  148.0, 148.25,  148.5, 148.75,  149.0, 149.25,  149.5,\n",
+       "       149.75,  150.0, 150.25,  150.5, 150.75,  151.0, 151.25,  151.5, 151.75,\n",
+       "        152.0, 152.25,  152.5, 152.75,  153.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cdc2d609-c01a-43aa-8878-8e49aaaa7e8e' class='xr-index-data-in' type='checkbox'/><label for='index-cdc2d609-c01a-43aa-8878-8e49aaaa7e8e' 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([  5.0,  4.75,   4.5,  4.25,   4.0,  3.75,   3.5,  3.25,   3.0,  2.75,\n",
+       "         2.5,  2.25,   2.0,  1.75,   1.5,  1.25,   1.0,  0.75,   0.5,  0.25,\n",
+       "         0.0, -0.25,  -0.5, -0.75,  -1.0, -1.25,  -1.5, -1.75,  -2.0, -2.25,\n",
+       "        -2.5, -2.75,  -3.0, -3.25,  -3.5, -3.75,  -4.0, -4.25,  -4.5, -4.75,\n",
+       "        -5.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3650821a-c43c-4cf6-b270-15e092d3e5fd' class='xr-index-data-in' type='checkbox'/><label for='index-3650821a-c43c-4cf6-b270-15e092d3e5fd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1991-01-01&#x27;, &#x27;1991-01-02&#x27;, &#x27;1991-01-03&#x27;, &#x27;1991-01-04&#x27;,\n",
+       "               &#x27;1991-01-05&#x27;, &#x27;1991-01-06&#x27;, &#x27;1991-01-07&#x27;, &#x27;1991-01-08&#x27;,\n",
+       "               &#x27;1991-01-09&#x27;, &#x27;1991-01-10&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2000-12-22&#x27;, &#x27;2000-12-23&#x27;, &#x27;2000-12-24&#x27;, &#x27;2000-12-25&#x27;,\n",
+       "               &#x27;2000-12-26&#x27;, &#x27;2000-12-27&#x27;, &#x27;2000-12-28&#x27;, &#x27;2000-12-29&#x27;,\n",
+       "               &#x27;2000-12-30&#x27;, &#x27;2000-12-31&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=3653, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b642ffd8-d8fc-4cdc-9049-842647072ae0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b642ffd8-d8fc-4cdc-9049-842647072ae0' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>2024-02-14 07:40:23 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data4/adaptor.mars.internal-1707896415.6796277-19686-8-cf1f5a1a-aa36-4c24-a9fb-29291e38f918.nc /cache/tmp/cf1f5a1a-aa36-4c24-a9fb-29291e38f918-adaptor.mars.internal-1707895649.3473864-19686-6-tmp.grib</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 25MB\n",
+       "Dimensions:    (longitude: 41, latitude: 41, time: 3653)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
+       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0\n",
+       "  * time       (time) datetime64[ns] 29kB 1991-01-01 1991-01-02 ... 2000-12-31\n",
+       "Data variables:\n",
+       "    t2m        (time, latitude, longitude) float32 25MB ...\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      2024-02-14 07:40:23 GMT by grib_to_netcdf-2.25.1: /opt/ecmw..."
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# loading a subsample of the ERA5 reanalysis dataset, daily from 1991 to 2000\n",
+    "link_id = \"z9xfv\"\n",
+    "url_ERA5 = f\"https://osf.io/download/{link_id}/\"\n",
+    "#filepath = \"/content/file_sample.nc\"\n",
+    "fname_ERA5 = \"file_sample.nc\"\n",
+    "\n",
+    "ds = xr.open_dataset(pooch_load(url_ERA5, fname_ERA5))\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "16xyHYikLmrd"
+   },
+   "source": [
+    "Let's visualize the distribution of the annual mean near-surface temperature for the year 2000 in the given area around the equator. After calculating the anomaly according to the hints included in the template, you should be able to visualize the answer to **Question 1** similarly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 610
+    },
+    "id": "7on_k6PakcVi",
+    "outputId": "7c793a9c-c42a-4c35-80ab-12cacadc460b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
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+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --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",
+       "}\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;t2m&#x27; (latitude: 41, longitude: 41)&gt; Size: 7kB\n",
+       "array([[300.1665 , 300.18317, 300.19998, ..., 300.12216, 300.10117,\n",
+       "        300.07745],\n",
+       "       [300.17957, 300.1994 , 300.21912, ..., 300.12817, 300.12216,\n",
+       "        300.105  ],\n",
+       "       [300.19885, 300.2193 , 300.23114, ..., 300.14175, 300.13776,\n",
+       "        300.13687],\n",
+       "       ...,\n",
+       "       [299.88504, 300.1443 , 300.40536, ..., 300.0386 , 299.20694,\n",
+       "        299.89627],\n",
+       "       [298.28378, 298.61932, 298.86646, ..., 299.7144 , 299.8679 ,\n",
+       "        300.66873],\n",
+       "       [294.1361 , 294.42368, 295.4092 , ..., 299.97037, 300.18015,\n",
+       "        300.3619 ]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
+       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0</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'>'t2m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 41</li><li><span class='xr-has-index'>longitude</span>: 41</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-dd0f9d24-13d2-49a7-95ec-09cf3b477b13' class='xr-array-in' type='checkbox' checked><label for='section-dd0f9d24-13d2-49a7-95ec-09cf3b477b13' 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>300.2 300.2 300.2 300.2 300.2 300.2 ... 299.4 299.3 300.0 300.2 300.4</span></div><div class='xr-array-data'><pre>array([[300.1665 , 300.18317, 300.19998, ..., 300.12216, 300.10117,\n",
+       "        300.07745],\n",
+       "       [300.17957, 300.1994 , 300.21912, ..., 300.12817, 300.12216,\n",
+       "        300.105  ],\n",
+       "       [300.19885, 300.2193 , 300.23114, ..., 300.14175, 300.13776,\n",
+       "        300.13687],\n",
+       "       ...,\n",
+       "       [299.88504, 300.1443 , 300.40536, ..., 300.0386 , 299.20694,\n",
+       "        299.89627],\n",
+       "       [298.28378, 298.61932, 298.86646, ..., 299.7144 , 299.8679 ,\n",
+       "        300.66873],\n",
+       "       [294.1361 , 294.42368, 295.4092 , ..., 299.97037, 300.18015,\n",
+       "        300.3619 ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-8cdb378a-373f-4756-8232-9468b4c43a87' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8cdb378a-373f-4756-8232-9468b4c43a87' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>143.0 143.2 143.5 ... 152.8 153.0</div><input id='attrs-acaf5997-4548-448f-a7d0-dad167ec5de8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acaf5997-4548-448f-a7d0-dad167ec5de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b719805-3164-491a-82f2-821d04d5c5b6' class='xr-var-data-in' type='checkbox'><label for='data-3b719805-3164-491a-82f2-821d04d5c5b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([143.  , 143.25, 143.5 , 143.75, 144.  , 144.25, 144.5 , 144.75, 145.  ,\n",
+       "       145.25, 145.5 , 145.75, 146.  , 146.25, 146.5 , 146.75, 147.  , 147.25,\n",
+       "       147.5 , 147.75, 148.  , 148.25, 148.5 , 148.75, 149.  , 149.25, 149.5 ,\n",
+       "       149.75, 150.  , 150.25, 150.5 , 150.75, 151.  , 151.25, 151.5 , 151.75,\n",
+       "       152.  , 152.25, 152.5 , 152.75, 153.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 4.75 4.5 ... -4.5 -4.75 -5.0</div><input id='attrs-33888794-6627-4678-a827-45530a806e64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33888794-6627-4678-a827-45530a806e64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b873021-ce18-4b90-b4d7-a2b5bb35b06d' class='xr-var-data-in' type='checkbox'><label for='data-0b873021-ce18-4b90-b4d7-a2b5bb35b06d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 5.  ,  4.75,  4.5 ,  4.25,  4.  ,  3.75,  3.5 ,  3.25,  3.  ,  2.75,\n",
+       "        2.5 ,  2.25,  2.  ,  1.75,  1.5 ,  1.25,  1.  ,  0.75,  0.5 ,  0.25,\n",
+       "        0.  , -0.25, -0.5 , -0.75, -1.  , -1.25, -1.5 , -1.75, -2.  , -2.25,\n",
+       "       -2.5 , -2.75, -3.  , -3.25, -3.5 , -3.75, -4.  , -4.25, -4.5 , -4.75,\n",
+       "       -5.  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d045a024-6f08-40d9-9908-f939ac402a11' class='xr-section-summary-in' type='checkbox'  ><label for='section-d045a024-6f08-40d9-9908-f939ac402a11' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0c191ef8-84f3-4671-8439-c113bee94fb5' class='xr-index-data-in' type='checkbox'/><label for='index-0c191ef8-84f3-4671-8439-c113bee94fb5' 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([ 143.0, 143.25,  143.5, 143.75,  144.0, 144.25,  144.5, 144.75,  145.0,\n",
+       "       145.25,  145.5, 145.75,  146.0, 146.25,  146.5, 146.75,  147.0, 147.25,\n",
+       "        147.5, 147.75,  148.0, 148.25,  148.5, 148.75,  149.0, 149.25,  149.5,\n",
+       "       149.75,  150.0, 150.25,  150.5, 150.75,  151.0, 151.25,  151.5, 151.75,\n",
+       "        152.0, 152.25,  152.5, 152.75,  153.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b5d43b8f-adf8-43a1-8d26-f86d982f7fbf' class='xr-index-data-in' type='checkbox'/><label for='index-b5d43b8f-adf8-43a1-8d26-f86d982f7fbf' 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([  5.0,  4.75,   4.5,  4.25,   4.0,  3.75,   3.5,  3.25,   3.0,  2.75,\n",
+       "         2.5,  2.25,   2.0,  1.75,   1.5,  1.25,   1.0,  0.75,   0.5,  0.25,\n",
+       "         0.0, -0.25,  -0.5, -0.75,  -1.0, -1.25,  -1.5, -1.75,  -2.0, -2.25,\n",
+       "        -2.5, -2.75,  -3.0, -3.25,  -3.5, -3.75,  -4.0, -4.25,  -4.5, -4.75,\n",
+       "        -5.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15df72d9-f5c5-49e0-bef8-3dfff145503e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-15df72d9-f5c5-49e0-bef8-3dfff145503e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 't2m' (latitude: 41, longitude: 41)> Size: 7kB\n",
+       "array([[300.1665 , 300.18317, 300.19998, ..., 300.12216, 300.10117,\n",
+       "        300.07745],\n",
+       "       [300.17957, 300.1994 , 300.21912, ..., 300.12817, 300.12216,\n",
+       "        300.105  ],\n",
+       "       [300.19885, 300.2193 , 300.23114, ..., 300.14175, 300.13776,\n",
+       "        300.13687],\n",
+       "       ...,\n",
+       "       [299.88504, 300.1443 , 300.40536, ..., 300.0386 , 299.20694,\n",
+       "        299.89627],\n",
+       "       [298.28378, 298.61932, 298.86646, ..., 299.7144 , 299.8679 ,\n",
+       "        300.66873],\n",
+       "       [294.1361 , 294.42368, 295.4092 , ..., 299.97037, 300.18015,\n",
+       "        300.3619 ]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
+       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calculate the annual average of the year selected\n",
+    "year_to_use = ds.t2m.loc[\"2000-01-01\":\"2000-12-31\",:,:].mean(dim=\"time\")\n",
+    "year_to_use"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 610
+    },
+    "id": "7on_k6PakcVi",
+    "outputId": "7c793a9c-c42a-4c35-80ab-12cacadc460b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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aj7emnsAPp5/Ex6eM4/zE2BZtvE1Gzk7QnUIiIiIiItJzrFmzhszMzHY9OsOyZcu4/vrrXfOJiYnNkiW/5efnx5133uma3717N1OnTmXdunXN2lVWVnLXXXfx97//vcU+flsyCxp7o/xWv379WLp0KaNHj262PDIyknfffZff/e53rmVLly5l0aJFB437aLz99tsEBwcTHBxMWFjYEZVH605UYktEREQ63V+G9GNLaQVbSivIrqnjrnXbeCwuEZPB0NWhdRtVVhsX/7Ca3RXVB23zbPJunk3e7Zq3tPL8nZ8YR7DnoWveioiIiIjIseNYGIMkOjq6U8cUaYtNmzZx9tlnU19fDzQmHhYuXEhgYOAht7vzzjtZvnw5X3/9tWs/Y8eOpU+fPvTq1Yuqqiq2bNni2u/YsWNxOp2uJEpr+/f19W2x7Omnnz5kLM8++yyffPIJNTU1ALzxxhvMmjWrDWfePvn5+a7yZqNHjyY2tuWNez2JepCIiIhIp/MwGfn3uKGE7B+wfXlBCd/+pqbq8e6V7XsPmRxpjfU3v4ICPSxcm9SrI8MSERERERE55u3YsYPZs2e7SluFhISwePFi+vfvf9htjUYjn332GX/6058wNimHvHfvXpYsWcLatWtdyZGZM2fy1VdfNSuhFRQU1GKffn5+zeaDg4M544wzDhnHb9ssW7bssLEfiV+TNwaDgYSEhE45hjupB4mIiIi4RZS3F/86YQjXrdgEwNu5OZweGopRvUgoqWvgrZ0ZLZZfPzCR9UWl7K6oZmCQHwEWCyaDgWqrjRqbnWqbjRBPD4aFBDIsJICxYcEEeGhsFxERERERkbZKS0tjxowZFBQUABAQEMDChQsZNmxYm/dhsVh47rnnuPHGG5k3bx4//vgjGRkZVFVVERERwejRo7nyyis599xzcTgcZGVlubZtLckQFhbWbH7EiBHNki8HM3r0aD7++GMAcnNzqaurw8urY8eojIqK6tD9dTUlSERERMRtTowIYVxYEGuKyshpaCCzvp5eHfxlrSdanl+E1dG8N0iivw83DemDp8nUor3T0QP7z4uIiIiIiHQzWVlZTJ8+3ZWw8PHx4euvv+aEE044ov0NGjSIJ5988pBtUlJSqK2tdc2PHTu2RZukpCQMBoOrlFVoaGibjv/bdqWlpURHd+wYlUlJSa7pzhoLxp2UIBERERG3Ka5voNJqc81X2myHaH38+Dn3QLmx/5w4gmgfL5IC/fEwqRqqiIiIiIgcmgPoifdQObr4+Pn5+cyYMYO0tDQAPD09+eKLL5g8eXKnHvenn35yTXt4eDBy5MgWbXx8fEhMTGTv3r0ArhJdh/Pbwd07uvcINPZm6devH7t372b9+vWUlpYSHBzc4cdxFyVIREREpEP9lFfEk8m7sTmdnBYbSR9/H7aWVrCmqJSdTcbY8DAY6O/j02L7tNpa/p62l4KGBgb5+nJiYCCDfHwxGiDc4kG4x7E3APm6olIAfMwmpkWHKzEiIiIiIiLSiUpKSpg5cyY7duwAGktkzZ8/nxkzZnT6sd977z3X9Jw5c/D29m613dSpU10Jkl+TOIeTnp7umvbw8Gh1fJOOcN1113HXXXdht9t5/PHH+de//tUpx3EHJUhERESkw3yblc9f121zzb+yM/2gbS+IiMSzlRqq/8xIZ8/+7sZrKipYU1HRbP2pIaHcnpCAbyulp3qirOpacmsa7/IJ9rQoOSIiIiIiItKJKioqmD17Nlu3bgXAZDLx/vvvc+aZZ3b6sZcuXcrKlStd89ddd91B25533nm8+eabAGzfvp2srCzi4uIOuf/Fixe7psePH4+hk8b8/Mtf/sJnn33GqlWrePrppxk9ejQXXXRRpxyrs+kXuIiIiHSIbWUV3Ldx+0HXG4GhQf5c0TeeZ8cN44bY2BZtKmw2tlVXt9y4ie9Kivn73r2uWqw9WX5NHZf8sNo1H3wM9o4RERERERHpLmpqajjjjDNYt24dAEajkbfffpu5c+d2+rHLysq48cYbXfMnn3wyM2fOPGj7WbNmER8fD4DD4eCJJ5445P6//vprV9IHGnundBaj0cinn37K+PHjsdvtXHrppVx//fXs2bOn047ZWdSDRDqdwWjE0Modwh3J6XBf1cLOPpcDx3HLYdrHaGg23b7nwj3/R+582hxuO1pXV+XsOoYmrzmD0dBs/vDbuq93gbsGzHbXe53RfGTP3d837aDO3hjjub1iuGVIX37IKaTebqeXvw9jw4IJ8LC42lcXtUyEBNjNeBqN1Dc510CzmVPCGweaW1hQSI3dwcqKcj4qK+LSuBjMbXhdmD3bcU4eB9qaPUzt2rY9z12D3cFtq7ZQWNcAQIKfD49PHN6mfThs9jYf52gc6Wuhvdz5Oe4u7fmEMDZ5DRuNBoymzrnLqydw12sO3PfebTB2v9e303zgFWoyGzFZ2v6KNXu67yekrd4941TZrd3v/6gnaM/3st+2b+/3Ondx1/uvw+6e47jrfc6djJa2f044mry3GSzGNm1rtB8bPZTl+ON0OnvkDWTujrm+vp45c+awfPlyAAwGA6+99hqXXXbZUe23srKSRYsWce6552I8yLWyvXv3cskll7B9e+NNhV5eXrzyyiuH3K+Hhwf/+Mc/uOaaawB44YUXGDNmDFdeeWWLtsnJyVx77bWu+dDQUP7whz8c6Skd1kMPPQQ0JnlSUlKorKxk3rx5zJs3jyFDhjBq1CgiIiLw9/dv134feOCBzgj3kJQgERERkaPmcDrZXlYJQKCHhQdHD8LDZOSyfvHt2o+nycjv4mN4PSPLtazcZqOooYGnhw7kpNBgbk9OBeA/ezP4MDuXp4YOZKC/X8edjJs8vjGVjUVlAMT6evPRrImEeKkHiYiIiIiISGd49tln+f77713zQUFBfPTRR3z00Udt2v7yyy/n8ssvb7G8urqa888/n+joaObMmcOECROIj4/HYDCQnZ3NokWLmD9/PrX7S0lbLBY++ugj+vfvf9hjXnnllXz22WcsWLAAh8PBVVddxSeffMJFF11EfHw8ZWVlfP/997z22muuAdoNBgNvv/02fn6d9zv5wQcfbFa+y2AwuBJeycnJbNu27WCbHpISJCIiItJj+ZhN1NjsmAy0qVfHr4obGvA1mfDaP6bIH3rH09fXhwV5BawsKcMJ/Fxcyk/FpUwLC+HsqAi+zCsAIL++gT9s2saM8FC2VVZhNhi4ulcc0/f3OOlsTqeTdSXl/FJUytbyCoqsNkaHB3NFUi+GhAQedLsv07J5d2cGABajgf9MHqXkiIiIiIiISCeqqalpNl9aWsrChQvbvP2ECRMOuT43N5eXX36Zl19++aBtwsLCePPNN9s83onRaOSDDz7gzDPPZMmSJQAsWLCABQsWtNrew8OD1157jTPOOKNN++9IRzPeidPp7LTxUg6nOxbxERERkR7GaDAwMSIEgJJ6K8mlFYdsX1rfwLf5hVy1fguzV6zjivVbqLI1llIxGAzMjAjjP8MH8/CgA3fULCsqAeD+pL48NXQgA3x9AKi22/kir4Dd1TWkVlVz97YdvJ6eyfqyckobrB1yftU2G4vyClmYV+i6K2Z9STnXrNnCdeu28lZ6FutLK8ioquGztGzO/W4Fv1+yjg2FpS329UtuEfetSXbN//2EIQwLPXgyRURERERE5FCczp77OBb4+vpyxhlnHLKcVEhICDfccAPbt29v92DwPj4+fP/99zz99NPEtjKWJzT+jj799NNZs2YNV1xxRbv2f6R+Le3WEY+upB4kIiIi0iGmRofxQ04hAEtzCxn+mx4UTqeTpblFvLB9D9tKK5ut21tTy9O707ksPoai+gb21tSwp7qGBbkFrjY++3uYGAwGpoaFMCYogKs3bCWtprb5cYCX0zNd84FmM339fUj09aGPX+O/ff18iPD0OOwdKovzili4ey9LC4pdy36KDqeswcaK4pbJj6aW5RayLLeQMeHBTIwMJcLHk63F5XyyN4tfS4Kf3zeOC9tZhkxERERERETa78EHH+TBBx/s8P36+/vz1VdfYbPZWL9+PWlpaeTn51NbW0tkZCSJiYlMmjQJi8Vy+J0dhNFo5M9//jO33norK1euZMeOHeTn5+Pj40NcXBxTpkwhPDy8A8/q0H7tzXIsUIJEREREOsTUqDDX9NLcIm4Z0q/Z+hdS9vLC9r0H3f7LvAJX6azWjAxsfjeOn9nMm6OH8XVeIVV2O7PCQ3lzX3aLfZTbbGworWDDb3q1+JpMJPr5kOjrTYDFTK3dQY3Njt3Pj5v2t3lyx16Ki4ubbfdNbmGz+URfb37fJ4HxoUFERgbyyd4sXt22l5yaxvqv6wtLWd9KT5LpsRH8/YTBBz1fERERERER6TnMZjPjx49n/PjxnXYMo9HIpEmTmDRpUqcdoy2mTp3apcfvSEqQiIiISIeI8PZiSLA/20or2V5WybbSCoYEBwBQbbXx2o50V9sBgX6M8w9gcmgwm8ormvX4+K0TggK4PD6GyaEhLdb5mc1cFBftmn9gYD8uiI1iW0UVaTU1pFXXklZTS2FDQ4ttq+12kssrSS5v3pslNNTRpvON9vLkhn69OD06wjXmiqfJxKX9e3FB33i+TMvh1ZS9pFVWN9vOYjRww5C+3Di0H8YuqrEqIiIiIiIiIkqQiIiISAc6v3cs20pTAbhv3TbemXYC/hYLP+UV0eBoTDyc0yuax04YQk1x4wB5o4MCGOTvx7bKKjJqagn1sNDH14d+vj4k+vjgaza1K4ZB/n4M8vdrtqzW6CStuoa91TXsrWpMnOytqiGnto5DVTv9Xa9YhvWJpbTBylc5+fiazYR6Wujn58up0eF4GFsfzs1iNDK3bxzn9Ykls7qW7aUVlNdbCff2ZGxEMH5H0bVaRERERESkKacDHG27z6tbcfbAmOXYowSJiIiIdJg5vWJ4fUc62TV1pJZXce2yDdw6pB8L9uW62pydEN1s7A+jwcCk0GAmhQZ3Wlz+FjPDgwIYHhTQbHmt3U5GdS11dgc+ZhPeJiPegUFU7V9/ee9YzNWNczOalBBrK4PBQIKfDwl+Pkd7CiIiIiIiIiLSwZQgERERkQ7jbTbx2kmjuWzJWkobrGwtreD3yze41gd5WBgb3nmJkPbyNpkYGNC8t4nNw+JKkEjHcTid1NkdeBgNmA/S80ZERERERETEnfTrVERERDpUor8vr500mlgfrxbr5vSK1sXx49DeymqmfvMzJ3y5hK8y81hRUHz4jUREREREREQ6mXqQiIiISIcbEhzAd6dO4oecQhZnF+BrNjEhIoSZsRFdHZp0ge+y8imubwDgnvUpGIFXJ43ixMjQrg1MREREROQY4HQ2Pnqanhjz8WDfvn0tliUkJBy2TUf47XHcQQkSERER6RRmo5HZcZHMjovs6lCkC6WUVvDB3qxmyxzAX9Ym8/HJ44jz9e6awERERERERKSF3r17Nxs31GAwYLPZDtmmI7R2HHdQjQsRERER6RTL8oq4cMkaivb3HhkREsjU/YPdlzdY+eOKjeTW1HVliCIiIiIiItIKp9PperSlTUc8uoISJCIiIiLSKVYXluJoMv/8hBE8MXYovf18ANhbWcOlS9fyTVY+Voej9Z2IiIiIiIiIdBKV2BIRERGRTjE8JMA1fWFiLKFeHgC8Mmkkv1++kczqWvLr6rlj3TYSfL2ZN2kUMT5eXRWuiIiIiEiP5HA2Pnqanhjz8eDKK6/skDY9hRIkIiIiItIpJoSHuKbXFpa6puN9fXhv6lhuW72F9cVlAOyrrmVlQQlze8e4O0wRERERERHZ78033+yQNj2FSmyJiIiIyFGzORzYflMm66vMPNe0n6X5fTmhXh7cNXyAa97HbGJKVGjnBikiIiIiIiLShHqQiIiIiMhRSSmt4A8rNlFltTEhIoSxYcGsKSxheX6xq80dw/o328bhdPLwplTX/E1JiYR7ebotZhERERERERElSERERETkqDy4MZXi+gYAfsor4qe8ombrL+gdywlhwc2W/Xf3PraWVgDQ19+Xy/rGuSdYEREREZFjjNPZ+OhpemLM0nkaGhrw8PBw+3FVYktEREREjli1zUZyWUWr66K9vbhlcF/uG5nkWuZwOnl1Rxr/2rrLtey+4QOwGPW1VERERERE5HizZcsWbr31VmJjY7vk+OpBIiIiIiJHzNtkYnx4MKubDMIOkODrzczYCGptdp5P2QtAbm0dKwtKXL1NAK7p34tx4c17l4iIiIiIiMixq6Kigvfee4958+axYcOGLo1FCRIREREROWJGg4FXThzF09t28c7uTNfyfdW1zNuZccht/zykH78f0Et960VEREREjoLT6cTp6HnfqZ36HXDcWbp0KfPmzePTTz+lrq6u2WvAYDB0SUxKkIiIiIjIUfEwGbl7eBKTI8N4OnkXqeVVB23rY27scXJB71imRYcD+mEkIiIiIiJyrMrJyeGtt97ijTfeIC0tDTjwG/DXpEhX/iZUgkREREREOsTkyFAmRYRQWNdAbm0ddXZ7s/XeJhMDg/zx0HgjIsec3Jo6fsgrYm9lNb5mE6fHRTIo0L+rwxIRERGRLmCz2fjyyy+ZN28eixYtwuFwNEuKGAyGxp5PTid+fn6cc845XHrppV0SqxIkIiIiItJhDAYDEd6eRHh7dnUoItLJiurq2VhSzve5RXyXU4C9yZ1/b+7JZHCgHzcmJTItKqwLoxQRERERd0lJSeGNN97gv//9L0VFRUDz3iK/JkU8PDw49dRTufTSSzn77LPx8vLqspiVIJFOZzQZMZo7905Rp6NratQdC9xVo9LgpruFnQ774Rv1MO567sDhpuO0XdP6kwaDAYPxeP9bd89rwV3Ps39U97yz2Ort7Zr2CvTG4tH2vw2nwz1/R+5773bPa8FgNLnlOAAOm3s+J9r13DVta+ym73V297zmjCb3nbvNTa8Fd/EK9D58o/2s3gd+gHr6e2Ext/258PD1ZG95FfOS9/D+zn2HbJtSXsWf1mxlRnwkD4wfSpy/T5uPA1BTfPByfR3JVl/vluO47z3VPcdp79+rsUlcRqOhXdvbre75fHXXt2Gzp3suxbjrM8/hps8IcN93IJGexuFsfPQ0PTFmaamqqooPPviAefPmsWbNGoBWe4sATJkyhcsvv5zzzz+foKCgrgq5GSVIRERERERE5JCqrTY+3pvNV1l5JBeXt1gf7Gnh8kGJTIkJJ7W0gg937nO1+z4zn+U5hdw0YgC/H9oHT5P7kqIiIiIi0jmWL1/OG2+8wccff0xNTQ3QmBj5bQmtpje/vvPOOyQkJHRVyK1SgkRERERERERcyuqtrMgvZndFFRUNNurtdpbmFlFY17ynhQGI9fPmr2MGMTMhEm9z48/LMZEhXJrUi6/TcnhkzTYKauupszt4akMqn+3J5JaRSQR4mPE2mxgVHoKHSeMSiYiIiPQE+fn5vP3227zxxhvs2rULaL23iNFoZObMmVx99dVccsklXRnyYSlBIiIiIiIiIgB8vS+XhzZsp7zBdtA2w8ICObtPLGcmxhLp03q9aIPBwJl9YpkaF8EzG3fw9vY0HE7YW17NbT9tcLXr5e/LLSP7c0ZirBIlIiIiIt2Qw+Hgq6++4o033uCbb77BbrcfdMD1pKQkrrzySq644gpiYmIAlCARERERERGR7q20voGHN6TyTWbeQducGBnC308cTv+gto8h5e9h4f7xQ5nbL54HVm5lQ2Fps/UZldX85edN/N/6VC4b2ItT4iMZFBJ4xOchIiJyXHKCsyeO59ETYz6O7Ny5kzfeeIN33nmH/Px8oPUB1wMDA7nwwgu5+uqrmTBhQleGfESUIBERERERETmOLcst5N61Kc1KaJ0WH8kZCdGEe3lgMRqJ8vYixMsDD1/PIzrG4NBAPjpjEosy8theWgFOJ8tziti4P2GSV1PHUxt28NSGHVw5KJEHxg/pkHMTERERkfabMmUKv/zyC9B6CS2DwcCMGTO46qqrOPfcc/Hyar1XcU+gBImIiIiIiMhx6qktu3hrY7JrPtDDzN9HD+b0hKgOP5bRYODU3tGc2jsagNtGOVlXUMLryXv4fl++6ybSt7enkV5RzclhwYwKCaSPn0+zwT1FREREpHMtX77cNd20t0j//v256qqruOKKK4iNje3CCDuOEiQiIiIiIiLHMKfTyRcZuXyRnsOY8GCuGnWgd8aCfbmu6SlRYTw8djCR3u65A9BgMDA2MpSxkaHsq6xmwd4c/r0xFYcTfsou4KfsAgDifLw4Oz6KOfFRxPp4uyU2ERERkePdr4kRgNNPP517772XiRMndnFUHU8JEhERERERkWPYouwC7l7T2EtkZUEJnxRU8NL517jWe5uM3DUyiYv6xHVZT40Ef19uGtGfxABf7l2xhfIGq2tdVk0dL+5I58Ud6ZwaE8H9wwcQ6GHpkjhFRES6I4fDicPR8wb06IkxH29+TZIsXLgQm83G1VdfzTnnnIOn55GVXe2OlCARERERERE5hm0vrWg23+BwuKaHBAdw77hB9PL3cXdYrTo9MYbpCZEkF5WzMi2XFYWlrC4qdZXf+i6ngE2l5fxr9GDGhAZ1ZagiIiIix7RfxxoxGAzY7XYWL17M4sWLCQgI4OKLL+bKK6/skYOy/5axqwMQERERERGRzlNvdxx03X9OHNFtkiO/8jSZGBMZwjX9e/H6iSNZPHMitwxMJNDSeH9fXm09V/+ykRUFJV0cqYiIiMixaeHChVx44YV4eHi4EiXQmDQpLy/n1VdfZdKkSQwcOJDHH3+c7OzsLo74yClBIiIiIiIicgw7p3cMvmaTa76334GEiKkHDH4e5e3F9QN688m0sZywv9eIA/j39j2uutgiIiLHM6ez5z6ke5o5cyYffPABOTk5PPPMMwwbNsz1vatpsmTnzp3ce++99O7dm9mzZ/PBBx9QX1/flaG3mxIkIiIiIiIix7CkIH/enHoCAft7YKRX1bjWfb0vl5t/2cT4z3/k9pXNx/7obqK8vZh34kgGB/oDsL28ip/Vi0RERESk0wQHB3PLLbewadMm1q5dyx/+8AcCAgKaJUucTid2u53vv/+eyy67jKioKP7whz+watWqLo6+bZQgEREREREROcYNDw3krWknEOrp0Wz5/23ZxeLsAsobbHyTmcd5i1eyt6K6i6I8PJPBwHX9e7nmv83O78JoRERERI4fY8aM4aWXXiI3N5e3336bqVOnutb9tgTX66+/zqRJk0hKSuqqcNtMCRIREREREZHjwODgAL47bRLXD0w8aJvs6jr+uHwDpfUNboysfXzMB37GeptMh2gpIiIiIh3Ny8uL3/3udyxZsoSdO3dy9913Ex0d3WoJrl27drnmAVasWIHDcfDx8bqCEiQiIiIiIiLHCX8PC5f2i3fNX9k/gbemjuH70yczINAPgH1Vtdy8YjMNhxjcvSutLipzTY8JDaLC2n3LgomIiLhDV48jojFIjl99+/blscceY9++fSxYsIA5c+ZgNptdA7v/mhz5tRTXZZddRnR0NDfffDMrVqzo4ugbKUEiIiIiIiJynLo6qTcTIkOJ8/PhlZNGE+7VWIJrXWEp/9iwvVsOgr6ppNw1fdeGFJ7dvrcLoxERERERo9HIGWecwWeffUZWVhZPPPEEAwcOxOl0thivpLCwkBdffJGTTjqJxMRE7r33XpKTk7su9i47soiIiIiIiHQb0T5evDBpFJ6mxp+Jn6Rl88aOjC6OqqVIb89m8z/nd4+B2jeWVfDojj38VFTSLRNLIiIiIu4QHh7OX//6V7Zt28Yvv/zC1Vdfja+vb6sluDIyMnj88ccZMWIEw4cP75J4lSARERERERERoHEw93+OHeqaf3LLTn7MLujCiFp6eORAbkzq7ZoP8bR0XTD7WR0O7tyWyme5+fwlOZUrN2xlU3lFV4clIiIi0qUmTpzIvHnzyM3N5bXXXmPixImuXiVNS3A5nU62bdvWJTEqQSIiIiIiIiIupydE8achfQFwAveu29atxiMxGwwsyStyzc9NiOnCaBoV1jdQarW55lMqq7hh0zY2lilJIiIinc/hdPbYhxwffH19ufbaa/nll19ISUnh9ttvJzw8vFkJrq6iBImIiIiIiIg0c9PgPkyNDgOgtN7KivziLo7ogMW5hWwvrwKgv78v5yREdXFEEOzRsheL1enk7m07KNcg8iIiIiIuAwcO5MknnyQrK4v58+dz+umnYzKZuiweJUhERERERESkGYPBwIV94lzz32Xld2E0zf2Qe6D3yF+H9MNi7Pqftd/kF7qmTwwJYlxwIADFViv/SN2NzaE7ZI8lTqeTlIpKntuTzn/3ZVNtsx1+IxEREWnGbDZz3nnn8dVXX5Gens7DDz/cNXF0yVFFRERERESkW5scFYqv2US1zc6P2QU02B14dHVQQGp5JQBeJiPjwoK6Nhgaxx95IyPLNf+H3vGEe3pw4ZpNVNntLCsu5fcbt3JnUh+GBPh3YaTSEZYVlfDs7nTSa2pdy97KyOLOAX2YHRnehZGJiIj0XLGxsdxzzz1dcuyuv9VGREREREREuh1Pk4mTYxov+FZYbaws6B5ltpr2GOkOvUe+zCsgv74BgMkhwQwJ8CfC05N/DUnCvH/g0eTKKq5ct4WHtu8it7auK8OVo7Crqpo7tqY2S44AlFlt3J+ykzUlZV0TmIgc95yOnvsQ6Wpd/21SREREREREuqVT4w+M7/HoxlR2lVV2YTSNgvaP91Fnd1De0PXje3yec6D8WL3DQUpF4/go40OCeGHEYBJ9vIHGAe+/yC3g7JXr+TavsLVdSTdmdzp5ePtubPsHkh0a4MfdA/pwcnjI/vVwd/IOCuvruzJMERERaSclSERERERERKRVJ0WFEuHtCcC+qlpO+3wp967YQkld110E7uvv65peV1zWZXH8KszzQOGxtWXlXLFhC1eu38JzezLYUVnNlQmxnBsdie/+wUcdwL93p1Hc0NBFEcuR+Cq3gG2Vjcmv3j7evDZ6GBfERfOvoQOZFBoMQLnNxuvpWYfajYiIiHQzGoNEOp3RbMRoNnV1GB3G4KZu/A6b3S3HcTra3p/RaDY2m246f/jjuGdgSve+1tzzf+QuDrflzNv+mjMYDc2mm84f9ig29/XVbU9cPeE47vp77a6anr/T4WzX83GsPXfues25k7s+x9vz+doTGE3H1vtc47GOrddCe5679ny+ehnNvD51DDcv30RGVQ0OJ7y/I4OVuUV8dNZkIny8jirutsbZ1LToMD5Izwbg5Z3pTI0OO6pSW0f7f/TIwH58mJ3HB9l5lFgbe7Rsq6xyXUz/VS8fb6r3l2YqbrByweqNXJoQw8UJMQTu7xXTEbrra/tovteZLO45J1v9wQdb31JW4Zq+vU8vzA4nDocdA/DggD6cuXoj9Q4Hn+fkc318LMGH+D91uOk91X2/jdz3u8h+jH3XEhGRrqceJCIiIiIiInJQSUH+fHnqifx5WH98LY0XXNMrqrny25WU17u/xNWJESGu6e3lVSzPL3F7DE35mExcnRDLl+NH8bf+ifTz9Wm1Xcb+5EigpfE+xXKrjZf27OO0ZWv5PDvPbfHKkXFy4MJ86G+SH0EWC4Hmxv9Xm9PJPo0zIyJu5sSJ09kDHyjpKV1PPUhERERERETkkLzMJv44pA9zh/TmwgXLyamuZXtJBdcuXMV/T5+It/nIf1o6nU42FpSyoaCUkrp6LknqRXyAb7M2DqeTtUVlrCgoYWvpgTv5DUC8b+f1YmkPT6OR86IjOS86kvz6evZW11Jhs5FdV89L6Zmudo8PH8jXuQV8m1uA3Qk1djsPJe8ixsuLcaFBXXcCckgGDvT6uCU5lV7eXkwOaSytlV/fQMH+kmm+JhO9OrFnlYiIiHQsJUhERERERESkTeL8ffjv6RO5cMFyiusaWJdfwh8Xr+WlGWPxsRz4eWl1OKhusFFltVFtPfDvnrJKlmcXEuBh4f6JQymrs/Lgyq0syypwbfvN3hwWnn8yAHsqq1mQmceCzDzyaluOe3JJYhz9Avw6/8TbKdLTk0jPxrFbntyd3mxdiIeFR4YlcUPfXjy/O51vcwtxAHdt3s77E0cTtX/MF+leJocG8WVeAQ4aS6QVN1jZUF7Zot21CbEEWTquZJqIiIh0LiVIREREREREpM36Bvnz9mkTueSrX6i02vgpq4ApH35Pgr8PGRXVVDbYaGjD+BSf7W59MOv0imqS3viKeB8vMmtaL1UU5unBRYmxXDeg11GdS2ersdv5KKd5+axbNm7jo4mjifXx4pFhSZQ32FhRXEqp1cZfNqUwb9xwvEzHzhiOx4qpoSG8O3oYr+3LJqu2jlKrlaKG5iXmZoSHcElsVBdFKCLHM6cDeuIQeM4eGLMce5QgERERERERkXYZGhbEvNkTuGbhKqqsNopq6ylqpYdHW8X4ejM1PoL3UzNcy5omR0wGA5MjQjgjPpKxocFE9JBeFj4mE6dHhvF1fpFrWX5dA9sqqjgxLBiTwcA/hydx6apNZNfWsa2iiuvXbuX6vgn09fMh0ssTo8E9A3rL4fX38+WJwQOos9tZX15B/f4Bww1Af18f4rxVWktERKSnUYJERERERERE2m1cdChfnDOFR1dt4+fsAqwOJ5E+XoR4eeBrMeNrMeO3/19fj8bpYC8PEgP9mL9jH1+n5QAws1cUz548Bh+LmfTyalbmNiYTPIxGhgX7MzMmgtNiIwnz8ujK02231aXlvLEvixCLhXdHD+OdnFwW5f16bgeSHoEeFp4eOYir1mym1u5gS3klf9qwDQAvk5HePt708fPhrJhIJoQGYVDCpMt5mUxM2j/+iIiIiPRsSpCIiIiIiIjIEekb5M8bp06g3m7H4XS2ebD2k+MjuTK3mMLaOmb3jsZsNAIwb/Z4VuQUYQCGeXjiexSDv3cWh9PJmtJyfMwmhgf4t1hf1NDAM3syWFhY7Fq2rryC/Z0NAAj4zRgVSQF+vD52OLdu2NasbFOd3UFqZTWpldV8k1vItIhQ/jygN718fTr+xMRlb3UNd6TsJKeuHg+jgSCzhSlhwZwaHsZgf18lqURERI4h3e/bpoiIiIiIiPQonkcwZsa46NAWy3wsZmb0ahzDobqw5QDYXS2tppb7U3exo6oGgGmhwdzapxdx3l7UOxx8kVvAi+mZVNvtzbYrs9pc02OCA+jv1zLBMSTQny9OOoGfC0tIragmvbqGtOpasmprse9PriwtKGZdSRnvTRhFgq93553oce7zvAL21TaWeLPZndTY6/kgO48PsvOI8/JkdkQYZ0eFE+Olkloi0j04nU6cTufhG3YzPTFmOfYoQSIiIiIiIiJyGHankzu37SC99sDYKEuLS1lWXMoJQYGkVlVRYTuQGAk0m/lj7zhWlJTzc0kpAONCAvn3qMEH7YHgazZzanQEp0YfWNbgcLA4r4gnUvdQbrVRZbPzTnoW9w3p3zknKhTUN7imE7y9yK2rx7r/Il5WXT3z9mXzVmYO1yXEck1CrHqUiIiI9GBKkIiIiIiIiIgcxg+Fxc2SI79yAGvKypstOzsynJv7JBBksTA3OpIfi0qoNDg5OyYST5OxXcf1MBo5IyaC8aFBnPnzWursDr7JLeTPSYndsgTZsaCkSZmz/40ejtXpYElRCd8VFLGurAInjQmzlzOyyKtv4K7+iZiVJBEREemR9G1KRERERERE5DA2lFe4pp8aksToQH8+zM7j49x8ihuseBoNnBIWyoUxkQxtMjaJwWBgengoFm9La7ttszBPD86IjuCTrDxq7Hb+umk7F8RH09vXh0Rfb/Vi6EDF1sYEia/JhJfJiBdGzo6K4OyoCIrqG/ggJ4+3M3OAxnJcmbV1PD00CZ8jKDUnIiIiXUsJEhEREREREZHDsDUpk/5zcSlTQoO5tlccv4uPIbO2jkhPD/w6uUfHZb1i+TI7H6vTycriMlYWlwEQ5eXJObGRnB8fTbi3xsU4WsX7e5CEerRMaoV5evCnxAT6+/pwX+puANaXV7C2tJypYSFujVNE5FcOZ+Ojp+mJMcuxRwkSERERERERkcM4PSKMBXkFOGjsNdDX15sLYqLwMBrp69ty0PXfKq5vYEdlNTsqq9hZWU1pg5Ugi4UIr8aeIUkBfofdRx8/Hx4cOoBHUnZRa3e4lufV1fPynn28vjeTWVHhXNwrhuFBAepVcgQcTifV9saxZHwP0SNkdkQYn+cWsG5/zyKTnmsREZEeSQkSERERERERkcMYHRTAX/v15ond6QA8tSeD1zOymRgSxInBQUR4emAADAYwAPn1DeysrmFnVTW7qmtcvRJa8056NidHhHJtn3gGBfgd8mL7GTERTAkPYVlhCZk1tWwpq2RVcSkOwOZ08k1uAd/kFjA4wI+Le8VyanREu8c9OZ4ZDQZCLBZKrFaKGhoO2XZmRKgrQZJXX++O8ERERKSDKUEiIiIiIiIi0gYXxESRU1fPu1m5AJTbbHxXUMR3BUVHve8lBcUsKSjGbDAQ5eVJP39fpoaHcGZMBBZj8wSHv8XMGTERrvnc2jo+zszl06w8yqw2AFIqqnhg6w6eTt3DJb1i+X3fXpiN6uXQFtFeHvsTJFasDkeL59/VztPTNV1Qf+hkioiIiHRPSpCIiIiIiIiItNEtiQkM8fdjcWExq0vLXeWYDiXQbCYpwJckfz8G+PsywN+XaG9Pyq02lheW8EZaFoX7L7DbnE6yauvIqq1jaUExb6Vl8c/hSQwO9D/o/qO9vbhlQCLX901gUX4x72dks72iCoAyq42XdmeQXVvHP4YlYVQpqMOK8vRkW2U1Thp7AsW1Mq6LzeFgU0Wlaz7I0nK8EhERd3E6nDh74IAePTFmOfYoQSIiIiIiIiLSRgaDgRnhocwID8XqcLC5opINZRXUOxw4gV8v9QSYzfT39aG/nw8RHh54+Hi02FeAxcIlvWI5Ly6aBTn5/FJUSnZtHdk1da7ES0ZNLdeu3cJTIwdzYljwIWPzMpmYExfF2bGRbC2r5P2MbBblFWJzOvkyOx9fs5m7BvXV2CSHEeV1oGdIXn19iwSJ1eHg6k3J7KiqcS2LbrKNiIiI9BxKkIiIiIiIiIgcAYvRyAlBgZwQFHhU+/E0GTk/Pprz46OBxoHCt5RV8n+pe9hWUUWt3cGtG7bx8gnDGBNy+GMZDAaGBwcwPDiA6Xlh3LExBQfwfkY2I4MDODU64rD7OJ7FNCmdlVPXcmyRwoaGZsmRQLOZ8cFH9xoQERGRrqGR2kRERERERES6EaPBwMjgAN4YN4LpEaEAWJ1O/rwxhQ/25bCxtJz8unoczsOXJpkRFc79Qwe45n8qKO60uI8VTXuD5LaSIIn29KSfr49rvtxmY1eThImIiLs5nT33IdLV1IPkMGpra0lNTSUjI4OcnBwqKyuxWq0EBAQQGhrK0KFDGTJkCGbzkT+VJSUlPP3003z55Zfs2bMHs9nMoEGDuOiii7jhhhvw8mpZ77StHA4HO3bsYPPmzRQXF1NeXo7RaMTX15eIiAh69+7NgAEDCA4+dFdtERERERERcS9Pk5F/jRjEzRuSWVlcRoXNxuPb97jWexgNjAoO5ML4aE6JCD1o6ayzYiN5bNsurE4nyWWVrbaRA7yaDMpua3L1zul0squ6hh+KSthd3TwhEuyhyysiIiI9kT7BW/Hmm2/y448/snr1avbs2YPD4Thkez8/Py688EJuvvlmRo4c2a5jLV++nHPPPZeioqJmy1evXs3q1at56aWX+Oabb+jXr1+79rtlyxZefPFFPvjgA8rLyw/bvk+fPowfP55Zs2Yxe/ZsoqOj23U8ERERERER6Xhmo4EnRgzizs3bWVlc1mxdg8PJ6uIyVheXMT0ilDsH9SXKx7vFPixGIwMD/NhaXsm+mloqrDYCLLoccDBNb2h2AFm1dXxbUMR3BUXsq61r0f7ufokkeLd83kVERKT70zeiVtx///1kZ2e3uX1VVRVvvPEGb7/9NjfffDP/93//16YeJcnJyZx22mlUVVUdtM2uXbuYPn06a9euJSLi8HVi6+vr+dvf/sazzz572MROU3v37mXv3r28//77zJkzh88//7zN24qIiIiIiEjn8beYeXHMUNaWlLOzsprs2jpyauvYWVntKgH1Q0Exy4tKeWLkIKZFhrXYx5Agf7aWN/Ye+aWwhNNiNA5JW7yTmcM7mTmtrgu1WHh1xGASWklKiYiISM+gBEkb+Pj40LdvXxISEggICMDhcFBSUsLWrVvJy8tztbPb7TzzzDOkp6czf/58TCbTIff7xz/+0ZUciY6O5v7772fcuHE0NDSwbNkyHnvsMSoqKti3bx933nknb7311iH3V19fz7nnnsu3337bYl1ERARDhgwhLCwMi8VCSUkJ2dnZbN++HZvN1v4nRURERERERNzGYDAwLjSIcaFBrmVOp5Pv84t4JGU35VYb9Q4Hj6fsZkpEKMbflNuaGBrMBxmNF/r/tX0340KDCPX0cOcp9BgJ3l54GAw0HKY4frHVytLiUq5QgkREupjD6cTh6HkDerRlLC2RzqYESSt8fX05++yzOe200zjxxBMZOnQoRmPr49mvWrWK++67jx9++MG17PPPP+fpp5/mjjvuOOgxtm7dyi+//ALAkCFDWL58OUFBQa71EydO5KKLLmL8+PEUFBTwv//9j2effZbAwMCD7vPuu+9ukRw577zzuOuuuxg3blyr29TW1rJ69Wo+//xzPvnkE7Kysg66fxEREREREek+DAYDM6PCGR8axO/XbnX1KLlr03YeGp6Ed5Ob9qZGhHJyRChLCoopbbByw9otzIgKZ0igP0MC/QnysOB0OrE5nTQ4HNTbHY3/Ohr/DfHwOG4SKlFenrw8YjCP7tzLnppa+vv6MCU0iHn7WvYkeT0ji9Miwgg/Tp4bERGRY40SJK1ITk7GYrG0qe2ECRNYtGgRV155Je+++65r+aOPPsott9yCp6dnq9utWLGiWdumyZFf9e7dm7vvvpvbb78dm83G6tWrmTVrVqv727VrF88//3yzZU899RS33377IeP39vZm2rRpTJs2jaeeeoovvviCXbt2HXIbERERERER6T4CLBZuT0rkhnXJOIFFeYWklFfywNABjA8LBhqTKQ8MHcDWX9ZTVN/AjspqdlRWu/bhZTLSYHdwqELNSf5+PDZiIP38fTv3hLqBYQH+vDdmOOVWG8EeFh7bude17sTgIGK8PJmfm0+tw8HTe9N5bGB/DL/ptSMiIiLdnxIkrWhrcuRXRqORF154gc8++4zq6sYvmOXl5SxZsoRTTz211W2Ki4td04MGDTrovocMGdLqNr/14YcfNiuVNWPGjMMmR37LZDJx3nnntWubtmiorqeB1nvgdBSD0X1fRC0+rSe9OprJs32vwyPlbEcXTJOHpdm0ydn2u6QcVveUcnPY7G45DoDRfOgyeh3F2Y7xhI5G5/6VHuBox5EMTXrvGYzGZvOHY3TjJ5zD5p7/o2NNe95/jpa7Pifc+XnkDu76P3Lna8Fods+7naMdH3tNXzcGo6Fd73XuYuh+IR01i7e7Xgvu+W5SV95y4OiDsdkOfEjWV9Zjr277tvWV9e2K62hYa61uO5Y7dPbf9sTwUJ4dM5S7N2+nxmYnq7aO69du4ZPp4xkcHABAlK8Hz04cwV9XbyX3N4ON19kP/31mR2UVf1y3hW9mTyLA26td8VmbDGLuFeiNxaPt358s3u7pndFQ3fL1/WsNh4rdBz6rVpSWMX/GBBYXF1PeYOP7whISQwv4y/D+bTpOdVH14Rt1ALvVPX9DFm/3/HYFcNjb/p2h5efr4b+nHWvf5URE5PCUIOkgAQEBTJ48mYULF7qW7d69+6Dtm5bK2rlzJwMGDGi13Y4dO1zTrfUy+dXPP//cbP6SSy45XMgiIiIiIiLiBk6nk12VVQRaLER4dd4NV1MjQpk/fTyXLllLWUPjxfG5P6zGZDCQFOjHIycMYXRYEN+fPpmMqhq2lVawtaSCbaUVlFuteJlMeBqNeJiMeJqM+6dNWIwGfs4rorCugcK6Bu5ak8z0+Ai+zshja0k5YV6eDAn2Z2xECGf1isbXcuxdavjj4D78kFPomj//+1XN1r+WmobN4eCWof3wdtONVCIiLk4nzp44nkdPjFmOOcfet5YuFBIS0my+srLyoG3Hjh3rmr7vvvuYOnUq/v7+zdpkZ2fz+OOPA429VJpu81s5Oc1rocbFxbU5bhEREREREekc9Q4Hj+7cy7cFRQCMCg5gdnQEMyLDCO+EZEmivy/PThjOlcvWu5bZnU5Syiq5+qd1vHbSaIaFBJLo70uivy9nJkS3ab+ZVTWctWgl9Q4HP+YW8mPugWRBpdVGWmU1X+3L48nNO7m0Xzw3DOl7TCUKhoUE8s9xQ3ho/XZqm/S2GRsezNrCUgDe3JnB4uwCrhuYyImRIcT7+XRVuCIiItJGx2Bn+a6TkZHRbD4mJuagbceOHcvQoUMB2Lx5M0OGDOGVV15hw4YNrF69mqeeeophw4a5Eh9z584lLCzsoPtrWl4LoKSk5EhPQ0RERERERDpAUUMDN2xOcSVHADaWVvB4ym5mLlnFtas38VFGDsX1DR163LHhwfxtRBITIkIYGhxA9P5yWOVWG9cs28DG4rJ27zPez4f7Rw3ktwWIgjwsWJqUJaq02nhlexrnLlzJ1pLyoziL7ufc3rF8f8ZJnBIT7lrWy8+Hu0ckuZ6DrOpa/r4+hZnfLOeOVVuxu7GMpIiIiLSfepB0kJ07d7J69WrXvMFgYOrUqQdtbzAYeOmllzjllFOwWq1kZmbyxz/+sdW2ERERPPXUU4c8fkREBKmpqa75L774gosvvridZyEiIiIiIiIdYUdVNX/ZtoP8/ckPL5ORKC9P0qtrAXAC60rKWVdSzhPbd3NVn3iu7ZuAt+noe10YDAau6J/AFf0TAKi22rjhl02sLSqlymbjtpVb+P70yVjaOS7K3MRYhoUE8PW+PMxmIxMiQhkXEYzN6SSltIIPd2fxZUYOVoeTtMpqLv1+DU9NHM6s+MijPqfuItTLk7+NTOLH/eW26ux2rkrqxUnRofxj/XbW7O9NArAkt5DTvlvO/BkTCPBw3zgdIiIi0nbqQdIBcnNzueCCC7DbDwzAeP7559O7d+9Dbjd58mQ++ugjfH19D9qmV69eLF68mPj4+EPua+LEic3mP/zwQ1544YXDBy8iIiIiIiIdanFhMb/ftM2VHInw8OCt8SP5/KSxzJ88huv6JpDgc2DQcqvTyWt79nHusrWsLio92G6PmK/FzCuTRzE6NAiAgrp6Xt+RTpXVdugNWzEg0J8/D+vPrcP6Mz4yBIPBgMVoZERoEI+NH8qXp05iaEjjoPANDge3rtjEx3uyOvJ0ulzTAe099ye0+gb48fa0E/hw+jhuG9oPgCqrjX1VtXyZkdslcYrI8cPp6LkPka6mBMkRsNlsFBYWsmzZMu68804GDhzIli1bXOv79OnD888/36Z9nXPOOaSmpnLbbbeRlJSEl5cXfn5+jBkzhn/+858kJyczfPjww+7nsssuw2A40K3Z6XTypz/9iTFjxvDiiy+2KP8lIiIiIiIiHaOgvoG1peWk19Tyt5Sd3LN9F3WOxqs+Q/39eHv0UAYF+mMwGOjv78efBiTy5ZSxfDhpDJf1isW8/7dcbl09N67bypdZeR0eo7fZxO+Tervm/7NtD2O/WMIp3/zM3WuT+S4rnwbH0V+p6hPgywczxnNO78aS0w4nPLBuG2sLj50y0HVNbo70NB24rGIwGBgRGsQfB/fhzaljXMvza+vcGp+IiIi0nUpstcFtt93Gs88+26a2J598Mv/973+JiIho8/7j4uL497//zb///e8jDZFhw4Zx/fXX88orrzRbvmHDBjZs2MBNN91ETEwM48aNY+zYsYwbN44JEybg5+d3xMcUERERERE53v1QWMw/duyhtpXkgqfRwFUJMXgYWt6baDAYGBjgx8DB/bgwIYbHUnaxurgMm9PJ/Vt38L+MbC5OiMHqcDIgwJcRQQHNboo7ElOjwzivdwyfpue4luXW1PFFRi5fZOQyNDiAZycOJ6ZJ75YjYTEa+ef4ofh7mPnvzn04nPCPdSk88/uj2m23YWzy/7CjrBKn09ni/8anyQD1TXuciIiISPeiBEkHOfvss7npppuYNWtWl8Xw3HPPUVhYyKefftrq+pycHD7//HM+//xzAMxmM2PGjOHCCy/k0ksvJSoqyo3RioiIiIiI9FwOp5OX0jN5KzPnoG3qHU7+um0nAH18fRgeHMCIoAAGBvg1G9jc4YQLE2JY3WTw9NSKKh5M3umav31gH65MPHTp5cMxGgw8esIQLu4Tx6fpOewsryK1rJKa/T0ikksruHTJWt6aMobe/gcvBd3WY90zaiBZVbUsySmk0mo//EY9xIBAP3r5+ZBRVcP6ojIWZxcwK675OCtL9o9RAtCgBImIiEi3pQRJB/n222+x2+14eXkxZcqULonBYrHwySef8Oqrr/LQQw+RnZ19yPY2m43Vq1ezevVq7r33Xm655Rbuv/9+9SoRERERERE5jOfT9vHfrLaPLbG3uoa91TV8foTls17elcHcuGj8LEf/M35YSCDDQgKBxov3v+QX88/NO8isriW/tp4rflrHm1PG0Dfg6H4bGg0Gnpw4nIsWr6LpyCr/2riDhspywr08mREXwaDggKM6jrtZjEbuHDGAm37ZBMA/N+1gQkSIayD2ealpvLw9zdV+YmRIV4QpIscRh7Mxcd/TOHpeyHIM0hgkbfDAAw+QlpbmeqSkpPDzzz/z3HPPccoppwBgtVr5+uuvmTp1Kn/605+aDdjubtdffz179uzhgw8+YM6cOfj7+x92m7q6Op544gkmTpxIenp65wcpIiIiIiLSQ+2qqj5scuSMiDBuSUzgopgoBvv7usYZOVI1djtlVutR7aM1HiYjJ8eE88Ep40gKbEyIFNY1MPf71dy5Jpk1BSU4j+Kim5/FzIsnjcLfcqDk1DeZeXy4J4vnt+3hvEUreT55N/YedpXslJhwTtyf+MitqePhDalA43gj/9m2x9Xu7hFJnBqvag0iIiLdlXqQtEFISAghIS3v+Jg8eTJ/+tOfWL58OZdffrlrIPQXXniB2tpa5s2b5+5QXTw9Pbnooou46KKLsNvtbN68mTVr1rBhwwbWr1/P5s2bW03iJCcnc/rpp7N69eo2JVbawublg9Xbp0P2dTBHW4u3PZyeHm45jrHJD4jO5GzHDxGrh3er023hMLknaeiwuS852Z7n7qi46ThH88O3PRz2drzmvHxanW4LZwcMMtpWDpVN6Pba8zlha/Jas7XzdXescdf7gtveTwGjyT33B7XnfcHW5HuSrZO/M4n7OW3u+Yywt+M4Nh/fVqfbwmB03/dum6njkwGtOZL/o1Kbg9DQ0IOunxgcyD39+zQbq8LhaWJnZQ3bKyrJaWXQ7iCLhb5+PqRV1/Bu+oFqAGaDgaFB/syOCicqPBzbYWIz+FjafT4A/j6+vH7qVO5Ys5VdFdUArKhuYMW2dGJ98jgtLoozEqII8mzcf3veT2O8fXl02oHf1MHBwc3Wv5dbRkrDbu4fPYgwb88W2zu9juyc2svqaN8lkoemjuean9ZTbbOzorqBR3ZkkV1bh19QMH7A3MRYLhvSl9++km1+7vk7ctfnq8FN/z8ANsvh/gIOsDd5f7O38b3OZtJlMhGR443B6a5fvse4tLQ0xo4dS3FxsWvZ559/zpw5c7owqoOrrKzk66+/5qWXXmLZsmUt1t911108/vjjR7z/rKws4uMb6+O+/vrrhIWFHfG+REREREREREQ6W1FREb///e8ByMzMJC4urosjEjm4ptferntkB/7BsV0cUftVlmbz2n1JgP7mpOuoxFYHSUxM5IEHHmi27IknnuiiaA7P39+fiy++mJ9++ol3330Xi6X5HR8vvPAC9fX1XRSdiIiIiIiIiIiIiEjnUt/BDnTxxRdz6623uuZXrVpFWVkZQUFBXRdUG1x22WUUFBRw++23u5ZVVVWxYsUKTj755KPef98fPyPK79gpsWX2Pr5LbKWOPw2Agau/xdJQ2+Zt3VX6SiW2jlx3LbGVPvM8AHov/hRLXU2bt1WJLWmqvSW2Mk67AIBe336MuR2vu2ONSmwdufaW2Mo84yIA4r/+EHPt8fuaOxZ11xJbueddCkD0p+9hrqlu87buLLFlreu+JbaOhMXHPb8jLEdYYutQcmvqeGfnPr7Lzncti/X14g+D+jAlOqxNn7M2Lx/SZ58PQO+F85t9vlY0WPnXph0szytuto23ychVA3tzyaBETG547VlrGo5oO7vTSWpZJXanEw+jkSBPC1HeXgdtX1Pqnvd5d32+WtxZYquhfSW2Ci6+AoCID97B1Ib3OmMr5e9EROTYpgRJB4qIiCA4OJjS0lIAHA4HaWlpjBo1qosjO7wbbriB++67j5qaA1/UUlNTOyRBYq6rwWLq3C9m7vyhZjG2/QvZ0TC2s/7tkTrSL82Whlo86tuRILG653k7FhMk7rrQ767zaU+CpClLXQ2W2rZfwHFrgsRNF1bkyB3p54S5na+7Y4373ufcmCAxuylBcoTvC+baGiztuFgt3Z+7vpsYrEf4mqupxlxd1fbjuPF7t7PWPQkSd/0fmZ0tx9boDBY6PhGTANw3IJaBFnho43asDifFxXDTvmxGhQVx18gkRoUFtXl/v/18DQX+NbQ373mZeHzjDhqafI97pKCQT5J38OiJwxkZHtzK3jpQ7ZFVUbAAI72a3uBmh0N8fzBXued9vrM+X2tsdvLr68mvb8AAjI4Mwcfsnhv8qD+y35WmmmrMVYd/rzPXqZKGiMjxRgmSDvbbUlU9pUyVl5cXAwcOZMOGDa5lvyZ6RERERERERM5PjGVIcACPb97BmsLG34sbi8q4+PvVnBofyV+GDyDB/8iqBxgMBi7rn8CYsCDe2pHBrooqtpVU4AS2l1Qw96vlXD6wN38ZM5AAD/f1WJBGZVYrT+1K4+fiUqrszZOKZoOBUcGBnBgWzLTIUPr4tW1AdBHpOA6HE4cbbzrqKD0xZjn2aAySDlRXV0dRUVGzZZGRkV0UTfuZTM3v+AgMDOyiSERERERERKQ7GhTkz1tTxvDySaPoE3DgQvh3mfmc/u1y/rkxleqj6D0+MDiAxycM45NZE/lo5ngGBvkD4AT+m5rOrE+X8E16jtvKQApsLq/gsnWb+bagqEVyBMDmdLK2pIxnd6Yx9+d1LCsobmUvIiIi3ZMSJB3ohx9+wNGkK7CPjw+xsbFdGFHb2Ww2du/e3WxZT4ldRERERERE3MdgMHBybAQLTj2RB08YTKhnY1kvq8PJWzsyuHLJWkrqj2w8j6aGhwbxyawJ3DN2MN77SzgV1NbzpyXr+f33a8iq1JhNne2znHyu37SN/P3/n34mE6MDAzgtMpyrEmKZGxNJbJPxVhzA2pKyrglWRETkCKjEVgdxOBw8/PDDzZadeuqpeHi4ZyC+TZs2MWLEiCMerPzDDz9sVlLLZDIxZcqUjgpPREREREREjjFmo5FL+sVzVq9oXt+exps70qmzO9haUsHvfljDx7Mm4GM+ussOZqOR3w/ty2m9o3lg5VaWZBUAsCSrgFWfL+XlU8ZyUmx4R5yONOF0OnklPZPXM7Jcy0YHBvDI4P5EeDYfT8fsZea73ELu3rwdAA+j7sUVEZGeQ59av/Hcc8+Rm5vbrm2sVivXXnstq1evbrb8pptu6sjQDum2225j1KhRzJ8/H5utfd2Z161bxy233NJs2amnnkpISEhHhigiIiIiIiLHID+LmduG9+fjmROI8G68eL67opp3d+3rsGPE+vnw+oxxvHDyGNcxam12bvxxLVuKyjrsONLow+y8ZsmRS+OieXHEkBbJEWjsUdR0kHZPJUhE3M7p7LkPka6mT63fmDdvHn379uXyyy9nwYIFVFZWHrRtbW0t77//PqNGjeKtt95qtu53v/sdp5xySidH29zmzZu54IILiImJ4dZbb2Xp0qVUV1cftP3evXu56667mDx5MiUlJa7lHh4ePPXUU+4IWURERERERI4RA4L8eXXKaNf8L7kdOxaFwWDgtN4xLDrvZKbHN473WW2zc+3i1WRXqdxWR/omv8A1fXvf3tzeLxGz8eAVKxrsB8qNe5p0qUlERHoOldhqRW1tLf/73//43//+h8FgoF+/fvTu3ZugoCA8PDyorKwkIyODlJQUrFZri+3PPPNMXnvttS6IvFFhYSH/+c9/+M9//oPJZGLQoEFER0cTGhoKQGlpKampqWRkZLTY1mKx8N5775GUlOTusEVERERERKSHGxQcQIS3JwW19WwpKafebsfTZDr8hu0Q4GHhuWljuHLRKtbml1Bc18DDq7fx8vSxHXqc45XN4WD3/oRTgrcXl8bHHHabuibjsaoHiYiI9CRKkByG0+lk165d7Nq167Btvb29ue+++7jjjjuwWCxuiO6AAQMG8PPPPzcbJB7AbreTnJxMcnLyYfeRlJTEK6+8wtSpUzsrTBERERERETnGjQoLYmFmPjU2Ow+sTeFfE4Z1+DG8zCZenT6WWZ8tpbC2nkX78lialc+0uMgOP9bxptRqo2F/3ZugNl7baGhyLUJjkIiISE+iT63feO2117jvvvuYOHEinq3U1mzNwIEDefjhh9m5cyf33HOP25MjAK+++ip5eXnMmzePyy67jMTExDZtZ7FYmDVrFm+//TZbt25VckRERERERESOys1D+7nKLH2ensOdq7ayu7yqw48T6OnBHWMGuebv+HkTBTV1HX6c402oh4VYr8brIVsqKsmoqT3sNj5Negm9sCud8oaW1TZEpPM4nU6cjh740CAk0g2oB8lvjB07lrFjx/Lwww9jtVrZvn07e/fuJTs7m6qqKqxWK35+fgQEBNC7d29GjRpFcHBwV4cNQHh4ONdccw3XXHMNAMXFxezcuZM9e/ZQUlJCZWUlJpOJgIAAgoODGThwIIMHD25zIkhERERERETkcPoH+nHf6IHcvzYFgC/Sc1heWc+8KxrX19vtdNRthef1i+PrtBx+yi6guK6BPy1dzyunjCXYy6ODjnD8MRoMnB8TxbN7G8tyP783gyeGJGEwHHwMkhHBAZwbF8VnWXkU1TcwZ9laXh03nAEBfu4KW0RE5IgoQXIIFouF4cOHM3z48K4O5YiEhoYyceJEJk6c2KVx+IQH4evv06UxdCRr9eHvnukITod7suiGQwy0d6i2BqOhXdsazR1bd7g7cP6mpF3ncU9nP3e95miwtbmp0WRoNt10/rA6uNZ1d+Cwues15x7teQ85Wu15fTe9i+nXO7E64zg9gTv/j9zFXX9H7vp8le7PXd+BHPa2v/8czWvO3o7P8Z7C7Omen8WVuZVuOY6Hr3sqGoT2Czvk+ksG9cZpNPKv9dupttmbrZu7aBWnhPhwfr94BgT5HfLCO4BPeOAh17947hRmvv0NOZU1rMsvYdYXS/n96IFkV1SzvaiMpNBAnpg9HvNhSj/5Rrrn/dfiXeKW45RmlB7xtnOiI3knM5tSq40lRSW8n5V70LFIDEYDsb7eNP2ELbVaeSstk3+OGnzEMbSmXX+vHgfamj3MbdrW7LAfto2IiBxblCARERERERERkQ5lMBi4bGAvzu4Tw5d7c/iqsMK1rtJq442UNN5ISSPY08Lo8GBGRwQzOjyYYWFBeLczsRjq48Vrc07iko9/pKLeSnFNPf9avtm1fn1OEVN6R3POoN4ddXrHvACLmbv69+HulJ0APL0nnUCLmTOiIg66zUPDk5gYFszdm7YDkFtX75ZYRaTxxi5HDyxXpRJb0h0oQSIiIiIiIiIincLfw8JlA3txwUgfduxfZmnSk6O03soPWQX8kFUAgNlgYFBIAOf1jeOypF6Y2tjDaUxMOMuuOYt7f1jL1zszW6xfmVmgBEk7zYgI47rqGl7LyALgodTd+JvNTAkLabW9wWAgyvtACe9IL5XzFhGR7k+DtIuIiIiIiIhIpzJwINHxxZmTuH/cYE6JiyDIs3lJMJvTydbicv6xZhvnffMLe9sxuHuUvw/zzpnKF5fO4snZ4/n4oumY9pfvWpaei9V+bJUrdYfre8dzQUwUAHbgbyk72VhWcdD2C3MKXdOTwltPpIiIiHQn6kEiIiIiIiIiIm4T6OHBVYMSuWpQIk6nk7SKajYUlLK+sJSNhaXsKmtMiiQXl3Pxdyv5NMSfpLCgNu9/fFwE4+MaS0GNiQljTXYhaWWVPLViC3efNLITzujYZTAYuKN/IhU2GwsLiqh3OLht63ZeHTmUJH/fZm1r7Xa+zskHwNNo5JTIQ49TIyIi0h0oQSIinWJ3SQXVVhsjInXXkIiIiIiItM5gMNAn0I8+gX6c3z8egA0FpfxtxRZ2l1dRXNfAeR8sZv5FMxkUHtTu/T94yhjO/t9CbA4nz6xMJq20kt+N6E+YrxfVDVaqG2xUW20Ee3kyNCoYf0+PDj7Dns9oMPDgwH5U2mysKCmj2m7nli0pvD5qKPE+3q523+UUUGG1ATA7Jhw/iy45ibiL0+HE6eh543n0xJjl2KNPKxHpME6nk09SM3hn6x425BUDcNOYgfxt0vAujkxERERERHqK0RHBfHTaiVyxeDXJxeUU19Qz94PFfHLxDAaFB7dvX9Fh3H3SSB75aSMAX6Rm8EVqRqttvc0m/jBuEDePH4qPh5mfM/LA6WRK72gMhraNhXKsshiN/GtIEjdtTmFLRSXFVis3b9nO66OGEuPrgdPp5IP0bFf7i3rFdmG0IiIibacxSESkwzyxMpnbFq9xJUcAXlifynNrU7owKhERERER6WkCPS38d9Z4hocGAlBSW8/cD75nW0Fpu/d107jBPHv6RIK9Dt07pNZm55kVyYx68RMGP/sRF37wPRd++ANn/28hWeXVR3QexxJvk4l/DxtIX18fALLq6rhi/RaSyyrIrq1je0VjabTBgX4MCwroylBFRETaTAkSEekQ63OLeWF9qmu+T5C/a/pfK5N5Y/OurghLRERERER6qAAPC2/PGs/o6MaxLEpq67ngw+9Jzi9p134MBgMXDe3Liuvm8MxpE7lmdBIXD+vL78ckcdvEofztpJFcMKQPFmPjJZKKeiuldQ2u7ddkFXL2/75jT8nBByc/XgRaLDw7bBBR+0uRFTQ0cNXKjXydne9qE+Xl1VXhiYiItJtKbIlIh/CxmPAwGamz2blocG+enD6Wlzfs4NFftgDw9+WbCfb25NwBCV0cqYiIiIiI9BQBHhY+uPAULvn4R9bnFLmSJB9dNINh7RzvMNjbk4uH9eXiYX1bXf+XycN44ufN/Lg3h7K6BhIC/SiqqaPGaiO7ooYz//sdT582kdMGxHfEqfVYUV6evD1mOHcm72BzRSUNDicv7Ex3rc+qqe264ESOUxqDROTIKUEiIh2ixmrn16q8G3JLqLHauGHMQKoarDy7djsAf/lhLX2D/Bke0b66wSIiIiIicvwK8PTggwsakyTrcooorWvggg+/58MLpzMiKrTDjpMYHMBLZ5+E0+mkwe7A02yioLqWiz/8gW0FpZTU1nPVp0sZHB5MgKcFAIvJyNjYcK4aPYBIP58Oi6W7C/Xw4OWRQ/i/XWl8mpvfbN2AAL8uikpERKT9VGJLRDpEvxB/YvwbfxDsKq1g1nuLuHfJevqFBBDg0fjjwepw8mNGbleGKSIiIiIiPZC/pwcfXDCdcbHhAJTVNXDhhz+wKbf4MFu2n8FgwNNsAiDC15tPL53Jaf0P9BpJKSxlVVYBq7IK+Dkjj6dXbGXavK94fV0qC1IzWJddiMN57N8VbTEauSepLw8NTyJ8f8mtE8ODuX/YgC6OTEREpO2UIBGRDhHo6cEbZ05yJUMyKqp5e+sebl64mooGKwARPl6cn9SrK8MUEREREZEeys/Twnvnn8KEuAgAyusbuPCj79mYW9Spxw3y8uTN86by9GkTiPD1brVNSW09936/lt9/vowz/vsdt3+7slNj6k7OiY/m25Mn8MXUcbw0djjeJlNXhyQiItJmSpCISIfpGxzAu+dMYVxMGCaDodm6hABfPj1vGnEBvl0UnYiIiIiI9HR+nhb+d/7JriRJRb2VCz/8gQ05nZskMRgMXDaiP1v+NJesOy5zPdb+8Vxm94tr0f7L1IxOjae78TAZSfTzwfCb34Ei4h4OZ899iHQ1jUEiIh1qdFQon55/ClUNVlbnFLEiM596u4ObRw8kwterq8MTEREREZEeztfDwv/OP4XffbKEFZn5VDZYueijH/jgwlMYExPeqcc2GAxYTAeSAAlBfrw9dxo/peeyvaCMB5esB6C6wUZWeTVxgbpBTEREpDtTDxIR6RR+Hham947m/pNG8si00UqOiIiIiIhIh/H1MPPfuSczKSESYH+S5EfWZhe6PRaDwcC0xBhuGD+YmX1jXctzq2rcHouIiIi0jxIkIiIiIiIiItLj/JokOalXFABVDVYu/ugH1mQVdFlMVofDNT33vUWs7cJYRERE5PCUIBERERERERGRHsnHYuad86YxtXc0ANVWGxd//COrMrsmMXH5iP6YjY0luOrtDq78dCnbC0u7JBYROX44nU6cjh74cGoQEul6SpBIpyutrdMbHlBjtZFXXYtDz4WIiIiIiEiH8baYeevcqUzbnySpsdq4dP6PrMzMd3ssZw3sxao/nOMaRL64pp657y1mW36J22MRERGRw1OCRDrdjHe+Jf7p9xnwn4+Y/vbX/H3Jekpq67s6LLdanVPExHe/Y9w735L02hec/vGPzN+Rgd2hZImIiIiIiMjR8raYeeu8aZycGAMcSJL8si/P7bHEB/rx9vnTGBkdCkBxbT1z31/Mlrxit8ciIiItlZWV8dlnn3HLLbcwZcoUoqKi8PT0xM/Pj4SEBM466yyeeeYZSkvb3wOwoKCAxx57jGnTphETE4OnpyeRkZGMGTOG++67j507d7ZrfwaDod2Pl19+ud1xH8+UIBG3sDudVDZYSSks49X1qUya9yVvbtyBrUl91mOV1e7g5u/XUFrXADR2s04uKuP2H9cz48PFfLMnu4sjFBERERER6fm8zCbePHcq0/s0JklqrXYun7+E5RnuT5IEeXny8UUzGBMTBkBpXQPnv/89G3OL3B6LiBz7nE5nj324U2pqKmeddRaRkZGcd955PPfcc/z888/k5+fT0NBAdXU1mZmZfPXVV/z5z38mLi6OZ555ps1xPvPMM/Tp04d7772Xn376idzcXBoaGigoKGDDhg08+uijDB06lEceeQS73d7JZyttZe7qAOTYNy4mnDq7ncp6K5kVVdgcTsrqGrjnh3W8tHY7U3pHMzkhkhPjI4nw9e7qcDvcypxC8qrrXPP9gvzZXVYJwJ6yKv64aDVzByTwyJSR+Fr0JykiIiIiInKkvMwm3jhnKtd+vozv92ZTa7Pzu0+W8M7caZzUK9qtsQR4efDhRdO59OMfWZNVSHl9A5d/vIRvzppMqJenW2MRERFITk7mq6++arbMZDLRr18/IiMjsdvtbN++nZKSxrKINTU1/PnPf2bbtm28+uqrGAyGg+771ltv5T//+U+zZYmJifTq1Yuqqiq2bNlCQ0MDVquV+++/n+zsbF566aV2xT9lyhS8vQ9/7TQhIaFd+z3e6WqsdLqXzppMjL8PAPlVtTy6bCMfp6QBkFlRzf+27OZ/W3YD0D8kgMkJUZy4P2ES4t3yS2OD3U5xTT1FNXUEeFoorq3n212ZnNQriskJURgP8WbVFV7ceKDr3L0Th/KHkQNYlVPIk2tSWJPb2MX6k5372FRQwqunTqB/cEBXhSoiIiIiItLjeZpNzDtnCtd9sYxFe35NkizlnfOmMaW3e5Mk/p4efHDhdC77+EdWZhZQVFPH/SuTeWHa6ENeaBMRkc5jsVg488wzufLKKzn55JMJCDhwLc7pdPLll19y0003kZ3dWPXl9ddfZ9SoUdx4442t7u/dd99tlhwZPnw4r732GuPGjXMtq6io4KGHHuKpp54C4OWXX2b06NFcd911bY777bffpnfv3u05VWkDJUjErSL9vPnP6Sdy5cgBPPHLZlZmFmBtUmZrV0kFu0oqeHNTY1Khb0gAfhYzZqORsvoGiqrrKK9vaHXfz69JIdrPm/hAP6L8vInw9SbS15tIv8ZHjL8vfYP93f4ldEdxhWs6zNsLgAkx4Xw8Zwqf78rknmWbqLba2FNWxR8WruaHi2boi7KIiIiIiMhR8DSbeP2cKVz/xc98tzuLOpudKz5d2jiY+/5xStzF18PCq+dMYdrrCyiurWfhvjy+2JvNOX3j3BqHiMjxzmKxcN1113H//fcTHx/fahuDwcCcOXMYPXo048aNIy+vsUzjAw88wHXXXYfFYmnWvqGhgXvuucc1n5iYyE8//URQUFCzdgEBATz55JP4+Pjw8MMPA3DfffdxySWX4Ofn14FnKe2lBIl0iTExYXx4wXRqrDbW5RSyfF8+K/blsymvGHuTun57SioOsZeWcqtqya2qPej6hEA//nbSCM4Z2PtIQ2+3ibFhfLV/nJE//7iOD7anc1a/WOb0j+fcAQmMjAjh6m9WsLe8it2llWwpLGNERLDb4hMRERERETkWeZhMvDrnJP745XK+2ZVJ3f6eJDeOG8ytE4fi48YSxxG+3jwxezzXfr4MgAdXb2N8VCjRx2CZaRFxP6cDHA73jufREZxuHpp4zpw5zJkzp01t4+Pj+cc//sEf/vAHAIqLi1m2bBnTp09v1m7RokVkZma65v/1r3+1SI409cADD/Dmm2+SlZVFQUEBr7/+Orfddlu7z0U6jgZply7lYzEzpVc095w0kq8um03qzRfw7nnTuOGEQQyLDMHLbOLXvhR+HmZ6B/kxNiaM2f3iuGx4P07uHU2Yjxe+FjOT4iMJ9PQ45PH2lVdxw1e/8JeFq8iuqO78EwSemT6WCwf2cs2vzi3ivp83M/adb7n9x3X8lJlPdlWNa/2yzHy3xCUiIiIiInKs8zCZeOXskzhjQGM9dqvDwbOrkpn6xgIW7c5yayxnDuzF3CGJAFRabfxtxRa3D1AsIiJtd9ZZZzWbT01NbdFmyZIlrmlPT8/DJmDMZjPnn3++a37+/PlHGaUcLfUgkW7Fz8PC9D6xTO8T22y5w+ls89gitVYbBdW15FfXkl914N/1OUWszCoA4L2te3hv6x48TEai/HwYGBbIwLAgBoUHMSgsiD7BAVhMHZM/9DAZ+b9poxkfHcaLG3ewp6wKgDqbnfk79jF/x75m7fsH+3fIcUVERERERAQsJiMvnzWZp1ds4fnVKVgdDjLLq7ni06Wc2i+Oh6efQHyge8qbPDZzLL+k5ZJXU8fPOUW8t2MflzW5oU5ERLqPkJCQZvOVlZUt2mRkZLimk5KS8PA49M3bAMOGDXNNr1y5kqKiIsLCwo4iUjkaSpBIp/Mf1JfAkMBOPYaz4cC4JAFAJDCslXbvLN/E7e99S22DDYAGu4N95VXsK69i0f4yWAAeZhNJ0WEMiY1ofMRFMDQ2gphgfxoKi48oxqsTorhqxliSc4v53/pUPty4g/K6xrhP6R9PWnE598+ewDnD+gJgqz54qbCOVF/e9p40zibdNZ0OZ7P5wzF0UMLpcNqaSOsI9oOMh9Phx9n/eu1s7fn/PBoOe9uP07Stw+5s37Y2e7viOhoGo3ted0aze/6O7A3uee7c+X9kNJvcdBz3/B+56+/VXdz1vAE4bO7px9+e94WmY48ZDIZ2beuu14K73ufc6Zj7OzK1/f/I2OT/02g0tG9bb8vhG3UQu9U9f6+2evd81/Lwdc9z11BtdctxStLa97vI7nfgu3NZZimmqpYXmQ66bQe9Fm5IjGd2aDAPrk1hVX5j/N/tzmJpWg43De3HDeMH49HJv1u8gYdPGMx1yzYA8M91KYwPCaKXv0+HH8vpcM/fUH1lnVuOA+37TmezHPjbtjXYoA1/6zY3/fYSkZ4hPT292XxERESLNuXl5a5pf/+23fTcdFB4h8PBli1bOOWUU44sSDlqSpDIceWKySOZ1D+B91ZuYdWeLIoqq0kvLKPqNxe6G2x2tmbms/U35a6CfLwYFBHM4MgQBkeFMigqhMGRIQR4ebbp+AaDgWExYTweM5m/nzqB73fso6S2jv5hQYxLiMLspiSCiIiIiIjI8ahPgB9vnzKWrzNy+eeGVArr6qmzO3hq806+2JfLoyeNZGJseKfGcFJUGBf3jeODPVnU2h3ctWYr70wb2+nJGRE5djmdzh5Zsq+7x/zZZ581m58wYUKLNk2TIq31MGlNRUXzMZdTUlLalCC54447SElJITMzE6vVSmhoKP3792fq1KlceeWVJCYmtun40pwSJHLc6RsZwv3nTHPNOxxO9hWXsS27gG1ZBSRnFbAtu4Bd+cXYf3NnYVlNHSvTc1mZnttseVyQH0OiQg8kTiJD6B8ehMV08LtbvC1mzhrap0PPTURERERERA7NYDBwZu8YpsaE8+yWXby7KwOHE3aXVnLRlz9z7oB47p04jAgfr06L4Y7hA/glr5jM6lo2FZdz95pknpwwzK094kVEupPc3NzDN/qNuLi4ToikUXV1Nc8//7xrfvjw4QwePPiQMezcuZOGhobDltnaunVrs/m0tLQ2xfTb8Uqys7PJzs5m6dKlPProo1x77bX8+9//xtvbu037k0ZKkMhxz2g00Ds8mN7hwZwxMsm1vM5qY0dukStxsi278ZFT2jIbnFVWRVZZFQtTD9Qd9DSbmN4/nnOH92P2wN74ebqvHIGIiIiIiIgcmr+HhftOGMx5fWL5+9ptbC5uLJPy2c5MfkjP447xg7l8cB9MnVBm0Ndi5qkJw7li6Vrq7A6+ycwjzMuDv41MalZ6UUTkeDFu3Lh2b9OZPVD+8pe/kJ19oBz/I4880mq7yZMn89xzzwFQV1fHggULmDt37kH3a7fb+eSTT5ota2vPk7CwMPr27Yufnx/l5eWkpqZSVdU41rHNZuOVV15hzZo1LFmyhMDAzh3u4FiiBInIQXhZzIxIiGJEQlSz5XnpWWzPL2FbXjEpeSWk5BezPb+EqvrmtX7rbXa+2Z7ON9vT8baYmZXUiwtH9efUgb31hVdERERERKSbGBwSyIezJvJ5Tj7/XJVMeb2VigYr9/+8mY9SM3hsykhGRIQcfkftNDw0kKcnDufmXzZjdzp5Z9c+Ajws3DS4j34zioh0oXfeeYdXXnnFNX/RRRdx1llntdr29NNPJzg4mNLSUgDuvvtuZs6c2WyckaYeffRRMjMzmy37NcnRmkGDBnHdddcxZ84c+vRpXonGZrOxcOFC7rnnHrZs2QLAxo0bufjii/n2228Pf6ICKEEi0m7BPl6cmBjDiYkxrmVOp5PMskpXwiQlr4Rf0nLIr6wBoNZq44vkPXyRvIfpA+J55txpxAb6ddEZiIiIiIiISFNGg4FLBycyOzGGx1cl8+H+6gBbC8s4+5OlXDo4kT+M7E/vDv4dd0pMBA+dMJh7124D4Plte8isquGhEwbjeYiSzSIiTTkdTpyO7j2eR2uaxrxmzRqio6O7MJpGy5Yt4/rrr3fNJyYmNkuW/Jafnx933nknf/vb3wDYvXs3U6dO5bXXXuOEE05wtausrOSRRx7hiSeeaLEPq9XaYtmvUlJSDrrObDZzxhlnMH36dM4//3y+/vprAL777jsWLFhw0KSONKcEiUgHMBgMJAQHkBAcwKmDegNgdzhYmZ7Lp1t282XyXkpq6gD4YWcmJ/3nIz65+kxGxUV0YdQiIiIiIiLSVKi3J/938hguHNibe5dtJLWkAifwv5Q0/peSxoSYMC5I6sUZfWPxsXTMJZW5ibGUN1h5YvNOAL7IyCW9qobnTxxJuLdnhxxDRKS7i46O7tQxRdpi06ZNnH322dTX1wMQGRnJwoULD1uu6s4772T58uWuBMWmTZsYO3Ysffr0oVevXlRVVbFlyxbXfseOHYvT6WTdunUAR10Oy8vLi/fff5/+/fuTn58PwHPPPacESRsZuzoAkWOVyWhkcp9Ynj5nKtv/dgVvXjKLKH8fAMpq6zn3jQVsyCro4ihFRERERETkt8ZGh/L1+adw/4nD8G2SCFmVU8Rflqxn5Ftfc+5nS7n/5018uD2d5MIyGuyOIz7eNUm9efbEEXibGi/TbC4u55xFK/ksPRtHJ9bYFxGRRjt27GD27NmUlzeORxUSEsLixYvp37//Ybc1Go189tln/OlPf8JoPHC5fe/evSxZsoS1a9e6kiMzZ87kq6++ch0HICgo6Kjj9/f354YbbnDN//zzz9TV1R31fo8H6kEi4gYWk4k5w/oytV8cl//3W1ak51JR18B5byzgk6vPZEx8ZFeHKCIiIiIiIk1YTEauG9Gfc/rH8/GODD5OzWBPWWOd+DqbnfV5JazPKznQ3mggKSSAIWFBDA0PYkhYEINDA9vc02R2XCTxvt7c9MsmcmvqKK5v4G9rtvHRnmweGD2QQcGt17MXETkWSmx1pbS0NGbMmEFBQeONzAEBASxcuJBhw4a1eR8Wi4XnnnuOG2+8kXnz5vHjjz+SkZFBVVUVERERjB49miuvvJJzzz0Xh8NBVlaWa9uEhIQOOY9p06a5puvq6sjMzGxTgud4pwSJiBsFeXvy4VVncPHb3/BLWs7+JMlXfHqNkiQiIiIiIiLdUbiPFzeOSuKGkQPYkF/CR6kZLM8qIHP/mJO/sjqcJBeVk1xU7hrDxAD0DfJnSFigK3HSx2ghyNOCc3/PkKYDsg8ODuDjGeN5aMN2Fu2vOLCxuIy536/i7F4xXJvUm/4az1JEpMNkZWUxffp0V8LCx8eHr7/+utn4Ie0xaNAgnnzyyUO2SUlJoba21jU/duzYIzrWb0VFRTWbLyoqUoKkDZQgEXEzXw8LH1x5Ope8/Q3L03KorFeSREREREREpLszGAyMiQplTFQoAGV1DWwrKmNbUTnbisrYWlTG3rJKmt4Q7QR2l1Wyu6ySL3Zntdinv8VMH39fJkWF8odBiXiaTIR5efKfE0eyPK+IRzamkl5Zg8MJn6fn8Hl6DlOjw/j9wN6cEBbcLLkiIiLtk5+fz4wZM0hLSwPA09OTL774gsmTJ3fqcX/66SfXtIeHByNHjuyQ/TZNukBjskcOTwkSkS7g62Hh/StP59J3vuHnvQeSJJ9ccyYnKEnSLdVabZTVW/lpXx5BXh7MTozp6pBERERERKQLBXl5MCkugklxEa5ltVYb24srSC4qJXl/4mRHcQUNjtbHJ6m02thcUs7mknKW5hTy74kj6LV/7MrJUWF8OetE3t6ZwWupaVRYbQD8lFvET7lFDA8J4NqBicyIicBkVKJERKQ9SkpKmDlzJjt27AAaS2TNnz+fGTNmdPqx33vvPdf0nDlz8Pb27pD9bt++vdl8ZKSuMbaFEiQiXcTXw8L7V5zOJU2SJHPf+Ipfbr2IuCB1me5u/vDdSrIra8iqrKbGaufUPrG8eMoYjLpjS0RERERE9vO2mBkdFcLoqBDXsga7g92lFWwrKmdrURnb8kqos9sxYqCwrp6cmsZBdFPKKpn7/SruGZnE2b2iMRuNeJiMXDcokUv6xfNJWjZv78xwtd9SUsGtKzbTy8+H6wclcl7vGPUoETlOOXDicHaP8Tzaw0HXxFxRUcHs2bPZunUrACaTiffff58zzzyz04+9dOlSVq5c6Zq/7rrrOmzf77//vmu6d+/eLUpuSeuUIBHpQj6tJEn+uy6Fv80Y19WhSRNZFdUsychrtuy7vdks6hPDqepJIiIiIiIih+BhMjI4LIjBYUFcQC9qiquard9RVsmtKzeTXllDldXGPWu38VLKXm4Z2o8zE6IwGAz4WcxcOaAXl/aL57vMfF5PTWNHeeN+MqpquHftNnKqa7l5aL+uOEURkR6jpqaGM844g3Xr1gFgNBp5++23mTt3bqcfu6ysjBtvvNE1f/LJJzNz5swO2feCBQtYsGCBa/7cc8/tkP0eD4xdHYDI8c7Hw8LLF87g1xt95m/a5RqsT7oHD5MJUyt3YhXW1ndBNCIiIiIicixJCvJn/owJnJFw4E7fzOpa7li9lYc3pjZrazEaOatXNJ/PmsjrU0YzMeJAT5UXUvby1JZd1NhsbotdRKQnqa+vZ86cOSxfvhxoHFvqtdde47LLLjuq/VZWVvLJJ5/gOEg5RYC9e/cye/ZsVxksLy8vXnnllYO2Ly8vZ+7cuaxfv/6wx3///fe55JJLXPM+Pj7ceeed7TiD45t6kIh0A9EBvkzpG8dPu7NIK6lg+d4cJjbpki1dK8LXi/875QQe+WUzJXUNAJwQFcqZfWK7ODIRERERETkW+FnMPDVhOBf2ieOV7WmsyC8G4L3dmZwaF8m4iOa/Dw0GA5OjwpgcFcY7OzN4bFNjDf3XUtP4LD2b+0cP4kRzx9S0FxE5Vjz77LN8//33rvmgoCA++ugjPvroozZtf/nll3P55Ze3WF5dXc35559PdHQ0c+bMYcKECcTHx2MwGMjOzmbRokXMnz/fNYi6xWLho48+on///gc9ltPp5NNPP+XTTz9l4MCBzJ49m5EjRxIdHY2vry+VlZVs3bqV+fPns3btWtd2BoOBN998U+W12kEJEpFu4uJRA/hpdxYAV763kGvGDOSq0UlE7R+gT7rWBYN6c/7AXtgcTmpsNgI8LDgarF0dloiIiIiIHEPGR4QwPiKEeanp/N+WnQC8s2tfiwRJU1cM6EWd3cEzybtwOKGoroHbV27h3dHD6Oer35MixwOnw4nT0fOqkbg75pqammbzpaWlLFy4sM3bT5gw4ZDrc3Nzefnll3n55ZcP2iYsLIw333yzXeOdpKamkpqaeth2/v7+vPLKK1x44YVt3reoxJZItzF3eH9GxoYDUFZbz9PLNzPhpU959pctlP6mlFNhdS0fb93D4l2Z1NvsXRHucclgMGAxGQn09NDghyIiIiIi0mmuGJBAhLcnAN9nF/DnlZv5NjOP7OraVksyXz8oka9mT2JseDAAdqeTJUUlbo1ZROR45evryxlnnIG/v/9B24SEhHDDDTewffv2NiVHvL29uf766xkyZMhhr0EFBgZyyy23kJyc3KzUlrSNepCIdBNmk5FPrzmLuxcs55Mtu7A7nNRYbTy6dAOP/7SRSb2jOCOpFx4mI//4YR1l+0s9DQwPYtE1Z+Jl1p+ziIiIiIjIscBiNHL1gF78a3NjL5JvM/P5NjMfgMFB/lwzsDdnxEc1u2jWJ8CXv48exJkLVwCQVlPr/sBFRLqxBx98kAcffLDD9+vv789XX32FzWZj/fr1pKWlkZ+fT21tLZGRkSQmJjJp0iQsFkub9+np6ekao6SsrIxNmzaRn59PUVERZWVl+Pj4EBISwvDhwxk+fDgmk6nDz+t4oSuqIt1IkLcnL184nftnj+fJxWt4Z8MOnDTe/bMsLZdlabkttkktLOOX9Dym94tzf8AiIiIiIiLSKa7o34uKBhvv7dlHecOBgddTyir566qtlNVbubx/QrNtevn7YDYYsDmdpCtBIiLiVmazmfHjxzN+/PgO3W9QUBDTpk3r0H3KASqxJdINxQb68X+nTWT5H87hj+OHkBDod8j2Yb5ebopMRERERERE3MFkNHDrsH78fNY0Xj1pFDcO7sOQ4ADX+tdT07A5HM22sRiN9No/juWu6ho+zM5r0UZEjj1Op7PHPkS6mhIkIt1Y/7AgHpoxlrU3zWXBFacxNTGGiQmRPDprXLN29h44EJeIiIiIiIgcnofJyJTocG4Z2o/5M8YzLToMgLzaepblFrVoPzI0yDX95J50bkvegU0XIUVERFqlElsiPYDBYGB8fCQfXzrLtWxXUTlvbdgBwDnvfsfFw/sxOjacMB8vQrw9ifDzJjbAV4OJi4iIiIiIHCMMBgOX9Itn6f7EyHPb9jApKhTPJrXnbxnSl5TSCraXVQKwuqycL3MLOC8msktiFhER6c6UIBHpof4xYyw7ispYuS+fOpudtzbscCVMfhXq48nYuAguGd6f2QPiMSpZIiIiIiIi0qNNjgyjb4Aveyqq2V5WyX1rU3h83FBMxsbfe5E+Xnw6cwJfb8vgrymNg7y/kpHFlNBgwjw9ujJ0ERGRbkcltkR6KG+LmY8umcV1YwfhbTa12qa4pp7vdmZy5fwfmf3GV+RX1bg5ShEREREREelIJqOBpycMx8vUeElnwb5c7lmb3Kz0ssFgYGpYCFNDgwEosVq5e/su7Cq1JXJMcjqcOHrgw6mS8dINKEEi0oN5mk08Oms8ybddxJtzT+afs8dzx0kjuWbMQGb0jSPI68DdQZvzirnru1VdGK2IiIiIiIh0hKQgf56cMBzz/ioBX2Tk8re1yVh/MyD7Pf37ELm/18jmikrWlJa7PVYREZHuTCW2pNOVrE3G08+7q8PoMNaaevccp9barvYTDGbwDwD/A8scIwfyU24R969Pobi+gW927OODHzcyMzbiiI5j8/VzTZdnlmCurmrztsG9w9rc9miYvT3dchwA40F67nT8cRrcchx7g80txzHY7G1u6zAfyOObzEZMlu6Z13e045yOhsHonjJ5Jg/3vLadDvf9f7rrzqRj7Q6oY+18AIzm7vk+Iu7nrvdUd2nP9xJDk78Dg9nYrm3d9ZnnTu56r7P4WNxyHKPFPZ/j9ZXt+45qNx74rtlQY8NU3fbvnjkbctp1rCPlFeSe3xKBcYEdsp/p0eE8PWE4t6/ags3p5MuMXHaWVfLoCUMYHBxASGIoIcDfvZzcuHQDAIuqKjhjTN8W+9pbXsUryXs4JS6C2b2i2xVHQ7V7fq8AVBVUtrmt0+ZoNt2W9y+H/dh7jxMRkUPTL0SRY5jRYODkmHDuGZnkWvbIxlQqre1LvoiIiIiIiEj3MzM2gmcnDseyvydJankVlyxZy6LsfFebk+MiCdnfi2TRvjwyK5uXXq5ssHLV4jXM353FjUs38OXebPedgIh0COf+clU98SHS1ZQgETkOnBYXyZSoxh4cBXX1PL11dxdHJCIiIiIiIh3hlJgI3j9lHEmBjT3+GxwOHli/nfr9vSE8TEbO7x/nWnfV92tYnVcMwJ7yKn7/w1qyq2td+7tj+WbW5Ze4+SxERES6hhIkIscBg8HAA6MG4mNq7G7/wd4sNhSVdW1QIiIiIiIi0iGGBAfw0fTxRO4vOVzeYKWgSXnom4b3J9rXC4D0imouXbiKQf/9llmf/8S6gtJm+7I5ndz+8yYqG1R5QEREjn1KkIgcJ2J9vbll6IFasw+sT6G03n21YkVERERERKTzeBiNrlJaRiDC58CYKn4WM/+dOZ5R4UGuZQ1NBnSP8fXm3VnjGRsRAkB2dS2PrdvulrhFRES6khIkIseRy/slMDQ4AIA9ldWcs3gVH2fkUKeB6ERERERERHo0u9PJ7ooqAOJ8vfHcX0HgV4mBfnx46ok8NH4oI8KCGBwSwJiIYG4bOYBF50xlYnQYT500Aj+LGYD5uzPZW17l9vMQkfZzOsHpdPbAR1c/cyJKkIgcV0wGA4+dMIRgDwvQOB7Jw8k7mf3jKl7YmUaxepSIiIiIiIj0SEbAYmy8zFNQV09edV2LNiajgcsG9uLTMyax4KyT+Oi0E7l5RH+8zY3JlFg/H/44rLHygMMJz2/Z5bb4RUREuoISJCLHmf6Bfnw+cwInRYW6lpU2WHllVwazf1zJjWu28M7eTCqsqjcrIiIiIiLSUxgMBk6NiwSgzu7gxqXrXQO1t8cVA3sT5Nl4U92iffnYmpTiEhEROdYoQSJyHIrw9uLVyaOZP308p8VEYDI0Lm9wOFleWMKT2/cw+8dVPJO6h3INzCciIiIiItIj/HVYf2J8Ggdj31xUxv0rk3G2s4aNr8XMpOgwAGptdlJKKjo8ThERke5CCRKR49iQ4AD+NWow35w8gSsS4wjZX3oLoNpm5409mVy9apPGKBEREREREekBgj09eP7EkXiZGi/3fLIni/+mZrR7P+MjD1QceG3b3nYnWUTEvZxOB05HD3w41UNNup4SJCJCtLcXfx3cjyUzTuTTKWO5ICEai7GxW8nuymqeTd3bxRGKiIiIiIhIWwwK8ueRE4a45h9Zm8KqvOJ27ePMxBh8949L8k16Lk9sSFWSREREjklKkIiIi8FgoJ+/L/cPS+LDySfguX+Av/+lZ7O6qLSLoxMREREREZG2OCM+iuuH9gHA7nRy89IN5FTVtnn7QE8Lj544nP3VmHk1eS//29H+nigiIiLdnRIkItKqfv6+3Dqwj2v+/s2pVFltXRiRiIiIiIiItNVfRw3kpJjGsURK6hv445J11NraXj75rMQYHpow1DX/4pbd2B3qRSIiIscWJUhE5KAu7R3L2NAgAPLq6nlxV1rXBiQiIiIiIiJtYjIaeGbKKBL8fADYVlLBvSu3tKtU1qVJvRgdHgxAfm09+6pqOiVWETk6Doezxz5EupoSJCJyUEaDgUdGDMRvf+3ZxblFXRyRiIiIiIiItFWQpwcvnzIGn/2/6b7Ym8MbKe278a1pr5MwL48OjU9ERKSrKUEiIocU7e3F3UP6d3UYIiIiIiIicgSSggP4v8kjXPOPr9/OLzltu/ltR2kF20srABgaGoi/h6VTYhQREekq5q4OQES6v7NiI1mSV8Qm24Gujz/lFzHFxwOL8eB5VqvDQaXVRrXNjqGugfL6BqqtNowGA95mE70C/doVh9PpJL28mk0FJdTb7DicThzOxkEHzUYDsf4+JIYHExfgi+f+O6RERERERESOd6f2iubGYf14cetuHE64ZdkGPjtjMgn+Pofcbm1+iWv6lLiIzg5TRI6Q0+lsV/m87qInxizHHiVIROSwDAYD9w8bwLXJe1zLHkneRX15GRFenviZzfhZTFgdTiqsViqsNir2J0Zclrbc73kDEnh25rjDHn91TiEfp2bwS1YBWZWHr3lrAKL9fUgKDWR6n1hm9Y0lvp3JGBERERERkWPJbSMHsL20giVZBZTVW7l04UqenjyScVGhB90muElJrWXZhVw3pA8+Fl1KEhGRY4c+1USkTUI8Pfj78KRmy6psdqqOYpC+tblFOJ1ODAbDQdvsKa3kws9/oj3jdjmBnMoaciprWJKey30/riMuwJc6m52qBis+FjPeZlPjvxYzgZ4ejIwO5cwBCQyPDDni8xEREREREemuTEYD/z5pJOd+/QtpFdXkVtdx6cJV/PPE4VzQP77VbWbERxLq5UFxXQObisq4Ycl6Xp8x9pCVBERERHoSJUhEpM2GBgWwb//0KZGh/FxdRYXVRo39QE8Rs8FAoIeZALOFAIsZf4uZ5YUlre7vgckjDpkcASiurW+WHJkcF8HE2HDCfbww0DiQvMlooM5mJ7OimqzqOvaVV5FeVklpXYNru6yKatd0XdOeLfv9vC+P51Zv48lZ47lseL/DPxkiIiIiIiI9jL+HhXdnTeD2nzeyOr8EJ/Do2hRm94oioJXxRTxNJl6cNoZrf1hLldXG8twihrz7HfePG0yghwVvs4kwo5l+Ab54mlTmWEREeh4lSETkiPxt6ADuT4wBGscAqbbZMBuMeJuMLZIe167cxNqSsmbL5p87lfEx4UBjwmJ3aQVJIYFYTM3vRCquq3dNnzsggf8cpiSX2dsTaKxjmVpUxsI92Szek8XG3GLCfLwI9fGk1mqn1majxmqjxto4lsmv7li0Gk+zifMHJ7bvCREREREREekBony9+O+sCdz+8ya+Ss+h0mrj7e3p3Dyif6vtT4gM4ZkpI/n9D+uAxt9/D67e1qyN2WCgX4AvF/WJ48I+cRgPcyOciHQsp8OJsz2lN7qJnhizHHuUIBGRo2YyGAiwtLzb6Ff3DO3PucvWNls2b/Nuony9WZldyFNrUsirriXaz5vbThjE/7d33+FRlGsbwO/tKZveC6mEltB7LyJWQAFFwU85CpajYu8exV6O3WNXFBUrioKioEjvvRMgIb33tn3n+yNhk4WU3WR3Npvcv+vK5czszLzPLuu7u/PM+7zzkhMAAHqTGW/sPm45ZnrPaJtjkkgk6BsSgL4hAbh3VApMZjNkzQwDFwQBRbVavLv7GD7bnwoB9UmSfiH+6BcSYHN7RERERERE7kImleCBIb3xR2Y+TIKAtw+eQl6tBqGeKiT4qTEtJhye8sYRIZOjw/Dw0D748MgZVOmNF5zPKAg4WVmDZw6cxF+5RXhpeDJCPT3EfEpERETtwgQJETldoo83/rruYjy6cR/2FdSX2/ojPRd/pOda7Zdfo8EjG/fDUy7HVb164PFN+3GytBIAkBzsj8mx4e2OobnkCFCfSAlTe+K5yUOhNZqw/PAZaI0m/Pu3bVh/0+UtHkdEREREROTOYny8cEOfWCw7kQEBwA+nsy2Pxfp44dtLRyPMqzHJcVtKIhb0jcORkkqkV9Wg1mBCndGItLJqnKioxqnKGgDA9qIyzN+4F19MGIoob0+xnxYREZFdeOWPiETRJ8gPK2dNxseXjoaf6sLRJr0DfS3Lr+w8itd2H8P3JzIAAAqpBP+dMhRyJyYrJBIJnp8yDCmh9aNGUksrsfpUVhtHERERERERua9HhvbB1YlRkJ1XEiuzug7/2XHkgv1VMhmGhQXi2qQY/KtfPO4ckISXh6fg14tH49PxQxDWUPI4p1aD/9u4FycrqkV5HkRERO3FBAkRiUYikeCyxChsmn8pHhqZjAR/NUZHheDHqyfi7+unYUKPMABAbk0d3tl70nLcOxePRH8Ryl15yGV4etIQy/on+062sjcREREREZF7U8lkeG3cIOy49iL8eNkYvD9pCII96pMc63OKkFFVa/O5xoYF4YcpI5Hg4w0AyNdocd0/u/FDeg4EwbZ5Bg6WVuD5Ayfx8O6jyK3V2P+EiLqpc3OQuOMfkasxQUJEogvyVGHxsL7YNP9S/HDVRIxqmKx9ybiBVnVuAWBERDCutGPukY4a2yMMvYL8AACHCspQZ7iwvi4REREREVFXEuShwpDQAFwSG4EF/eIs27flldh1nlBPFb6cOAzJAT4AAJ3ZjKf3n8D1G/ZgW2Fpq4mSlRl5mLdhD5anZWN1Vj4e2n3E5sQKERFRe3EOEnK6qtwKeHo6986PutI6p56/KZPeJEo7MqWs7Z0cwKi1PQFg8jVblqvyqiCrsn24tC13BQQB+HLkIHyZkYN8jRax3l5YmBiDsvRim9sxGzv+75Pi4YFTqIRJELBp23EMDvBrdj//mMAOt2ULqVyc94JYjDrb33NGaeO+Rr0REjuOFfNOFLPR3PZODmlHnP5HLBKppO2dHKSr/X8k1msn5r9RV2NPH9T04o8g2HcnnVj/Rl3x7r6u9pwEs+2fRU2fe/3dm7YfazaJ97qJ9f5WeitFaUdbIc7d8GL9G3n4qeza36hufJ1VvkrIpbYfL5WJ816oSrd9tERHGOoMorQTN6Fnu44bERsB7E8FAByprIHSx6vV/f3irOeKDAGwdmAcnlq7G0v3ngAAHCqrxMIt+xEf4IOrUxIwvEcY+oT4w89DhZI6LZbuOY6P9h5D03fvgdJKpHorMD4+0rKtx9Qwm5+HVqpAQcNyz6vGwMPc9uvuW14F7DpgcxtEROT+mCAhok6ll68azw/o49IYwj0af6xVd7EL0kRERERERK2J91Nbln88mQFvhRz/GTcQSpntRUg85HK8esUYXJQUjef+3oOTxRUAgLPl1Xhjy6FWj/WUy6Bp+B32xpaDVgkSIiIiR2OJLSKi80jAO6aJiIiIiKh7Cld7YkH/xtEnXxw5g+e2tp7UaMklvWKw6far8cnsyZgQH9nqLy2VTIb7xg3E2cduREKgLwBgy9l8lHAuEqI2CTDDLLjfnwBxqjEQtYYjSIiIiIiIiIiIyOKZCYOQGOCDpzYfgABgxckMPDNhEKQS+28mk0mluDolAVenJCC/qhY7swpxIK8Y2RU1qNbpYTQLmJwYhfmDeyHY2xMAcHFSD3y06xgA4HhROSbEezry6REREVkwQUJERERERERERBZSiQQLBvTEluxCrDubhxqDEcdLKpASEtCh80b4eluSJa2J8vO2LHMECRERORNLbBERERERERER0QVGRoZYlh/dsA8ag1GUds+NJAGAnMpaUdokIqLuiQkSIiIiIiIiIiK6wA0pCZZJ2w8VlePm37eJkiTpHx5oWV51/CwEQXB6m0TuTDADgllwwz9Xv3JETJAQEREREREREVEzvBRyvHfJKKgV9RXat+YU4ebft0FrNDm13T4hAUgOq0+SHMwrwWd7Tji1PSIi6r6YICEiIiIiIiIiomb1Dw3A8pkTrJIkH+w/6dQ2JRIJnrl4hGX96XW7kVpc7tQ2iYioe2KChIioFbkaratDICIiIiIicqkh4UH4esYEyKUSAMD7+1ORW13n1DYnJUZh0Yh+AACdyYRPdh13antE7sz1pbLa/0fkakyQEBGdJ6Whxi4AvHcmExuLSl0YDRERERERkesNjQjCTf17AgC0RhOe33bI6W0+OnmIJSmz+kQGqrW6C/bRGYx4+fetuOvrNSisrHF6TERE1LXIXR0AEVFnMzLQH2OCArC9tBy1JhPuO3QC08KC8e/EWMR6e7o6PCIiIiIiIpe4b0Q//HIqC6UaHX47k4N7SisxOMjXae35eagwo188fj6ajtI6LSa89DnmjkhBXLA/Arw8UFGnxdO/bEROeRUA4Eh2IdY9+H9QKXi5i4iIbMNPDCKi80gkErwyoDeeOXYafzeMHllXWIK/C0swJjgAo4MCEO/tiQGBHojw9oRUInFxxERERERERM7np1Li7mF9sWTLQQDAssNnMLhXD6e2+ejkofjtRAb0JjNOF5bh+dWbW9x3X2Y+3v9nD+67ZLRTYyIioq6DCRIiomao5XK8OqAPVuYW4n9nMlFuMMAMYGtJObaWNEwOuP8YPOUyJPipkeinRqK/un7ZX414XzU85DKXPgciIiIiIiJHu7ZvHP678yhqDUasScvBm4IAiRNvGksI9MWPN1yKlzfux47Mgjb3330212mxEHVWgiBAENxvPg93jJm6HiZIiIhaIJFIMCs6HJeEB+O77HysyClAwXk1bzVGE46VVuJYaaX1sQCifbzqEyd+aoyODMaE6FAopJz6iYiIiIiI3JePUoFRkSFYn5mPMq0e6WVVSAzyc2qbY+MisHrBFciXK7E7PRdldRqU12ohlUjgrVJgUp84jH7+MwBAea3WqbEQEVHXwgQJEVEbvOVy3BLfAwvionG8qgbpNXXIqKtDjtmEtMpqZFXVwXTeXQ8CgOzqOmRX12FjThE+O5aOIA8lZiZGY3ZSD/Rz8g8IIiIiIiIiZxkSEYT1mfkAgD3ZRU5PkJwTHxKA+JCAC7bnNsxBAgD+Xh6ixEJERF0DEyRERDaSSSTo7+eD/n4+AAD/mEAAgN5kRlZ1LdIqapBWWYO0iuqG/9agxmC0HF+q1WPpsXQsPZaOURFBeHfyMAR7qkSLv0yjw5fH0lFUp8VFseGY1CMcMinnTyEiIiIiIvsMCw+yLH9/+DSuG5TkwmgA3ya/q4qra10YCRERuRsmSIiIOkgpk6Knvw96+vtYbRcEAcUaHQ4Wl+OXMzlYn1UIvdkMANiZX4pZq7fg82mjkOivdlpseTV1OFhUjiq9Aa/vOYGiuvrh5j+kZiLc2xOLBiTh/5LjOdE8ERERERHZbERkMOL81MiorMGWs/nYnpmPMbERLovHx0OF2CA/ZJZWYld6Lv48cgaTBvZ1WTxEYjObzTA3XG9wJ+4YM3U9LIZPROQkEokEoV4emBYbgfcvGo5d86bhuTH9Ed4w5Du7ug5zftuCnfklTml/fWY+xn+7Dnf8tRuPbDpgSY4AgMEsILu6Dk9tO4QXdx51SvtERERERNQ1yaVS3DO8MQHx6sYDLoym3r3TRlmWb/xkJdYeTXNhNERE5C6YICEiEom/Sokb+sZj5Yzx6BvoCwCo0Bkw/4/teGXPcVTpDA5t78tjZ2E0W8+NMjQsEM+NG4iJ0aGWbZ8cPoOtOUUObZuIiIiIiLq2q3rFIN6vfjT81ox8bMvId2k8CycMwZUDewEA6vQGLF7+h0vjISIi98AECRGRyMK9PfH9FWMxISoEAGAWgA8Pn8GQ5X9izuoteOdAKnKq6zrczqHicsvygBB/3D4wCd9NH48bkxPw5RVj8fSY/pbH12cWdLg9IiIiIiLqPupHkfSzrL+ycb8Lo6kfwf/5LTNx3ciUCx7TNpkbkoiIqCkmSIiIXMBHqcDSaaPw8LC+UDRMlG4SBOwrKseb+1Nx8U8b8OGh0zB0oB6nvGFekSi1J1bPmozHRqVAKWvs9q9MjLYsn66obnc7RERERETUPc3s1QOJQX4AgO2ZBdhyNs+l8XgqFfhkwXS8MGuK1TyL93zzJ/RGkwsjI3IuQRAgmN3wTxDafnJETsYECRGRi8ikEtwxMAm/XTURC/rFI9GvcbJ2rcmEV/aewIxfNyO7urZd5/dVKQAApVo9jM0kWuqa3EWlVsjb1QYREREREXVfcqkUD04YZFl/ZeN+l1/wlEgkuGfaKHyyYLpl28aTGXj6lw0ujIqIiDorJkiIiFysV4Avnh7dH3/PmYIt107Fgn7xaBhUgpNlVZj32zZo2jEkPDnIHwCgNZpworTygsePlTRui2+SnCEiIiIiIrLVrJQE9GwYRbIzqxBbXDwXyTnjesVYrX+8cR8KK2tcFA0REXVWTJAQEXUi0T5eeHp0f6ycPgFRak8AQFZVLT49kmb3uYaFB1mWPzh4GqYmE7bX6A348NApy/royOAORE1ERERERO6oQqvHz6mZKK7TtvscMqkUD04cbFl/tROMImmOzmjCu3/vdnUYRE4hCGa3/SNyNSZIiIg6oQQ/NYI8VJb1YyUVdp9jRs8oBHgoAQC/p+di7Ddrcc2vm3HlTxsweNkaHC6uP2e02gvjokMdETYREREREbkJsyBgwW9bcc9fu3HJd+twvB2/Oc65OjkevYL9AdSPItns4rlIznduLsZPNu9DaU2di6MhIqLOhEXnyelUvip4eHk4tQ2ziJOt1RS2bz4Iexm19pdUag9tqcHmfc2mxph05UZIK2w/VqoQ53VTh3qL0g7g3Pfd7X/vxuEmP1CG9giF0sfLrnOE+XjhuSnDcNea7QCA/FoN8ms1Vvt4KeR48/LR8PD1Fu3/I0257T9ITPLGmEwGE6R622MUOjDBvb0UXqq2d3IAwSwTpR2zUZzXTsy+WzDbfhdj033PTR5o+7HivHYSqTj3uEjlXe9eGnv+PamRWK+bRCppeyc3a0u81872/1+bPneJVGLXsXKVeP2CUSfO92GxmAzifEZ4+InzvUTlY99vPEOT34Qevh5QyG3/HuAX5WdXW+3l4VclSju5G4ou2FYoGFAGI3rDw2py8Y6oSiht9fFfs/Kxr6B+n+I6HS757i+svXg0or097WonaMQAAMAT116Mm97/EQDw2N97sWXJMPh4Ovb9KPGwPTYZGr87Xzt2MN5euQ61OgM+2nkM/7n2kmaPkQpd77sPERG1jj0/EVEnk1tTh235JZb1mwf3wm3D+rbrXLP7xeOb2ZMxLTEK8oaLIRIAUT5emNMvHr/Nm4ZxMeGOCJuIiIiIiOxgEMz41FSMhcazuMV0Fg+ZsvGyOR8mEcpT/ZVXhGcPpV6wfeH2gyjS6Np1zlkjUjAoLhIAcCq/BP/+7JdOU2pr4bTRkDeMInn/j62o6kBJMSIi6lo4goSIqJOpMTTeTTcmIggvXDS8Q+ebHB+JyfGRMJjq71qUSyWQOOiuNCIiIiIiap9VQgV+Ecqttm0XavCxuQi3S0Od8p29Qm/AC4dPYU1OoWXblPBgFGl1OFpRjexaDe7adRjfTRxm90gWmVSKr+68FmOf+gBVGh1+2n0UY3rF4o5poxz9NOwWHeSPeeOH4suNe1BRq8HzP67DqzfNcHVYRA5j78j3zsIdY6auhyNIiIg6mThfL/gpFQCAPYVl+O1UFkwOKNujkEmhkEmZHCEiIiIicjGTIOB3c4VlvR88LQWhfhcq8ao5H3UOnrxYYzRh/uZ9VsmRiyND8PrwFLw9sj+iGsqgHauoRkY75+lIDAvCR4tmWdYf/fZPHMrM71jgDvLQ1VOglNe/yu/+vhl/NzOChoiIuh8mSIiIOhmVTIb5vWMBAAazgEWrtiDpnR9w+dd/4oG1O/HZ/lSklYlTH5mIiIiIyBlyajV4Yt8xvHXsDNKqajtNKSaxHBU0KEL9PDvDJN54Vd4Di6Vhlse3CDV40JSFCsFxc/FUGYwXJD5eHNIPSpkU4Z4euComwrL9TFX755CcOawf7r1sLADAYDLh3T+3tftcjtQzIgTPz7/Csr7ove9QzgnbiYi6PSZIiIg6obsG9sT0+EjLusZowoGCUnxzJA1P/rMXEz7/DYvXbEduB364EBERERG5QqFGhxs378XKzHx8nJqB6X/vwJjfN+NfW/bh5cOpWJmZh+MVVajQ6VGo0SKjqhYnyqqQWl4Fncn2yd3FoDGZUKE32J3gOQmNZXmCxAcAcJHUD09KI+HVcKkmC3osMeU6bCRJmKcKzw/uAw9Z46WglVnNj+7Qd3AE+1OzL0JAw2TvP+8+hup2zmviaHdeNg7TBvUGABRUVOO1X/5xcURERORqnIOEiKgTUslkeHP8IIyNCMbm4nKcKK5ARkU1zv3sMgsCfjx+Fhsz8vHjtRehd7C/K8MlIiIiIrLZg3uOouC8C+aVegN2FZdjV3F5C0fVk0skGBsRjFv6xWN0eBAEAEdLK1FQp8WgYH+ENpSJEsOesgr8e/8xGAUBarkME0MCsSg+BrENiYFzirQ67CuvQohKiWQ/NYyCgF3mxhudekkaYx4lVeNNSQyeMOWgBEacgQ4fm4twryzcITFfHRuJGG8v3Lh1PwBgX0kF5idEA4DVaz8kyK9D7XgoFZg2IAnf7zgMrcGInLJK9I0K7dA5HUEqleKdRbPRf/ErMJhMeGPVRjw252KoPVSuDo2oY8xuOp+HYysJErULEyRERJ2URCLBNUk9cNOY/gCAWr0BqaWV2JxZgI/2nkCFVo/iOi1mf/83frj2IvQLCXBxxEREREREbcutrR89Eaf2wpy4KOwsLkNqZTWKtfo2jzUKAjblFWNTXjECVUoYBTOq9PVlqBRSCWYnRiPYQ4UyXf25wr08EOHtiQgvD0R5eyJa7emQOfkEQcAbp87C2DBypMZowu/5xfizoBhTQ4NxSXgIfBVy7C2rxBcZOdA2jMiQSySWYwAgFHJEQoFSwYgqmBAJBaIkSjwji8KDpmxoYMZWoRp3CmFQOGguweQAH8vy2rwi/Gf/CQR7KLG3tAIAEOPtiUgvzxaOtp3e2Djax0PReS4/xYYEYnBCNHafzqxfX7QEH90xF3PGDHJtYERE5BKd5xOKiIha5a1UYEhEMIZEBOPGgUmYt+IfHCosQ6lGhznf/43vrrkIA8ICXR0mEREREVGbwj1V+HTcEER6eeDmXvXz75Xp9DhZWY3UyhqkVlajXGeAp0wGLw8FPGVS1BlN2F1Yhvw6rWX/pgxmAd+dzm613UCVEhf1CMXCfglI9FO3O/7dZZU4WV0/CiRAoYBJEFBlNMIkAGsLS7C2sKTZ45omR+SQ4CFZBL40l2CFUD96IwZKvCGLQaxEhVESb2wQqqGFgBOCBgMkXu2OtymVVIoApQLlegMA4OfzymxdExfZ3GF2Ka6qxZ+HTgEAPJUKhHXgtXaGaYN6WxIkdToD/u+tr7HzVCbuumyciyMjIiKxMUFCROSGAj1V+OHaizBvxQbsyy9BuVaPmd+uw/2j++P2YX2hkHGKKSIiIiLqnKQAXhvRH5HnlcMKVCkxJjQIY0KDrLYrvZWWZaPZjLVZhViRlo19ReXwUyoAwJI0aUuZTo8fz+RgxZkcXNwjDLcmJ2JQiL/dzyGzrnEOkRGBfvhPv574JisPX2fmocp44cTqg/19EePlgUMV1cip00AFKe6WhkENKX4SGktbZUGPNUIFZkkCMaQhQQIA+4VaDIBjEiQSiQSvD0/B+yfP4mBZpVXSxksuw+zYjidIPt+4F5qGBMzY3rHwUinbOEJcj8+5GH2iw/Dhn9uw9UQ6AOC9NVuwpyFpQkRE3QcTJEREbspXpcS3c6bghp83YHduMbRGE17cchC/nMzA8tmTEa52zA8oIiIiIqKO0psaC8338lNjSJB/u84jl0pxRVwEroiLsGyr1hswasV66ExmJPh6475BvdBD7QWTICC/VoP8Oi0K6rRIr6zB7qJy1BqMEACsyy7EuuxCXBEbgRdH97crjkvCg/G/MxmoNpqwtrAEYR4q3JMUh5viorGtpBzbSsohl0rQU+2FUYEBiG6SDMrdUGRZ/stcifNnDdgn1GEWAjG4yYiRA0IdFtgVYetGhgRgZEgAtCYTjpZXYWNBKUp1elwVE2FJOnWEuUnS5VBGPgRBcEhpM0eRSCSYPXogZo8eiM/+3on7PlsJg8mE3aezXB0aUbuYBTPMgvtN6OGOMVPXwwQJEZEb81Ep8O2cKXhl6yF8uj8VZkHA8eIKPPLXbiy7epKrwyMiIiIiAgCYmmQBDA6eSNhHqcAvl49FRlUtJkSFQCWTWR4bGOxvtW+13oDvT2fj8xNnUdgwUfzvmfmo1Bvwbkofm9v0Uyhwb1I8njtxBgDwZWYudGYzHu2TiMmhQZh83iiYlihwYdLgkFCHZ0y5VpO3p0EHrWCGh8SxI8U9ZDIMCw7AsGDHzmd496Vj8P32Q0jNL0FxdS22n8rE2N5xDm3DUW6ZOgopMRG45tWlKK6qdXU4REQkMtZgISJyc14KOZ6ZPBRr5l9i2bYtq9CFERERERERWfOUN15+SKuuvWD+kI5K8vfBxTHhVsmR5vgoFViYnIB/rp6E50elQCGtj2trfgkyazWtHnu+WdHheKJvouXCyvfZ+finqPm5R1oSLml+tMYeoRbLzaWWdU9IIG8mmdJZeauUuOfyxvk8tpzMcF0wNhjZKxYrH1sIDyXvIyYi6m6YICEi6iIGhgehV5Cfq8MgIiIiImrT+rxil7avkslwXVIM7urf07Jtc0mZ3eeZEx2Bx/s2nuO542egM9leMiYeKsuFmWgoMF3iD+/zLtVIAcyVBkHeiUpU2cJD0Zhs0BounJelsxma2APv3DLb1WEQEZHImBonIupChIZav27224mIiIiIuplv0rMxJy7S5fNSXNwjDG8eOgUAOFxRBcRG2X2OWVFh+Dg9C0U6PSoNRhgEc5O0R+tUEikioUQO9CiGEbfJQrFICEEuDMgWdPCEFEkSD6glrY+M6Yy+237IsjwoNqKVPTuPyQOSXB0CUbsIZgGCg8sXisEdY6auhyNIiIi6kHNfLWr0RpwsrnBlKERERERELUqtrMG+0gpXh4EEP28oG8psnampa9c5TtXUoqihZNgAPx+o5fbdi6psKJ11btyJVCJBD4kSY6Q+GCz1dsvkCADomowakUl5+YmIiDonfkIREXUhTUtsXfTlGizdn+rCaIiIiIiIWubqMlsAIJdKkeinBgBk1WlQZWcpKI3JhMePnLKsT7Fxcvamzo2hMaNr3Un972mjLcuvrt5kGe1ORETUmTBBQkTUhTw1cTBiG37gmQUBT23Yh4P5pW0cRUREREQkviKtztUhAABGhgUCqB/B8Vt+kc3HCYKAF0+kIb22fuRJktoL1/awv5TUuQszZtR/hwcAvWDGEaEOaYIWRjdNLFw5pA+So0MBAPvSc1FUVeviiIi6LkEwQzC74Z9g+5xNRM7CBAkRURcS6++Djf+6Ev8a1AsAYBIE3PXHdtS5waSIRERERNS9DAr0a3snEVyb1MOy/FFaFpZl5CBPo21zxMOveYWWhIqXTIZXB/SBh8z+clh+kvqSXAKAl8z5+NBUhP8zpeMxUw7uMWXhJlM69phr7D6vq0kkEgR4e1nWvVUKF0ZDRETUPCZIiIi6GA+5DM9MHoqBDXfCpZVV4cXNB10bFBERERFRE3KJBLPiIl0dBgCgl78PxoTXl8aqMhrx1ukMXLF1L8Zt2Ilb9x7BN1m5yNNorY45WFGFl0+mW9af7NsTcU2SAfaYJQmwLO8QavCbUIFaNN5VXQkTnjPn4ajQvjlSXCmnrBIAEODtCbWHysXREBERXci+mcPIKU6cOIHXX38d69evR35+Pnx8fDBixAjcfPPNmD17dofOrdVqcejQIZw8eRIVFRWorq6GSqWCWq1GZGQk4uLi0Lt3b3h4eDjo2RBRZ6CQSfHu5WMw7as/oDWa8NmBVFycGIWJcfYP+SciIiIicjSjIKBEq4e3unNclnhz/CA8veUw1hYUW2YCqTOZsKe8EnvKK/Hf1LPo7eON/n4+KNDqsLO0wlL6anZUOC6LCGl32wOkXrgNIfjSXAJNk3lIRki8USWYcBJamAG8aMrHbdIQDJV4wxtSSCSSlk8KIK9Ogw0FpdhdXA6NyYRwTxXuT+4Jf6U4IzlMZjNyy6oAANFBnWO0UFsqazWuDoGIiETWOb6JtOC6667D999/b7UtNjYWGRkZTmtz0qRJ2LRpU7uP//zzz7FgwQKb93/vvfdw7733wmhsLH+j0+mwZs0arFmzBtOnT8e3334Lb29vu+JYs2YNPvroI/zxxx8wGAyt7iuXy5GcnIyxY8fikksuwZQpU6BWq+1qj4g6n6QgPzw5YTCe/GcvAOC+P3dg/U1XIMCTd24RERERkevdsnU/Ph07GHE+9v3edYYgDxVe6t8bC+N74I+CIpyursOpmloUNJknJbW6FqnV1vNoDA3wxYO94zvc/nRpAC6V+OGwoEEO9Ogj8UBviSdMgoCnzLk4JNShCib811wAAPCEBEFQIEQiRxDkCIYcgRI5ZJCgSDBgt1CLs+tOXdCOXCLFU4N6dzheWxRW1sBgMgEAerhJgiS/vMrVIRC1i2AWIJjdb74id4yZup5OW2Jr1apVFyRHupply5bhrrvuskqOnG/16tWYO3cuzGbbJi3Kzc3FZZddhiuuuAKrVq1qMzkCAEajEYcOHcL777+PmTNn4tNPP7X5ORBR5/avwb0wITYcAJBfo8GUZb/j2yNpMNnYpxAREREROVq0tycAIK9Oi/mb9iKzpvOUjkpUe+GunnF4e3A/rBk3DCtGD8FdPWOR7Gt9E6G3TIaF8T3wwZCUds070hyFRIqhUm/MlAagt6T+NZJJJHhMGoF+8LTaVwMBOdDjgFCHv4UqfCeU4X1zEd41F+J7oQxnoWuuCazNK4JRpN8COaWVluUeQf6itNlRdbq2r6EQEVHX0ilHkFRUVOCOO+5wdRhOVVpaisWLF1vWhw0bhocffhh9+/ZFQUEBfv75Z3z00Ucwm834/fff8eWXX7Y5MiUzMxOTJk1qdoRNfHw8+vTpAz8/P5jNZpSXl+PMmTM4e/asg58ZEXUmUokEb146GlOX/Y5yrR4FNRrcv3YnPthzHHOS43FFUgwSA31dHSYRERERdSNvjeiP2ysrkFpZg3K9AUtPZeKZIX1dHdYFJBIJEtVeSFR74Zb4HijS6pCn1SHSQ4UQlbLNEleOopbI8JIsGnuEWhwQapEDA0oFA4phhA4t332d4u+DyRHBmBQejNkb9gAALo4MgVEQRLkYdG7+EQCIdpPfHBq93tUhEBGRyDplguSBBx5AXl4eAMDHxwfV1dUui8XeBEJwcLBN+33zzTeoqqofunnDDTdg2bJlkErrB/SkpKRg6tSpuOKKKzB9+nQIgoD333+/1QSJ2WzG7NmzrZIjKpUKixcvxh133IH4+OaH/JaVlWHDhg34+eefsXr1ape+1kTkHJE+Xvht/iV4duMBrE3LAQCcLqvCS1sO4aUth9An2A9XJMXg8sQo9A7yE+2HHhERERF1T4EeSnw6dggm/7EFRkHAzuIyV4dkk1APFUJdNNG4TCLBKIkao9A4kkUQBNTCjGIYUSIYUAETBABKSNBf4oVhk+IAAAazGYFKBcr0BvyZU4SnBopTYivbDUeQxIYEuDoEIiISWadLkPz9999YunQpgPq5MZ599lncd999LosnLi7OKefdvn07gPrn+Oabb1qSI02dS5CsWrUKe/fuhU6ng0rV/JexZcuWYd++fZZ1T09PrFu3DuPGjWs1jsDAQMyePRuzZ89GdXU1vvjiC0RFRXXgmRFRZ5QQ4Isvrp6IHdmFeGnrIezJLbY8drKkEidLjuD1HUeQEOCDy3tG44qePdA/NKDVZInRbMbRonLsLyhFdlUt8qrrkFtdh/zqOnjIZZjROwbzkhPQw49zGhERERGRtSAPJaK9PZFRU4dKPcsatYdEIoEaMqghQ7yk5cTNurwilDW8xiNDAiAV6Yao7NIKy7K7zEESHcwECbknQTBDENyvlLY7xkxdT6dKkNTW1mLRokWW9fvvvx+DBg1yXUBOVFpaCgAICQlpddRJcnIyVq1aBUEQUFZWhoiIiGb3W758udX6E0880WZy5Hw+Pj64++677TrGFkpvFVRqD4eftymjVrwv1AovcYbcypTiTBHkE2H7xWujtxo5DctBvf0gr7W91q6+RpzXzahreU4fd21LcGCN4FFRIfh17lRkVtRgzZlsrDmdjX35pZbH08ur8b89J/C/PScQ4++DK5PjMTQ6FAIAs1mAySwgs7wKOzLzsTe7ELX6ll+Dd3Yfx7t7jmNKzx64eUQyLu0TC4lEApPO9veCXuVlWfYO9oFSZ/t7riJLvDsR9cXijL7zChJnAlODRpw+VSoTb7SSYDbZvq/RbLVsNtp+rFjseT4da0ecHyxSuWNqt3cm9kw42XRfeyfY7GoTW3a15wOI9/+RXe8bN+jnAIgWl1h9kNyja/V19v77NPe+82i4UbDOaILRYGzxwr3ZJE7fEDE4TpR2xPpOJ5FKIQgCvkrPtWy7qV88FF6OHQVjqq5pdntWYePvjAiVosX97FG6Za/N++o9vICL6qtpFG/ZDaW27bluijvRfDhERCSOTpUgeeyxxywlohISErBkyRLs2rXLtUE5iZ9f/d0TxcXFKC8vR0BA83cppKamWpb9/f1bPN/WrVut1q+//vqOB0lEXVasvxp3DOuLO4b1RV51Hf44k4M/z+ZhR2YBzEL9D9Csimq8v+2w3ecOVXuitE4Lk1mAIADrT2dj/els3DdhMP4zbaSjnwoRERERualILw+crKqBURBworIGyf4+rg6py9lfUoEjDXOB9A3wwYjQQNHazi2vv4lIKpEgwk1GlX9y+IyrQyAiIpF1mgTJ9u3b8d5771nWP/roI3h6erowIucaPnw4VqxYAaPRiIceegiffPLJBaVs1q9fj19++QUAMGDAgBZfj9LSUuh0Oqtt0dHRTombiLqeSB8v3DK4F+68aDiKazT4/cRZrD6Wji3peTC2ctdrhK83RsdGYFRsOPqEBiDKT41IPzVUchkKqmvxzb5UfLn3BLIq6n8Yvbn5AJIjgjDNTSZoJCIiIiLnGh8ahH8KSgAArx8/g09GD4KM8+E51BcnG+dVXdA7XrT5BgVBQEZDia0IPzXkMnEqJLSXIAj47EgaPj9m3zy0RETk/jpFgkSn0+Hmm2+GueFC3E033YSpU6e6OCrnmjdvHp566inodDp89tlnOH78OB544AH06dMHRUVF+OWXX/D+++9bXpPbbrutxXMZjReWuCkrK0N4eLjT4ieirilE7YkFw/thwfB+KK/T4u9TWSiq1UAqkUAqkUAmkcDPQ4kRMeGICfBp8QdWuI837p80BPdOGIz/bTuEJWt3AgAW/7wRK6+eiL5uUoOYiIiIiJzn8uhQfHgqA4VaHXaXVGDJoVT8p38vKDv5xXR3kV1Th79yCgEAIR4qXBHbfMluZ9ifVYDyWi0AoF9ky2XFOwOD2Ywl249g+YkMV4dC1G5mc31ZbHcjUiVSolZ1igTJkiVLLKWkQkJC8Prrr7s4IueLjo7GM888g0cffRQAsGPHDsyZM6fZfSdMmGA1N8v5goKCIJPJYDI11oD99ddfW02qEBG1JcDLA9cM6tWhc0ilEtw9biBSi8rw7YFTqDMYsfCPHfh9zhT4eygdFCkRERERuSNvuRz/HdoP/9p+ECZBwMqsfKRX1+KNYSkI83TsPBnd0VepmTh3vXR+rxhRE09fbm8s1Tu9g78pnKlCq8e/1+/B9rwSV4dCREQu4vLbMvbv34/XXnvNsv7WW28hKCjIhRFZu+eeezBixAiEhoZCqVQiMDAQSUlJmD59Ol599VWcOnWq3ed+5JFH8Pjjj7e6z5QpU/DLL79AoVC0uI9cLsewYcOstj366KPYtm1bu2MjInIUiUSC12dMwOCoEABAdnUd7vp7N0xueHcLERERETnWkCB/vDS4L1QNE7YfKq/CNZv2YF9DeSZqnxqDET+mZQMAVDIprusZI1rbtTo9ftx7HADgrVRgztC+orVtj7SKalz162ZLckQpleKpUSkujoqIiMTm0gSJ0WjEzTffbCkRdemll2LevHmuDOkC77zzDvbs2YPi4mIYDAaUl5fjzJkz+O233/DII4+gb9++mDVrFtLS0tp1/hdeeAE7d+7EvHnzEBUVBYVCgcDAQFx88cVYvnw5/vrrrxYncG/q//7v/6zWKyoqMGHCBMycORM//PADysvL2xUfEZEjeCjkWHb9JQj29gAAbM4uwlNbDzJJQkRERES4PDoMX40bgkjP+u+KZXoD7th5GKeralwcmftakZ6DWmN9lYmZcVEIFHH09sr9qajW6gEAs4f1hY9H5xsNtCWnCFf9uhkZVbUAgGBPFb69ciwui490cWRE7WQ2Q3DDP9bYos7ApQmSl19+GYcOHQIAeHt744MPPnBlOO1iNpuxcuVKDBkyBD/99FO7zjFy5EgsX74cOTk50Ov1KC0txbp16zBv3jxIpbb9Ey1atAgDBgy4ILZVq1Zh7ty5CAoKQp8+fXDjjTfi3XffxZ49e5qdu4SIyFmi/dX4/LppkEvr5y356thZLPxzB6r1BhdHRkRERESu1s/fBz9MHIZRwfU3CNaZTLh79xFUG/i71V4ms4CvTmVZ1hf0iRW1/WXbDzW2PWagqG3b4ufT2Vjw505U6+vfW30CffHLzAkYGhbo4siIiMgVXJYgOX78OJ5//nnL+nPPPYe4uDhXhXOB/v374+GHH8Y333yD3bt349SpUzhy5AjWr1+P559/Hv3797fav6qqCnPnzsWaNWtcEq9SqcTq1avRt2/zQ1cFQUBqaiq++uorLF68GCNGjIC/vz8uv/xyLF++HBqNRuSIiag7GhsfiVcmDoGsYXL39ZkFmLVyE7Ib7twiIiIiou7LX6nAuyP6o6+fGgCQU6fFN2dzXByV+1mfW4Sc2vrf+OMjgtHTz0e0trUGI3am5wIA4oP9MSxOvInhbVFrMOKpbYdhEupHsk+NDcdPM8Yj2sfLxZEREZGruCRBYjabccstt0Cn0wEAhg4disWLF7silAvMmzcPR48exeHDh/HKK6/g+uuvx/Dhw5GUlISUlBRMmTIFTzzxBA4fPoyvv/4aPj6NXzRMJhPmzp2L3Nxcl8QeExODPXv24KGHHoKXV9sf7rW1tfjjjz9www03ICEhAZ9++qkIURJRd3dNn1h8deVY+Knq51ZKLavC9J82YHc+J0YkIiIi6u485TK8PjTZcrHi27O50JtMLo3J3XyRmmFZXtA7TtS2lTIZGu6Fgq+nCpJzK53E7+m5qGkYlXR5fCQ+vngEvBVyF0dFRESu5JIEydtvv42dO3cCqJ9g/NNPP4VMJnNFKBe49dZbkZycbNO+8+fPx/r1662SETU1NXjmmWecFV6bvL298eqrryIjIwNvv/02xo4d2+oE7+cUFBRg0aJFuPbaa6HVakWIlIi6s3HRoVg1azISGu4OLNPqcf2qLfjhZIZrAyMiIiIil4tRe+GiiBAAQIlOj39yi1wckfvIq9VgX0kFAKCnnxrjIoJFbV8qlSAxpL5U1ZGcIpTWdK5qFT+kNpYeWzQgEdJOlsAhai9BECCY3fBP4Lyk5HqiJ0jS09Px5JNPWtbvv/9+DBo0SOwwHGb48OFWpcIAYNmyZaitdW25mJCQECxevBhbt25FRUUF/vnnH7z66qu4/vrrkZiY2OJxP/74IxYtWiRipETUXcX7q/HL7EkYFx0KADCYBTy4YT9e2H4EZn5JIiIiIurWrooJtywfaLjgT23LrK6zLE+MDHHJCI7L+/cEAJgFAX8ePSN6+y3JqqrF3sIyAECvAB8MCglwcURERNQZiDqOUBAELFq0CHV19R/YCQkJWLJkiZghOMW///1vLFmyBFVVVQAAvV6PDRs24Morr3RxZPW8vLwwefJkTJ482bItNzcXK1aswDvvvIP09HSr/b/++mvMnz8fl156qUPaN3h4Qu/h3HqeBi/xLqaafET6gqkQJ38p8bC9GzB6eTe7bAsTxJmIW1CJNxrN4Gnfa9BeeqWnKO1I5CpR2tGrGvsDL5UXPp09FW/tOYEfG+7mWpFTBvWJXNwxuBcMqsbn3nTZFkZvvWMCtoFJIc7koQZPcWojG81tjzx0BKm0c96xZ2zyOhtFes07LZH+jaRyl02L5zSC2fbvJnzPdW32vBc6pAu+54xycT5fpXJxvj+aBHE+X6EU5/lIPOx7Pva+7/pGKhEUlAcAyBHkonz31on0vduZz6VAWoWgoCAAQHx4mCivm1aqtFq/fPgALD+YBgDYkFGM2WOUzR3WLvZcWzj/t0SJRGF5bcYlRjX72hjE6XaIiKgTkQgijmX6+OOPcdttt1nW//rrL0ydOrXVYzZu3Gh1YT82NhYZGRnOCrHdrrrqKvz666+W9RdeeAGPP/64CyOyjcFgwEMPPYS3337bavvUqVPx119/tfu8OTk56NGjBwDg008/RXCwuMN6iYiIiIiIiIjsUVJSgoULFwIAsrOzER0d7eKIiFrW9NrbyEtXQuUZ6uKI7KfTFGHXn1cD4P9z5DqijiB5+umnLcuXX345evbs2Wayo6CgwGrdaDRecExkZCSUSsfdkdAecXFxVuvFxcWuCcROCoUCb731FjIyMqwSPFu2bIFGo4Gnpzh30BAREREREREREZH9BMEMQTC7Ogy7uWPM1PWImiDRaBon51qzZg3i4+PtPkdubu4Fxx04cMDl85icn0ho+lzdwQMPPGCVINHpdMjIyEDfvn07fO5e21YjXO3coft1peLN+VJbLE5bMpFKbMntLLFVMOcGAED4iq8hr7P9tTDUilNiSypiiS3vIHFKbKnD/EVpR+XvI0o7tQWlrT7++p7j+OFkJgAgKiQIz778KgCg947fodDZ3rdW5VW0O0Z7mfStj8Uv1RuwMqcA2RotEr29MDTQD8m+arvb8QwQpwyKvlac8mSducRWzszrAQDRv34LuaaujSO6MJbYajd7S2xlXzEXANDj9++793uuC+qsJbbcoZ8ztvH56ihildjS1+hEaUcmUoktRTtKbNn7vpv613YIAPr4++DjScPaE6Zd1BHizElRndv69+GO+Ce3GM/sOw4AmNezB25P6em0ts4JHXbhdYOnftmIH/bWx/H9bbMxsEeYQ9oqP277nCYGlSdOj5sBAEjaugoKnQYHCstw9z/7YGoopjK5RyieHzfQMll7QU3n7A+JiMh5RE2QdGUlJSVW6+5WUqq5BFN5eblDzq3QaqB08jvNUFfj3AaakFWL05ZMKVKCxNS+WsjyulrIa21/Lcw14lxwlRnE69YUIpXsVurFmRtEZRRnJJ5B1/qPjodTYnEmNx9/ZeRbbVfoNFC2cWxT9rw/O0rSwgUcsyDgo/QsLMvIhc7ceGfMiEA/BCgUeLhPAgLtGAGp8BDnIpu5VpwLOFJZ50yQNCXX1EEu4mdMZyMRLUEiXnJbLO29KC7X1EFhxw0I1PkJZnHujOzIe67T9nO6rpUgMdVoRWlHrhLn+7C8A3OW2fq+KymtTyRUwwiFxvl9o0rv4fQ2AEDrxOcyzEuG0obXbaNgxN2JEU5r6xwP84W/9Xp4yy1xpOXkYmSUY5JPSm37EhgKnQZKbR1G+nng8f4xeHDjARgFAStKS5GokOCOQUn1+2nd62ZXIiLquK53u56L7Nq1y2o9MjLSRZG0j0x24Y8CPz8/F0RCRN2dTCrBu1OHY0CIv6tD6RBBEPBqajo+Ts+2So4AwO6ySqwtLMH8XYeQXccfYURERETNkTXc1a8zsQSLrYI8VEjyqx+pfKy8EgUu+q6ZFBZkWT5dWOaSGFpyVc8e+GTaSJy7DeTN/SdRqRPnhj4iIup8RE2QVFRUQBAEu/42bNhgdY7Y2NgL9nF1ea0jR47gyJEjVtsmTZrkmmDa6fjx4xdsc7ckDxF1HV4KOd68aJhVdZ+Myk56d2szBEHA26cz8H12/SgYKYD5MZH4YdRg3JbQw7JfgVaH2/YdRaFWnBEbRERERO4kxru+lPXZqloYRRqN1RVMjaqfqNksAEtPZLgkhqTQQMvyygMnUVrTuW4KmhwThuv6xAIA9CYztuWWtHEEUecmmAW3/SNyNY4g6SCTyYT77rvPalvPnj3Rr18/UeM4cOBAh47/6KOPrNYHDx6MgABx6q8SETUnKcAXVyc1JhOe2HIQWqPJhRHZ7uP0bCzLzAUASAA8m9ILD/ZOQJKPN25PjMW6CSOQ4F1foy1fq8Pt+46iTM+71oiIiIia6uVbP+ef3mxGakW1i6NxH//XKwYqWf3lnu/PZKNMK/73zIQQf8QF1VelSCsqR8pTH+LpXzehrLbzJEouigm3LB8rrXBdIERE5FJdOkEikUis/jZu3Njq/u+++y60Wtvrwur1eixatAjr16+32v7000+3J9wOGTJkCGbMmIGdO3fafezSpUvx2WefWW2bN2+eo0IjImq3u4f2sSyfKa/GE5sPoEpnwN6CUpR30lEXyzJy8GF6lmX9ib49cUVEqNU+ISolPhyagh6e9XWuM+o0uGPfMVQZxKm1TkREROQOBgf6W5Y/PJbmukDcTJCHCtckRAMANCYTvjqVKXoMEokEH954BQK86r/v1uj0eGPdTkx57StUd5Lv8VFqT8tycV3niImIiMTXpRMk9lq8eDHi4+Px0EMPYdeuXTAam79QZTQa8euvv2LkyJH4/PPPrR6bOnUq5s+fL0a4F1i9ejVGjx6Nfv364aWXXsL+/ftbfA5A/bwp11xzDW655RYIQuOQtqSkJNx9991ihExE1CpPufUkoz+mZiFl6WrMWrkJo7/6E+vO5rkosuZ9n52Ht05nWNYf7BWP2dHhze57LkkSpqqfpP1UTS3uPnAMBpaPICIiIgIAzIqJQFDDd6W12YXYml/s4ojcx8194iBvmMPlq9QM1LjgRpyxPXtg5xM3Y+H4wZA11M5NKyrHd7uPiR5Lc0xNroPImtb2JSKibkXe9i7dS0FBAV577TW89tprUKlUSE5ORkREBPz8/GAwGFBUVIR9+/ahpubCWvjDhg3Dzz//DInEtR+sJ06cwOOPP47HH38cXl5eSE5ORkhICAICAqDX61FUVISjR4+itLT0gmMjIyOxatUqqFQqF0RORGS7OqMJd/61G99MH4/hEUFtH+BkO0vL8fLJdMv6XT1jMT82qtVjIj098NHQ/rh572GU6Q04XFmNjcVluDgs2NnhEhEREXV6XnIZ7uwdh2cPnwIAPLzjMFZdNg7Bnvy92pYob09Mj4vEyrO5qDIY8d3pLCzslyB6HJH+PnjzumkYFheB279aAwDIq+gccwvmNpkXJYTvKXJzgtkMwQ1vtnPHmKnrYYKkFTqdDvv3729zP4lEgrvvvhuvvPIKPDw8RIjsQr1790ZqauoF2+vq6rBnzx6bznHppZfigw8+QFxcnIOjIyLquA+njcQH2/ahTKtHRmUtKnR66Exm3LZ2J7bOvwReCtd9pOlMZrx4orHswy3x0bglvkcrRzSK9fbEs8m9cNeB+jvp1hYUM0FCRERE1GBObCQ2FJdhS34JSrR6PLzjMD6dPAxSF9+Y6A4W9UvAL2dzIQD4/GQGbuwdC6VM5pJY4oL9Lcv6TjKv4NqMfMtycpP4iNyRXlfm6hDaxV3jpq6FCZIm/vvf/2LDhg3YtWtXs6MrzhcSEoJrr70Wd911F/r06dPm/s508uRJpKamYvXq1di8eTO2b99u03Pw9fXFjBkzcNNNN2Hq1KkiREpE1D6DwwLxxRVjAQB6kxk3/r4N23OLUaLRYUVqJm5MSXRZbD/l5iNbUz+H1WB/X9yZGGvX8aOC/OEpk0JjMuNMTZ0zQiQiIiJyS1KJBK+OHoAZa7ahWKvD1oISrM0uwGUxEa4OrdPr6afGxT3CsC67EMVaHXYVlmF8ZIhLYmmamDGYXJsgqdDq8eKuY/j5dDYAQCmVYmS460ekE3XE4a23uzoEIrfV6RMkkyZNspofwx72Hvfggw/iwQcfBADk5OQgNTUVOTk5KC0thUajgUwmQ0BAAIKDgzFo0CAkJrruYlxzevfujd69e1ueQ3Z2Nk6fPo2MjAxUVlaitrYWHh4e8PHxQWhoKFJSUpCYmAiplFPREJF7UcqkeGpMf1z64z8AgE8OncH8fgkuqx38a26RZfmRPgl2l1qUSSQIViqRrdGiVK93dHhEREREbi3IQ4Ulw5Nx55b6Cg9b8kqYILHRFbERWJddCADYkl/isgSJSt6YING5cATJuox8PLblIEq1jd+5HxreF/4eSpfFRERErtXpEySuEh0djejoaFeH0SE9evRAjx62lXghInI3/YL9MS46FFtzipBZVYt1GXm4LKH1OT+c4VRVDU7V1AIAUnzV6O2jbtd5glT1CZIaowkGsxkKJq+JiIiILCZEBkMplUJvNmNXUdvVEqje2PBgyCQSmAQBm/KL8Tj6uiQOZZMEiatKbG3PLcYdf++xTM6uVsjx6Ih+mN83ziXxEHVUeHg4srOzXR2Gw4SHh7s6BOqmmCAhpwu8dg6CQ507XDWsosSp57dSUyVKM4bCQlHakXl52ryvVqpATsNy/JWj4WE22HysoazczsjaJ3/3hXPxOIvCU5y7jMwi/YAwizTU3dOO+r5SReO8Th6BvlAZrF/zuycMwtZv1gEAPj12FrNGJFse01ZqIIa16ZmW5RlxkVB6t+99oZA1JkTkXopm60N7Bfm069z2MtSJM4pFW6UTpR0AkClsTzgZpY19m0FrgKCxva9TeCrsiquzMxnEmTRRMLdvtHB7SEQaaWbPcxKMZqtlsfp9e8iU4vxsEPO9YK9SrR5/5RXBT6lAuU6PocH+6O0nTr9sC3v+fzU1ec+ZjGZI7DhWzMlUJSLdLGDPZ0RHSOXizP0gVt8N2P75CHTs89UzwAteCikGh/hjV2EZsms0KNTpEKm2/beMLSQi/RsFJolzU4+hVgM1gCERQdiTV4KzVbUo85Qjxq99N/S0pOzwqTb30VXXWpbz8opsOqY5p349Y/O+Zn9f4KL65fS1GViSfsCSHBmt9MFinwgEnTbh9OnGuQSLTfa9r4lcSS6Xu/3N3USdARMkRETktiYnRKFvSABOFJdjT04Rvj5wCjcM7iVa+2ZBwKqMPACAXCLB5VGh7T6XsUlZSBknHSUi6jRyazV48VAqNhWUwAzAX6lAld4AM4AZMeF4tH8v+KtYmoVIDCPDg7CrsH5C371FZZihFn/0sDsaHxOOPXn1NxUeKSx3eILEFlFqLwR7qlCi0WFHXglyqusQ7eMlagylZqNl+Sm/HvzOTUREAADW7yAiIrclkUhw37iBlvUHf9+GdafFG2J8urIGhZr6URBjQgMR0IELZNomI3j4Y42IyPXMgoDv0nMw4++d2NCQHAGAiobkCACsyirAjL934nRljavCJOpWwrwaRxdrOuFou84quMnrVmtwzQgJqUSCS+Lq543RGE24dtVmZIjcd3pL6kcIeUqk/L5NREQWTJAQEZFbuyo5HreNrC+tZRIELPppA/bnFovS9sGSCsvyiOCAdp8np1aDkw0/EH0VcrsneSciIscyms24e8chPHvwJDRtlKAs0elx89b9yGhSPoaInKNK33hx30/VtcpaOpOXorF0mCsTS/cO64tE//rRK7k1GlyzagsONIwIEsO5b9g6QbxSgURE1PkxQUJERG5NIpHg2YtH4Kp+8QCAOoMR8777C2ernH+hqmkb/fzbX4f+z9winCuwdW0cS0UQEbnab9kF2FDQ9hx38oaEdqlOj9eO2l4Xn4jap2mCxEfJBImtPBWN1dU1BmMrezpXqJcHvp8+Hr0DfQEARXVazPp1E/67+zj0JucmLXbqqpFpqh/5HSVjWUQiImrEBAkREbk9qUSC/82cgHENw/ZL67S4ZcMeFGucOwl407JYPor2T+t1uLzSsnx1bESHYiIioo4xms344ORZy/r1CS1Pfjo+LAghHvUX2jYXlKBCz8l9iZzJagQJEyQ2U0gbL/24ujRZiJcHvp8+DkPCAgEAZgH434FUzFy5ESdLK9s4uv3ercm3LE/3DHRaO0RE5H6YICEioi5BJZdh2TUXITm0vtRVTq0GizbuRY0T6yyrZI0fo6U6fbvPU6StP1YCIKpJjWgiIhLfobIqZNdqAABquQwLkmLg20ISfFNBiSVBbhQEZNfUiRYnUXdUpW8c/eDLBInNDjcpYxUl8sTozQnwUOHHGePxwPC+kEvrR+IdL63EjJUbsT4zv42jO2aSypcJEiIissIECRERdRm+Hkp8N+8SRPt5AwCOl1fhri0HnDZkPyXQz7K8vah99ZPrjCacbahbH6hSWN3hR0RE4otRe1oSIjVGE65evwtVTUrSyCUSXNEjHABgBpBe3ZgU6choQiJqW9MRJL5K/v9mqw0ZjUmHiZ1ktLJcKsXiIX3w69WTLCW3dCYzbl23C7+eyXZIG6Vm63JiC73D8JhvNCdoJyIiK7wKQ0REXUq4jxe+v/4SS9mF7QWleHzXEZgFoY0j7Tc6LAjKhoTGjxm5KLKzpFexVod/7zyEmoZSB6NCeDcbEZGrhXiosHzicMSq6++yrmtSjkYlleLZIX3x8rBk9PJVWx0nk0gQ6slRgETOZDUHiYIjSGxRWqe1jCDpF+KPMLWniyOylhLsj9WzJmFGYn05Q6NZwD3r9+LbE2fbOLJtf2jKLcuXevjjWu9gSJgcISKi8zBBQkREXU6vEH98NHGopQTWqow8vHYw1eHtBHuqcF3PHgAAjcmMxbuP2FR/3iwI+O5sDqb/vQt7SioAAAqpBLckxTo8RiIisl+irze+nzQcE8ODAQDhnirc1jsOv08bg6tiIyGTSHB5jzCrY8aEBsJLLnNFuETdxrkEiVohh0zKC9222JRZgHO3CU2O6xyjR86nksnw1pRhmN83DgAgAHhy6yFU6dpfKtckCNihr7KsX+UV1MEoiYioq2KChIiIuqQhIQF4c+wgnPvt/OmJs1h2MsPh7fw7pSdCGyboPVJehZu27EdhKyNJTlRUY/7mfXju0ClUG+uH/QepFPhs7GD09lO3eBwREYnLV6nA+6MHYuNl4/HXpeNwT3JPRDaZJ+qGxBiMCgmEp0yGSeHBeG5IPxdGS9Q9nBvR5clkpM02NimvNamTlNdqjkwqwQvjB6FfUH0JW6NZQEdyYAcNtagwN44A9JHwPUNERM1j0U4iIuqypkaHYcmwZDy15xgA4MX9JxDiqcLlDvxxGOihxIejB2HR9oMo1elxproW8zfvxSdjBiHep34uFJ3JhPX5JfgpMw87i8utjr8qJhwPJvdEgErpsJiIiMgxJBIJQj1VzT7mJZdh6fghIkdE1L15yuovcmublL6jlpkFARsbJj33UsgxPCrYxRG1TiKRWEaNKKVSFGt0UCvbV0ptnaYCUPOSFxERtY2fFkRE1KVdlxSDQo0W7x1NgwDgoR2HEOihxKgwxw2z7+2nxtcThuDWbQeRXadFvkaH6zbtxcWRocip1eBweRV0ZuuJ4hPUXnhqUG8MDw5wWBxEREREXZmXov4SRq3RCEEQOJ9EG86UVaGkrn5k89geYVDKOv8oCn3Dd2a92YyLvv8b1/aOxeKhvRHZMC+ULTKMWmzSVSJAzbJaRETUNpbYIiKiLm9x/yTMaZj40WAW8O/N+3G8rNKhbcR4e+GrCUPRu2HS3hqjCSuz8rGntMIqORLj7YmHUnrip8kjmBwhIiIisoO3ov4Cv1kAdCZzG3vTiYa57gBgSIR7JAueGzcQgQ3la02CgG9PZmDK939jc3ahTccXmwx4rjIbfHcQEZGtmCAhIqIuTyKR4NnhyZgUGQIAqDEYMf/vXdiaX+zQdkI8VPhi3GDMjo2AUtr4ERvl5YGrYyLw+djBWDN1FBb0jIFSxo9gIiIiInv4KhrLLZ2qqHZhJO4htaTxhqBeDXN7dHaXxkdiy/XTcP+wvvBR1o8Y0hhNWLh2J7bntv7d/bC+FreXpSHbpAcAhEnbV56LiIi6F5bYIiKibkEuleKtcYPwr3/24EBJBWqNJty8YS9Ghgbixt5xmBod6pAyDb5KBZ4d3Bf39UtEalUN4tReCPf0aPtABxAEAb+n5eL3tBzoTWb4qhRYPLQv4v05+TsRERG5vwlRIfirYSTBx0fT8L9JQ10cUed2qrQxQdLbTRIkAKBWKnDP0D64MTkeD286gHUZ+dCZzLj5zx14Qd0DKUrvC45JM2jxZGUWtEL92JEwqQKP+0WLHToREbkh3r5KRETdhpdcjs+nDMdF0aGWbbuKynDnlv349+b9KNboHNZWgEqJUSGBoiVHDCYz7li7E3es3YnfzuRg3dk8rDiZiVvWbONEpkRERNQlzEiIQlBD+aU/MguwLb/ExRF1XiazGUeKygEAKpkUsX4XJhU6uwAPFd6bOgIXxYQDqB9J8lxVDjTm+u+2pwwa3FhyCgtKT+OO8jRLcmSYUo33AhMQzBEkRERkAyZIiIioW/GSy/G/cUPwn6H9EO/T+ENxfW4Rpq3ehDcPncKB4nKYzIILo7TfV0fT8Hta7gXbT5dX47Vdx1wQEREREZFjqRVyPDykj2X9lb0nXBhN5/bJnhPIrqoFAAwMD4JM6p6Xf5QyKT6YNgJjGkrllpuN+FlTBq1gxotVOSgwG5DXUFILAIKkcjzl1wO+UhZMISIi27jnJyQREVEHyKQS/F/vWPx55Xi8M26Q5U7EWqMJHxxLw9y/dmL0yvX47nSWiyO1jcks4N19Jy3r7148Ar/OnmyZB+Xjg6esJukkIiIiclezekajd4APAOBYWRW+P+Ue39fElFVRjZc27LOsPz5uoAuj6TiVTIYXxg+ErKEc7o91Jfi+tsQqMXLObK8geEh4qYuIiGzHTw0iIuq2JBIJLo2JwE+XjMHI0ECrxyp0Bjy15xi+O9P5f3RnVdWgpKE82OSYcFzVKwZDwoMwMSYMACAA+PTQGRdGSEREROQYUokEl8dGWNYf33EEG3OKXBhR5yIIAh5csx11BiMA4KaBSRgRFeLiqDouwd8Hc/vEAgDqBDOW1zVO2P6afxye9I3Gk77RmO0Z5KoQiYjITTFBQkRE3V6ktye+mjoSW6+ejBdHpmBMeOMPq2f3HMe+4nIXRtc2D7kMcxp+MG7IKgAAfLA/FX9l5Fv2OVhY5pLYiIiIiBxtQb94JPg2lkpd2/D9h4AVR9KwoaHsaoTa0+1HjzR195DeF2wLlMrRX+GFCR5+mODhB0nDKBMiIiJbMUFCRETUINTTA3MSe+CLKSOwoHccAMAoCLhn6wGUah03gbujBXl6YHNWIQBAKgF+OZWFF3ccsdrHJLjXnCpERERELVEr5BgY4m9ZD/PycF0wnUhJrQZPrttlWX/xomHwUXWdicoj1V7oKbf+tx6qVDMpQkREHcIECRERUTMeHtzbUnarSKPDW4dPuziilillUtw8oCcAwCwAd/+1+4J9Zib1EDssIiIiIqc5VV5tWb6xT5zrAulE/rNuN8oayq7O7BePSxKjXRyR4w1Relutz/MKdlEkRETUVTBBQkRE1Ay5VIo3xg6Ct1wGAPgxLRtnKqvbOMp1bhmYhKlxEc0+tuvGy3HfiH4iR0RE1H5GsxmbCkrw0J6juH/XERRrOu8oPiJyjUq9AQAQqFIi0EPp4mhc7+8z2VhxNA0A4O+hxIuXjHJxRM4xUeVnuZB1i3coouQql8ZDRETuT+7qAIiIiDqrYA8l/FVK1Bo1MAvA9oJS9PTzcXVYzfKQy7D08jHYkFmA13Yfw/GSSviqFJiZ1AMRak9Xh0dEZJOz1bX4OTMPq7LyUazVW7aX6/X4bNwQSFlGhYgAmMwCShv6CL8uVEKqvWr0Bjy0Zrtl/ZmLRyBU7QlDrcaFUTlHksITr/jHoUYwYYyyc34vJyIi98IECRERUQveOXIauU1+WI4KC2plb9eTSCSYEheBKXEREASB9ZiJyK2szsrHo7uPwtzMY7uKy7E6Kx8zYyNFj4vIndUaTSjS6aA3C4j39oRS2jWKSJyprIbGaAIA9A7gRfKXNuxDTmUtAGB8XASuH5jk4oica+B5ZbaIiIg6ggkScrr5//OHytO5FxV9gno69fxNDRzbS5R2RowUZ5h4SlCOzfsaDHog9RgAIH/09VAobI9RJ4gz9Dl2ykFR2gGA7GdfEaUdwdzcpSLHU0eHitKO3N/P5n0l0sY7Aj0iQ+FhNth8bHlavl1xnU9nMuGLkxmW9QcH9UIv/+Z/gPtE+HeoLVspvMWZgNQrSC1KO7oafds7uRlpQ0k2ZzNobP9/oSPMDRefnE0Q6XUTk0Rqe4LSbBasls0moZW9rYn1XlA5uVso0epRqdFZkiNyiQSTIoKR6OONj1IzAAC7S8odmiCRysW5UGzSi/M5bs97rum+EqnErmNlCvF+Qiq8xPk+XFNUI0o7KrU4z0fuoUBOrQYfnziLlRm5MDT0MT4KOe5KTsSVMeEI8uj4d/OwIfZdhNfJVchtWO4xrh9URttL5ymDA63Wf/q7cSLyccOTET5+iF2xtERXVOKQ87RFpnLMe0EQBKw4kIpP9hwHUD+i+J3rp0EV4AsAqMoqdEg7bdGU14nSDgAE9LE9IWZSq1HQsOzfUw1ZTdufr3qdDihtZ3BEROSWmCAhIiJqxpb8EtQ2XBy+Kj4Kt/ZLdHFERERd19GKKrxw5LRlfePl4xGoUuLz05mWbZFeLBdIZIsf03PwwoGT0Jqsk3PVBiPeOHIa7x5Lw4MDknBdYo92nV9rNOHXzHyguhqzBiYhuoUbSJxFEAR8ve2QZf3SAV17tERrlvy+DW9t2GdZf/zS0UgI9nddQERERG6ICRIiIqJm7ChovHXssphwF0ZCRNT1+TYZleCrkCOzpg6lWj3ePpZm2T6uk5c5JHI1syDgjeNp+CIt27LNWy7DlMhQmAQBa7ILoDOZoTOZsWTfCZRo9bizX4LdJTmXpmbgnYb/N9/atB+fXDcNk3pGQ2Zn+S6d0QSzIMDTzlFJ646m4XB2/ciIYfGR6BsZYtfxXcXBnCKr5MglfeNw54TBLoyIiIjIPTFBQkRE1IzsmsZSAcmBvi6MhIio6xsY6IdRwQHYWVKOKoMR8zfttXr8hsQeGBhoe4lGou5odU6hVXLk+sRo3Ns/CX7K+pKlTwzujTePnMGKs/WFrv53LA1quRwLesfa1c72wsabSMrrdJizdDX8PFSYkBiFiUk9MKlnNBKC/FpNvOzJKsTCz1eivE6Lm0Yk44EpQxHm0/a8EkaTGU/8+Jdl/e6LR9kVe1fy2t+7LcuPXDwCj10yivPPERERtQMTJERERM3IaZicXSmVItgBdbqJiKhlMokE/x3aD3ftOYpDZZVWj/XyVeOBFPHmmyNyV5ubJC4W9o7DgwOt504M8lDh+eHJ6OmrxsuHUgEAy89k2ZUgMQsCjldUX7C9UqvD6mPpWH0sHQAQ7a/G5J49MGdQL4xPjLrgwv3Cb9ehsLr+ZpRPdhzB13tP4NYxA3DPxMEI8Gp5zrUvthzAibz6eUJGJERh9vB+NsfelZwsKMXqI/WjeMJ9vXH/RcOZHCEiImoncWYmJCIiciOCICC3pj5BEqX2hJQ/OImInC5ApcTXE4fhkSZ3vHvKpPjviBSoZDIXR0fU+ZmExgmotxWWIrO6+Ymzx0cEwUte//+U1mSyq42M6jrUNczRdnHvWLw3Zwqu6p+IAC/rm0lyKmrw1d4TmPnprxj++nI8/OtmvL/loOVxo7l+fpRzX7E0BiPe3rQfA1/5Cl/uPg5BuHAy7azSCjz7ywbL+stzp3XLpEBJTR0W/7jesn7XxCHwsLNMGRERETXipygREdF5ynR6aBouGER7c1JgIiKxyCQS3JQUizlxUdheVIYkPzXi1F6uDovILdzTNwH7SytQpjfgREU1ZqzbjsXJPTErPhKCAKRWVmNPcTmWncq0JDnibChrBQBFGh2OllfijcOnLdvGJkRi3rC+mDesL0xmM47klWDjmRxsPJONnRn50DW0kVZSibSSIwgKCsJn/2o85y2jUvDAlGF4d9MBfLbzCPQmM6p1etzz8wZsPJONN2dNgl/DKN7yWg2ue+9HlDbcwDJneD+MTIx2xMvmNgRBwLJdx/D0b1tRodEBAAK9PPCv0SkujoyIiMi9MUFCRER0nnKdwbIcxPJaRESi81bIcXFUqKvDIHIr8WovfDpmEO7afQR5dVroTGb89/Ap/PfwqWb37+fvg9dG9W/1nIdLK/HhiXRsyCtG0zEdcokEcwf3tqzLpFIMig7FoOhQ3DtpCDQGI/48cRbLdh/HpjM5F5z3+SvGYm7faEgkErw4fRzuGD8QL63bjW/3nwQArDx8BvuyC/H2rMmo0urx1NqdyCypAAAkhATgrRsut+/F6QJeXLsTr/7VOO9IoJcHvlpwBdQqpQujIiIicn9MkBAREZ3HQ9ZYgVJvMrswEiIiIiLb9fJVY/UlY/D6kdP45kx2s/tIAFweE45nh/aDdwulmYxmM/576BS+PJ2F84td+Snl+M+Qvgj3bXn0iadCjqsHJOHqAUkorqlDemklCvSNj189oCckRp1lvYe/D96/9iJc2jcOi3/agEqtDlnl1bj6s1VW5w1We+H7u65FQCcY4ZtRVoWcimqMjouATOrc6uVLdxyxSo7MHdoHz08fh1AbRwARERFRy5ggISIiOo+nvLHWfbXB0MqeRERERJ2Lt0KOp4b0xeSIEGzML0Z6VS0AoJefGr38fDAmPAgRLUyELggCTlXW4JVDp7C9yaTvYZ4qTI+JQEqgLyZEBMNLbvulhBC1F0LUXtDJVdjZxr4z+idiUHQIFn77F/ZkFVg9NjwhCl/fPhvRgX42t+0MBpMJb2zcj9c37IfRbEbfsEA8cfEIXNQrBh9sO4T8ylr0DPHHuIQo9AkN6NA8KeV1Wny2/TBe+LPxlXt55gTcMWGwI54KERERgQkSIiKiCwSolPBXKVChM+BwaSXMgsCJ2omIiMitjI8IxviIYJv2PVRagV8z8rGpoBi5tVrLdoVUgntTknBjrxgonDxK4pyYAF/8fttVeHvjAWxJz8GAyBBMGNgL01J6QtHkJhZXMJsFzPvqD6w/1Tg650RhGW74+s9m9w/38UJisD8SgvwwPCYMI2PD0TPY/4L9BEHAvqxC/HE8HWeKK5BRWomM0krLXCPn3D1pCJMjREREDsYECRER0XmkEglGhAZiXXYhKvUGnKyoRr8AX1eHRURERORwHx5Px1tHz1ywPcRDhf+O6o9RoYGix6SQyfDgRcPw4EXDAADKYPFjaM7+3CJLckQmlaBXSABOFJa1uH9BdR0Kquuw7Wwevtp7AkD93CEj4yIwMDoUKrkMJTUarD5yBlnl1S2eRyqR4N8TBuHZK8Y59gkREREREyRERETNOZcgAYDdhaVMkBARUbdSoNGiQKNDkU6HEq0exTo9PGVSjA4NREqAL2QcWen2zIKAL05lWiVHFFIJhgUHYGJECGbHR8JHqXBhhJ3P5rTGCedfvGIsFo5KwR8nMvDZrmPYnJaDSF817h4/CAazCRtO52B3VgGqtHqrc5TVafHH8bP44/jZFtuRSiSIDvBBbKAveoUG4JYxA5Bs42ggIiIisg8TJERERM0YGRZkWd5VVIYFfeJdGA0REZE4sms1ePZIKnaVVjT7+P9OZcBPIceY0ECMCw3CpLAg+PEiutsxCwKe3nccP6bnWm3fNmMSfPnv2aKDucWW5clJPSCRSHB5v3hc3i8eGoMRCqkUcll9KbI7xg4EAFTr9DiSV4LdWQXYlVmA3ZkFKD+vdJZMKsGkpBhcPTAJYxKjEBPgA4XMteXEiIiIugsmSIiIiJqR5KeGp0wGjclkmdyUiIioqzIJApafzcH/Us9Caza3um+lwYg/covwR24RvOUyzIuPxo0J0QhQKUWKljrq/ePpFyRH5sRHMTliB+/zXitPRfOXV3xUSoyJj8SY+EgA9fONpFfWIq2kAmZBgEwqwYjYCAR6ezo9ZiIiIroQEyRERETNkEokiFJ74kxlDXJrNDCZ63/AEhERdTXVBiPu2H0YhyuqLNsiPVWYFBaMEJUSIR4qhKiUyNNosb2kHDuKy1BjNAEAao0mfHI6E9+ezcFDyT0xKyYCEpbf6tSOlFXig+PpAACpBFgypB8mRgQj1FPl4sg6P2WTUR0Gk6ld55BIJOgVFoheYZ1jXhUiIqLujgkSIiKiFvT0VeNMZQ30ZjNWpOdgbs8erg6JiIjI4T49k2lJjkgAXB8XhcW94+Elv/Dn4jXxUTCYzThSXoXVOYVYmZUPoyCgxmjC04dSka/R4q4+CSI/A7KVzmTCY7uPwiQIAIB/90vEtYnRLo7KfSgbymcBgN7U+kgrIiIicg/StnchIiLqnq5LakyIvLz/BMq0ulb2JiIicj8mQcDvuYUAALlEgs9HD8KjyUnNJkfOUUilGBLkj6cH9sYfU0fhqh7hlsc+PJWJA2WVTo+b2ufdo2k401A6tF+AD27ryznW7NF0XhC9sX0jSIiIiKhzYYKEiIioBWPCgzEzrr5edK3RhM35JS6OiIiIyLEOlVeiSKcHAIwPDcSQQH+7jo/w9MDzg/vi7j6NF9pXZxc4MkRykN+z8vFZagYAQCGV4OURKVBIeUnAHk1HkBg4goSIiKhL4LchIiKiViT6qS3LVXqDCyMhIiJyvL/yiy3LV0SFtfs8TY9NrarpUEzkWCazgEd2HcEDO49AaNi2OLknevn5uDQud6SQNxlB0s45SIiIiKhzYYKEiIioBZV6A5aeOGtZHxjk77pgiIiInGBjUSkAwEcuw8TQoHaf580T6Zbl3r7qVvYksb1/PA2/ZuZb1i/vEY6be8e5LiA3xhEkREREXQ8naSciImrBN6ezUNEwauSK2AgMDPZ3bUBEREQOZjTXjymYFhEKVZP5FeyRX6fF3w0jUXwVcixKinVYfNQxWwpK8P7x+uSVVAK8MqI/rowJh0QicXFk7slqDhImSIiIiLoEJkiIiIhacKKsyrJ8U29e7CEioq6r6UTr9tCZTHjzRBpMQn2iZV58FCK8PBwZGrXD4dJK/Jieg18y8yxlte5NScL02AiXxuXurEeQsMQWERFRV8AECRERUQtqjUbLcpyPtwsjISIicq5HDhzHzxNGwEve9igSrcmEw+VV2FNSgRWZeShumORdLZfh/xJ6ODtUaoXOZMITu05aldQCgIkRwVjYJ841QXUhSo4gISIi6nKYICGn0+vKnN6Golbv9DbOqSr3EqWd0kKlKO0U6PPb3qmBwahHSUkJAEDtlw+F3PYYdYI4z0deXdz2Tg5SoNGK0o6HUpwSCNoKcSZUlQu2Px+dRI4S1L/n8soqoRKMbRzRqLBOY3ds5yvW6CzLtQYTtKbmz6mSCc1udzQFxGlHU9vx184WlVpd2zs5iFRm+/vOKJVb+jq5Rgu5HXEq21edxm5Gre3/L3SEWaS7Y6XtLOvTqdkx059JIrO851R1Gsjs+HwR672glIlzIVDM94JULs50jCaDOP9GJoPt/0ZN33O+MilKAeRpdFibX4TRwQHNHpNZq8Ff+UU4UlmNU5U1aK53WNgzBhqTCRqNY/oOBcR539WK9Hnk7P+Pqg1GPHXwJI5UVFu2ecikmB4TgVt6x6FI49jnaay077ujXtb4WyKvohpKk+2/4ZRScS5d6Np4TnUNpVcBoKCqFrl2vgbnyJSKdh1nrzKRvtNpRfpdBABane3vY7NMYXnPSTVaSG04tqTJvzEREXUPEkEQxLnaQt1KTk4OevTg3WNERERERERE5H6ys7MRHR3t6jCIiMjJxLltioiIiIiIiIiIiIiIqBPhCBJyCqPRiIKCAleHQURERERERERkt/DwcMjlrExPRNTVMUFCRERERERERERERETdDktsERERERERERERERFRt8MECRERERERERERERERdTtMkBARERERERERERERUbfDBAkREREREREREREREXU7TJAQEREREREREREREVG3wwQJERERERERERERERF1O0yQEBERERERERERERFRt8MECRERERERERERERERdTtMkBARERERERERERERUbfDBAkREREREREREREREXU7TJAQEREREREREREREVG3wwQJEXUaZWVlePLJJzFgwAB4e3vDz88Po0aNwptvvgmtVuvq8IiIHIJ9HREREREREVHnIBEEQXB1EERdTXp6Ovbs2YO9e/diz5492L9/P6qrqy2Px8bGIiMjw2ntHzlyBEOHDoXBYLDa/vnnn2PBggUObeu6667D999/b7WtPc9v69atuPrqq1FSUtLs40lJSVizZg169uzZ5rmWLFmCZ555xq72WzJz5kz88ssvDjkXUVfj7n3dggULsGzZMofEYutzZV9HRJ3F2bNncfDgQeTl5aGmpgYRERGIjY3FmDFjoFAoOnTusrIyvPHGG1i1ahXS0tIgl8vRt29fzJ07F3fccQc8PDwc9CyIiC6k0Whw8uRJZGZmIi8vD9XV1TAYDPD19UVQUBBSUlKQnJwMuVzeoXbY1xERdQ0d+zQgIouNGzfipZdewt69e1FWVuayOEwmE26++eYLLhg6w6pVqy5IjrTH0aNHcdlll6GmpqbFfU6fPo2LLroIe/bsQWhoaIfbJKL26Y59nS08PT3b3Id9HZH7cWYiWCKRdCi2s2fPIi4uzu7jVqxYgTfeeAM7duxo9vHAwEDMnTsXzz77LIKDg+0+f0uJ4F27dmHXrl344IMPmAgm6mS6Ql/3+eef459//sGuXbuQlpYGs9nc6v5qtRrXXnst7r77bgwaNMjuuNjXERF1HSyxReQgBw8exLp161x6wRAAXn/9dezdu9fp7VRUVOCOO+5wyLluv/12ywXDiIgIvP/++9i7dy+2b9+Ol19+Gb6+vgCArKwsPPzwww5pk4jap7v1dbaaPXt2m/uwryNyDxs3bsQll1yCoKAgJCYm4rrrrsNrr72GTZs2WV0wdDc1NTW4/vrrcc0117SYHAHq74j+4IMPkJKSgrVr19rVxrlEcEuj5IDGRHBRUZFd5yYix+pqfd1//vMffP311zh9+nSbyRGgvk9cunQphg0bhvvuuw9Go9HmttjXERF1LRxBQuRkKpUK0dHRSEtLc3pbp0+fxtNPP21Z9/HxcdqX2wceeAB5eXkdbufIkSPYtm0bACA5ORlbt26Fv7+/5fHRo0dj7ty5GDlyJIqKirB8+XK8/fbb8PPzs7mN2bNn47XXXmtXfF5eXu06jqi7cce+7rXXXsOSJUvsPm7v3r245pprLOsSiQQ333xzq8ewryNyH+cSwV2JyWTC3LlzsWbNGqvtISEhGDx4MPz8/JCWloYDBw7gXAXmwsJCzJw5E3///TfGjRtnUzvnJ4L/85//YMSIEdDr9di8eTNefPFFVFVVWRLBX3zxhUOfJxHZriv2dU15eXkhMTERMTEx8PX1hdlsRllZGY4cOYKCggLLfiaTCW+99RYyMjKwYsUKyGSyNs/Nvo6IqGthgoTIgRQKBZKTkzFs2DAMHz4cw4YNQ//+/bFt2zZMnjzZqW0LgoBbbrnFMsHvnDlzUFxcjE2bNjm8rb///htLly4FAMjlcjz77LO477772nWu7du3W5ZfeOEFqwuG58TFxeHRRx/F/fffD6PRiF27dmHatGk2t6FWq9tVgoKImtdV+rrg4OB2lY95+eWXrdYnT56MhISEVo9hX0fk/pyZCB45ciS+++47u46Jjo62ed9HH33UKjmiUCjwxhtv4NZbb4VSqbRsP378OBYuXGgZYaLT6XDVVVfhyJEjiIiIaLUNJoKJugZ37eu8vb0xY8YMXHbZZRgzZgxSUlIglTZfNGXnzp148sknsX79esu2X375BW+88QYeeuihVtthX0dE1PUwQULkIDfddBNuv/12l03E9v7772PLli0AAD8/P7zzzju4/vrrHd5ObW0tFi1aZFm///7721Wz9ZzS0lLLct++fVvcLzk5udljiEhc3aWva4lGo7ngh/0tt9zS5nHs64jci9iJYA8PD6clONPT0/H2229bbfvxxx8xc+bMC/bt168f1q9fj4suusiSJCktLcUzzzyDDz/8sNV2mAgmcj9dqa87evQoFAqFTfuOGjUK69atw0033YSvv/7asv2FF17A4sWLoVKpWjyWfR0RUdfDOUiIHCQgIMBlFwyzsrLw6KOPWtZfffXVNu/ya6/HHnvMMkFfQkJCu8rTNNX0TppTp061uF9qaqplubkvoUQkju7S17VkxYoVqKystKz7+/tj1qxZbR7Hvo7Ifdx0002oqqrCgQMH8Mknn+DWW2/FkCFDbL7w1tk888wzMBgMlvUFCxY0mxw5x9PTE1988YXVyJLPPvsM6enprbbDRDCRe+lqfZ29cUulUrz33nvw9va2bKusrMSGDRtaPY59HRFR18MECVEXcOutt1pqoI4fP95qhIcjbd++He+9955l/aOPPoKnp2eHzjl8+HDL8pNPPtnsPAK5ubmWkjZSqdTqGCLqPsTq61rz2WefWa3Pnz/fpoQR+zoi9+HKRLCjaTQarFixwmrbI4880uZxvXr1wlVXXWVZNxqN+Oabb1o9holgIvfSlfq69vL19b1gjqUzZ860egz7OiKirocJEiI398UXX2Dt2rUA6uvFfvzxx5BIJA5vR6fT4eabb4bZbAZQf8fR1KlTO3ze4cOHIyUlBQBw6NAhJCcn46OPPsL+/fuxa9cuvP766+jfv79lQvjZs2e3a84AInJvYvV1rUlLS8PmzZutti1cuNCmY9nXEZErrF27FnV1dZb10aNHo0+fPjYd+69//ctq/eeff251fyaCicgdBQYGWq0313c1xb6OiKjr4RwkRG6soKAA999/v2X9iSeesPlHr72WLFliuQsmJCQEr7/+ukPOK5FI8MEHH2DKlCkwGAzIzs7G7bff3uy+oaGhDmuXiNyHmH1da5YuXQpBECzrQ4YMsXkOJvZ1ROQKf/75p9X6pEmTbD52/PjxkMvlMBqNAIADBw6gsLAQYWFhze5/LhF89OhRSyL4iSeewPDhw2EwGLB161a88MILKC8vB8BEMBF1DpmZmVbrkZGRre7Pvo6IqOthgoTIjd15552WL17Jyck2lUxoj/379+O1116zrL/11lsICgpy2PnHjRuHH374ATfccANqa2ub3Sc2NharVq1Cjx49HNYuEbkHsfq61phMJixbtsxqmy2TszfFvo6IxHb06FGr9dGjR9t8rLe3N/r3748DBw5Yth07dqzFBAkTwUTkbk6dOoVdu3ZZ1iUSCSZOnNjqMezriIi6HpbYInJTP/74o6XUgUQiwccff2w1maajGI1G3HzzzZa7By+99FLMmzfP4e1cddVVOHnyJO6991707t0bHh4eUKvVGDp0KF566SUcPXoUAwYMcHi7RNS5idXXteXPP/9Ebm6uZd3T07NdfSH7OiJqTlZWFv71r38hOTkZAQEBUCqVCAsLQ3JyMm644QZ8/PHHKCsrs/u8J06csFrv2bOnXccnJiZarR8/frzV/c8lgptOeny+2NhY/PXXX0wEE3VDzurr2iM/Px/XXHMNTCaTZducOXMQFxfX5rHs64iIuhaOICFyQ2VlZbjrrrss6//+978xZswYp7T18ssv49ChQwDq7yT84IMPnNIOAERHR+PNN9/Em2++6dDz1tTUICMjo13HxsTEQCplLpnIFcTs69qydOlSq/XZs2e3e8JN9nVEdL6zZ8/i7NmzVtuKiopQVFSE48ePY/ny5bj//vuxaNEiPPfcc1Cr1W2es6ys7IILjTExMXbFdf7+p0+fbvOYc4ng119/HX/88QcyMzMhl8vRu3dvzJkzB3fddZdN8RNR1+OMvs5WRqMR5eXlOHHiBH777Td89NFHqKqqsjyekJCA//3vfzafj30dEVHXwQQJkRu65557UFRUBACIiorCiy++6JR2jh8/jueff96y/txzz9l0R01n89NPP+Gnn35q17Hl5eXtvghKRB0jVl/XluLiYqxevdpqm73ltcTAvo6oa6utrcVbb72FNWvW4Oeff0ZycnKr+1dUVFite3l5tXq3c3NCQ0Ot1isrK206jolgImove/u6ltx77714++23bdp38uTJ+Oqrry7o89rCvo6IqGtggoTIzaxZswZff/21Zf29996Dr6+vw9sxm8245ZZboNPpAABDhw7F4sWLHd4OEVFzxOrrbPHll1/CYDBY1hMTE9usT01EZAu5XI5x48Zh6tSpGDBgAKKjo+Hj44OamhpkZWVhy5Yt+PLLLy3JYqC+Zv7UqVOxc+dOxMbGtnjumpoaq3VPT0+74zv/mOrqarvP4UhMBBO5J2f2dR0xY8YM3HnnnZg2bZpTzt9e7OuIiMTFBAmRG6mqqrKaAG727NmYOXOmU9p6++23sXPnTgD1X2g//fRTyGQyp7RFRNSUmH2dLT7//HOr9ZtvvhkSicRF0RBRV/H8889j0aJFLd6xPGjQIMyYMQPPPfccnnnmGbzyyisQBAEAUFBQgFmzZmHv3r0t9kfnJ0g8PDzsjvH8BMn55yQiaouz+7qO+OOPP2AymeDh4YEJEyY4/PxEROQeOO6OyI08/PDDyM7OBgD4+fnh3XffdUo76enpePLJJy3r999/PwYNGuSUtsRw0003QRCEdv3x7hsi8YnV19li586dOHbsmGVdJpNhwYIFLounNezriNzLE088YVM5Fw8PD7z00ksX9IX79+/Ht99+a3N77bm4yGQwEXWU2H3dOU899ZRlzpOzZ8/i+PHj2LJlC959911MmTIFAGAwGPD7779j4sSJuOuuu6wmbCciou6DCRIiN7Fx40Z8/PHHlvVXX30VERERDm9HEAQsWrQIdXV1AOonq1uyZInD2yEiao5YfZ2tPvvsM6v1yy67DJGRkS6Khoi6szvvvBMzZsyw2vb++++3uP/5kwNrNBq72zz/GFdPOMxEMFHXZ29f15LAwEDExcVZ/vr27Ytx48bhrrvuwvr167Flyxar0l3vvfcebr311g7H7wjs64iIxMUECZEb0Gg0WLhwoWWo8fjx47Fo0SKntPXJJ5/gn3/+sax/9NFH7apZTURkLzH7OlvU1tbi+++/t9rWGSdnJ6Lu47HHHrNa37lz5wWTsZ/TFRMkRNQ92NPXtde4ceOwYcMGBAUFWbYtXboUv/76q0PbISKizo9zkBC5gS+//BJpaWkAAKlUiscffxyZmZltHqfVaq3WS0pKkJGRYVn38vK6YLjz008/bVm+/PLL0bNnT6tjmlNQUGC1bjQaLzgmMjISSqWyzZiJqPsSs6+zxY8//mg1IXFYWBiuvPJKu89DROQoI0aMQEBAAMrLywEAJpMJx48fx5gxYy7Y18/Pz2q9rq4OtbW18Pb2trm9phMmA+CdyUQkCnv6uo6Ij4/HU089hXvuucey7dVXX3Xp3HdERCQ+JkiI3EDTu/fMZjMuu+y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+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 611,
+       "width": 804
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot temperature \n",
+    "fig, ax = plt.subplots(1, subplot_kw={'projection':ccrs.PlateCarree()}, layout='constrained')\n",
+    "\n",
+    "extent = [year_to_use.longitude.min(), year_to_use.longitude.max(), year_to_use.latitude.min(),\n",
+    "          year_to_use.latitude.max()]\n",
+    "#im = ax.imshow(year_to_use, extent=extent, transform=ccrs.PlateCarree(),cmap=\"coolwarm\")\n",
+    "im = year_to_use.plot(ax=ax,\n",
+    "                      transform=ccrs.PlateCarree(),\n",
+    "                      cmap=\"coolwarm\",\n",
+    "                      add_colorbar=False)\n",
+    "\n",
+    "# add coastlines, labeled gridlines and continent boundaries\n",
+    "ax.coastlines()\n",
+    "ax.gridlines(draw_labels={\"bottom\": \"x\", \"left\": \"y\"})\n",
+    "ax.add_feature(cfeature.BORDERS, linestyle='-.')\n",
+    "\n",
+    "# create colorbar and set cbar label\n",
+    "cbar = plt.colorbar(im, ax=ax, orientation='vertical', shrink=0.9, pad=0.1)\n",
+    "cbar.set_label('Air temperature at 2m (K)')\n",
+    "\n",
+    "# set title\n",
+    "plt.title(\"Annual mean temperature in 2000\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yPwmRiYClFdf"
+   },
+   "source": [
+    "Additionally, you can calculate the air temperature trend. We choose a longer time period and therefore another subsample from 1991 to 2020, that we load in the next cell:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 19MB\n",
+       "Dimensions:    (longitude: 21, latitude: 21, time: 10958)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 84B -80.0 -79.75 -79.5 ... -75.5 -75.25 -75.0\n",
+       "  * latitude   (latitude) float32 84B 0.0 -0.25 -0.5 -0.75 ... -4.5 -4.75 -5.0\n",
+       "  * time       (time) datetime64[ns] 88kB 1991-01-01 1991-01-02 ... 2020-12-31\n",
+       "Data variables:\n",
+       "    t2m        (time, latitude, longitude) float32 19MB ...\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      2024-02-20 03:51:34 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...</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-b7234d5b-3db2-42a4-9936-cbd0d4d64728' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b7234d5b-3db2-42a4-9936-cbd0d4d64728' 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'>longitude</span>: 21</li><li><span class='xr-has-index'>latitude</span>: 21</li><li><span class='xr-has-index'>time</span>: 10958</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-811d25a2-0f5e-4bdc-a158-0ec8f48b8c18' class='xr-section-summary-in' type='checkbox'  checked><label for='section-811d25a2-0f5e-4bdc-a158-0ec8f48b8c18' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-80.0 -79.75 -79.5 ... -75.25 -75.0</div><input id='attrs-9c2332e7-853c-4d38-b9c2-508702693c23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c2332e7-853c-4d38-b9c2-508702693c23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a50e41c9-7e9a-4cd9-a664-ae5aabb21742' class='xr-var-data-in' type='checkbox'><label for='data-a50e41c9-7e9a-4cd9-a664-ae5aabb21742' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-80.  , -79.75, -79.5 , -79.25, -79.  , -78.75, -78.5 , -78.25, -78.  ,\n",
+       "       -77.75, -77.5 , -77.25, -77.  , -76.75, -76.5 , -76.25, -76.  , -75.75,\n",
+       "       -75.5 , -75.25, -75.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 -0.25 -0.5 ... -4.5 -4.75 -5.0</div><input id='attrs-983c600f-3146-4973-81da-9ad93ff815b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-983c600f-3146-4973-81da-9ad93ff815b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-297fb94e-e892-4dd5-be29-705e0725ec97' class='xr-var-data-in' type='checkbox'><label for='data-297fb94e-e892-4dd5-be29-705e0725ec97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 0.  , -0.25, -0.5 , -0.75, -1.  , -1.25, -1.5 , -1.75, -2.  , -2.25,\n",
+       "       -2.5 , -2.75, -3.  , -3.25, -3.5 , -3.75, -4.  , -4.25, -4.5 , -4.75,\n",
+       "       -5.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1991-01-01 ... 2020-12-31</div><input id='attrs-9bab4c4b-b4f9-495f-8b22-b5598304d4cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bab4c4b-b4f9-495f-8b22-b5598304d4cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86d27622-e92b-4806-925a-d6238137e26c' class='xr-var-data-in' type='checkbox'><label for='data-86d27622-e92b-4806-925a-d6238137e26c' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1991-01-03T00:00:00.000000000&#x27;, ..., &#x27;2020-12-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-12-30T00:00:00.000000000&#x27;, &#x27;2020-12-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ad0c38d-753b-49b6-86fb-00a42e1d92cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ad0c38d-753b-49b6-86fb-00a42e1d92cd' 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>t2m</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a4e551dc-d6a6-495f-afdb-fc84e078daa2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4e551dc-d6a6-495f-afdb-fc84e078daa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a52e513-09a0-45a3-a532-3b1b51cea412' class='xr-var-data-in' type='checkbox'><label for='data-2a52e513-09a0-45a3-a532-3b1b51cea412' 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>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><pre>[4832478 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a708111-cd7d-429e-9350-bc2a77a77509' class='xr-section-summary-in' type='checkbox'  ><label for='section-5a708111-cd7d-429e-9350-bc2a77a77509' 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>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c7db68a4-aba0-4c22-b437-fb0f15bee79d' class='xr-index-data-in' type='checkbox'/><label for='index-c7db68a4-aba0-4c22-b437-fb0f15bee79d' 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([ -80.0, -79.75,  -79.5, -79.25,  -79.0, -78.75,  -78.5, -78.25,  -78.0,\n",
+       "       -77.75,  -77.5, -77.25,  -77.0, -76.75,  -76.5, -76.25,  -76.0, -75.75,\n",
+       "        -75.5, -75.25,  -75.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e9f636df-33df-4413-8403-b7dce255a09c' class='xr-index-data-in' type='checkbox'/><label for='index-e9f636df-33df-4413-8403-b7dce255a09c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0, -0.25,  -0.5, -0.75,  -1.0, -1.25,  -1.5, -1.75,  -2.0, -2.25,\n",
+       "        -2.5, -2.75,  -3.0, -3.25,  -3.5, -3.75,  -4.0, -4.25,  -4.5, -4.75,\n",
+       "        -5.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-10247d3d-d0f8-4976-88d0-4380fcac5bdd' class='xr-index-data-in' type='checkbox'/><label for='index-10247d3d-d0f8-4976-88d0-4380fcac5bdd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1991-01-01&#x27;, &#x27;1991-01-02&#x27;, &#x27;1991-01-03&#x27;, &#x27;1991-01-04&#x27;,\n",
+       "               &#x27;1991-01-05&#x27;, &#x27;1991-01-06&#x27;, &#x27;1991-01-07&#x27;, &#x27;1991-01-08&#x27;,\n",
+       "               &#x27;1991-01-09&#x27;, &#x27;1991-01-10&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2020-12-22&#x27;, &#x27;2020-12-23&#x27;, &#x27;2020-12-24&#x27;, &#x27;2020-12-25&#x27;,\n",
+       "               &#x27;2020-12-26&#x27;, &#x27;2020-12-27&#x27;, &#x27;2020-12-28&#x27;, &#x27;2020-12-29&#x27;,\n",
+       "               &#x27;2020-12-30&#x27;, &#x27;2020-12-31&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=10958, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d8ec7da-31f2-4b83-9a57-b454dec56b87' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4d8ec7da-31f2-4b83-9a57-b454dec56b87' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>2024-02-20 03:51:34 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data8/adaptor.mars.internal-1708401052.8403652-9777-7-0baf55cc-b968-4b17-b272-4d86faa5643c.nc /cache/tmp/0baf55cc-b968-4b17-b272-4d86faa5643c-adaptor.mars.internal-1708398180.7979972-9777-9-tmp.grib</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 19MB\n",
+       "Dimensions:    (longitude: 21, latitude: 21, time: 10958)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 84B -80.0 -79.75 -79.5 ... -75.5 -75.25 -75.0\n",
+       "  * latitude   (latitude) float32 84B 0.0 -0.25 -0.5 -0.75 ... -4.5 -4.75 -5.0\n",
+       "  * time       (time) datetime64[ns] 88kB 1991-01-01 1991-01-02 ... 2020-12-31\n",
+       "Data variables:\n",
+       "    t2m        (time, latitude, longitude) float32 19MB ...\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      2024-02-20 03:51:34 GMT by grib_to_netcdf-2.25.1: /opt/ecmw..."
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "link_id = \"3xbq8\"\n",
+    "fname_ERA5 = \"data_sample_91_20.nc\"\n",
+    "ds_long = xr.open_dataset(pooch_load(url_ERA5, fname_ERA5))\n",
+    "ds_long"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 502
+    },
+    "id": "k5D4Vu-9k5ZJ",
+    "outputId": "6840837d-eb0e-44b4-b2b5-dfe0195bbad7"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1539ddca0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+KJUpk/748uWl4GBzH9sVobBxTBxQ36BgGjtWKl78Wjs5WXr3Xc/Fk6+8/rotEzx7duZJgwoVpEcflTZulHbskCZO1LHk6mlWbwwYIHXu7N6QkXcKxZX1K1eueDoEAAAAAAAKrFmzpISEa21/f2ncuMyPcdyuiMQBCpOICFtCzR6Jg4IpMFC65x5z30cf2YpfF3l9+mR8n5+fNGiQ9P33tl+G2bNtH/wWiyTp2Wel+Phrw3180tbJQcHm8sRBXFycq6fM1JUrVzxSTwEAAAAAgMLg8mXpww/NfaNH27a4zoxjgeTwcFdGBXiWYwHdMmWkBtR6LbDGjTOud0uSrlyR5s/3WDh5JyVFWrVKOnMm/fubNZOuv97c17at9P770qlT0tKl0q232vZ8srNxo7RwofmwBx6QrrvOdaHD81yeOBg0aJCSk5NdPW26oqKi1KNHD/3L1xoAAAAAAMiRDz6QoqKutb28pCefzPo4x8QBBZJRmKRX34AdsQuu+vWlW24x982aZdu2qFDav196/nmpbl2pSxfps88yHjtqlFSzpjR5srR3r7R+vW2vunLl0h1utUrjx5v7SpdWmiLJKPhc/pH3yy+/aNSoUa6eNo3IyEj17NlT4XylAQAAAACAHLl61XbxzN4dd0j16mV9rGPiID7eViAZKAyob1D4PPGEuX3woPTTT56JxS0uXbLtwdS+vW15zMsvS0eO2O5bsMB2xT89Dz4oHT5sG+/Esprvv5fWrDH3TZpkK4OAwsUtudIvv/xS//d//+eOqSXZVhr07t1bYY7rxgAAAAAAgNPmz5fOnjX3PfWUc8eWKyfVrm3u47t9KAySk9O+l0kcFHydO6fdlefNNz0SiuskJko//igNHixVqWLbL2j9+rTjdu6UNm9Ofw5/f6eX0yQkSBMnmvtq1bLVTUbh47ZFVh988IGee+45l88bHR2tPn36aMOGDS6fGwAAAACAoiIpSZo509zXo4fUsqXzcziuOmAnYRQGO3dKMTHmvjZtPBMLXMdiSbvqYPVqadMmz8STY1arLejHHpOqV7fVIFi61Fzh3lHjxrYVCbn04Ye2XZDsTZsmFSuW66mRD7ktcWC1WvXaa69p9uzZLpszJiZGffr00bp162SxWGT9b4nNRMdUFwAAAAAAyNTSpdKhQ+a+p5/O3hytWpnbJA5QGDhuUxQcLFWq5JFQ4GJ33pm28Ptbb3kmlmy7cEF6/XUpJMSWtZ09Wzp3LuPxFSvaqkL/+6+0Y4fUvXuuTn/5svTii+a+G2+0PaconFyeOJg4caKsVqtxYX/8+PH6LLMCHE6KiYlR3759tXbtWlPSYMKECZo6dWqu5wcAAAAAoKiwWm3fErXXurXUtWv25qFAMgoj6hsUXn5+kuPu6osXSydOeCaebLl82bZP0I4dGY/x87MVqvnhB9uDmjXLtozMYsn16V99Vbp40dz3xhsumRr5lMsTB9OmTdN9991nJA9SUlJ033336YcffsjxnLGxserXr5/WrFkji9278YknntD06dNdETYAAAAAAEXGihW2i/z2nn46+xeA0iuQvHNn7mIDPM2xpCaJg8Llf/+TAgKutZOSpPfe81w8TqtbV7rppvTva9dO+uAD6dQp6euvpf79JV9fl5368GHp7bfNfYMGSR06uOwUyIfcslXRRx99pMGDBxvJg6SkJA0dOlSrVq3K9lxXr17VLbfcotWrVxtJA6vVqscee0wzHTdjBAAAAAAAWXL8Dl6DBtKAAdmfJzBQqlPH3EeBZBRk0dFpk18kDgqX8uWlu+829334Ydq6Fnlu/37p+eeld9/NeIx94LVqSZMnS/v2SevW2QojlyvnltCeecZcQsHXN+2qNRQ+bkkcWCwWffHFF+rZs6eRPIiLi9Ott96qTdmoOJKaNAgNDTUlDR599FG9WeDLngMAAAAAkPc2bJBCQ819EydK3t45m48CyShMwsOllJRrbR8fqUULz8UD93jsMXP70iXJBTutZ9+lS9JHH0nt29syuC+/bNv/x/5NaG/IEGnMGNuH+KFDtvH167s1xLVrpa++Mvc9/LBUr55bT4t8wG3FkX19ffXdd9+pXbt2RvIgKipKvXv31r59+7I8PjXRsHLlSlPS4OGHH9asWbPcFTYAAAAAAIWa42qDatWkkSNzPh8FklGYONY3aN7cvK0NCoeGDaW+fc19b72V8fV6l0pMlH76SRo8WKpSxbZSYP36a/cfOSL9/Xf6x5YpI33yidS5s+Tltsu6huTktDUhAgOl555z+6mRD7j1HRYQEKCff/5ZzZo1M5IH58+fV8+ePRUREZHhcalJgz///NOUNHjwwQf1zjvvuDNkAAAAAAAKrd27pWXLzH2PPy75++d8zvQKJNtvaQEUJBRGLjqeeMLc3r9f+vlnN53MapU2b7Ytdahe3VaDYOnSjD8sPbL8Ia2PPpK2bDH3TZnith2RkM+4PTVVpkwZrVixQnX+2/TQYrHo2LFj6tGjh86fP59mfHx8vG677Tb98ccfpqTB//73P71XICqVAAAAAACQP73+urldtqw0dmzu5mzZ0txOSKBAMgoux8RBmzaeiQPu162bFBJi7nP5zugnT9o+eENCbB+Ws2dL585lPL5iRWncuLRf8/eAc+ekSZPMfU2b2rYpQtHg/jUtkqpUqaI//vhDVapUkWRLHuzdu1e9e/dWVFSUMS4hIUG33367fvvtN1PS4P7779cHH3yQF6ECAAAAAFAoHT8uffGFue/hh6XSpXM3b2CgVLeuuY/tilAQRUTYrvPaY8VB4WWx2FZc2Vu5Mu037HMkOVm65RYpKMhWRGbHjozH+vlJgwZJP/wgnTghzZolXX+9C4LInWeflS5fNve9+66t7geKhjxJHEhScHCwfvvtNwUGBhp9mzdv1q233qr4+HglJCRo4MCB+vXXX01Jg3vvvVcfffRRXoUJAAAAAECh9NZbtq21UxUrJj36qGvmdtyuKDzcNfMCeSkszNwuU8ZWrxaF17BhUuXK5j6XlFb19rYlDzIrmtC2rfTBB9KpU7Zti/r3l3x9XXDy3AsLk+bONfcNG2YrrYCiI88SB5LUpEkT/fzzzypRooQsFousVqtWr16twYMHa9CgQfr5559NSYPRo0frk08+ycsQAQAAAAAodC5elObMMffde69UqZJr5ndMHLDiAAVRetsU5UH9WXiQv7/00EPmvkWLbNfyneL4lXx7d9+dtq9mTWnyZGnvXltB5AceyHcFA1JSbKvRrNZrfSVLpt3qDoVfnn/8tWnTRt999538/PyM5MHy5cv1s131EavVqrvvvlvz5s3L6/AAAAAAACh03ntPiom51vb2lp580nXzt2plbm/bRoFkFDwURi6aHnzQXCA+MVF6//1MDrh0yVY1uEMHqVEjKSkp/XEDBkilStmuut9zj20fpMOHpZdfztdLWebOTbtq7PnnbTWdUbRYrFb7/FHe+e677zRkyBClpKTIMYQRI0bos88+M1YfABEREQoKCpIkHT9+XDVq1PBwRAAAAABQMMTESLVqSRcuXOsbPlxauNB157h82VbrwN6//6YtnAzkV8nJtq2J7BNsP/5o26Yehd/990v2m56UL2+rCxMQ8F9HYqK0YoX02We2WgTx8dcG//yz1KdP+hNv2GArjFy8uNtid6WLF205Dft/Lxo2lLZutZViQNHisQVXt99+uz7++GNJMhIEFotFw4cPJ2kAAAAAAICLzJtnvggk2Wp1ulLZshRIRsG2c6c5aSDZtipC0fDYY+b2hQvS559Zpc2bbXdWr26rQfD11+akgWRLJmSkbdsCkzSQbLsoOf578fbbJA2KKpfXwf4ss18WB15eXrrpppu0Zs0aWSwWVatWTd27d9fnn3+e7fPend6+YQAAAAAAFGGJidIbb5j7+vSRmjd3/blatZIOHrzW/vdf27d4gYLAcZui4GDX1QBB/tekidSrl21RQVWd1AgtVJdxn0nxO7I+eO1a24dtPilsnFObNkkffmjuGzRI6tHDM/HA81yeOBg9enSOVgtYrVadPHlS9913X47OS+IAAAAAAACzr76Sjh419z39tHvOdcMNtvOlctwjG8jPqG9QxMXGasb1y/TYis/UQ7/LWylSfCbj/fxsKxBGjZJ69y7wSYOUFOn//s9cEDkgQHrzTc/FBM9zeeIgVXZKJ9gnGnJScoFtjQAAAAAAMLNapenTzX3t2kkdO7rnfDfcYG5v324rkMwWFygIwsLMbRIHRcz99ytk0SKFZDWubVtbsmDIEKlcubyILE98/rm0fr25b9IkqWZNz8SD/MFjNQ7sWa1W4wcAAAAAAOTezz9LOxx22XjqKcld371zLISckJD2/EB+FB1tq3Fgj8RBEXPnnRnelVC1pm3z/717bVfXH3igUCUNLl9OW/emXj3pySc9Eg7yEZevOOjUqRMrAAAAAAAA8LBp08ztRo1sO2u4S9mytotNBw5c6wsPT5tQAPKb8HDbVi2pfHykFi08Fw/c4NIlW2Hju++WihVLe3+fPlKFCtL585KkKJXU1xqsz3S36vXupE9ezhffvXaLF16Qzp41982eLfn7eyQc5CMuTxyEhoa6ekoAAAAAAJANf/9t+7H31FOSl5uvfbVqZU4c/Puve88HuIJjfYPmzW37u6OAS0y0VTtesED64QfbMqhy5aQ77kg71tdXuusuaedOLS1xt+7+7nZdVXFJ0vqF0qtTpcqV8zj+PLB9u/Tuu+a+W2+V+vb1TDzIXwpvugwAAAAAgCLKsbZBjRrSsGHuP69jnQMSBygIKIxciFit0ubN0uOP2z74+veXli61JQ0k6bPPMj72jTekFSvU8YMRSvYrbnQnJEgffODmuD3AarUVRE5Ovtbn7y+99ZbnYkL+QuIAAAAAAIBCZMcO6aefzH3jx+dNkWLHxMG2bVJ8vPvPC+QGiYNC4ORJaeZMKSTEtj/arFlp99+RpF9+Sb9fMgrAVK4sjRhhvuv996W4ONeG7GmLF0urV5v7nn5aqlPHM/Eg/yFxAAAAAABAITJjhrldrpx03315c27HegaJiRRIRv4WEWG75myvTRvPxIJsio2VvvxS6t1bCgqSJkzI/APHz08aMECKispy6scfN7fPnZMWLsxlvPlIVFTa4sfBwbYt7YBUJA4AAAAAACgkjh6VFi0y9z3yiFSyZN6cv0wZqX59c194eN6cG8iJsDBzu0wZqUEDz8QCJ+3aJY0ZI1WpIg0fbqtjYF/d2lHbtra9hk6dsm1bVLdulqdo1ky6+WZz31tv2bb3KQxefjltwuytt6jtATMSBwAAAAAAFBJvvGHerzogwLaHdV6izgEKEsdtitq0cX8RceTSqVPSvHmZrxyoWVOaNEnau1dav1564AHb8qtscFx1sHOn9PvvOYg3n9m9O20dg969bYsxAHt8FAIAAAAAUAicPy998om57/77pQoV8jaOVq3MbRIHyM+ob1AAde1q25rIUcmS0j33SCtXSocPS6+8kqvlI717Sw0bmvsKeuFgq1V69FEpKelan6+vNHu2UeIBMLg0cfDss88qyol9wvLCqlWrtHTpUk+HAQAAAABAnnjnHenq1WttHx/piSfyPg7HFQfbt1MgGflTcnLarbRIHHhYYqKtuvuQIWn3XUvl5SWNHHntds+etgIEZ87YViJ06eKSZSNeXtJjj5n7fv3VtlNSQfXNN9Iff5j7nnyS7bmQPpcmDqZNm6b69evrnXfeUZyHSo1v3rxZAwYMULdu3bSrIP8mAwAAAADgpOhoW+LA3rBhUq1aeR9LixbmdmKiLXkA5Dc7d0oxMeY+EgceYLVKmzfb9gaqUUPq31/6+uu0S6js3XOPrRL8sWO2GgfDh0vFi7s8tLvuksqXN/fNmuXy0+SJmJi0yeQaNWw7OgHpcflWRefOndNjjz2m4OBgTZs2TefPn3f1KdL1xx9/qG/fvmrVqpV++umnPDknAAAAAAD5wSefSJcumfsmTvRMLOkVl2W7IuRHjtsU1a4tVazomViKpJMnpZkzpZAQqWVL2xX5s2ev3b9ypa3ie3rq15cmTJCqV3driMWL28oj2PvsM+ncObee1i1ee006ftzc9+abUokSnokH+Z9LEwddunSR9b/y4mfPntWkSZMUFBSkESNG6Oeff1aS/QZaLnDw4EFNmzZNDRo0UK9evbRixQpZrVZZrVaVLFlSrVu3dun5AAAAAADIbxISbEWR7fXvLzVt6pl4pLTbFTluBwPkB+kVRoabxcbatiDq3dtWp2DCBGnHjozHf/FF3sWWgYcfttUBSBUfL334oefiyYn9+205Gnvdukl33OGZeFAwuDRx8Ndff+nzzz9X5cqVjb74+HgtXrxY/fv3V6VKlTR06FB99NFH2rVrl1JSUrI1//nz5/Xzzz9rwoQJuv7669WgQQNNmjRJBw4cMBIGkjRkyBDt2bNHffr0ceXDAwAAAAAg31m0SIqIMPc9/bRnYknlmDhgxQHyIwoj55GUFGnVKmnMGKlKFWnECNv2QpldF2zXTvrgA+mhh/IuzgxUrWrb+s3ee+8VnNotVqs0bpwtyZzKx8e2vR0FkZEZizX1arsLxcbG6s0339TMmTMVGRkpi8ViXNS32L0j/f391ahRI9WqVUs1atRQYGCgAgIC5OPjo6tXryomJkanTp1SRESE9uzZo9OnTxvH2s+XertTp06aOnWq2rVr5+qHBA+LiIhQUFCQJOn48eOqUaOGhyMCAAAAAM9LSZGaNJH27LnWd9NN0po1notJsl0j7NLlWtvXV4qMlIoV81hIgElUlG1bLfurYmvXSu3bey6mQunUKduTeuRI1mNr1bIVFbj7bttWRPnIli1p67d8+qk0erQnosmeH36QBgww940fn3YFAuDILYmDVJcuXdK7776r999/X2fOnLGd0O5CvxGEE+kt+2NSx6f29e7dWxMmTFDXrl1dFTryGRIHAAAAAJDW999Lt91m7vvpJ6lfP4+EY4iMtF2UtRcWJrGjMPKL0FDJ/jKSj4/tfRsQ4LGQCierVWrYUNq3L/37S5aUBg+WRo2SOnaUvFxejtVlunWzlV1I1ayZtHVr/v7W/tWrUuPG5rxN1aq2ZHPp0h4LCwWEW38bAwMD9dxzz+no0aNauHChevbsaUoSWCyWNEmD1C2HMksuWK1WValSRRMmTNCuXbv0888/kzQAAAAAABQpVqs0daq5r2lTqW9fz8Rjr3Rp6brrzH1sV4T8xHGboubNSRrkWGKidOxY+vdZLLYVBPa8vKSePaWFC6UzZ6R586TOnfN10kCSHn/c3N6+XfrrL8/E4qwZM9Iu9nj9dZIGcI5PXpzEz89Pw4YN07Bhw3ThwgX98ssv+vXX/2fvvuObqr8/jr/SxSp7C2UpW0CGA1ABleFgqKD+QEFw4RYHLpyICg5wooIiRUQFFFG+gIogKDhYKsgSGS1lUwRaRmnv74+PbXOTtHQkuUn7fj4ePMg9ubk5lQrpPZ/POfP4+eef+eeff3J8nXvxoEyZMrRp04ZOnTpx2WWXce655+Zpp4KIiIiIiIhIUbR4sffNz4cfDp3Vr23bwoYN2ccqHEgo0XyDQrIs079n8mQzaKVePbOtyJcbboARI8zS90GDzIyDWrWCma1fXH656aC0aVN2bOxYuPhi53LKzZYt8OKL9tgFF0D//s7kI+EnKIUDd5UrV+b666/n+uuvB0w7o40bN7J161b27NlDamoqJ0+epFSpUpQrV464uDgaNGjA6aefTkSIVx5FREREREREgsXzhlDdunDttc7k4kvbtuZ+Yqbly53LRcSTCgcFlJRkdgrEx8OaNdnxvXth3Tpo2tT7NXXqmN44jRqFTmWzACIi4L774M47s2Nz5pgvrUkTx9LK0bBhcOxY9nFkJLz5Zlj/EUiQBXTGgYi/aMaBiIiIiIhItt9/h7POssfeeAPuusuRdHxavNh0H8kUFWUG0mpAsjgtMRH+u8WQZd260Lz5GxJSU2HWLFMs+PZbM5Xdl0cfheefD2pqwZaSYr53kpOzY0OHwvjxzuXky9y53m3r7rkHXnvNmXwkPGkJv4iIiIiIiEiYGT3aflylCgwZ4kwuOWnd2r6y9eRJ0xNcxGmeuw3KlzeL4cVNRgb88APcdBPUqGHaC82fn3PRAHIegFyElCkDt91mj02eDPv3O5OPL8ePmyKBu2rV4JlnnMlHwpcKByIiIiIiIiJh5J9/4NNP7bF77oHSpZ3JJydly3rfjFW7IgkFnq34zzkn5OfyBs+mTfDkk3D66dC5sxlcfPhwzufXqWPmF2zYADNmBC1NJ911l9lBlenoUXj3Xefy8fTKK/D33/bY6NFQoYIj6UgY01+LIiIiIiIiImHklVfsi37LlLH33A4l7drZjzUgWUKB5hvk4rrrYORI2Lo153NiY2HwYFi40EzgHTmyWG3ZqFXLe57Mm2/CiRPO5ONu+3Z47jl77LzzYOBAZ/KR8KbCgYiIiIiIiEiY2L3bLAB2d9ttUKmSM/mcStu29mPtOBCnpad7fx+qcOAmpzvMERHQrRt89FH2X0SdOxfbrRrDhtmPd+703gnmhAceMDsgMrlc8NZbxfaPSQpJ3zYiIiIiIiIiYeL11+HYsezj6GjvG1ihxLNwsHatPX+RYFu71gy4dVdsCgeWBStXmiXpluX7nP/7P3sfnubNYcwYs5R9/nwz6yDU+qI5oG1buPBCe2zs2Jz/swbDd995d4u67TZo08aZfCT8RZ36FBERERERERFx2qFDZuWou+uvh9q1ncknLzIHJGfeTDt5Ev74w/SUF3GCZ5ui+vWhalVncgmapCSYOhXi42HNGhO79FLvyh6YKbo33ADlypndB55TziXLsGGweHH28apVZp50587Bz+XECbj7bnuscmUYNSr4uUjRocKBiIiIiIiISBh47z3499/sY5cLHnrIuXzyomxZaNwY1q/Pji1frsKBOKfYzDdITYVZs0yx4Ntv7YNRACZP9l04AO9+aOJTz57QoIEZWJ9p7FhnCgevv27/exbg+edDt42dhAe1KhIREREREREJccePw6uv2mN9+kDTpo6kky+e9yY1IFmc5Fk4KFJFrIwMs+T9ppugRg3TVmj+fO+iAcC0aaExzTeMRUbCfffZY199BZs2BTePpCR45hl7rF07820gUhgqHIiIiIiIiIiEuClTzPBNdw8/7Ewu+dWunf1YhQNxyuHDZsaBuyKx42DTJnjySTj9dLPc/YMPzBebkzp1YOhQU5GUQhk8GMqXzz62LHjtteDm8NBDcOSIPfbmm6awIVIYKhyIiIiIiIiIhLD0dDOb1F3nzuFzw9Nzx8GaNXD0qDO5SPG2YoV9eG1UlGnhH7a+/ho6doRGjWDkSNi6NedzY2PNXe6FC2HLFnN+2bJBS7Woio2FW2+1xyZNggMHgvP+P/wAH39sj910U/j8+yChTYUDERERERERkRA2a5Z364tHHnEklQLxnK2anm4GJIsEm2ebolatoFQpZ3Lxi127YOnSnJ93uaBbN/joI9i92+xE6NwZInQ70J/uvtu+uj81FSZMCPz7njwJd91lj1WoAC+8EPj3luJBf1OIiIiIiIiIhCjLghdftMfOOsvcCwwXsbHQpIk9pnZF4oSwHIxsWfZtEu769YOSJb3jzZrB6NGQkGBmHAwYAKVLBzbPYiwuzvxRuHvjDUhLC+z7vv222cHlbuRIqFo1sO8rxYcKByIiIiIiIiIh6vvvYflye+zhh+0r+MOBZ7siz69JJBjCqnCQlAQvvQQtW8KSJb7PKV/eTEkHqFIF7rnH/M+1Zg0MHw61agUt3eJu2DD78Y4dMH164N5v92544gl7rFUrM7pCxF9UOBAREREREREJUaNH248bNIC+fZ3JpTA0IFmclpho7sW7C7nCQWqqaVjfo4dZxj58uCkCxMfn/JoHH4QvvzRf3GuvmSpduFUWi4BzzoEOHeyxsWNz3ixSWI88AocO2WNvvmnmdoj4iwoHIiIiIiIiIiFoxQr49lt77KGHwvPGkOeOg7VrNSBZgstzt0H58tCwoTO52GRkmAm3N90ENWqYtkLz55t4ps8+M0UFX9q2hV69IDo6OPlKju6/3368fDn8+KP/32fZMvjwQ3vshhvg/PP9/15SvKlwICIiIiIiIhKCPHcbVKsGgwY5k0thnXWW94Dk3393LB0phn791X58zjkOzwjetAmefBJOP90MLP7gAzh82Pe5hw/DnDlBTU/yr08fqFfPHhs71r/vkZ4Od95pj5Ut6/3vhYg/qHAgIiIiIiIiEmI2bYIZM+yx++6DUqUcSafQYmOhaVN7TO2KJJhCYr5BcjK8+67padOokZlku3VrzufHxsLgwbBwIVx9ddDSlIKJjIR777XHZs2CzZv99x7vvQerVtljzzwDNWv67z1EMqlwICIiIiIiIhJiXn7Z3hu7bFm4/Xbn8vEHz3ZFKhxIsKSnew/kDnrh4McfTSuioUNNr5mcuFzQrRt89BHs2mV2InTu7PD2CMmrIUPM39eZLAtef90/1963Dx5/3B5r3hzuuss/1xfxFFKdEVNSUjh06BBpaWn5fm2dOnUCkJGIiIiIiIhIcO3c6d2/euhQqFDBiWz8p21bmDIl+9jzRq5IoKxdCykp9ljQCwdt20LJknDihO/nmzUzvcgGDIBatYKbm/hNuXJwyy3w6qvZsfffN7sCCvt3+GOPmU0r7t54Q+MtJHAcLRxs2bKFDz/8kAULFvD777+TmtOgl1NwuVycPHnSz9mJiIiIiIiIBN+4cfZ7izExpk1RuGvXzn78119mQHK4tl+S8OHZpqh+fahaNQBvlJRkhhifcYb3c6VKQb9+5i5ypipVoH9/UzBo3do+CETC1t13m7/HM+dbp6TAxInw4IMFv+Zvv5lruLv2WujSpeDXFDkVR/Y5HT9+nPvvv59GjRrx3HPPsWzZMlJSUrAsq8C/RERERERERMLdwYMwfrw9NmgQnHaaI+n41Vln2butaECyBEtA5xukpsLHH0OPHhAXByNG5HzuoEGmEnj11TB7tik0vPYatGmjokERUq8eXHWVPfb661DQNc8ZGaYdkfvtzzJlTEs7kUAK+o6D48eP07NnTxYsWJB1w9/lcuFyuVQAEBERERERkWLtnXfg8OHsY5cLHnrIuXz8qUwZaNLE7DTItHw5nHeeczlJ8eBZODjnnEJeMCMDliyB+HiYPt3+P+2sWaYC6Ksvzfnnm15klSoVMgEJdfffbx9wn5AAM2eaXQL5NWkS/PqrPfbEE1C7duFyFDmVoBcORo0axXfffedVLGjUqBENGzakfPnyREWF1OgFERERERERkYA7etS0t3B39dXQsKEj6QREu3b2woEGJEugHT5sZhy4K/COg02bzKCOKVNg61bf5xw/booJt9zi/ZzLpaJBMdG+vfk+cy9avfoqXHNN/jaXHDgAjzxijzVqBMOG+SdPkdwE9Q790aNHeeWVV2wFg9tvv51HHnmEuLi4YKYiIiIiIiIiElImT4bdu+2xhx92JpdAadvWLNLOpAHJEmgrVthbvERFmXECeZacDJ9+ar5xly079fmxsd4TbKVYuv9++w6DX38130IdOuT9Gk8+Cfv22WNvvGE6XokEWlALBwsXLuTo0aNZuw2eeeYZRuTW+01ERERERESkGDh5El56yR675BLvgcLhrm1b+/Fff5kW8aVLO5OPFH2ebYpatcrDQO70dPjf/0yxYPZs+7RyXyIizP+wAwfClVfqG1oAM+egTh3Yvj07NnZs3gsHq1d7z7y56iro1s1vKYrkKqjDkf/55x8ALMuicuXKPProo8F8exEREREREZGQNHMm/Pcjc5aittsAvAckZ2RoQLIEVoEGI2dkwE03mSb1uRUNmjWD0aPNneH582HAABUNJEtUFNxzjz32+eewZcupX2tZcOed5lsxU6lSpt2RSLAEtXBw6NAhwAxDbt++PZGRkcF8exEREREREZGQY1nw4ov2WNu2cPHFzuQTSGXKQNOm9pjaFYUHy4L33jM3Qt9+28z4DQcFKhxER0P//r6fq1oV7r3X9EBaswaGD4datQqdpxRNN99suldlysgwrYZOZcoUWLrUHnvsMahb17/5ieQmqIWDihUrZj0uW7ZsMN9aREREREREJCR9841pSeHukUfyN0AznHi2K9KA5PAwYgTcdpu56XnnneZeeceO8MoreVtB7YTEREhKssfOPRfTH+vjj+Hpp3N+8cCB2Y9jYqBvX9O2aMcOM8W8TZui+z+p+E358jBkiD02cSL8t7bap3//NfUod6efDg8+6P/8RHIT1MJB8+bNsx7v3bs3mG8tIiIiIiIiEnIsC0aNsscaNjRt0osqz7kNKhyEvj//NB153FmWWRH94IPQoIEZOPzcc2ZuhfswYie57zZwkcFlsT/QaPRNUKOGaSv03HPeE8kztW4NN9xgmszv3AnTp0PPnmY3gkg+3HuvvcZ0+DC8/37O5z/9tPe35WuvQcmSAUlPJEcuywreX+dpaWlUr16dgwcPUq5cOfbv3692RZIniYmJxMXFAZCQkEDt2rUdzkhERERERKTwPv0UrrvOHnvvPbjlFmfyCYalS81K9UwREWb1bZkyzuUkObMs6NQJlizJ+2saNzZDXK++2tmF+cOHwxcvbeIGpnADU6jPVu+TXn0Vhg0Lem5SvFx1FXzxRfZxvXqwaZOZg+BuzRozCyY9PTt2xRXw1VfByFLELqg7DqKjo7njjjsAOHz4MB9++GEw315EREREREQkZBw+DPffb4/Fxdk7pBRFGpAcXqZM8S4aeN7s9LRhA7zwgtldUq+euS+/ZIn9ZmhAJSfDu+8y8N0ObKIRTzLSd9EAID4+SElJceb5d/3WrTBrlj1mWXDXXfb/T0qUMJ2xRJwQ1MIBwOOPP07jxo2xLIvhw4ezYcOGYKcgIiIiIiIi4rhnnvHuvz5unLlRVJSVLg3NmtljalcUmg4ehIcessfq14d9++B//zODX6tUyf0a27eb7+sLLzRzEYYONXM90tL8nGxaGnz9NfTrZ1oRDR3KmYeW5Xx+RAR062Z6LYVKbyUpsjp29G7TNnas/fjTT+GHH+yx4cPNfAMRJwS9cFCqVCnmzZtH3bp1SU5OpmPHjkybNi3YaYiIiIiIiIg4Zs0a71Wk3bsX7dkG7jwHJC9f7kwekrsnnoA9e+yx1183A18vvRQmTDDt/xcuhLvvNoWB3OzeDe++a77Xq1Uzu2u+/BKOHi1koi+8YN68Z0+YMQNOnMj53GbNzMCG7dth/nwz60BDjiXAXC7vXQdLl2bP4Th8GB54wP583brwyCPByU/El6DOOHC3b98+brzxRv73v//hcrmoW7cuXbt2pWnTplSoUIGIiPzVNAYW9b2cxZxmHIiIiIiISFFhWdCli31laUyMKSY0bOhcXsH05pvmRnOm5s3N1y+hY+VKOPts00oqU69e5kZ/TjIyTBHo889h5kz4+++8vVfp0nDZZaYP/OWXQ7ly+Uz28cfh+edzfHoPVZlTrj+DFw40Q49VKBAHpKWZQeKJidmxa6+FTz6Bhx+GMWPs53/+efEpJktocqxwYFkWU6ZMYdiwYSQnJ5tkCvEXd3rQGuWJE1Q4EBERERGRomLqVLj+entsxAgYOdKZfJywbBl06JB9rAHJoSUjw/z5ZK6GBihVCv76y8wsyAvLMsWgzz83v/74I2+vi4mBrl1NEaFXL7dWSCdOmCd9Wb8emja1hdIiYvgyoyeTGcQ8etD3umjU8EKcNnq0fRdBZKRp+3X55XDyZHa8e3eYO1c1LnGWI4WDhIQErr76alb4aGKYn3RcLheWZeFyuVQ4KOJUOBARERERkaLg33+hSRPYtSs7VreuuSFburRzeQVbaiqULWtfzf7jj6YPuDhv4kS45RZ77LnnzML+gvr7b/jiC7MTwb0gkZtIVwZ3tVrCrSXjafLXTCL++N38D+PLuefCr7/CeefBoEF0fO0alq6vlPX02LFw330Fz1/EH5KToXZt83dgppgYe3et6GhTdGvUKPj5ibgL+oyDvXv3cuGFF7JixQpbkcCyrHwVDTJfIyIiIiIiIhIunn7aXjQAeO214lU0APP1Nm9uj2lAcmjYv9+7r3rDhmaGcGGccYYZtPzzz5CQAG+8YVp2+epUfQabeIYn2WSdzrjVnWn28wdEHPqXd8+fwksvwebNPt7g9ddhwwZYtozDA4aybEMl29PnnFO4/EX8oWJFGDzYHvMcyfHAAyoaSGgI+o6DG264galTp2a1JbIsiwYNGtC1a1caNmxI+fLliYqKyvd1Bw0a5O9UJYRox4GIiIiIiIS7P/6ANm3AfcP8ZZfB118Xz3YUgwfDhx9mHw8cCJMnO5aO/Oe22+C99+yx+fOhW7fAvN/evTB7NnzzaTKVF3zG9RmT6cAyn+dupCGN2QC4aNXKtDO66ipThHL/f2jRIlOUyBQVZVphlSoVmK9BJD82bYLGjU07L0+1apnOW7Gxwc9LxFP+79AXwq5du/jkk0+yWgzFxsby/vvv069fv2CmISIiIiIiIhJUlgV33mkvGpQoYRZJF8eiAUDbtvbCwfLljqUi//n1V5gwwR7r2zdwRQPS0qj68zxumhfPTT/MhowTuZ5+Bn/TlHWsoxm//w6//w5PPWV2RFx1FVx9NbRr590KqVUrFQ0kdDRsCD17moKZp1deUdFAQkdQCwc//PBD1iwCl8vFlClT6N27dzBTEBEREREREQm6KVNMD393jzwCp5/uTD6hoF07+/H69ZCSogHJTklPh9tvt6+CLlPGzAbwK8uCVasgPh4+/thsOTiFdRHNmJQxiKkMIIlaXs9v2mSGzo4eDXFxZuCsu3PP9VfyIv4xbJh34aBLF7jmGmfyEfElqDMOtm/fDpiiQYMGDVQ0EBERERERkSLv4EHT291d/frw8MOOpBMyWrWy3+DNyIDVqx1Lp9h7911YudIee+opM8jV72/Utq0Z7pFb0aBKFbjnHlixgtNT13DxvOFccWstqlXL/fIJCbB1qz2mwoGEmk6dzBzvTNHRZu5Hcd2BJqEpqIWDSLdPBK1atQrmW4uIiIiIiIg44sknYc8ee+z119U6pVQpaNbMHlO7Imfs2QOPP26PNW0K994bgDe77LKc747GxJjeSLNnQ1KSKS60aUNMCRfdu5uaQ1ISLF5scvtvFOIpqXAgocblgs8+M9/unTqZb3nPgfEiTgtqq6JatbK3k8XExATzrUVERERERESCbvVqeOste6xXL7jiCkfSCTnt2sGff2Yfr1jhXC7F2cMPm50x7t5+29zHz7eMDFiyBOrVg7p1vZ+vU8f0ZPn+++xY+/ZmOvY110ClSrlePjISLrjA/Bo71nzPfP45zJwJGzd6n3/aaaanvEioiYuD6dOdzkIkZ0HdcdC0adOsxwkJCcF8axEREREREZGgysgwA5EzMrJjJUvCuHGOpRRy2ra1H2vHQfD99JN9SDVA//7QuXM+L7RpEzzxBDRoYF78zjs5nztwoCkqjBhh7vYvXQpDh56yaODJ5TLFp+efNzMy1q6FZ5+Fs84yz8fGmt09EUG9+yUiUjS4LMt97E3gtWjRgrVr1xITE8Pu3bspX758MN9ewlRiYiJx/+1BTEhIoLbfmyyKiIiIiIj416RJMGSIPfbss+beqhi//GLv8+1ywaFD5oavBN7Jk6Z488cf2bGyZWHDBqhZMw8XSE42/VYmT4Zly+zP1a5thg14TirOfOOIiIDe0T90yLy1hm2LiBRM0Guud999NwBpaWm8+OKLwX57ERERERERkYBLTobhw+2x00/3HpJc3LVsab+vbFkakBxMb75pLxqAKW7lWjRIS4Ovv4Z+/aBGDbNTwLNoAJCYCAsX+r5GVFTAtwGUK6eigYhIYQS9cHDrrbdy+eWXY1kWL730Eh9//HGwUxAREREREREJqBEjYN8+e+yNN0yrIslWqpT3QFC1KwqOnTvN4G53LVvCXXf5ONmyYNUqGDbM7CTo2RNmzIATJ3J+g2bN4Phxv+YsIiLB40iXt+nTp3PVVVeRkZHBDTfcwM0338zmzZudSEVERERERETEr1asgPHj7bErr4RLL3Umn1DXrp39WAOSg+PBB+HwYXvs7bfNZoAsBw/CSy+ZikKbNmZAx549OV+0alW4917zh7hmDVx+eQAyFxGRYAj6jIMhbg0ev/zyS5KTk3G5XACcfvrpNG3alIoVKxKRjy1rLpeL999/3++5SujQjAMREREREQkHGRnQoYPp3Z+pVClYt87MghVvb79thkhnatoU/vrLuXyKg4UL4aKL7LEbbzRzOWz27oXTTjMzCXISEwO9epmBxz16QHS0v9MVEREHBL1wEBERkVUoyOSegudzp2JZFi6Xi/T0dL/kJ6FJhQMREREREQkHEyfCLbfYY6NGwWOPOZNPOPA1IPnff82QXvG/EyfgrLNMMStThQpmIHK1aj5e0Ls3zJ7tHW/f3hQLrrkGKlUKULYiIuKUqFOfEnj5LRaIiIiIiIiIhJr9++GRR+yxRo3ggQecySdctGxp2uNkLmrPHJB8wQWOplVkjRuXXTRoyEZuYApdLyhFtWo5VLcGDswuHNStCzfcYH41ahSUfEVExBmOFA6CvMlBREREREREJOAef9wUD9y98QaUKOFMPuEic0Dy779nx1asUOEgEBIS4PVnkrmNTxlIPB1YBoC1tDKceNC0HfJ0xRVw++1w7bXmDyUfraVFRCR8Bb1wsGXLlmC/pYiIiIiIiEhA/fYbvPeePda3L3Tr5kw+4aZtW3vhYPly53IpktLSYN48Em+PZ3PqbEpwwva0a/9+mDvXtCXyVKKEGUQhIiLFStALB3U1DUpERERERESKkPR0uOMO02InU+nS8OqrzuUUbtq1gw8+yD5escK5XIoMy4JVqyA+Hj7+GPbupX1u58fH+y4ciIhIsRQSMw5EREREREREwtXEid4r5J98EuLinMknHLVtaz/esAEOH9aA5AJJSoKpU00hYM2aU59ftSr07w+DBgU+NxERCRsqHIiIiIiIiIgU0L598Oij9liTJjBsmDP5hCtfA5JXrYILL3Q2r7BiWXDVVWaQcUZGrqceJ4YdbXvR4KmB0KMHREcHKUkREQkXmmgjIiIiIiIiUkCPPgrJyfbYm2/6njErOStZEs480x5Tu6J8crnMpOlcigZLac9QxtOr3U7q/TodevZU0UBERHxS4UBERERERESkAH7+2bQpcnfttXDxxc7kE+482xWpcJCD/ftzfm7gQK/QnlJ1GMkIGrGBjixlQsRQXnyvEhG6IyQiIrnQPxMiIiIiIiIi+ZQ5ENldmTLwyivO5FMUeBYOPOdGFGvJyfDuu9CxI9SvD6mpvs+75BKoWRNiY2HwYJY9v5AaR7fwJCPZRCPAfN+2bh3E3EVEJCxpxoGIiIiIiIhIPr37runB7+7pp6FWLUfSKRLatbMfb9wIhw5BuXLO5OO4tDSYP98MOZ49G44fz35u1iwz0NhTVBTMmQONG3PUVZoBzcFye7paNRg5MtCJi4hIURDwwkFqaipz585l4cKFJCUlceLECWrWrMk555xDz549qVGjRoGuu2PHDi644AIAXC4Xmzdv9mfaIiIiIiIiIj7t2QOPP26PNWsG997rTD5FRYsWvgckd+rkbF5BZVmwejVMngwffwx79/o+Lz7ed+EAsrYTvPgUbNlif+qll6BCBb9lKyIiRVhACwcffPABjzzyCPt99N/74IMPuPPOO7n++ut5/vnn811AOHnyJFu3bgVM4UBEREREREQkGB55BA4etMfeekszZgurZElTPHDfybFiRTEpHCQlwdSppiCwZs2pz//9d0hJMf2xfPj7bxg92h674AK44QY/5CoiIsVCwGYcDBs2jFtuuYV9+/ZhWRaWZdmetyyLkydPMnnyZJo1a8ann34aqFRERERERERE/OKnn2DSJHusf3/o3NmRdIqcYjUgOTXV7Cro0QPi4mD48NyLBjEx0LevaVu0fXuORQPLgrvvtnc2iow0xS2tuxQRkbwKSOFg7NixvPbaa1iWhcvlytoRkFlAcI9blsXBgwfp378/99xzj1eBQURCX0aG2ardti089pg5FhEREREpak6ehDvvtMfKljXtX8Q/itWA5AcfhAEDzByD3H6IOu88GD8edu6E6dOhZ89ct7d88QXMm2eP3Xuv2c0hIiKSV35vVbRlyxYef/xxW7EgNjaWq666itatWxMVFcXff//NF198wfbt223nvfXWW+zdu5ePPvqIyMhIf6cmIgHy6qvw/PPm8cqVULEiPPSQszmJiIiIiPjb+PGmQ4y7Z56B005zJp+iqFgNSO7f33xT+VKnDgwcaHoLNWqU50umpMB999ljNWvCU08VPE0RESmeXJafl/jff//9jBs3LqsgcOGFFzJt2jSvGQaWZTFjxgweeOABEhMTs3YfuFwuevXqxWeffUZ0LhX0bdu2Ub9+ffNFuFykp6f788uQEJOYmEhcXBwACQkJ1K5d2+GMJNPhw1C/PriPMqlcGbZuhdhYx9ISEREREfGr3bvN/dtDh7JjLVqYhTNRAZ0eWLwcP252caSlZccWLQrTOQfJyfDZZ3DNNWZ1lSfLgtNPz55gHBtrWhENGgQXXggR+W8S8eij8OKL9ti0aXDddQXIX0REijW/tyqKj4/PKhq0aNGCefPm+Rx87HK56NevH2vWrKF3795ZRQPLspg9ezZ9+vThuHtDPhEJSW+9ZS8agDl++21n8hERERERCYSHHrIXDcB8FlbRwL9KlIAzz7THwqpdUVoafPUV9OsHNWrA0KGmvZAvLhcMHgzdupnByLt3mwEanTsXqGiwfj288oo9dtFFcO21+f8yRERE/Fo4WLduHQcOHMiaUzBu3DhKlCiR62vKlSvHF198wYgRI2zFg3nz5nHFFVdw9OhRf6YoIn505Ai8/LLv515+2WyTFREREREJd4sXw5Qp9tgNN8AFFziTT1Hn2a4o5AckWxasWmV6BNWqBb16wYwZcOKEeX7y5JxfO2KEmXHQvz+ULl2oFO68075TIzpaA5FFRKTg/Fo4WL16ddbj0047jc6dO+f5tc8++ywTJkwgIiIiq3jw/fffc+mll5Kiu48iIcnXboNMe/fCO+8ENx8REREREX9LS/MeiFyuHIwZ40w+xYHngOSQLRwkJZnJ2C1bQps28Npr5gchT0uXwt9/+76Gn+7qf/opfP+9PfbAA9CkiV8uLyIixZBfCwf7/7uD6HK5aN26db5ff9NNN/HJJ58QHR2dVTxYsmQJ3bt35/Dhw/5MVUQK6cgR8xk5N2PGQGpqcPIREREREQmEt96CNWvsseeeM11oJDA8CwcbN8K//zqTi5fUVPj4Y+jRA+LiYPhw728QdzExZm6B+1YAPzt8GO6/3x6LizObGURERArKr4WDQ24NH8uXL1+ga1x99dXMnDmTEiVKZBUPli1bRteuXfk3ZD4piIiv3Qaecw327IF33w1eTiIiIiIi/rRzJzz5pD3WqhXcfrsz+RQXLVqYNjvuVq1yJpcsGzfCTTeZitGAAaa9UEZGzue3bw/jx5tvounToWnTgKX29NPmbdyNGwdlygTsLUVEpBjwa+GgtFs/vgMHDhT4OpdffjmzZ8+mVKlSWcWD3377jYsvvpjk5GR/pCoiheBrtkHPnuYHqB497PExY0CjSkREREQkHD34oFnN7U4DkQOvRAlTPHDneLuilBT44APvbwh3deqYZf4bNpj2REOHQqVKAU3rzz9NhyR3PXrAlVcG9G1FRKQY8GvhoHLlylmPk5KSCnWtSy65hDlz5lCmTJms4sHKlSu56KKLsloiiYgz3n4b9u2zx556yvzuuSJr1y6YMCE4eYmIiIiI+MuiRaYjjbsbb4SOHZ3IpvjxbFe0fLkzeWQ56yzvagZAbCwMHgwLF8KWLTByJDRqFJSUMgcip6dnx0qUgDfe0EBkEREpPL8WDho3bgyAZVmsXbuWI0eOFOp6nTp1Yu7cucTGxuL671+933//nc6dO7N79+5C5ysi+edrtsEVV2R/sG/fHrp2tT8/ejQcOxac/ERERERECsvXQOQKFcznWgmOdu3sxwHdcZCWBl9/DddcY7aU+OJywcCB5nFEBHTrBh99BLt3m50InTubeBBNmQJLlthjDz8MZ5wR1DRERKSI8uu/as2aNSMiIgKXy0V6ejo//PBDoa/ZsWNHvvnmG8qVK5dVPPjrr7/o06dPoa8tIvmX226DnI6TkuD99wObl4iIiIiIv7z+Ovz1lz02ahRUq+ZMPsWR546DTZv8PCDZsszghPvug1q1TO/V6dNh4sScXzNggKkebd9uZhwMGABuLZuD6eBBeOghe6x+fXjkEUfSERGRIsivhYPY2Fhat26NZVkATJ061S/XPffcc/nuu++oWLFiVvFg165dWY9FJDhSUnzvNvBcDdSxI1x0kT324otw/Hhg8xMRERERKawdO8ywWXetW8NttzmSTrF15pkQE2OPrVzphwsnJZkfalq2hDZtzICAvXuzn1+9Gv74w/dra9aE4cNNocFhTzwBe/bYY6+/DqVKOZOPiIgUPX7fR9elSxfAtCuaNWsWBw8e9Mt127Zty4IFC6hSpQqAigYiDsjLboOc4omJMGlSYPISEREREfGXBx4w7Tndvf02REY6k09x5dcByampZmBFjx4QF2du/q9Zk/P58fEFfKPgWLnSfE+669XLLOoSERHxF78XDnr16gWYG/vHjx9n3Lhxfrt2q1atWLhwIdWrV/fbNUUkb1JSYMwYe+zyy713G2S68ELT5tPdCy/AiRMBSU9EREREpNAWLIBPP7XHbroJzjvPmXyKO892RfkqHGRkwA8/mD/AGjVMW6H58008J+edB+PHw2OPFSjfYMjIgDvusH8ZJUuajRMiIiL+5PfCQceOHalRowaWZWFZFuPGjfPbrgMwcxR++OEHatWqldUSSUQCLz+7DTI9+aT9ePt2+PBDv6YlIiIiIuIXJ07AXXfZYxUrmsUv4gzPRUrLl+fxhYcOwemnm5VMH3wAhw/nfG6dOjBiBGzYAMuWwdChUKlSQVMOuEmT4Jdf7LHHH4d69RxJR0REirAof1/Q5XLx888/c9jtH+bo6Gi/vkfDhg356aef+P777/16XRHxzddsg8svh7PPzv11nTvDBRfAkiXZseefh8GDwc9/LYiIiIiIFMq4cbB+vT32wgtQtaoj6QjeOw7+/tsMBa5Q4RQvLFfO7DLYutX387Gx0K8fDBxotkpH+H1NZUDs3w8PP2yPNWzoPSRZRETEH1yWlu1LGEhMTCQuLg6AhIQEateu7XBGxcvLL3t/GP3111MXDsBs977kEnts4kSzY1hEREREJBQkJECTJqYVfqZ27eDnnzXbwEknTkDZsvZ2pwsWwEUXAWlpsG0bnHGG7xePH296+mSKiDA/mAwaBH36QOnSgUw9IG67Dd57zx6bPx+6dXMmHxERKdrCo6wuIo7xNdvgssvyVjQA86G+Qwd7bNQo8zlfRERERCQU3H+/vWjgcmkgciiIiYGWLd0jFglfroT77oNatcwPJjmthbz2WnOB5s1h9GjTN3X+fOjfPyyLBr/+ChMm2GN9+6poICIigeP3VkUiUrSMHw9799pjp5pt4M7lMud3754d27IFpk6FG2/0S4oiIiIiIgX2zTcwY4Y9dssteV8oI4HVti3sWJ7EAKYykHhavL4m+8m9e822kPbtvV9YqZLpPVWvnvmhJIylp8Ptt9trJGXKwNixzuUkIiJFn3YciEiOfM02uOwyOOec/F2na1c491x77Lnn4OTJwuUnIiIiIlIYx4/D3XfbY5Urm7lc4rDUVPj4Yx7/sQcJxPESw2nBGu/z4uNzvkb9+mFfNADTnmjlSnvsySdBHXxFRCSQVDgQkRy98w7s2WOP5We3QabMXQfuNm+GadMKnpuIiIiISGG9+ips3GiPvfiiKR6IAzIy4IcfYMgQM9x4wADi1s4nkoycX7N9e/Dyc8CePfDYY/ZY06amW5OIiEggaTiyhAUNRw6+lBRo0MBeOLj0Uvjf/wp2Pcsyuw5++y071qgR/PWXeseKiIiISPBt22ZuwB49mh075xxYtszM0ZUg2rTJ7ByYMsX8wZzC0ep1KXXLDTBwIDRsGIQEnTN4MHz4oT22cCF07uxENiIiUpzo45CI+OSv3QaZXC6zndbdxo3wyScFv6aIiIiISEENG2YvGmQORFbRwAF33GF6meZSNDhMLB8wmE4s4o37/oGRI4t80eCnn7yLBv37q2ggIiLBoY9EIuIlNRXGjLHHLr3Ue05Bfl1+ObRpY48995wZ9iUiIiIiEixz58IXX9hjQ4eaQbzigIEDfccjIqBbNz64eCrV2c1NfMBiOrFiVdG/lXHypKmnuCtbFl5+2Zl8RESk+Cn6/9qKSL75e7dBJl+7Dtavh+nTC39tEREREZG8OHbMeyBylSowapQz+RR5lmUm+z72WM4rhq68EsqUyT5u3tysZNq+HebPJ+O6/hyldNbTK1YEOOcQ8NZb8Mcf9tizz0LNms7kIyIixY9mHEhY0IyD4ElNhfr17YWDHj3Mqix/sCyz62D16uxYs2bw55/aFi4iIiIigTdypPdilg8+ML3kxY+SkmDqVDO7YM0aE/vmG+ja1ff5DzxgCgsDB0Lr1mbV0X9WrfLeuXzgAFSsGKDcHbZzJzRuDIcPZ8datjQFk6go5/ISEZHiRbfpRMQmULsNMvnadfDXXzBzpv/eQ0RERETEly1b4Pnn7bH27WHQIGfyKXJSU+Hjj83Ko7g4GD48u2gApoiQk1degXHjTIXArWgAZgNCiRL201eu9F/aoebBB+1FAzDzN1Q0EBGRYFLhQESy+Jpt0KMHnHeef9+nd29o0cIee/ZZyMjw7/uIiIiIiLi77z7TqihTRIQGIhdaRgb88AMMGQI1asCAATB/vu8P959/7n1HPA9iYsyKe3dFtV3RwoWm9uLuxhuhY0dH0hERkWJMH49EJMu778Lu3faYP3cbZIqI8N51sGYNzJrl//cSEREREQH4+muYPdseu/NOOOssR9IJf5s2wRNPQIMG0LkzTJqUe1Ggbl3TjujkyQK9Xbt29uPlywt0mZCWlma+J91VqACjRzuSjoiIFHPa6CYigNlt4PmBtHt3/+82yHTVVWbL8dq12bFnn4U+fbTiS0RERET86+hRuOcee6xaNfP5U/Jp/nx45hlYtuzU58bGQr9+phfUBRcU6oN+27b246K442DcOFi3zh4bNcp8r4qIiASbbs+JCBC83QaZIiLMAiV3v//uvQpMRERERKSwRo828w3cvfSSWc0t+ZSSknvRICICunUzg5F37zaTpzt1KvTqIM/CwT//QHJyoS4ZUhITTT3GXdu2cNttzuQjIiLisizLcjoJkVNJTEwkLi4OgISEBGrXru1wRkXL0aNmh/GuXdmx7t1h3rzAvm96upl14L6qpnVrs3rIYx6aiIiIiEiBbN5sdroeP54dO/98WLxYnzlzlHmbwNd/oOPHoWZN77v2zZubnQX9+0OtWn5PKS0Nypa1/zl++y1cconf38oR/frBjBnZxy4X/PwznHOOczmJiEjxFvQdB/Hx8Vm/UlNTC3ydlJQU27VEpODefddeNIDA7jbIFBkJI0bYY6tWmf6zIiIiIiKFZVmmRZH7zebISHjrLRUNfEpKMlsxWraEL7/0fU6JEnDddeZx1apw771m5c+ff8JDDwWkaAAQHQ2tWtljRaVd0Tff2IsGALfcoqKBiIg4K+g7DiIiInD99wlty5Yt1KlTp0DX2bZtG/Xr18+6Vnp6ut9ylNCjHQeB42u3QbdupnVpMKSnm8VJGzZkx9q1g19/1Q9zIiIiIlI4s2dD79722H33wdixjqQTmlJTYdYsiI83S/gzMky8Tx/44gvfr1m3zgxHvvRSc0c/SO64A8aPzz7u2xemTw/a2wfEkSPQpo35z5mpcmXz81Hlys7lJSIi4siMA3/WKtRpSaRw3nvPmd0GmXztOli+HObODV4OIiIiIlL0rFvnPRC5Rg14+mlH0gktGRnwww9w003mP8qAAWblUGbRAGDOHNi3z/frmzaFXr2CWjQAs8DIXbjvOMjIgBtvtBcNAF58UUUDERFxnoYjixRjR4+aD6XuunWDDh2Cm8d118EZZ9hjzzyT3VpVRERERCSv1q0zbfabN4dt2+zPvfwylC/vTF4hYdMmeOIJs+W4c2czuPjwYd/npqXlvOPAIZ4DkrdsgQMHnMnFH0aOhJkz7bHzzoMhQ5zJR0RExF3YFg7cdxq41M9EpECc3m2QKSrKe9fBr7+aXp8iIiIiInnhXjCYNs17EUqnTub5Yic5Gd55x6wOatQInnvOu6LiLjYWBg+GhQvNjoQQ0qyZGbHgLlx3Hcyc6b37pUIF0zEqImzv1IiISFEStv8cpaSkZD0uXbq0g5mIhCdfuw26dg3+boNMAwaYhU/utOtARERERE7lVAUDgLg4eP/9YjhDa/Vq04ro9tth2bKcz4uIMFuPP/oIdu82OxE6dw65O9jR0XDWWfZYOBYOfv8dBg60xyIi4LPPoGFDZ3ISERHxFFqfAvJh7dq1WY8rVqzoYCYi4WnChNDYbZApKgoef9weW7YMvvvOmXxEREREJLStX28Wn+RWMChb1uxsXb0aTj896Ck6r0WL3JvlN2sGo0fD9u1mxsGAARDiC/M82xWFW+Fgzx4zHiI11R5/9VWzkEtERCRUhGXh4NChQ4wdOxYwbYqaNGnicEYi4SWn3QYdOzqTT6YbboB69ewx7ToQEREREXeZBYNmzeDjj3MvGGzdavrIV6oU9DSDJykJ3BbW2URGwvXX22NVq8K995o77mvWwPDhUKtW4PP0E8/CwfLlzuRRECdOQN++pk7jbsgQ70HeIiIiTosKxEWH5HGSz4MPPkhsbGyer3v8+HF27tzJb7/9Rqpbef7CCy/Md44ixdmECbBzpz3m5G6DTNHR8NhjcOut2bGffjLtVS+6yLm8RERERMR569eb9vzTpkFGhu9zypY198SHDSvixYLUVJg1yzTE//ZbuPjinAeEDRwIr71mlrkPHAg9epgP3mGqXTv78datsH9/7hsrQoFlwV13wZIl9niHDvD228WwjZaIiIQ8l2X5fy1vREREjgOL/THU2LIsXC4XlmVRqlQp1q9fT1xcXIGuJeEhMTEx6884ISGB2rVrO5xR+Dp2zMwScC8cXHKJ+XkjFJw4Yfp6uq/C6dQJFi1yLCURERERcdCGDWbXwKkKBvfcYwoGoX4DucAyMsxd58mTYcYMOHw4+zmXCxISct458O+/UL58cPIMsJMnzZ/3sWPZsW++Cf02P2+9ZQoH7mrXNjsmqld3JicREZHchGWrosyiQVRUFG+//baKBiL5EKq7DTLFxMCjj9pjP/xgfomIiIhI8bFhg+my06wZTJ3qu2gQG2vmZG3ZYnYjFMmiwaZN8MQTZvVP584waZK9aABmOftHH+V8jSJSNAAzG61VK3ss1NsVff+92QnjrlQpmD1bRQMREQldASscWJbl81dezjnVr7p16zJ48GB+++03Bg0aFKgvQaTIOXYMXnjBHrvkEjj/fGfyycngwWb1jbtnnnEmFxEREREJrg0bzOyrvBQMtm4togWD5GR45x3Tx6ZRI/NFbtuW8/mxsfYl+EWcZ7uiUB6QvHkz9OsH6en2+OTJ0Lq1MzmJiIjkRUBmHGzZssVn3LIsGjRoAJhdA4sXL85zyxmXy0WJEiWoUKECJUqU8FuuIsVJqO82yFSihNl1cOed2bGFC83O7AsucC4vEREREQmcDRvM/fGPP865JVFsrGlJdP/9RbBYkJEBc+aYuQWzZ5senrmJiDCrgAYNgj59oHTpoKQZCjwHJIdq4eDQITNa4sABe/yJJ0wxQUREJJQFZMZBbtznH2zZsoU6deoE8+0lTGnGQeEdOwannw5JSdmxiy+G775zLqfc+Mo3lGYxiIiIiIh/bNxoZhgU24JBJssyw742b879vObNTbGgf/+cZxoUcX/+CS1b2mP79oXW90Z6Olx5JXz1lT3epw/MnGnqPiIiIqEs6P9U1alTJ+tXVFRANjyIiA8TJ9pvwkNo7jbIVLIkPPKIPfbdd7B0qTP5iIiIiIh/bdwIAwdC06amPX9OLYkee8y0JBo1KrRuDPudy2X+g/hStappkr9ihblr/tBDxbZoAOZ7plQpeyzUdh088YR30aBFC5gyRUUDEREJD0HfcSBSENpxUDjhttsg07FjZgace3ul7t1h3jznchIRERGRwtm40bQkyml+AZiCwd13mx0GVaoEN7+ASU2FL74wd7hffdX3Of/8Yz64A8TEmD43gwaZD8HR0cHLNQy0bw8//5x9/Pzzpt1pKJg2zWwIcVe5Mvz2G9Sv70xOIiIi+aUl/yLFQLjtNshUsiQMHw7DhmXH5s+HX36Bc891Li8RERERyb9Nm0zBIKfdBVAECwYZGbB4sZlbMH06HDli4rffbtoSeWrQwAz6atECrrkGKlYMbr5hpF07e+Fg+XLncnG3fDkMGWKPRUXBjBkqGoiISHjRBjmRIu7YMXjhBXvsoovCZ8jwrbdC9er22LPPOpOLiIiIiOTfpk1m0XyTJub+ua+iQZkyZrX4li1m5XjYFw02bTK9aho0gC5dYNKk7KIBmH41OXnzTbjtNhUNTiEUByTv3GlmGBw7Zo+/8QZ07uxERiIiIgWnwoFIEff+++G52yBT6dKmhau7//3PbPMVERERkdCV14LBI4+YGQZhXzBIToZ33oEOHaBRI7O9Yts23+dOmZLztgvJE8/CwbZtZkCyU44dM8OQd+ywx2+/HYYOdSYnERGRwgipGQeJiYkkJyfz77//kpHPD1EXXnhhgLKSUKAZBwVz7BiccYb9w+tFF8GCBc7lVBApKWZb79692bErrvAeNiYiIiIizvv77+yWROnpvs8pU8a0JHrggTAvFqSlmQFc8fEwezacOJH7+RERcMklpqJyzTWmh40UyMmTUK4cHD2aHZs3z4yDCDbLgsGDYfJke7xzZ/jmG42nEBGR8OTop5STJ0/y8ccfM3XqVH755RcOHz5coOu4XC5Onjzp5+xEwt/773uveAmn3QaZypQxuw6GD8+Off01rFwJbdo4l5eIiIiIZCtWBQOAsWNNT1D31S05ad7cFAv694datQKfWzEQFQVnnQXLlmXHVqxwpnAwdqx30aBePTPWQkUDEREJV44VDn755Reuu+46tm/fDkAIbXwQKRKOH/eebdClC4Tr5pzbb4cxY+zbj599FmbNciwlERERCQPbt8OHH0KNGnDTTRAZ6XRGRc/ff8OoUab7Tm4Fg7vuMgWDqlWDm1/AZGTkXjSoWtUUCgYOhNatweUKXm7FRNu29sKBEwOS583zbq1apozZgBL2xTERESnWHCkcfPfdd1xxxRWkpaVhWRYutw9Q7o/diwkujw9ZKjSI5K6o7DbIFBtrftB89NHs2JdfwurVZqWRiIiIiKcjR6BTJ9M/H2DJktxn0kr+FIuCwdGjUKqU7+f69zdbYt3b7MbEQK9epljQo4eWmwdYu3b242APSN6wAa67zntcxUcfQYsWwc1FRETE34JeONi9ezf/93//x4kTJ7KKAVWrVuXSSy+lbNmyvPnmm4ApFDz11FMcOnSIpKQkli1blrU7weVyUa1aNW699VYitWRIxMvx42a4nLsuXcwPzuHszjvhpZfgwIHs2MiRMHOmczmJiIhI6Prii+yiAZibeVdfDX36OJVR0bB/Pzz4YO4Fg9KlTcHgwQfDsGCQkWGqTJMnw4wZ5nGrVt7n1axp+uLMnQvnnZc9t6BSpeDnXEx5Dkjevt1sAgnG99zBg6ZG9O+/9vjIkfo7RkREioagD0d+9NFHGT16dFbR4MYbb+TNN9+kVKlSbNu2jfr165vEXC7SPT6Ffv/99zz66KP89ttvuFwuOnXqxOzZs4mNjQ3mlyAO0HDk/Hn7bXOT3d2iReFfOACzqm3ECHvs99+hZUtn8hEREZHQddVVpnjg7rTT4K+/oHx5Z3IKd6mpcM45sHat7+fDumCwaZMZcjxlCmzblh2//3545RXfr/n9d7MjoVGj4OQoNr4GJM+dazZ7BFJ6Olx+Ocyfb4/36weffqquVCIiUjREBPsNJ06cmFU06NKlC++//z6lctr66eGiiy7ip59+4sYbb8SyLH744Qf69u0byHRFwo6v2QadOxeNogGYYXoVKthjzz3nSCoiIiISwlJTTe9xT0lJ8PDDwc+nqBgxwnfRoHRp07Vn61YYPTqMigbJyfDOO9Chg7n5/9xz9qIBwNSp5g61L61aqWjgoKgoMz7CXTDaFT38sHfRoHVrmDRJRQMRESk6glo4WLduHfv378+aTzBq1Kh8XyMqKoqJEydywQUXYFkW3377Le+//76/UxUJWx98AImJ9lg4zzbwVK4cDBtmj82YkfOqNxERESmevvnGvgrZ3bvvwuLFwc2nKPjpJxg3zh4rXdoMht2yJYwKBmlp8NVXZnl4jRpw++32Cbue9u4NfvN8yTPPdkWB/qOaPNl7A0q1ajBrlpnpISIiUlQEtXCwevXqrMc1atTg3HPPLdB1IiIiePnll7OOx48fX9jURIoEX7MNOnc2v4qSe+6xtxewLNNLVERERCSTZ4siT7fcAseOBSeXouDoURgyxHzuylSiBCxfDmPGmBunIc2yYOVKuO8+qFXLNKefMQNOnMj5Nc2amWrI9u1QwJ9dJfA8ByQvXx649/r5Z7j1VnssOtr8fVOnTuDeV0RExAlBLRzs378fMPMLWrRo4fW8y2NP37FcPsmfffbZ1KtXD8uyWLVqFf/8849/kxUJQ0V9t0GmChXMz3zuPvsM1q1zIhsREREJNZkLyt15tjPZuFELD/LjySfNfzN3I0dC06bO5JNv06aZpemvvWZ2EOSkalW4916zbH3NGtN/qVat4OUp+ea54yAhIfc/4oJKTIQrr/SuNWV2uhIRESlqglo4OHToUNbjypUrez1funRp2/GRI0dyvd6ZZ56Z9fj3338vZHZ5c/DgQb744gvuueceLrzwQmrUqEGJEiWIjY2lTp069OzZk3HjxpGcnJzna6ampjJ37lweeughLrnkEmrXrk2pUqUoXbo0tWrVolu3bowaNYqkpKSAfV316tXD5XLl69cjjzwSsHwk/3ztNujUqejtNsh0772mbVEmy9KsAxERETEWLzat6919/rn3ovExY8xsW8ndzz/Dq6/aY+eea2YGh41LL4WYGN/PxcRA374wezbs2GH6MbVpo2b1YaJJE9Myy52/2xUdPQp9+sCuXfb4ffeZnTgiIiJFUVALByVLlsx6bLnvcf1P2bJlbcenulFe3q1XyS7Pf8H9bP369fTs2ZPq1atz1VVX8cYbb7BkyRJ2797NiRMnSElJISEhga+//pphw4ZRu3Ztxo0b5/PrzLR7926uu+46qlatymWXXcbLL7/MggUL2LFjB8eOHePo0aMkJSXx7bffMmLECOrVq8djjz3Gidy200qxNWmS926Dp592JJWgqFjRtCxy98knsGGDM/mIiIhI6PBsU9S2LdSrBxMnmmGqmU6ehJtvznnurZh2ToMHQ0ZGdiwmxux0jYx0Li8vGRnwww+wfr3v5ytWNO2J3LVvD+PHw86dMH069Oxp+s5IWImM9N5R5M92RZYFN93kXYzo2hVeesl/7yMiIhJqok59iv9UqlQp67H77oNMJUqUoHz58vz777+AuVnfsmXLHK+X2foIyHpNoKxZs4avv/7aFouMjOSMM86gevXqpKens27dOg4cOACYXQTDhg1j7dq1vPfee15tmAASEhL49NNPbTGXy0WDBg2oUaMGkZGRbNq0iZ07dwKQlpbGCy+8wOrVq5k1axYxOa2YKaSzzz7b9meVk8aNGwfk/SX/ittug0z33WcWhGVuTsrIgFGjID7eyaxERETESRkZZkipuyuvNL+feSY8+qi9RdHy5fD662G2ej6InnnG+178M8+Y9v8hYdMm8+FvyhTYts00oH/3Xd/nDhwIv/0GN9xgHjdsGNxcJWDatjXDuzP5c8fBiy+aTlfuzjgDPv3UXogUEREpaoL6z1xDtw9mW7Zs8XlO8+bNWbp0KQCLFi3immuu8XleWloav/zyS9ZxOfeeJQEUHR3NFVdcwaBBg+jSpYvtfS3LYvbs2dx5553s2LEDgIkTJ9K6dWvuuOOOHK8ZERHBxRdfzODBg+natStVqlSxPf/DDz9wxx138NdffwEwd+5cRowYwZgxYwLwFcKYMWPoXNTvOBcxkyaZXp7uiuJsA0+VK8Pdd8MLL2THpk6FJ57Qz4EiIiLF1fLlptuMu8zCAcDjj5vF5e43w0eMMG1IGjQISoph47ffTDsnd+3awYMPOpNPluRkc9c2Ph6WLbM/9+mnZo6B2273LJdfbn5FBHXjvQSB55wDfxUOZs82f2e4K1fOxCtW9M97iIiIhKqgfmJq1qwZLpcLy7LYtGmTz5Y77du3B8xN+E8++SRrBb+nd955xzZHINCr36Ojo7nlllvYvHkzn3/+Ob179/YqVrhcLnr37s2yZcuoUaNGVvzJJ58kLS3N65oRERH07duXNWvW8M033/B///d/XkUDgE6dOrF06VKauS3rGTduHLt37/bjVyjhytdugwsvLPq7DTLdfz+UKZN9nJHh/d9DREREig/PNkWNGtkH+JYoYVoWuW8IPnoUbrvNtCQR4/hx7xZF0dFmwYojq6wzJ1736wc1asDtt3sXDQD+/dd7MnamiAgVDYqodu3sxwkJsGdP4a65di0MGGD/e8HlMrsPwmYouIiISCEE9VNTxYoVswYap6ens3jxYq9z+vXrB5ib8P/++y89e/Zk27ZttnMmTpzIgw8+mNX+p3Tp0nTo0CGguffu3Zv33nuPuLi4U54bFxfHM888k3W8f/9+n19rmzZtmD59Ok3z8KmjfPnyjB07Nus4LS3Nq3WSFE8ffui92+Dpp4vPLLcqVeDOO+2xKVNg82Zn8hERERFneRYOrrzS+3NRx47guSH4u+9g8uTA5hZORo40N07dPfmkafcUNJYFK1ea/pS1apkZBTNmQG4z35o3V/+YYqhxY/tiIijcroP9+823W2ZL1EyjR8NllxX8uiIiIuEk6MstunbtmvXY143vc845hwsuuCDreNmyZZx++um0aNGC888/n+rVq3PbbbeRlpaGZVm4XC5uvvlmSpUqFZT886pnz5624/U5DenKh4svvtj2dfrjmhLeTpwwPf3dXXBB8dltkOmBB6B06ezj9HR7+yIREREpHtatgw0b7DH3NkXunn8eate2x+6/H7Sp19xwffFFe6x1a3j44SAlcPiwmTrbsqXpQfPaa7B3b87nV60K995rigx//pnzH7oUWZGRcNZZ9lhBCwdpaWZjyz//2OPXXx8CbbpERESCKOiFg8yZBZZlMWXKFI4dO+Z1zltvvUVsbCxgdh5kZGSwdu1ali1bxt69e7MKBgCnn346I92nm4UIz+HChw8fLvQ1IyMjKV++vF+vKeHN12yD4rTbIFO1ama3urvJk2HrVkfSEREREYd47jY47TQ4+2zf55YrB+PH22PJyXDPPYHJLVycOGFaFKWnZ8eiosznzujoICXhcpkJzGvW5HxOTAz07WvaEu3YAePGmepGcfsgLFk82xUtX16w69x/PyxcaI+dcw5MmKBvLxERKV6CXjg455xzmDlzJtOnT+e9994jJSXF65wzzzyTOXPmUL16daz/GgpmFgoyf7csi5YtW7JgwYKsIkMo2epxx7JatWqFvubRo0fZ49ao0R/XlPB14oR3L/8LLoAuXZzJx2kPPQTuG49OntSsAxERkeLGs3DQp0/uLe2vuAKuu84e++wzM/i0uBo1yizadzdiBLRqFcQkYmNNUcCXDh3gnXdg1y4z5fqKK4JY0ZBQ5o8Bye+9B2++aY/VrGn+bvE1b1tERKQoc2Qy1JVXXsnVV1/N1VdfTeXKlX2ec/7557NhwwZefPFFOnToQJUqVYiKiqJq1ap07dqViRMnsmLFijzNHHDCFx4/tZx33nmFvuaXX35Jhtt0Mn9c05eXX36Z1q1bU6FCBUqUKEHNmjXp0KEDjzzyCH96/hQhjvnwQ9i+3R4rjrsNMlWvDkOH2mMffggeI1JERESkiEpI8F5hnJeONa+9Bh6bhbnjDjNjt7hZvdp74UXLlvDoo35+o02bzMCE3HofDRyY/bhuXXjiCdi4EX76yUyyrljRz0lJuPMsHCQm5q/12OLF3rPTSpSAWbPM7iUREZHixmVlLukXv0lJSaFx48bs2LEDgJYtW/L7778X6poZGRm0a9eOVatWAWa3wfbt2ylRokSh8wWoV6+e1xBqX1wuF1dddRXvvfeeVzum/EhMTMzX+Tt37uScc84BICEhgdqeDWmLmRMnoGFDe+Hg/PPNh93iWjgA2LkTGjQA9w5oQ4d6tyEQERGRoueNN+xthipWNDcN87IYPT4eBg2yx26/Hd5+2785hrK0NNOOZfXq7FhkJPz2m+kAVGjJyWY7x+TJsGyZiZUqZXYOlCvnfX5Ghmko37u32Vab29YREUx7rfLlwb2pwZw5eRtmvG2baXW0b589PmWKmW0gIiJSHEU5nUBR9MADD2QVDQCee+65Ql9zzJgxWUUDgBEjRvitaOCpfPnyNGrUiHLlynHkyBE2bdrEgQMHANMiaubMmSxfvpwlS5YUeMdHqO4UCRfabeBbzZpw663w+uvZsfffh8ceA33LiYiIFG2ebYry08Hmhhtg6lT45pvs2Pjx0L+/WZxRHLz4or1oAGanQaGKBmlpMG+eqczMnm1Wv7g7ehRmzjRDFTxFRMCrrxbizaW4iYw0368//pgdW7Hi1IWDI0egVy/vosHw4SoaiIhI8aYdB34WHx/PILflStdeey2ffPJJoa75/fff0717d06ePAlAhw4dWLx4MZGRkYW6rrvOnTtzySWXcOWVV9K8eXPbc5Zl8eOPPzJixAgWL16cFW/VqhW//vorMTEx+X4/VyHucBf3HQfabZC7pCSz6+D48ezYnXd69yoVERGRomP/ftO20H2g7+ef561VUaYtW+DMMyE1NTvWuLG5mV7Ue5v/+adp85KWlh0780zT+infa5UsC1atMsWCjz+GvXtzP79TJ1i0KL8pi/h0332m/VimXr3gyy9zPj8jA665xtSv3F12mal1+fFHbhERkbCjwoEfLV68mG7dunH8vzuW9evXZ9WqVZQvX77A11y3bh0dO3YkOTkZgIoVK7Jq1Srq1q3rl5zzIyMjg6FDhzJhwoSs2Ouvv87dd9+d72upVVHBTZhgVtW7++47uPhiZ/IJRXfdBW+9lX0cEwP//AO1ajmXk4iIiATOhx/aF62XKmVWD5cunb/rjB0L999vj40YASNHFjrFkJWWBuedBytXZsciI+Hnn03rljxLSjLbNuLjYc2aU59ftarZ0jFwILRpk++8RXz56COzgyhTrVpm1kFOnnnG7Nx217Sp6aZViB/jRUREigQVDvxk9erVdO7cmX//m6JWvXp1lixZQsOGDQt8zYSEBDp27EhCQgIAZcqU4bvvvgvYUOS8OHnyJG3atMkaktywYUM2btwY8PdNTEzMam9UnAsHJ05Ao0b2gb8dO8KSJdpt4C4xEU4/3b4b/p577KuPREREpOjo3dusDs7Up49366K8SE+H9u1NX/9MUVGm3UnLloVOMyQ9/zw8/rg99uij3kOSczVgAHzyiVm+nZuYGLMEfOBA6NEj772kRPJo3Tpo1swe27kTatTwPnfmTOjb1x6rWBF+/RXOOCNwOYqIiIQLTZjygw0bNtC9e/esokGlSpX49ttvC1U02L17N5dccklW0aBkyZLMnj3b0aIBQFRUFA888EDW8aZNm/I0VFn8Y/Jke9EANNvAl9q1YcgQe+y998wPDSIiIlK0pKTYZxNA/loUuYuMhIkTTbEg08mTcPPN9jZIRcXatWbFtbtmzeCpp/J5oYoVcy8atG9vhkbs2gXTp0PPnioaSEA0agSxsfbYihXe5/3+u6lfuYuMNPO7VTQQERExVDgopC1btnDJJZewZ88eAMqVK8f8+fNp0aJFga954MABunbtmrWSPzo6munTp3PRRRf5JefC6ty5s+04GDsOxKyeHzXKHuvYUS2KcvLoo/afR48dgzFjnMtHREREAmPePPPvfKbISDMYuaBatoRHHrHHfvsNXn+94NcMRSdPmvZO7js0IyJg0qQc5hrs2pXzxTzvwALUrWv6PG3cCEuXwtChpsAgEkCZA5LdeRYO9uwxG1/c55mAmcV9ySWBzU9ERCScqHBQCImJiVx88cVZ/fpLly7NnDlzaJevZqB2hw4donv37lmtgCIjI5k6dSpXFOanHz+r4bHPc9++fQ5lUrzEx2u3QX7UqWPvdQzwzju5/8wrIiIi4cezJVHnzlCpUuGu+fjjZjCyuxEjzADlouKVV+wtmQAefBD+GytmJCebD1AdOpgPVzl97j/7bPMfLDbWfABbtMgMmBo5EgqxC1ukINq2tR+7Fw5OnDDtibZvt59z001QgNF9IiIiRZoKBwWU2Upoy38/PZQoUYIvv/yS888/v8DXTElJ4bLLLmP58uUAuFwuPvjgA/r16+eXnP3l6NGjtuPS+Z06J/nma7dBhw7abXAqjz5qbzVw7Bi8/LJz+YiIiIh/nTgBX39tjxW0TZG7kiVhwgR7LDUVbrsNisKEuHXrvNsRNW78X9uitDT46ivo1880hr/9djMpNi3NzDHwxeWCzz+H3bvhgw+gUyezfUHEAZ7r+P778RrLgrvuMvPh3HXsCG+9pQVZIiIinvRprgAyWwlt2LABMK2EZsyYwSWF2Nd47NgxevXqxU8//ZQVGz9+PAN9bft12Lp162zH1atXdyiT4iM+HrZutce02+DU6tWDQYPssbffNtuTRUREJPwtWgT/jRnL0qePf659wQXmnrm7b7+FKVP8c32npKebWVDHj2fHXFh89shKSj5yH9SqZfq4zJhh72ME5kNpTpo1Ay0okhDgueMgKcnsOn77be+CYFycGZLssz2XiIhIMafCQT75aiU0bdq0QrUSSktLo2/fvnz//fdZsbFjx3LbbbcVOt9AmDZtWtbjUqVKcdZZZzmXTDGQluZ7t4H6b+bNY4+ZXqeZjh41W/NFREQk/Hm2KTrnHHPf219efNH7esOGhfcihHHj4OefzeOaJPEgL7GjcgtaDm4Lr70Ge/fm/OKtW3NuVyQSInwNSH7pJbj3XnusVCn48kvQOjgRERHfVDjIh9TUVC6//PKsVkIRERFMnjyZq6++usDXTE9Pp3///syZMycr9vzzz3PfffcVNt2AWLFiBe+9917WcY8ePShZsqSDGRV92m1QOA0awA032GNvvaWfeUVERMJdRoa56efOH22K3JUrZ1YpuztwwPsGZLjYuBFGPZ5Kf6Yyj+4kEMdLDKfm/rU5vygmxjSFnz0bduyAKlWCl7BIAUREQJs29tirr5rdNu4mT/YepCwiIiLZVDjIo+PHj9O7d29+/PFHwMwfmDBhAgMGDCjwNS3L4qabbmLGjBlZsSeffJJHH3200PmCyTHz14033pjjeX379mXhwoVYp2jYumDBAi699FKO/7ev2eVy8ZRnc1Txq7Q0eO45e6x9e+02yK/HH7e32U1JMT88iIiISPj65RfYudMe83fhAEzXnmuuscc++cR7tkKoy2xR9ODx55jK9XTnGyLJyPkF7dvD+PHmP/L06dCzJ0RHBy9hkULwbFfk6YknzBgPERERyVnUqU8RgNdee43vvvsu67hChQp89tlnfPbZZ3l6/fXXX8/1119vi02fPp3JkydnHZcsWZJffvmFHj165OmaXbt25YEHHsjTubn57rvvmDlzJnXq1OGyyy6jdevWxMXFUbZsWVJSUtiwYQNffvmlrZUSwJgxY2jVqlWh319ypt0G/nHGGTBggL0n8RtvwAMPQOXKzuUlIiIiBefZpqhpUzPgNxBef93MN0hOzo7dfjtceKHZlRAO3ngDfvoJkhnAY7zg+6S6dc1WzYEDoWHD4CYo4ke5FQ769DE/U4mIiEjuVDjIo9TUVNtxcnIy8+fPz/PrzzvvvFNe89ixY/m6Zo0aNfJ8bl5s376dd95555TnxcTE8MILL3D//ff79f3FLqfdBl27OpNPuBsxAqZONW0NAI4cgbFjvf8bi4iISOizLO/CQSB2G2SqXt3sVhw8ODuWmGhmKb35ZuDet0AOHIDPPjM7BP4b0PD33yZXgL9oznLa0o4VJhAba5ZeDxpkJkJHaFO6hL927XzHW7Qwi4n0bS4iInJq+udSuPXWW2nTpg1RUbnXkUqVKsWNN97IqlWrVDQIgilTtNvAnxo1gv/7P3vslVdgyRJn8hEREZGCW7vW3Ax3F8jCAZj76p7tIt9+26zid1xaGnz1lZlFULOm2Q7x0UeAWTRx001w9Gj26ZMYzIGzu5lVFbt3wwcfQKdOupsqRUbDhlC2rD1WubKZi+I5OFlERER8c1mnamwvxUZKSgqrVq1i586d7Nu3j+TkZEqUKEHFihVp1qwZbdq0ISYmxpHcEhMTiYuLAyAhIYHatWs7kkcwPf44vPhi9gr5886DpUtVOCiMdeugeXOzSjFTuXKwcKH3ADUREREJXSNHwpNPZh/HxcG2bYH/nPTPP3Dmmfab8E2awOrVUKJEYN/bi2XBqlWmt+XHH8PevfbnmzaFtWt58y0Xd99tf+quu0zrIpGibMgQmDTJPI6KMu3GOnd2NCUREZGwosKBhIXiWDgA2LQJRo0yC8bmzIHu3Z3OKPzdfbd3S4EqVczOgyZNnMlJRERE8qdNG3PPPNPdd5s5BMHwyivw4IP22JNPwjPPBOf9SUoyOwUmTzZbL3KR+MVvNB7QDvcOqfXrwx9/aNW1FH3JyaYtaWIi3HsvdOjgdEYiIiLhJSQKB6tXr2b27NksWbKEzZs3c+DAAQ4fPozL5eLkyZNe5x88eJBDhw4BUKJECapXrx7slCXIimvhINO2bVCnjnYb+MPJk6aN76xZ9njt2vDjj2YmoIiIiISurVvNzW93338PXboE5/1PnjRzp5Yvz45FR8PKlWY3QkCkppoPL/HxZtl05pbUnMTEYPXsxc3bnuCD5S1tTy1YABddFKA8RURERKTIcHQ48p9//smwYcNYuHBhViwvdYyFCxfSt29fAMqUKcOuXbsoXbp0wPIUcZpuZvtPVBRMmwaXX25uMmRKTDR9i3/80QxAFBERkdDkWfyvXNnM9A2WqCiYONEMX81c45SWBjffbOYdREb68c3++cdsP50+HQ4fPvX57dvDwIFw7bW880lFPrjD/vTQoSoaiIiIiEjeODb96sMPP+S8885j4cKFXsUC1ymWVffu3Zs6depgWRYpKSnMnDkzkKmKSBFTsqS56XDuufb433+bdlDJyY6kJSIiInnwxRf24549zc38YGrVCoYPt8d++cW7HWKhuVxmcHFuRYO6dWHECNi40QzEGjqUrf9W5KGH7KfVqQNjxvg5PxEREREpshwpHMycOZObbrqJo25TxSzLIi4ujrPOOuuUuw4iIiK49tprs45nz54dsFxFpGgqWxb+9z/vlgK//252I6SkOJOXiIiI5GzvXrM70N2VVzqTyxNPQKNG9tjjj5tWSn5Tvz5ceKF3PDYWBg+GRYvMroSRI6FhQ8DMTL75Zu/PMhMnms8/IiIiIiJ5EfTCwc6dOxk0aBCQvbPgjjvuYPPmzWzdupXPP/88T9fp3bs3YAoOP/zwQ2CSFZEirVIl+OYbaNDAHl+2DK66Co4fdyYvERER8W32bHt7/zJloGtXZ3IpWRImTLDHUlJMO6A8TZFLS4OvvzbDl557LufzBg40v0dEQLduZjDy7t1mJ0KnTibuZsIEM8fA3S23OPffSURERETCU9ALB88++yypqalYlkVERASfffYZb775JvX/m3B2qjZFmc4++2yio6MB2L9/P1u2bAlYziJSdNWsCd99B6edZo9/8w0MGJDdu1hERESc59mmqEcPKFXKmVzAbAa47TZ7bP58c2/fJ8syU5Tvuw9q1TJ9lmbMMNsBchp43K8fjB4N27ebi/fvDznMd9u+HR580B6rXRteeilfX5aIiIiISHALB+np6UybNg2Xy4XL5eLhhx/m6quvLtC1oqKiaNKkSdbx+vXr/ZWmiBQz9evDt9+a4YruZs6EW2/N+ed4ERERCZ7Dh82/1+6calPkbvRo7wUI991n2iplSUoyd+9btoS2beG11+wnbNsGS5b4foNy5cxAhVq1cs3DsszOAs9xCBMmQPnyef5yRERERESAIBcOfv75Zw4dOoRlWURHRzPcc6JYPtWuXTvrcUJCQmHTE5FirFkzmDvXtAx2N2mSWbmXp5YDIiIiEjBz58KJE9nHUVFmLpHTypeHt9+2x/bvh+F3pcLHH5ttEXFx5ub/mjU5X2jKlELlMWmS2THpbvBg8/YiIiIiIvkV1MLB33//DZh2RGeffTblypUr1PXcX3/o0KFCXUtE5Oyz4auvoEQJe3zs2NxbD4uIiEjgebYpuugiqFDBkVS89O4NffuCiwwu5AfeZwivfVbD9D2cPz/37Yvt28P48TBmTIHfPzERhg2zx047DV59tcCXFBEREZFiLiqYb7bXbTtuXFxcoa8X4TYI7KQakYuIH3TuDNOnm9YH6enZ8SefNCsK77nHsdRERESKrePHYc4ceywU2hS5e+Pl44z+vDkNMjaf+uS6deGGG8zg44YNC/W+lmVaK3quo3rvvdAprIiIiIhI+AnqjgP3wcfp7nfkCujAgQNZjyvoU7GI+EnPnhAfD56z2u+918RFREQkuL7/3t673+Uyq/xDSY26JYhu3ijnE2JjTe+gRYvgn39g5MhCFw3AfDaZO9ceGzgwNNo4iYiIiEj4CmrhoGrVqlmPk5KSCn29NW49Qit7TjUVESmE/v3hrbe840OGwKxZQU9HRESkWPNsU3TeeVCzpgOJpKXBX3/l+HTtxwbZjtOJYD7d2PT0VNi9Gz74ADp1ggj//BiWlGQGMburWRPGjfPL5UVERESkGAtq4aBOnToAWJbFqlWrSEtLK/C1Nm7cyI4dO7KOW7ZsWej8RETc3X47PP+8PZaeDtdeCwsWOJOTiIhIcZOeDl9+aY8FtU2RZcHKleYOfa1a0KWLKSD44Ordi/TYcqx1NechxlCH7fRgPr0/7c/xyNJ+T+u22+DgQXv8nXegYkW/vpWIiIiIFENBLRy0b9+eUqVK4XK5OHr0KNOmTSvwtV5//fWsx9WrV6dx48b+SFFExOaRR+Chh+yxEydMe4Sff3YmJxERkeJk2TLYs8ceC0rhICkJXnoJWrSAtm3htddg716TzDff+H5NqVJErlvL3NF/8jIPkUQtANatgxde8G96U6fC11/bYwMGQK9e/n0fERERESmeglo4KFGiBBdffDGWZWFZFo8//jgHPZfI5MFPP/3Eu+++i8vlwuVycdVVV/k/WRERTA/l0aPhllvs8ZQUuOwy+PNPZ/ISEREpLjzbFJ15JpxxRoDeLDXV3JHv3h3i4mD4cFi71vu8yZNzvkbt2tw3zEWbNvbw88/7vlRB7NoF99xjj1WvbmobIiIiIiL+ENTCAcDjjz8OmEHJO3bsoFu3buzxXEKUi4ULF9KrVy8yMjKwLIvIyEgefPDBQKUrIoLLBePHmxZF7pKToVs3+PtvZ/ISEREp6izLu3Dg990GGRlmYPGQIebu+/XXmx0FGRk5v2bv3lyfj4qC99+HyMjsWFoa3Hyzab1UGJZl2ikmJ9vj48eDxr6JiIiIiL8EvXBw7rnnct1112FZFi6Xi+XLl9OkSRNGjhzJhg0byPDxATw9PZ0FCxZw3XXXcckll5CcnJz1+nvvvZd69eoF+8sQkWImMhLi4+HSS+3xXbuga1dwG7kiIiIifvLHH7Bliz3mt8LBpk3wxBPQoIGZWzBpEhw5kvP5devCiBGwcSMsXHjKAcdnneXd7vDnn+HttwuX9qefwqxZ9ti11wZ57oOIiIiIFHkuy7KsYL/p0aNHOf/881m1ahUulyurCAAQExPD8ePHTXIuF40aNWLLli1Zg5Qzz7Usi44dO7Jo0SIi3ZfySJGUmJhIXFwcAAkJCdSuXdvhjKS4Sk2FHj1gyRJ7vGlTWLwYqlRxJi8REZGi6Omn4Zlnso/r1jWFhP9+dCica6+Fzz7L/ZyyZaFfPxg4EC644JTFAk9Hj0KrVqZGkalMGfjrL6hTJ/8p794NzZvD/v3ZsapVTQukqlXzfz0RERERkZwEfccBQKlSpZg/fz4XXXSRrWhgWRbHjx+3HW/YsIETJ06QWd/ILBp069aNOXPmqGggIkFVujR89RW0bm2Pr1tndiMcOuRMXiIiIkWRrzZFfikagCkG+BIRYWYcTJ1qtha+/z506pTvogFAqVIwYYI9lpICQ4ealkP5dddd9qIBwFtvqWggIiIiIv7nSOEAoEqVKnz77beMHj2aKlWq2AoDmb+7/wJTSChfvjyjRo1izpw5lCtXzqn0RaQYK18e5s+Hxo3t8eXLoVcvs7pQRERECueff0yrInd5bsdjWbByJTz4YM7/MHfrBtWqZR83bw5jxkBCAsybB/37mxUDhdSpE9xyiz02dy5Mm5a/60yfDjNm2GN9+5oNESIiIiIi/uZIqyJPx44dY9q0aXz77bf8+OOPJCUl2WYdVKxYkQ4dOtC9e3duuOEGypcv72C24gS1KpJQlJAA558P27fb41dcAZ9/DtHRzuQlIiJSFLzyirnvn6lqVdi50z5w2EtSEnz0kRlMtHatiX3yiWlL5MvIkWYJ/6BBZiiB37Yz2B08CM2amfwzValidizmpc3h3r3m9fv2ZccqVzYtj9xrHyIiIiIi/hIShQNPlmWRnJzMiRMnqFy5MtG6+1bsqXAgoWrTJlM82LPHHu/fH6ZMKVBXAxEREcH8+/rTT9nHN90EEyf6ODE11UwLnjwZvvsO3BYgAXDZZTBnTiBTzZMvvoCrrrLHrr/efF44leuuM0OR3U2bZuIiIiIiIoEQ1MLBpk2bmDt3btbxNddcQ40aNYL19hLGVDiQUPb776YNwb//2uO33276Dgdo8aKIiEiRtXs31KxpnwPw9ddw+eX/HWRkwJIlplgwYwYcPpzzxSIjITERQuDnjquvNrsS3c2dCz165Pyazz83r3PXp4+J6zOGiIiIiARKUAsHb7zxBvfddx9g2g/t3LlTuwkkT1Q4kFC3dCl07WoWPbp79FF4/nlnchIREQlX770Ht92WfRwba9r1lEzYZNoQTZkC27ad+kJ165ohyHffHRIThHfuhKZN7YsN6tQxXZViY73P37/ftChy39lYsaJpURQCdRARERERKcKC2kTjyJEjWUOQW7duraKBiBQZHTqYFgSef6298IKZsygiIiJ598UX9uMH2y2i5EUdoFEjeO653IsGsbEweDAsWmQmLD/7bEgUDcDsonj5ZXts+3YYMcL3+ffc490O8Y03VDQQERERkcALauGgitvkr+rVqwfzrUVEAq5bN9Nv2HOuwcMPm5WTIiIicmr//gsLFthjF1wALFuW84siIsw/xFOnmj5HH3xg+giG4LChm26CLl3ssddfh59/tse+/BI+/tge69nTzFESEREREQm0oH6SrlmzZtbjQ4cOBfOtRUSC4uqrYcIE7/jQod5DDUVERMSNZUFGBv/7H6SlZYdjYqDd/ReatkOemjc3W/u2b4f5881d9dKlg5dzAbhcZkFByZLZMcuCm2+GEyfM8YED5rODuwoV4J13NNdARERERIIjqIWDc889l8jISADWrFkTzLcWEQmaIUPg1VftMcuC66+H//3PmZxERERCVlISvPQStGwJH37o1abokkugXIUIuOEGE6haFe69F1asgD//hIceglq1gp93IZxxBjzzjD22di28+KJ5PGwY7Nplf37cODjttKCkJyIiIiIS3OHIAF27dmXBggW4XC5++eUX2rVrF8y3lzCl4cgSjp56yrRVdleypFkQeeGFzuQkIiISElJTYdYsM+j4228hIwOAjAs6UX7VIo4cyT51wgSzGp9t2+CPP6BHD++hQmHo5Ek45xxYtSo7Fh0Nzz9vaiHuLrsMvv5auw1EREREJHiCXjhYsmQJXbp0wbIsOnTowMKFC4mKigpmChKGVDiQcGRZZkHkG2/Y4+XKwcKF0KaNM3mJiIg4IiMDliyByZNhxgw4fNjnafXYwjbqAWZEwc6dUK1aEPMMopUrTfEgPT3nc8qVM7sR9PFXRERERIIp6NPCLrjgAh577DEsy2Lp0qX07duXgwcPBjsNEZGAc7lMW4GBA+3xQ4ege3dYv96RtERERIJr0yZ44glo0AA6d4ZJk3IsGgBczcysxx07Ft2iAZhFBA88kPs5Y8eqaCAiIiIiwRf0wgHAs88+y9ixY4mMjOSrr76iSZMmPPXUU6xcuZKTJ086kZKISEBERMD770OfPvb4vn3QtavpuiAiIlLkJCebSb4dOkCjRvDcc7n/oxcbS8agwfQuv4ixDMsKX3llEHJ12FNPwemn+36ue3cYPDi4+YiIiIiIgAOtiho0aJD1ePfu3Rw9etQk8l/DzsjISMqXL0/ZsmXzfE2Xy8XmzZv9m6iEFLUqknB37BhccQUsWGCPn3EG/PgjVK/uTF4iIiJ+988/0LQpnDiR+3kREWby8aBB0KcPP/xWms6d7ads2QL16gUq0dCxcCFcdJE9VrYsrFkDdeo4k5OIiIiIFG9BHy6wdevWrCIBZBcMMusXJ0+eZP/+/ezfvz/P13RpSpiIhLiSJc0MyEsugV9+yY7//Td06waLFkHFik5lJyIi4kf165sl9OvW+X6+eXNTLOjfH2rVygp/8YX9tLPOKh5FA4AuXcwA6IkTs2OvvKKigYiIiIg4x5FWRb64XK4C/RIRCRexsfC//8GZZ9rjf/wBl18OKSnO5CUiIpJvSUmwYoXv51wu7wE/VavCvfea1/z5Jzz0kK1oYFnehYPi0KbI3dtvw8MPm1aGEyfCLbc4nZGIiIiIFGdBb1VUr169gNzw37Jli9+vKaFDrYqkKNm5Ey64ADw7rHXtCl99BSVKOJOXiIhIrlJTzfa5+Hj49lto3RqWL/d9bmKimW1w+eWmiNCjB0RH53jplSuhbVt77I8/oEUL/6UvIiIiIiJ550irIhGR4qxmTXO/5fzzzYLNTN9+CwMGwCefQFTQ/3YWERHxISMDliyByZNhxgw4fDj7uRUrYO1a03rIU+3asGeP2W6XB567DU4/3XuHnoiIiIiIBE/ItCoSESlO6tc3hYLKle3xmTPh1lvNfRoRERHHbNoETzwBDRpA584waZK9aJBpypScr5HHogH4blOkrqQiIiIiIs7RmlYREYc0awbz5sFFF9nvxUyaBOXLw6uvhsZNE8sy8xf27YO9e3P+vXRpuOceOO88pzMWEZECSU6Gzz4zuwuWLTv1+WXL+uUfqk2bzMYFd8VtvoGIiIiISKhR4UBExEHt2sHs2ab18/Hj2fFx46BiRXjySf+/Z3o6HDiQexEg8/fMx8eO5e3aX34JCQlQqZL/8xYRkQCwLJgzxxQLZs+GEydyPz8iwgzlGTgQ+vQxVeNC8txtUKOGitAiIiIiIk5T4UBExGGdO5u20VdeCSdPZsefegoqVDCr+HOTmup9oz+33w8cMPeJAiE1Fb7/Hvr2Dcz1RUTEz1wuePppM68gN82bw6BBZhjPaaf5NQXPwkHv3qY+ISIiIiIizlHhQEQkBFxxhVnsef319pv6994Lu3dDmTI5FwNSU53L25cNG5zOQERE8mXQIN+Fg6pVoX9/8/xZZwWkf15SEvz8sz2mNkUiIiIiIs5T4UBEJET07w///gt33GGPP/+8M/n4Eh1t7iNVqZL9+7p18Mcf2eesX+9cfiIi4iE1FWbNgoUL4b33fN/8v+46uP9+s+0tJgZ69TLFgu7dzV/8AfTll/bj8uWhS5eAvqWIiIiIiORB0AsHQ4YM8fs1XS4X77//vt+vKyISbLffDgcPwmOPBef9ype3FwF8/e7+2NcczNdfNzsjMmnHgYiIwzIyYMkSiI+H6dPh8GETv+UWOOcc7/OrVoXhw6FOHbjmGjNkJ0g82xRdfrmpXYiIiIiIiLNclhWoTte+RURE4PLjNmfLsnC5XKSnp/vtmhJ6EhMTiYuLAyAhIYHatWs7nJFI4FgWPPIIjBmTv9dFRfm+2Z/T75Ur++fmzPz5ZrhzprJlzc6JAHS0EBGR3GzaBFOmmF9bt3o/f+ed8OabQU8rJ8nJUK2afb7P9OmakyMiIiIiEgrCqlWRZ43DnwUIEZFQ4XLBiy9C9eowbRpERp66CFClitk94MRfi02a2I8PH4Zdu6BmzeDnIiJS7CQnw6efmt0Fy5blfu60afDKK1CiRHByO4U5c+xFgxIl7IVoERERERFxjiOFg8JscsgsFliWVajriIiEMpfLtJu+/36nMzm1uDgoVQqOHs2OrV+vwoGISMCkpcG8eaZYMHs2nDiR+/kREdC1KwwcGFLbwTzbFHXrBrGxzuQiIiIiIiJ2QS8cbNmyJV/np6enk5yczNq1a5kzZw6ff/456enpVKpUiYkTJ9K6desAZSoiInkREQGNGsHvv2fH1q/XcEsRkYAYPx6eegr27j31uc2bmyHHAwbAaacFPrd8OHrU1D7cXXmlM7mIiIiIiIi3oBcO6tatW6DXtW3bloEDB7J27Vr69evH+vXrufHGG5k7dy7t27f3c5YiIpIfTZrYCwcakCwiEiAlSuReNKhaFfr3N7sLWrcOqR0G7r75BlJTs48jIqBnT+fyERERERERuwinE8iv5s2bs3DhQurUqcOhQ4e48sor2bNnj9NpiYgUa40b24/Xr3cmDxGRIiE1FTIyfD/Xt6/pD+cuJsbEZ8+GHTtg3Dho0yZkiwbg3abowgvNvB4REREREQkNYVc4AKhevTovvfQSAHv37uWZZ55xOCMRkeLNc0CydhyIiORTRgYsWgRDhkD16vDDD77PK1cuu6dP+/amddGuXTB9ulmyHx0dtJQL6uRJ+Oore0xtikREREREQovLCtMJwxkZGVStWpXk5GTKly/Prl27KFGihNNpSYAkJiYSFxcHQEJCArVr13Y4IxFxt2qVWdyayeWCI0egdGnnchIRCQsbN8KUKebXtm3Z8RtvhEmTfL9mwwbT26dhw6Ck6G/ffw8XX2yPbdsGdeo4k4+IiIiIiHgLyx0HABEREZx99tkAHDp0iMWLFzuckYhI8dWokf3YsmDTJmdyEREJeQcOwDvvQIcOptfbc8/ZiwYAM2ZASorv1zduHLZFA/BuU9S2rYoGIiIiIiKhJmwLBwBV3Bqhbt++3cFMRESKtzJl4L9NQVnUrkhExE1amunP07cv1KwJt98Oy5blfH5qKixdGrz8gsSyYNYse0xtikREREREQk+U0wkURorbKqy9e/c6mImIiDRpAgkJ2ccakCwixZ5lmV5ukyfDtGmQl8+rzZvDoEHQvz/UqhX4HINs+XJITLTHVDgQEREREQk9YV04WL58edbjcuXKOZiJiIg0bgzffpt9rMKBiBR7//sfXHHFqc+rWtUUCgYOhNatzaCYIsqzTVGjRtC0qTO5iIiIiIhIzsK2cPDJJ5+wY8eOrON69eo5l4yIiNCkif1YrYpEpNi7+GIoXx7+/df7uZgY6NXLFAt69IDo6ODn5wDPwsGVVxbpOomIiIiISNgKyxkHs2fP5pZbbsH1308ZUVFRXHjhhQ5nJSJSvDVubD/esMF06RARKbIyMmDRItOOyJeSJeHaa+2x9u1h/HjYtQumT4eePYtN0WD9eu/daGpTJCIiIiISmoK+42Dx4sX5fs3Jkyc5ePAgf/31F1999RXLly/H+u9ulMvlYsCAAcTGxvo7VRERyQfPHQcpKbBjB9Su7Uw+IiIBs3EjTJlifm3bBv36wWef+T534ECYP9/8fsMN0LBhcHMNIZ67DU47Dc4+25lcREREREQkd0EvHHTu3Dlrp0BBuBcMLMuiRo0ajBo1yl/piYhIAdWqBWXKmIJBpvXrVTgQkSIiORk+/RTi42HZMvtzX35pnq9Y0ft1HTrAP/9ARFhu9PUrz8JBnz76zyIiIiIiEqoc+6huWVa+f4EpGGQWDerXr8+8efOoWbOmU1+GiIj8x+XyblekAckiEtbS0uCrr8yOgho14PbbvYsGACdO5LzjwOXS3XEgMRF++80eU5siEREREZHQ5chwZKuATa8zXxcXF8fNN9/M/fffT5kyZfyZmoiIFEKTJrByZfaxBiSLSNixLDOzID4ePv4Y9u499WuaNzdDkCVHs2bZjytWhE6dHElFRERERETyIOiFg6eeeirfr4mKiqJcuXJUq1aNNm3a0LAY94YVEQllnnMOtONARMLG0aPw5pumYLBmzanPr1oV+vc3swtatzY7CyRHnm2Krrii2MyEFhEREREJS2FROBARkfCgVkUiEraio+Hll2HPnpzPiYmBXr1MsaBHD935zqP9++GHH+wxtSkSEREREQltargqIiJ+47njIDERjhxxJhcRkXyJioIBA3w/1749jB8Pu3bB9OnQs6eKBvnw9deQnp59XKoUdO/uXD4iIiIiInJqKhyIiIjfNGzo3a1j40ZnchERsdm4EZ54Am67LedzBg7MflynDowYYYa1LF0KQ4eaxvySb55tirp3h9KlnclFRERERETyxpHhyCIiUjSVKgV168LWrdmxDRugTRvHUhKR4uzAAfjsMzO3YNkyE4uMhGeegRo1vM8/6yx4/HG45BK48EKI0BqbwkpJgfnz7TG1KRIRERERCX1B/2koMjKSyMhIoqKi2L59e4Gvs23bNtu1REQkNGhAsog4Ki0NvvoK+vaFmjXh9tuziwZgeuZ8/HHOr3/uOejcWUUDP5k/H44dyz6OjDSDkUVEREREJLQF/Sciy7KyfoXStURExD80IFlEgs6yYOVKuPdeqFXLDDCeORNOnPB9fnx8cPMrxjzbFHXuDJUqOZKKiIiIiIjkgyNL9V2eDbBFRKTI8NxxsGGDM3mISDGQlARTp8LkybB27anPr1oV+vc3swwsy3soi/hVWpoZjOxObYpERERERMKDI4UDy7JUPBARKaJ8FQ4yMtT1Q0T87OabYdIk8xdMbmJizA6EgQOhRw+Ijg5OfsKiRXDwoD3Wp48DiYiIiIiISL6F7XCAE25bz2NiYhzMRERE3Hm2Kjp2DLZvh3r1HElHRIqqmjVzLxq0b2+KBddeCxUrBi8vyeLZpuicc0wnKRERERERCX1hu/4zMTEx63HZsmUdzERERNzVqAHlytljalckIgXi9nnPy8CB3rG6dWHECNi4N1wFOgAAvRFJREFUEZYuhaFDVTRwSEYGzJplj6lNkYiIiIhI+AjbwsEX/y1hcrlc1NMyVhGRkOFyaUCyiBRCcjK88w506AB16sCWLb7Pa9jQ7CqIjYXBg01fnH/+gZEjzXPiqF9+gZ077TEVDkREREREwkdAWhUtXrw4T+f9/PPPbN26Nc/XPX78ODt37uSbb77hk08+yYq3bds2vymKiEgANWkCv/2WfawdByKSq7Q0mDcP4uNh9mxwa0nJlCnw5JO+XxcfD6edBqVLBydPyTPPNkVNm3oXlUVEREREJHQFpHDQuXPnUw4/tiyL//u//yvwe1iWlfX4mmuuKfB1RETE/zwHJGvHgYh4sSxYtQomT4Zp02DvXt/nxcfDE0+Y7UyezjgjsDlKgViWd+FAuw1ERERERMJLQIcju9/cL8jzOXG5XLhcLizLolevXnTp0qVA1xERkcBQqyIRyVFSEnz0kSkIrF176vMPHTIT1uvWDXxu4hdr18Lff9tjKhyIiIiIiISXgBUOCloUyOu1Y2Njue222xg1alTA3kdERArGc8fBzp3m3p/n0GQRKSZSUsyk3Ph4+O47Mzk3NzEx0KsXDBoE3btDdHRQ0hT/8NxtEBcH6iwqIiIiIhJeAlI4eOqpp3J87plnngHMroF77rmHChUq5OmaLpeLEiVKUKFCBZo2bUq7du0orX62IiIh6YwzICLCfm9wwwY4+2znchIRB731Fjz88KnP69ABBg6Ea66BihUDn5cEhGfhoE8f352mREREREQkdLmsQG4N8CEiIiJr/sGWLVuoU6dOMN9ewlRiYiJxcXEAJCQkULt2bYczEpFTOeMM2Lw5+3jKFLj+eufyEREHJSZCnTqm+b2nunVNseCGG6Bhw+DnJn61dSvUr2+Pff89qLOoiIiIiEh4iXDiTYNcqxAREQdoQLJIMZKcDO+8Axs3+n6+dm24+OLs47JlYcgQWLQI/vkHnn1WRYMiYtYs+3HlynDBBY6kIiIiIiIihRDQ4ci+LFy4MOtxjRo1gv32IiISJI0bw5w52ccqHIgUMWlpMG+emVswezacOAEPPQRjxvg+f8gQiIw0uwv69AG1nCySPNsU9ewJUUH/iUNERERERAor6K2KRApCrYpEws+ECXDrrdnHZ54Jf/7pXD4i4geWBatWmWLBxx/D3r3252vWhIQEUyCQYmfvXqhRwz7f5ssvzZxrEREREREJL1r/IyIiAeHZqmjjRkhP1/1EkbCUlARTp5qCwZo1OZ+3cycsWADdugUvNwkZs2fbiwZlykDXrs7lIyIiIiIiBafCgYiIBETjxvbjEyfM0MzTT3ckHRHJr9RU07A+Ph6+/dZ+R9iXmBiztLxSpaCkJ6HHs01Rjx5QqpQzuYiIiIiISOGocCAiIgFRtSpUrGhmpmbasEGFA5GQl5gITz0F06fD4cOnPr99ezO34Nprzf/0UiwdPmzqS+6uvNKZXEREREREpPAcLxwcOXKEr776iqVLl7Ju3TqSk5P5999/yTjVqjY3LpeLzZs3BzBLERHJL5fL7Dr4+efs2Pr1cNllzuUkInlQsiRMmWKGH+ekbl1TLLjhBmjYMHi5SciaO9fsLMsUFQWXX+5cPiIiIiIiUjiOFQ7S0tJ46qmnGD9+PIcOHcqKF2RWs8vl8mdqIiLiJ02a2AsHGzY4l4uI5FGVKuaO76xZ9nhsLPTrB4MGwQUXQESEI+lJaPJsU3TRRVChgiOpiIiIiIiIHzhSONi3bx+XXnopK1euzCoUuN/8z0shwLIsXC5XgQoNIiISHJ4DktevdyYPEflPWhrMm2fmFsTFwauv+j5v4EBTOIiIgEsuMcWCPn2gdOlgZith4vhxmDPHHlObIhERERGR8Bb0wkFGRgbXXXcdK1asAMi6+R8dHU2lSpXYtWtXVlGgTp06HDp0iIMHD3oVGMqWLUslDd8TEQlpngOSVTgQcYBlwapVpljw8cewd6+JV6oEL7wAJUp4v+ayy+CVV8zcglq1gpuvhJ3vv7ePw3C5oHdv5/IREREREZHCC/oe808++YTvv/8el8uFy+Widu3azJgxg8OHD7N06VLbuVu2bGH//v2kpKSwYMECrr/+eqKiorAsi5MnT/LEE0+wZcsWtmzZEuwvQ0RE8sBzx8GePfZhySISQElJ8NJL0KIFtG0Lr72WXTQAOHAA/vc/368tUQLuv19FA8kTzzZF550HNWs6k4uIiIiIiPhH0AsHr/63Jd6yLKpVq8ZPP/3EVVddRXR0dI4tikqWLEmXLl2Ij4/np59+ol69ehw9epRbbrmFt956K5jpi4hIPpx+uhmQ6U5zDkQCKDUVpk6F7t1NK6Lhw2Ht2pzP/+ij4OUmRVJ6Onz5pT2mNkUiIiIiIuEvqIWDffv2sXLlyqzdBqNGjaJ27dr5uka7du347rvvqFq1KpZlMWzYMFavXh2YhEVEpFCio6FBA3tM7YpE/CwjAxYtgiFDoHp1uP56+OYbE89J+/YwfjxMmBC0NKVoWrbM7CZzp8KBiIiIiEj4C2rh4JdffgHMboNSpUrRv3//Al2nQYMGjBo1CoD09HReeOEFv+UoIiL+5dmuSDsORPwoI8O0IurSBSZNgiNHcj63bl0YMQI2boSlS2HoUDPnQKQQPNsUnXkmnHGGM7mIiIiIiIj/BLVwkJSUBJgBxy1btqRkyZK5np+WlpbjcwMHDiQ2NhbLsvj6669JSUnxa64iIuIfGpAsEkAREdCmTc7Px8bC4MFmR8I//8DIkdCwYdDSk6LNsrwLB9ptICIiIiJSNAS1cJDsNhHTV4uimJgY2/GxY8dyvFZMTAznnHNO1nk//fSTn7IUERF/0o4DkUJKS4Pc2jIOHGg/joiAbt3MrIPdu+GDD6BTJxMX8aM//oAtW+wxFQ5ERERERIqGqFOf4j+WZWU99rXboGzZsrbj3bt3e8XcVa9ePetx5m4GEREJLZ6Fg7//NvdBo6OdyUf86+RJ+PNPKFXK+89aCsGyYNUqiI+Hjz82Q49374YyZbzPvegiqFULKlSAQYOgf39zLBJgnrsN6tWDs85yIhMREREREfG3oC49K1euXNbjw4cPez1fpkwZoqKyaxlbt27N9XonTpzIerzHcyqbiIiEBM9WRWlp3itUJTxlZJjF7m3aQLNm8OabTmdUBCQlwUsvQcuW0LYtvPYa7N0LKSnw+ee+XxMZCStWmArOQw+paCBB46tNkcvlTC4iIiIiIuJfQS0c1K1bN+uxrxv9LpeLhm59d3/99ddcr7dmzZqsx9FauioiEpIqV4YqVewxtSsqGv73P5g2zTy2LBg1yvwu+ZSaanYV9OgBcXEwfDi4fcbJEh+f8zWqV9cdWwmqf/4xrYrcqU2RiIiIiEjREdTCQZP/ehhYlsVff/3l85yz3PY3f/LJJzle65dffmGD252n0047zT9JioiI33m2sNGA5KLhpZfsx7t2gY8NheJLRoYZWDxkiLnpP2AAzJ9v4jk5ehTcdluKOMlzt0HVqtChgzO5iIiIiIiI/wW1cNCgQQOqVasGwKFDh1jv485R7969sx6vXbuWF154weucPXv2MHjwYFxuK+s6duwYgIxFRMQfPNsVqXAQ/n7+GRYv9o5r5NApbNoETzwBDRpAly4waRIcOZLz+XXrmvM3boQff4SYmODlKpKL2bPtx717m65ZIiIiIiJSNAR1ODJA586d+eyzzwCYO3du1i6ETFdccQVVq1Zl3759WJbFiBEj+Oabb7jiiisoX74869evZ/LkyRw4cADLsnC5XHTu3JnatWsH+0sREZE88txxoFZF4c9zt0GmpCQNSc7VK6/Au+/mfk7ZstCvH9xwA1x4IUQEdZ2HyCkdO2aKh+7c1v6IiIiIiEgR4LKs4HYj/uyzz7juuusA05Zo5cqVXudMnDiRW2+9FZfLlVUccJcZsyyLmJgYlixZwtlnnx2U/MUZiYmJxMXFAZCQkKBCkUiY+eor6NUr+7hyZdi3z7l8pHA2bTK7SHx9goiPN/e7JQdLl4KvXZIREdC1q5k23acPlC4d9NRE8mrJElPTyuRyQXIylC/vXE4iIiIiIuJfQV/CdsUVV9CzZ08uv/xyatWqxfbt273Oufnmm7n33nu9igaZNY7MokFUVBTvvvuuigYiIiHOcwX6/v0qHISzV17JeQhysW5VZFmwciXcdx8cOOD7nPbt4Ywzso+bN4cxYyAhAebNg/79VTSQkLdkif24VSsVDUREREREipqgtyoqXbo0X3755SnPGzt2LB06dODpp59m3bp1WfHM4sH555/P6NGjad++fcByFRER/6hfH6KjIS0tO7ZhA1Sp4lxOUjC7d8OHH+b8fLEsHCQlwUcfme0Wa9eaWJMmMHSo97kuF9xzD2zeDIMGwVlnmZhIGPEsHLjvPhARERERkaIh6IWD/OjXrx/9+vXj77//ZtOmTRw8eJCKFSvSqlUratas6XR6IiKSR1FRZpG1Wx2Y9et9d2yR0Pbmm3D8eM7PF5vCQWoqfPGFKRZ89x1kZNifj4/3XTgAuPvuwOcnEiDp6fDTT/bYBRc4k4uIiIiIiAROSBcOMp1xxhmc4b6tX0REwk6TJvbCgQYkh58jR+Ctt+yxsmXh8OHs4x07gptTUGVkwOLFpigwfbr5D5KTZctg40Zo1Ch4+YkEwe+/2/+fBxUORERERESKoqAWDtLT00lJSck6jo2NJSIi6GMWRETEAZ5zDtavdyYPKbgPPjADUN098gg8/nj2cZHccbBxI0yZYn5t23bq8+vWNUOOy5ULfG4iQebZpqhhQ6he3ZlcREREREQkcIJ6137y5MlUrFiRihUrUqVKFXbu3BnMtxcREQc1bmw/1o6D8HLyJLz6qj3WsydcfLE9lpSU8+DksLN0KXToYL55n3su96JB2bIwZAgsWgT//APPPgs1agQtVZFgWbzYfqz5BiIiIiIiRVNQdxzs3r07a7hxmzZtqFWrVjDfXkREHOS542DzZjhxAmJinMlH8mf6dO/75sOHg+c/5WlpsG8fVK0avNwCpkwZ03IoJxER0LWr2V3Qpw+ULh201EScYFneOw7UpkhEREREpGgKauGgTJkyALhcLurUqRPMtxYREYd57jhITzfFg6ZNnclH8s6yYMwYe+y888xw65MnweWy7zJISgqjwoFlmW/GKB8fiVq1gpYt4Y8/7PHmzWHQIBgwAE47LTh5ioSAjRth7157TIUDEREREZGiKaitimpoy76ISLFVoYJ3H2y1KwoPCxbA6tX22PDhpmAQHQ3VqtmfC4s5B0lJ8NJL0KIFjBuX83mDBpnfq1aFe++FFSvgzz/hoYdUNJBix3O3wWmnQf36zuQiIiIiIiKBFdQdB43dlpsmJCQE861FRCQENGkCu3dnH2tAcnjw3G3QsCH06pV9fNpp9j/XkC0cpKbCF19AfDx89x1kZJj45MnwwAOmEuLp+uvNF9yjh6mSiBRjvuYb+PrfRkREREREwl9Qdxy0atWKM844A8uyWLFiBcnJycF8exERcZhnuyIVDkLfqlXw7bf22IMPQmRk9rHnnIMdOwKfV55lZJiBxUOGmC0v118P33yTXTQAWLMGfv/d9+urVTNToFU0ENF8AxERERGRYiSohQOAW265BYD09HRefPHFYL+9iIg4yHNAsloVhb6XX7YfV6tmZgG78+zYExI7DjZuhCeegAYNoEsXmDQJjhzJ+fzPPw9ebiJhKDERtm61x1Q4EBEREREpuoJeOHjggQc477zzsCyLV199lU8//TTYKYiIiEM8Cwfr19uH6kpo2bYNPP+ZvuceKFnSHguZwsGBAzB+PLRvb7a3PPec+SJyUras2YmwaBE8/XSwshQJS567DSpWNHPCRURERESkaAp64SAiIoLPP/+cc889l/T0dPr378+tt97K5s2bg52KiIgEmWerooMHYe9eR1KRPBg7FtLTs4/LlIHbb/c+LyQKB3v2mETuuAN+/jnn8yIioFs3mDoVdu2C99+HTp1MXERy5Dnf4Pzz9b+NiIiIiEhRFtThyADPPvssAF26dOGvv/7i8OHDvP/++7z//vs0b96c1q1bU61aNcqWLZuv6z755JOBSFdERPyobl0oUQKOH8+OrV9v2t9IaNm/HyZMsMduvhkqVfI+NyRmHFSrBm3bwtKlvp9v3hwGDYIBA7wrHSJySppvICIiIiJSvLgsK7hNIiIiInC5XLaYewqez+VVuvuSSClyEhMTiYuLAyAhIYHatWs7nJGIFFSLFmYWbaZ334Vbb3UuH/HtuefMiIBMkZGwebMp/nhavRpat84+drngxAmI8vfyhKQk2LIFOnb0/fy778LQodnHVatC//6mYHDWWSYxEcm3/fuhShV7bNkyOO88Z/IREREREZHAC4kNxi6XK+tXfgW57iEiIoWkAcmh7+hReOMNe+zaa30XDcB7Ab9lwe7dfkomNdW0FereHeLizGTmnP7tv+YaKFcO+vaFr74yWx/GjTNVDRUNRArsp5/sx6VKQZs2zuQiIiIiIiLBEfRWRaCb/SIixZmvAckSWuLjzcgAdw89lPP5VapAdDSkpWXHkpK8WxjlWUaGaageHw/Tp8ORI9nP/fOPuYt5/vner6tY0cwtKFWqgG8sIr54zjdo3x5iYpzJRUREREREgiPohYOFCxcG+y1FRCSEeA5IVuEgtKSnwyuv2GPduplOPzmJiICaNWH79uzYjh1w9tn5fPNNm0yxYMoU2LYt5/Pi430XDkBFA5EA0HwDEREREZHiJ+iFg06dOgX7LUVEJIR47jjYuhWOHYOSJR1JRzx8+aW5f+8ut90GmU47zV44SErK4xsmJ8Onn5piwLJlpz6/bFkoUyaPFxeRwkpJgZUr7TEVDkREREREij5HWhWJiEjx5bnjICMD/v4bzjzTmXwkm2XBmDH2WOvWcPHFp36t55yDUxYO5syBDz+E2bPNJOXcRERA165mvkGfPlC69KkTEhG/+PlnOHky+zgqSkORRURERESKAxUOREQkqMqWNTeZ3W8sb9igwkEo+PFH+OUXe+yhh/I2V9hznsEpCwevvw7ffJP7OWeeCYMGQf/+3pUJEQkKz/kGbdtq04+IiIiISHGgwoGIiARdkyb2G8uacxAaPHcb1K0L/frl7bWe9/V37DjFCwYO9F04qFoVBgwwz591Vt6qFiISMJpvICIiIiJSPEU4nYCIiBQ/GpAcev76C77+2h67/37TliQv3AsHpUjlrL8+hv/7PzNt2Zc+fSA21jyOiTEViq++MhWHsWNNjyQVDUQcdeKEaVXkToUDEREREZHiISR2HKSlpbFs2TKWLFnC5s2bOXDgAIcPHwZgwYIFDmcnIiL+5jkgecMGZ/KQbC+/bD+uVAluuinvrz+tRgYXsoRBTKYvMyiXeBg+AW68Ebp3935BmTIwYgRUqADXXAMVKxYiexEJhJUr4ehRe6xjR2dyERERERGR4HK0cJCSksLYsWN588032bt3r+05y7Jw5bDScNq0aTz++OMAVKpUid9++y3Hc0VEJPR4Fg7WrzeDefVXuTOSkuCjj+yxO+/MYx/zTZsgPp4LP5jCD2zzfj4+3nfhAODhh/Odq4gEj+d8gzPPhMqVnclFRERERESCy7HCwR9//ME111zDpk2bsCwLIM83/3v27MnQoUM5fPgw27Zt49tvv6Vbt26BTFdERPzIs1XR4cOwc6fm3zrltdcgLS37uGRJuOuuXF6QnAyffmqKAsuWARCT07lffAGHDkG5cv5KV0SCRPMNRERERESKL0dmHPz111906tQpq2iQWTCwLCuriJCb2NhY+rlNa5w5c2bAchUREf+Li4NSpewxtStyxqFD8M479tiNN0K1ah4npqWZGQT9+kGNGnD77VlFA1/SiSD5nO4wcSKUKOH3vEUksDIy4Kef7DEVDkREREREio+gFw6OHTvGFVdcwb///psVa9GiBe+//z7//PMP69aty1PxoHfv3lmPNQdBRCS8RERoQHKoeO89UzzI5HKZocg2kyZBrVrQqxfMmGEmpuZgDc15iDHEkcC3D8yD/v1VOBAJQ2vXms1F7lQ4EBEREREpPoLequj1119n69atWbsM7rnnHl599VUiIkwNY9s2H/2RfejSpQsulwvLstiyZQt79uyhmtfySBERCVWNG8Pq1dnH2nEQfCdOwLhx9thVV0HDhh4nVqwIHrOIbKpWhf/7P275cRATV7YGzL/xSUn+zFZEgslzvkH9+lC7tjO5iIiIiIhI8AV9x8Ebb7yRVTTo06cP48aNyyoa5EdsbCz16tXLOl63bp2/UhQRkSDwNSD5/9m77/go6vyP4+8NKdTQIaGDVFEREAREwAPEBhwIoqDg2TunIHo/sZdTbFjOgoICh3RBEU8QBREBG6CCEFBqQug9EBKS+f0xZrMz2fTdnS2v5+PB4/L9Znb2g8dkYN77/X4QWNOnSykp5tfldFLRytSDD3o58Ior8nZEjY2VBg0yty9KSZFee01pLdopJzSQcs8NIPTQ3wAAAACIbAENDn7//XelpKS4tyJ68cUXS3W+s846y/311q1bS3UuAEBgsVWRs7KzpZfGZau7lmmibtJe1dZDZy/QhRd6OTg2VrruOvPrzp2lt9+W9uyRZs+WrrpKiomRlLe5NSsOgNBkGAQHAAAAQKQL6FZF6/7ak8Llcumcc85RkyZNSnW+KlWquL/27JkAAAh+9hUHO3dKJ09K5cs7U09E2bxZfz42VQt+n6pGyt0i8J74KZIGen/N6NHSffd52ccoF8EBEB62bs17/RIcAAAAAJEloMHBfo/9kZsV8OChqOI8mi2ePHmy1OcDAARO8+bWsWFIW7ZIbdo4U0/YO3RImjVLmjxZWr1a3u7CtX9aaPYyqFkz7zcbNiz0LerWtY4JDoDQZF9tUKtW3p/ZAAAAAMJbQLcqSk9Pd3/t+dC/pDxXGVSqVKnU5wMABE6FClL9+tY5tivyscxMswfBoEFSYqJ0553S6tX5Hu7KzpZWrCjx29lXHKSkmIEQgNDibZsil8v7sQAAAADCU0BXHNSoUcP99YEDB0p9Ps++BtXtTRsBAEGvZUtp167ccVKSc7WEDcOQ1q41VxZMn26uICjEpujWavrUCEWPGJb36X8x2F+aliYdPy7Fx5f4lAAcQH8DAAAAAAENDhISEiRJhmFo7dq1pTrXwYMHtXHjRve4adOmpTofACDwWraUvvwyd8yKAx9YsULq1q3Qw/appj7SUE3WCA199nw9OKb0Hyf2ljns3k1wAISSPXvMbeM8ERwAAAAAkSegWxV16dJFUVHmWx48eFBff/11ic81adIkGX/tf1ChQgVdcMEFPqkRABA4LVpYx6w48IEuXfI2G8gRG6ufmwzSVVqgukrR/RqvrfFtddvtvtmDpFw5qWpV6xx9DoDQYl9tUKkSvWcAAACASBTQ4KBq1arq0KGDe/zoo4+6H/4XR0pKip5//nm5XC65XC717t3bHUgAAEJHy5bW8aZNUna2M7WEjOxsadky6bvvvH+/TBnp+uutc126SO+8o/2/7VHX3bO1UFfpjGIkSbffLlWu7LvyvPU5ABA67MHBRReZP1YAAAAARJaAP20fOXKk++vVq1frjjvuKNbr9+7dq379+unw4cPu0OGBBx7waY0AgMCwrzg4eZIHzfnavFl69FGpSRPpkkukxx/P/9jhw6VGjczjN282Q4bbb9cb/62q9PTcw2JiJI/bsk/YgwNWHAChhf4GAAAAACQHgoNrr71W559/viSz18H777+viy++WN/a/5Vik5aWpnfeeUfnn3++1q1b515tcOmll+qiiy4KQOUAAF+rW1eqUME6x3ZFHg4dkt55x1wx0KKF9Mwz0o4d5ve+/traWdrT2WdLW7dKTz0lNWsmyWxU/J//WA8bNiz/XY1KiuAACF1Hjki//GKdK0LLFAAAAABhKKDNkXPMmTNHnTp10sGDByVJ3333nXr06KGEhIQ8TY7vvPNObd68WatWrdLp06dlGIZcLpcMw1DdunU1depUJ34LAAAfcLnM7Yp+/jl3btMmqVcv52pyXGam9MUX0pQp0qefShkZ3o8zDGnaNOnhh71/32XtWzBpkplDeBo92gf12tiDCIIDIHSsXGn+aMkRFyd57DIKAAAAIII4Ehw0adJEn332mQYMGKDU1FR3EJCamqo9e/a4jzMMQxMmTHB/Lcl9bL169fTZZ5+pRo0aTvwWAAA+0qKFNTiIyBUHhiGtXStNnixNny7t31/4a845J+/H+/Nx5oz0yivWuSuvlFq3LkGthaDHARC67AuAO3Y0wwMAAAAAkcexjsIdO3bUmjVrdPnll1tCgZz/zfmVI+drwzDUu3dv/fDDDzrvvPMCXzgAwKe8NUiOGBkZ0osvSueeK7VvL73+esGhQc2aZlOCNWukX381exkUwZw50vbt1rkxY0pedkHYqggIXfQ3AAAAAJDDseBAkmrXrq2FCxfqxx9/1PXXX6+EhAQZhuH1V3x8vAYOHKilS5dq0aJFSkhIcLJ0AICP2BskR1RwEBMjTZggbdiQ/zGxsdKgQdKCBebH98ePl9q2zbMVUX4MQxo3zjp34YX+eyDoLTjw3PoEQHA6dUr64QfrHP0NAAAAgMjlyFZFdu3bt9eUKVMkSVu3btWuXbt08OBBZWRkqEaNGqpdu7Zat26tqChHcw4AgB/YVxwkJ0snTkgVKzpTT0C5XOaqgccey/u9zp3N711zjVStWonf4uuvzV2QPD34YJFzh2Kz9zjIzJQOHpTYWRAIbj/8YF6vOaKizB9DAAAAACJTUAQHnpo0aaImTZo4XQYAIECaNTMfYnt+Kn3zZqldO+dq8pktW8wmx0lJ0qxZ3o+54Ybc4KBhQ3M8fLj5H8YH7KsNmjaV/v53n5zaq9q18/7/mZJCcAAEO/s2ReefL8XHO1IKAAAAgCAQdMEBACCylCtnPi/33IN/06YQDg4OH5ZmzjQDg1WrcueTkvLuyyRJjRpJ//63+dHeiy82P+brI+vWSYsXW+dGjZLKlPHZW+QREyPVqiXt3Zs7t3u31KaN/94TQOnR3wAAAACAJ/b+AQA4zr5dUVKSM3WUWGam2YNg8GApIUG6805raCBJU6fm//qHH5a6d/dpaCBJL71kHdesKY0Y4dO38IoGyUBoOXNGWrnSOkd/AwAAACCyBW1wcOrUKaWnpztdBgAgAOzBQUg0SDYMac0a6Z//NDf279dPmjNHysjwfvzUqVJ2dsDK27FDmjHDOnfvveYKD3+z9zlISfH/ewIouXXrzN4ynrp2daQUAAAAAEEiKLYq2rdvnz766COtWLFCP/74o/bt26eMvx68xMbGqlatWurQoYO6du2qoUOHqlatWg5XDADwJfsOPkG94mD3bmnaNGnyZGnDhsKPr1lTGjrU7Fvgr47EXowfL2Vl5Y7Ll5fuuisw782KAyC02LcpatHC3HIMAAAAQORyNDjYv3+/Ro8erZkzZyozM1OSZHh2U5R0+vRp7dq1S8nJyZo3b54efvhhDRkyRC+++CIBAgCECW9bFWVn+3znntIbOVJ6883CVw7ExporEIYPly67zNz4P4AOH5bee886d8stUvXqgXl/ggMgtNDfAAAAAICdY49kPv/8c7Vu3Vr//e9/lZGR4Q4MXC6X11+SGSpkZGTov//9r1q3bq3//e9/TpUPAPAh+4qD9HRp505nailQo0YFhwadO0tvvy3t2SPNni317Rvw0EAyS0hLyx2XKSPdf3/g3p/gAAgdhpE3OKC/AQAAAABHgoMFCxZo4MCBOnDggAzDsAQDhmEoLi5OtWrVUq1atRQXF+eel8xgwTAMHTx4UAMGDNBnn33mxG8BAOBDCQlSfLx1zrHtirZtM5+keTN0qPkU3lPDhtKjj0qbN5vdRe+4Q6pa1f915iM9XXrtNevcNdeYmUeg0OMACB2bNkkHDljnWHEAAAAAIODBQXJysoYNG6aMjAx3YOByuTRw4EDNmTNHu3bt0smTJ5WamqrU1FSdPHlSu3bt0pw5czRgwADLKoSMjAwNHTpUKTyRAICQ5nI53CD50CHpnXekLl2kJk3MTqHe1K5tbj1UqZJ0003SsmXS1q3SU09JzZoFsOD8TZki7dtnnXvwwcDWYF9xsHevdOZMYGsAUDT21Qb16pl5KAAAAIDIFvAeB2PGjNGJEyfcKwdatmyp//73v2rXrl2+r6lbt64GDhyogQMH6ueff9b111+vpL8+ipqWlqYxY8Zo2rRpgfotAAD8oEUL6Ycfcsd+Dw4yM6UvvjCbHC9YIGVk5H5v8mSpbVvvr3vrLalGDbPbcJDJypJeftk616tX/r8Vf7EHB4Zhhgf2lQgAnOetv0EA+7gDAAAACFIBXXFw/PhxzZs3zx0aNG3aVN9++22BoYFd+/bttXz5cjVt2tR9nnnz5unEiRN+rBwA4G/eGiT7nGFIa9aYTY7r1jUbGM+daw0NJOmjj8xgwZsGDYIyNJCkTz81d0zyFOjVBpKZq9hbO9DnAAhOy5dbx/Q3AAAAACAFODhYtmyZTp8+7e5r8Pbbb6t69erFPk/NmjX11ltvufsenD59WkuXLvV1uQCAAPLrVkW7d0svviidd57Uvr30+uvS/v0Fv2bLFh8W4H+GIY0bZ51r00bq3TvwtURFSYmJ1jl2FQSCz86deRvR098AAAAAgBTg4CA5Odn9dYMGDdSzZ88Sn6tXr15q6LEB665du0pVGwDAWS1aWMepqdKxY6U4YVqaNG2a1KePVL++NGaMtH59/sfHxkqDBpnbFqWkSGefXYo3D7zvvpNWr7bOjRnj3JYj9u2KWHEABB/7NkXVqkmtWjlTCwAAAIDgEtAeB8f+egLkcrl07rnnlvp85513nnbs2CFJbFUEACGuaVPzk+rZ2blzSUlShw4lPOFHH0m33Vb4cV26SMOHS9dcI1WtWsI3c559tUGDBtLgwc7UIhEcAKHAHhx07Wr+HAYAAACAgP7TINFj34KKFSuW+nwVKlRwf127du1Snw8A4Jy4OKlxY+tcqbYrGjzYPKk3jRpJY8eaDQG++066/faQDg02bjQXSnh64IG8fQYCieAACH70NwAAAACQn4AGB55bC6X4YLNjz3N4nhsAEJqK1SD50CHp7beltWu9f79KFenvf88dV6ok3XST9M030p9/Sk8/LTVrVsqKg8NLL1nHVatKN9/sTC056ta1julxAASXAwfM0NET/Q0AAAAA5AhocHDRRRepZs2aMgxD33//vY4cOVLicx05ckSrV6+Wy+VS9erV1bVrV98VCgBwRKENkjMzpU8/NXsRJCZKd90lvfVW/ie88Uazx8G0adKePdLEieZHasNoL47du6X//tc6d9ddkg8W9pUKKw6A4LZihXVcvrzUtq0ztQAAAAAIPgF9chIdHa0bbrhBkpSZmamnnnqqxOd68sknlZmZKUm6/vrrFR0d0HYNAAA/sDdI3rRJkmFIP/8sjRxpPo3u31+aO1fKyDAPmjVLOnXK+wkvu0z64gtp6FDzqVgYev313P8Ukrk70733OldPDoIDILjZtynq3NnZ7c0AAAAABJeAf+Ty0UcfVYMGDWQYhl5//XW9/fbbxT7H22+/rddff12SVL9+fT322GO+LhMA4ADPFQeJ2q1+m8bJOPdc6YILzCfkBw7kfdGxY9InnwSuyCBy7Ji5W5OnESOkYGj7Y9+q6NAhKT3dmVoA5GVvjEx/AwAAAACeAh4cVK5cWYsXL1aDBg2UnZ2te+65R4MGDdKGDRsKfe2GDRt09dVX65577pFhGGrQoIEWLVqkKlWq+L9wAIDftWxwUkM1TV+oj3apvp7Lekiugu4PsbFmE+RGjQJWYzB57z0zPMjhckmjRjlXjyf7igOJVQdAsDhxIm97GPobAAAAAPDkMgzDCOQbTpkyRZJ04MABPfvsszp8+LBcLpck6dxzz9WFF16o5s2bKz4+Xi6XS0ePHtXmzZu1evVqrV+/XpJkGIaqVaum//u//1ONGjWK/N7Dhw/3/W8IAZGcnKz69etLknbt2qV69eo5XBEAn9q7V/rXv2TMni3XiROFH9+lizR8uHTNNWYn4AiUkSE1aWJtOjxggPTxx87V5MkwzD4LJ0/mzn37rURLIsB5X34pXXpp7jgmRjpyJGx3dAMAAABQAgEPDqKiotxBQQ7PEuzfK84xhcnKyirR6+A8ggMgzJ08ae6vU1Bo0LChGRbccIPUrFngagtSU6aY2xJ5WrVK6tTJmXq8adZM+uOP3PHMmWbWA8BZjz4qPfNM7rhTJ/PnBwAAAADkCPhWRTnsQUDOr5zvef7ydoy347z9sr8XAMBB+f08Ll/e3HLI5pgqaUXzm6Rly6StW6WnniI0kPmfcdw461zXrsEVGkh5+xywVREQHOhvAAAAAKAwjgQHng/083vYbz++KMcV9F4AAIdkZkoLFpjBwE035X/cXx+fz3ZF6Qv10VBNU4L26JGEiVL37lKUY1l30Pnf/yR764cxY5yppSD2Pgee2yoBcMbp09L331vn6G8AAAAAwC460G/4wQcfBPotAQCBZhhm580pU6SPPpL27zfny5aVxo+XKlfO+5qLL5beeENfVhioy2/KfeK8aVNgSg4lL75oHbdqJV15pTO1FMQeHLDiAHDezz9L6em5Y5dLuugi5+oBAAAAEJwCHhyMsG/IHGKOHDmipUuXaunSpVq3bp02b96sw4cPKyYmRtWqVVObNm3Us2dPjRgxQlWL2LDz5MmT+uabb/T1119r7dq12rRpkw4ePCiXy6WqVauqdevW6t69u/7xj3+ojv0pjB/s2bNHkydP1vz587V9+3YdPnxYCQkJatmypa677jpdc801KleunN/rABCCdu+Wpk2TJk/O+5F4yXxaNWeOdPPNeb8XFSXdc48a2oKCffukw4cjtgdyHj/+aO7c5Gn06OBckEFwAASf5cut43PO4ecrAAAAgLwC3hw5VG3atEkPPvigFi9erIyMjEKPL1++vJ599lmNHDky32bOe/fu1ciRI7VgwQKdPHmy0HPGxMRo9OjReuKJJxQbG1vs30NRzJgxQ3fccYeOHj2a7zEtW7bURx99pLZt2/qlBm9ojgwEsZMnpfnzzbBgyRIpO7vg4//2N+mrr/L9dmam2fLgzJncuWBr+uuka66RZs/OHScmStu2SXFxztWUn5kzpWuvzR03by4lJTlXDwBzddLnn+eO775bevNN5+oBAAAAEJwCvuIgVK1fv16fffaZZa5MmTJq2rSpateuraysLG3cuFGHDh2SZK4iuP/++7VhwwZNmDDBa3iwa9cuzZw50zLncrnUpEkTJSQkqEyZMtqyZYtSU1MlSZmZmfr3v/+tdevWaf78+T4PD6ZOnarhw4db5po3b67ExERt375dO3bskGSGKD169NDKlSvVunVrn9YAIERkZ5sfW50yxXyKfeJE4a/p0kUaPtx88l2AmBjprLOsD5g3bSI4kKQ//5TmzrXOjRwZnKGB5L3HgWGYW6MACLysLOm776xz9DcAAAAA4E0QbmwQ3GJiYjRgwADNnz9fhw4d0qZNm/TNN99oxYoVOnDggObPn6+6deu6j3///ff19ttvF3jOqKgo9e7dWx999JH27dunP/74QytWrNA333yj3bt3a9myZTr77LPdx//vf//T2LFjffr7+u2333Trrbe6x82bN9dPP/2kpKQkLVu2TNu3b9fixYtVu3ZtSdKxY8fUr18/pXtukgsgMhiGdOGF0iWXSB98UHBo0LCh9Oij0ubN5tOq228v0p4YLVpYx3xK3fTKK9YFHZUqmf9Jg5U9OEhLk44fd6YWANL69ZJ9USnBAQAAAABvCA6KKCYmRrfeeqv+/PNPffzxx+rfv7/i4+Mtx7hcLvXv31+rVq1SQkKCe/6xxx5TZmZmnnNGRUVp0KBBWr9+vRYvXqzrrrtONWrUyHNc9+7dtXLlSkt4MH78eO3du9dnv79HHnlEp0+fliTVqFFDy5cvV/v27S3H9O7dW1999ZXi/vpo69atWwsNRQCEIZdL6tw5/+9XqiTddJO5Ef/WrdJTT0nNmhXrLVq2tI5pkGz2l540yTp3221SlSqOlFMk3try0OcAcI69v0GTJt6vUwAAAAAgOCii/v37a8KECe599gtSv359Pfnkk+7xwYMHtdz+LzVJ7dq10+zZs9WqVatCz1m5cmW9+uqr7nFmZmaerZNK6vfff9eCBQvc42eeeca9ssCudevW+uc//+kejxs3TtmF7WcOIPRkZkrff5//923bmikqSurTx2yMvGePNHGi1L17iTv22lccEByYe5B7LvKKjpY8fhwHpXLl8i4wITgAnPPtt9Zxt27O1AEAAAAg+AVFj4PTp0/rt99+0969e3Xs2DGvn84vjH1vfqf17dtXt3vsH7Fp0yb17NmzVOfs2bOnypUrp1OnTrnP6Qsff/yx++uKFStq2LBhBR5/22236YUXXpAk7dmzR6tWrdJFF13kk1oAOMgwpLVrzSbH06dLhw+bm9LXqpX32PbtpVatzGBgxAhp2DCffmzVvuLgzz/NLCMmxmdvEVLS0qT//Mc6N2yYFAp94uvUMf8o5UhJca4WIJIZRt7ggG2KAAAAAOTHseDAMAxNnTpVEydO1KpVq5SVlVWq8wVbcFCtWjXL+LgPNnUuU6aMKleu7A4OfHFOSVq4cKH7665du6pixYoFHt+kSRO1aNFCSX9tOr5w4UKCAyCU7d4t/fe/ZqPjDRus35s+3ey+a+dymU+gqlXzS6db+4qDzExp2zapeXOfv1VI+OAD6eBB69zo0c7UUlx16lj/WLHiAHDGn3+aC8I8ERwAAAAAyI8jwcG2bds0ZMgQ/fzzz5LMEKEkXC6XDMOQyw8PrUpr+/btlnEtb5/YLaZTp05p3759Pj2nYRj67bff3OPOBe1b7qFz587u4OCXX34pdR0AAuzkSWnePDMsWLLE2nHX05Qp3oMDSape3W/lVa8u1aghHTiQO5eUFJnBwZkzZlNkT1dcIZ1zjjP1FJd9IQrBAeAM+66ZtWtLTZs6UwsAAACA4BfwHgepqanq0aOHfv75Z3dgUNIH/yUNHAJh3rx5lnGnTp1Kfc5PPvnE0k/AF+fcuXOn0tLS3OOzzjqrSK/zPG7jxo2lrgNAAGRnS998YzYurl1buv56afHi/EMDSSpbVvLR6qbiokGyae5cc7WFpwcfdKaWkqhb1zomOACc4a2/QRB+9gYAAABAkAj4ioP7779fu3btksvlcq8YaNKkiXr37q1mzZqpcuXKio4OitYLJZaWlqY333zTPT7vvPN09tlnl+qc2dnZGjdunHtcq1atUvdMkKQdO3ZYxg0aNCjS6zyP27FjR7FXfiQnJxf5WMkMnACU0B9/mH0Lpk6VbNe8Vw0bms2Pb7hBatbM//Xlo2VLacWK3HEkBgeGIXn86Jckdehg9p0OFfYVB/Q4AJxBfwMAAAAAxRHQJ/T79u3TnDlz3IFBxYoVNXHiRA0ePDiQZfjdqFGjlOLxZOSZZ54p9TnHjRuntWvXusdjx45VXFxcqc977Ngxy7hy5cpFel18fLz76+zsbJ08eVIVKlQo8vvWr1+/yMcCKKVJk6R//7vgYypVkgYPNgODiy82mx47zN7n4K/d0SLK0qXSmjXWuTFjQutTwmxVBDhv926zx4EnggMAAAAABQnok6GlS5cqOzvb/en0adOmhV1oMGXKFL377rvu8ZAhQ9S3b99SnfPrr7/Wo48+6h536dJFd911V6nOmcNzmyJJKlu2bJFeV65cuQLPAyCI3HCD9/moKKlPH2naNLNj5sSJ5kfZgyA0kNiqSMq72uCss6QBA5yppaS8BQdBvNMgEJbsqw3i46Vzz3WmFgAAAAChIaArDnb/9TFDl8ulpk2blvqBerBZvny5brvtNve4cePGlhChJDZu3KhBgwbpzJkzkqSqVavqo48+UpkyZUp13hyZmZmWcVG3ibIfl5GRUaz33bVrV7GOT01NVceOHYv1GiAiGIa0dq3ZxPiBByRv2421amXub/Pjj+b4nHOkESOkoUPzPtUNIvYVBwcPms2Sa9Rwpp5A+/VXadEi69yoUZKPfvwHjL3HQWam+f9lpPz/CAQDe3DQtWvo/SwBAAAAEFgBDQ48Hy6fd955gXxrv1u3bp369eun06dPS5Jq166tRYsWFXnrH2927dqlPn366PDhw5KkChUq6PPPP1fDhg19UrMklS9f3jJOT08v0uvsxxVnmyJJqlevXrGOB2Cze7e5UmDyZGnDBnOuVi3p//7P+/H33GMGDMOHS+efHxJ73TRuLMXEmA+acyQlRc4D5xdftI5r1JBuvNGRUkqldm3zj5vnKoOUlMj5/xEIBvQ3AAAAAFBcAd2PolatWu6vQ70BsqekpCT16dNHR48elSRVq1ZNX375pZqVoqno3r171atXL/cn88uWLatPP/1UnTp18knNOSpWrGgZnzp1qkivO3nyZIHnAeAHJ0+aYUGfPlL9+uZm9zmhgWSuOshvD5jhw6VXX5Xatg2J0ECSoqPz9maOlO2Kdu6Upk+3zt17r2TbJS4kxMSYmZYn+hwAgXP4sPTbb9Y5ggMAAAAAhQlocNCuXTv318XdqiZYbdu2Tb169dK+ffskmU2DFy1apHNLsXHsoUOH1Lt3b23evFmSFBMTo9mzZ+tvf/ubT2r2VMP2kc/U1NQivW7Pnj3urytVqqSYmBif1gXgL9nZ0rJl0k03mR/dvv56afFic94uKUn64YeAl+hPkdog+fXXpays3HG5cpKPWts4ggbJgHO++86aKZctK11wgXP1AAAAAAgNAf3Yf5s2bXTWWWfpzz//1I8//qijR4+WaisfpyUnJ6tnz55KTk6WZG77s3DhQl1Qin+NHTt2TH369NFvf300rEyZMpo2bZquuuoqn9Rs17x5c7lcLhl//Yty586dRXqdZ/DT0t7BFEDpbd4sTZ1q/tqxo/DjGzY0VxWE2TZgkdggOSvLXFji6eabQ3trn7p1zZ2ychAcAIFj36bowguluDhnagEAAAAQOgK64kCSHnnkEUnSmTNn9Pzzzwf67X0mZyuhbdu2SZLi4uL0ySefqGvXriU+Z1pamq644gr99NNPkswm0pMmTdLgwYN9UrM3FStWtPQbWLduXZFet9bjCVCrVq18XRYQudaskbp0MT9q/8wzBYcGlSqZKxGWLZO2bpWeeipvJ9oQF4nBwfLlkseiLknS3Xc7U4uv2FccpKQ4UwcQiehvAAAAAKAkAh4c3Hjjjerfv78Mw9BLL72kmTNnBrqEUsvZSijprz0zYmJiNGfOHPXq1avE50xPT1e/fv303XffuefefvttDR8+vNT1FqZbt27ur1esWFHo8ZmZmfr++++9vh5AKVWvLq1alf/3o6LMHgfTpplPlydOlLp3N+fDkH2roq1bpYwMZ2oJlBkzrOO2bfMGKKGGrYoAZ5w8Kf34o3WO4AAAAABAUTjypGnGjBm66qqrlJWVpaFDh+r222/X1q1bnSil2LxtJTR9+vRSbSWUmZmpQYMG6euvv3bPvfrqq7r99ttLXW9R9O/f3/31xo0bLasJvPn00091/PhxSVJUVJT69u3r1/qAsGMY+T/9bthQ6tEj73zr1tK4cdKuXdIXX0hDh0rly/u1zGBgDw6ysqQ//3SmlkDIzJTmzLHODRniTC2+RHAAOOP776UzZ3LHZcpInTs7Vw8AAACA0BHQHgc54uLi9Omnn+qll17SE088offff1/vv/++mjZtqlatWqlKlSqKKsanZ10ulyZOnOjHik0nT57UlVde6d5KKCoqSpMnT9bVV19d4nPmhCcLFy50zz333HP65z//Wdpyi+yKK65QzZo1tX//fknSM888o7lz53o9NisrSy+88IJ7fPnll6tWrVoBqRMIebt3S//9rzRlitS/v/Tss96PGz7c3H6oZk0zIBg+3PzYucsV0HKDQZUqZk/ovXtz5zZtksJ1h7QlS6RDh6xz4RAc2HfQYqsiIDDs2xS1bWvucgcAAAAAhXEkOJCklJQU/f7778rKynI35t2yZYv++OOPYp3HMIyABAenT59W//793Vv5uFwuvffeexo2bFiJz2kYhm6++WbN8fh46WOPPaZ//etfpa5XMmvMMWLECH344Ydej6tQoYL+9a9/6YEHHpAkffzxxxo/fnye8MIwDI0ePVo//rXm3eVy6amnnvJJrUDYSkuT5s83w4IlS6TsbHP+2DHp6ae9bzE0aJAZGvTpI8XEBLTcYNSypTU4+GuXuLBk36aoUyepUSNHSvEp+4qDvXvNT0FHO/a3ECAy0N8AAAAAQEk58k/2xYsX65prrnFvd+MKgU/Rvvbaa1qyZIl7XKVKFc2aNUuzZs0q0uuvv/56XX/99Za52bNna/Lkye5x2bJl9f333+uyyy4r0jl79+6tUaNGFenYwtx9992aM2eOVq5cKUm6//779dVXX2nYsGFKSEjQ9u3bNXHiREsPhNGjR6tdu3Y+eX8grGRnmx1up0yRZs+WTpzIe8yuXeaqgr/9Le/3KlWSSrH9Wbhp2VL65pvccbg2SE5Pl+bNs85de60ztfiaPTgwDDM8CLNe3kBQycyU/vprnRvBAQAAAICiCnhwsG7dOg0YMECnTp2SZIYGOSsOgtnJkyct48OHD2vRokVFfn2nTp0KPWd6enqxzpmQkFDkYwsTGxur+fPnq2fPnu7+DZ999pk+++wzr8cPHTpUzz//vM/eHwgLmzdLU6eav3bsKPz4Tz/1HhzAwt7nIFxXHPzvf9Jfebokc2eqwYOdq8eXatQwF89kZubO7d5NcAD409q1ZnNkT127OlMLAAAAgNAT8ODgnnvu0alTp9yBQdmyZXXdddepd+/eatasmSpXrqxo9i5wRM2aNfXDDz/oscce04QJE3T06NE8xzRq1EiPPvqobrrpJgcqBILQoUPSzJnm6oLVqws/vlIl82nw8OF89LOIWra0jjdtMj+xHgKL1Ypl5kzruHv3vJ/UD1VRUVJiorRzZ+5cSorUoYNzNQHhzr5NUatW5i54AAAAAFAULiOAH/ffuHGjWrdu7d6aqGXLlvr888/VsGHDQJWAIkpPT9eyZcu0fft2HT58WLVr11arVq3UqVMnR7aWSk5OVv369SVJu3btUr169QJeA5DHiRNSQoLZx6AgUVFS795mWPD3v0vlywekvHCxbZvUpIl1bs8es2lyuEhLk2rVsn46+J13pNtvd64mX+vc2Zqt/ec/0l13OVcPEO7+/nfpk09yx7fdJr37rmPlAAAAAAgxAf1o/6pVqySZTXajoqI0a9YsQoMgVbZs2SL3WgAiVsWK0iWXSPls6aXWraURI6Rhw8Lno+MOaNBAiouTTp/OnUtKCq/gYMECa2hQpox09dXO1eMP9ktg925n6gAiQXY2jZEBAAAAlE5UIN9s3759ksy+Bm3btlXr1q0D+fYAUHy7d0tffZX/94cPt45r1pRGjpTWrJF++0168EFCg1IqU0Zq3tw6F24NkmfMsI579zb7AoQTggMgcDZuNHfS89StmzO1AAAAAAhNAV1xUKlSJffXzZo1C+RbA0DRpaVJ8+ebfQuWLJGqVTOfcsbE5D22b19zu6KuXc3VBX36eD8OpdKihZnD5Ain4ODIEbMxsqdrr3WkFL+yN0JOSXGmDiAS2FcbNGhg/gIAAACAogpocFDX46lBRkZGIN8aAAqWnS0tX26GBbNnm/0Lchw4IH3xhRkS2JUtK+3YIcXGBq7WCGRvkJyU5Ewd/jB/vuR5S4yNNfcmDzesOAACh22KAAAAAJRWQIODCy64QFFRUTIMQ1u2bAnkWwOAd5s3S1Onmr927Mj/uClTvAcHEqFBANiDg3BacWDfpuiKK6TKlZ2pxZ8IDoDAMAwzB/dEcAAAAACguALa46BevXrq0aOHDMPQ+vXr9eeffwby7QHAdOiQ9M47UufO5h44zzxTcGhQqZLZu8AwAlcjLFq0sI63b5fS0x0pxaf27zd3w/I0ZIgztfibPTg4dCg8/j8Egs2OHVJysnWO/gYAAAAAiiugwYEk/d///Z/761GjRgX67QFEsi++kAYNkhITpTvvlFavzv/YqCizX8G0adKePdJbb0kuV+BqhYU9OMjOlv74w5lafGnuXCkrK3dcvnz+C1tCnb3HgcSqA8Af7NsU1aiRd9UWAAAAABQm4MHB3/72Nz311FMyDEMLFizQnXfeqczMzECXASASffih+aS2oB4r55wjvfiitGuXGTQMHWo+zYWjKlXK++A5HLYrsm9T1LevVKGCM7X4W3x83kuJ4ADwPXtw0LUruTcAAACA4gt4cCBJY8eO1WuvvaaYmBhNmDBBbdq00aRJk7SbJwgA/Gn4cO/zNWtK//yntGaN9Ouv0ujRefdVgePsqw5CvUFySkrefcivvdaZWgLB5aLPARAI9DcAAAAA4AsBbY4sSU2aNHF/HRMTo4yMDG3atEm33nqrJKlixYqqWrWqoqKKnmm4XC76JQCR7uRJaf58adYs6aOPvK8SuPRSqXZtae9es6Fx//5mmNCnjxQTE/CSUTwtW0pff507DvUVB7NnW9tmxMdLl13mXD2BUKeOdYspggPAt/btyxuq0t8AAAAAQEkEPDjYvn27XC6XDMOQy+WS66+108ZfT0+OHz+u48ePF+ucLtZfA5EpO9v8aOWUKeZT2BMnzPlPPpGuuy7v8dHR0mOPmf0LhgyRqlYNbL0olXBbcTBzpnU8YIBUtqwztQSKfbuplBRn6gDC1YoV1nHFitL55ztSCgAAAIAQF/DgIIf9YX9JH/4bnh/XBBAZNm+Wpk41f+3Ykff7U6Z4Dw4k6a67/Fsb/Mbe3HPTJvMT+6GYHW/blrc3dzhvU5SDrYoA/7L3N+jc2czMAQAAAKC4Av5PiQYNGrBCAEDxHTpkbkM0ZYq0alXBxy5ebD6RpE9BWLEHB8ePS6mpofl/86xZ1nH16lLPns7UEkgEB4B/0d8AAAAAgK84slURABRJZqb0xRfS5MnSggVSRkbBx0dFSb16SSNGsA1RGKpXTypXTjp1KncuKSk0g4MZM6zjQYMio82GfasiggPAd44dk9ats87R3wAAAABASbF4GUBwmjFDuu8+af/+wo9t3doMC4YOzftkEmEjKsrsc+D5YGzTJumSSxwrqUQ2bcr7cC8StimS8oY8KSmhu90UEGxWrTJb/+SIiZE6dnSuHgAAAAChjeAAQHCqU6fg0KBmTTMoGD5catuWJ48RwltwEGrsTZETEyNnOxF7cJCWZm45FR/vTD1AOLH3N+jQwVylBQAAAAAlQXAAwDknT5oP/L092ejaVWrUSPLc3iw2VurXz1xd0KdPZOztAgt7n4OkJGfqKCnDyLtN0TXXSGXKOFNPoHnbVmr3boIDwBfobwAAAADAl6KcLgBAhMnOlpYtk266SapdW5o2zftxUVHSDTeYX3fpIr3zjrRnjzR7tnTVVYQGEcoeHITaioNff81b85AhztTihHLl8rYfoc8BUHqnT0s//GCdo78BAAAAgNIIyhUHR44c0fHjx2UYhho0aOB0OQB8YfNmaepU89eOHbnzkydLt9zi/TV33WWGB82aBaZGBL0WLazjnTvNhSvlyztTT3HZVxs0bCh16uRMLU6pU0c6fDh3nJLiXC1AuPjxRzM8yOFymZk7AAAAAJRUUAQH8+fP16effqpvv/1W27dvV/Zfnd1cLpfOnDmT5/jt27dr586dkqQKFSqoffv2Aa0XQBEdOiTNmiVNmWJ2bfRmxQrpzz+ls87K+72EBPMX8Jfmza1jw5C2bJHatHGmnuLwtk3RkCGR156jTh1pw4bcMSsOgNKzb1N03nlSlSqOlAIAAAAgTDgaHCxatEj33Xef/vjjD0mSYRhFet2ff/6p3r17y+VyKTY2Vrt371ZV+94HAJyRmSl98YW5kmDBAikjo+Djo6Kk777zHhwANhUqSA0amCsNcmzaFBrBwQ8/WFt2SNK11zpSiqPsfQ4IDoDSszdGpr8BAAAAgNJyrMfBU089pSuvvFJ//PFHnsDAVcjHL3v27KlWrVrJMAxlZGRo5syZ/iwVQGEMQ1qzRho5Uqpb12xgPHduwaFB69bSuHHSrl3S8OGBqxUhz75dUag0SLavNmjeXDr/fEdKcVTdutYxwQFQOllZ0sqV1jn6GwAAAAAoLUeCg9dff11PPPGEe0siSYqLi1O3bt101VVXFWnlwRCPbpILFy70S50Aimj9eql9e+n116X9+/M/rmZNM1xYs0b67TfpwQfzfvwYKEQoNkjOzjZ37fJ07bWRt02RlPeSp8cBUDq//iodO2adY8UBAAAAgNIKeHCwZcsWjR49Wi6XSy6XS3FxcRo3bpwOHjyoZcuW6Y033ijSefr16yfJ3N7o22+/LfI2RwD84JxzzBUE3sTGSoMGmdsWpaRI48dLbdtG5hNT+IR9xUEoBAcrVuT9ZL1H/h1R2KoI8C17f4OmTWkPBAAAAKD0At7j4LHHHnM3PC5XrpyWLFmizp07F/s85513nsqWLav09HQdP35cW7ZsUXN710wAvpGdbT6ZyMyUevfO+32Xy9xu6KGHcuc6d5ZGjJCuuUaiBwl8yL7iICnJ/CMa5djme4Wzb1N03nnS2Wc7U4vTvAUHhkGWCJQU/Q0AAAAA+ENAH7OcPn1an376qXu1wTPPPFOi0ECSoqKi1KpVK/d4Uyh85BQINZs3S2PHSo0bS5dcYg0G7IYNk5o0kR591HzdypXS7bcTGsDn7MHByZPBvd3NmTPS7NnWuUhsipzD3uMgM1M6eNCZWoBQZxh5gwP6GwAAAADwhYCuOPjuu+906tQpSVKFChV01113lep8derU0dq1ayVJu9nrAPCNQ4ekmTOlKVOk1aut31u71uxNcO65eV9Xt670xx98bBh+V6eOVLGidOJE7lxSklS/vnM1FeTrr6UDB6xzkbpNkSTVrm3+mPDcYTAlRapRw7magFC1ZYu0b591jhUHAAAAAHwhoCsOtm/fLklyuVzq2LGj4uLiSnW++Ph499fHjx8v1bmAiJaZafYgGDRISkyU7rorb2iQY8qU/M9DaIAAcLlCq8+BfZuijh3NxTmRKiZGqlXLOkf2D5SMvb9BYmJk/3wBAAAA4DsBXXGwf/9+99cJPujalp2d7fVrAEVgGOYKgsmTpenTJY/rM1/nnJP3iS3ggJYtpZ9/zh0Ha3Bw+rT08cfWuUjepihHnTrS3r25Y4IDoGS89TcgwwcAAADgCwENDjxXGJw+fbrU5zvosSlyVfZRB4omO1t66SVz5cCGDYUfX7Om2b9g+HDp/PN5IoGgYM+vkpKcqaMwixZJR49a5wYPdqaWYFK3rplb5iA4AEqG/gYAAAAA/CWgwUHNmjXdXycnJ5f6fL/88ovXcwMoQFSUNHduwaFBbKzUr580YoTUp4+5twgQROwNkoN1xYF9m6KLL5bq1XOmlmBSp451HMzNrYFglZwsbdtmnaO/AQAAAABfCWhw0OSvTVcNw9C6deuUlpamChUqlOhca9assWx91K5dO5/UCESE4cOlH37IO9+li/m9a66RWMWDIGZfcZCcbDZLrljRmXq8SUuTPvnEOsc2RSZ7cMCKA6D47KsNqlQxdxQEAAAAAF8IaHPkjh07Kj4+Xi6XS5mZmZo0aVKJz/XKK6+4v27YsKEaNmzoixKB0Ld5s/Too9Kll5p9DLy59trcVQQNG5rHb94sffeddPvthAYIes2a5d01a/NmZ2rJz8KF0smTueOoKLP/OAgOAF+wBwddu5o/ZwAAAADAFwL6z4syZcroyiuvlGEYMgxDjz/+uHbt2lXs88ybN08fffSRXC6XXC6XrrvuOj9UC4SQQ4ekd94xVwy0aCE984z05ZfeVxVIUvXq0quvSsuWSVu3Sk89ZT6JBUJEuXJSo0bWuWDbrmjmTOu4Z0+pVi1nagk2detaxwQHQPF5a4wMAAAAAL4S8M8lPfroo4qKipLL5dKRI0fUo0cPbShKg9a/fPjhhxo6dKhcLpcMw1DZsmU1cuRIP1YMBKnMTGnBAvMjzImJ0p13SqtWWY+ZMiX/1999t9S9Ox9PRMgK5gbJx46ZKw48sU1RLvuKg717pTNnnKkFCEWHDknr11vnCA4AAAAA+FLAnxi2bNlS9957rwzDkMvl0rZt29SuXTvdfPPNWrRokfbt25fnNbt27dLEiRPVuXNn3XzzzTp9+rT79U8++aRq8RFORArDkNaskUaOND+y26+f2eg4I8P78dOnS6dPB7ZGIECCuUHyJ59YL72YGGnAAOfqCTb24CA72wwPABTNihXWcblyUvv2ztQCAAAAIDwFtDlyjpdfflm///67vvzyS3e/gw8//FAffvihJLlXE0hShQoVlJ6e7n5tTmBgGIYGDBig0aNHO/FbAAJr927pv/81VxAUZYVOzZrS0KHSiBFSbKz/6wMcEMzBwYwZ1vFll9E6xFONGmaYkpmZO7d7d94tjAB4Z9+mqFMnbvcAAAAAfMuRPUqioqL0ySef6MYbb3QHAZLcvQ8kuedOnTplmc857qabbtIM+5MZIBz9619S/frSQw8VHBrExprbFi1YIKWkSOPHS23b5u0gC4QJ+1ZFmzebn1x32sGD0uLF1jm2KbKKijJ3WPOUkuJMLUAoor8BAAAAAH9zbHPzsmXLatKkSZo5c6Zat25tCQY85TRAlszA4KyzztK0adP0/vvvKzrakQUTQGC1bFnw09AuXczGyHv2SLNnS1ddZX6UFwhz9hUH6enSzp3O1OLp44+t+/WXK2fuKgYr+3ZFNEgGiiYtTfr5Z+scwQEAAAAAX3P8yfvgwYM1ePBgLV26VF9++aVWrFihXbt26eDBg8rIyFCNGjVUu3ZtdenSRX369NHll1+uMmXKOF02EDgDB0p33SWdPJk717ChNHy4dMMNUrNmztUGOKh2bSk+3mxEnCMpSWrUyLGSJOXdpuiqq6SKFZ2pJZgRHAAls3q1NZwsU8bcqggAAAAAfMnx4CDHJZdcoksuucTpMoDgU6mSGR588ok0eLAZGFx8sbnXBxDBXC5z1cEPP+TObdok9enjXE2pqdLSpdY5tinyjuAAKBn7NkXt2xNOAgAAAPC9oAkOABTgxReld9+Vypd3uhIgqHgLDpw0Z47kufNexYrS5Zc7V08wszdCpscBUDT0NwAAAAAQCAQHQChISHC6AiAo2RskJyU5U0cO+zZFf/+72eMAebHiACi+jAxp1SrrHMEBAAAAAH8IeHDwt7/9TZLZ9Hj69OmqVatWic6zd+9eXXfdde5zffXVVz6rEQAQGuwNkp1ccbBjh7RypXWObYryR3AAFN+aNdKpU9a5rl2dqQUAAABAeAt4cLBs2TK5XC5JUnp6eonPk56ermXLlkmS+3wAgMhiDw5SU81myfHxga9l1izruGpVqXfvwNcRKuzBwaFDUnq6VLasM/UAocC+TVHr1lL16s7UAgAAACC8OdJd1fDcABoAgBI666y8fcKd2q5o5kzr+OqrpdhYZ2oJBfYeBxKrDoDC0N8AAAAAQKA4EhwAAOALcXFSkybWOSe2K9qyRfr5Z+sc2xQVLD4+b793ggMgf9nZ0ooV1jmCAwAAAAD+ErLBwZkzZ9xfR0fT4xkAIlUwNEi2rzaoXVvq0SPwdYQSl4s+B0BxbNggHT5snSM4AAAAAOAvIRscHDhwwP11hQoVHKwEAOCkYGiQPGOGdTx4sFSmTODrCDUEB0DR2bcpatRIql/fkVIAAAAARICQDQ6WL18uyWyMXMf+5AEAEDGcDg7Wrzc/CeyJbYqKxt7nICXFmTqAUEB/AwAAAACB5OgePy6Xq1jHZ2ZmKjU1VYsXL9azzz7rnj/33HN9XRoAIETYtyraskXKygrcJ/7tqw3q15c6dw7Me4c6VhwARWMY0l+fmXEjOAAAAADgT34JDsoU4WmNYRhq1KhRid/DMAz31/369SvxeQAAoc2+4iAjQ9q+XTrrLP+/t2HkDQ6GDJGiQnY9X2ARHABFs21b3uuD4AAAAACAP/nl0YZhGPn+Kupxhf3KWa3QsmVLDRo0yB+/DQBACKhRQ6pWzToXqO2Kfv5Z+vNP6xzbFBUdwQFQNPZtimrWzLvaCgAAAAB8yW+fiSzuNkTFZRiGLrjgAn322WeKiYnx63sBAIKXy5X3AVpSUmDe277a4KyzpHbtAvPe4cBbjwPbZwwAyHt/Az//VRsAAABAhPPLVkXdunXLNzj45ptvJJnBQseOHVW2bNkindPlcikuLk5VqlRRq1atdMkll+hi1mgDAGRuV7RqVe44ECsOsrOlmTOtc9dey8O84rCvOEhLk44fl+LjnakHCFb0NwAAAAAQaH4JDpYtW5bv96KiotyhwsyZM9WgQQN/lAAAiCBOrDhYuVJKTrbOsU1R8SQm5p3bvZvgAPC0Z4/Z9N0TwQEAAAAAf3OkfaO91wEAAKVhb5AciBUH9m2KWreWzjnH/+8bTsqXl6pUsc7R5wCwWrHCOq5USWrTxplaAAAAAEQOv6w4KMjjjz/u/rqK/WkBAAAlYA8O9u2TDh+Wqlb1z/udOSPNnm2dY7VBydStKx05kjtOSXGsFCAo2fsbdOkiRQf8b/AAAAAAIo2jwQEAAL7QpIn5IO3Mmdy5pCSpUyf/vN8335jhhKchQ/zzXuGuTh1pw4bcMSsOACv6GwAAAABwgiNbFQEA4EsxMdJZZ1nn/LldkX2bovbtpWbN/Pd+4czeIJngAMh19Kj0yy/WOYIDAAAAAIFAcAAACAuB6nOQkSHNnWudY5uikqtb1zomOAByrVwpebYGi42VOnZ0rh4AAAAAkYPgAAAQFlq0sI6TkvzzPl9+afZP8HTNNf55r0hgX3FAjwMgl72/QceOUtmyztQCAAAAILIQHAAAwkKgVhzYtym66CKpQQP/vFckYKsiIH/0NwAAAADgFIIDAEBYsK84+PNPKTPTt+9x6pQ0f751jm2KSsdbcOC5NQsQqdLTpR9/tM4RHAAAAAAIFIIDAEBYsAcHmZnStm2+fY/PP5dOnMgdR0VJgwb59j0ijb3HQWamdPCgM7UAweSHH8yeKjmioqQuXZyrBwAAAEBkITgAAISF6tWlmjWtc77ersi+TVGPHlJCgm/fI9LUri25XNY5+hwAefsbtGkjVa7sTC0AAAAAIg/BAQAgbPizQfLx49Jnn1nn2Kao9GJipFq1rHP0OQDobwAAAADAWQQHAICw4c8GyZ9+au45niM6Who40Hfnj2Q0SAaszpyRVq60zhEcAAAAAAgkggMAQNjwZ3Bg36bo0kvN7ZFQevY+BwQHiHS//GLtpyIRHAAAAAAILIIDAEDY8NdWRYcPS4sWWefYpsh37CsO6HGASGfvb9C8udkPBAAAAAACheAAABA27CsODh6UDhwo/XnnzZMyM3PHcXFS//6lPy9MbFUEWNHfAAAAAIDTogP5ZllZWUpLS3OPK1asqKgosgsAgG80amQ22/V8yJ+UJNWoUbrz2rcpuvJKKT6+dOdELoIDIJdhSCtWWOcIDgAAAAAEWkCf2k+ePFlVq1ZV1apVVaNGDaWmpgby7QEAYS46WmrWzDpX2j4H+/ZJX31lnWObIt+ixwGQKylJ2r/fOtetmzO1AAAAAIhcAQ0O9u7dK8MwZBiG2rVrp7r2JwUAAJSSrxskz5kjZWfnjitUMFccwHfsKw727pXOnHGmFsBp9v4Gdeuaq6kAAAAAIJACGhxUqFBBkuRyudSgQYNAvjUAIEL4ukGyfZui/v2l8uVLd05Y2YOD7GwzPAAikbf+Bi6XM7UAAAAAiFwBDQ4SEhIC+XYAgAjkyxUHycl5P/3LNkW+V6OG2ZvCE9sVIVLZf+bQ3wAAAACAEwIaHLTw+Bjorl27AvnWAIAIYQ8Otm6VMjJKdq5Zs6zjKlWkSy8t2bmQv6goKTHROkdwgEizf7/03HPSjh3WefobAAAAAHBCQIODNm3aqGnTpjIMQz///LMOHz4cyLcHAEQA+1ZFWVnSn3+W7Fz2bYoGDJDi4kp2LhTMvl1RSoozdQCBZBjSypXS9ddL9epJjzxi/X7VqtLZZztTGwAAAIDIFtDgQJJuvfVWSVJWVpaef/75QL89ACDMVa4s2XfGK8l2RX/+Kf34o3WObYr8xx4csOIA4ezECWnCBKltW+mii6Rp07yvjLriCnNFDgAAAAAEWsD/KTJq1Ch16tRJhmHolVde0cyZMwNdAgAgzPmiQbL99lSjhvS3v5W8JhSM4ACRYONG6b77pLp1pdtvl375Jf9ju3SR/v3vwNUGAAAAAJ4CHhxERUXp448/1oUXXqisrCwNHTpUt912m/4s6T4SAADY+KJBsj04GDxYio4ueU0oWN261jFbFSFcZGZKs2ebwePZZ0tvvCEdO+b92NhY6YYbpFWrpBUrpPr1A1srAAAAAOQI+COQp556SpJ0ySWX6Pfff9fx48c1ceJETZw4Ua1bt1bbtm1Vq1YtVapUqVjnfeyxx/xRLgAgBJU2OPj9d+nXX61zbFPkX6w4QLhJTpbee8/8lZpa8LGNG0t33CH94x9SzZqBqQ8AAAAAChLw4OCJJ56Qy+Vyj10ulwzDkCStX79eGzZsKNF5CQ4AADm8bVVkGJLH7adA9tUGdepIXbv6pjZ4R3CAcGAY0tdfS2+9JX3yidmcPT8ul3TlldKdd0p9+khlygSuTgAAAAAoTFBsuuAq6pMcLwzDKNXrAQDhx77i4MgRad8+qXbtwl9rGNKMGda5IUNoUOpv9uDg0CEpPV0qW9aZeoDiOHJEmjxZevvtwnuq1Kgh3XKLdNtt5koDAAAAAAhGjgQHOSsMAADwhwYNpLg46fTp3LmkpKIFB+vWSZs3W+fYpsj/7D0OJHPVQZMmga8FKKo1a8zVBR99JJ06VfCxXbpId90lDRpk/nwCAAAAgGAW8OBg6dKlgX5LAECEKVNGat5c+u233LlNm6Ru3Qp/rX21QePGUocOvq0PecXHS+XLSydP5s4RHCAYpadLs2aZgcH33xd8bIUK0rBh5nZE558fkPIAAAAAwCcCHhx079490G8JAIhALVvmDQ4K422bomuvLXpvBJScy2VuV/THH7lz9DlAMPnzT+ndd6VJk6SDBws+tlUrc3XBDTdIlSsHpj4AAAAA8KWg6HEAAICveWuQXJjVq6WdO61zbFMUOAQHCDZZWdLnn5urCxYtMsPF/ERHSwMGmIFB9+4EjgAAAABCG8EBACAs2RskF2XFgX21QatW0rnn+q4mFMze5yAlxZk6gH37pIkTzRUGO3YUfGzdumaj41tuydvkGwAAAABCFcEBACAs2YODbdvMvcnLlvV+fFaWuW+5pyFD+NRwINkfurLiAIFkGNLKlebqgtmzpczMgo/v1ctcXdC3r7naAAAAAADCCf/MAQCEpebNrWPDMLfBOecc78cvXy7t2WOdGzLEP7XBO4IDOOH4cWnaNOntt6Vffy342MqVpX/8Q7rjjrzboQEAAABAOCE4AACEpUqVzC1EPLe72bQp/+DAvk3R+efnXbUA/yI4QCBt2GCGBVOmmOFBQdq1k+6+2+x5Ur58YOoDAAAAACf5NDjYae8oKalBgwaFHuML9vcBAKBFC2twkF+D5MxMae5c6xxNkQPPW48Dw2C7KPhORoY0f765HdE33xR8bFyc+XPgrrukDh34cwgAAAAgsvg0OGjUqJFcHv+qcrlcOnPmTIHH+IK39wEAoGVL6euvc8f5NUj+6ivp4EHrHNsUBZ59xUFamvlJ8Ph4Z+pB+DhzRnr+eek//8m7JZldkybSnXeaWxJVrx6Y+gAAAAAg2PhlqyLDMHxyDAAApWHfaii/4MC+TVGnTlKjRn4pCQVITMw7t3s3wQFK74Yb8l7nnqKipKuuMgODSy81xwAAAAAQyehxAAAIW/bmpUlJebe+SU+X5s2zHsc2Rc4oX16qUkU6ciR3bvduek2gdD77LP/QoGZN6dZbpdtukxo2DGxdAAAAABDMfBocjBgxwifHAADgC/YHzsePS6mp1i1xvvhCOnYsd+xySYMHB6Y+5FW3rjU48OxRARRXerr0z3/mne/a1exdMHCg2csAAAAAAGDl0+Dggw8+8MkxAAD4Qr165qfYT57Mndu0yRoc2D+J3L173r32ETh16kgbNuSOd+92rhaEvpdflv780zr3/vvSzTc7Uw8AAAAAhAp2cAUAhK2oKKl5c+tcUlLu12lp0oIF1u+zTZGz7KENwQFKaudO6dlnrXNduphNjwEAAAAABSM4AACEtYIaJC9YYF2NUKaMdPXVgakL3hEcwFdGj5ZOncodu1zSm2/S+BgAAAAAiiIs/umUkZHhdAkAgCDlrUFyDvs2Rb17SzVq+L8m5K9uXeuYHgcoia++kmbPts7dcYfUtq0z9QAAAABAqAnp4ODXX3/VyJEjVdf+lAEAgL/kt+LgyBHpf/+zfm/IkICUhAKw4gCllZkp3Xuvda56demZZ5ypBwAAAABCkU+bIwfCsWPH9NFHH2nixIlas2aN0+UAAIKcPTjYscPcnmj+fMlzwVpsrPT3vweyMnjjLTgwDHObGaAo3nhD2rjROvfss1K1as7UAwAAAAChKGSCg2XLlmnixIn6+OOPlZ6eLsMw3N9z8TQBAJCPZs3yzm3Zknebossvl6pUCUhJKIA9OMjMlA4eZAspFE1qqvTEE9a5du2kW25xpBwAAAAACFlBHRzs3r1bH374oSZNmqRt27ZJkjswyAkLPAMEAADsKlSQGjSQdu7MnVuxQlqyxHrctdcGti54l5Bgri7wvL2npBAcoGgeflg6ftw69+abZuNzAAAAAEDRBV1wcObMGX366aeaOHGiFi9erOzsbEtY4HK5ZBiGDMNQxYoV9fe//11Dhw51uGoAQDBr2dIaHLz4opSVlTsuX17q2zfwdSGvmBipVi1p797cud27pTZtnKsJoeG776QpU6xzN94ode7sSDkAAAAAENKCJjj4/fffNWnSJE2dOlUHDhyQZF1dkBMWxMbG6rLLLtPQoUPVr18/lS1b1smyAQAhoEULafHi3PGOHdbv9+1rrkxAcKhTJ29wABQkK0u65x7rXHy89PzzztQDAAAAAKHO0eDgxIkTmjFjhiZOnKgffvhBkryuLpCkbt266frrr9egQYNUhU2oAQDFYG+QbMc2RcGlbl1p7drcMcEBCjNhgrRunXXuySel2rUdKQcAAAAAQp4jwcGKFSs0adIkzZ49WydPnpRkBgb2rYg8mx5PmTJFDRo0cKJcAECIa9Ei/+/Fx0uXXRa4WlA4e4PklBRn6kBoOHBAeuQR61zr1tLddztTDwAAAACEg6hAvdHevXs1btw4tWzZUt27d9fkyZOVlpaWZzsil8ulSy+9VNOnT6fxMQDAJwpacTBggMSud8HFHhyw4gAFGTtWOnzYOvfGG2a/DAAAAABAyfh1xUF2drY+++wzTZo0SZ9//rmysrLybXTcokULjRgxQsOHD1edv54YXHfddf4sDwAQIerUkSpWlE6cyPs9tikKPgQHKKqffza3KfI0ZIh0ySXO1AMAAAAA4cIvwcHmzZs1adIkTZkyRXv/6m7ordFx5cqVdc011+gf//iHOnXq5I9SAACQy2VuV/Tzz9b56tWlnj2dqQn5q1vXOiY4gDfZ2WZDZM8FquXLSy+95FxNAAAAABAufB4cdOvWTd99950k742OXS6XevXqpRtvvFEDBgxQWfaHAAAEQMuWeYODQYPYziQY2Vcc7N0rnTkjRTvSmQnBasoUafVq69zYsVK9es7UAwAAAADhxOf/BF+xYoX7a8/VBc2aNdONN96o4cOHq679o4QAAPiZtz4HQ4YEvg4Uzh4cZGeb4QF/fUCOo0elhx6yzjVtKj3wgDP1AAAAAEC48ctn93ICA0m64oor9Mgjj6hz587+eCsAAIrknHOs44QEqVs3Z2pBwWrUMFeCZGbmzu3eTXCAXE88Ie3bZ517/XUpLs6RcgAAAAAg7ET568Q54cGiRYv05JNPaubMmTp9+rS/3g4AgAJdeaV11cELL0hlyjhXD/IXFSUlJlrn6HOAHOvXS2+8YZ3r10+6/HJn6gEAAACAcOSX4MCzt0FWVpa+/PJLDR06VAkJCbrzzju12r4hLQAAfhYTI61ZI82YIf30kzR8uNMVoSD27YpSUpypA8HFMKR775WysnLn4uKkV191riYAAAAACEc+Dw4WLVqka665RrGxse5myJIZJhw9elQTJkzQRRddpJYtW+r5559XCk8CAAABUq6c2degfXunK0Fh7MEBKw4gSbNmScuWWefGjJGaNHGkHAAAAAAIWz4PDnr37q0ZM2Zo9+7dGj9+vM4991zLCgTJDBE2b96sRx55RI0aNVKfPn00Y8YMtjICAACSCA6Q14kT0qhR1rkGDaSHH3amHgAAAAAIZ37rcVC1alXdd999WrdunX788Ufdfvvtio+Pt4QIhmEoKytLS5Ys0bBhw5SQkKDbb7+drYwAAIhw9kbIBAd47rm8W1a9+qpUvrwz9QAAAABAOPNbcOCpffv2evvtt5WamqrJkyere/fu7u/ZtzJ6//33ddFFF6lFixaBKA0AAAQhehzA0+bN0ksvWed695YGDHCmHgAAAAAIdwEJDnKULVtWN9xwg5YuXarNmzfr4YcfVmJiotetjLZs2eIeS9LKlSuVnZ0dyHIBAIBD2KoIOQxDGjlSyszMnYuOll5/XfL4qyIAAAAAwIcCGhx4Ouuss/Tcc89p586dWrBggfr376/o6Gh3Q+Wc0CBnS6Nhw4YpMTFR9957r1auXOlU2QAAIADswcGhQ1J6ujO1wFkLFkhffGGd++c/pZYtHSkHAAAAACKCy8j5uH8Q2L9/vyZPnqwPPvhAGzdulGRdheA5btCggYYOHarrrrtO55xzjjMFI2CSk5NVv359SdKuXbtUr149hysCAPjT0aNSlSrWua1bpcaNHSkHDjl1SmrdWtq2LXcuMVHatEmKj3euLgAAAAAId46tOPCmZs2aGj16tDZs2KDvvvtO//jHP1ShQgWvWxnt2LFDzz//vNq0aaPzzjvPybIBAICPxcfnbXpLn4PI89JL1tBAkl58kdAAAAAAAPwtqIIDT507d9bEiROVmpqq9957T507d5ZhGHm2MjIMQxs2bHC4WgAA4EsuF30OIt327dJzz1nnunaVhg51pBwAAAAAiChBGxzkqFChgm6++WZ99913+v333/XAAw+oZs2a7hABAACEJ4KDyDZqlLWvRVSU9OabNEQGAAAAgEAI+uDAU8uWLfXSSy8pOTlZc+bM0RVXXKEyZco4XRYAAPCDunWtY7YqihyLF0sff2ydu+suqU0bZ+oBAAAAgEgT7XQBJREdHa2BAwdq4MCBSklJ0eTJk50uCQAA+BgrDiJTRoZ0333WuRo1pKeecqYeAAAAAIhEIbXiwJu6devq//7v/5wuAwAA+BjBQWR6/XUpKck69+9/S1WrOlMPAAAAAESikA8OAABAeCI4iDy7d0tPPmmd69BBuukmZ+oBAAAAgEhFcAAAAIKStx4HhuFMLQiMMWOkEyesc2++aTZGBgAAAAAEDv8MAwAAQcm+4iAtTTp+3Jla4H/Ll0vTplnnbr5Z6tjRmXoAAAAAIJIRHAAAgKCUmJh3ju2KwtOZM9K991rnKleWnnvOmXoAAAAAINIRHAAAgKBUvrxUpYp1juAgPL37rvTrr9a5p5+WatVyph4AAAAAiHQEBwAAIGh563OA8LJ/vzR2rHXu3HOlO+90ph4AAAAAAMEBAAAIYvY+B6w4CD//93/SkSPWuTfflKKjHSkHAAAAACCCAwAAEMQIDsLbDz9IEyda54YOlbp1c6YeAAAAAICJ4AAAAAQtgoPwlZ0t3XOPZBi5cxUqSOPGOVcTAAAAAMBEcAAAAIIWPQ7C14cfSj/+aJ177LG8/58DAAAAAAKP4AAAAAQtVhyEp8OHpYcfts41by7985+OlAMAAAAAsCE4AAAAQctbcOC5tQ1C0+OPS/v3W+feeEOKjXWmHgAAAACAFcEBAAAIWvbgIDNTOnjQmVrgG7/+Kv3nP9a5AQOkSy91ph4AAAAAQF4EBwAAIGglJEgul3WOPgehyzDMhsjZ2blzZctKr7ziXE0AAAAAgLwIDgAAQNCKiZFq1bLO0ecgdM2YIX37rXXu4YelRo0cKQcAAAAAkA+CAwAAENRokBwejh+XRo+2zjVqJI0Z40g5AAAAAIACEBwAAICgRnAQHp55Ju//d+PHS+XKOVIOAAAAAKAABAcAACCo1a1rHdPjIPRs2iS9+qp17rLLpH79nKkHAAAAAFAwggMAABDUWHEQ2gxDGjlSyszMnYuJkV57LW/jawAAAABAcCA4AAAAQY3gILR98om0eLF17oEHpObNnakHAAAAAFA4ggMAABDUCA5C16lT0j//aZ2rU0caO9aRcgAAAAAARURwAAAAgpq9x8HevdKZM87UguJ54QVpxw7r3MsvSxUrOlMPAAAAAKBoCA4AAEBQs684yM42wwMEt61bpeeft8517y4NGeJMPQAAAACAoiM4AAAAQa1GDbOZrie2Kwp+DzwgnT6dOy5TRnrjDRoiAwAAAEAoIDgAAABBLSpKSky0zhEcBLcvvjCbInu6+27p3HOdqQcAAAAAUDwEBwAAIOjZtytKSXGmDhTu9GnpvvusczVrSk8+6Uw9AAAAAIDiIzgAAABBzx4csOIgeI0fL23ZYp174QWpShUnqgEAAAAAlATBAQAACHoEB6EhOVl6+mnr3IUXSiNGOFMPAAAAAKBkCA4AAEDQq1vXOiY4CE4PPiilpeWOXS7pzTfNPhUAAAAAgNDBP+MAAEDQo8dB8PvmG2nGDOvcLbdIF1zgTD0AAAAAgJIjOAAAAEGPrYqC25kz0j33WOeqVpWee86ZegAAAAAApUNwAAAAgp49ODh0SEpPd6YW5PXWW9L69da5Z56RatRwph4AAAAAQOkQHAAAgKBn73EgSampga8Dee3dKz36qHXu/POl2293pBwAAAAAgA8QHAAAgKAXHy+VL2+do89BcPjXv6Rjx6xzb7whlSnjTD0AAAAAgNIjOAAAAEHP5aLPQTD6/nvpgw+sc9dfL3Xt6kw9AAAAAADfIDgAAAAhgeAguGRlSXffbZ2rWFEaN86ZegAAAAAAvkNwAAAAQoK9zwHBgbNeeEH6+Wfr3BNPSImJjpQDAAAAAPAhggMAABAS7CsO6HHgnEWLpLFjrXOtWkn33edMPQAAAAAA3yI4AAAAIYGtioLDtm3SdddJhpE7FxUlvf22FBPjXF0AAAAAAN8hOAAAACGB4MB5J09KAwdKhw9b5597Ture3ZmaAAAAAAC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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 575,
+       "width": 775
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# find the last year of the dataset\n",
+    "last_year = ds_long['time.year'].max().item()\n",
+    "\n",
+    "# filter the last 30 years\n",
+    "ds_30y = ds_long.sel(time=slice(str(last_year-30+1),str(last_year)))\n",
+    "\n",
+    "# calculate the mean temperature for each year\n",
+    "mean_time_dim = ds_30y['t2m'].resample(time=\"Y\").mean(dim=\"time\")\n",
+    "\n",
+    "# apply cosine of latitude as weights to the dataset variables\n",
+    "weights = np.cos(np.deg2rad(mean_time_dim.latitude))\n",
+    "weighted_mean_time_dim = mean_time_dim.weighted(weights)\n",
+    "\n",
+    "# calculate the global mean in degrees celsius\n",
+    "weighted_global_mean_temp = weighted_mean_time_dim.mean(dim=[\"longitude\",\"latitude\"])\n",
+    "weighted_global_mean_temp_c = weighted_global_mean_temp - 273.15\n",
+    "\n",
+    "# calculate the trend line\n",
+    "years = weighted_global_mean_temp_c['time'].dt.year.values\n",
+    "annual_temperature = weighted_global_mean_temp_c.values\n",
+    "trend_coefficients = np.polyfit(years, annual_temperature, 1)\n",
+    "trend_line = np.poly1d(trend_coefficients)\n",
+    "\n",
+    "# draw data\n",
+    "plt.plot(years, annual_temperature, color=\"blue\", label=\"ERA5 Reanalysis - annually resampled\")\n",
+    "plt.plot(years, trend_line(years), color=\"red\", linestyle=\"--\", label='Trend line')\n",
+    "\n",
+    "# aesthetics\n",
+    "plt.xlabel(\"Time (years)\")\n",
+    "plt.ylabel(\"Air temperature at 2m (K)\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ERA5-Land hourly data from 1950 to present \n",
+    "This [ERA5 Reanalysis dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview) of hourly data has an increased spatial resolution and focuses on the land variable evolution over the last decades. It is updated regularly by ECMWF and accessible via the CDS API (cf. [`get_ERA5_reanalysis_data.ipynb`](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D2_StateoftheClimateOceanandAtmosphereReanalysis/get_ERA5_reanalysis_data.ipynb)). A similar dataset of lower temporal resolution, i.e. monthly averages can be found [here](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview).\n",
+    "\n",
+    "Depending on your research question it is essential to choose an adequate frequency, however, due to the huge amount of data available, it might become necessary to focus on a regional subset of the ERA5 dataset.\n",
+    "\n",
+    "In the following, we show how we downloaded global ERA5-Land data via the CDS API to answer **Q2** by calculating global temperature trends. Please note that the API request serves as an example and the downloading process should not be triggered if not necessary. Please think about an adequate frequency and domain of interest beforehand, to request a subset that is sufficient to answer your questions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cdsapi\n",
+    "\n",
+    "c = cdsapi.Client()\n",
+    "\n",
+    "# Uncomment the following block after adjusting it according to your research question\n",
+    "# and after successfully working through the `get_ERA5_reanalysis_data.ipynb` notebook.\n",
+    "\n",
+    "#c.retrieve(\n",
+    "#    'reanalysis-era5-land',\n",
+    "#    {\n",
+    "#        'variable': '2m_temperature',\n",
+    "#        'year': ['1974', '1975', '1976', '1977', '1978',\n",
+    "#                 '1979', '1980', '1981', '1982', '1983',\n",
+    "#                 '1984', '1985', '1986', '1987', '1988',\n",
+    "#                 '1989', '1990', '1991', '1992', '1993',\n",
+    "#                 '1994', '1995', '1996', '1997', '1998',\n",
+    "#                 '1999', '2000', '2001', '2002', '2003',\n",
+    "#                 '2004', '2005', '2006', '2007', '2008',\n",
+    "#                 '2009', '2010', '2011', '2012', '2013',\n",
+    "#                 '2014', '2015', '2016', '2017', '2018',\n",
+    "#                 '2019', '2020', '2021', '2022', '2023'],\n",
+    "#        'month': ['01','02','03','04','05','06','07','08','09','10','11','12'],\n",
+    "#        'day': '15',\n",
+    "#        'time': '12:00',\n",
+    "#        'grid': ['0.4', '0.4'],\n",
+    "#        'format': 'grib',\n",
+    "#\n",
+    "#    },\n",
+    "#    'reanalysis-era5-land_1974_2023_04x04.grib')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see in the request code block, we downloaded the 2m_temperature / $\\text{t2m}$ variable from the *reanalysis-era5-land* at noon on every 15th of the month in the last 50 years. In other words, the requested data is not averaged over the whole month but just a sample. To reduce the resolution, we chose a grid of 0.4° in both spatial dimensions. As we want to calculate global trends over the whole time period, this choice should be adequate and save us a few computational-intensive averaging calculations. Furthermore, it helps to emphasize how the downloaded data looks like. \n",
+    "\n",
+    "The output is given as a file named `reanalysis-era5-land_1974_2023_04x04.grib` in the [`grib`](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them) format, which needs a small addition in the known file reading method [`xr.open_dataset(path, engine='cfgrib')`](https://docs.xarray.dev/en/stable/generated/xarray.open_dataset.html#xarray.open_dataset), check out [this resource](https://docs.xarray.dev/en/stable/user-guide/io.html#grib-format-via-cfgrib) for more information. Additionally, we get an `idx` file that is experimental and useful if the file is opened more often but can be ignored or deleted.\n",
+    "\n",
+    "Again we uploaded these files to the OSF cloud for simple data retrieval, and we converted the file format to `.nc` (NetCDF) as an additional option. The following lines help to open both file types.\n",
+    "\n",
+    "***Note that the frequency of our example file `reanalysis-era5-land_1974_2023_04x04.grib` is monthly and not daily, hence no further question of the template can be answered by using it. Please increase the frequency and spatial resolution, and reduce the domain of interest when downloading the data to allow for investigations of regional heatwaves.***"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The file reanalysis-era5-land_1974_2023_04x04.grib exists locally.\n",
+      " Loading the data ...\n",
+      "\n",
+      "<xarray.Dataset> Size: 974MB\n",
+      "Dimensions:     (time: 600, latitude: 451, longitude: 900)\n",
+      "Coordinates:\n",
+      "    number      int64 8B ...\n",
+      "  * time        (time) datetime64[ns] 5kB 1974-01-15 1974-02-15 ... 2023-12-15\n",
+      "    step        timedelta64[ns] 8B ...\n",
+      "    surface     float64 8B ...\n",
+      "  * latitude    (latitude) float64 4kB 90.0 89.6 89.2 88.8 ... -89.2 -89.6 -90.0\n",
+      "  * longitude   (longitude) float64 7kB 0.0 0.4 0.8 1.2 ... 358.8 359.2 359.6\n",
+      "    valid_time  (time) datetime64[ns] 5kB ...\n",
+      "Data variables:\n",
+      "    t2m         (time, latitude, longitude) float32 974MB ...\n",
+      "Attributes:\n",
+      "    GRIB_edition:            1\n",
+      "    GRIB_centre:             ecmf\n",
+      "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+      "    GRIB_subCentre:          0\n",
+      "    Conventions:             CF-1.7\n",
+      "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+      "    history:                 2024-06-11T01:46 GRIB to CDM+CF via cfgrib-0.9.1...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# specify filename and filetype\n",
+    "filetype = 'grib'\n",
+    "#filetype = 'nc'\n",
+    "fname_ERA5 = f\"reanalysis-era5-land_1974_2023_04x04.{filetype}\"\n",
+    " \n",
+    "# check whether the specified path/file exists or not (locally or in the JupyterHub)\n",
+    "isExist = os.path.exists(fname_ERA5)\n",
+    "\n",
+    "# load data and create data set\n",
+    "if isExist:\n",
+    "    _ = print(f'The file {fname_ERA5} exists locally.\\n Loading the data ...\\n')\n",
+    "    if filetype == 'grib':\n",
+    "        ds_global = xr.open_dataset(fname_ERA5, engine='cfgrib')\n",
+    "    elif filetype == 'nc':\n",
+    "        ds_global = xr.open_dataset(fname_ERA5)\n",
+    "    else:\n",
+    "        raise (\"Please choose an appropriate file type: 'nc' or 'grib'.\")\n",
+    "\n",
+    "else:\n",
+    "    _ = print(f'The file {fname_ERA5} does not exist locally and has to be downloaded from OSF.\\nDownloading the data ...\\n')    \n",
+    "\n",
+    "    # retrieve the grib file from the OSF cloud storage\n",
+    "    if filetype == 'grib':\n",
+    "        link_id = \"6d9mf\"\n",
+    "    elif filetype == 'nc':\n",
+    "        link_id = \"8v63z\"\n",
+    "    else:\n",
+    "        raise (\"Please choose an appropriate file type: 'nc' or 'grib'.\")\n",
+    "        \n",
+    "    url_grib = f\"https://osf.io/download/{link_id}/\"\n",
+    "\n",
+    "    # The following line is the correct approach, however, it sometimes raises an error that could not be solved by the curriculum team\n",
+    "    # (cf. https://github.com/ecmwf/cfgrib/blob/master/README.rst & https://github.com/pydata/xarray/issues/6512)\n",
+    "    # We, therefore, recommend to download the file separately if this EOFError arises. \n",
+    "    \n",
+    "    fcached = pooch_load(url_grib, fname_ERA5)\n",
+    "    \n",
+    "    try:\n",
+    "        if filetype == 'grib':\n",
+    "            ds_global = xr.open_dataset(fcached, engine='cfgrib')\n",
+    "        elif filetype == 'nc':\n",
+    "            ds_global = xr.open_dataset(fcached)\n",
+    "    except EOFError:\n",
+    "        print(f'The cached .grib file could not be parsed with Xarray.\\nPlease download the file to your local directory via {url_grib} or download its NetCDF equivalent.')\n",
+    "\n",
+    "print(ds_global)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 575,
+       "width": 767
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the mean 2m temperature of 1974 as example\n",
+    "t2m_1974 = ds_global.sel(time=slice('1974')).mean(dim='time')\n",
+    "_ = t2m_1974[list(t2m_1974.keys())[0]].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YFB352Tmjil7"
+   },
+   "source": [
+    "## Weekly Mortality Data (Optional)\n",
+    "\n",
+    "[The Organisation for Economic Co-operation and Development (OECD)](https://en.wikipedia.org/wiki/OECD) provides weekly mortality data for 38 countries. The list of countries can be found in [the OECD data explorer](https://data-explorer.oecd.org/vis?tm=weekly%20mortality&pg=0&hc[Measure]=Mortality&hc[Frequency%20of%20observation]=Weekly&snb=3&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_HEALTH_MORTALITY%40DF_MORTALITY&df[ag]=OECD.ELS.HD&df[vs]=1.0&pd=2023-W01%2C&dq=.W.M._T._T.&ly[rw]=TIME_PERIOD&ly[cl]=REF_AREA&to[TIME_PERIOD]=false) in the filters section, under *reference area*. This dataset can be used to analyze the impact of heatwaves on health through the general mortality of a country."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 652
+    },
+    "id": "qAGgrbtLljHg",
+    "outputId": "45bc3215-d89f-418c-b9bd-c7db6d3bd82b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 16168 entries, 0 to 16167\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column                    Non-Null Count  Dtype \n",
+      "---  ------                    --------------  ----- \n",
+      " 0   STRUCTURE_NAME            16168 non-null  object\n",
+      " 1   ACTION                    16168 non-null  object\n",
+      " 2   REF_AREA                  16168 non-null  object\n",
+      " 3   Reference area            16168 non-null  object\n",
+      " 4   FREQ                      16168 non-null  object\n",
+      " 5   Frequency of observation  16168 non-null  object\n",
+      " 6   MEASURE                   16168 non-null  object\n",
+      " 7   Measure                   16168 non-null  object\n",
+      " 8   AGE                       16168 non-null  object\n",
+      " 9   Age                       16168 non-null  object\n",
+      " 10  SEX                       16168 non-null  object\n",
+      " 11  Sex                       16168 non-null  object\n",
+      " 12  UNIT_MEASURE              16168 non-null  object\n",
+      " 13  Unit of measure           16168 non-null  object\n",
+      " 14  TIME_PERIOD               16168 non-null  object\n",
+      " 15  OBS_VALUE                 16168 non-null  int64 \n",
+      "dtypes: int64(1), object(15)\n",
+      "memory usage: 2.0+ MB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>STRUCTURE_NAME</th>\n",
+       "      <th>ACTION</th>\n",
+       "      <th>REF_AREA</th>\n",
+       "      <th>Reference area</th>\n",
+       "      <th>FREQ</th>\n",
+       "      <th>Frequency of observation</th>\n",
+       "      <th>MEASURE</th>\n",
+       "      <th>Measure</th>\n",
+       "      <th>AGE</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SEX</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>UNIT_MEASURE</th>\n",
+       "      <th>Unit of measure</th>\n",
+       "      <th>TIME_PERIOD</th>\n",
+       "      <th>OBS_VALUE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mortality by week</td>\n",
+       "      <td>I</td>\n",
+       "      <td>SWE</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Weekly</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Mortality</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>DT</td>\n",
+       "      <td>Deaths</td>\n",
+       "      <td>2015-W01</td>\n",
+       "      <td>1927</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mortality by week</td>\n",
+       "      <td>I</td>\n",
+       "      <td>SWE</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Weekly</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Mortality</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>DT</td>\n",
+       "      <td>Deaths</td>\n",
+       "      <td>2015-W02</td>\n",
+       "      <td>1966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mortality by week</td>\n",
+       "      <td>I</td>\n",
+       "      <td>SWE</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Weekly</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Mortality</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>DT</td>\n",
+       "      <td>Deaths</td>\n",
+       "      <td>2015-W03</td>\n",
+       "      <td>1935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Mortality by week</td>\n",
+       "      <td>I</td>\n",
+       "      <td>SWE</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Weekly</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Mortality</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>DT</td>\n",
+       "      <td>Deaths</td>\n",
+       "      <td>2015-W04</td>\n",
+       "      <td>1946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Mortality by week</td>\n",
+       "      <td>I</td>\n",
+       "      <td>SWE</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Weekly</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Mortality</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>DT</td>\n",
+       "      <td>Deaths</td>\n",
+       "      <td>2015-W05</td>\n",
+       "      <td>1975</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      STRUCTURE_NAME ACTION REF_AREA Reference area FREQ  \\\n",
+       "0  Mortality by week      I      SWE         Sweden    W   \n",
+       "1  Mortality by week      I      SWE         Sweden    W   \n",
+       "2  Mortality by week      I      SWE         Sweden    W   \n",
+       "3  Mortality by week      I      SWE         Sweden    W   \n",
+       "4  Mortality by week      I      SWE         Sweden    W   \n",
+       "\n",
+       "  Frequency of observation MEASURE    Measure AGE    Age SEX    Sex  \\\n",
+       "0                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
+       "1                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
+       "2                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
+       "3                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
+       "4                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
+       "\n",
+       "  UNIT_MEASURE Unit of measure TIME_PERIOD  OBS_VALUE  \n",
+       "0           DT          Deaths    2015-W01       1927  \n",
+       "1           DT          Deaths    2015-W02       1966  \n",
+       "2           DT          Deaths    2015-W03       1935  \n",
+       "3           DT          Deaths    2015-W04       1946  \n",
+       "4           DT          Deaths    2015-W05       1975  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read the mortality data from a csv file\n",
+    "link_id = \"rh3mp\"\n",
+    "url = f\"https://osf.io/download/{link_id}/\"\n",
+    "#data_mortality = pd.read_csv(\"Weekly_mortality_OECD.csv\")\n",
+    "data_mortality = pd.read_csv(url)\n",
+    "data_mortality.info()\n",
+    "data_mortality.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YHJ5l944lstE"
+   },
+   "source": [
+    "# Hint for Q3\n",
+    "For this question you will calculate the percentiles, you can read more about percentiles [here](https://www.britannica.com/topic/percentile) and e.g. in W2D3 Tutorial 1. \n",
+    "Furthermore, as a recommendation for this question, a definition was given to calculate heatwaves, however, there is a great diversity of definitions, you can read about it in the following article: [Perkins & Alexandar (2013)](https://doi.org/10.1175/JCLI-D-12-00383.1)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hU7ut6Zgl6Ml"
+   },
+   "source": [
+    "# Hint for Q4\n",
+    "For Question 4, to understand the method of calculating the percentage of an area under heatwaves, please read the following article: [Silva et al. (2022)](https://doi.org/10.1016/j.jenvman.2022.116193)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vJwFdRc3l7pv"
+   },
+   "source": [
+    "# Hint for Q5\n",
+    "\n",
+    "The following articles will be helpful: [Heo & Bell (2019) (not open access) ](http://dx.doi.org/10.1038/s41370-018-0076-3) and [Smith et al. (2012)](https://doi.org/10.1007/s10584-012-0659-2)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1T2EcomF7U9M"
+   },
+   "source": [
+    "# Hint for Q6\n",
+    "\n",
+    "The following article will be helpful: [Reddy et al. (2022)](https://iopscience.iop.org/article/10.1088/1748-9326/ac3e9a)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GvVxbZFYfLLK"
+   },
+   "source": [
+    "# Hint for Q7\n",
+    "The following article will help you learn about a method to determine the influence of heatwaves on health by analyzing mortality: [Nori-Sarma et al. (2019)](https://doi.org/10.3390/ijerph16122089)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "d5GaD8vdmOfO"
+   },
+   "source": [
+    "# Further reading\n",
+    "\n",
+    "\n",
+    "*   Geirinhas, J. et al. (2018) ‘Climatic and synoptic  characterization of heat waves in Brazil’, International Journal of Climatology, 38(4), pp. 1760–1776. [doi: 10.1002/joc.5294](https://doi.org/10.1002/joc.5294) (not open access)\n",
+    " \n",
+    "\n",
+    "*   Perkins-Kirkpatrick, S. et al. (2016) ‘Natural hazards in Australia: heatwaves’, Climatic Change. Climatic Change, 139(1), pp. 101–114. [doi: 10.1007/s10584-016-1650-0](https://doi.org/10.1007/s10584-016-1650-0) (not open access)\n",
+    "\n",
+    "\n",
+    "*   Sutanto, S. et al. (2020) ‘Heatwaves, droughts, and fires: Exploring compound and cascading dry hazards at the pan-European scale’, Environment International. Elsevier, 134(March 2019), p. 105276. [doi: 10.1016/j.envint.2019.105276](https://doi.org/10.1016/j.envint.2019.105276)\n",
+    "\n",
+    "\n",
+    "*  Lo, Y. et al. (2022) ‘Estimating heat-related mortality in near real time for national heatwave plans’, Environmental Research Letters, 17(2). [doi: 10.1088/1748-9326/ac4cf4](https://iopscience.iop.org/article/10.1088/1748-9326/ac4cf4)\n",
+    "\n",
+    "\n",
+    "*  Wilks, D. (2020) 'Statistical Methods in the Atmospheric Sciences', Elsevier. Available at: [https://doi.org/10.1016/C2017-0-03921-6](https://doi.org/10.1016/C2017-0-03921-6) (not open access)\n",
+    "\n",
+    "\n",
+    "*   World Meteorological Organization & World Health Organization. (2015) 'Heatwaves and Health\n",
+    "Guidance on Warning-System Development'. WMO-No. 1142. Available at: [https://library.wmo.int/idurl/4/54600](https://library.wmo.int/idurl/4/54600)\n",
+    "\n",
+    "* https://ecmwf-projects.github.io/copernicus-training-c3s/reanalysis-heatwave.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From efe42590ce607ee47f0f279ee019b42d120d7a4e Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Tue, 11 Jun 2024 01:54:25 +0200
Subject: [PATCH 22/65] Removed cell output.

---
 .../Heatwaves_project_notebook_24.ipynb       | 1667 +----------------
 1 file changed, 25 insertions(+), 1642 deletions(-)

diff --git a/projects/project-notebooks/Heatwaves_project_notebook_24.ipynb b/projects/project-notebooks/Heatwaves_project_notebook_24.ipynb
index 96f9a914e..2201e51d7 100644
--- a/projects/project-notebooks/Heatwaves_project_notebook_24.ipynb
+++ b/projects/project-notebooks/Heatwaves_project_notebook_24.ipynb
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -37,22 +37,7 @@
     "id": "mUt8EEPe0hKk",
     "outputId": "3e1145b7-ff68-4475-d51c-88784416b5e8"
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "78d2861846d84c73956fba516fcd182d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Tab(children=(Output(), Output(), Output()), selected_index=0, titles=('Youtube', 'Bilibili', 'Osf'))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# @title Project Background\n",
     "\n",
@@ -103,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -113,37 +98,7 @@
     "outputId": "7036341a-1ed4-420a-b198-cecffce9cc01",
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "If you want to download the slides: https://osf.io/download/zeyxn/\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"854\"\n",
-       "            height=\"480\"\n",
-       "            src=\"https://mfr.ca-1.osf.io/render?url=https://osf.io/zeyxn/?direct%26mode=render%26action=download%26mode=render\"\n",
-       "            frameborder=\"0\"\n",
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-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x103fc6700>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# @title Slides\n",
     "# @markdown These are the slides for the video introduction to the project\n",
@@ -200,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -212,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -237,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,436 +256,11 @@
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   {
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-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "id": "y_cz1nOyk7fe"
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-       ".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",
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-       "\n",
-       ".xr-var-dims {\n",
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-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
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-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
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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",
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-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 25MB\n",
-       "Dimensions:    (longitude: 41, latitude: 41, time: 3653)\n",
-       "Coordinates:\n",
-       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
-       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0\n",
-       "  * time       (time) datetime64[ns] 29kB 1991-01-01 1991-01-02 ... 2000-12-31\n",
-       "Data variables:\n",
-       "    t2m        (time, latitude, longitude) float32 25MB ...\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.6\n",
-       "    history:      2024-02-14 07:40:23 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...</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-8b86f741-eddb-4860-961f-308eee45a2e4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8b86f741-eddb-4860-961f-308eee45a2e4' 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'>longitude</span>: 41</li><li><span class='xr-has-index'>latitude</span>: 41</li><li><span class='xr-has-index'>time</span>: 3653</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-af2c63ee-5859-4d16-9283-52811f95f78c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af2c63ee-5859-4d16-9283-52811f95f78c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>143.0 143.2 143.5 ... 152.8 153.0</div><input id='attrs-3d089d11-e546-46e3-9893-34b2b4748e93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d089d11-e546-46e3-9893-34b2b4748e93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b608161-9aaf-4390-b75e-7fc4d92187ee' class='xr-var-data-in' type='checkbox'><label for='data-5b608161-9aaf-4390-b75e-7fc4d92187ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([143.  , 143.25, 143.5 , 143.75, 144.  , 144.25, 144.5 , 144.75, 145.  ,\n",
-       "       145.25, 145.5 , 145.75, 146.  , 146.25, 146.5 , 146.75, 147.  , 147.25,\n",
-       "       147.5 , 147.75, 148.  , 148.25, 148.5 , 148.75, 149.  , 149.25, 149.5 ,\n",
-       "       149.75, 150.  , 150.25, 150.5 , 150.75, 151.  , 151.25, 151.5 , 151.75,\n",
-       "       152.  , 152.25, 152.5 , 152.75, 153.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 4.75 4.5 ... -4.5 -4.75 -5.0</div><input id='attrs-b23c0d42-1fa4-41a5-925f-0097bd406639' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b23c0d42-1fa4-41a5-925f-0097bd406639' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc53d2b4-d4fe-4e6e-b06e-ee39f817e98f' class='xr-var-data-in' type='checkbox'><label for='data-dc53d2b4-d4fe-4e6e-b06e-ee39f817e98f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 5.  ,  4.75,  4.5 ,  4.25,  4.  ,  3.75,  3.5 ,  3.25,  3.  ,  2.75,\n",
-       "        2.5 ,  2.25,  2.  ,  1.75,  1.5 ,  1.25,  1.  ,  0.75,  0.5 ,  0.25,\n",
-       "        0.  , -0.25, -0.5 , -0.75, -1.  , -1.25, -1.5 , -1.75, -2.  , -2.25,\n",
-       "       -2.5 , -2.75, -3.  , -3.25, -3.5 , -3.75, -4.  , -4.25, -4.5 , -4.75,\n",
-       "       -5.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1991-01-01 ... 2000-12-31</div><input id='attrs-2e400ee5-ef3e-4fb7-a340-7fa5c4135437' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e400ee5-ef3e-4fb7-a340-7fa5c4135437' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06eb4ce3-8ed3-45a1-a110-fdf8b1687616' class='xr-var-data-in' type='checkbox'><label for='data-06eb4ce3-8ed3-45a1-a110-fdf8b1687616' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-02T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1991-01-03T00:00:00.000000000&#x27;, ..., &#x27;2000-12-29T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2000-12-30T00:00:00.000000000&#x27;, &#x27;2000-12-31T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a07546be-afac-49b7-8c1d-631e10afa25f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a07546be-afac-49b7-8c1d-631e10afa25f' 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>t2m</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a8a981bc-83e8-437d-9ad7-5a30e4657687' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8a981bc-83e8-437d-9ad7-5a30e4657687' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52203164-ca8f-4cf9-b2b7-af327d62e0cb' class='xr-var-data-in' type='checkbox'><label for='data-52203164-ca8f-4cf9-b2b7-af327d62e0cb' 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>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><pre>[6140693 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-84c38f25-a102-4400-83c8-bafd41780b77' class='xr-section-summary-in' type='checkbox'  ><label for='section-84c38f25-a102-4400-83c8-bafd41780b77' 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>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-51257331-d851-43a5-9e0a-7df8257eec6d' class='xr-index-data-in' type='checkbox'/><label for='index-51257331-d851-43a5-9e0a-7df8257eec6d' 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([ 143.0, 143.25,  143.5, 143.75,  144.0, 144.25,  144.5, 144.75,  145.0,\n",
-       "       145.25,  145.5, 145.75,  146.0, 146.25,  146.5, 146.75,  147.0, 147.25,\n",
-       "        147.5, 147.75,  148.0, 148.25,  148.5, 148.75,  149.0, 149.25,  149.5,\n",
-       "       149.75,  150.0, 150.25,  150.5, 150.75,  151.0, 151.25,  151.5, 151.75,\n",
-       "        152.0, 152.25,  152.5, 152.75,  153.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cdc2d609-c01a-43aa-8878-8e49aaaa7e8e' class='xr-index-data-in' type='checkbox'/><label for='index-cdc2d609-c01a-43aa-8878-8e49aaaa7e8e' 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([  5.0,  4.75,   4.5,  4.25,   4.0,  3.75,   3.5,  3.25,   3.0,  2.75,\n",
-       "         2.5,  2.25,   2.0,  1.75,   1.5,  1.25,   1.0,  0.75,   0.5,  0.25,\n",
-       "         0.0, -0.25,  -0.5, -0.75,  -1.0, -1.25,  -1.5, -1.75,  -2.0, -2.25,\n",
-       "        -2.5, -2.75,  -3.0, -3.25,  -3.5, -3.75,  -4.0, -4.25,  -4.5, -4.75,\n",
-       "        -5.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3650821a-c43c-4cf6-b270-15e092d3e5fd' class='xr-index-data-in' type='checkbox'/><label for='index-3650821a-c43c-4cf6-b270-15e092d3e5fd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1991-01-01&#x27;, &#x27;1991-01-02&#x27;, &#x27;1991-01-03&#x27;, &#x27;1991-01-04&#x27;,\n",
-       "               &#x27;1991-01-05&#x27;, &#x27;1991-01-06&#x27;, &#x27;1991-01-07&#x27;, &#x27;1991-01-08&#x27;,\n",
-       "               &#x27;1991-01-09&#x27;, &#x27;1991-01-10&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2000-12-22&#x27;, &#x27;2000-12-23&#x27;, &#x27;2000-12-24&#x27;, &#x27;2000-12-25&#x27;,\n",
-       "               &#x27;2000-12-26&#x27;, &#x27;2000-12-27&#x27;, &#x27;2000-12-28&#x27;, &#x27;2000-12-29&#x27;,\n",
-       "               &#x27;2000-12-30&#x27;, &#x27;2000-12-31&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=3653, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b642ffd8-d8fc-4cdc-9049-842647072ae0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b642ffd8-d8fc-4cdc-9049-842647072ae0' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>2024-02-14 07:40:23 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data4/adaptor.mars.internal-1707896415.6796277-19686-8-cf1f5a1a-aa36-4c24-a9fb-29291e38f918.nc /cache/tmp/cf1f5a1a-aa36-4c24-a9fb-29291e38f918-adaptor.mars.internal-1707895649.3473864-19686-6-tmp.grib</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 25MB\n",
-       "Dimensions:    (longitude: 41, latitude: 41, time: 3653)\n",
-       "Coordinates:\n",
-       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
-       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0\n",
-       "  * time       (time) datetime64[ns] 29kB 1991-01-01 1991-01-02 ... 2000-12-31\n",
-       "Data variables:\n",
-       "    t2m        (time, latitude, longitude) float32 25MB ...\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.6\n",
-       "    history:      2024-02-14 07:40:23 GMT by grib_to_netcdf-2.25.1: /opt/ecmw..."
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# loading a subsample of the ERA5 reanalysis dataset, daily from 1991 to 2000\n",
     "link_id = \"z9xfv\"\n",
@@ -753,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -762,446 +292,7 @@
     "id": "7on_k6PakcVi",
     "outputId": "7c793a9c-c42a-4c35-80ab-12cacadc460b"
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       ".xr-attrs dt,\n",
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-       "  margin: 0;\n",
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-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
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-       "  padding-right: 10px;\n",
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-       "\n",
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-       "  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-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t2m&#x27; (latitude: 41, longitude: 41)&gt; Size: 7kB\n",
-       "array([[300.1665 , 300.18317, 300.19998, ..., 300.12216, 300.10117,\n",
-       "        300.07745],\n",
-       "       [300.17957, 300.1994 , 300.21912, ..., 300.12817, 300.12216,\n",
-       "        300.105  ],\n",
-       "       [300.19885, 300.2193 , 300.23114, ..., 300.14175, 300.13776,\n",
-       "        300.13687],\n",
-       "       ...,\n",
-       "       [299.88504, 300.1443 , 300.40536, ..., 300.0386 , 299.20694,\n",
-       "        299.89627],\n",
-       "       [298.28378, 298.61932, 298.86646, ..., 299.7144 , 299.8679 ,\n",
-       "        300.66873],\n",
-       "       [294.1361 , 294.42368, 295.4092 , ..., 299.97037, 300.18015,\n",
-       "        300.3619 ]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
-       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0</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'>'t2m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 41</li><li><span class='xr-has-index'>longitude</span>: 41</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-dd0f9d24-13d2-49a7-95ec-09cf3b477b13' class='xr-array-in' type='checkbox' checked><label for='section-dd0f9d24-13d2-49a7-95ec-09cf3b477b13' 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>300.2 300.2 300.2 300.2 300.2 300.2 ... 299.4 299.3 300.0 300.2 300.4</span></div><div class='xr-array-data'><pre>array([[300.1665 , 300.18317, 300.19998, ..., 300.12216, 300.10117,\n",
-       "        300.07745],\n",
-       "       [300.17957, 300.1994 , 300.21912, ..., 300.12817, 300.12216,\n",
-       "        300.105  ],\n",
-       "       [300.19885, 300.2193 , 300.23114, ..., 300.14175, 300.13776,\n",
-       "        300.13687],\n",
-       "       ...,\n",
-       "       [299.88504, 300.1443 , 300.40536, ..., 300.0386 , 299.20694,\n",
-       "        299.89627],\n",
-       "       [298.28378, 298.61932, 298.86646, ..., 299.7144 , 299.8679 ,\n",
-       "        300.66873],\n",
-       "       [294.1361 , 294.42368, 295.4092 , ..., 299.97037, 300.18015,\n",
-       "        300.3619 ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-8cdb378a-373f-4756-8232-9468b4c43a87' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8cdb378a-373f-4756-8232-9468b4c43a87' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>143.0 143.2 143.5 ... 152.8 153.0</div><input id='attrs-acaf5997-4548-448f-a7d0-dad167ec5de8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acaf5997-4548-448f-a7d0-dad167ec5de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b719805-3164-491a-82f2-821d04d5c5b6' class='xr-var-data-in' type='checkbox'><label for='data-3b719805-3164-491a-82f2-821d04d5c5b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([143.  , 143.25, 143.5 , 143.75, 144.  , 144.25, 144.5 , 144.75, 145.  ,\n",
-       "       145.25, 145.5 , 145.75, 146.  , 146.25, 146.5 , 146.75, 147.  , 147.25,\n",
-       "       147.5 , 147.75, 148.  , 148.25, 148.5 , 148.75, 149.  , 149.25, 149.5 ,\n",
-       "       149.75, 150.  , 150.25, 150.5 , 150.75, 151.  , 151.25, 151.5 , 151.75,\n",
-       "       152.  , 152.25, 152.5 , 152.75, 153.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 4.75 4.5 ... -4.5 -4.75 -5.0</div><input id='attrs-33888794-6627-4678-a827-45530a806e64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33888794-6627-4678-a827-45530a806e64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b873021-ce18-4b90-b4d7-a2b5bb35b06d' class='xr-var-data-in' type='checkbox'><label for='data-0b873021-ce18-4b90-b4d7-a2b5bb35b06d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 5.  ,  4.75,  4.5 ,  4.25,  4.  ,  3.75,  3.5 ,  3.25,  3.  ,  2.75,\n",
-       "        2.5 ,  2.25,  2.  ,  1.75,  1.5 ,  1.25,  1.  ,  0.75,  0.5 ,  0.25,\n",
-       "        0.  , -0.25, -0.5 , -0.75, -1.  , -1.25, -1.5 , -1.75, -2.  , -2.25,\n",
-       "       -2.5 , -2.75, -3.  , -3.25, -3.5 , -3.75, -4.  , -4.25, -4.5 , -4.75,\n",
-       "       -5.  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d045a024-6f08-40d9-9908-f939ac402a11' class='xr-section-summary-in' type='checkbox'  ><label for='section-d045a024-6f08-40d9-9908-f939ac402a11' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0c191ef8-84f3-4671-8439-c113bee94fb5' class='xr-index-data-in' type='checkbox'/><label for='index-0c191ef8-84f3-4671-8439-c113bee94fb5' 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([ 143.0, 143.25,  143.5, 143.75,  144.0, 144.25,  144.5, 144.75,  145.0,\n",
-       "       145.25,  145.5, 145.75,  146.0, 146.25,  146.5, 146.75,  147.0, 147.25,\n",
-       "        147.5, 147.75,  148.0, 148.25,  148.5, 148.75,  149.0, 149.25,  149.5,\n",
-       "       149.75,  150.0, 150.25,  150.5, 150.75,  151.0, 151.25,  151.5, 151.75,\n",
-       "        152.0, 152.25,  152.5, 152.75,  153.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b5d43b8f-adf8-43a1-8d26-f86d982f7fbf' class='xr-index-data-in' type='checkbox'/><label for='index-b5d43b8f-adf8-43a1-8d26-f86d982f7fbf' 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([  5.0,  4.75,   4.5,  4.25,   4.0,  3.75,   3.5,  3.25,   3.0,  2.75,\n",
-       "         2.5,  2.25,   2.0,  1.75,   1.5,  1.25,   1.0,  0.75,   0.5,  0.25,\n",
-       "         0.0, -0.25,  -0.5, -0.75,  -1.0, -1.25,  -1.5, -1.75,  -2.0, -2.25,\n",
-       "        -2.5, -2.75,  -3.0, -3.25,  -3.5, -3.75,  -4.0, -4.25,  -4.5, -4.75,\n",
-       "        -5.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15df72d9-f5c5-49e0-bef8-3dfff145503e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-15df72d9-f5c5-49e0-bef8-3dfff145503e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 't2m' (latitude: 41, longitude: 41)> Size: 7kB\n",
-       "array([[300.1665 , 300.18317, 300.19998, ..., 300.12216, 300.10117,\n",
-       "        300.07745],\n",
-       "       [300.17957, 300.1994 , 300.21912, ..., 300.12817, 300.12216,\n",
-       "        300.105  ],\n",
-       "       [300.19885, 300.2193 , 300.23114, ..., 300.14175, 300.13776,\n",
-       "        300.13687],\n",
-       "       ...,\n",
-       "       [299.88504, 300.1443 , 300.40536, ..., 300.0386 , 299.20694,\n",
-       "        299.89627],\n",
-       "       [298.28378, 298.61932, 298.86646, ..., 299.7144 , 299.8679 ,\n",
-       "        300.66873],\n",
-       "       [294.1361 , 294.42368, 295.4092 , ..., 299.97037, 300.18015,\n",
-       "        300.3619 ]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
-       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# calculate the annual average of the year selected\n",
     "year_to_use = ds.t2m.loc[\"2000-01-01\":\"2000-12-31\",:,:].mean(dim=\"time\")\n",
@@ -1210,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1219,23 +310,7 @@
     "id": "7on_k6PakcVi",
     "outputId": "7c793a9c-c42a-4c35-80ab-12cacadc460b"
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 611,
-       "width": 804
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# plot temperature \n",
     "fig, ax = plt.subplots(1, subplot_kw={'projection':ccrs.PlateCarree()}, layout='constrained')\n",
@@ -1273,426 +348,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
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-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 19MB\n",
-       "Dimensions:    (longitude: 21, latitude: 21, time: 10958)\n",
-       "Coordinates:\n",
-       "  * longitude  (longitude) float32 84B -80.0 -79.75 -79.5 ... -75.5 -75.25 -75.0\n",
-       "  * latitude   (latitude) float32 84B 0.0 -0.25 -0.5 -0.75 ... -4.5 -4.75 -5.0\n",
-       "  * time       (time) datetime64[ns] 88kB 1991-01-01 1991-01-02 ... 2020-12-31\n",
-       "Data variables:\n",
-       "    t2m        (time, latitude, longitude) float32 19MB ...\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.6\n",
-       "    history:      2024-02-20 03:51:34 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...</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-b7234d5b-3db2-42a4-9936-cbd0d4d64728' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b7234d5b-3db2-42a4-9936-cbd0d4d64728' 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'>longitude</span>: 21</li><li><span class='xr-has-index'>latitude</span>: 21</li><li><span class='xr-has-index'>time</span>: 10958</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-811d25a2-0f5e-4bdc-a158-0ec8f48b8c18' class='xr-section-summary-in' type='checkbox'  checked><label for='section-811d25a2-0f5e-4bdc-a158-0ec8f48b8c18' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-80.0 -79.75 -79.5 ... -75.25 -75.0</div><input id='attrs-9c2332e7-853c-4d38-b9c2-508702693c23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c2332e7-853c-4d38-b9c2-508702693c23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a50e41c9-7e9a-4cd9-a664-ae5aabb21742' class='xr-var-data-in' type='checkbox'><label for='data-a50e41c9-7e9a-4cd9-a664-ae5aabb21742' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-80.  , -79.75, -79.5 , -79.25, -79.  , -78.75, -78.5 , -78.25, -78.  ,\n",
-       "       -77.75, -77.5 , -77.25, -77.  , -76.75, -76.5 , -76.25, -76.  , -75.75,\n",
-       "       -75.5 , -75.25, -75.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 -0.25 -0.5 ... -4.5 -4.75 -5.0</div><input id='attrs-983c600f-3146-4973-81da-9ad93ff815b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-983c600f-3146-4973-81da-9ad93ff815b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-297fb94e-e892-4dd5-be29-705e0725ec97' class='xr-var-data-in' type='checkbox'><label for='data-297fb94e-e892-4dd5-be29-705e0725ec97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 0.  , -0.25, -0.5 , -0.75, -1.  , -1.25, -1.5 , -1.75, -2.  , -2.25,\n",
-       "       -2.5 , -2.75, -3.  , -3.25, -3.5 , -3.75, -4.  , -4.25, -4.5 , -4.75,\n",
-       "       -5.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1991-01-01 ... 2020-12-31</div><input id='attrs-9bab4c4b-b4f9-495f-8b22-b5598304d4cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bab4c4b-b4f9-495f-8b22-b5598304d4cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86d27622-e92b-4806-925a-d6238137e26c' class='xr-var-data-in' type='checkbox'><label for='data-86d27622-e92b-4806-925a-d6238137e26c' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-02T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1991-01-03T00:00:00.000000000&#x27;, ..., &#x27;2020-12-29T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-12-30T00:00:00.000000000&#x27;, &#x27;2020-12-31T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ad0c38d-753b-49b6-86fb-00a42e1d92cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ad0c38d-753b-49b6-86fb-00a42e1d92cd' 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>t2m</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a4e551dc-d6a6-495f-afdb-fc84e078daa2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4e551dc-d6a6-495f-afdb-fc84e078daa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a52e513-09a0-45a3-a532-3b1b51cea412' class='xr-var-data-in' type='checkbox'><label for='data-2a52e513-09a0-45a3-a532-3b1b51cea412' 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>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><pre>[4832478 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a708111-cd7d-429e-9350-bc2a77a77509' class='xr-section-summary-in' type='checkbox'  ><label for='section-5a708111-cd7d-429e-9350-bc2a77a77509' 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>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c7db68a4-aba0-4c22-b437-fb0f15bee79d' class='xr-index-data-in' type='checkbox'/><label for='index-c7db68a4-aba0-4c22-b437-fb0f15bee79d' 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([ -80.0, -79.75,  -79.5, -79.25,  -79.0, -78.75,  -78.5, -78.25,  -78.0,\n",
-       "       -77.75,  -77.5, -77.25,  -77.0, -76.75,  -76.5, -76.25,  -76.0, -75.75,\n",
-       "        -75.5, -75.25,  -75.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e9f636df-33df-4413-8403-b7dce255a09c' class='xr-index-data-in' type='checkbox'/><label for='index-e9f636df-33df-4413-8403-b7dce255a09c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0, -0.25,  -0.5, -0.75,  -1.0, -1.25,  -1.5, -1.75,  -2.0, -2.25,\n",
-       "        -2.5, -2.75,  -3.0, -3.25,  -3.5, -3.75,  -4.0, -4.25,  -4.5, -4.75,\n",
-       "        -5.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-10247d3d-d0f8-4976-88d0-4380fcac5bdd' class='xr-index-data-in' type='checkbox'/><label for='index-10247d3d-d0f8-4976-88d0-4380fcac5bdd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1991-01-01&#x27;, &#x27;1991-01-02&#x27;, &#x27;1991-01-03&#x27;, &#x27;1991-01-04&#x27;,\n",
-       "               &#x27;1991-01-05&#x27;, &#x27;1991-01-06&#x27;, &#x27;1991-01-07&#x27;, &#x27;1991-01-08&#x27;,\n",
-       "               &#x27;1991-01-09&#x27;, &#x27;1991-01-10&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2020-12-22&#x27;, &#x27;2020-12-23&#x27;, &#x27;2020-12-24&#x27;, &#x27;2020-12-25&#x27;,\n",
-       "               &#x27;2020-12-26&#x27;, &#x27;2020-12-27&#x27;, &#x27;2020-12-28&#x27;, &#x27;2020-12-29&#x27;,\n",
-       "               &#x27;2020-12-30&#x27;, &#x27;2020-12-31&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=10958, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d8ec7da-31f2-4b83-9a57-b454dec56b87' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4d8ec7da-31f2-4b83-9a57-b454dec56b87' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>2024-02-20 03:51:34 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data8/adaptor.mars.internal-1708401052.8403652-9777-7-0baf55cc-b968-4b17-b272-4d86faa5643c.nc /cache/tmp/0baf55cc-b968-4b17-b272-4d86faa5643c-adaptor.mars.internal-1708398180.7979972-9777-9-tmp.grib</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 19MB\n",
-       "Dimensions:    (longitude: 21, latitude: 21, time: 10958)\n",
-       "Coordinates:\n",
-       "  * longitude  (longitude) float32 84B -80.0 -79.75 -79.5 ... -75.5 -75.25 -75.0\n",
-       "  * latitude   (latitude) float32 84B 0.0 -0.25 -0.5 -0.75 ... -4.5 -4.75 -5.0\n",
-       "  * time       (time) datetime64[ns] 88kB 1991-01-01 1991-01-02 ... 2020-12-31\n",
-       "Data variables:\n",
-       "    t2m        (time, latitude, longitude) float32 19MB ...\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.6\n",
-       "    history:      2024-02-20 03:51:34 GMT by grib_to_netcdf-2.25.1: /opt/ecmw..."
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "link_id = \"3xbq8\"\n",
     "fname_ERA5 = \"data_sample_91_20.nc\"\n",
@@ -1702,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1711,40 +369,7 @@
     "id": "k5D4Vu-9k5ZJ",
     "outputId": "6840837d-eb0e-44b4-b2b5-dfe0195bbad7"
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1539ddca0>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 575,
-       "width": 775
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# find the last year of the dataset\n",
     "last_year = ds_long['time.year'].max().item()\n",
@@ -1793,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1843,41 +468,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The file reanalysis-era5-land_1974_2023_04x04.grib exists locally.\n",
-      " Loading the data ...\n",
-      "\n",
-      "<xarray.Dataset> Size: 974MB\n",
-      "Dimensions:     (time: 600, latitude: 451, longitude: 900)\n",
-      "Coordinates:\n",
-      "    number      int64 8B ...\n",
-      "  * time        (time) datetime64[ns] 5kB 1974-01-15 1974-02-15 ... 2023-12-15\n",
-      "    step        timedelta64[ns] 8B ...\n",
-      "    surface     float64 8B ...\n",
-      "  * latitude    (latitude) float64 4kB 90.0 89.6 89.2 88.8 ... -89.2 -89.6 -90.0\n",
-      "  * longitude   (longitude) float64 7kB 0.0 0.4 0.8 1.2 ... 358.8 359.2 359.6\n",
-      "    valid_time  (time) datetime64[ns] 5kB ...\n",
-      "Data variables:\n",
-      "    t2m         (time, latitude, longitude) float32 974MB ...\n",
-      "Attributes:\n",
-      "    GRIB_edition:            1\n",
-      "    GRIB_centre:             ecmf\n",
-      "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-      "    GRIB_subCentre:          0\n",
-      "    Conventions:             CF-1.7\n",
-      "    institution:             European Centre for Medium-Range Weather Forecasts\n",
-      "    history:                 2024-06-11T01:46 GRIB to CDM+CF via cfgrib-0.9.1...\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# specify filename and filetype\n",
     "filetype = 'grib'\n",
@@ -1929,25 +524,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 575,
-       "width": 767
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# plot the mean 2m temperature of 1974 as example\n",
     "t2m_1974 = ds_global.sel(time=slice('1974')).mean(dim='time')\n",
@@ -1967,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1976,203 +555,7 @@
     "id": "qAGgrbtLljHg",
     "outputId": "45bc3215-d89f-418c-b9bd-c7db6d3bd82b"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 16168 entries, 0 to 16167\n",
-      "Data columns (total 16 columns):\n",
-      " #   Column                    Non-Null Count  Dtype \n",
-      "---  ------                    --------------  ----- \n",
-      " 0   STRUCTURE_NAME            16168 non-null  object\n",
-      " 1   ACTION                    16168 non-null  object\n",
-      " 2   REF_AREA                  16168 non-null  object\n",
-      " 3   Reference area            16168 non-null  object\n",
-      " 4   FREQ                      16168 non-null  object\n",
-      " 5   Frequency of observation  16168 non-null  object\n",
-      " 6   MEASURE                   16168 non-null  object\n",
-      " 7   Measure                   16168 non-null  object\n",
-      " 8   AGE                       16168 non-null  object\n",
-      " 9   Age                       16168 non-null  object\n",
-      " 10  SEX                       16168 non-null  object\n",
-      " 11  Sex                       16168 non-null  object\n",
-      " 12  UNIT_MEASURE              16168 non-null  object\n",
-      " 13  Unit of measure           16168 non-null  object\n",
-      " 14  TIME_PERIOD               16168 non-null  object\n",
-      " 15  OBS_VALUE                 16168 non-null  int64 \n",
-      "dtypes: int64(1), object(15)\n",
-      "memory usage: 2.0+ MB\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>STRUCTURE_NAME</th>\n",
-       "      <th>ACTION</th>\n",
-       "      <th>REF_AREA</th>\n",
-       "      <th>Reference area</th>\n",
-       "      <th>FREQ</th>\n",
-       "      <th>Frequency of observation</th>\n",
-       "      <th>MEASURE</th>\n",
-       "      <th>Measure</th>\n",
-       "      <th>AGE</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>SEX</th>\n",
-       "      <th>Sex</th>\n",
-       "      <th>UNIT_MEASURE</th>\n",
-       "      <th>Unit of measure</th>\n",
-       "      <th>TIME_PERIOD</th>\n",
-       "      <th>OBS_VALUE</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Mortality by week</td>\n",
-       "      <td>I</td>\n",
-       "      <td>SWE</td>\n",
-       "      <td>Sweden</td>\n",
-       "      <td>W</td>\n",
-       "      <td>Weekly</td>\n",
-       "      <td>M</td>\n",
-       "      <td>Mortality</td>\n",
-       "      <td>_T</td>\n",
-       "      <td>Total</td>\n",
-       "      <td>_T</td>\n",
-       "      <td>Total</td>\n",
-       "      <td>DT</td>\n",
-       "      <td>Deaths</td>\n",
-       "      <td>2015-W01</td>\n",
-       "      <td>1927</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Mortality by week</td>\n",
-       "      <td>I</td>\n",
-       "      <td>SWE</td>\n",
-       "      <td>Sweden</td>\n",
-       "      <td>W</td>\n",
-       "      <td>Weekly</td>\n",
-       "      <td>M</td>\n",
-       "      <td>Mortality</td>\n",
-       "      <td>_T</td>\n",
-       "      <td>Total</td>\n",
-       "      <td>_T</td>\n",
-       "      <td>Total</td>\n",
-       "      <td>DT</td>\n",
-       "      <td>Deaths</td>\n",
-       "      <td>2015-W02</td>\n",
-       "      <td>1966</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Mortality by week</td>\n",
-       "      <td>I</td>\n",
-       "      <td>SWE</td>\n",
-       "      <td>Sweden</td>\n",
-       "      <td>W</td>\n",
-       "      <td>Weekly</td>\n",
-       "      <td>M</td>\n",
-       "      <td>Mortality</td>\n",
-       "      <td>_T</td>\n",
-       "      <td>Total</td>\n",
-       "      <td>_T</td>\n",
-       "      <td>Total</td>\n",
-       "      <td>DT</td>\n",
-       "      <td>Deaths</td>\n",
-       "      <td>2015-W03</td>\n",
-       "      <td>1935</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Mortality by week</td>\n",
-       "      <td>I</td>\n",
-       "      <td>SWE</td>\n",
-       "      <td>Sweden</td>\n",
-       "      <td>W</td>\n",
-       "      <td>Weekly</td>\n",
-       "      <td>M</td>\n",
-       "      <td>Mortality</td>\n",
-       "      <td>_T</td>\n",
-       "      <td>Total</td>\n",
-       "      <td>_T</td>\n",
-       "      <td>Total</td>\n",
-       "      <td>DT</td>\n",
-       "      <td>Deaths</td>\n",
-       "      <td>2015-W04</td>\n",
-       "      <td>1946</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Mortality by week</td>\n",
-       "      <td>I</td>\n",
-       "      <td>SWE</td>\n",
-       "      <td>Sweden</td>\n",
-       "      <td>W</td>\n",
-       "      <td>Weekly</td>\n",
-       "      <td>M</td>\n",
-       "      <td>Mortality</td>\n",
-       "      <td>_T</td>\n",
-       "      <td>Total</td>\n",
-       "      <td>_T</td>\n",
-       "      <td>Total</td>\n",
-       "      <td>DT</td>\n",
-       "      <td>Deaths</td>\n",
-       "      <td>2015-W05</td>\n",
-       "      <td>1975</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      STRUCTURE_NAME ACTION REF_AREA Reference area FREQ  \\\n",
-       "0  Mortality by week      I      SWE         Sweden    W   \n",
-       "1  Mortality by week      I      SWE         Sweden    W   \n",
-       "2  Mortality by week      I      SWE         Sweden    W   \n",
-       "3  Mortality by week      I      SWE         Sweden    W   \n",
-       "4  Mortality by week      I      SWE         Sweden    W   \n",
-       "\n",
-       "  Frequency of observation MEASURE    Measure AGE    Age SEX    Sex  \\\n",
-       "0                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
-       "1                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
-       "2                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
-       "3                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
-       "4                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
-       "\n",
-       "  UNIT_MEASURE Unit of measure TIME_PERIOD  OBS_VALUE  \n",
-       "0           DT          Deaths    2015-W01       1927  \n",
-       "1           DT          Deaths    2015-W02       1966  \n",
-       "2           DT          Deaths    2015-W03       1935  \n",
-       "3           DT          Deaths    2015-W04       1946  \n",
-       "4           DT          Deaths    2015-W05       1975  "
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# read the mortality data from a csv file\n",
     "link_id = \"rh3mp\"\n",

From 5783c4ef03792cb18f301d827a6ddae37e0e7254 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Tue, 11 Jun 2024 22:57:03 +0200
Subject: [PATCH 23/65] Updated template file for 2024 w/ resources and.

---
 projects/template-images/2024_SeaIce.svg | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/projects/template-images/2024_SeaIce.svg b/projects/template-images/2024_SeaIce.svg
index 0d4f7608a..6a93b29a0 100644
--- a/projects/template-images/2024_SeaIce.svg
+++ b/projects/template-images/2024_SeaIce.svg
@@ -1 +1 @@
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From b7519df85790edb014d61237eb37865d78bddc57 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Tue, 11 Jun 2024 23:01:00 +0200
Subject: [PATCH 24/65] Major update after review by curriculum specialist,
 CMIP6 data retrieval via intake_esm, more coding clarity, hints for every
 question, etc...

---
 projects/project-notebooks/SeaIce_2024.ipynb | 3043 ++----------------
 1 file changed, 218 insertions(+), 2825 deletions(-)

diff --git a/projects/project-notebooks/SeaIce_2024.ipynb b/projects/project-notebooks/SeaIce_2024.ipynb
index 9387adaaa..e202e4677 100644
--- a/projects/project-notebooks/SeaIce_2024.ipynb
+++ b/projects/project-notebooks/SeaIce_2024.ipynb
@@ -5,7 +5,7 @@
    "id": "ada5523b",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/neuromatch/course-content-template/blob/main/projects/example_dataset/Example_based_on_CMA_structure.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/course-content-template/main/projects/example_dataset/Example_based_on_CMA_structure\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/projects/project-notebooks/SeaIce_2024.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/projects/project-notebooks/SeaIce_2024.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
    ]
   },
   {
@@ -15,74 +15,17 @@
     "execution": {}
    },
    "source": [
-    "<h1>\n",
-    "Arctic Sea-ice Decline\n",
-    "</h1>\n",
+    "# Arctic Sea Ice Change\n",
     "\n",
+    "**Content creators:** Alistair Duffey, Will Gregory, Michel Tsamados\n",
     "\n",
+    "**Content reviewers:** Paul Heubel, Laura Paccini, Jenna Pearson\n",
     "\n",
-    "__Content creators:__ Alistair Duffey, Michel Tsamados, Will Gregory\n",
+    "**Content editors:** Paul Heubel\n",
     "\n",
-    "__Content reviewers:__ Names & Surnames\n",
+    "**Production editors:** Paul Heubel, Konstantine Tsafatinos\n",
     "\n",
-    "__Content editors:__ Konstantinos Tsafatinos\n",
-    "\n",
-    "__Production editors:__ Konstantinos Tsafatinos\n",
-    "\n",
-    "__Our 2024 Sponsors:__ Sponsors\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "60881a20",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "The notebook should have the following outline:\n",
-    "\n",
-    "- Project background (video + template outline)\n",
-    "- Imports and functions common to multiple data sources\n",
-    "- Data source 1\n",
-    "- Data source background\n",
-    "- Imports and functions common to multiple datasets from the same data source\n",
-    "\n",
-    "- Dataset 1:\n",
-    "\n",
-    "    Dataset background (if needed)\n",
-    "    \n",
-    "    Imports and functions specific to the dataset\n",
-    "    \n",
-    "    Code to load the dataset\n",
-    "    \n",
-    "    Code to pre-process the dataset (if not taught and likely difficult for students)\n",
-    "    \n",
-    "    Code to visualise the data table shape/content\n",
-    "\n",
-    "...\n",
-    "- Dataset N:\n",
-    "    same as for Dataset 1\n",
-    "...\n",
-    "\n",
-    "- Further reading\n",
-    "\n",
-    "---\n",
-    "We encourage you to explain the key parts of your code through comments and text cells as you see fit.\n",
-    "\n",
-    "---\n",
-    "If the same or analogous dataset is covered in the curriculum tutorials, we can expect students to know how to load that data. In that case, please provide only the dataset background and refer students to the respective curriculum day while maintaining the structural hierarchy of the notebook.\n",
-    "\n",
-    "---\n",
-    "If you wish to include any plots in your template, those should follow the standard figure formatting. Please include the Figure settings cell from the tutorial notebook template. Let us know if you have any questions about this or are unsure how to proceed!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c08b96da",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "To include videos:"
+    "**Our 2024 Sponsors:** CMIP, NFDI4Earth\n"
    ]
   },
   {
@@ -133,7 +76,7 @@
     "  return tab_contents\n",
     "\n",
     "\n",
-    "video_ids = [('Youtube', '<video_id_1>'), ('Bilibili', '<video_id_2>'), ('Osf', '<video_id_3>')]\n",
+    "video_ids = [('Youtube', 'gt6o3lqu5zA'), ('Bilibili', '<video_id_2>'), ('Osf', '<video_id_3>')]\n",
     "tab_contents = display_videos(video_ids, W=854, H=480)\n",
     "tabs = widgets.Tab()\n",
     "tabs.children = tab_contents\n",
@@ -142,30 +85,19 @@
     "display(tabs)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "a9fb54e0",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "To include slides:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "id": "b52fef8b",
-   "metadata": {
-    "cellView": "form"
-   },
+   "execution_count": null,
+   "id": "0f7b933c-9888-4051-8a62-c7f44246feda",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# @title Tutorial slides\n",
-    "# @markdown These are the slides for the videos in all tutorials today\n",
-    "\n",
+    "# @title Slides\n",
+    "# @markdown These are the slides for the video introduction to the project\n",
     "from IPython.display import IFrame\n",
-    "#link_id = \"n7wdy\""
+    "link_id = \"y6em2\"\n",
+    "print(f\"If you want to download the slides: https://osf.io/download/{link_id}/\")\n",
+    "IFrame(src=f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\", width=854, height=480)"
    ]
   },
   {
@@ -175,15 +107,15 @@
     "execution": {}
    },
    "source": [
-    "In this project, you will be given the opportunity to explore data from climate models to examine the modelled change in sea ice coverage over time.\n",
+    "In this project, you will be given the opportunity to explore data from climate models to examine the modeled change in sea ice coverage over time.\n",
     "\n",
     "The project aims to:\n",
-    "* Download, process and plot data showing modelled sea ice coverage over the historical period from a CMIP6 model.\n",
-    "* Calculate the total sea ice extent and assess it's rate of decline over the recent historical period, and projected into the future under a middle-of-the-road emissions scenario.\n",
+    "* Download, process, and plot data showing modeled sea ice coverage over the historical period within a CMIP6 model climate.\n",
+    "* Calculate the total sea ice extent and assess its rate of decline over the recent historical period, and project it into the future under a middle-of-the-road emissions scenario.\n",
     "* Assess the dependence of future projections on emissions scenario, e.g. to assess whether any emissions scenario is sufficient to keep late-summer sea ice in the Arctic.\n",
-    "* Examine the spatial and seasonal variation of sea-ice and how this changes during its decline with warming.\n",
+    "* Examine the spatial and seasonal variation of sea ice and how this changes during its decline with warming.\n",
     "\n",
-    "We also include a dataset of satellite observations, in case you would like to check the realism of the model's representaiton of sea ice in the recent historical period. \n"
+    "We also include a dataset of satellite observations, in case you would like to check the realism of the model's representation of sea ice in the recent historical period. \n"
    ]
   },
   {
@@ -195,17 +127,18 @@
    "source": [
     "# Project Template\n",
     "\n",
-    "<img src='../template-images/Sea_ice_template_questions.svg'/>"
+    "<img src='../template-images/2024_SeaIce.svg'/>"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "f51243f3",
+   "id": "a4cc9acb-3363-4a13-879d-73378ddb6a74",
    "metadata": {
     "execution": {}
    },
    "source": [
-    "## Data Exploration Notebook\n",
+    "# Data Exploration Notebook\n",
     "\n",
     "## Project Setup"
    ]
@@ -214,42 +147,37 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "b68292ec",
-   "metadata": {
-    "execution": {}
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# google colab installs\n",
     "\n",
-    "# !mamaba install netCDF4"
+    "# !pip install --quiet"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "1e541a40",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Imports\n",
     "\n",
-    "#Import only the libraries/objects that are necessary for more than one dataset.\n",
-    "#Dataset-specific imports should be in the respective notebook section.\n",
-    "\n",
-    "#If any external library has to be installed, !pip install library --quiet\n",
-    "#follow this order: numpy>matplotlib.\n",
-    "#import widgets in hidden Figure settings cell\n",
-    "\n",
-    "import random\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import xarray as xr\n",
-    "import dask"
+    "import dask\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "from xmip.preprocessing import combined_preprocessing\n",
+    "from xmip.utils import google_cmip_col\n",
+    "from xmip.postprocessing import match_metrics"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "07545499",
    "metadata": {},
    "outputs": [],
@@ -258,7 +186,36 @@
     "\n",
     "#import ipywidgets as widgets       # interactive display\n",
     "%config InlineBackend.figure_format = 'retina'\n",
-    "plt.style.use(\"https://raw.githubusercontent.com/ClimateMatchAcademy/course-content/main/cma.mplstyle\")"
+    "plt.style.use(\"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6569502-58dc-43a7-a95f-5fa5f8828179",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper functions\n",
+    "import os\n",
+    "import pooch\n",
+    "import tempfile\n",
+    "\n",
+    "def pooch_load(filelocation=None, filename=None, processor=None):\n",
+    "    shared_location = \"/home/jovyan/shared/Data/projects/SeaIce\"  # this is different for each day\n",
+    "    user_temp_cache = tempfile.gettempdir()\n",
+    "\n",
+    "    if os.path.exists(os.path.join(shared_location, filename)):\n",
+    "        file = os.path.join(shared_location, filename)\n",
+    "    else:\n",
+    "        file = pooch.retrieve(\n",
+    "            filelocation,\n",
+    "            known_hash=None,\n",
+    "            fname=os.path.join(user_temp_cache, filename),\n",
+    "            processor=processor,\n",
+    "        )\n",
+    "\n",
+    "    return file"
    ]
   },
   {
@@ -268,1914 +225,232 @@
     "execution": {}
    },
    "source": [
-    "## [datasource 1]: Climate model simulations of sea ice concentration\n",
+    "## CESM2-WACCM: Climate model simulations of sea ice concentration\n",
     "\n",
-    "Here we use output from CESM2-WACCM, the Community Earth System Model (version 2, CESM2), with the The Whole Atmosphere Community Climate Model (WACCM) as it's atmosphere component. \n",
+    "Here we use the output from [CESM2-WACCM, the Community Earth System Model (version 2, CESM2)](https://doi.org/10.1029/2019ms001916), with the [Whole Atmosphere Community Climate Model (WACCM)](https://doi.org/10.1029/2019JD030943) as its atmosphere component. \n",
     "\n",
-    "We use the historical scenario, which runs with historical forcings 1850-2014. Note that this scenario is just one instance of internal variability under a world forced by historical GHGs. \n",
+    "We use the historical scenario, which runs with historical forcings from 1850 to 2014. Note that this scenario is just one instance of internal variability in a world forced by historical GHGs. \n",
     "\n",
     "A note on the sea ice output for CMIP6 models: \n",
     "\n",
-    "Sea ice component of models is generally output on the ocean grid, which is not normally a simple lat/lon grid, unlike many atmosphere model components. Here we use the variable 'siconca' which stands for 'sea ice concentration atmosphere' - this is the sea ice concentration regridded onto the model's atmosphere grid, and is somewhat easier to work with.\n"
+    "The Sea Ice component of models is generally output on the ocean grid, which is normally not a simple lat/lon grid, unlike many atmosphere model components. Here we use the variable ***siconca*** which stands for *sea ice concentration atmosphere* - this is the sea ice concentration re-gridded onto the model's atmosphere grid and is somewhat easier to work with.\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "80b20bd4-a095-4bef-9284-e896245e8df8",
+   "cell_type": "markdown",
+   "id": "867ff5a5-6b39-459d-b89f-d224126147b6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/users/a_duffey/.conda/envs/main/lib/python3.10/site-packages/xarray/conventions.py:516: SerializationWarning: variable 'siconca' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
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-       "}\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-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
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-       "  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",
-       "  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",
-       "  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-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-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",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:    (time: 1980, lat: 192, lon: 288, nbnd: 2)\n",
-       "Coordinates:\n",
-       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
-       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
-       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
-       "Dimensions without coordinates: nbnd\n",
-       "Data variables:\n",
-       "    siconca    (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
-       "    time_bnds  (time, nbnd) object dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
-       "    lat_bnds   (lat, nbnd) float64 dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
-       "    lon_bnds   (lon, nbnd) float64 dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
-       "Attributes: (12/45)\n",
-       "    Conventions:            CF-1.7 CMIP-6.2\n",
-       "    activity_id:            CMIP\n",
-       "    branch_method:          standard\n",
-       "    branch_time_in_child:   674885.0\n",
-       "    branch_time_in_parent:  20075.0\n",
-       "    case_id:                4\n",
-       "    ...                     ...\n",
-       "    sub_experiment_id:      none\n",
-       "    table_id:               SImon\n",
-       "    tracking_id:            hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89\n",
-       "    variable_id:            siconca\n",
-       "    variant_info:           CMIP6 CESM2 hindcast (1850-2014) with high-top at...\n",
-       "    variant_label:          r1i1p1f1</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-370a4cef-676a-44f2-9336-76047eda225d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-370a4cef-676a-44f2-9336-76047eda225d' 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>: 1980</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1a4ac358-1748-4bb4-8860-d7be0ba94071' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a4ac358-1748-4bb4-8860-d7be0ba94071' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-d9d6f81c-1b57-4f9c-a0b7-97332dd35263' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d6f81c-1b57-4f9c-a0b7-97332dd35263' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c1e9b7d-ad58-42b0-94e1-4aa9a66d07c0' class='xr-var-data-in' type='checkbox'><label for='data-3c1e9b7d-ad58-42b0-94e1-4aa9a66d07c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
-       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
-       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
-       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
-       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
-       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
-       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
-       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
-       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
-       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
-       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
-       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
-       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
-       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
-       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
-       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
-       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
-       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
-       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
-       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
-       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
-       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
-       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
-       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
-       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
-       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
-       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
-       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
-       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
-       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
-       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
-       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</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'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-2104bea4-3266-4e82-ae39-6df0f15f9e07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2104bea4-3266-4e82-ae39-6df0f15f9e07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07efbccc-b568-4d6c-95d1-f516945b34fe' class='xr-var-data-in' type='checkbox'><label for='data-07efbccc-b568-4d6c-95d1-f516945b34fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</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'>1850-01-15 12:00:00 ... 2014-12-...</div><input id='attrs-dfc1d58c-ef17-472a-bee4-493b2862820a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfc1d58c-ef17-472a-bee4-493b2862820a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3caf291a-b5fc-4c93-904a-4c926a653d77' class='xr-var-data-in' type='checkbox'><label for='data-3caf291a-b5fc-4c93-904a-4c926a653d77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(2014, 12, 15, 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-af98ba17-1ba3-4af1-9b4d-6c74385f0357' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af98ba17-1ba3-4af1-9b4d-6c74385f0357' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>siconca</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=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-fa31497f-affc-4b24-8780-7182c8c2e393' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa31497f-affc-4b24-8780-7182c8c2e393' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-392008d9-7eff-4370-a4dc-751b0f2fb1c4' class='xr-var-data-in' type='checkbox'><label for='data-392008d9-7eff-4370-a4dc-751b0f2fb1c4' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>ICEFRAC</dd><dt><span>description :</span></dt><dd>Percentage of grid cell covered by sea ice</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>siconca</dd><dt><span>long_name :</span></dt><dd>Sea-Ice Area Percentage (Atmospheric Grid)</dd><dt><span>mipTable :</span></dt><dd>SImon</dd><dt><span>out_name :</span></dt><dd>siconca</dd><dt><span>prov :</span></dt><dd>SIMIP [seaicemon]</dd><dt><span>realm :</span></dt><dd>seaIce</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Sea-Ice Area Percentage (Atmospheric Grid)</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>variable_id :</span></dt><dd>siconca</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\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> 417.66 MiB </td>\n",
-       "                        <td> 3.81 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1980, 192, 288) </td>\n",
-       "                        <td> (100, 100, 100) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 121 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"171\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-71a9d479-3aa2-42a1-8d34-99aee3283334' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71a9d479-3aa2-42a1-8d34-99aee3283334' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36b15c4a-92a0-4fae-ac25-c9623da50949' class='xr-var-data-in' type='checkbox'><label for='data-36b15c4a-92a0-4fae-ac25-c9623da50949' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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-       "                        <th> Array </th>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 30.94 kiB </td>\n",
-       "                        <td> 1.56 kiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1980, 2) </td>\n",
-       "                        <td> (100, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 21 Tasks </td>\n",
-       "                        <td> 20 Chunks </td>\n",
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-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> object </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-90ef9d13-60dd-4b0f-932f-547389144329' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90ef9d13-60dd-4b0f-932f-547389144329' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a46c377-2b60-45f3-a03d-96bcc4d18ea4' class='xr-var-data-in' type='checkbox'><label for='data-4a46c377-2b60-45f3-a03d-96bcc4d18ea4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\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> 3.00 kiB </td>\n",
-       "                        <td> 1.56 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (192, 2) </td>\n",
-       "                        <td> (100, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 3 Tasks </td>\n",
-       "                        <td> 2 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-3f9f38d6-c160-4ad4-8971-dac8e1dec4c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f9f38d6-c160-4ad4-8971-dac8e1dec4c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-527c62b4-d6b2-4878-9d48-9a2767aa47f9' class='xr-var-data-in' type='checkbox'><label for='data-527c62b4-d6b2-4878-9d48-9a2767aa47f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\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> 1.56 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (288, 2) </td>\n",
-       "                        <td> (100, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4 Tasks </td>\n",
-       "                        <td> 3 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "                </tbody>\n",
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-       "        </td>\n",
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-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-35d9584f-1d6b-48a3-9d71-2beebb4f8a93' class='xr-section-summary-in' type='checkbox'  ><label for='section-35d9584f-1d6b-48a3-9d71-2beebb4f8a93' class='xr-section-summary' >Attributes: <span>(45)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>20075.0</dd><dt><span>case_id :</span></dt><dd>4</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BWHIST.f09_g17.CMIP6-historical-WACCM.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-05-02T15:33:18Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2-WACCM.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>native gx1v7 displaced pole grid (384x320 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>seaIce</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); atmosChem: WACCM (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb; landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC CHEM AER</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>SImon</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89</dd><dt><span>variable_id :</span></dt><dd>siconca</dd><dt><span>variant_info :</span></dt><dd>CMIP6 CESM2 hindcast (1850-2014) with high-top atmosphere (WACCM6) with interactive chemistry (TSMLT1), interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:    (time: 1980, lat: 192, lon: 288, nbnd: 2)\n",
-       "Coordinates:\n",
-       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
-       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
-       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
-       "Dimensions without coordinates: nbnd\n",
-       "Data variables:\n",
-       "    siconca    (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
-       "    time_bnds  (time, nbnd) object dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
-       "    lat_bnds   (lat, nbnd) float64 dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
-       "    lon_bnds   (lon, nbnd) float64 dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
-       "Attributes: (12/45)\n",
-       "    Conventions:            CF-1.7 CMIP-6.2\n",
-       "    activity_id:            CMIP\n",
-       "    branch_method:          standard\n",
-       "    branch_time_in_child:   674885.0\n",
-       "    branch_time_in_parent:  20075.0\n",
-       "    case_id:                4\n",
-       "    ...                     ...\n",
-       "    sub_experiment_id:      none\n",
-       "    table_id:               SImon\n",
-       "    tracking_id:            hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89\n",
-       "    variable_id:            siconca\n",
-       "    variant_info:           CMIP6 CESM2 hindcast (1850-2014) with high-top at...\n",
-       "    variant_label:          r1i1p1f1"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "# read in the historical scenario run data \n",
-    "# this is for one ensemble member - r1i1p1f1 \n",
-    "# and one scenario - 'historical' \n",
-    "directory_mod = '/badc/cmip6/data/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/SImon/siconca/gn/latest/'\n",
-    "files_mod = ['siconca_SImon_CESM2-WACCM_historical_r1i1p1f1_gn_185001-201412.nc']\n",
+    "Let's search the CMIP6 catalog for some sea ice concentration (`siconca`) data from the CESM Earth System Model. We pick a certain ensemble member (`r1i1p1f1`) to reduce the amount of data to download. At first, our scenarios of interest are the `historical` and the middle-of-the-road `ssp245` ones. As we get the data on the native grid (`gn`), we must also download the grid cell area in the next step.\n",
     "\n",
-    "SI_ds = xr.open_mfdataset([directory_mod + file for file in files_mod],\n",
-    "                                chunks=100)          "
+    "*Hint: for a detailed explanation of the following `xmip` functionalities and other CMIP6 related terms, please refer to:*\n",
+    "* *W1D5 [Tutorial 7](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D5_ClimateModeling/W1D5_Tutorial7.ipynb),*\n",
+    "* *W2D1 [Tutorial 1](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W2D1_FutureClimate-IPCCIPhysicalBasis/W2D1_Tutorial1.ipynb),*\n",
+    "* *our [CMIP Resource bank](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/CMIP/CMIP_resource_bank.md),* \n",
+    "* *xmip's [tutorials](https://cmip6-preprocessing.readthedocs.io/en/latest/tutorial.html)*\n",
+    "* *[this lecture](https://earth-env-data-science.github.io/lectures/models/cmip.html) by Ryan Abernathy, Professor of Earth and Environmental Sciences at Columbia University*"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "ce3a66c5-421c-4fb9-9253-ef156113ddf6",
+   "execution_count": null,
+   "id": "62ca691c-6efe-4598-932f-32f5e9aac31b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/users/a_duffey/.conda/envs/main/lib/python3.10/site-packages/xarray/conventions.py:516: SerializationWarning: variable 'areacella' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
-      "  new_vars[k] = decode_cf_variable(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# also get the models grid areas as an input file:\n",
-    "areacella_file = '/badc/cmip6/data/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/fx/areacella/gn/latest/areacella_fx_CESM2-WACCM_historical_r1i1p1f1_gn.nc'\n",
-    "grid_cell_areas = xr.open_dataset(areacella_file)\n",
-    "grid_cell_areas"
+    "# create a collection object from the Google Cloud Storage\n",
+    "col = google_cmip_col()\n",
+    "\n",
+    "# search via keys\n",
+    "cat = col.search(\n",
+    "        source_id=[\"CESM2-WACCM\"],\n",
+    "        variable_id=[\"siconca\"],\n",
+    "        member_id=\"r1i1p1f1\",\n",
+    "        #table_id=\"SImon\",\n",
+    "        grid_label=\"gn\",\n",
+    "        experiment_id=[\"historical\", \"ssp245\"],\n",
+    "    #, \"ssp126\",  \"ssp585\"],\n",
+    "        require_all_on=[\"experiment_id\", \"variable_id\"]\n",
+    ")\n",
+    "# key word arguments that allow efficient and useful preprocessing\n",
+    "kwargs = {'zarr_kwargs':{\n",
+    "    'consolidated':True,\n",
+    "    'use_cftime':True\n",
+    "},\n",
+    "          'aggregate':False,\n",
+    "          'preprocess':combined_preprocessing\n",
+    "}\n",
+    "\n",
+    "# create a dictionary of the datasets from the catalog entries\n",
+    "ds_dict = cat.to_dataset_dict(**kwargs)\n",
+    "list(ds_dict.keys())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "f3eed487-0116-4d52-b533-2c99cbb1f7cc",
+   "execution_count": null,
+   "id": "5a348976-8faa-4a0f-aedd-c427b212120c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# files for the SSP2-4.5 scenario, for the orange extension questions:\n",
-    "directory_cmip_data_ssp245 = '/badc/cmip6/data/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r1i1p1f1/SImon/siconca/gn/latest/'\n",
+    "# repeat the procedure with the grid cell area\n",
+    "cat_metric = col.search(\n",
+    "    source_id=['CESM2-WACCM'],\n",
+    "    variable_id='areacella',\n",
+    "    member_id=\"r1i1p1f1\",\n",
+    "    experiment_id=[\"historical\", \"ssp245\"]\n",
+    ")\n",
+    "ddict_metrics = cat_metric.to_dataset_dict(**kwargs)\n",
     "\n",
-    "files_cmip_data_ssp245 = ['siconca_SImon_CESM2-WACCM_ssp245_r1i1p1f1_gn_201501-206412.nc',\n",
-    "                   'siconca_SImon_CESM2-WACCM_ssp245_r1i1p1f1_gn_206501-210012.nc']      "
+    "list(ddict_metrics.keys())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "e0b537fa-4a11-4c89-9c98-d73327cc229f",
+   "execution_count": null,
+   "id": "08a37581-9a3f-4650-8811-5cd1aea35f27",
    "metadata": {},
-   "outputs": [
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-       "  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-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",
-       "  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",
-       "  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-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-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",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:    (time: 1980, lat: 192, lon: 288, nbnd: 2)\n",
-       "Coordinates:\n",
-       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
-       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
-       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
-       "Dimensions without coordinates: nbnd\n",
-       "Data variables:\n",
-       "    siconca    (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
-       "    time_bnds  (time, nbnd) object dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
-       "    lat_bnds   (lat, nbnd) float64 dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
-       "    lon_bnds   (lon, nbnd) float64 dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
-       "Attributes: (12/45)\n",
-       "    Conventions:            CF-1.7 CMIP-6.2\n",
-       "    activity_id:            CMIP\n",
-       "    branch_method:          standard\n",
-       "    branch_time_in_child:   674885.0\n",
-       "    branch_time_in_parent:  20075.0\n",
-       "    case_id:                4\n",
-       "    ...                     ...\n",
-       "    sub_experiment_id:      none\n",
-       "    table_id:               SImon\n",
-       "    tracking_id:            hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89\n",
-       "    variable_id:            siconca\n",
-       "    variant_info:           CMIP6 CESM2 hindcast (1850-2014) with high-top at...\n",
-       "    variant_label:          r1i1p1f1</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-4c242a38-6df1-4862-b5bb-8d05b11d3f12' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4c242a38-6df1-4862-b5bb-8d05b11d3f12' 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>: 1980</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2f9c43f3-813b-47b5-a740-844d91e2ec62' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2f9c43f3-813b-47b5-a740-844d91e2ec62' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-e323c14f-8d48-43ed-8da5-21b94312232d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e323c14f-8d48-43ed-8da5-21b94312232d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a06b0f7-c19d-4f13-b5d2-2e4b5b68da05' class='xr-var-data-in' type='checkbox'><label for='data-5a06b0f7-c19d-4f13-b5d2-2e4b5b68da05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
-       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
-       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
-       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
-       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
-       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
-       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
-       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
-       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
-       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
-       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
-       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
-       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
-       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
-       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
-       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
-       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
-       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
-       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
-       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
-       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
-       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
-       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
-       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
-       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
-       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
-       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
-       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
-       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
-       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
-       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
-       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</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'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-68c02fa0-6df4-411c-a3f6-78b03c293d1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68c02fa0-6df4-411c-a3f6-78b03c293d1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c05b7a4-e489-47a6-a1a4-fff7df6e59e5' class='xr-var-data-in' type='checkbox'><label for='data-1c05b7a4-e489-47a6-a1a4-fff7df6e59e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</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'>1850-01-15 12:00:00 ... 2014-12-...</div><input id='attrs-1c7f3f19-2bf9-4369-9fd9-67b82065c523' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c7f3f19-2bf9-4369-9fd9-67b82065c523' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e87dfae9-d4ff-43da-a277-cb721261400f' class='xr-var-data-in' type='checkbox'><label for='data-e87dfae9-d4ff-43da-a277-cb721261400f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeNoLeap(2014, 12, 15, 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-d387521d-105b-4dd6-919b-97bf3f14d9b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d387521d-105b-4dd6-919b-97bf3f14d9b8' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>siconca</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=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-04d1c1fa-af30-46e4-b915-85df51558d58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04d1c1fa-af30-46e4-b915-85df51558d58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-721a8df2-b23a-454c-b0e0-1494e727d748' class='xr-var-data-in' type='checkbox'><label for='data-721a8df2-b23a-454c-b0e0-1494e727d748' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>ICEFRAC</dd><dt><span>description :</span></dt><dd>Percentage of grid cell covered by sea ice</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>siconca</dd><dt><span>long_name :</span></dt><dd>Sea-Ice Area Percentage (Atmospheric Grid)</dd><dt><span>mipTable :</span></dt><dd>SImon</dd><dt><span>out_name :</span></dt><dd>siconca</dd><dt><span>prov :</span></dt><dd>SIMIP [seaicemon]</dd><dt><span>realm :</span></dt><dd>seaIce</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Sea-Ice Area Percentage (Atmospheric Grid)</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>variable_id :</span></dt><dd>siconca</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\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> 417.66 MiB </td>\n",
-       "                        <td> 3.81 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1980, 192, 288) </td>\n",
-       "                        <td> (100, 100, 100) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 121 Tasks </td>\n",
-       "                        <td> 120 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"171\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"20\" x2=\"80\" y2=\"90\" />\n",
-       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-1e5229a1-a22b-4a80-a537-d3504356bbc6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e5229a1-a22b-4a80-a537-d3504356bbc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65b14fce-e00e-4a34-8e3b-3869812dc2b2' class='xr-var-data-in' type='checkbox'><label for='data-65b14fce-e00e-4a34-8e3b-3869812dc2b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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-       "                <thead>\n",
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-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 30.94 kiB </td>\n",
-       "                        <td> 1.56 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1980, 2) </td>\n",
-       "                        <td> (100, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 21 Tasks </td>\n",
-       "                        <td> 20 Chunks </td>\n",
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-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> object </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-9ed0c0a0-dde1-4865-8850-b1dd053a0378' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ed0c0a0-dde1-4865-8850-b1dd053a0378' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33add893-6a05-4811-af51-1a3a0aeb2a92' class='xr-var-data-in' type='checkbox'><label for='data-33add893-6a05-4811-af51-1a3a0aeb2a92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\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> 3.00 kiB </td>\n",
-       "                        <td> 1.56 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (192, 2) </td>\n",
-       "                        <td> (100, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 3 Tasks </td>\n",
-       "                        <td> 2 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-20cca63f-4987-4e1a-aa75-c35b92dc894f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20cca63f-4987-4e1a-aa75-c35b92dc894f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a13e8f02-3ee9-4798-92d8-e9fcc0714ed9' class='xr-var-data-in' type='checkbox'><label for='data-a13e8f02-3ee9-4798-92d8-e9fcc0714ed9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\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> 1.56 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (288, 2) </td>\n",
-       "                        <td> (100, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4 Tasks </td>\n",
-       "                        <td> 3 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float64 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8cf74968-975c-48c5-924d-12b73c2ccfe9' class='xr-section-summary-in' type='checkbox'  ><label for='section-8cf74968-975c-48c5-924d-12b73c2ccfe9' class='xr-section-summary' >Attributes: <span>(45)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>20075.0</dd><dt><span>case_id :</span></dt><dd>4</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BWHIST.f09_g17.CMIP6-historical-WACCM.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-05-02T15:33:18Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2-WACCM.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>native gx1v7 displaced pole grid (384x320 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>seaIce</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); atmosChem: WACCM (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb; landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC CHEM AER</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>SImon</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89</dd><dt><span>variable_id :</span></dt><dd>siconca</dd><dt><span>variant_info :</span></dt><dd>CMIP6 CESM2 hindcast (1850-2014) with high-top atmosphere (WACCM6) with interactive chemistry (TSMLT1), interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:    (time: 1980, lat: 192, lon: 288, nbnd: 2)\n",
-       "Coordinates:\n",
-       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
-       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
-       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
-       "Dimensions without coordinates: nbnd\n",
-       "Data variables:\n",
-       "    siconca    (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
-       "    time_bnds  (time, nbnd) object dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
-       "    lat_bnds   (lat, nbnd) float64 dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
-       "    lon_bnds   (lon, nbnd) float64 dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
-       "Attributes: (12/45)\n",
-       "    Conventions:            CF-1.7 CMIP-6.2\n",
-       "    activity_id:            CMIP\n",
-       "    branch_method:          standard\n",
-       "    branch_time_in_child:   674885.0\n",
-       "    branch_time_in_parent:  20075.0\n",
-       "    case_id:                4\n",
-       "    ...                     ...\n",
-       "    sub_experiment_id:      none\n",
-       "    table_id:               SImon\n",
-       "    tracking_id:            hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89\n",
-       "    variable_id:            siconca\n",
-       "    variant_info:           CMIP6 CESM2 hindcast (1850-2014) with high-top at...\n",
-       "    variant_label:          r1i1p1f1"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# we can take a look at our dataset shape by calling it: \n",
-    "SI_ds  "
+    "#ddict_metrics[CMIP.NCAR.CESM2-WACCM.historical.r1i1p1f1.fx.areacella.gn.gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/fx/areacella/gn/v20190227/.20190227']\n",
+    "ddict_metrics[list(ddict_metrics.keys())[0]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "id": "59497066-568b-434e-9557-8fb8d9d5ad9f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0\n",
-      "99.999916\n"
-     ]
-    }
-   ],
-   "source": [
-    "# we see that it contains the variable siconca\n",
-    "\n",
-    "# lets print the minimum and maximum of this variable to check its format:\n",
-    "print(SI_ds.siconca.min().values)\n",
-    "print(SI_ds.siconca.max().values)\n",
-    "# note that it is formattwd as a percentage not a fraction, ranging from 0 to 100. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae442fb9-ddb5-42c3-92ad-fb6f906f2d8c",
+   "execution_count": null,
+   "id": "5d2ffd64-8c4c-4934-b2de-00da88cc5703",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Great, now you are all set to use these model sea-ice data to address the questions you are interested in!"
+    "# add the grid cell area metric to both scenario data sets\n",
+    "ddict_matched = match_metrics(ds_dict, ddict_metrics, ['areacella'])\n",
+    "list(ddict_matched.keys())"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "19e740fe-c069-4d24-b29d-a5936c6b0255",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db90f606-c419-416b-99cb-aecfb810ba2a",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "# Solutions for the Green questions"
+    "# select the historical scenario and print its summary\n",
+    "SI_ds = ddict_matched['CMIP.NCAR.CESM2-WACCM.historical.r1i1p1f1.SImon.siconca.gn.gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/SImon/siconca/gn/v20190507/.20190507']\n",
+    "SI_ds"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "d1826b08-3b1b-4eea-97e0-31ec140113a6",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb72400d-8b18-48aa-aedb-8ef0bc28795b",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Q1. \n",
-    "Plot the seasonal mean (DJF, MAM, JJA, SON)\n",
-    "sea ice concentration fields from one CMIP6 ensemble member."
+    "# select the ssp245 scenario and print its summary\n",
+    "SI_ds_245 = ddict_matched['ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i1p1f1.SImon.siconca.gn.gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r1i1p1f1/SImon/siconca/gn/v20190815/.20190815']\n",
+    "SI_ds_245"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "3a324ef6-0ee4-469e-b3ec-df9151850c6a",
+   "execution_count": null,
+   "id": "58e3e557-9eee-4116-89e1-cb3ba4d75c53",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import cartopy.crs as ccrs\n",
-    "import cartopy as cy"
+    "# let s print the meta data of the areacella that we added as a coordinate before\n",
+    "SI_ds.areacella.attrs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "1677999d-e7fd-43eb-a55d-3a1ab8e16a50",
+   "execution_count": null,
+   "id": "59497066-568b-434e-9557-8fb8d9d5ad9f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, '%')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x576 with 5 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# we'll do this for the preindustrial period:\n",
-    "timeperiod = [1850, 1900]\n",
-    "SI_ds_PI = SI_ds.sel(time=slice('1850', '1900'))\n",
-    "\n",
-    "# select northern hemisphere high latitudes:\n",
-    "SI_ds_PI = SI_ds_PI.sel(lat = slice(50, 90))\n",
-    "\n",
-    "# now use groupby function to get seasonal means\n",
-    "SI_ds_PI = SI_ds_PI.groupby(SI_ds_PI.time.dt.season).mean()\n",
+    "# we also see that it contains the data variable siconca\n",
     "\n",
-    "\n",
-    "# now plot\n",
-    "#fig, axs = plt.subplots(2, 2, figsize=(8, 8))\n",
-    "fig, axs = plt.subplots(2, 2, figsize=(8, 8), \n",
-    "                    subplot_kw={'projection':ccrs.NorthPolarStereo(central_longitude=0.0), \n",
-    "                                'facecolor':'gray'})\n",
-    "i=0\n",
-    "for season in SI_ds_PI.season.values:\n",
-    "    ds = SI_ds_PI.isel(season=i)\n",
-    "    \n",
-    "    p = ds.siconca.plot.pcolormesh(ax=axs.flatten()[i],\n",
-    "                               levels=  np.arange(0, 110, 10),\n",
-    "                               cmap='Blues_r',\n",
-    "                               transform=ccrs.PlateCarree(), \n",
-    "                               x=\"lon\", y=\"lat\", \n",
-    "                               add_colorbar=False\n",
-    "                               )\n",
-    "    \n",
-    "    p.axes.set_extent([0, 360, 66, 90], ccrs.PlateCarree())\n",
-    "    axs.flatten()[i].gridlines()\n",
-    "    axs.flatten()[i].set_title(season)\n",
-    "    p.axes.coastlines()\n",
-    "    #p.axes.LAND()\n",
-    "    p.axes.add_feature(cy.feature.LAND, color='gray', zorder=1000)\n",
-    "    i = i+1\n",
-    "fig.subplots_adjust(right=0.85, top=0.95)\n",
-    "cbar_ax = fig.add_axes([0.9, 0.35, 0.05, 0.6])\n",
-    "cbar = fig.colorbar(p, cax=cbar_ax, orientation='vertical')\n",
-    "cbar.ax.set_ylabel('%', rotation=0, \n",
-    "                       verticalalignment='center', labelpad =0)"
+    "# let's print the minimum and maximum of this variable to check its format:\n",
+    "print(SI_ds.siconca.min().values)\n",
+    "print(SI_ds.siconca.max().values)\n",
+    "# note that it is formattwd as a percentage not a fraction, ranging from 0 to 100. "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fa2cdf6f-a84c-42e9-902b-643c430e06b5",
+   "id": "ae442fb9-ddb5-42c3-92ad-fb6f906f2d8c",
    "metadata": {},
    "source": [
-    "## Q2. \n",
-    "For your CMIP6 model, compute the time series of mean sea ice extent over the period 1950-2014. Compute this for the months of March and September. "
+    "Great, now you are all set to use these model sea ice data to address the questions you are interested in!"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e47283b0-eeb9-47e9-8e39-69fbc7cb278c",
+   "id": "0823d446-6b6a-4684-814a-9892caee2a46",
    "metadata": {},
    "source": [
-    "We define sea ice extent following the convention that a grid cell is 'sea ice' where the concentration is higher than 15%. \n",
-    "\n",
-    "Extent is then the sum of the grid cell areas with concetration above 15%. "
+    "# Q1: \n",
+    "Plot the annual mean of Arctic sea ice concentration (SIC) for three example years (e.g., 1996, 2007, 2012) using one CMIP6 model ensemble member."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "001c2472-f031-45a0-beef-a710d1adc768",
+   "cell_type": "markdown",
+   "id": "b12170fa-fc2c-45ba-9021-c3ebd03f9143",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f89e2587df0>"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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6YsGuzGo+4NLySp6bt4cuYQHcPto8aAN9vRjfPYwfEzPRWvPJukNMe2UVi5Oy+GbrUafrVV5RaYyFhChGdQ3lpmEd+WJTGr/+dAv9Ytvy0KTuVe1k6d4szpRW8FPiMaYlRNM5NIAfHhzLxb1NT/SqAR1QSuHr5cmorqHVgvUHc4p4fWkKC+t4oBaWlPPt1vSz7kHtzcwnwMeTi3qEs26/4x5CpRMB3guNBof+t3ReW5LCgsRj/P2avgzpXNuStGfBrkxyCkv507e7eP/2YZw8XUZ+cXmDgg7w2OQeHM49TRsfL7pFBNI9IpAP1hzigzWHmDG8I/+8th/b004xa+MRbh0Vx/trDpJ3poy/XZ1Q7ThdwgLoFhFYlcteUalZsz+HS3pHMq67eVD8a+FeVu7LwdfLgyv6R/PAxO4E+tb+V03rF820ejJDGsvI+FBmrjyAj6c5b4i/N/eOjyc6xN9l57AyNK498x8ax/a0U7XcRfXh4aG4a2wXnvoukQ0HT1QL1p5rzpRWcP1ba9lj+d+Nig9l1r0j6yz/6pIU9h4r4KZhHekdHczH6w5xILuI928fio+XzZaakhDN4qQsFu05zj8X7GVMtzB6Rgbx3uqDFJWUE+Dgf1+TgzlFlJZX0jva9L7un9iNOdszGN01lJdvGoS/jychbbzpHR3Mgl2ZlJRXUlRawdUDzQM5NNCXN28ezI70PHpH28YBXNQjnKV7szicW8SsjWm8bQnse3koZgX5MiyuPRmnzhAV7IeHh+KVxft4d9VB9h4r4E/Tejf+IlvYe6yAnlFBjOkWxpK9pica09bWDn9MPMZjX21n3kPjnLp/LxRapYVutbBLyiv435qDJGXmc+Pb63jy253sSndsMReXVbDp0Eli2vqzLDmbb7Ye5WCO6XrGhzfcICb0jOC20XGAsU4XPjyOxb8bz91juzBrYxr3fLyFm2au56vN6Vzx2mq+3XqU+y7qWnWD2dMrKoi9li7lnox8Tp0uY2y3MDq2b0N8WACLk7JIiAlm1RMT+c8NA+gU6pyVfbaMjDfCOrFXOG3b+KCUOidibsXP25OR8aFOu1usXD84lvYBPry3yjapZ97pMnIbGTz7ZN0h3liW6lRZrTXPzEkk6Vg+/7mhP7ePjmPjoRN1ZiWlZhVW/Y+/2HiE7IISXlmcwoSe4UyqEQC+tHckXh6KR77cTkWl5u/XJDA0zvwvUizukYawPmSs7S06xJ+Nf7qEd24ZWi0+dGmfSHak5/HSon3EtPWvClaDadcDO7bF18tW/qIeZqDaK4tTeGflfq4e2IFFj44ntp0/v/1sK/d+vJnR/1rK3+btIe90GZ9vOEKQnxczVx6oNV7C/lp+syWdY3nFde7fe6yAXtHG7QPVkwW01ry2NIWi0gqn/n+VlZrEo451wd1odYL+/bajjP/PMpIy81mSlMWp02W88cvB3DyiM99uPcqVr69m6iur+GDNQU6dtt1sGw+eoLS8kr9fk8DwuPb87YfdrD9gBtV0CQtsdD28PD3oFhHEU5f35paRnVmcdJxBHduy8vcT+c2ErkzuE8kDk7o5/G7v6GAy84o5dbqUVammOzvG4sa5Z3w8NwyJ5dO7R9C2zfldmnVEl1AGdWrLXWPjz+t5G4uftye/GtmZxUlZJB8rQGvNre9vYMa7652+aSsrNa8sSeGlRfs4eqrhATTfbTvK7C3pPDipO78Y2pHrB8dSUalZnHSc4rIKXvwpmawCm0At2JWJUjC6ayjfbjvKP+bv4UxZBU9f3qfWsUPaeDOqayinSyu4fUwcnUMD6BFp2uS+4+b3fbDmYJXv2xFJmQV4eyq6Rdjacs1AP8BvJ3Tl83tG8P39Y5j34Fg8G5jBMi4sgE7t2/DttqNEBfvxj2v70T0yiLdvGUJhcTnr9ucyvEt7Plx7iEe/2k5RaQWf3jWCAR3b8siX23lv1QFOFJXyw46Mqsnjftp9jMe+3sEdH25ymEFzLL+YvDNl9IoKokdEEDFt/Xl50b4qQ279gRPszsinU/s2fLftaIOxrH//uLfKyHJ3Wp2gT+gZTrCfN09+u4uvNqcRFezHlIQonrsmgY1PXcJz1yTg4+XBX3/Yw4h/LuEPs3eQd7qM1ak5VT7wf9/Qn9KKSv67aB9eHorYdk23QpVS/PWqvsy6ZySf3DWCTqFteGJKL2beOhQ/79o3FNisqD2Z+axJzaFXVFBVMHHG8E68eOOAqrlMzicBvl5899sxDoOgLY07RscR6OvFq0tSWJKUxY70PPYdL2TrkVNOfX/n0TxyCkupqNR8uOYgZRWVvLRon8NpHSorNa8uSaF/bEhVOmtCTDAxbf35efcxXl2SwuvLUnlruW2cwfydmQzr3J4HJ3WnoLic77dncPvouGqCa88tIzvTLyaE+ycaI6BzaAA+Xh6kZhVyMKeIv/6wh1v/t4Esu2kT8ovL2GDJAEnKzKdbRFA1V44j/Lw9Gd01jIEd29IuwDmDwWql//WqvlWuv15Rwfz86HhWPzGJj+8cTreIQJbuzWJCz3AGdGzLu7cOYXTXMP4+P4nBzy3iwVnbuPndDaRmFfCfH5OJCPIlKTOfP89JrPUQtvZsekUF4+GheOeWIeQXl3Pb+xvZlZ7Hu6sOEBrgwyd3DcfTQ/HmcsdWemWliUm8s/IAXh6K99ccRGtjrb+9Yr9b+uBbnQ+9bRsfnr6iN49+uQMwFofVygjx9+aWkZ25ZWRndmfk8dmGI3y9OY3MvGKyC0oY0rkd/j6edAkL4PeX9eK5eXuIDwuollbXFDzqCBzWRS+Lj3J72ik2HTrJrSM7n9X5L0TaBfhw++g4Xl+Wys6jp4ht58+JolK+3pzGkM4NTxm8dG8WHgrGdg9n1sY00k+eYWHiMU4WlfLcNdXjHitSsjmUe5pXpg+samtKKSb3jeSz9UdYnpyNj5cHszen8/jknmScOkPy8QL+elVfRsa3Jz4sgLwzZTx0Sd3T/07uG8XkvlFVnz09FF3DA9l3vKAqyyS3qJR7Pt7Ml78ehZ+3J28sTeWdlQdY+PA4kjLzGWuJwbia307sytC4dtXqB9Cxvc0V+PJNA/ndV9t55JIeAEQE+fG/24ayYNcxUrIK6B8bwmNf7eDaN9ZSUFLOe7cOZWf6KV5dmsr6Aye4uHcEQzq3o3tEEOstA516Wub0SYgJYeatQ7j9g01c+fpqAB6+uDudQwOYPqwjn284wj3j4okPNw9LrTVPfruLH3ZkUFRawYSe4UzsGcGzc3ezJCmLp79P5Fh+MQXFZfz+MvdaXK3VWegA1wyMYYzFt3b9kFiHZfp2COGf1/bjuasTWJWSw95jBdUa/O2j4xjXPaxRQuwqIoL8CAv04bP1Rygtr2TMOboR3Z27x3UhyNeLtBNneOji7kzrF828nZmcLi1v8LtL9x5ncKd2/H5yTwpLylmYeIxAXy8SM/Jqlf147SHCg3yZmlA9AH1Z3yhKKyoJ9vfm7V8NpqCknC83pfG3eXvw8lBMTYhCKcXMW4fy+T0jq6ZlcJbuEYGkHC9kVUoOnUPb8OqMQexIz+PLTWamS+sozn//uJesghL6OIjXuILoEH+uHhhTb5mEmBB+fvQiBtrNk6+U4vL+0TxySQ8m9Yrk1RmDKCwtZ1hcOy7uHcHDl/TgP9f3Jz48gM82HOGBz7dx2csreWflAWLb+RPSxna9RncNY/UTE3ll+kDun9iVOy3puA9M6oavlwf/Wri3quzujHy+2JTG2O5hvHjjAN66eQg3Do0lyM+L33y2hZzCEib1iuCNZfv5blu6ay9WM9PqLHQwDeWV6YPYduQUXcPr939PH96JHemnmLUxrarrCMYC+vjO4Y0OyLmK3tHBrErJwdtTNSrLQ7DRto0PD1/SnZ93H+e6QTF0bt+G2VvSWbjrWK0HfVlFZVVP7FheMYlH8/nDlJ70iw3hrrFdiAz25Xh+CZ9tOEx5RSVelrKHcopYvi+bhyZ1r+XOGBbXnkt6R3Dj0I5M7BlBv5gQ/j5/D5Ua/n19v6pxDHW5WRqiR2Qgc3dkkFtUwo1DOnJZ3yh6Rwfz7dZ0xnYP40BOEZHBvixPNha8owB8S2Jc93Bm3zeKTu0DUErhqeAXwzryi2EdKS2vJPlYAYdyi9DYrHN7IoL8aj1YIoL8+M2Errz48z7WH8hlZHwo3207io+nB/+5fkC1h8L0YR15d9VBnpjSi7vGduHW9zfwh9k7CQv0ZVx391i0vlVa6ABhgb5c6mT+8nNXJ/D9/WNIiAmptr25xBxsN9/gTu2axV/uLtw9Lp6v7huFl6cHw7u0p2t4AM8v3MuBbFsAcdneLBKe/YldlgW0lyWb4e/W6QaeuaIP947vSkJMMMVllRzIsc3o+PnGI3gqxc0jai+75+mheO+2YVzW11jid4/rQqWGJ6b04qZhZ79MX3eLqBWXVValtF4/OIYd6Xm8Y0kffG3GYKxxzZYu6ABDOrd3OPjMx8uDfrEhXGkZQNczqrag18VdY+OJDvHjL3N3U1hSzpztGUzsFV5NzAEevqQHr84YxL3j4/Hx8uCdW4bSNTyQX3+yhUe+2MaAv/7MzJV1z7fUGmi1gt4YvDw9qnUFWwK9LA12nLhbXIZSinduGYrWml+9t4G0E6cpLa/krz/spqS8krdWpFJZqfl43WG6hNkySawkdDAP/MSjRvhLyyv5Zks6l/SOrLK26+PqgTGsfmIi913kmiwh67zw9oO7rhrQAQ8FX21Op28HM0fP1QNjiA8LoL2TQU53w9/Hk39e24/k4wXc+PY6cgpLuHZQbVdsoK8XVw3oUC3m9vGdwwkP8mVJUhb+3p58vO5wqw6WXhCC3hIZ1TWUXlFBLh0YJBj3xid3jaCotIJr31zLs3MTOZRrFs7+MfEYb63YT1JmPg/XmMwMID48ED9vDxKPmkyXJUnHyS0q5abhHZ0+f2y7Ni7r+XVq3wZfLw8Gd2pHkMX/HhHsV+UeuKS36WH86/p+fHf/GJecs7UysVcEj13ag6TMfIL9vJjYyzkXSkSwHz89Mp7Nz1zCE1N7kn7yDFuO1D2vTkFx48c71GSZZaDWuUAEvZmIDvHnx0fGV0XmBdfRp0Mws+8bhZ+3B7M2pjGhZziv/XIQnh6KF35KpldUUNW8OPZ4eij6RAdXBUa/2JRGdIgf45vJv+rpofjj1F48dHH17JjpwzrioWCKZe1YXy9PQvwbF3B1R+6f2I17x8fz+GU9qw2Oagg/b098vTy5rG8UbXw8681Xf+DzbUz6vxVVs7Q2hqOnznDPx5u548NNvGs3KM6VNCjoSqn3lVJZSqnEespMUEptV0rtVkqtcG0VBaHxdI8M4vv7x3D32C48d3UCkcG2gNrjk3viUceAmoSYEPZk5LP3WD4rU7K5cWjHBgffnEvuGNOlVjri1H7RrH/y4lbhMz+fKKX407Te3Doqrknfb+PjxWV9o5i/M4OS8toDntJOnGZlSjZ5Z8q468NNbDtykq82p/HQrG2Mfn4J83eakbG5hSXM3pJOeY35bO7+aDOrU3J4Ykov/nxF3ybVsSGcsdA/BKbUtVMp1RZ4E7hKa90XuNElNROEsyQs0Jenr+hTlS/9xJRevHBD/3oX00iICaGwpJxr31hLW39vZjTC3XI+ccanLzSeawbFkF9czork2jNMfrPVpDi+fNNA0k6e5to31/KH2TtZuz8XT0/FH7/dSWpWAbd/sInHv97BY1/vqBL1jFNnSMrM57HJPfjNhK4NDgBrKg2mV2itVyql4uop8kvgW631EUv5rHrKCkKzER7ky41D6xdoa/A8PjyAd24Zck7nshFaHqPiQ/FQsOtoXrWBVJWVmtlb0hnTNYxrBsXQNTyQAzmF9O0QQnxYAOknzzDt1VVc/upqyioquXZQDN9tO4qnh+KlXwxkdYqZi+Zcp0e6Il+uB+CtlFoOBAGvaK0/dlRQKXUvcC9Ap05nn9YlCK6mR2QQPzwwlu6RgXVO3SC4Lz5eHnRs34YD2dV95OsP5JJ+8gy/v6wnAP1iQ+gXa0uD7hTahr9e1ZfHvt7Bn6/ow51juxAd4seby/dzx+gurEzJJiLIt1ZmlatxhaB7AUOAiwF/YJ1Sar3Wel/NglrrmcBMgKFDh7be3CDBrbG/UYULj/iwgGpjEQC+3pJOkJ/xsdfF9UNiubh3RNWkevdN6MpHaw/xv9UHWLs/lwk9w8/52BdXOHLSgR+11kVa6xxgJTDABccVBEE478SHB3Iwp7AqHz2/uIwFuzK5emCHBntt9jOkBvt5c8OQWL7fnsGJotLzMubEFYI+BxinlPJSSrUBRgBJLjiuIAjCeSc+PIDiskoyLTNbztthFgS5cUjjA+TWNRTANkX2uaRBl4tSahYwAQhTSqUDzwLeAFrrt7XWSUqpH4GdQCXwnta6zhRHQRCElox1BaQD2YXEtPXnq81p9IwMon8TXHHx4YFM6RvF8YJiIoLOfWaSM1kuM5wo8wLwgktqJAiC0IxYJ/w7mFNEVLAf29NO8fTlvZvs/35lxkDO12JJMiuUIAiCHRFBvgT4eHIgu4j0k2fw8lBcM6j+6YProzGjVs8WEXRBEAQ7lFLEWxYX2Xe8kEm9IggLrD1DZEtE5nIRBEGoQXx4AGv355JTWNLgYLSWhAi6IAhCDayB0bBAXyb0bD2LX4igC4Ig1MA6C+p1g2POes3h80nrqakgCMJ5YmSX9oyMb88trWwBdwmKCoIg1CAi2I8v7h3V3NVoNGKhC4IguAki6IIgCG6CCLogCIKbIIIuCILgJoigC4IguAki6IIgCG6CCLogCIKbIIIuCILgJoigC4IguAki6IIgCG6CCLogCIKbIIIuCILgJoigC4IguAkNCrpS6n2lVJZSKrGBcsOUUhVKqRtcVz1BEATBWZyx0D8EptRXQCnlCfwb+MkFdRIEQRCaQIOCrrVeCZxooNiDwDdAlisqJQiCIDSes/ahK6VigGuBt50oe69SarNSanN2dvbZnloQBEGwwxVB0ZeBJ7TWFQ0V1FrP1FoP1VoPDQ9vPQuvCoIgtAZcsQTdUOALpRRAGDBNKVWutf7eBccWBEEQnOSsBV1r3cX6Xin1ITBPxFwQBOH806CgK6VmAROAMKVUOvAs4A2gtW7Qby4IgiCcHxoUdK31DGcPprW+/axqIwiCIDQZGSkqCILgJoigC4IguAki6IIgCG6CCLogCIKbIIIuCILgJoigC4IguAki6IIgCG6CCLogCIKbIIIuCILgJoigC4IguAki6IIgCG6CCLogCIKbIIIuCILgJoigC4IguAki6IIgCG6CCLogCIKbIIIuCILgJoigC4IguAki6IIgCG6CCLogCIKbIIIuCILgJjQo6Eqp95VSWUqpxDr236yU2mn5W6uUGuD6agqCIAgN4YyF/iEwpZ79B4GLtNb9geeAmS6olyAIgtBIvBoqoLVeqZSKq2f/WruP64FYF9RLEARBaCSu9qHfBSysa6dS6l6l1Gal1Obs7GwXn1oQBOHCxmWCrpSaiBH0J+oqo7WeqbUeqrUeGh4e7qpTC4IgCDjhcnEGpVR/4D1gqtY61xXHFARBEBrHWVvoSqlOwLfALVrrfWdfJUEQBKEpNGihK6VmAROAMKVUOvAs4A2gtX4b+DMQCryplAIo11oPPVcVFgRBEBzjTJbLjAb23w3c7bIaCYIgCE1CRooKgiC4CSLogiAIboIIuiAIgpsggi4IguAmiKALgiC4CSLogiAIboIIuiAIgpsggi4IguAmiKALgiC4CSLogiAIboIIuiAIgpsggi4IguAmiKALgiC4CSLogiAIboIIuiAIgpsggi4IguAmiKALgiC4CSLogiAIboIIuiAIgpsggi4IguAmNCjoSqn3lVJZSqnEOvYrpdSrSqlUpdROpdRg11dTEARBaAhnLPQPgSn17J8KdLf83Qu8dfbVEgRBEBpLg4KutV4JnKinyNXAx9qwHmirlIp2VQUFQRAE53CFDz0GSLP7nG7ZJgiCIJxHXCHoysE27bCgUvcqpTYrpTZnZ2e74NSCIAiCFVcIejrQ0e5zLJDhqKDWeqbWeqjWemh4eLgLTi0IgiBYcYWgzwVutWS7jATytNaZLjiuIAiC0Ai8GiqglJoFTADClFLpwLOAN4DW+m1gATANSAVOA3ecq8oKgiAIddOgoGutZzSwXwP3u6xGgtAQ5aXg5dPctRCEFoeMFBVaFxnb4J8dICe1uWsiCC0OEXQrFWXw6Q1wcGVz10Soj/TNUFkGx3Y2d00EocXRoMvlgiEnBVIXwZmTcPdiUI6yMYVm58QB83rqSPPWQxBaIGKhW8nea16Pboa0DZB3FDb9Dyorm7deQnVy95tXEfRzw6HVUFLY3LUQmkjrFfTd38OHV7hOcLOTQXmAX1tY8hx8MBXm/w6OrHXN8QXXcMIi6Hlp9ZcTGk9Rrrmnlj/f3DURmkjrFPSyYvjxSTi0CopcNOI0OwnadYFhd8Ph1VB8ClBweJ1rjl+T0tOw6T3zW9yZfT/BxndBOxw87Bw/Pmn+DxXlcPKQ2SYWuuvJTQE07JoNlRXNXRuhCbROQd/8PyiwDEYtcDgotfFkJ0N4Lxj5W+h/E9w6FyL7wuE1rjm+PZWV8P19MP8xSPnZ9cdvSax+GRY8Dj/9qWmiXpQL69+EjTMh7whUlpte1KkjZ/eQEGqTa8kcKjwmyQGtlNYn6CUFsOr/INgy/1fBMfNalAun65sUsh4qykxjDu8JAaFw3UzoMBA6jYK0jWa/K1n+POyZY97n1ki/K8qxWaHuwIkDRoDXvwlf/LLx6YbW63NoNeRaAqLxF0HZaTid69KqXvDkpoKHN/gGw84vm7s2QhNofYK++3tzI097wXzOt1jos++AT65tmtV24oCx/MJ7Vd/eeTSUFUGmC1PkinJg5QvQfzoERNh8wlYW/gE+v8l152tOSgqNtTf6Qbj0OTiwAt4Ybv6HzpKbYl6LsmDfj+Z9/ATzeuqwK2sr5KZC+y7Q52pI+gFKi87v+bV2vfF0gdH6BH3Qr+DeFdD9MhPEtFroWUmQud1Yco3FmuES3rP69s6jzasrA6M5Fj9l/xshtJvN6rRyfLfJ5HAHH+bJg+Y1tCuMeQge3g7t42F9I9ZAse/B7PwKfAIhZqj5fEoCoy4ld79pk/1/AaWF5gF8PtnyAfy3L5SdOb/ndSNan6ArZdwhnl7Gwi3IMAHGoiyzf/2bjT9mdjKgIKxH9e1BUUaAUn6GFf+BDTPPtva2POr28RAaX12wKissvYUy24PqbMhOtj0YtDY59ueKykpIXVy9h2RNMWwfb14DI0x8Im29SQt1hpwU838JioaSPHOstp3MPgmMuo7KSougd4UOg8y27KTzW4esJCg8bjPKjqyHzB3ntw6tnNYn6PYERxvhs97YYT0geaFNSJwle68RCZ82tfd1Hm0CRMv+AUv/fvaBuBMHwMMLQjpB+67mQVScb/adOgIVpZb3Z+lOKMqBN0fBmlfM522fwos9nRdSR2z7zKR0OroG+xbCp9fD3nm2bfYPLyt9rzGvSXOdO2fufgjtDp3HmM+hXcG/LfiFiKC7kvx0qCgxFrpvkIlRZe9r+vEqKxufwWXNWNv3E5SXmJjLHJkmqjG0bkEPiob8TJv4XfwseHrD5vcbdxxrhosjRt4PYx6GEfcZC7Eo5+zqfGK/eXh4epmbB2zCZ/8gOluxyksDXWEGR1WUw9rXzA2burhpx6soh8V/gVUvmoyTmlizIvbYCfWJA6YX5Rtk2xbWHSITnPOjW3ssoV0hbqzZ1r6reQ3pJIJeFx9fA6v/27jvWHuK1jYZ1gNykptehy0fwMsJRpidxXpv7fvJ+PBP58KxXa7prV4gtH5BL8i0ZYV0HG4yUxrj+8tJMX7r2KGO90f2gUv/Bt0uMZ9rZqVs/7xxIn/igM1iDbWIkzUwag0AwtmLVaHFBZWfblIGrTfn/qVNO97h1aY3EdIJfnrKzKliz8FV5nXfj2Y2RKj+W+3pc41xu2TurL/Hc+qIeQiFdTeBUOUJ0f3NvradZHCRI4rz4cAy05uqj7Iz1V1wVmPCKujhPY2F3tSBe8d3G4s7a4/z3ynKMf/jvCOw9DnwsRgCqUuaVocLkNYv6GdOQM4+8G4DAeHGkjue6Ly/eN0b4OkDQ26vv5xVfGuK7ve/gc0fOP7Onrnw9R2w/N+mF6A1nDhoE7l2XSzHtAp6KviGQGBk3S6X9C3OBY0Kj5tXn0DY+A60CYOEG+DA8qYFXHfNNse662cTW1j4B9u+olzI2g2xw6Ek32atW63rmiRcZ675O+Pgpd6wfVZ1Yd/0Hqx/u7rItO8Cj+yE3leZbW07nZ9c9MPr4LMbW88AsOO7zWtuSv3prz89Be9PtX3OTTX/38BI8zmsh8nwym+ii87a/jK2Of+d0znQ/VLz/uQh0zMOjGx6r/ICpHULenC0eT2ywdzgSll8rdq5EZ6F2cbCHjDdBOzqo21nk6Nrb6HnWHyM1iyZmqz8D+ydb/LOv/+N6UKW5NsE3aeN8VVahSsnBcK6mXNZxeqr2yDxG7M/bSO8N8mI4IoX6hcz6w01/F7zOuR26DnVjIDN2O74O6Wnjc8yu0ZXu7zU+Lx7XW6u+eiH4OgW83AB2+CrSU8bUUiaa1LeCjKNENcktCv8Zh1c/pL5v31/H3x2gwmGLfunGXD14x9h97eW8t3Na0isbdK0dp1NJoa1J3Ku2L/EBMUPLD83x89Ohv9Nrn3Nm8rxRNv7+izbA8tNu7W6RHJTzf/Fen2tGV/2bpeVL5qUWmceolY3ibOCXllp7o+ofhDV31jqg35lesb7lxqXn9AgrVvQg6LM6/FEI4IAMUPA09e5EZ4bZ5ou/egHGy7r4WmE2N7PbX3v6GYszDL+v4v+AJOeMgJ4ZL3Z197Oag3taudySTXiZbU+Tx6CPd8bfyLYrK/wXrDs75CxtcbveRcSv7Wd3y/E/LbBt8HI39jyt+tyu2x8xwRPa/rI9y+B4jxIuN58HjDdZvmDEWLvNsbd1X2yeYhZH3LtHVjoYB5cw+6COxbCZc9D+ib48HJY8W+TCePfDrZ/ZnosAWG1v99hsHlN2+D4+K4i37KaorNB3MaSNNf8hlnTmz4wzp5ju8y1a9upumWblw4L/mDJCMu1tDltS/3MTa3+vwqzCLo1MFpWbOIw+36s3e4c0VgL/cxJ0JWmJ3nxn2HKv4zx0O0SixHixDmF1i7oHSxvNLSLM2+9/SB2WMP56EfWw5qXTRc+rLtz5wvtVsNCt7hfclNquzH2LzOv3S6GHlPM+w1vm1d7v3L7rubBUGrp3oZ2s/iH021WoXV05Yn94OUH1//P8htqiNmaV4yog7mhAiOhTXu46lUjigFhED3AsaCfOWkLpCXNs/lOy0uN1RwQDvETzTa/YBj4S/PwKMwyc+p0GmlWERp0s+k6f31H7d/qCA9PGPVb+N1euPoNMwDpmrdh4p/M/rBujqcy7jDIXIsj52iuHSvWqSWSF5ybQS9H1ptreyoNvrnr7F1IxxNN0LnbpSaWZI1nLP6reQAnLzAPTysnDxkX3snD1dN2A8LMg8Fqoe+dZ5vfaMtHpr3Pf9xkldVEa2OhK084vsc5F+HpHNt5u18KIyw9y/gJZryJ+NGdopULepTtfbvOtvdxY8wCCMV5jr938jB8cbPpwl/5ivPnC+1qyRO3iLfVn15eXNvnvX8ptAmFqAHmBguOMcKnPGx51NZjnjlhs6bCupnfUlkOu762nCfVlifcPh5CYkyPxF7MKsrMA8HqNy3MsvlD7el2qbEIaw7KWf1fE1Ab87AZ3XnU4k5Z/ry5lle8XH3Zt+H3mnz5F7ubwJc1rbDbJXDZP23XoyFBt+LTxnSxxzwEHh4w5A4j2tbBXTXx8jEDjA6f49kw8zNNcO7MyaYNWquPygpI2wS9roCJT5o2k5d+dsc7vse4LbpdYnzgh9cYKztxtimT9AOkb7R95+RBi5GiIdxO0JUyVrrVQt/6kWlzA39p4ilL/gab3rXFj7SGlEWmDqdPmLbRebTJtDpm5waqC2vKYs3eWJv25rxW96ZQL61b0P3bGSsNbC4XMOKiK2tbsFZW/sdYDTO+NA3GWcK6mzxxa3ZFTqqtZ2Cfs6u1uTnjJxpxUgp6XGb2hXSsLow9phi3wuy7zGerhQ7mZvTwhvIzxlK0CjoY98aR9TaLLj/D/OaCTNM9tlroNRl6h6mP/WjN4nwzaKrfjTD2dyZPfu8P5jesedkIbe8ral+La2fC+N/DhD8ZAbYy6n7Tbe4+2VjzTcHTC+5eCpP/XneZzqPMw6akoGnncIaCDDMU3ruNzfXlKrKSTCpsp1G2B2LNrJCKMuet9hMHTFuJTIAu48398c3dMPdB8PKH3lca0T24yvipvfyNAWB1GVrdLFbCLamLuftNoHvwLSYWU1Zk2oWHlxmdDeZh99kNpgdQaPGf97QEXR25XQ6sqP5/s2aKBYTXLmt1QQoN0roFXSmblW5voXccbny8e753/L3cA2a0qb1F4gzWlK6cVOOLzE831hVUD4weTzQpfl0n2bZZ3S41Ldaw7nD7DxbhU8YFY/9w6nOV7fgnD9rq0GmkOUetFXy0eeDUZaGHxELf64zFdeaU2ZY01wjB8HvNoJ0u403myaxfGn/9lH85vh4DbjKB0AlPmEnN7Bn3GNz8tePvOYtHA82z0yjzEEvbWH+5plJ62vTy2ncxboDd39WfPZW2CV7uZzKZnMHaw+o0wjYOwhonASPkMyeYAW3OcGyXeY1KAN9AuPNnY/GmrYfh99jEOH0jdBxhjJGTh4z1qzxsbctKWE8TqHxzlDEsBt5s3JnRA0w7veiPxnDIz7T1lLL32gKiHQabcQgpP8HSf9geiMcS4eOrqsdqrBZ6GwfxkrYdm5aiWloEH0xzfc+qBeOUoCulpiilkpVSqUqpPzrYH6KU+kEptUMptVspdYej45wTrH50exH09jeBu12zTQCoJnlpRtgai7XB56baApkxgyEwqnpg1Oo+sRf0LuPNQ8bRAKboAWbZu198bFwP9nWzWr77l5negTUNsNMo82oNtNo3+OO7TQZIXZk7ox8w+7d8aD7v+MLcoNZc/F5XmIdFu85mGmH7gUEtiY7DjRCdKz96gSUgGtzBPKCK8+DnZ+ouv/IF82Dd+pFzxz+y3qTetu1sHqTBscZqt5KdbIwDZ9dPPZ5orGZrGwvvAXcvgStfNT2puPGmNwjVBT072dTB26/68bpPho4jTfD6rp/MdVDKtIn7VtkGe2Vut/0PclJsAdGgSOM2S11sesVzHjAiu92SI59m58u3zpzpqMcc0skcs7Gpo/uXml7uuiZMB9JKaVDQlVKewBvAVKAPMEMp1adGsfuBPVrrAcAE4P+UUj6cD4I7GF91za79sHtMBsu2j6tvryg37omQjo0/V0C4mVo0N9UWEA3tbuuaWtkzx1gn1rRKMA+ZuxebrBdHtI+3WeNevuZGD4wyN41PoG2mQfuRfP7tbDeSfZfUarE6stDBPEDiJ8Cql8zNdmi1ySyxBh8HTDeW920/QKCDLnBLwTfIuA7O1SIk1pk8g6LNNRv9IGz7xPHAtexkY4l6+pjejTNpdkfWm56W9bpH9qnucrEaBtZ61EfpaRM4DOth2o8V30AYcpt59fKBnpaeYsdh1S30mhPTgWnXd/0EU5432WNW/NuCT4Dx1aMsKawWcc5JsVnogVFmUN5Vr8P0z01QdctHtql50zfZ3ElF2aY9e3rXrofVBdnY+II1YJvys2syiFoBzljow4FUrfUBrXUp8AVwdY0yGghSSikgEDgBnJ/E0fG/N/OX1ySiF8SNs6wLapeBUpBpAjVNsdCVgojeZiSe1ZIK7Wosoux9toFDGdug77UO6tTbeZ997ytNF1mp6tk11tQyDw9jPVkt9FNpRsC9/GxBr/py6696zVhkn92Imf3xF7Z9PgHmujaUm98SiBtrfq/VfXRkfe051/Mz4dt7HQfJtTYBcuucN/ZUWeiWufcn/NGI4NLnapdd94a59lP/Y3zI+xvIysjdb1x2HUfatkX0Ng8GazZN6iLz2pCQnT4BH19tJrJqKAV33GMmvtG2s/ktpYWmLdecmM4ZfAPN93Z8YY4TEG7aacExY/j4tDH34eBboOc081BY/BdjjfeYajJbrMHzohzH7hYwLhcwI0idpbLCTCEQ0dcEaK3rD7g5zgh6DGDvwEq3bLPndaA3kAHsAh7WWtcaM6yUulcptVkptTk720VLx0X0sg3Lr8nwe4wrwn71Fatrom0TLHQwN0RuqpnVMTjGiF94TygtMJbU7u9MOeskVE1l2gsm8wFsVrlPUHWR7TTSZNoUWW6Mtp3Nn3XgUF0WOhir55dfmcBYp9GOBwC1BhKuM66oPd8b//Yn18HPT1Uvs+VDYxU6sqwztpmUvKR5tfdZLWNrT8vb3wSOj261TagGRlB3fGF6NgNvNsK0/F/w1lh4obtJJa1psa95xVjzva+0bbOKjzWN9fBa8/8pPmWbm9xRgHTp343b4xcfmyyU+gjvadqwUnb/c+3YQneGDgNt99SAGWbg3LFdtdueUjDqAdNrDoyy9VStU0gU5TgOiILd7JqN8KOnbzYPjHG/Mw8da8aYm+OMoDtIAqZmq7oM2A50AAYCryulaqU3aK1naq2Haq2Hhoefh6583Djzaj96zmrthHSqXd4ZelxmbsLSQpvQRlg8UBveMqMbY4dVT008W6x58qHx1XOy7f3oeWnmIdWusxEFqF/QwdyMv1kDv3DS59sS6TDYuL12fGm682VF1bMqtLaNtLUGDbW2CaM1jpCVVFssCywpi/YxhLixpodnP6Apc4cRqr7XGrfGgOlmIEz5GdNGFjxuMk2snDxk/MiDbzMpqFYiLe0oa7fJRKkotfX08jPNTJnPx9ZeHu7YLuMTt7rsnMWaoQW1M1ycJXqgeQ2OtQ1cS99UPaXYSu+rjE995H3GVebdxibop3NqB9atBHUwOe32bsXyEsejd0+fMNdp30ITT+h2sXkIH17jXiuB1YEzgp4O2JuzsRhL3J47gG+1IRU4CNQxfeF5pE1789S3D1haG0VIzU5GI5jyb3OjR/UznzuOhEG3mJF0x3Y5drecDdYHR80shA4DbaNi89LNQ8QaHFaezrl32ndpHa6VulDKZNscWQtrXzW/u/C4zY97bJdtvIBV0Ne9Dq8NMZZ24jfGPVBaUDs1Lj+jehwEzHw1Ht5mTIEVa450eG/zOvFP8Ktv4bcb4I4FJu3xoF3vYOWLpp7jflf92GE9bINxkueDdwD0s4zOzT9qHhKlhbD1E9t3tDbnr9k2nMHe6GhsxpeVDgPNa6eRNsOjssyxoHt6wb3LYeyj5n2HQTbfe30WuqeX6Q3bB/43vWfcTPapyRXl8L9L4b99TA+o82jjlx8ww/R0FvzB7dehdUbQNwHdlVJdLIHO6UDNcdBHgIsBlFKRQE+gxlI8zURYz+qDEvLSTBDVJ6DpxwyJgfs32EYzengYn/TEp0ywte91Z1fnmlhvlJrD6L18jV9yzxwzEKltJ5vVFRBuRmFeCPSz+P9P59p8yFa3U+I3xlKLn2gT9J1fmSyl9y4xAmnt/ttnmICx0INqCLpPG5MNZJ8Kl73XTLNgfTD6BBjL0NPLPHCiBxhBLikwy/LtmGX8ysEdqh/by9cI8+b3YevHxjCwTuCWn2Frx8kLTBDU+puLTzXNB+7tbwu++4U0/vtgfltgFPSaZqx0L3+zvaHeIZi2e2yn+S2nc+v2oYPpfdo/cK3TL9uvfbrrK+MOHXaPmdFz7KO2717yrAla75jVmF/X6mhQ0LXW5cADwE9AEvCV1nq3Uuo+pdR9lmLPAaOVUruAJcATWuuznDjcRYT3sM10CMaSbUqGS01CYqo/FJQywvBoYm2r7mwJt8QJrLns9nQaaZsRL6STLR+/NVvdjaVdZ+hykblO4x4DlPEpa21cYPETjTsgP934p4/tggG/tGWvDL7VHCdrd/Xj5mfWFl0wbpeM7TY/unU+fUdTFIBNbHNTTRZLZXn1lFZ7IvuYkcODb4UrX7adPz/dMnhNmYdQys9mu1XknZ2+oibRA+qeOtoZfALg8WQzz4+Hhy2t1pGFXpPYYcatlPIzoB3P2WOlbSebDz0/wwTCPX3N/7e81FjnK/5jXDnTXoAbP6h+jYf/2sSKFv6x7hHkboCXM4W01guABTW2vW33PgOY7NqquYiwHsaCKco2IncqremNv7nw8oVffeN4n9WPDqbRV1hmz3PmhnInbvrEZDb4BZv/b8Z24xY5dcSMZLU+4Na/BWiTynf5/xn3gF+Iecgft0sZrKw02So1LXQwgr7yBeNH736pEXTrqEhHWAU9J8WIMZjRnI6Y9Izp4fW+0jwgPL3Bv73NQo8ba863+1sTeLemzza1Td/wQd0PoqYQ2s3ErAKdaH/xF5me5I+WoS31CXpIRzNqt6LMTP4GcPEz8PPTJr2z8JgZeDf9c8e/x8MDxj8On15n2kb8RY3+aa2B1j1S1BmsN5PVSrf6mt2FjsOoilu37WjzoV9IFjoYUbbGDKIHmkDl+reMe63vNbZ4x/bPjG+6w2DjPrG6GiJ6V88BL8o2lrQjC93qRz+40gxcO51T94pXYNwmytMI8rFEM7inrjYY2tUEN+1FKSTGBPpyUkw9+15jUvJKCswxPX2b3uv0aWNcL67C+mAJcsLl4hdiRiFb00MbcrnoStMb3TPHuFJH3Ge+M/8xmPeo+b/0nFb3MaIsi6PYJ0m4Ge4v6PbzOp85abIgmpKD3lLxb2eybALCzY3pF2y6mtYMnwuRDgONNZe8AIbeaa5LYISxGstOmzlg7OfTAXMNc/bZZie0zrLoyEL3aWMmgNs7zzblQ31pf14+JraRs88yG2LfxlnFwTGWgGiBMVD6XmcmhEv+0TLlcreWEy/pMMiM3rX6/hsi4XozYRzUHRQF2wNw59cmCaDPVab3MmC6+V+NuA9unVP/dQ0MN23AGktxQ5xyubRqgmPMSMvsfXYZLi7wobckRvy6+uCTW75rvrq0BKIHmFcPbxh6l217VD9IPWamYahJZF9jkeemGj+2dbX5uizphOtNKuKur8znhvK4wyyxnLz0hnPFaxLcwTZSOKyHSVEMjjEB35x9tt5HS6DnNHh4h/PjPJQy0yZv/6z+Xo71nl32dzM/zMCbzedJz5iHtqOVsRwRleDc7I+tFPe30JUy3cCcZJvoNXVQUUtlyG1mEQ3BYF3xJuG66gFqq/A5EnTrWIKsPcY1t/5t4+euSyx7XWEeGNs+NfnUwQ30+sK6G2u+tNA8PBqDvdsnrIfxB/e91viOa85j3two1XiXZlCkSeGsbzK2dl3MwKQrXjZLEVoHRXn7OS/mYP6f2XttPTE3w/0tdDD+toMrbKsYuZuFLlTHLxhun2f8zfYMvsV006MG1P5OWHfTk9vwjqVHlwTXvFV3F75Ne5OauO9HI/wNzQxpL7qRjbSorQ8LnyBbsDvhOpNPD7Yl+twZDw+47B9nf5zIBBMIz0luWT0bF+H+FjqY1MWCTDNcv+c0EygT3BvroBJ72sebsQOOxNfLF65+3aTDfXWryaO2LrlXF9b9zgybtwq68qj9oGkIq4Ue3sP2gOkw2DbmoLVlbTUn1sCom7pdLgxBj59orJxpL8JNn7k2TUtwH/peCxOeNKmfw+6pPmuhI3pONcZBx+ENH9t+cJhPm8bVyzo5mL2Vr5QZUOXpK4LeGEK7msFPx3aZAVxrX2vuGrmUC8PlEjMYfre74XKCcNETlhWE6lj6zh7fIHh0T8PCD8ZFExhlC9g2hpAYk2ppnTfFyvjfm1kyW+p89S0RD08T9N72qVktysPbzKnT1JW1WhgXhqALgrMo1bhBJzUXhaiPm7+uf/BMnefwhwc3107r8/IR67wpRCaYOdxjh5m5ZFIXNexeayVcGC4XQWgJRPd3PFDJGYI7OF78QWg8w+8x6Y63LzADk/YuaPg7rQSx0AVBuLCI6mfLcOk5BfbMNWmMNQebtULEQhcE4cKl1xVmUY7D7rGQtAi6IAgXLvETzMAwRytWtUJE0AVBuHDx9jdTUyfNdW5h7xaOCLogCBc2/W40s2seWtlw2RZOiwqKlpWVkZ6eTnFxcXNXxa3x8/MjNjYWb2/JmhAEuk82yxDuml33wiOthBYl6Onp6QQFBREXF4eS0ZznBK01ubm5pKen06WLk1OcCoI74+1ngqNJP8DlLzVubEELo0W5XIqLiwkNDRUxP4copQgNDZVekCDY0+96k+2Suqi5a3JWtChBB0TMzwNyjQWhBl0mmBWUUkTQBUEQWjeeXmYmxla+PJ0Ieg2UUtxyyy1Vn8vLywkPD+eKK65wyfEDAwNdchxBEFxMZAJkJZnFxhti26eQuuTc16mROCXoSqkpSqlkpVSqUuqPdZSZoJTarpTarZRa4dpqnj8CAgJITEzkzJkzACxatIiYmJhGHaO8vPXnswrCBUdkX7Pm7ImD9ZdL/Abm3A/L/nl+6tUIGsxyUUp5Am8AlwLpwCal1Fyt9R67Mm2BN4EpWusjSqmzXnL+rz/sZk9G/tkephp9OgTz7JUNL/81depU5s+fzw033MCsWbOYMWMGq1atAmDjxo088sgjnDlzBn9/fz744AN69uzJhx9+yPz58ykuLqaoqIi5c+fy4IMPsnnzZpRSPPvss1x/vZnR7amnnmLevHn4+/szZ84cIiOdWCFdEIRzS1SCeT2+C8K6mfeVlXDigO1z5g74/n6zxOGxnS1uDhhnLPThQKrW+oDWuhT4Ari6RplfAt9qrY8AaK2zXFvN88v06dP54osvKC4uZufOnYwYMaJqX69evVi5ciXbtm3jb3/7G3/605+q9q1bt46PPvqIpUuX8txzzxESEsKuXbvYuXMnkyaZ/NaioiJGjhzJjh07GD9+PO++++55/32CIDggvLdFqO386Mv+Dq8PgUOrjbh/f79ZCWvqv6Gi1Ih/C8KZPPQYIM3uczowokaZHoC3Umo5EAS8orX+uOaBlFL3AvcCdOpU/0KyzljS54r+/ftz6NAhZs2axbRp06rty8vL47bbbiMlJQWlFGVlZVX7Lr30Utq3bw/A4sWL+eKLL6r2tWtnlkPz8fGp8scPGTKERYtad1RdENwGbz8zv/xxy2I4Jw/DWsu6rYuehRG/NgJ+/f+g4whY8Dikb4GYIaZMTgrMfQiufxdCGlg0/BzhjIXuKMdN1/jsBQwBLgcuA55RStVailxrPVNrPVRrPTQ8PLzm7hbFVVddxeOPP86MGTOqbX/mmWeYOHEiiYmJ/PDDD9XyuQMCAqrea60dpgd6e3tXbff09BR/uyC0JCITbJkui/9i1oCd8Cc4uhnmPWoW+O57nRHswEizUAaA1jD/MTiy1iwc3kw4I+jpQEe7z7FAhoMyP2qti7TWOcBKoAlrbbUc7rzzTv785z/Tr1/1lcHz8vKqgqQffvhhnd+fPHkyr7/+etXnkydPnpN6CoLgQqISIC8N1r8Nu7+FMQ/BuMcgvBeUFsIlz5pFxpWCmKFG6AH2zIGDllyQtI3NVn1nBH0T0F0p1UUp5QNMB+bWKDMHGKeU8lJKtcG4ZJJcW9XzS2xsLA8//HCt7X/4wx948sknGTNmDBUVdac3Pf3005w8eZKEhAQGDBjAsmXLzmV1BUFwBZEWA+7HJ6DLeBjziMlRv/49mPwPMzOjlZjBkJtqXC0/PWW+23MaHFnfLFUHUFrX9J44KKTUNOBlwBN4X2v9D6XUfQBa67ctZX4P3AFUAu9prV+u75hDhw7VmzdvrrYtKSmJ3r17N/5XCI1GrrUgOKAwG17pb4T7+vfqXwD8wHL4+GrwawvlJXD7PDiyDn5+Gh5LhqCoc1JFpdQWrfVQR/ucmpxLa70AWFBj29s1Pr8AvNDUSgqCIDQ7geHwuyQzDUBDU2R0GAwoqCiDX82G2KHGlw7G7dLnqnNe3ZrISFFBEAR7/Ns2LOYAfsHGir9jAcSNNdui+4OnL6RtcPwdrWH2XWZw0jmgRU2fKwiC0Krod0P1z16+xrdel6AfWA6Js6HTyHNSHbHQBUEQXEnH4ZCxHcrOVN+uNaz4NwR1gMG3npNTi6ALgiC4kpihUFkGWXuqbz+40gRNxz5af7D1LBBBFwRBcCXR/c3rsRrTAqx8AYKiz5l1DiLoDvnHP/5B37596d+/PwMHDmTDhjr8YfWwfPly1q5d67I6xcXFkZOT47LjCYJwjmjb2axRmrnTtu34Hji0Ckbcd06XuJOgaA3WrVvHvHnz2Lp1K76+vuTk5FBaWtro4yxfvpzAwEBGjx59DmrZOMrLy/Hykn+1IJwXlIKoftUt9M3vm+yXQbfU/T0X0HLv8oV/rN1lOVui+sHUf9VbJDMzk7CwMHx9jY8rLCwMgC1btvC73/2OwsJCwsLC+PDDD4mOjmbChAkMHDiQjRs3kp+fz/vvv09ERARvv/02np6efPrpp7z22mv06tWL++67jyNHjgDw8ssvM2bMGP7yl79w8OBBMjMz2bdvHy+99BLr169n4cKFxMTE8MMPP+Dt7Q3ACy+8UDXi9PPPP6dbt25kZ2fXedyMjAwOHTpEWFgYn3/+uWuvpSAIdRPVD7Z+YhbLKDsDO76AvtdCQOg5Pa24XGowefJk0tLS6NGjB7/97W9ZsWIFZWVlPPjgg8yePZstW7Zw55138tRTT1V9p6ioiLVr1/Lmm29y5513EhcXx3333cejjz7K9u3bGTduHA8//DCPPvoomzZt4ptvvuHuu++u+v7+/fuZP38+c+bM4Ve/+hUTJ05k165d+Pv7M3/+/KpywcHBbNy4kQceeIBHHnkEoN7jbtmyhTlz5oiYC8L5JqoflBWZxTJ2fQWlBTDsrnN+2pZroTdgSZ8rAgMD2bJlC6tWrWLZsmXcdNNNPP300yQmJnLppZcCUFFRQXR0dNV3rDMyjh8/nvz8fE6dOlXruIsXL2bPHlvUOz8/n4KCAsAsqOHt7U2/fv2oqKhgypQpAPTr149Dhw7VOs+MGTN49NFHGzzuVVddhb+//9leEkEQGkuUZU6YjG1mCt6o/hA77JyftuUKejPi6enJhAkTmDBhAv369eONN96gb9++rFu3zmH5mtPkOpo2t7KyknXr1jkUWKt7x8PDo9r0uh4eHtWm17U/rvV9fce1n85XEITzSHgv8PCCZf+Akwdh+iznRp+eJeJyqUFycjIpKSlVn7dv307v3r3Jzs6uEvSysjJ2795dVebLL78EYPXq1YSEhBASEkJQUFCVpQy1p9Pdvn17o+tmPc+XX37JqFGjXHZcQRBcjJevEfWTB82cLz2nnp/TnpeztCIKCwt58MEHOXXqFF5eXnTr1o2ZM2dy77338tBDD5GXl0d5eTmPPPIIffuaVZXatWvH6NGjq4KiAFdeeSU33HADc+bM4bXXXuPVV1/l/vvvp3///pSXlzN+/Hjefvvt+qpSi5KSEkaMGEFlZSWzZs0CcMlxBUE4B0T1M4tlTHr6vFjn4OT0uecCd5k+d8KECbz44osMHepwNssWS2u81oLQqji6BfYvhXGPu1TQz3r6XEEQBKGRxAyxrTd6nhBBP0uWL1/e3FUQBEEAWmBQtLlcQBcSco0FwT1pUYLu5+dHbm6uCM45RGtNbm4ufn7nbj4JQRCahxblcomNjSU9PZ3s7Ozmropb4+fnR2xsbHNXQxAEF9OiBN3b25suXbo0dzUEQRBaJS3K5SIIgiA0HRF0QRAEN0EEXRAEwU1otpGiSqls4HATvx4GtNble1pr3VtrvaH11l3qff5pDXXvrLUOd7Sj2QT9bFBKba5r6GtLp7XWvbXWG1pv3aXe55/WXHcQl4sgCILbIIIuCILgJrRWQZ/Z3BU4C1pr3VtrvaH11l3qff5pzXVvnT50QRAEoTat1UIXBEEQaiCCLgiC4Ca0GEFXSr2vlMpSSiXabRuolFqvlNqulNqslBpu2R6nlDpj2b5dKfW23XeGKKV2KaVSlVKvKkcrNjdfvW+2q/N2pVSlUmqgZd9ypVSy3b6Ic1nveuo+QCm1znINf1BKBdvte9JyXZOVUpfZbW8J19xhvZVSlyqltli2b1FKTbL7znm95o2sd4tp402oe4tp50qpjkqpZUqpJKXUbqXUw5bt7ZVSi5RSKZbXdnbfaRHtvElorVvEHzAeGAwk2m37GZhqeT8NWG55H2dfrsZxNgKjAAUstH6/JdS7xvf6AQfsPi8HhraAa74JuMjy/k7gOcv7PsAOwBfoAuwHPFvQNa+r3oOADpb3CcDR5rrmjax3i2njja17je81azsHooHBlvdBwD5LW/4P8EfL9j8C/9YtrJ035a/FWOha65XAiZqbAauFGAJk1HcMpVQ0EKy1XqfNf+Bj4BoXV7V6BZte7xnArHNYtQapo+49gZWW94uA6y3vrwa+0FqXaK0PAqnA8BZ0zR3WW2u9TWttvf67AT+llO+5rF9dNPJ6O6Q5rjecVd2btZ1rrTO11lst7wuAJCAG054/shT7CNs1bDHtvCm0GEGvg0eAF5RSacCLwJN2+7oopbYppVYopcZZtsUA6XZl0i3bzjePUHe9rdxE7Yb+gaUb+kwzducSgass728EOlrexwBpduWs17alXPO66m3P9cA2rXWJ3bbmvub11bslt3Fw7pq3mHaulIrD9Ng2AJFa60wwog9YXT8tvZ3XS0sX9N8Aj2qtOwKPAv+zbM8EOmmtBwG/Az63+O8cNY7myMusq94AKKVGAKe11ol2m2/WWvcDxln+bjlfla3BncD9SqktmC5qqWV7Xde2pVzzuuoNgFKqL/Bv4Nd2m1vCNa+r3i29jUPD17zFtHOlVCDwDfCI1jq/vqIOtrWkdl4vLV3QbwO+tbz/GhgOYOkO5Vreb8H4uXpgnpr2S/HE0oCb5hzhsN52TKeG1aK1Pmp5LQA+d/Cd84LWeq/WerLWegimjvstu9KpboFZr22LuOb11BulVCzwHXCr1nq/3Xea/ZrXVe9W0MbrveYWWkQ7V0p5Y8T8M6219b48bnGjWN1YWZbtLbqdN0RLF/QM4CLL+0lACoBSKlwp5Wl5Hw90xwReMoECpdRIS1fuVmDO+a+243oDKKU8MN3TL+y2eSmlwizvvYErMN3Z844168BSz6cBa3bFXGC6UspXKdUFc803tpRrXle9lVJtgfnAk1rrNXblW8Q1r6feLb2N19dWWkw7t1yj/wFJWuuX7HbNxRheWF7n2G1vse28QZo7Kmv9wzzJM4EyzNPwLmAssAUTdd4ADLGUvR4T4NoBbAWutDvOUEwj2Q+8jmU0bEuot6X8BGB9jWMEWMrvtPyuV7BE1puh7g9jMgH2Af+yv37AU5brmoxdhL+FXHOH9cYITRGw3e4vojmueSPr3WLaeBPbSoto55Z7UVvOaf3/TwNCgSUYY2sJ0L6ltfOm/MnQf0EQBDehpbtcBEEQBCcRQRcEQXATRNAFQRDcBBF0QRAEN0EEXRAEwU0QQRcEQXATRNAFQRDchP8HMscJGp85juAAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "SI_ds_mar = SI_ds.where(SI_ds.time.dt.month == 3, drop=True)\n",
-    "SI_ds_sep = SI_ds.where(SI_ds.time.dt.month == 9, drop=True)\n",
-    "\n",
-    "# now define a function to convert concentration to extent:\n",
-    "def get_extent(ds):\n",
-    "    SI_binary = xr.where(ds.siconca>15, 1, 0)\n",
-    "    SI_cells_areas = SI_binary*grid_cell_areas\n",
-    "    # take only northern hemisphere:\n",
-    "    SI_cells_areas = SI_cells_areas.sel(lat=slice(0, 90))\n",
-    "    SI_extent_timeseries = SI_cells_areas.areacella.sum(dim=['lat', 'lon'])\n",
-    "    return SI_extent_timeseries\n",
-    "\n",
-    "SI_extent_mar = get_extent(SI_ds_mar)\n",
-    "SI_extent_sep = get_extent(SI_ds_sep)\n",
-    "\n",
-    "# quick plot to check:\n",
-    "plt.plot(SI_extent_mar.time.dt.year, SI_extent_mar, label='March')\n",
-    "plt.plot(SI_extent_sep.time.dt.year, SI_extent_sep, label='September')\n",
-    "plt.legend()"
+    "*Hint: Select latitudes of the arctic, years of interest and use `cartopy` for pretty projections, e.g. [ccrs.NorthPolarStereo()](https://scitools.org.uk/cartopy/docs/latest/reference/projections.html#northpolarstereo).*"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "88cb816c-aba8-4859-b195-6e405e4a36be",
+   "id": "7be2c8be-21a8-4989-be07-81ab873feb51",
    "metadata": {},
    "source": [
-    "Q3. Compute the linear trend in each sea ice extent time series between 1979-2014. Does the trend reflect a decline in sea ice extent within the model? Are these trends different between March and September? \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "60aa0476-e656-460a-b253-2971fd9d4b6d",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# we can use the scipy stats package to calculate linear trends and\n",
-    "# test for differences:\n",
-    "from scipy import stats\n",
+    "# Q2:\n",
     "\n",
-    "# also define a conversion from m2 to million km2, to make our units more interpretable:\n",
-    "unit_conversion_m2_Mkm2 = 10**(-12)"
+    "Plot the Arctic total sea ice extent (SIE) as a function of time for the last 40 years. Compute seasonal means of SIE for June-August (JJA) and December-February (DJF)."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "5a5dc0a9-5bd1-4818-812c-4a70cb3e0dab",
+   "cell_type": "markdown",
+   "id": "e47283b0-eeb9-47e9-8e39-69fbc7cb278c",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "march_test = SI_extent_mar.sel(time=slice('1979', '2014'))\n",
-    "linear_regression_march = stats.linregress(march_test.time.dt.year, unit_conversion_m2_Mkm2*march_test.values)\n",
+    "*Hint: We define sea ice extent (SIE) following the convention that a grid cell is 'sea ice' with a sea ice concentration higher than 15%.*\n",
     "\n",
-    "september_test = SI_extent_sep.sel(time=slice('1979', '2014'))\n",
-    "linear_regression_september = stats.linregress(september_test.time.dt.year, unit_conversion_m2_Mkm2*september_test.values)"
+    "*Extent is hence the sum of the grid cell areas with a concentration above 15%. Use e.g. xr.where() and an appropriate condition to mask the data, then apply the grid cell areas. Have a look at W1D1 Tutorials 8 and 9 to help apply it. Finally, combine results from both data sets into one time series.*"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "49351d9a-69a8-4531-ba1a-aa7d2d51cafa",
+   "cell_type": "markdown",
+   "id": "f40e7c26-7588-46bd-ae26-6cb28b470b4e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LinregressResult(slope=-0.026916248481935644, intercept=70.09838824698096, rvalue=-0.7085028184389062, pvalue=1.3324180848619235e-06, stderr=0.0045978893749957, intercept_stderr=9.179810401024811)\n",
-      "LinregressResult(slope=-0.10324355173521954, intercept=213.0497016999347, rvalue=-0.9053126699550053, pvalue=3.3882764111605495e-14, stderr=0.008307186154928226, intercept_stderr=16.58552167065367)\n"
-     ]
-    }
-   ],
    "source": [
-    "print(linear_regression_march)\n",
-    "print(linear_regression_september)"
+    "Compute seasonal means of SIE for June-August (JJA) and December-February (DJF).\n",
+    "\n",
+    "*Hint: Apply e.g. `groupby(\"time.season\")` to calculate seasonal means, note that months have a varying amount of days. Check xarray's [documentation](https://docs.xarray.dev/en/stable/user-guide/groupby.html)*"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "287978fa-34ee-4d45-8ca3-8e9c8be6f287",
+   "id": "fd1cf207-201b-4c38-8589-e1085b151124",
    "metadata": {},
    "source": [
-    "Both months show a negative trend, but the trend is around 5 times larger in september in this model. September sea ice is declining at around 1M km2 per decade in this model. \n",
-    "\n",
-    "The code below shows that if this continues, this model sees sea-ice free period in late summer around 2060:"
+    "# Q3:\n",
+    "Repeat the analysis in Q2 for the Antarctic. How does this compare with the respective SIE in the Arctic?\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "633aac8f-107f-486c-8230-def833d71d44",
+   "cell_type": "markdown",
+   "id": "3690109a-32cc-4f25-a415-2bdd1aa1f239",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2063.5642431822394\n"
-     ]
-    }
-   ],
    "source": [
-    "x_intercept_sep = -linear_regression_september.intercept/linear_regression_september.slope\n",
-    "print(x_intercept_sep)"
+    "# Q4: \n",
+    "Are there long-term changes (trends) in SIE between 1979-2014 in this model? How do these changes differ across seasons and also between the Arctic and Antarctic?\n",
+    "\n",
+    "*Hint:* `from scipy import stats` *might offer the linear regression techniques that you are looking for. Check its [documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html).*"
    ]
   },
   {
@@ -2189,15 +464,17 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "378b7ff3",
+   "id": "d63d99be-fc26-405e-8b17-3c574eca874e",
    "metadata": {
     "execution": {}
    },
    "source": [
-    "* On the CMIP6 projections for arctic sea ice, see Notz et al., 2020  https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019gl086749\n",
-    "* This paper by Steove & Notz, is a nice summary of the observational trends across all seasons, and how they compare to models' predictions: https://iopscience.iop.org/article/10.1088/1748-9326/aade56\n",
-    "\n"
+    "* On the CMIP6 projections for arctic sea ice, see [Notz et al. (2020), doi.org/10.1029/2019gl086749](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019gl086749). \n",
+    "* This paper by Steove & Notz, is a nice summary of the observational trends across all seasons, and how they compare to models' predictions: [Steove & Notz (2018), doi.org/10.1088/1748-9326/aade56](https://iopscience.iop.org/article/10.1088/1748-9326/aade56).\n",
+    "\n",
+    "* [CMIP6 Preprocessing with XMIP](https://cmip6-preprocessing.readthedocs.io/en/latest/postprocessing.html)"
    ]
   },
   {
@@ -2205,862 +482,32 @@
    "id": "b1fa5d34-263b-4182-ad58-a57122716944",
    "metadata": {},
    "source": [
-    "# Optional additional data source 2: Satellite observations\n",
+    "# Optional Additional Data Source: Satellite Observations\n",
     "\n",
-    "National Snow & Ice Data (NSIDC) \n",
-    "Gridded Monthly Sea Ice Extent and Concentration, 1850 Onward, Version 2\n",
-    "https://doi.org/10.7265/jj4s-tq79\n",
+    "**National Snow & Ice Data (NSIDC) \n",
+    "[Gridded Monthly Sea Ice Extent and Concentration, 1850 Onward, Version 2](https://doi.org/10.7265/jj4s-tq79)**\n",
     "\n",
     "From 1978, this is satellite passive microwave data. Prior to this, a range of sources are used (e.g. ship logs), but we will subset to only the satellite era here. \n",
     "\n",
-    "The passive microwave observations are the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4. This data product uses a combination of two algorithms to generate the sea ice concentration; the NASA Team (NT) algorithm (Cavalieri et al. 1984), and the NASA Bootstrap (BT) algorithm (Comiso 1986). \n",
+    "The passive microwave observations are the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4. This data product uses a combination of two algorithms to generate the sea ice concentration; the NASA Team (NT) algorithm [(Cavalieri et al. (1984), doi.org/10.1029/JD089iD04p05355)](https://doi.org/10.1029/JD089iD04p05355), and the NASA Bootstrap (BT) algorithm [(Comiso 1986)](https://doi.org/10.1029/JC091iC01p00975). \n",
     "\n",
-    "This version has been gridded onto a 1/4 degree latitude-longitude grid, and has been averaged to monthly resolution. It includes only latitudes North of 30°N (so is arctic-only)."
+    "This version has been gridded onto a 1/4 degree latitude-longitude grid, and has been averaged to monthly resolution. It includes only latitudes North of 30°N (so is ***arctic-only***)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "id": "ee26e97b-14d9-4577-b3bc-b75fae4c471f",
    "metadata": {},
-   "outputs": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:          (latitude: 240, longitude: 1440, time: 2016)\n",
-       "Coordinates:\n",
-       "  * latitude         (latitude) float32 89.88 89.62 89.38 ... 30.62 30.38 30.12\n",
-       "  * longitude        (longitude) float32 0.125 0.375 0.625 ... 359.4 359.6 359.9\n",
-       "  * time             (time) object 1850-01-15 00:00:00 ... 2017-12-15 00:00:00\n",
-       "Data variables:\n",
-       "    seaice_conc      (time, latitude, longitude) uint8 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
-       "    seaice_source    (time, latitude, longitude) uint8 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
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-       "    Gridcell_Area    (latitude) float32 dask.array&lt;chunksize=(100,), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    version:       2.0\n",
-       "    release_date:  February 28, 2019\n",
-       "    Conventions:   CF-1.4\n",
-       "    citation:      https://doi.org/10.1111/j.1931-0846.2016.12195.x\n",
-       "    dataset_doi:   https://nsidc.org/data/g10010</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-5ea8bd87-44e8-4fa8-a4ed-a8af404920c6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5ea8bd87-44e8-4fa8-a4ed-a8af404920c6' 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'>latitude</span>: 240</li><li><span class='xr-has-index'>longitude</span>: 1440</li><li><span class='xr-has-index'>time</span>: 2016</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-592c11ec-a21d-472b-9556-45503b5063f7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-592c11ec-a21d-472b-9556-45503b5063f7' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>89.88 89.62 89.38 ... 30.38 30.12</div><input id='attrs-ec6ca268-74d8-45a0-83ea-c6a6f3288ec3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec6ca268-74d8-45a0-83ea-c6a6f3288ec3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1be8d7e-acc1-4ec5-9b21-a99e6d965ae9' class='xr-var-data-in' type='checkbox'><label for='data-d1be8d7e-acc1-4ec5-9b21-a99e6d965ae9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>valid_min :</span></dt><dd>30.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>array([89.875, 89.625, 89.375, ..., 30.625, 30.375, 30.125], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-3b923ac8-0471-4415-b9d7-526f6fb088c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b923ac8-0471-4415-b9d7-526f6fb088c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f4b62ab-5a2d-4a68-889c-b41754811d4d' class='xr-var-data-in' type='checkbox'><label for='data-5f4b62ab-5a2d-4a68-889c-b41754811d4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>360.0</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
-       "       3.59875e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-15 00:00:00 ... 2017-12-...</div><input id='attrs-9f8f4d0c-5e57-46ad-a983-fba6a2736d9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f8f4d0c-5e57-46ad-a983-fba6a2736d9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-224b4c53-c2b9-4bc0-9756-2f792206b6c9' class='xr-var-data-in' type='checkbox'><label for='data-224b4c53-c2b9-4bc0-9756-2f792206b6c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeJulian(1850, 1, 15, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeJulian(1850, 2, 15, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeJulian(1850, 3, 15, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       ...,\n",
-       "       cftime.DatetimeJulian(2017, 10, 15, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeJulian(2017, 11, 15, 0, 0, 0, 0, has_year_zero=False),\n",
-       "       cftime.DatetimeJulian(2017, 12, 15, 0, 0, 0, 0, has_year_zero=False)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9960d924-d7a1-40f1-8f84-395b88d66d9a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9960d924-d7a1-40f1-8f84-395b88d66d9a' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>seaice_conc</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-478771b6-e822-45d1-ac30-232c015955a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-478771b6-e822-45d1-ac30-232c015955a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62602096-d6e2-4022-bc5c-0a60316199c3' class='xr-var-data-in' type='checkbox'><label for='data-62602096-d6e2-4022-bc5c-0a60316199c3' 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>short_name :</span></dt><dd>Concentration</dd><dt><span>long_name :</span></dt><dd>Sea_Ice_Concentration</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Values are percent-concentration from 0 to 100.  Sentinel value of 120 in the concentration field indicates land</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                        <th> Array </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 664.45 MiB </td>\n",
-       "                        <td> 0.95 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (2016, 240, 1440) </td>\n",
-       "                        <td> (100, 100, 100) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 946 Tasks </td>\n",
-       "                        <td> 945 Chunks </td>\n",
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-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> uint8 </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seaice_source</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-ae375d8f-a9a8-464c-8086-18566f4d6292' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae375d8f-a9a8-464c-8086-18566f4d6292' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88915b7d-c078-4077-af53-2d76314db7a9' class='xr-var-data-in' type='checkbox'><label for='data-88915b7d-c078-4077-af53-2d76314db7a9' 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>short_name :</span></dt><dd>Seaice Source</dd><dt><span>long_name :</span></dt><dd>Coded_Source_of_Sea_Ice_Concentration</dd><dt><span>standard_name :</span></dt><dd>source</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>name_source_00 :</span></dt><dd>Land</dd><dt><span>name_source_01 :</span></dt><dd>Satellite passive microwave</dd><dt><span>name_source_02 :</span></dt><dd>Danish Meteorological Institute</dd><dt><span>name_source_03 :</span></dt><dd>Dehn</dd><dt><span>name_source_04 :</span></dt><dd>NAVO yearbooks</dd><dt><span>name_source_05 :</span></dt><dd>AARI</dd><dt><span>name_source_06 :</span></dt><dd>Hill</dd><dt><span>name_source_07 :</span></dt><dd>Whaling Records - Complete Sea Ice</dd><dt><span>name_source_08 :</span></dt><dd>Whaling Records - Partial Sea Ice</dd><dt><span>name_source_09 :</span></dt><dd>Whaling Records - No Sea Ice</dd><dt><span>name_source_10 :</span></dt><dd>DMI yearbook narrative</dd><dt><span>name_source_11 :</span></dt><dd>ACSYS</dd><dt><span>name_source_12 :</span></dt><dd>Walsh and Johnson</dd><dt><span>name_source_13 :</span></dt><dd>JMA charts</dd><dt><span>name_source_14 :</span></dt><dd>Kelly ice extent grids</dd><dt><span>name_source_15 :</span></dt><dd>Landmask correction fill</dd><dt><span>name_source_16 :</span></dt><dd>Change-of-landmask ocean</dd><dt><span>name_source_17 :</span></dt><dd>Analog fill - spatial</dd><dt><span>name_source_18 :</span></dt><dd>Analog fill - temporal</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
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-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 664.45 MiB </td>\n",
-       "                        <td> 0.95 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (2016, 240, 1440) </td>\n",
-       "                        <td> (100, 100, 100) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 946 Tasks </td>\n",
-       "                        <td> 945 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> uint8 </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LandRegion_mask</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</div><input id='attrs-b4f1ac6a-e40c-40e9-928f-cdfa83511acb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4f1ac6a-e40c-40e9-928f-cdfa83511acb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f07d0d34-30ea-436c-bd63-bc0690654672' class='xr-var-data-in' type='checkbox'><label for='data-f07d0d34-30ea-436c-bd63-bc0690654672' 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>short_name :</span></dt><dd>LandRegion Mask</dd><dt><span>long_name :</span></dt><dd>Land_and_Ocean_Region_Mask</dd><dt><span>standard_name :</span></dt><dd>source</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>name_regionmask_00 :</span></dt><dd>Ocean, no region specified</dd><dt><span>name_regionmask_01 :</span></dt><dd>Beaufort Sea</dd><dt><span>name_regionmask_02 :</span></dt><dd>Chukchi Sea</dd><dt><span>name_regionmask_03 :</span></dt><dd>East Siberian Sea</dd><dt><span>name_regionmask_04 :</span></dt><dd>Laptev Sea</dd><dt><span>name_regionmask_05 :</span></dt><dd>Kara Sea</dd><dt><span>name_regionmask_06 :</span></dt><dd>Barents Sea</dd><dt><span>name_regionmask_07 :</span></dt><dd>Greenland Sea</dd><dt><span>name_regionmask_08 :</span></dt><dd>Baffin Bay and Gulf of St Lawrence, on polar stereo grid</dd><dt><span>name_regionmask_09 :</span></dt><dd>Canadian Archipelago</dd><dt><span>name_regionmask_10 :</span></dt><dd>Hudson Bay</dd><dt><span>name_regionmask_11 :</span></dt><dd>Central Arctic</dd><dt><span>name_regionmask_12 :</span></dt><dd>Bering Sea</dd><dt><span>name_regionmask_13 :</span></dt><dd>Baltic Sea</dd><dt><span>name_regionmask_14 :</span></dt><dd>Sea of Okhotsk</dd><dt><span>name_regionmask_15 :</span></dt><dd>Yellow Sea, on polar stereo grid</dd><dt><span>name_regionmask_16 :</span></dt><dd>Cook Inlet, on polar stereo grid</dd><dt><span>name_regionmask_17 :</span></dt><dd>Yellow Sea, off polar stereo grid</dd><dt><span>name_regionmask_18 :</span></dt><dd>Baffin Bay and Gulf of St Lawrence, off polar stereo grid</dd><dt><span>name_regionmask_19 :</span></dt><dd>Cook Inlet, off polar stereo grid</dd><dt><span>name_regionmask_20 :</span></dt><dd>Land</dd><dt><span>name_regionmask_21 :</span></dt><dd>Unnamed ocean on polar stereo grid</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\n",
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-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 337.50 kiB </td>\n",
-       "                        <td> 9.77 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (240, 1440) </td>\n",
-       "                        <td> (100, 100) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 46 Tasks </td>\n",
-       "                        <td> 45 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> uint8 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Gridcell_Area</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100,), meta=np.ndarray&gt;</div><input id='attrs-00febd06-dc4a-422e-a2a7-4f931119b918' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00febd06-dc4a-422e-a2a7-4f931119b918' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55d72adc-e116-4942-93c8-e3cdb44117be' class='xr-var-data-in' type='checkbox'><label for='data-55d72adc-e116-4942-93c8-e3cdb44117be' 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>short_name :</span></dt><dd>Grid_Cell_Area</dd><dt><span>long_name :</span></dt><dd>Grid_Cell_Area</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>units :</span></dt><dd>km^2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table>\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> 0.94 kiB </td>\n",
-       "                        <td> 400 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (100,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Count </th>\n",
-       "                        <td> 4 Tasks </td>\n",
-       "                        <td> 3 Chunks </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                    <th> Type </th>\n",
-       "                    <td> float32 </td>\n",
-       "                    <td> numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-68905e9b-b5a8-424a-956d-702d3ca41358' class='xr-section-summary-in' type='checkbox'  checked><label for='section-68905e9b-b5a8-424a-956d-702d3ca41358' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>version :</span></dt><dd>2.0</dd><dt><span>release_date :</span></dt><dd>February 28, 2019</dd><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>citation :</span></dt><dd>https://doi.org/10.1111/j.1931-0846.2016.12195.x</dd><dt><span>dataset_doi :</span></dt><dd>https://nsidc.org/data/g10010</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:          (latitude: 240, longitude: 1440, time: 2016)\n",
-       "Coordinates:\n",
-       "  * latitude         (latitude) float32 89.88 89.62 89.38 ... 30.62 30.38 30.12\n",
-       "  * longitude        (longitude) float32 0.125 0.375 0.625 ... 359.4 359.6 359.9\n",
-       "  * time             (time) object 1850-01-15 00:00:00 ... 2017-12-15 00:00:00\n",
-       "Data variables:\n",
-       "    seaice_conc      (time, latitude, longitude) uint8 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
-       "    seaice_source    (time, latitude, longitude) uint8 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
-       "    LandRegion_mask  (latitude, longitude) uint8 dask.array<chunksize=(100, 100), meta=np.ndarray>\n",
-       "    Gridcell_Area    (latitude) float32 dask.array<chunksize=(100,), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    version:       2.0\n",
-       "    release_date:  February 28, 2019\n",
-       "    Conventions:   CF-1.4\n",
-       "    citation:      https://doi.org/10.1111/j.1931-0846.2016.12195.x\n",
-       "    dataset_doi:   https://nsidc.org/data/g10010"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Code to retrieve and load the data\n",
-    "Sea_ice_conc_obs_file = 'Data/G10010_sibt1850_v2.0.nc'\n",
-    "SI_obs_ds = xr.open_dataset(Sea_ice_conc_obs_file, chunks=100)\n",
+    "\n",
+    "link_id = 'fpr3j'\n",
+    "Sea_ice_conc_obs_url = f\"https://osf.io/download/{link_id}/\"\n",
+    "Sea_ice_conc_obs_fname = 'G10010_sibt1850_v2.0.nc'\n",
+    "\n",
+    "SI_obs_ds = xr.open_dataset(pooch_load(Sea_ice_conc_obs_url, Sea_ice_conc_obs_fname))\n",
     "\n",
     "# note the use of the chunks keyword. These data come on a high resolution grid, \n",
     "# so are potentially too large to load into memory. Chunking (using dask) \n",
@@ -3072,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "id": "88016832-949b-47c9-9729-478e22716279",
    "metadata": {},
    "outputs": [],
@@ -3094,33 +541,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "id": "778eabdf-3594-476e-ae69-fda6d6d67001",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f89e0038910>"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# We can select and plot the first month \n",
     "# using .isel(time=0) and inbuilt xarray plotting,\n",
@@ -3133,52 +557,29 @@
    "id": "64abc79c-689a-4e73-91c3-5fe86f7c7d6c",
    "metadata": {},
    "source": [
-    "Note that the dataset also includes variables for grid cell area. Grid cell area is in km2. \n",
+    "Note that the dataset also includes variables for grid cell area and given in $\\text{km}^2$. \n",
     "\n",
-    "We will need this to convert the spatial data into a timeseries of total Arctic sea ice area. \n",
+    "We will need this to convert the spatial data into a time series of the total Arctic sea ice area. \n",
     "\n",
     "The code below shows how this can be done:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "id": "b7bf75ee-8e48-4e26-ac0d-200e940bd66e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f89dff311b0>]"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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3Lj3kc5sVji6PcXR5nHiR9KJOyiZjR0YGzct4CRkDo9mcFTm0CteAidanFVbGJSYjp0YdGw1nMBmVSdQyb9JoV8alem5SSJcemagKk5TgZYcn5nXR8TVOYGNetVXXMjKoa49Z5TEZFRgVLvlNvPc4tznHnS9Zic7FhQb6DOHmmucfdYNVjr26Ocex5TGWx4VqbM5uzHFsZWymG9MxzxqGamMWnklt/Rxm0bK4IgJ5AEbaD6GoS4vIPn9mHdd8/DRe8PvvVXkBbsB20g+Lrt0yKpSQYQ2S+twta/jl139sIVXn+yUH0hgstcZAZBPNaiyNgwcK6Lj94aUSK+MyCbtJOU8aDzbB9GeUDaR7iVXtsT6rTE5glXnfQPoCTOd1F5WoxqDGyrjAREl7pchgMipQFq79d7dvV7mted58bZpSIiV5l+PLKgy0OatxaFLi6PIoiRymPOJSsrRWpxW8B+58LHjnUrHMq7qNNjRlztMaNc9/cwAmWp9VOLRU4sjSSFXo5zbnOLpsJxUQlHNOUTreh2fimBFNrm+DM9Bgoq3Uk3zulrWFVXjTXA412mRr19qv8CK7M0oG2pDQb2Wpcpr/YRnM137wi3jJmz6xb1HVIuRAGgMLJtqck/esb6c6gZVJmbx8LUxU6gQyeXWXHNKVOXncJw7rkUMHE+mKYVrVLV+hwkTzCkstgRyvnTyvUVFgXKaRRWcMJqqHSZ9dsjLu9X4vP7qkeufr03Dfjy2PE2NABntpVGJUKMagOfadGvJbKnxufPQ2Hf3Kns5fFs5sS74yLrE8Tp8JIHiWRxouR+dT7DqCwFsBl6wEAtqCNcPf288m4tstD/exv/QmfN0v/l9125C8/rov4U0ftYcpUZSoFfPxtQ310uob5WpJFxno28mpsSK2W9aoGHB/o6vdlANpDJZKizMgkjV4BVLxnN0MJO3KuEyUIo8MNA+WPDPCj2VU0nIKRmRAD6UVOcwrj0lZYGzUOEznAXfXCGT6/qh0GBcpnEGK5tLDk94meMeWx2o2Eb3Ylx1eMg3VCkUGwhOLIq5RChPRuYlLkb8LJ781hcuVZB9ncOLQRPVgp1WIyJZGhXrtlE00MYx0CxNp5HXjOVNkkMCa3BgYdQaHmtoSiky1c4djK3Uxzb2xBiINyb/8X+/Bc175bnXbrKrb+2HBayRfUjoA832sBoev/eAXzZ5QlJ23NtXTkSmpwWrNcmaV9tehx88Z7UEuZFmoMXDOvcI5d6Nz7kMD3/tq51zlnHvqItdDQpxASsiF9EuCiTQv9OjyCMsaTCQIZEvZX2KE/JITSJTelNJeG8Ug1jaraoxHzlQ6m/MAgakEMo8MNEPGOIPNea0U63VZWH0psycO68aC7vvSuEwMESnApbLAWIkM6Hh0X6V3Tl7joUnZO3CIr1OuDQj1Hdr+03mIyJZGZZKqXDWkb1+xHynktWmVYOOk4C9Z0R2ImEDWs4la50VRWtzr1fb/2Jd3J+VUU7Z9NTVArOy12SB8/1sVmGhtOscLfv+9+I6Xvk1dE09HlhFhVfvOUBnGgKq7NUP23utvxWN/6U34o3fvzzCmncqiI4NXAnhS3xeccyWAXwTw+gWvpZVJ2aSWasZgbBuDkE2kw0R0rHHjnafZPuGhOTQZYVSkKZJEWLWcQhUff7OqW3JaW/usgYlCVJK+fNN5jUmpKyXiCMrCYVz2cQZNwdws9VBHhcOhSYnpPE3JpZTZQ5NR4t2G/UNEtqRkYdF1EpeTGINZbAykwiQo4i6XLA8O/VEjg3mNwoWoR9t/RpHBuEiuLcowM2EiOy12KOlgfdo9M9oY13MNrBkMYT/BrMFMH//S7hgDDVdfjYywXUgI6G08YmOQRgZv/2SoOLe66HKPX5LE/Jk4qxQSAh1PoT0z1L7+DR/5srrvhSoLNQbe+2sA3DLwtR8E8CoAi5nUrkhfBfJS8+ICSi7/Zsh3XxkXycvFI4MlRWnRi0thu1R65wQMNJuLWoB5B0fQv+PzU42DnotP+2ucAmUTjUsXtiuRwWRU4MhSUEprQiluzuuIa5HGaG0ajChBKVq+/PK4xHiUrr29r2WAiRJDJbxnaaSJpDx5RG/TMTQhbrPhWg4tjUxj0WaQKQYaAMaFHi2Gc87b31QaA3ltifMyJ+hxYhDITcLDRI8MuNc71NvIkvdef+sgZv/JG88ln632GEH+2Z2PLatTB3narJaU8MGmVfoVJ/T6DX7OPi7Gigw6ziC9b+RMasT3hSz7yhk45+4G4NsBvGwL332ec+5a59y1p0/rlaRbFSu1tCWQy1Spee9xbjpvPK2RyRksDRDIhwzIQBLEEgZqs4V6IoNJQyDLMn3vfYtta2uj6xyV5H2nENXKuMThpRBRScJvY1ZFRjT1YBuopCzgfQdLkWzOuvuekPqzzsiOFD5jk7UQAVIDTr/TySNLZjYQEIyNpeyXxjYnQFzM0qhMts/b++rUaBEIyuQulwTyW3qopMDp2rR0YiBEBloW1+a8xvKoNCEyrhA1zuHsgLF456duxne89G34FWWoEDf4X1bGxNLz7pxOIJ9tjOSxlZEataxuznG0afqotY6h983KNOLXJp93/m6bnEEPgUyf3dxTEPeUl/wdvu93dD5lv2S/CeT/DuDHvPf9CeAAvPdXe++v8t5fderUqfM6qeVdE1yhwURrTQrjkaWGM+ghkDXvm75PSjHJVGpeCGpXocEllKkEpAo3wESOeefd9i4jJ0Qtsrq6yyYKMJE8N90XqkOQXiZFBlTZLRXTxiwYE5ur6e57kjJbddlEk7JIIoNN1nZcP3YDcR0JBXFWxHbyyETPJprVXRsPsa80svLc1AZ9VDb3fZ4+M9OqbjOhpOLZGIDA6N8hMkgV4rzyGJVOTRoAgjGgoXpD2UhaseCfNWNK9eaD3fE0hUwe9ckj+myRcxuBnwtGVoe4LqGaHKUOgYzfuc15b20MkMLBbVuapTShAQi/+61baM9ysxEZeO/x/htuw998xAZDbl2d7nlfpP02BlcB+EPn3GcAPBXAS51z37bok5oVyPNQpTumbCK2nX7gABMFbJx735wz0CODedvKQjMWLcl6SMeHW6VjZELN5jVGJdvO9u8UaqFCOUQIB2OgE8gr4xKHJqPmWlJlT+S0tva16RwrE9tY0PG1+xZFBkraKymdcPxCNURAV58hIa711lgsmXUGS2O9fqOqPbxHQyBrnEFTv9EYaat25PLGGEil2GZpGaml69MKhQuGUPPsZ3VtPo9AeKZPNq0yNM+fK3kNiqFIRoWoBlqQ8IQJvQ9Y117F2n5kKfBvWlU6f0ZvvD1VyudY1XpqDBoje1iH327fmLfvfl/34VADkxqqzysjcePzV3jEz7wBP/VnvXk3uy77agy89/fy3l/pvb8SwJ8AeIH3/s8WfV6LEyAvcKwotdYYLI3atg38QZFkofTEVjcrHBqXcM6pFcptncGhnshgVLRrSz3ori+S3F+mvcpra7OJGs5AelpErHeevVRadeu58/ORECewZBiLrjo6vS9EpFPEZXlxS0Z65wZT9gBwTniJpIAvsyIDlpKr1XbQ2rRsos7IFuq1dSm3er8pWntHIKew5tKoxPJIr3HokgrSqnMgPJNkJIeK1jSFTorw1lUlm4cZXS0yoOf9+CF7dsiRpYZn0mCiJlNqVLoEdgRiw6oVzZ3dmHXvmpE9d8mKvjbOkWgQFzcQ2rV/WGkDz+WtTUvvN31sz2hUAItPLf0DAG8HcH/n3A3Ouec6557vnHv+Is87JFYFclBKuvdNL8aRJlUPiLFFrhjGChSzPg259PQdK7X02MoYzhkVxszzTyCJqm490PDv7twcJhor10YE8qjQ+yqRMl9uPHsJKQSupTBhoPVZ3Xr+gN4t1uIMptHa0zoDXpSmFX6tz6omG0jvHEqZUZcdtudALBndXlsjW1I2kcXF6Cm/pEgshdxN3tM5A2rTsTwubJio6Es3rtrsNW3/sxvzNkrWjUFY3y0KgTwEMbUc2aGJCmGd3WTGQI1qKhxeGqnpxoCsH4nvK7Uwaft8iWQNnsU1repk+E7UuHGgelrjS4YiA2oX/4C7HOv93m7LaJEH994/fRvf/RcLXEoklgfLX3wgVqj08B5uOAMgViyyNxFADdbCd9dmoZEcANVLXJsGhUpQUkIgD8FErAJZbueRAXlR/AXiSmuktKvYbDB96qgqldbmrPOetbVtTCusHFvuYCKFM1gaF6i9N2EgKzLY5JGBopA3GkNEv1micFlkMKsC8V4W3RBqigw0Yp3fVx0m6p4JvXX4ACcgyHGr6lzjsNrtowJeua90vo5416Geux1fwWduXlN5ATJmtypFX9ywntGMARVhGs0N16cVTh6ZYGlUmsV6VxxfCZGBYkzWpxWOLo9wdmOuZsfVHmZkkEZkNZaLst3Or1eHiXhUsokH3DnezqMF+byF/e32KYuU/eYM9kWcc2qqXyBCdQL5bAQTpYolgmKUbKT16RwrDeauKTXCQAE03nnqAU9GBYqG5NVqBcaN9wwIzqA1VDqUE7ejsDkDS5nLNh5aRg9h+tq5p1VQ2BqXwo3suCzSTKRmO0UuibKnqGZMa9c5g85DTslxq1iPp732ZxPp+5MhI05Arq1rLa6T4yFadKFYb556sPPKY2xEBt6HwipSeGpR2sYMlx9dbu6LrpCBkIIqz00KbWVcmhCTc+HaNWXPs+csAvnwUomR0YtrbdbT9HGDIrKwfV6nzyvQVX7Ld41SbpdGhQkTkeOnQVT8fmipq+QEbCW1dzflQBoDAEle+LyqMa99Ow0MiB+C1c0UJuLhp/QCgVjpUYtp+k5CMG/OW4I2RA5p5gkpU7k/ZbWMWVQSZxN1uLumsKN2FFpqaatQ9chA1mdYnIGW0kt/m5wBg4m0qGWDEcxaZNBBXEX0fZK1aegKeri596kxqKJsIg79ychAFtx12UTO4ENizz+B32b9xmDWeP4rY91I80JEy5BZ9RlAQzAf7eEUGg+49mlGEb0bdz2+rNYhnNuk2pNSnR0SIuGyByZqCGYjU2p9Wpn1GW1rFwMm2hAOgoQ1CSa64sSK2dL9XifDCNkvK32VuDHQitoIHtxJA77zkQNrDKTC3Wg9TF2pnWMwkcoZCDhD7h8ZA5UzCBgo0EQOWtFZc1y5P3nLk0aZy3NzhWp550BTHKW0wN6YV3HqqBEZdNtTKOYQiwz4/uSRLo+6vklcMXBOYFIWiRe3Oa8wLh3KwmFZSUPsIC4DJmp4otbQKVDNEutXpWVpmUaWMsyMorMWBjJ6D9Hx6bmwakssIz2rfBeVGPAbnVteNxCyZk612UZ6ZEBjXGV/IHpf7np8BbetK8V+m+GZ6PqA6c/70ljnO6hBYCCQ9e1kDJIeYxsxcW9lE1mV37euTlG4MGLWMqKXHp7g6NIINysQGjeOWh0DRYxnlIhrkXKgjQG3+PSjxkVnzLtuvrsyKbE8STkDNWNnHh+flKWWkx6qdMN2qxKXCNqRUIptpWupQ1yqoeLZRNSOonRqZ9DZ3GNcuvb8VmRg1W+0MNM4bQNCSoj698i18bWPDAKZiO0lhUgliKozBul9PzTReSA6vsUjRQRyT5bWuHRqhTJ9l+BB6YFyPkSDBqdz33IGQFo4RkkFYyUpQLbx0CKi6bxus7Dkdmq5TjCSJGkpTfeul6yoBHLSITgxVlWXpSXWXtce87qbcaFxBmszFhnIuSQbInPPyCayuJpb16Y4cWiClXG6NqDpVLA0CoZMgbA4h6I2KGx+m9rbXVMXIQfWGMj0ztYYGI3q5qwwS8vlb6GWwqkPeHh4gxek5ayvNmEvbdfgEjrvWAx5oShiXOqpo5uKBxsRyNSOotBhonkdahjMLKyeF3vWwG9UbBf2ZxGZaoR1KEZVak0dAAC1QIma4Fne8/qsjmogtMjBIsdbIzwqWkPHFXoHvxUqzMQhsqVRkSjzzSoYWeK4VAJ5xCODePu8qttzawkJABquxiX7EvxxtOm4KtdGWLnVcp2ekUuPhDqCxMGog4NhJR20Kb0KZyAz96xsojYySKLweJ64RSBbXM6ZtRmOHxqbBXFrTeaglcV129oMd790JVpLfO08LXfveIMDawwk1EIvw1JEIKeFWSVT9twj2WyyfcKL20AKQnHQcdV2FNOqbfegEalEqAFNZKDAFeORvnZSUJZSI5goXFvqgc4aIpJqJGRhl8wm4gpxnSl7LfW0qxPQ50hsNlPQSjJUtXxxu4wtLcUyiQwkgTyN+ZC0RQkppTSq0TLI+P70fJDSk604kmwkhTOge6K2EWkI5GXDkM2a2pOlMuUz2qhjXKjEe5s9NwkerlwbTw2ltcTnbmCoxruW2U6zeVcDwe8Fvzd0X2aVj+ASTtxrdQbUdfSYAROlBHEKUY3ZfdUKSA+T568o+xY2HeuZUGfWZ7ji+CEA/ZEBsLe8wYE2BptC6QBBKRGBzLfzyIBGNEooZokpeyCFYmi4tppz3hBqYQ16W4bOGLioMKxriKZ7WloFcmyo4t5EsqJz1niYAJJcfu99xymM0+sm2GWFFZ1p8NzKRI/IuBG06gzouHpkULeFWfRvLuuzkPlhwUibsirdgLC0qInDd7qh636XJQVy4KnJ2rVRx9RlhcMCqAJ5uPYknFvPsjq0pNdvSGOQwprhXEca8ntjmq6dGivSv0naNh9NlpY8fpRUoMCaa21rFx3moefviNHbKGn9oiQtLPeQ29w5kvvWtceZtSnudoIiA722hchrLWV4UXJwjUGpRwbL4y4s5w8Z5QM751TcnSstapEd5/LXHUykEsjzmEBWYaImcihihR1xBqMepVXq6Z/zdn8DJmo8TADJCxAIXzvqaI3BpHuxOWTR3nfDUHEjSE34uJe4OataiEaLDDZncUFc2tW0MusQvPcNJ6ETxDExr8BEjDMYqv/oi7joO1ZtiXZtvFVGX78qq2iNRwZhuzQGXZ2APDZd22RU4JBhqOYEE2nPY7P2yMjO9PumQa50rqPLRgGnSNmVBDRlaZnjcRtoUuMz6PttoWKCAMxR+1DXAkDlOzbmFY6vZGOwZyKLWTYZnAEgadg2Z8UhGkwkYSAghYkoMtAU7lC2UQwTueTcQICJVPJ73kECGt8xZzDRSCEqycOk+xMNYmcRlXbdbYO+qAJZ5wy0iIzf1zZyqGOlFkUGRt+jgL1rfZECZ7CiRAatoRvrNRKzFgbi1dccJgrfpXGi2toBNBlBqWIJ7SZsaHGzgVpWevmKoQwzo3J72kVsAUbSjcVxw/umTCeqydEaO3IeyjKS7TPDMv9o+5hgItl1lDoEU+2KjCZlZCBhomo4MlgyDHjd1M1YfAfdR4LPrHnlNB5X60a7KDmwxkD+UJxkBZBM/KrquoWHNJhoVvnWKx8rnAH3rqWyp5bT5F3KB5h3xwQCnDOLsOc0tVTzYPkDPhNrA7oUSBkReY/WkC2LXH7epllLLY04Ay21lMFEWo0E8RX8vvKXnwheAA0JK1/cAGER3yG3r0/nTWSQetedoTOytFjfpCXlvnMjTfvPLahGI0qjaFOPDMiQyLWrdS+ag9BGBpIgDv8+3GRaJVxLUqUbK1TqoqsZWb723gyy0ogM2Lsq3wW+NiJx06r2DgLj5+vO79vpdfz7fP+lxrnRoD2gc7y0LgdAF5VoNRKbs6ptoqel9C5KDrgx0BUiQAo5VkoUGWjN4uY14wQ0SKHqvGuZWcK9uHa7gfmHNcadGrvhNDokoKa9CkNXODTVzYHoJFJZrm1JkI1dk7tCNYJDuHoHE+mRBWXEAJ1BihVySrJyopTaYwNpVAN0kYNGMHNlPZz2qkWL9Ew5dTth062hSviKGCbSeh9xzoCvveWBDB6Jd4PVPH/C3QP5nhoLWbmtGirOZwjOgGAi3ch2CrXN0jKcG/ku8Guj303LVHKOqtrTuhru2ctzAzwyKDGvfXT+7txGF955HJVoHVc35nWGifZSZGpp1SjUFgoqi6gysao7z37cKqU424giBs3DndeeKdRYsXRFY50XqGP+AzBRyWAiK+zWUiSZIZMvJ8+VB5TIoG1yF/gUmcY4Z1BK4Fvil7eDiXTvO2TExEZYpq7yyID24dvpuFrWDKUBagRzlOnUh/k3cEW4l7EhA2JDGcNEXVSzrJC4kjyXcAh531pqKT+3asiqTmlpnj/VDRxuCWRpLOLIQOspFWogdK6GYKKh+6rCSFFkYI9pJehRWxul7Op1NXE1v6bQrYQJHnFpBpzWRt0G5LnrJhPq+CG78ntRcmCNgSw66/DfzjuXyrwUcEWq7GOFSg9R3cBAHfYdV12S4iaFMhZtF7gyp+NzpTJldQaawqQ5vlENROJ9C4iLjAHDvQElMmAeKIA2jbG9r8xY0BpNfNjI5e+McAoTzeYx/MbX7r2PfheNKN2gvkkqTNR5qNZ9pfOSg8DXxluDj5TtXNlrmSnUojrcv7T6ekoKV0kt5emXamSQwES6sj80HiW/OT/XMZMz8L2cQQITzfX72nnneoGn1ptIdunVanY6SDbNUKNo0ySQG2hSg7BkNGnVXwTnxyUQF22/JEcGeydpZNDBHQASj4JzBiGrKA7xeGQgldKMwThA2jWVT8Si/bWwueUMCj0ymIxcYmhof6qBUCuQI0MXRxa8hgFIFSpXeACS/kBVayw4ea5EFoYh4xGVHjl0MFJyX9lwmbD2MsHVZ5XHIYNgHqrP6I7fRQZa/UcUOQhjwsnvxHuuRNJAnSotXoG8rhnpUncAOEy0NE6Jd6ogJpgo6fkkIgOtBoKT2xZMNJyl1b99Umq1J3EhY5qO3E/ME8RlEchysp8WGSyPy8QxAuJElVGRpnHT/m2bkMwZLF5kNhGvIAYUpcU4A+ccxkVM8nKlJRXHPIk6YuJKKi3Te25hoiJRiLSfFXZPDIVJ60sydpo1dVwKKfsYzqjYLAQ6fnRs1qwtXHt834aw7SgLS4GBYi4mvq9zYWSXxmVEIHOikXD79SgyqNr9+rJeAvZNMBCHDrtra8nv2ogMFIXLU0u1tgsEkdF3NpRsIrMqnSlUFT6bVa2y1rgWihzalg0K7j4ZFWofLwDJZD7L+dH6YUXZRMp9SSIDpV8Vj7JThRzXZ0incSrue1w3IyIDgzOw5nPQ/pTUkLOJ9kBkNpH0cMelxMZ9ayjCdkHiVt12STB3hkbAGa2xSLdrrS7G7QMce4n8xSdcXqaWEhGntcDm1yYhMN6GGUBCdM4ETCRfgAR+U4wsrV3t78M5AzWLK27gB6RGtuMMYiKUCqHIs5bYuDYUSEaLQJOS28JAcbTYt53DQJpCTjLIEg82GPGigf82o6gnrn4GdB5pqVE6MsuKd9HVCOQNAZXoNRCu7eOVVEc3sKlaW9I6P126sRpxtTCRzRmEKNsuVNTSvENkwNqnKDUO1uS/TXZftagj5jPsyX3UXDHDRHsgqQfb5doDKVRTMewZSF/Oqo4rjIHuwWhfTKbMAQYjCWUv1zYT3rkML6dC4crIYpNFBuq1KxBXa8haiKuDWiIYqJbGQkYOEiZySeTgXLjvWhXwvI7rM8J648gi5Qyq6Bpo7bLKl9dAAKlCjjqmKkqL+us45xhMxKMW32YLdTCRHhmoSmvGlVbsAFSCh5JwiBYZbApDFK7N9vyp7mVZKa6iNh/kXGhFZ30wERmLwSwtJVkjyiZSupam3ncKxXDOQO8DFjLAysLF3Y1Z80AtaokIZA0mYmtTyW8GM60ov8si5cAag6VRgdp3HkdbhVvoL9eccQZAWgswq2sTJuLtIujc8fZY2SfktfDOZffOtlWyxTkwT4i2S0OYpm/GMFFHIEsPVMBrI53PKC34jUUlGvY9bYb20HWHz5hCnteRQgS6F06uXaYhSiMsPWStPiOOWlJDJbOJJN8xNwrmtCZ8UWRgVZ03tS2ylXPExagEcjDCo8K1nj9PyY2MQaOUku1tgabeRG/SRFSjwtm9iTRYk9Vv0P2rasUYNMbCyiZa6hmYRF69dt9nVUzsa+S2PZ+jHyYaigw4TxUig3j7IuXAGgNJXFUJTOQShVz2wEQVU2rSW5LKXCqWuYAzxmUwVG2uv8Ddx4Xdwpr+PzU8UNpuKWQJE8k6AzsyYFGJODZfW2roOoXaNouLiNC6M5IGvkwKMTWycWQgPTHeb4qubUOcm/bTFCq/b/R/efy+7VHqqNIgcJO1PZcOACenw/FlZNBFixbfQemVy6MStY+9b2rtDYTfjD+PQDM0iFXMpxBWF42uKKmpBBNpNTtRyi7dtzr+zem8Vuoo0ClkK7U03B+99Qt/XvXGikY2EaszoDoEft8k9CizibixWBql8Nwi5cAbA+mdc0/PevFpu8yHL4tY4cpsoQ43j4nOqVC4UvHIgrikzkAqXAUm6o0MuAdr8B30Yi83k6m6orQYXkuJ9/TaJIHMDQ0QQwpztl0v7OrjDGIjORKZJzItNjEGzFhYfIZcm+QzUmJe/12obXk0SY15qIkDwLzjcHwj6jFweQ4dtmNco9biXbFeC4ExxbXR9HSicyQZOSzld3kSY980k5nzGZuK902RRbieNDJYKsvGSKZGFLBTS8MIWTu1lNJi6dq0Bn9WRX3U+kW571zZj5SCOT5ka2WSYaI9kdQ7j5WWJH8kZyBzhCvWv0fun3rukqSNFe7Q2hJDJLKNpLckI4OkMEzhO1qYSEkdBbqHvlIM0azH+56I7I55HSvMUeHiStq6Sx2V962qPWofRx3RfRNrGxspua3SEq2aeWtvMzJoPfNUYXJjoCnUuKgsrvyuax8ppVI4ANPE0MWcAo82tap0vralcUrycq5G62eVjnFVCuI4/GY00dN4IJ46Sk5GpUQG45Fro+haeN98DkQyT5y1MLGMhcXf6ZzBQNW6YujayMAgkKnT7h3GGDjnXuGcu9E59yFj+zOdcx9o/nubc+5hi1wPF9llUlNaMh++lJGBSM/k2/lDJGEgMyppFWrc1oF3v6Q1qtlErCuq1bJBro32H4uoxMqEolnCdN9mLKOG1tjHd4xHsSfGs7CAACmsT+P9x0X/2qz6Dfo/997nwgjy7bISt3smglIqC3ltrF+Vgm1HkYPSSoMTmZ3CDvvL2pKx/M3nwtAJpdZGm8YwJurCC6S/KV37iD1vdD0kNFua1qh6360RTmFJWnNbs2PAQBbxDhCBnHI1G2xtsscY7d+m7JYuGS3Jo800M49FBsp8Di3pYFMQ0KOmPYnVkj0cXy8GXKQsOjJ4JYAn9Wz/NIB/5L1/KICfAXD1gtfTisw8IWXvHPO+57bSGpdF7ImxzA7aTg+1hIEkXyE5AenJydTToNRsDzfNJuqUDpCSYhusDbQkaWWNROtFUmSg9XSKMm46pQRoRja+r0tSIVc8MtAV5ti4r7KVhozmUkNXCg9W/C4adMh4HH7McO1p1bnJGQiFXUkHoNQHGkUwkmKEo6p0kaHWRURKZMCe9w6ei58ZgolktEf3YcLJbTWq0XmmuPdQEd0P2l64sC7NUMWcgMLFzLsK5LKI32M6fpzlZUUGGmfQwUTUvDB+12rWGTmtKueRwcrkDpRa6r2/BsAtPdvf5r2/tfnnOwBcscj1cEkyTyQnMEpz8bnnL7HKeRVHDlzh0oOaFH6Rh5vAPFbkwJUaVwxxVKO9XBNhqPja12d19GLzNcnCrY7kjbfzJn5Ryq22toSv6NYmIYVppSjUWtw39uLG901AKUJhSvLbrK42aijmtW+VVVE4FE7wGfM6UuZ8TeHaOGeg13eUjCfSDNmk1BUuJ881XL5ika6W0jurFHiOnV+2XNdy9aP7rvAdFnzXFpWNCpRK5basv+D3K1wHi1rKNKOHZxONChcZGjpXzBnokYEWlQzDRFUUlWgQFh3fGpu5KBk0Bs65hzjn3uGc+5xz7mrn3Am27V27uJbnAvirnnU8zzl3rXPu2tOnT5/3yWSIp3n+s3msOBLPXyoGyRkIpZTk8gsP1owMRJXvqEg7i1I+O+0/xBnErQmqVsl3KZAxQdySrKM4MpDed5LvLuo3xgJSmDHvGkCSWz2vUwjLKiqzUnpHBvE+RCBXYu2yelo6AAlBHdVIpApVVsLyNcukA+n5T4VCla2ctap0GbWkjRPja+cGHIiL1jhMJGtHaH3ciGs8El+7Bt+NS9f1fJJcS2KoYoXcly20Oa86LkbAb3XtoyhfJjzQ8xGR2xr5XerpyDIy0Ma4AsExGZVFYqgWKVuJDH4dwH8E8BAAHwfwd865ezfbxruxCOfcNyAYgx+zvuO9v9p7f5X3/qpTp06d9zllCCc9VImDyshgosBE0XamkJOiMvGQaDAPYKe9yj433AOltSXtKKQxqOIXe4W92PzYku/oGrpJD9biDMLaWkMlYaKqbl94IK0C5uNCJUwk71tr4Nv72kRko06p8ZcvJZBLvb+P0WojZEIxByEhqH2kzOl6ScLA+04xAGkGGTdknCjl2HR37nhtdNy26pw7N1UX1UwUuCMix1sHIX6m+vv7+Mh7l/vSNYU1WmNcOYGsZ1lpXAyPDLSeTmlkYMNvEgLj2XVaUsDmvGr7gFkNAvsMFYeJRkWaKbVIGW3hO0e8969r/v5l59x7ALzOOfcsAOdttpxzDwXwcgDf5L2/+XyPt1VJWkIImCgd8hIXnY1Kh9mGCKtlaqlUqDI7Q3q44uWcVfp22edmJgyZ9MSS1FKh1NabNs607nhtsYe63BLvMjLQH/BKMaIJFyMiA55aysnt7uULx5fY85JI2U24lsKoM2CZUmo2kZlhFhth6eGq2UTN+b33os5A/uZSYXaQxFLR9dVaYumf8bWl+8vKb/pdtBTJWdXVd3RtQOKoiHM5fLA7DWPqYCJRJMkUKu0v71tZuGDEXLwPoEcGFmcgf3O67+QMysgg4TNGzmzMKDPvgEDst2sbpds3ZnXHzxl9lQoXzq8ZskXKViID55y7hP7hvX8TgO8E8L8A3PN8Tu6cuweAVwN4lvf+4+dzrO2KLCWXSolS1uZMoSfZROyHrBRjksAVIpdfKq02G0iJWvj+ss8Nx9WB7cNEG7Mq8VCHvO8NsTZeZ5AQldxIjkTkkBDIRZLRY9VfSKUy1Jto1ERzyVAhRiDzGorhtNi0EFE2qtOUOdApHV5nENYe9q/EuSVRSumMXKFqJCzPYJORbmtElRTJqh6o7xAwk6y5CWvrrl2HgRh2bmSYOReyuJIRswa8BsjIIIZaknMLzoCntYZrMLoBFB15LbMOu6SCfr7DigxoMp/W1XSRshVj8IsAHsg/8N5/AMATEBS5Kc65PwDwdgD3d87d4Jx7rnPu+c655zdf+WkAlwF4qXPufc65a7d9BTuULhWQRwaxZw/E+fYxZ5CmUEbeOcOXtaIwIIWJTDgkqfJ1yf4TYciiDpVVmloqp411kYGAiaT3bEQGnO9I2njItc3ly9OtjUcGNAdC5ru3EZFIr7QJ5PjlpP1lphQpkK6GQhi6UerhckOXtIyoY++Yn3NTKp2RvO/kAHQeLF+7jAxkZorO5cTPaxsZKCmSvIhSPm+0Tm6EZaoyrSlcg/C+JUxUpMaEJzxI7z2q7Fahmjgy4NCg7KVVFtvLzOMZZu1vItbe7qvVd8xrZiSVrqVzZsiKvY0MBmEi7/3/Nj6/HsD3D+z79IHt3wfg+4bWsAgZidBXKi2O66+gTOAOadWT3kUMw+2yJ3TPn/fF5+eWqaVdBXLsqc2Zp0THoWO3YTFLLeV9bKglL8E/8gGWUEs3YlHfnhjJOq7MnghDNRPEPW93QS9xhz276H7KWQtUC8DThfn94vdtXKYE8QrjQw5NOgVTMr4jVgyxg6AR1HwGBr9fvL9OuG8WNBhHFrQ/JyoBBQITSk22VeBGWIOJNOKejinnYidFkAJ3T1tliLUrmVCRAyH4EN7CRB2FOo85Bd9wLQXD4HnEpfEZHL6TzyvtR7zANHEQ9GiO7mucIi4jg7qt+6BIdq9kK5wBAMA5dxWA/xcBGhoBcAB8UyNw0Qm9CBXzEmOYJ/XUkjqD5keum0pY6QHPmaGhz4C0jXSKD4f/Jw3XjCIg7o0AMUwk4Qi6dtqXCKuVSex9z9ix+Zq63Go9MqD6C3r5iEDW1hauoW574ABxZCCzfVoDzrgSvjZaf2tk5/HaeebJCspk6JDMt69q386GprVLCGx5HP/mkgilY7etxRMCODYGCYEsOYd2/64ZW3vuCI4QEJdMi2UEr9Z9kxP3srpaPq9JwkK7vXMQemGiQkY1cbQolWIMv2kK1yf3jbgW+S6VZQwTaYWMkgPj1zaS11aldTHSmNAzY006izmFvYOJtmwMAPw+gH8P4IMA9m6FCxKJ93GLDaSemtaoTrY9SAhm4eVJY2GllsoZylJpafizTHttC7OEBwkgqmaVbZy1+xLWZkQG4tol0WlxMRRpcU4gHL/jDKRClLMapIcJiJRecd+kEdVSS4HOGGjQ4dlZR5TyVhpAA2cYBHI4T+cJWpHBVN73lsSNnwmJbaftKNIsLpkKfUjwQJsipdfqu5QU8xne84TtH1dPC+dHpOxO5zFMJL33oUynkMwROxAJD8QjKpVTYPAaS3iQRZRpNwCWQaZwLbL9isYZRNXRLKpZtGzHGJz23r9mYSvZY5Epa5rSAuJsI6lw54bCpO304JhKS7zYY8EZpGmGAidlJG/CGcyF0hjLyCB8Tl44eSPUHiCBK1pcPVYccl4BjyyWRjYXM50HnkJGZDzXX+ba0/5zYQxkVGR1g5WVtJ2HS4aug4no2mQh4c3Ce47qDDRsu5QOgvDsqR2FwlHRPvz/knOIyMi5VIh6tBiO3ylMFSZiv0uyNgENavMx+PaxSJGUHFoCAwnIVsuUkl12eW8knsxRts5NHKXz7RU/dpsqTTCRGH+rRVyGAyD5Nzp+Z0j09tvLwjGb1x6TwsF7j42mNqVcgHEohr/Syouccy93zj3dOfcd9N+ur2iPRIbd3NsAUuxcKgaOg6qRAcNwpYcLiN5FdZdKB9icQckeIr5deqC8elpiy3TtHVwRRwY0JznJJmrnGcSQgsRg2xeAXVsEv4kaCxmRLTV1Bt77JNeeztNFPbFSoetM1pbARPHvZrXaSKDDnowcWkcUGczTiK1NixW/S5pOHCtU6QGnMJPM1RdFkIKr0ZQ9HdN7r0Itc8NB0BQi0L1DCScg+Y4eroXugWzPLSPRODLo1i75Dg161CIDq5pfFlHKpAFZsMbvB/0dF+PFkcFm1C02vrbbN+Z44E+/Dr/91k9jEbKdyOA5AB4AYIwOJvIYyCi6UEWmZyZKq0w9CqtdhXzAgPBDyh46iWJhBLJUKnw/PlEr7Csw3HkctSw14SeRx3S+9vgFjwzC/8kYhGsvmCGKcfNxGdouWLh6mi/vhREUMJSIyCbMSEtl3t43CRONYviOcyl0vUD6m0sCua2unnXZRKX4XSTfERkqAdXMhLHgDsKmiNjsYj+CieLnMSWQ7RqH9r4JmIjuq3PN2My5jPZ0I6rj6rGy5muWRWdaDcW5eQe/SdhzVMaFYVXtE1gzhWLiiCqFiSgyiFNPZQZZmjRQt+QxAHWYU5JOLA0Z265VRx8/NImvrUUv0kh4N2U7xuBh3vuHLGQV+yAyMpBZLdL71rqS9sFEE2b1JVxB+3OlNjaglLB/nUQl9DkQXp5jk3G0v/fh2mRFJV07YZHrrBcK3875Cn5dFDlwQ6bCQCyqkcQ7v7ZZnXrPdE80eG0IJuJ55ZJglh0wZdST9l2ya0e07Ul3zirloSwuR94XaagkrMkrXenaEu9aGLLVTcZ3iLWFCXYxJ2DVtWjDmmgMJ68JaL3zQqTkKrn+MhMq5lpk/UaNcmkUHSOFYmKeaCYUalSI2DPjInUABHdYKDBRUhwaPxM85VZyAtEcCclxid9lt2U7JuYdzrkHLWQV+yDS6vIiGwBR9WAbNguFTESoFRlwpVU4xMZE9C7iqaGywpg3RAvHTpXauNDW3ilUlRyv6ySbiLZzQ8aPDcSeYCVTchXsW+NioshAMRazyqsPP8fdJeYfrpMb4djLk5BCGvX0Q4NaRk4vtq1tb2GemKvZaosSTiAvjcRvJrzvlEsRfEbEh5QJvNYqNTOhQb9vsgtvUpk9T/eX6chxRFVEuD6PpLU6g1nFIgPqempkx5VF3OZDZgvJKW+SO0zTYj0bRRob+HBvUoI5ar89jwvmgJTj4rpgN2U7R/06AO9zzn2smT/wQefcBxayqj2QlECWXlwXttNzFnMK3Xb5AIW/O29Ger9ASnSqCrGnOhrolFry4rf7+y6zY8Rfrs7jWJ/FRCbtT3i87NlE19adO62/oDXROeIKZLE9MRbdfbUMWZc2G8MR3drovncD6eV1h+1pPyq6pnZtRouRbu1xVMI92GkVd4vlhsqKDKwMszS1VBqD1PtOYSJ9ghwQd9lNRplKElZwOUnrcAnPJXxGauhknYB8l5LU0546g2gWA0st5ddWisih8rH3LR0I7mDI5116/laRpNzePevsmWGtNlKYaLGRwXZgor65BBeddI3DuMXXlZps08z3j5VWvJ2H1YkxKCVnECsNgBOd4uWQSq3ycWRBL2dVJWExwD0OH43h666dKfs6XTvnQ9JurmFtnByPFKqCg1opvRI3D/t3iqPSYCLWn342T6uf6dh0/eMeQ6ZlEyXV09FvnubTJ+nEBmcgDVFCICuppVEhYYN9e+/hnEuaFy4phowXAy6NO85Adqq1s7AIV49/cwnPyY6rCUwk02Jr30IldH7Z9bRsvetYoXofV623kQFzzPja6TjhObb5kmkVuo0mUUuZjnHl1wUAcgiWBb/RtWlFkmFbGgnvpmznqP/Ye/9Z/h+Af7WQVe2BFEU8YUnrPUSfazAQfwilJ0X7U3jJw1a+Pc4G6ran+fT9Skvuz8lvvcah85Zk9XOytrmydqb0kglwA55/KQyZGfVUXk/ZZYRd68UlJG13bhnS8/3SqvLuxW/XrmQTUW8jjk3T8XkhIldKdG1WBbE8t6WUOAE9UaDFOOFBRgaxIYuMBYOJ0srtFLbk5zQzoRRsHEhhIpliqXEGcoKcVZmtwUB8Te3QIKOgTkYGSUPLhDPQakviSDSKinidgdLiOm4dHnMOkpjfbdmOMXiqc+6Z9A/n3EsBnH8v6X2UMfciDe97WtXJAwJ0P+S8qhNPCuC4fp1k1ADxyyk9/3AsHlmkYXPYr/MiZQ0DEF46Wf1Mxw7H7TJ2xhxG4jCRUOa0nYeu/OFMc9LT+oxo7XWaOQKE+05rmIi1DxX7kWKZCiM5lJIr+/5rkACfIyEVKp9nIFtptGurYwK5zSZSEhb4/UhSS1lxkrZ9KqNF4V1XwpAtsXYV0vmxemlJPiMd86pj4+n+Yma3NLKF7ANWJ4Zqxgw8wOoERDYRHacj5ov2ftCx+XE1PsSCLYHYAZFOHd2brpAwNvB0fpk0kMJEi4kMtgMTfQeA1zjnagDfBOAW7/0LFrKqPRJeMZp0HWXejvTSgO7lnUbbU0+NUiQnijGgl286T6EYjgHLATCpUtMVLodaLEOlwUg8E0pmOtH+3POX6Zfx2mKFKnv0pG1AUviN8x087VVm3ND5V5s0RanwpHdtRSUct5cEMhB+c2rpkXqJElfXIa4WJmrabheFi5SeVT3N05UjY5Dg+nFSQdJKQ+nFZbUYSWHJ+JkZHsbU7U+FiPzatJTd2MimbaRlZfY8uW8yMoh/F+m9twhBrRvCGesmK9e2Posx/7QtTTh2Wr8Rw0B0fF6UxrdLiGu3ZdDEOOcudc5dCmAFoancjwK4HcB/bj6/aIUXnMiQn8MVnVJiuPqoe8DbofCKwp43Clf+gBGBXPfDSHIAjFRqWnETEBRO15/dMlQKLs9IMakwAcKAGdeirI3DHfLlof3kyxG2K1GLBRMJohKIO1zO6nQSWTi37uXJLC2ZYcYbDFprt9Iv6fht1KJUhvOU3aGqcz4ljd8ji6eSxkA6P3FkECtzzYCHc8YKs8tGEgpXKL2phImSjByljYfgFKzKbC01lK897VoaRw5JZCGLJBU4WZLb4ySqEfCawkfwa+sK2oShUp733ZStRAbvQSguc+z/T27+8wC+YiEr2wMJxSxGyM9gIvnwhu1p5KApxVnlg2evpmfqcEXYP07vjFMUJSRQRzAP99S0GofIUCkw0rjs+rFI7JnuW/eAplAKEHvXccjfKQY14mIviFpHUBQ4VzWef13DOUR9W3i1amKIFC+xr9gv6bjK8ONKvNh0fIIr2voO8butNu0/iKyVRjqtai+i//PffCUi/WN4blrVODruXm+qa+lmOaSppTfNp9Ex6N7IJnuSU7AG83AYCIhhoqhwq5RpsWmGmcTlk3bvtTBkIqKSMFGKy8v7ToYu5hxkP6pxGc8x5qmlQNwGpINk47VzY8J5LOmcSAhrt2UrLazvtZUDOeee6L1/w/kvae+k5C0lKh0SiCOD1BPjSstKsZSeO5BmE0ljwb2lmfBAZaqfhHI4bi8nXoW1MUOlEcyjAuvrs/Y7Mmrh2R3SO9by5fm1lczb0eoIJsyYbM41Q+Xa7A1J8NL2inlSVlIAoBHIsVJLptsxDFclr4VzQfeqPT4ntyuf1J7IaJDfm5RArnDJSldoOFKU1kTcN1p70RaqMc5gnFYglyJSngnPv+1tZHAGadYMiyYFP5dUEMtoz4RSRJQsyG3JKWgVyPyaq1re9/Ta+uZec0NF187hXL5muXZZz0Tn7jKh0ih+N2U3441f3MVj7YlwxSG987gSNvWuJwwm0lIgeWgsH/6wf1x0xg0NEFcoS/yXp/pZ7bOB8ADLlgxA7GVqhSy8sZgatRRxYZeqMA2ohRN62rm7qIfVSIjfhWcLyYZdXHHIqKaDqPRro3kIEaeg3ndj7UUczfH7Qee3ahzk9rTOIPYiN2e2sgcCxq1Gk2ztyThSgbvHPFUMqYZzysggxraTFMuKODIFV699FLXIhAZZJcxhHt5YMa0wJoWqcwJD2URJOrJ0fkqZCeVjpzGCNUXEJBwnQsLIQKXwXMrv7abs5lEXY64WKPzFTxrRRdlAOlxB23WopQuNtdTSKJtoIDLQKlmB2EPV014ZQazsP2sMWSGglj6ISq7N6qvEvaGRULj83PwzvvYIJkoUg87ztGs3+I4uY4dHNWlkwQlqKyVXJ6/5fVGeGbE2mSI4KtJOt20VsIS46ljpyJx1KzuOr53/btII8nPTdaQwEEEppNT0ORSJoap0Q8YjXVmbEvd86t4n51xDzPvoGJ3nb0AtQuGSsZDJIKmxSJ0fTn7LSJi3q5AGPq11MqLBWr+23ZbdNAZ++CsXloxLnlqqk4WzyjMSVlN6Xu2eKdM3pRfIU/0szsAiOrlS06qfOVSjkrAsslDPPeq8Ha16mitczbsGRPqlAsVUzJCp93Vu9CYquLJOiflR0bUkTlqItFALh7DSa7M4g/g31aPBJCc8gYk6pZQ+E7FzwvkQWfiVDltKFaqMqGjtnRG2+ArdgbAMnTVutMPtxdorWf8RRz3Su+ajKes6TFmTjpvsEybTXisR1SSZUkLh0vHp/teRAyJgIgnzJJGsuG+sMpvfl7QflbgvitO5m7KYo14kUhYOPItAaxY3YwSy7s1what757PKq6mlROhpCplXNkoPlT9EctgGX2cEEylEKkExWlqr1SqD9p8bhizJ+65ihRpzBgquzmCilriXufo9nAFvCVEl2T4CP1b4EJm6aqbkGpwA9bKSVbr03c5z17kYbky0flTz6L7aEVmqtNJnRiZEyKwV2SKFQyX8mB1/FitUWVAXef49xH7iXbOEBQlB0f5JLn6rUGOIShLzsgiyEu9T69ywdhWx0+gSB2Ai3lWrdkQmLMiECi3hgX++27KbxuAzu3isPREK8aiEXebDExYpswAAySnY3jdBNdKDXWLeuwZXTJi3IzuD8spG68Vtz60orZHYrqWOckhAj2q6l0u2iwBiuELfPlTZrVdHc4UpfzM6Fvf8tRYicW2Jcm2s4E6DsOa1DnHxUap61MMycpSoJEpDFM+MhLiS2hPhXc+quPkhdwA0iCs2srqRtjp/LonIQEKTMhOKekbxY9N+mnetQVhbN1RppBqtLdkuIgOXbpetXfjsjt77JrOwRFKA/F0SAlnRM7sp2yk6g3PuwQAeBGCZPvPe/27z/+/Y3aUtXij7Q/Oew79DCKjCFQwmajFW4cECnfctX/zIe1eyjWIyMm1XQUpPbZ8ddS21FS5BWClMFKc4aveFV9JGL658uQRuX7b3pd+ITufG9oJ3LU3vK6/yrWovJrzFL9+srrE0jl8BPtkqzSYq2s81ZR//5jrPFEElo/i+8jREGfVo2LYseKN7Qv8fK2ufVx61H+IEFPK7iJ0Tfu1pla7wcGVkYNTFcA5MTu5LUj9FxCdJWsvzl3xIB8V0ERdfO+1f++74MuLq5jwo0GDpkiyspJWGQW6bUc1+cwbOuRcB+J/Nf98A4JcAfOvAPq9wzt3onPuQsd05537VOfeJphPqV21j7ectZRGstpZKB3T4tMYJcA9W4xQ4wRyyhaRC7V4QDTIYj+z0TaBTen2RwXQ+UGfQ9CaSRGZS8Kadmz2gMabfGUny8kolqrE8VAkTFS71vudMWcvIIMJoxbllTrqEgeg7MWegwxkdnKBdu95xVaYLJ5XdIhNKf970yEIWdtnwnU5+8/4/Wq+tiLgXDkjavyetI6DPaW0a7MkdAEl+pxk1emQgSVb+PPG1k1Mi6xBkZFC2kQHa70lu0SLW6fwJ/CYiAw4Hh3PH23ln5bDm/ecMngrgCQC+5L1/DoCHAVga2OeV6O92+k0A7tv89zwAv76N9Zy3jIuQWqphqAARqXpbBG7VtTTDyah7eWVoGbaHgiF6AfrSN0MdgQ7lqAQyw3DnTWGWBneEqCaFgSbi5UrhjJj8tvgMqzALgBmRSS5GM0Q8Z1x6SaECuTMW2rm5lygdgH7OoFO4ff2orHTjyNAo0OGojLO4+G/mnBPXZvAZUSaVBsXotSWk7CO+o9AVchoZpB6sxPTp87BGvdMtTyeOoiLxm8u1a1BMm9bKOCq+dul9S+iRDBndAg7VyDoC6lfVOWaxkSZlL53GdNqisTbBxVwIdQbr3vsawNw5dwzAjRioPvbeXwPglp6vPAXA7/og7wBw3Dl3l22s6byEsEgZGpKMmx9STxNsXoA5b/ameN8NAa3VGQCd965h11ZqKZ2f54xbfZXkCMGwr4gMDHiMjpFud5G3o/EZUdqrEpVEBXHcu2b3ZVql9Re8HbLq2Qsysa8vUnjxEe/Prl0aC65wpZfH/54Z5DgnOjUjOy7j7Sl0GJPnpfBQgQDBeB9apHCohSu9LrU0ht/ouHrdDF9b7PzIeQayjoBzVLRGHk3ywi8dGiwSz122fLBnHHfPG52DH1+LDDR4jmCidAhW7LzI+zZhMJFVZ9ByBoKHkpHshdTC+lrn3HEAv4nQouK9AN51nue/G4DPsX/f0HyWiHPuec65a51z154+ffo8TxuEHvCut5DunWsPKO/kqKcZ8shArzMI+1dJOwnav/O0tAKlInqx1Xz4xtCk+ewMojK8bxpjqHctjTuD8oiJe7CaoeoIO8ZnaARzrTf4G5VdO2QJQdF9aT014YFqbRU0hWxFBhH5bRRm0fYOWow9WJo5oD0TEaegEfulgA61qKT53byXRGa3Nq3ojEc9FozEnZPCsQlxSr686vkPZBNZzzM9j9779v4kVcCVMFSk7NnzFtYQ/k+7p51BRcW8IJBnMotLgQYTnsjgBOi95D3K+JrTVhq607pbsmUCmXUofZlz7nUAjnnvP3Ce59euyhvnvxrA1QBw1VVXqd/ZroQfqmKkkg4TtRO1jAwIDSaKYSRtuA3hpOEBl/jxpOS5/Aqu3xCpaljdZEJNK8pkSqOOsHZd2ctMKQ0mimcc6x6s9mLzIqC+bq+zeY2Z2s2Ved/ixY2PH7an2Uadwq09ku3jkrcW90m7CbpmrdiPv7y9hYgNjKTVGVjtUej885pnv2kRFye3dZ6IfEAVnotg03h/PiCGr70oHArHiNDKIL+pwrj2OKzBSJVeRBk7L7rzkyjcMoZaeBbWuHRs+l0TlfCIi527rTNgkYFslQEAdW1AWKPUUKVdSzvngx9TRjUa/7ebsuWjNmTvP3fO/bT3/jMAzjjnHnWe578BwN3Zv68A8IXzPOaWhcJLzRMCOk9MqzPgnj+lMBbKA9wVnemRwca8Qu3TH3hUdg2w9NGTRbR2rlCdc207Cz2fvfPENGXPx/Vpufhxpa0BZ3D8VzGiAWNNvWuuzGdaxCQUR/qbcYWsV3bz4iq5nWf0yGyiGNtWIC5mRPV2FDHPlNZvdF6kHJlJ27kRHWtrq/QiSRmpAoBK7Fd6UkLgalgmVHJfWQGneCZkIaIcmMTTO+v2XUzhN4pWw7VJmEgo3JaE7fal/6vQIdseZcclqaW17iBw6FBUT8s6g5RrEXwIGQMZ1Sj8327KdkzMSwF8DYCnN/8+C+DXzvP8rwHw7MbQPAbAbd77L57nMbcsXWqpnr87KpzZFiH2UHXcHSDvPC06o+1rm1W7Frmd52VrUM2MQVxpnULIl9c8d57xo0ExEczUA1GFYyhwR2OoOk6AeVrNnwGeS6EUMmSz2mOzj+9olKIGtfDtffdVFm7Rdu6JUZ55dGyjpQOHarTW4VuB57hSWxqlzyNPa42VUre23gwyTm6rMBFvIKgr3K3Ud2hFjpyEjY0Fcz76IgMGPcoMtKlQuLRP+7xFyRipU8c5A81Y8O1jY7tVv5HUOAhDZc1nTqKaKs1A203ZTp3Bo733X+Wc+3sA8N7f6pyb9O3gnPsDAI8DcNI5dwOAFwEYN/u/DMBrAXwzgE8AWAPwnG1fwXkI5fJrcAYQvMR5VbMxfYoXOPcqSRtnb2htE8J2GkivGQuZqhdtL+zIAAjh6bSqIJt+8euck6GSSkfwIXqmkzerpwmX1zxMXiOhpWe2+zf1F6ahapSiBqXQ9pmSLSRJWKs3kfce67MKK5OuTfRQX6UIqhmAFi14jhSDlnRAxqKv9Tevi1GzvOoazsXKJtqfR2yR0otTS9PJfCLi4jCSUrilZhuxZ0JL6a0YjCS5mLUptTWP32XiiaJ+VgaxHvaP3zUJE0l4L0qV9hpkm/ZN6rKJumiMjsE/l1GNloG2m7IdYzBzzpVoMH3n3CkAdd8O3vunD2z3AH5gG2vYVaF2FBYxw4vOnEtx+cL1p0ACHYarFUcBnTHQlNK8CZtrnyrMJJtIU2pNppM2qQzovPNDCZ/RQFjN2jRIIFxb2jGV7k384qbn5/c9VeiFfV+F5z9hPf359uCpWUODejzc5txUGX5kaRTtS+fWC+Y4hNWnkPVCxL42H+3aa26IOByhEJnK9nnl4Zzm+WuRQWwsOmwbUcREx+JEp16s1yQdiGwj7l3LPH++zlAjkToQcqAR3Sttu1X3YkYGos5AQouleN4A2fSRtVQX2UCy46o0hLSdp54uijwGtgcT/SqAPwVwuXPu5wD8HYCfX8iq9kjawi3DQ6V2FdPGO3ZOUSw1EZ2p5w4wz18J+YFO4WpQTEwGKkrL8L7pfOShWlHJrCLvW6y9iSTWptbaYkOmVk/XXcdUjcSNKrcNpTdToxZmZDWCONpuk7AAVCM9aYzFagPfHWaRAfeudUiAcwJKZBBFDoqhYmTjVLv2QlSdCzgCIEPVnHukbGe/i1qnwA2dULh0zbXGxRTxwCN+3VquvtV+W4+40muTUVFvu4qCp2nH913lDKLIoFl7FBkoa6t9azCk02itTXZcVbmaglc4pw7CbsqWIgMX4spPI4y8fAJCFtC3ee8/srCV7YGQJ6ZhsEBQqKubcxVXBxqlNfd6Z8/mRVwnhaooRKAzBolCbpSSlVtMg1K0ojPaf7NRHFLhdR6HN9IrGz5jSspeN3Rt5GAQzLYhI+zb4jsCTCQH2vNjEb6sKSWgI5j7cvWr2iceLsFEq5sBdjjEIwPmXWt8B28JoeWcR0VptZJNxBSqDhPFBLIsvAIQGWG1n1UUGaT7k6GTE+Q4hKUT97JiXokMjGZv3LuuFagl6vmkJCVw4l2rYC6ZA5A0fRRRixUZhG6paTozbxNC5+DPFG+lQddmdVzVU3pZjYXi2O2mbMkYeO9r59yveO+/BsBHF7aaPRbKgJD9StrthWtfbK3qj1LatK6kkhOwYKTOWKSKAUA7CFzLirGKzgAikPWiMrrWynv1AaO1WoaMjtcZi9TYWBWZdLxZ331nMBGHafixyBBqVbwAGQu9XUVcv6FvX20waH5+3bvmZCQnaXu2N0SoBr91fWz0FibRhDjDe1bJa3buoiLMWsPtvUpUEvQHBKUm7yuPHKbzGJqkP+Ncfh1312AiXuE8r9NokxPvGp/CZyjPa98qeH4cvjYrE6qvoj5AuvE+9LdMe5Xkd1/vIUInACSt7HdbtnPkv3bOfaeTWMlFLBTaWh7quLSx6/D9ooVaNMwe6BRqmvUioBZZaTsi73zeriVZm1Gks5W108urpTB2yn7eXqe8bn5tVlfTrphPURw8xdHKlOrLdCLv2jCipDRThdvhx3Vt1xlQZHBYMQazysK2m+0RTKRHDlZVOYcEUgcjJpDV1uARea0bC+15j9Ni9eeZt4TQ4LeoHQWHqJp1Utpo7WPvma9dS4jgClsnz+OoJb025n17r6aAV8w7t+oMNAShVO6rbCDIIahkO7uv2u8aZSMZjt1uyXYI5BcCOAxg7pzbQICKvPf+2EJWtgdCeJ6Fu48b3H1TCdmBLqtGU7hUiWvh7qU0BgbcsWEQzKQYZsrLQ9+nqEcLLUfs2s3IwMx0orXPm+26wu08qTRq4nUI2rVXldehEpHeqXmoQPBOwxAUhc/gkYHKtXicUzgDrf22xgm0GWSFi3imeBSqUgzICuJsmEiPDLraEqvgrYuYCg2bFvCbxvNwziAxogKXHzEjSp44V4r80jTOgP9sMmkAsKujLWPCWz5EkUEZry1pR8EyobRWGDzjR4OByqKriLcMmdWbiPa3srR2W7ZTgXx0YavYJ5mUhYgMUi+Tqh4lmUffD8NCUmwaCA95q8wVDxQIs2z5v9u1tZEBGQtdMchhHPz403nwzo+M05+ZcFRVKQnOwCKg1wyIq1P2esRFJG5XdKbt30BcJoHcKVxt7evG2onPoCwtrWiNcwY8MiiIazGiycg7VyGsOHLQopbaB2UbYKI0Mgi9rAxivvGAqRWGmslU1W3uvd4zKlybBv1xI1oKgCDK6BEebNfWXPfOdc5Ag5GsdhUxXwF09QVAjNtXAuKS2UR9RWnafY+jlviYfJ187YXY3mW3aTUUcq7JhREZwDl3AqHDKJ9ncM1uL2qvpCVpe1JLqc5AC89aOEPJhweCArc4gzYyGFC4vUqNedfa9o0ZNcEzIoM6eN9mQdxANlFrqKysF+O+0rm1kJ7WTpXf1toIUrCyiTbmBtfSGBotJ5yOP689zm2knAGANvtDzVphXqaW0htHFrYRntV6c8NRWWB1WtnEfHtflYp5BlEVRc/aKVpUjGSnMHUjGhcipucmZem9LOaLoxLAgN84POeEwhR1Ak7AULRdRjUpZxBDj127CT0hghuTNrVUu7bKiAwKhTMQ184N1b4TyADgnPs+AD+E0DLifQAeA+DtAB6/kJXtgYyb3Oip4cGGwi3dSwO6sN7aPmKRgaW0LCimVbhW6ulA0VnYPm8K3gzOoMXdBVTSetcNX2F433YdguxNpK9de/jp+1QZbnEtbTaRYWT77vv6rGeGRXO8M+tTAHFkQPtXRjSZEMQ9abFqVTlTHHrHVtk+xeBqlIhLzciJlE63di0Li88h1uZIhCiZZwsxArmFWhgub3IGqREfgmJGRXxuvTraigy6Y7dr02Air9d38KK1NjLQokWTHOf3Vb+2qGX7BUIg/xCArwbwWe/9NwB4BIDdaR+6T5KmfyovX623gQY6pWfBRKOyMGEimW2kzRQAgI2prXBpHgH9O1o7ba910omgHK1hWloQl3r+gJ1NRC0frEwn8nasYR0ESehQSwdnyJxwvpYNIyKjtVmGiO77rWszAMChiSxqc5GHqrZNML3rmKTVoD+gy+jRHASrmRvfrnbRpbXxazciB61+I/K+FXiNe98yU4pHBt4HeE7t41VzhWvAb0qGWtRpVrnvPGlAphPTV+PqarY9igzsQkLenFCmltLaW2Xv4v1l7yIJkVUi6lmUbMcYbHjvNwDAObfkvf8ogPsvZll7I60H3IPrz4zhM0CDVTYwkUUwr89SLw1IPVgLX7agmjHD1bW1d7h7jyGrrVz+fs5gCJcn/NiCgbqUXQuqIQhMbzENdFCL5gUCHRdjeYmUJlko5waAM2szjAqX9AdqjYkKE8XKXGvTAQRyuPa6AQeAzXnV9F1SeKJab9lA57daKfO1WdPC2rUrRrgsOj5DnzAXF1dpCrWqA6kPSIXYeedWxg0QZxPxxyKtMNbeBT0tltqjdFXCRtqs1yOqkkF/Wo3EcO+iIs0mEpFFXyfb3ZTtcAY3NPMM/gzAG5xzt2IPO4wuQugFWLWaxY2KVmmtiLYHQIeNaznhtL9NIMdRiYWNW4ZqCIqhtUsMlKQsXNv7KCGvhbK3ICwT4iri2dGa508vR+FShUwQl5ZKx7OJNIirjQzmVkEcZeQYxHuj/M+sTXF4aZRUnbfeuUImRnUISg1DW2ho3Pek2E/WGRSx5686LwYM1HrAzIDqrTL0qIVnI0mYJ9yHArM6rFtvIxIUKnE1fOkRCasVnfEMMo1gLrs5EbLrKB0rGmjUQ35rEV3pHKoavRGVFdVIQ1Y4RM9UNPxmINuoquOZ3rst28km+vbmz//onHsTgEsAvI62O+dOeO9v3eX1LVRShZvCRETmHVtObxURdpoHS9vpxbZSIPt6EwF25CBz+RPFUHSDeXSYyA1GJasD1dN2nUHwtLTOnvT9eV0nffH52tr7anjXBNVoNQwA5wwUqIUpHc27BoDb1meGA1CAw2/8xZbdYK3IYMO8r3E0mBrhmCfSo55aLTrjg31KDWpha+9L2aWsGO1dqThMJNZeFE549gpMZFwb/W3Vd0RT2oykAl5bIl9VSmUGQuQgq9KLguoMtAZ+XdRjpZaGa9PrYnj1tNqkr4yjnkMXCGfQivf+zd7713jvp+zjN+7SmvZMOu+ciqtSxeB9UCx66igRyFZqaWF79gMVymkVsOFdWzARw5e1DISyKNrqZssL7FP2gN2uQtZvWFDNXEmvlGvXoBKgU0rp2skYpO0gwrkLM7MD6BTw6rQykwIoKrGSAuiZ0IhxwOZiOuekqd/QsrQi+E2BkYwaCLp2ynqRffHl2vuIeyuLKyKQld88tHQI/7bTNzVOgMFIakTWKVwNV4+8a6N6Oq6B0CIDg3gXhkiurYsMarVym6eWWjwUJ5gvFM5gSBa3ygUJvWxd4ZeueNamVZIZErZ3HqwGE/FsIo0zcI7VGSgeA9BlE2n9fcI8A0PhMhLXilrWjWNPhFLSPPuw3TZk1sSsbm16eiVtb2Eew3u2PFjZ88mqI7AMVQvVbM51Y8Cilr5usLKtAV+7dd/biGzTbmEyr7sURp1r0Wfx0v5tJpNhyGa1XhAnUyS1+86J0OR5d3FExnePlL3BCQBN0oBiTIbamktl39dxVdZAhLW4uOiMXRvnBKjCWq2uboyJXNuk5MOU9Gwiq2/SbstuGgO/i8faE5kwZQ+kbRHoIVudztU6Ao6Nawp3XHbet/YjRgpZGJOJzHTSCOQmFU/mVYftXXW0ptTKwjZUCXmt9BYKa9MjKgq7Le87KFQd86ftfa0uAGDTiLgS+M2CiQxym0c9VnNCqy9S3EbanjC3Ya2tLeajym6Ni+k3ZFZqadi/45msgUeUsaM5F7Rd864pQ8x7anEifpfSNeRzqjBbPoNHPSJbKJxbN4TSUKnJFAwGGjIW2jNV++7cWmqqleXFt1uRwVxEBjJimzEY6WKJDC46GWrI1lYBb1qKwaEXJlKqKLmMisKEgRJcXnmIap/2hufX1rawNhTu5lz3IOV9SSIHWR2tGBNqQa1dW9vVtNIhrFFRmB1RO4Wq9zXqUkvtojPe0iHJ1W+u7dzm3I4GGxJX7tvOAq50viNNJ7YNEV8L3x4pnYEsLZVzaKNFC+LS05FjmEiD/mRHVT0yqBWFxzN6NC4nqpEwMnLC2g34roi7lkoCOU7fVAhmJ+ae8Gwfllray4dQUoFiZNNJZ/H+PDKQUctuyoGGicYCJrJw/WmVzuIFOrhjZm5PMdn4+K6n/08/FNOmUM4NGKgsGGegRwbrZp2A8K6H4A614I23m0gV9rQhv/WIypnKXM6JsJQaRQ5atLclzmBzjiUrGmxhIP2ZILjCUqibFp/B+Aq+Fr69r0aCohbzvvPIwDCi1AI7jahiXF5TqNHa1NTULv1SQjXEMxEnoNZv1LVqTPiUtkrJnkvqDHoig1rZXrSRgV4HADRdS9X229yIpsfmMJFaZ1DGw5gumMjAOfd1zrnnNH+fcs7di21+wq6ubA9EemIavtz9rXn+XcaOCiNFKWiKQu4xFnJtVvHVxqwyIgPXZZVokUHpWlxeKnvy1Mx5BjJyUMZq9tUZ0AtgdWEsC8eqwnUIy4Zamu1zw1C1CjUtEOL7z+t0XGi49q4KWIP+SOGqnMHA2ul3IvgtaWFdiEJDQ+nNDGPROS/6OFBgK5FBc+1KxEZV42GtKUc2rxhnoHrvemdQuTZA1ClwiMuKDDhMpEYGHWdgRwZ2P6poNrUBE+kQFieI6ZmU2ztjcUFwBs65FwH4MQA/0Xw0BvB7tN17f8vuLm3xwsc7yjRBIM7m0JV9gFpqryt7bXB2tH/kAVhwh+V9k3duEcSxdyGl5FCMptTKwowctsIpRJ09tbYJc52IlOcbgolsI2kUZjUK1YoMxgO/ObUP6Ks6D+mdehohgBaes7KNbPitiVTnVkFdx2Fpz/OoIPI8jWpkla+ZKUXZRNrUP6P6mY5fedbz3yBx9SrdIjq3HLzDexdZrTKsdhO0ti5ySA1VyNyzZiWwyMDA/MN23VDx6XbEoclOt223WH/hRAbfDuBbAawCgPf+CwAu6k6mvMhnSFmb7SYM71juY+X6t9uNzJS1nrRXoIkMDM9/6NwbRnU07dOe24DPNgyIi3vf8sUFwr3qnROheFbtvm3hltF7qPWuadyornBbhWpkUoV9dWMwrQhK0X9zC0ZqISxr7cLIWsV+du0JV/Y236EpRIoGQ4baQDaRopTKgpoT2hX3cW8hsTYBM0ncPJxbb0EiCxG1Z4K8+trrRY7x4J54bVRnoHFgPJtIj1rYfdOMAVubSswXHYEcrm1xNO92jjxtBth7AHDOHV7MkvZOONwhlXHYPgwTrRkKEdDDSS70WVk4FUsEwNpZ6J6cBRPFM2Z1zqDbruw/KtA820pGTz+ExVt/6/e1aAfOqzUQPfAZkbR2z6fYiNpQjd36W/ubfzYz4IiwPVSUqk30XH9UQ/eqXbswRm32m9mEjxr8WUkDVOxnRTUdBJam9HYKt6pS7FtCVGpkwDx/GbUQjFRr3reMSqyoxYJiyrjzp3zkkjoDAyZS22e39yWsPY1aOvJbXzurgdBqU4quIO5C4gz+yDn3GwCOO+e+H8DfAPjNoZ2cc09yzn3MOfcJ59yPK9svcc79H+fc+51z1xEnsRfSpm/OqsRDBOKXUfP8S5YDbCkOEoso5f/n0k0TI+9cV8hbiQws8lpbp7bendQZAMEDtgxV24hOhde2EJEZFcZlawwoMjAii546hHadSmQwGfV732QspkrbczJkFBkk85ebZ2xtU3cw2rqYnsaKLYelRjVdnYLOd3RFa8l95QpZiQxazN9qQeJEZKAS0Ea2kEjfTCGqAWNRxLn8miEjvL5WvO+igbj6K5D1tXV1MbUNr7FsIc1YtNuVXly7KdtpR/HLzrknArgdoUHdT3vv39C3j3OuBPBrAJ4I4AYA73bOvcZ7/2H2tR8A8GHv/T91zp0C8DHn3O+L6uaFCL3M5zbnOL4yTrezh8JKLSUZUrh6FbBLziOPbaUh8gEz1uAd7TrkueU6232UsYXy+1bq6ZgpLYsTICJ00JCpSsuZFcbUdmEQapkbCpV9Xzapi9ZuRDXU8mFep0Vp4fxFC1GZ6cRbrUo3vG+z0JD4DityKLuiteQ3FUSoCrXUvut3Je4ded9mKw3GGZRFOo+Ahgr1FRpSVCP793CCWCOQeWQQFHJ8X0oXaiSsrqLtfTHqCABmLBTngxuqob5Ki4wMtjPP4F4A3kIGwDm34py70nv/mZ7dHgXgE977TzX7/CGApwDgxsADONrMVj4C4BYA821dxQ6FXi5tkEjY3u8lxri8sv8ofWi082v57EnTMoNTWJ9Vag+d8YAhiiKDnvOHv3UoZVoZBW8scrDgiFZpqQp1O5GBbmTNlNwBgpn/zpbnH6aN6S8mdbrtGzdK55b6esgBGOqyy9tRWM7HrKoxqYse8ls3dJwI1VuHxxCYOsWt9i30KCep88hAKutwvKI1JlaGGRlp2b9HdgbVcPm+CmQysloLa5p+VzXwmcVhzRoYSXPqaCzmUF8lrRfXbsp2YKI/BlCzf1fNZ31yNwCfY/++ofmMy0sAPBChA+oHAfyQ974W34Fz7nnOuWudc9eePr07YxSGvMDRkFIaihyUcJILfdZnKCy4gxelWS+29nd37v7IgT7TlD15auHYundMax+6tmE+QzfSFmdAayfcPWlBzbgWbX9+PisapK6nVtX5rOqZgVG4FiaycvmtQsOxgIl0pVY3FcA2TDQz4AaeumpFg2Qs0jqBxsga6cpt0ZnRILBs1q5V6dJ2Sh1N4LU228gimDvvujaUPa8zkMaocE4QyDrEpaalFsQZ1DrENeIwklKUVlyYvYlGHLpp/p4M7KOtXLat+CcIk9PuCuDhAF7inDuW7OT91d77q7z3V506dWoby7ZlKHNkiIQdhIkGOAP6YS2FGDyO8HL0ZZ4MQViyNQA/t7V2eki1bUBnLPq4EovP4B6uVR3du7aSt/nQI48NY7Y0r88I++seLmBHTNbgHdqf+tBYXNCmkRqaGCrJGQyl9JbDld0tQWyszWoTwonQuicysJIKupYOOmdQtobIhrBmlX5uaai0d6X2aNthpLh+3JtIN1T9k/taiCvx/LuoRTN0vIWJxRlQm48LKZvotHPuW+kfzrmnALhpYJ8bANyd/fsKpDMQngPg1T7IJwB8GsADtrGuHQvHxVXCjW3vI3nD9n4Yqc9YaMfmx9S283kIQ2mtQwVvfQpdixrC2m1DxrFtK22VtmsP92BK7kBkwI+ZEMhteqc9IKbd1ySI9RbVYW0NjNTTntscvCMgrKQ6WlTMy0vvoBa7sttqmBbOz42JHhmYSkvwSPLetdlCVtFZSQq3VqEQgnJ03L1TqJZ3DdgEM0UG3oeuqmkL6y6iou/L41v1FxFnYFRHA3YmFLXRJ3jtQokMng/gJ51z1zvnPodQgPYvB/Z5N4D7Oufu5ZybAHgagNeI71yPpnrZOXcnBHL6U9tY145lyPMfgoH4D6Nt559pOGgLEyn7Ap2nqEcOHW4/mKs/mE2kQy3WvnxNfYZqbWpUR7PeRlYFcrdOHcKycu3lmrQ2zoDd6C4yBj11BtO5DsVQptTMas8dwUS64uibvAeE1NNJWSiDdzrP3rqvwXu2JvN1g3+sGgnKCEqgFpZBpq1dFp2lld9FyxnokUHRwEBaUVmn7C3vulu7XnXO529oyp5HNUnabNllSg2R2ylP1B8ZlA2nYBUa7qZsJ5vokwAe45w7AsB5789uYZ+5c+5fA3g9gBLAK7z31znnnt9sfxmAnwHwSufcBxFgpR/z3g9FHLsiBMV4P6zMh1JHh4yJ1mBq3ONdA43SnFZ6Vssgrt6/Nq3RV3zuAUPVrF1T1hwG0o3F1iKHcH4jMljv6QYbGQNdcWwYL1c5aOBdmzpqJRWsGxAVEBTH5rreCiPtuySjmvDv1U39vnbpnZbnT32TCvO+zarghfYRoZrC7lpp2C3bZzPeiC5dOylkeV/o+LPKo/IaZs8jAxtGmrXZSPGxZauM5JlwXVTCz8ePT6NQrftGnMKkiJM9ttJXCbALFXdTBo2Bc+6fe+9/zzn3QvE5AMB7/1/79vfevxbAa8VnL2N/fwHAN25jzbsmIQ2xMF/sCCYayCaylFaflC3urn9v1G7X4QiSoYK3IVy+T2Frx+Zr60trXZvOcdnhpWQ7HZOykaxj83XI7VZ/H6BTqpNR6j0nE+R6MFgrMvDeTukdl4WJ+dPa2nYUlqHqmW4HhPuqPo/Mi7Tao7QwkJF6anItpHDntQqlpK00Ug+3qqveRnUzAyoBOu9bjQw4Z9DTd6lqFL4FUbWtMsT2oggT0rThNvT9qu5PLTX5jC30VQLsFia7KVuJDKjS+KJuPWHJpDEG2y28AmI8fYhg1qSPQA5rC9uH01ptZQ7ohmwom4iu3TJo9LmeLdR5ieNj/WvbUZ0B4wz6srSsGRQAI5B7fqO+dGHLGEzKglU/62vrgyP42uS9pwhxdVoZyrzzIg8vKWNaGzJSa6UMBGNhQVS01raBoEEgWym9pbMHwNDaN2Z6E7ywtq65YV8uv16B3BiyWi/8ouJRSh3Vye0AnxXOmtndP2zJKvaj56nlOxIHgWDNCwAm8t7/RvP//7SwVeyj0EOsQTGTbXjffYrHEnpoLWvfEcz9nMCQIdKVfX9UQ9XZ1trGPWvrGgAaKYxRRDVkhHUPto9Qo2P2GVGLpI2uo6f+4tymMfCodG0F8VBEJ5VOO6fYnAPRGKLNOS5RiiQ7Y1Lj2IoeDQalVpiR7NkNvY1HW1VO5LXBZ1iFiKRwbUNYYF5XZlU6kdtqRg5T9loufhsZWMaioFkLUK+tYDCRmoxRdNlGtqGqoc2BIHhtZkQObSRrcDG7KdspOlsG8FwAXwlgmT733n/vAta1ZzLuURzLE2YMjGlh7XGUjJ5Dk7QYjAs9KNqxw9rsyCFSmAZkIM/DZTiX31b24fi0NkXZjxgM1ONdW/tvp5VGH4HcFzH1RRZHlkZB2asOQvd9q0LZKhqTa1dx+6LArKqSGcX8elanFU4eTeG31ouc2y1KiFOw6j+skZy0FguukHUxSTuKAi2UAvSTuHq0ZxPMURWwMUAGAGZzPZOJwzwA1N5FtacxrfpvamYTMWWvzYFoayQMiKy97z2p1Lsl2zny/wJwZ4S6gDcjpIkOksgXuqw0Clvz8pZGnTLfSe+ho8up98aFftgh79vqM0MyFLUMdkztMTY2TGQbi6HU0CjLaoDPsAq7+ra3MJExwxjoCGTt3t/1+HKzzn6Iy4oc2lm5PVCO/Ft+ptZnDFRH88hArb9ooRY77dVqnz2UkttlQlmjUAtUTeomkKbF8kwntQK5dG1RWYr5d1BLP+7eOADGYB4rauEEsgVLWpGBc45VMOtzIAC0jRutOoS9iAy2Ywzu473/KQCr3vvfAfBkAA9ZzLL2Tg5NQnCkefZchqAaTSEfW+kPvPpSR/nnmlKKsl4G2klYZKF2rO6YpJT6o5Zh73fIWPSvbShysBquWceWkYGW5XW34yvJZ9p6h4r91Ol3/N6oSo8chH7Yso9r2ZhbWVxB4U6NMa2jsmBzraVnLxSqOL+cJy7vTVG4fpioJO/adhDMXHyWWmp1LQ1rpzYgemRg1UDQ2ufGfSuZMbGMBU1psyKqrj23ft/7Itndku0Yg1nz/zPOuQcDuATAlbu+oj2WI0sUGeiQzskjIRzXfoOYpE2/cGwwMug3Blsp7Ap/93uwOoy0NeNnZzrZa4s82CHOwCAy2+0DEFmfUuyLmKziKAC4a2MMvnTbRro2PvxmJ0aYfdYXFQ31bOqNemZ6g0DC7WfKwPpwfGfCRB1n0F+st2akllLRmD3pjDgFC4rpaiiGM3LScwM9UQ2DeQCFQG7aUdhty5v5z8bwmXER2lDrHVebtZs1EjFMNJSheD6yHWNwtXPuBICfQigc+zCAX1rIqvZQKOvCwu1f8Lh7A4BO2EVKTYOJ+iODI812y9pTA7qdNMk7eaTrFKIp5CEPoy+tNZx/azDRkFKz5jN33+03Jn3ZRGq/qea6Vmm0pHL+5/w/V2JlXOLxD7hTso0r0Z1keQ1GNW1ksBOYiDgDa/BO8FDtoUIdMa9VENOxtbUPpcUSCet7W1jbcyKIQFYHxFCufrs9va6wdh0mKosw3IaMgdmorqpN52ZW6RAW7U/zDqz7NjWqp9tI9kKoMyDx3r+8+fPNAL5iMcvZezncwESapwQA3/t198IzH3OPiD8gGapQPqYYEC73PnUEAHDTuU19bY2hGk577ecrVO96wMPYetHZkOffHzn0ZQMBFu7ef+195HdbHd0Ubmkv730uP4qP/MyTks/lMYf6WfXxGfLvdp/Svu9badMBhCJKizPwvhnzOlg3Y3nXukKVMJE2prVqYCBArzMghazBZ6OywOq0gkN6bYTLd1CNMGSkcA2YqIOo9DqCogi9jcy01zJwMYVzJvTX9nySEVcb1ejFgpJAviA4A+fcnZxzv+Wc+6vm3w9yzj13YSvbIyHv0YoMwnd0CIl7/hpMNBQZ3PfyYAw+e/Oauv1wD7k9xFdE3x3gHDShY2qeUDh/s31IIQ5GBul27tGrldtDCrWPQC67yGDovmnC1649F8Pw3BCMtDUjO+Qg9MFntVFxH9fNxPuTwh2KDFY39RoL6u/Ted/p2mYGVELXQ9lGVhGlpTDHLGIC9GyhKJsoIZCDIZjV+uS+yagwU0PpeO0cCKNracsZJPBcE3EZA5F2U7bzNrwSoa3EXZt/fxzAD+/yevZc+jJ2hoQTjdrLaRkRkvs0xuDzZ9bV7X0Q1hAcAXR8x1CdQZ/c/dJD6udt+uaAZ9834c1a2/3vfDTpdx8fn0cettLT++80kYFRNDYkg7h9dG22oQJShciPrxlJqkOQ6yCJW4z0G5OdQI99TfaGUnZJmfe3sNZ7/tP+Vvts2m7OxRaRgc4Z1GZBXEd+W3UzITLQYCDabk+IC/dtWhlRTdtLa/GcwZZhIgAnvfd/5Jz7CaDtO1QtaF17JmSZLWy8T44fYlDMDsK3yxpl/a8aXkLKkRYm6veuNQ8UAP7sB74W13z8pjZ9lstQvvJ3ftUVOLo8wvd87ZXq9q1AMYCBfW8hcvjwf3oSvnx7SuCGfRgJa5CNQH9kUNV+h8agPyKLr20AiulJPbW6xYZGeHa2UHdszUPth5mGsrzGZbGlbKJxqc/A4PMM1EZ1DQEsh9PQuWmCmwUtWpGBJJA1TqD2UEdu0rVR80Grl9Z0HgyFHRnQ8Jsi2RfohttYPZ+6iGz7z+xWZTvGYNU5dxmaeQTOuccAuG0hq9pDGTPlsF3hD7x8+Lcqn/mFJ5vbKDKwPCESy5BdceIQnvHoe6jb+qAUALjy5GE87+t1IwUAlzUE9RC2PQgTWeT5pMSVJw+r26Jsox1mE8l1bFVimGgHuD6PDJRLp/0tGI+aFw5FBnpK77Dn37f2rcBE61arjMK18wQAq+ispzdRQW089BbX49KZzQfpGW0jA6MwrOUUtDnFRhM82j5tJsiZ5HcLgaXXBcBuZyEirguiAhnACxGyiO7tnHsrgFMAnrqQVe2hkMKgFgAXkpAxoH4wXKI6gx0oNd/MGHr43Y/vaG13PxHgI3pIuQx6mJxA3sHatzo0SE395KT/omEiI5cfCL+f5kB0rcP1tfVHZP1GeCttPvq2j0tmDIwUybVZ1XJdXMqGEzBhIqYwVeen7Np0qARz1LzQigz02hLeDl5b22QU+pdZY1onA8airZHwWmTQ6J+apuelUUtY+wXQm4jJvQF8E8Kwmu8E8Oht7n9Bylh4DduVk0cmuOnc1Nz+28/56h2TPvRSaYaKK5KdeLhfvj1kMD3u/jubGndFYww0KIfCZasIZ6h4akiGOQNSmHbUINexVZkMDUQaVMjhM+ul7qtABhixP5AU0JepxI8Tr73/mQqRgTEhrugibJ2rCTBQ2xlUTS3V20m0+9c1ChMmYs0LZSaU5AwGIgO5fCKILWU/GRWYzXWCOByvm9+cpr12MFFf2uyFFhn8lPf+j5tag38M4FcA/DqCUbhopbXMive9FXn9D389vmRg2wDwDfe/fEfHBVhkMO+nZoa6o2ry9K++B9amFZ77dffa0drufmlTmGXh+o0x0Jvg9XugQ0KKxzk92+j6JjvrIVccV9dFosE8g+fmkcEgX2JDOZp3C3SGzCIK6d4NcTVD5LVeHT0cWZgEMjue1WaD0jMBJAkCVHRmDX1vW1gbsxrG2+AMrPTNPphoWtUtZ6Gde1bZFci8lUZaENchExpnMFT5vZuyHWNAGunJAF7mvf9z59x/3P0l7a20A6nrnRmDy44stUTwbgsVnQ1FLRaB3CeXHBrjhU+8347WBXSRwZm1mbp9Uoa+/RYRSrKTh5uUjaVQX/C4e+MBdz6KZz4q5Uucc60XugiYaNhYNMre4gR6UkvD/i45DwlXNPps6CGYqD9yGDGYyOpaGtZuQ3ebPaMjAZrFoEUGASYqnJ16umH2Hoqjf63OAOBT2uL1k9OwPqtw4pCeTky9hawiyqnRC6ubd2ARzCKb6AJpVPd559xvAPhuAK91zi1tc/8LUr75wXcBAHzbw++2zytJpet1rvMZJ5pspp3AHecrJw6N8Zz/50q8/HuuUreTdzek7HcS1QxFE197n5P4D9/yIDVqABjBvANjwGtHdgITtZGBcd0dTKSv7XBPhtlgN9eBpIOtEMxUVLZkFKUB/Vlcm0bqadkqZD19c1Q2MJPSwpq2W9XPiXetZDrRuYE05ZdnSqmGauSabKN0XCiAaEyrRbwPEcx7UYG8nbfhuxHqDJ7kvT8D4FIA/34Ri9pLufLkYXzmF56MB97l2H4vJRE+EUyT+94pzBvaKd9xPuKcw4v+6Vfiq+5xQt1OofbwgJ8dEMjNC7FTyr+dhLYDI8oruwdhoh7OYqcw0fGmqn3Is7cUZt/atwIT0eAeqfB5DYRFogLds5zCRI1CnlUGQdwRzFZB3roxYa71rg1OIOEMjOl41pjWQCBTzyc9MrDmZ3R9lbwKI7UFcxdSbyLv/Zr3/tXe+39o/v1F7/1fL2xlWVqy0uIzvvuquwPo0jwvJKFHdihbaCcPNx2T+txsf/+dRwZcNM6B2psA/ZXjZuroAEx0/FD4rXdUgXy+kUHJIoOe6murPTZgK9y2uGpuNKqjlhGV3v9nVDisT5uRnYb3bXECw9lE4VrNyKAMfEff2tsW1OLehKwyNO0qtFkMexcZXPTZQHdkedBdLsF9Lj+Cn/imB6rbn/rIK/DY+57EnY4tq9v3U4oBpUbpejuJDEhp7TQy6Cah9VeIW/Kkr7wzXnfdl7CspFCSsubnic5ddKmlmhxqYCDLiB5e6mleyGGigWwiPdOq6N8+BAUVdjQ4Srxz3fs2YaKiU7j6ue3Z01vpWgrYnMJQZNCm1W7q9R99rTKA4P1Pqxq1t9fWEvcLbEeRjcEFLCuTEn/zwn/U+50L0RAAAc44fXbTNAZLZAx2EhkUFBnsbG3kOe6Ua/kfT384/uHL59QW5by7bZ93bq2dYCDrvmmzjdtjc5hoIHIYams+VIegjondQqYTKVytQllbp1zbtKoTviKszZkT5mTRmVaBzLcnTfj45D4DoqLtOvFu8xl0fptgDsfrIosLACbaqTjnnuSc+5hz7hPOuR83vvM459z7nHPXOefevOg1ZVm8nDhkVygDnTLZCYE8NE50SPpmJG9FlkYlHny3S9RtvEVJX8FdZVgDum+bBg90pIGhqCEclyGYaDvT8awq4Hb/niZ8fa0w+voDaefp1tafxTVqoBrt/EMdV9M6g3g7N3xDrcWt2hIyBtb0O+IUZGIBrcUaGrSbstDIwDlXAvg1AE8EcAOAdzvnXuO9/zD7znEAL0Ugpq93zu08MT/LBSMnDhPRaUUGQaHvpNfK+UZD9MLupM5gSJbHnaHSlBr1m9qY6rUjZEzObugpuxQZqMZggEAuBzz/I0udIVOro9nx1VYcPX2+OhjIninQt/ahJnvjnshiy3UGlV1n0B5rBz2dRmVhtsqgfTaNsZZt0ZkxNGg3ZdGRwaMAfMJ7/ynv/RTAHwJ4ivjOMwC82nt/PQB4729c8Jqy7IFcejh4uFZaLCmTnVRU0ozinUqbTbQAYzAklJpKw3WkEOdwdkPfTsZkVTEmQ5zBUN+koUQErrCXxn0Eso75Axyqkftu3ZBtd+Z3kjoqI4My3i7XH8/f0LOJunPrEFcbtRiRgzXwns5nTaDbTVn023A3AJ9j/76h+YzL/QCccM79rXPuPc65Zy94TVn2QEipnVnTW3XQC7YTPuzOl+xOZLAf9RnEM1h9EU+0kYFuDA41BLIeGRTq3yRHGN+gKa1TA8WT4wGF3Dd7esj7LgcMWQwTKYOmeq6dCg0tGEhCWHJt/FpVz37Ub2QHuRgGI1kQV2cMLlKYCF2GIRf5GowAPBLAEwCsAHi7c+4d3vuPRwdy7nkAngcA97iH3okzy4UjlzbG4JZVHe6gyGAnNRJDcyKGhF72/YwMLCEjek5R9gDw2Pucwt0vXcELHnefZNtQZMDhNe3ahyODcEzndKXXwUQ2zGNlxQxxBpEhGoSJ9MiiK/yKtw3BSEPKflIOKPst1H9Y5y4Kh8Kh5RQWaQwW/TbcgNDYjuQKAF9QvvM67/2q9/4mANcAeJg8kPf+au/9Vd77q06d2llztSx7J1ddeQIA8NArdKL18kYx7UevWOqcuT/GoH8UKkUGljG45NAYb/nRx+Mhyn0dUqh8roWmVA5N+g1VOwiqLFROoSOQ7UykNjIwvPOw/w7I6y3g+nadwUA2URQZ2Kml8u92PQNzJHiTPT0DrWjWfXHXGbwbwH2dc/cC8HkAT0PgCLj8OYCXOOdGACYIje/+24LXlWXB8oh7nMC7fvIJOHVUhx5e/NSH4k/ecwMescMW2s989D3MjJshud+djuK6L9yO02f12dPnK1Qtq8lQZECpqTuZrzEEtXDZCfZMx7eI90kvZ9B539rS4glw/XzHThoE8r5KZp1BpfMZk4HIYIiL2UrabF9R2ah0mFaLjQqABRuDZhrav0ZoY1ECeIX3/jrn3POb7S/z3n/EOfc6AB8AUAN4uff+Q4tcV5a9kct7sn6OH5rg+x77FTs+9s99+0N2vO8Ln3g//Onffx5fd9+TOz5Gn7ztxx+PWwyuZMgYjMoCT33kFfiWh95l2+fl3vrgjOsdREVd5bYO0xFM1Fd5bfceGuIMeGRge8+Ajdt33ne8vi4y6J/ipm0L5+uPDIYm0PFusH2G9KI2BgDgvX8tgNeKz14m/v1iAC9e9FqyZAHCXOdP/5dv3vF0uiG5/NiyaQgPD0AxAPDL35WgpNuWIcUx1E22bx8rMiAuR2/JEPbZnFVGz39uyPoL4ialNsaVKVwDqrltPfBX0hDKTCer6AzQ7ys3TkPdZK2xmWc3q+S7cp9FZhIBuQI5ywGVRRmCIbE6qe62WJEBtdKw1vGWH/2GtsApOeZAfcZJGoU6UEFstZuQ37W2691imUI2OAfKyJHrl5lOaauMoWK+IRhpOG3WamTHP7voI4MsWbLsnVyyMsZt6zOTM/ifz3gEzhlpq0CImixp23gYxoCylayxlUDwvociA7UR3UDR2VBabV/R2khmOkmYiH1/JzBRXOynR01Wi2tg74zB3qdTZMmSZWFCBLTZFbUscOLwzrrclgMwEUFjtyvV07yNtLa0reDqJHqNw7D3be2f1ED0tKPQYaLhorPub91QrVO7iR5Dl2GiLFnuYPLqF3xtO8Vut4U3ytttGbcwkb72y5vMsVtWU/Kcp28OKWt1bvaoHyYa6rjaN6FueJ5Bf2TAf8s+vsTaTjOSgf7q6gwTZclyBxNrINBuCBmD23ugoJ0KRRRrM/3YxBncrBiDMSNptUaDQ5wBbwCoRSajgdTSvjqFpM7A6F0E6IYmmn43GBkohmqohqLZvsi+RECGibJkuUPJd391qPG88jIb+9+p0DTAT51eVbdfejhEBlqTPVLW6zO95/9QZMCNgV6BPFTYFbY7p7V86CAs6/zd2tJz8wyxvsE+YW39hqxvAt0kw0RZsmTZqnzrw+6Kb37wnRfiRT7wLmHMqpVtdM9LD+FZj7knnvaouyfbtjoYB9CV8Qk2NGio6EztuErkt1I9LWck9xXsaYamGIgcygGYKa6RyDBRlixZdkkWBSdcfjQQxBYvURQOP/NtDx5ck475D0ApA/sPtdIgJdxniDZmdTOGUs/oCQPrB4r5BsjtvrnYQ+tb5GAbIBuDLFmybENe+28eG0E2W5W4xXR//56hGReawuybAMfPqfENVD+xOddnHNPxQ8pu/9o0QxxxDmoNRveZzodQNlGODLJkyXKByIPuemxH+w3BRNvpq6Rtp9nQlpQMJrKON6s8Dk90hXukNQb9axvKZJKTzPj55Xfl9kW3XM8EcpYsWRYukfc7QLIOQTEajHNkMDKwYaIh8hrojM0QVKMp7MHIYIsFdYuGibIxyJIly8JlKDJYGveTrEMyBBPRMYdbTNuRgfyuJhpMxFNphyqY+ziDDBNlyZLlohca0lJ7XeGtDMyOBoDffPZVuPazt6jbhiIDUtLaufnp7MggHH9e97dN12CiY4xwH2px3TcpbdEwUTYGWbJk2RMZNQNmNIXHoR8Ll3/ig+6EJz7oTuq2ocigVaiKMaCxmPPaD0YGq5t6Wi0ZOm1/XpSmZioN1BmQMckwUZYsWe4Q0jW62xrZux0ZIpC7wi1d5dE5rXPT7GrqfCrlN599FR7zFZfqynxgut2YrU0zFnduej4tcsoZkCODLFmy7JH0ZfRwGcLlNRmai024vdVxdVQ4bMKOSn7smx4AD48nP0QfOvSEB94JT3igHrUcG+gXRffF8vzvdEkwBjSPYVGSjUGWLFn2RMY9uD2XHdiCQbncGL9KMqSQLz08wS89dWdDhw4rvZi4EAQ1r/RRpxQZ3HROn563W5JhoixZsuyJkBEY6rGzk8hgSC4/FozB7YZ3TQTzUMHbTmRokNKxlWAMqIW2lLs0kYHWDXY3JRuDLFmy7IkQyWtFBstjUsi7j43TrIUzhjHoUk8Xi8trMtR2nAzZzec2F7qODBNlyZJlT4SyaixjcHgywsZsOljluxMhmOhWw7seDRDIi5Qhgvnk4SV8xanDeOET77fQdWRjkCVLlj0RavWsDbQHgJVJCejdsbckb/i3X2/Odr5TO4VNn8XQRgaLICwGZIhgLgqH//sjj1v4OrIxyJIly54INWHriwwAYHW6s8E8973TUXPbpU0LbJ7zz2U/I4NFTqfbjizcGDjnngTgfwAoAbzce/8Lxve+GsA7APwz7/2fLHpdWbJk2VuhlhMWLv8/n/EI/NqbPoH79yj1nUpROLzkGY/Ag+6iN9obyiY6X3n9D389zqzpENVQZLBXslBj4JwrAfwagCcCuAHAu51zr/Hef1j53i8CeP0i15MlS5b9E6oF0No0A8D97nQU/+Npj1jY+b/loXc1t1EG0yL4CgC4/51tA3d0oHp6r2TRANmjAHzCe/8p7/0UwB8CeIryvR8E8CoANy54PVmyZNknISNg4fr7KV0F8t5zBhfK/Vj0ld8NwOfYv29oPmvFOXc3AN8O4GV9B3LOPc85d61z7trTp0/v+kKzZMmyWCFjQIPnLyTpRkteGIp5P2TR8Yl2Z2WZ3X8H8GPe+6qvOMN7fzWAqwHgqquu0kv1smTJcsHKUtOZdPNCNAb7SCADwLMec09cefLwvpybZNHG4AYAfDr2FQC+IL5zFYA/bAzBSQDf7Jybe+//bMFry5Ilyx4KRQabswvPGKw0LSMWxRkMiTU7ei9l0cbg3QDu65y7F4DPA3gagGfwL3jv70V/O+deCeAvsiHIkuWOJ1RcdYFA5JGsjJvhNQueGXAhy0KNgfd+7pz71whZQiWAV3jvr3POPb/Z3ssTZMmS5Y4jz/qae+LWtSme+9h7DX95j+XQPkcGF4IsPKfJe/9aAK8Vn6lGwHv/Lxa9nixZsuyPLI9L/OiTHrDfy1ClNQYHmEA+uDFRlixZsjTScQYHVyUe3CvPkiVLlkYoMtivbKILQbIxyJIly4GXQ01fpKo+uFnr2RhkyZLlwMtKUwMxMwbMHATJxiBLliwHXggmuhCro/dKsjHIkiXLgRdqq50jgyxZsmQ5wDJuis2sOcQHQbIxyJIly4GXSdtELxPIWbJkyXJgZZIjg2wMsmTJkqWLDKp9Xsn+STYGWbJkOfCy3KSWjnOjuixZsmQ5uPKIux/Hv3n8ffCMR99zv5eyb5KNQZYsWQ68FIXDC7/x/vu9jH2VgxsTZcmSJUuWVrIxyJIlS5Ys2RhkyZIlS5ZsDLJkyZIlC7IxyJIlS5YsyMYgS5YsWbIgG4MsWbJkyYJsDLJkyZIlCwDn/cXXpc85dxrAZxd0+JMAblrQsbPsj+Tf9I4n+TfdmdzTe39K23BRGoNFinPuWu/9Vfu9jiy7J/k3veNJ/k13XzJMlCVLlixZsjHIkiVLlizZGGhy9X4vIMuuS/5N73iSf9NdlswZZMmSJUuWHBlkyZIlS5ZsDLJkyZIlCw6AMXDOvcI5d6Nz7kPss4c5597unPugc+7/OOeONZ+PnXO/03z+EefcT7B9Htl8/gnn3K8659x+XE+WINv8XSfOud9uPn+/c+5xbJ+/dc59zDn3vua/y/f+arI45+7unHtT895d55z7oebzS51zb3DO/UPz/xPN55c13z/nnHuJOFZ+V3cgd3hjAOCVAJ4kPns5gB/33j8EwJ8C+PfN598FYKn5/JEA/qVz7spm268DeB6A+zb/yWNm2Vt5Jbb+u34/ADSfPxHArzjn+LP/TO/9w5v/blzssrMYMgfwI977BwJ4DIAfcM49CMCPA3ij9/6+AN7Y/BsANgD8FIB/pxwrv6s7kDu8MfDeXwPgFvHx/QFc0/z9BgDfSV8HcNg5NwKwAmAK4Hbn3F0AHPPev90Hxv13AXzboteexZZt/q4PQlAkaJT9GQC5YOkCEu/9F733723+PgvgIwDuBuApAH6n+drvoHnvvPer3vu/QzAKreR3dedyhzcGhnwIwLc2f38XgLs3f/8JgFUAXwRwPYBf9t7fgvBQ3sD2v6H5LMuFJdbv+n4AT3HOjZxz90KI+u7O9vvtBiL6qQwp7L800fgjALwTwJ28918EgsEAMATj5Xd1h3JQjcH3IoSh7wFwFCECAIBHAagA3BXAvQD8iHPuKwBoCiLn5F54Yv2ur0BQCtcC+O8A3oYASwABInoIgMc2/z1rLxecJRbn3BEArwLww97723dyCOWz/K5uQUb7vYD9EO/9RwF8IwA45+4H4MnNpmcAeJ33fgbgRufcWxHghLcAuIId4goAX9i7FWfZili/q/d+DuDf0vecc28D8A/Nts83/z/rnPvfCA7B7+7tyrMAIYEDwRD8vvf+1c3HX3bO3cV7/8UGAhridG5Afld3JAcyMqCMkYZE/A8AXtZsuh7A412QwwhE1keb8PSsc+4xDYzwbAB/vg9Lz9Ij1u/qnDvU/J5wzj0RwNx7/+EGNjrZfD4G8C0IUFOWPZbmvfotAB/x3v9Xtuk1AL6n+ft7MPDe5Xd153KHr0B2zv0BgMchtLz9MoAXATgC4Aear7wawE94730Tov42AuHoAPy29/7FzXGuQshgWQHwVwB+0N/Rb94FLNv8Xa8E8HoANYDPA3iu9/6zjYG4BsAYQAngbwC80Htf7eGlZAHgnPs6hAj8gwi/EwD8JAJv8EcA7oHgrH1Xw+PBOfcZAMcATBCSAr6xMfL5Xd2B3OGNQZYsWbJkGZYDCRNlyZIlS5ZYsjHIkiVLlizZGGTJkiVLlmwMsmTJkiULsjHIkiVLlizIxiBLlkFxzh13zr2g+fuuzrk/2e81Zcmy25JTS7NkGZCmTuEvvPcP3u+1ZMmyKDmQ7SiyZNmm/AKAezvn3ofQxuKB3vsHO+f+BUJHzBLAgwH8CkIB1LMAbAL4Zu/9Lc65ewP4NQCnAKwB+P6mdUaWLBeMZJgoS5Zh+XEAn/TePxzdjASSByP0tHoUgJ8DsOa9fwSAtyO0QgDC8PYf9N4/EqH//kv3YtFZsmxHcmSQJcv5yZua/vtnnXO3Afg/zecfBPDQpsXJ1wL4Y9Yde2nvl5klS79kY5Aly/nJJvu7Zv+uEd6vAsCZJqrIkuWClQwTZckyLGcR5iNsW5qe/J92zn0XELpzOucetpuLy5JlNyQbgyxZBsR7fzOAtzrnPgTgxTs4xDMBPNc5934A1yGMcsyS5YKSnFqaJUuWLFlyZJAlS5YsWbIxyJIlS5YsyMYgS5YsWbIgG4MsWbJkyYJsDLJkyZIlC7IxyJIlS5YsyMYgS5YsWbIA+P8BdDCFHVEs2G0AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# select only the ocean regions by ignoring any percentages over 100:\n",
     "SI_obs_ds = SI_obs_ds.where(SI_obs_ds['seaice_conc'] < 101)\n",
     "\n",
-    "# then multiple the ice fraction in each grid cell by the cell area:\n",
-    "# factor of 0.01 to convert from percentage to fraction\n",
-    "SI_obs_ds['seaice_area_km2'] = 0.01*SI_obs_ds['seaice_conc']*SI_obs_ds['Gridcell_Area']\n",
+    "# then multiply the ice fraction in each grid cell by the cell area:\n",
+    "# factor of 0.01 servees to convert from percentage to fraction\n",
+    "SI_obs_ds['seaice_area_km2'] = 0.01 * SI_obs_ds['seaice_conc'] * SI_obs_ds['Gridcell_Area']\n",
     "\n",
-    "# finally, we can sum this sea-ice area variable over the spatial dimensions to get\n",
-    "# a timeseries of total Arctic sea ice area:\n",
+    "# finally, we can sum this sea ice area variable over the spatial dimensions to get\n",
+    "# a time series of total Arctic sea ice area:\n",
     "SI_total_area_obs = SI_obs_ds['seaice_area_km2'].sum(dim=['latitude', 'longitude'])\n",
     "SI_total_area_obs.plot()"
    ]
@@ -3190,14 +591,6 @@
    "source": [
     "Now you can add the observational decline of sea ice to your analysis if you wish!"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bc91b53b-1877-45f1-b9a1-e9fbf47b84b8",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -3213,9 +606,9 @@
    "name": "python3"
   },
   "kernelspec": {
-   "display_name": "main",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "main"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -3227,7 +620,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

From d32002f2630a238483e3069a4f89965af8f189ca Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Wed, 12 Jun 2024 18:52:34 +0200
Subject: [PATCH 25/65] Major update of project notebook after review from
 curriculum specialist.

---
 .../Regional_precipitation_2024.ipynb         | 370 ++++++++++++------
 1 file changed, 256 insertions(+), 114 deletions(-)

diff --git a/projects/project-notebooks/Regional_precipitation_2024.ipynb b/projects/project-notebooks/Regional_precipitation_2024.ipynb
index 38267848c..c1abced30 100644
--- a/projects/project-notebooks/Regional_precipitation_2024.ipynb
+++ b/projects/project-notebooks/Regional_precipitation_2024.ipynb
@@ -5,7 +5,7 @@
    "id": "ada5523b",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/neuromatch/course-content-template/blob/main/projects/example_dataset/Example_based_on_CMA_structure.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/course-content-template/main/projects/example_dataset/Example_based_on_CMA_structure\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/projects/project-notebooks/2024_Regional_precipitation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/projects/project-notebooks/2024_Regional_precipitation.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
    ]
   },
   {
@@ -15,57 +15,27 @@
     "execution": {}
    },
    "source": [
-    "<h1>\n",
-    "Regional Precipitation and Extreme events\n",
-    "</h1>\n",
+    "# Precipitation Variability and Extreme events\n",
     "\n",
+    "**Content creators:** Will Gregory, Laura Paccini, Raphael Rocha\n",
     "\n",
+    "**Content reviewers:** Paul Heubel, Jenna Pearson\n",
     "\n",
-    "__Content creators:__ Laura Paccini & Will Gregory\n",
+    "**Content editors:** Paul Heubel\n",
     "\n",
-    "__Content reviewers:__ Names & Surnames\n",
+    "**Production editors:** Paul Heubel, Konstantine Tsafatinos\n",
     "\n",
-    "__Content editors:__ Konstantinos Tsafatinos\n",
-    "\n",
-    "__Production editors:__ Konstantinos Tsafatinos\n",
-    "\n",
-    "__Our 2024 Sponsors:__ Sponsors\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c08b96da",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "To include videos:"
+    "**Our 2024 Sponsors:** CMIP, NFDI4Earth\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "64da422f",
    "metadata": {
-    "cellView": "form",
-    "execution": {}
+    "cellView": "form"
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bc75a50d6fd94a54812d7f38975591ab",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Tab(children=(Output(), Output()), _titles={'0': 'Youtube', '1': 'Bilibili'})"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# @title Project Background\n",
     "\n",
@@ -123,31 +93,31 @@
     "display(tabs)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "a9fb54e0",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "To include slides:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "b52fef8b",
    "metadata": {
-    "cellView": "form",
-    "execution": {}
+    "cellView": "form"
    },
    "outputs": [],
    "source": [
-    "# @title Tutorial slides\n",
-    "# @markdown These are the slides for the videos in all tutorials today\n",
+    "# @title Slides\n",
+    "# @markdown These are the slides for the video introduction to the project\n",
     "\n",
+    "from ipywidgets import widgets\n",
     "from IPython.display import IFrame\n",
-    "#link_id = \"n7wdy\""
+    "\n",
+    "link_id = \"\"\n",
+    "\n",
+    "download_link = f\"https://osf.io/download/{link_id}/\"\n",
+    "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
+    "# @markdown\n",
+    "out = widgets.Output()\n",
+    "with out:\n",
+    "    print(f\"If you want to download the slides: {download_link}\")\n",
+    "    display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
+    "display(out)"
    ]
   },
   {
@@ -188,34 +158,23 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "b68292ec",
-   "metadata": {
-    "execution": {}
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# google colab installs\n",
     "\n",
-    "# !mamaba install netCDF4"
+    "# !pip install s3fs boto3 botocore --quiet"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "1e541a40",
-   "metadata": {
-    "execution": {}
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Imports\n",
     "\n",
-    "#Import only the libraries/objects that are necessary for more than one dataset.\n",
-    "#Dataset-specific imports should be in the respective notebook section.\n",
-    "\n",
-    "#If any external library has to be installed, !pip install library --quiet\n",
-    "#follow this order: numpy>matplotlib.\n",
-    "#import widgets in hidden Figure settings cell\n",
-    "\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import xarray as xr\n",
@@ -233,9 +192,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "3692a9ca",
-   "metadata": {
-    "execution": {}
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# @title Helper functions\n",
@@ -262,8 +219,9 @@
     "# @title Figure settings\n",
     "\n",
     "import ipywidgets as widgets       # interactive display\n",
+    "\n",
     "%config InlineBackend.figure_format = 'retina'\n",
-    "plt.style.use(\"https://raw.githubusercontent.com/ClimateMatchAcademy/course-content/main/cma.mplstyle\")"
+    "plt.style.use(\"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\")"
    ]
   },
   {
@@ -292,17 +250,19 @@
     "### Indices for Extreme Events\n",
     "The Expert Team on Climate Change Detection and Indices ([ETCCDI]( http://etccdi.pacificclimate.org/list_27_indices.shtml)) has defined various indices that focus on different aspects such as duration or intensity of extreme events. The following functions provide examples of how to compute indices for each category. You can modify these functions to suit your specific needs or create your own custom functions. Here are some tips you can use:\n",
     "\n",
-    "- Most of the indices require daily data, so in order to select a specific season you can just use xarray to subset the data. Example:\n",
+    "- Most of the indices require daily data, so in order to select a specific season you can just use `xarray` to subset the data. Example:\n",
     "\n",
-    "`daily_precip_DJF = data_chirps.sel(time=data_chirps['time.season']=='DJF')`\n",
+    "```python\n",
+    "daily_precip_DJF = data_chirps.sel(time=data_chirps['time.season']=='DJF')\n",
+    "```\n",
     "\n",
-    "- A common threshold for a wet event is precipitation greater than or equal to 1mm/day, while a dry (or non-precipitating) event is defined as precipitation less than 1mm/day.\n",
-    "- Some of the indices are based on percentiles. You can define a base period climatology to calculate percentile thresholds, such as the 5th, 10th, 90th, and 95th percentiles, to determine extreme events."
+    "- A common threshold for a wet event is precipitation greater than or equal to $1 \\text{ mm}/\\text{day}$, while a dry (or non-precipitating) event is defined as precipitation less than $1 \\text{ mm}/\\text{day}$.\n",
+    "- Some of the indices are based on percentiles. You can define a base period climatology to calculate percentile thresholds, such as the 5th, 10th, 90th, and 95th percentiles, to determine extreme events (cf. in particular W2D3)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "e9697620",
    "metadata": {},
    "outputs": [],
@@ -317,7 +277,7 @@
     "    Parameters:\n",
     "    ----------\n",
     "      - data (xarray.DataArray): The input daily precipitation data should be\n",
-    "      a dataset (eg. for chirps_data the SataArray would be chirps_data.precip)\n",
+    "      a dataset (eg. for chirps_data the DataArray would be chirps_data.precip)\n",
     "    Returns:\n",
     "    -------\n",
     "      - cdd (xarray.DataArray): The calculated CDD index\n",
@@ -359,13 +319,13 @@
    "source": [
     "# code to retrieve and load the data\n",
     "\n",
-    "years=range(1981,2024) # the years you want. we want 1981 till 2023\n",
-    "file_paths=['https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.'+str(year)+'.days_p25.nc' for year in years] # the format of the files\n",
-    "filenames=['chirps-v2.0.'+str(year)+'.days_p25.nc' for year in years] # the format of the files\n",
+    "years = range(1981,2024) # the years you want. we want 1981 till 2023\n",
+    "file_paths = ['https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.' + str(year) + '.days_p25.nc' for year in years] # the format of the files\n",
+    "filenames = ['chirps-v2.0.'+str(year)+'.days_p25.nc' for year in years] # the format of the files\n",
     "\n",
     "downloaded_files=[ pooch_load(fpath,fname) for (fpath,fname) in zip(file_paths,filenames)] # download all of the files\n",
     "\n",
-    "#### open data as xarray\n",
+    "#### open data via xarray\n",
     "chirps_data = xr.open_mfdataset(\n",
     "    downloaded_files, combine=\"by_coords\"\n",
     ")  # open the files as one dataset"
@@ -399,7 +359,7 @@
     "\n",
     "As we learned in the W1D3 tutorials, all the National Atmospheric and Oceanic Administration Climate Data Record (NOAA-CDR) datasets are available both at NOAA National Centers for Environmental Information (NCEI) and commercial cloud platforms. See the link [here](https://registry.opendata.aws/noaa-cdr-terrestrial/). We are accessing the data directly via the [Amazon Web Service (AWS) cloud storage space](https://noaa-cdr-ndvi-pds.s3.amazonaws.com/index.html).\n",
     "\n",
-    "For this project we recommend using the Normalized Difference Vegetation Index (NDVI). It is one of the most commonly used remotely sensed indices. It measures the \"greeness\" of vegetation, and is useful in understanding vegetation density and assessing changes in plant health. For example, NDVI can be used to study the impacts of droughts, heatwaves, and insect infestations on plants covering Earth's surface. A good overview of this index from this particular sensor can be accessed [here](https://digitalcommons.unl.edu/nasapub/217/).\n",
+    "For this project, we recommend using the Normalized Difference Vegetation Index (NDVI). It is one of the most commonly used remotely sensed indices. It measures the \"greenness\" of vegetation and is useful in understanding vegetation density and assessing changes in plant health. For example, NDVI can be used to study the impacts of droughts, heatwaves, and insect infestations on plants covering Earth's surface. A good overview of this index from this particular sensor can be accessed [here](https://digitalcommons.unl.edu/nasapub/217/).\n",
     "\n",
     "Recall what we learned in W1D3 Tutorial 3, the data files on AWS are named systematically:\n",
     "\n",
@@ -410,7 +370,7 @@
     "> Satellite platform: `NOAA-07`  \n",
     "> Date of the data: `19810624`  \n",
     "> Processing time: `c20170610041337` (*This will change for each file based on when the file was processed*)  \n",
-    "> File format: `.nc` (*netCDR-4 format*)\n",
+    "> File format: `.nc` (*netCDF-4 format*)\n",
     "\n",
     "In other words, if we are looking for the data of a specific day, we can easily locate where the file might be. \n",
     "\n",
@@ -418,7 +378,9 @@
     "\n",
     "`s3://noaa-cdr-ndvi-pds/data/2002/AVHRR-Land_v005_AVH13C1_*_20020312_c*.nc`\n",
     "\n",
-    "The reasaon that we put `*` in the above directory is because we are not sure about what satellite platform this data is from and when the data was processed. The `*` is called a **wildcard**, and is used because we want *all* the files that contain our specific criteria, but do not want to have to specify all the other pieces of the filename we are not sure about yet. It should return all the data satisfying that initial criteria and you can refine further once you see what is available. Essentially, this first step helps to narrow down the data search. You can then use this to create datasets from the timeframe of your choosing.\n"
+    "The reason that we put `*` in the above directory is that we are not sure about what satellite platform this data is from and when the data was processed. The `*` is called a **wildcard**, and is used because we want *all* the files that contain our specific criteria, but do not want to have to specify all the other pieces of the filename we are not sure about yet. It should return all the data satisfying that initial criteria and you can refine further once you see what is available. Essentially, this first step helps to narrow down the data search. You can then use this to create datasets from the timeframe of your choosing.\n",
+    "\n",
+    "*Note the download might take up to hours depending on your time frame of interest and your device. Choose your time frame wisely before taking action to avoid unnecessary data retrieval amounts.*"
    ]
   },
   {
@@ -428,20 +390,133 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# we can use the data from W1D3 tutorial 3\n",
+    "# we can access the data similar to Tutorial 3 of W1D3\n",
     "# to access the NDVI data from AWS S3 bucket, we first need to connect to s3 bucket\n",
     "\n",
     "fs = s3fs.S3FileSystem(anon=True)\n",
     "client = boto3.client('s3', config=botocore.client.Config(signature_version=botocore.UNSIGNED)) # initialize aws s3 bucket client\n",
+    "# choose a date of interest, we pick its year or month while defining the file_location\n",
     "date_sel = datetime.datetime(2001,1,1,0) \n",
-    "file_location = fs.glob('s3://noaa-cdr-ndvi-pds/data/'+\n",
-    "                        date_sel.strftime('%Y')+'/AVHRR-Land_v005_AVH13C1_*_c*.nc') # the files we want for a whole year\n",
-    "files_for_pooch=[pooch_load('http://s3.amazonaws.com/'+file,file) for file in file_location]\n",
     "\n",
-    "ds = xr.open_mfdataset(files_for_pooch, combine=\"by_coords\")  # open the file\n",
+    "file_location = fs.glob('s3://noaa-cdr-ndvi-pds/data/'\n",
+    "                        + date_sel.strftime('%Y')\n",
+    "                        + '/AVHRR-Land_v005_AVH13C1_*'\n",
+    "                        + date_sel.strftime(\"%Y%m\") # or \"%Y\" for the whole Year\n",
+    "                        + '*_c*.nc') # the files we want for a whole year/month\n",
+    "# load the files, which might take up to a few hours depending on your device, in the JupyterHub it usually faster\n",
+    "files_for_pooch=[pooch_load('http://s3.amazonaws.com/'+ file, file) for file in file_location]\n",
+    "\n",
+    "ds = xr.open_mfdataset(files_for_pooch, combine=\"by_coords\", decode_times=False)  # open the file\n",
     "ds"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "a453828b-1200-4ddf-b8ab-abc428e9b375",
+   "metadata": {},
+   "source": [
+    "If the above downloading procedure is not working as expected, please retrieve the following sample to execute the remaining cells of the notebook. Ask for the help of your project TA to download your data of interest.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d8b70d6-e52c-4131-bd2d-3505f22bd13c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# uncomment to get a subsample of AVHRR-Land_v005_AVH13C1_NOAA-16 NDVI data if data retrieval above is not working properly\n",
+    "\n",
+    "#fname_ndvi_200101 = 'AVHRR-Land_v005_AVH13C1_NOAA-16_200101.nc'\n",
+    "#link_id_ndvi = \"a9v7h\"\n",
+    "#url_ndvi_200101 = f\"https://osf.io/download/{link_id_ndvi}/\"\n",
+    "#\n",
+    "#ds = xr.open_dataset(pooch_load(url_ndvi_200101, fname_ndvi_200101))\n",
+    "#ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "658a800d-e83e-422b-ae96-e16bb235e609",
+   "metadata": {},
+   "source": [
+    "*Note that the downloaded satellite data is quality-controlled and flagged accordingly. Please refer to W1D3 Tutorial 3 and others to decipher it. Choose the feasible helper functions, e.g. `get_quality_info_AVHRR(QA)` depending on the sensor of interest. VIIRS is a state-of-the-art sensor that produces measurements that have been available since 2014.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "088d05ba-643f-4886-919c-2bf7fce08193",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# AVHRR: function to extract high-quality NDVI data\n",
+    "def get_quality_info_AVHRR(QA):\n",
+    "    \"\"\"\n",
+    "    QA: the QA value read in from the NDVI data\n",
+    "\n",
+    "    High-quality NDVI should meet the following criteria:\n",
+    "    Bit 7: 1 (All AVHRR channels have valid values)\n",
+    "    Bit 2: 0 (The pixel is not covered by cloud shadow)\n",
+    "    Bit 1: 0 (The pixel is not covered by cloud)\n",
+    "    Bit 0: \n",
+    "\n",
+    "    Output:\n",
+    "    True: high quality\n",
+    "    False: low quality\n",
+    "    \"\"\"\n",
+    "    # unpack quality assurance flag for cloud (byte: 1)\n",
+    "    cld_flag = (QA % (2**2)) // 2\n",
+    "    # unpack quality assurance flag for cloud shadow (byte: 2)\n",
+    "    cld_shadow = (QA % (2**3)) // 2**2\n",
+    "    # unpack quality assurance flag for AVHRR values (byte: 7)\n",
+    "    value_valid = (QA % (2**8)) // 2**7\n",
+    "\n",
+    "    mask = (cld_flag == 0) & (cld_shadow == 0) & (value_valid == 1)\n",
+    "\n",
+    "    return mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70a41745-170f-4902-b680-674e25bf7489",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the quality assurance value from NDVI data\n",
+    "QA = ds.QA\n",
+    "\n",
+    "# create the high quality information mask\n",
+    "mask = get_quality_info_AVHRR(QA)\n",
+    "\n",
+    "# check the quality flag mask information\n",
+    "#mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d07685f-f0a6-4e1f-8d5b-3d852f3b808d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# mask ndvi data \n",
+    "ndvi_masked = ds.NDVI.where(mask)\n",
+    "ndvi_masked"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8f5b584-18fc-45af-9c47-c118353d4e88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# draw data to an example plot\n",
+    "ndvi_masked.isel(time=30).coarsen(latitude=10).mean().coarsen(longitude=20).mean().plot()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1d4ab262",
@@ -464,22 +539,36 @@
    "outputs": [],
    "source": [
     "filename_cereal = 'data_cereal_land.csv'\n",
+    "\n",
     "url_cereal = 'https://raw.githubusercontent.com/Sshamekh/Heatwave/f85f43997e3d6ae61e5d729bf77cfcc188fbf2fd/data_cereal_land.csv'\n",
-    "ds_cereal_land = pd.read_csv(pooch_load(url_cereal,filename_cereal))\n",
-    "ds_cereal_land.head() "
+    "\n",
+    "path_cereal = 'shared/data/'\n",
+    "if os.path.exists(path_cereal + filename_cereal):\n",
+    "    ds_cereal_land = pd.read_csv(path_cereal + filename_cereal)\n",
+    "else:\n",
+    "    ds_cereal_land = pd.read_csv(pooch_load(url_cereal,filename_cereal))\n",
+    "\n",
+    "ds_cereal_land.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "396c635a",
    "metadata": {},
    "outputs": [],
    "source": [
     "# example\n",
-    "ds_cereal_land[(ds_cereal_land[\"Country Name\"] == \"Brazil\")].reset_index(\n",
-    "    drop=True\n",
-    ").iloc[0].transpose()"
+    "country = 'Brazil'\n",
+    "unit = 'MT' # metric tons\n",
+    "\n",
+    "ds_cereal_land_brazil = ds_cereal_land[\n",
+    "(ds_cereal_land[\"Country Name\"] == country) & (ds_cereal_land[\"Series Code\"] == f'AG.PRD.CREL.{unit}')\n",
+    "].drop(\n",
+    "    ['Country Name', 'Country Code', 'Series Name', 'Series Code'], axis=1\n",
+    ").reset_index( drop=True ).transpose()\n",
+    "\n",
+    "ds_cereal_land_brazil.head()"
    ]
   },
   {
@@ -493,11 +582,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "75867210",
+   "id": "dfbed1e8-0dec-486b-b2bb-2c8506c1a9bc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Code to print the shape, array names, etc of the dataset"
+    "# example\n",
+    "plt.plot(np.arange(1972,2021,1),ds_cereal_land_brazil[1:].astype(float).values)\n",
+    "\n",
+    "# aesthetics\n",
+    "plt.title(f'{country}')\n",
+    "plt.xlabel(f'Time (years)')\n",
+    "plt.ylabel(f'Cereal production ({unit})')"
    ]
   },
   {
@@ -508,6 +603,61 @@
     "Now you are all set to address the questions you are interested in!"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "09c1aae5-2286-4c4c-873a-c8bcb3e44e13",
+   "metadata": {},
+   "source": [
+    "# Q1\n",
+    "\n",
+    "Plot the annual mean of precipitation for three example years (e.g., 2001, 2005, 2016) using the CHIRPS data sets.\n",
+    "\n",
+    "*Hint: Check out the tutorials from [W1D1 (e.g. T4)](https://comptools.climatematch.io/tutorials/W1D1_ClimateSystemOverview/student/W1D1_Tutorial4.html#section-2-aggregation-methods), [W1D2 (e.g. T2)](https://comptools.climatematch.io/tutorials/W1D2_StateoftheClimateOceanandAtmosphereReanalysis/student/W1D2_Tutorial2.html#section-3-plotting-time-series-of-reanalysis-data) and [W1D3 (e.g. T5)](https://comptools.climatematch.io/tutorials/W1D3_RemoteSensingLandOceanandAtmosphere/student/W1D3_Tutorial5.html#section-2-2-global-mean) to recall averaging with `xarray`.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "356909bc-d52a-4c4c-9bb0-febf2b5979b2",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "# Q2\n",
+    "Plot the global mean of land precipitation as a function of time for the last 40 years.\n",
+    "\n",
+    "*Hint: Check out the tutorials that contain time series calculations e.g. from [W1D2 (e.g. T1)](https://comptools.climatematch.io/tutorials/W1D2_StateoftheClimateOceanandAtmosphereReanalysis/student/W1D2_Tutorial1.html#section-2-3-compute-the-climatology-and-anomalies), [W1D3 (e.g. T5)](https://comptools.climatematch.io/tutorials/W1D3_RemoteSensingLandOceanandAtmosphere/student/W1D3_Tutorial5.html#section-2-2-global-mean) and [W1D5 (e.g. T8)](https://comptools.climatematch.io/tutorials/W1D5_ClimateModeling/student/W1D5_Tutorial8.html).*\n",
+    "\n",
+    "Compute seasonal means for June-August (JJA) and December-February (DJF).\n",
+    "\n",
+    "*Hint: Check out the code snippet in the **Indices for Extreme Events** section of this notebook. Furthermore, recall [Tutorial 5 of W1D1](https://comptools.climatematch.io/tutorials/W1D1_ClimateSystemOverview/student/W1D1_Tutorial5.html#section-1-groupby-split-apply-combine)*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81f8a45a-d2ba-4e02-b0a7-036192fdb0e9",
+   "metadata": {},
+   "source": [
+    "# Q3\n",
+    "Repeat the analysis in Q2 for a selected region of interest. How does this compare with the respective mean precipitation in the northern and southern hemispheres? How does this compare to the global mean?\n",
+    "\n",
+    "*Hint: Check out tutorials that showed how to select certain regions of interest, e.g. [T8 and T9 of W1D1](https://comptools.climatematch.io/tutorials/W1D1_ClimateSystemOverview/student/W1D1_Tutorial9.html#section-3-using-where-with-specific-coordinates), [T1 of W1D2](https://comptools.climatematch.io/tutorials/W1D2_StateoftheClimateOceanandAtmosphereReanalysis/student/W1D2_Tutorial1.html) and others.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0607dde-7c65-4ec7-a11b-b8185e33ad19",
+   "metadata": {},
+   "source": [
+    "# Q4\n",
+    "Examine the time series in your region of interest and identify extreme values. \n",
+    "\n",
+    "*Hint: Select a method from a predefined list of indices (provided above in Section **Indices for Extreme Events**) based on relative or absolute thresholds.*\n",
+    "\n",
+    "Do extreme events occur more often in wet or dry seasons? \n",
+    "\n",
+    "*Hint: We briefly discussed wet and dry season differences in [Tutorial 4 of W1D3](https://comptools.climatematch.io/tutorials/W1D3_RemoteSensingLandOceanandAtmosphere/student/W1D3_Tutorial4.html#questions-2-2-climate-connection) already, however for another data set of monthly frequency namely the Global Precipitation Climatology Project (GPCP) Monthly Precipitation Climate Data Record (CDR) ([cf. Tutorial 4 of W1D3](https://comptools.climatematch.io/tutorials/W1D3_RemoteSensingLandOceanandAtmosphere/student/W1D3_Tutorial4.html#section-1-obtain-monthly-precipitation-data)). You also examined extreme events on W2D3.*"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c62a0f3e",
@@ -525,20 +675,12 @@
     "execution": {}
    },
    "source": [
-    "- Anyamba, A. and Tucker, C.J., 2012. Historical perspective of AVHRR NDVI and vegetation drought monitoring. Remote sensing of drought: innovative monitoring approaches, 23, pp.20.[https://digitalcommons.unl.edu/nasapub/217/](https://digitalcommons.unl.edu/nasapub/217/)\n",
-    "- Zhang, X., Alexander, L., Hegerl, G.C., Jones, P., Tank, A.K., Peterson, T.C., Trewin, B. and Zwiers, F.W., 2011. Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdisciplinary Reviews: Climate Change, 2(6), pp.851-870.\n",
-    "- Schultz, P. A., and M. S. Halpert. \"Global correlation of temperature, NDVI and precipitation.\" Advances in Space Research 13.5 (1993): 277-280. \n",
-    "- Seneviratne, S.I. et al., 2021: Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V. et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1513–1766, https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/\n",
-    "- IPCC, 2021: Annex VI: Climatic Impact-driver and Extreme Indices [Gutiérrez J.M. et al.(eds.)]. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V. et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2205–2214, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_AnnexVI.pdf"
+    "- Anyamba, A. and Tucker, C.J., 2012. Historical perspective of AVHRR NDVI and vegetation drought monitoring. Remote sensing of drought: innovative monitoring approaches, 23, pp.20. [https://digitalcommons.unl.edu/nasapub/217/](https://digitalcommons.unl.edu/nasapub/217/)\n",
+    "- Zhang, X., Alexander, L., Hegerl, G.C., Jones, P., Tank, A.K., Peterson, T.C., Trewin, B. and Zwiers, F.W., 2011. Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdisciplinary Reviews: Climate Change, 2(6), pp.851-870. [doi.org/10.1002/wcc.147](https://doi.org/10.1002/wcc.147)\n",
+    "- Schultz, P. A., and M. S. Halpert, 1993. Global correlation of temperature, NDVI and precipitation. Advances in Space Research 13(5). pp.277-280. [doi.org/10.1016/0273-1177(93)90559-T](https://doi.org/10.1016/0273-1177(93)90559-T)\n",
+    "- Seneviratne, S.I. et al., 2021: Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V. et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp.1513–1766, [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/](https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/)\n",
+    "- IPCC, 2021: Annex VI: Climatic Impact-driver and Extreme Indices [Gutiérrez J.M. et al.(eds.)]. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V. et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2205–2214, [https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_AnnexVI.pdf](https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_AnnexVI.pdf)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "80a8bf0d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -568,7 +710,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

From 9abd6e2aa4d62abbcaa6b3a1c19d7b335094624d Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Wed, 12 Jun 2024 18:53:19 +0200
Subject: [PATCH 26/65] Update of template acc to 2024 conventions.

---
 projects/template-images/2024_Precipitation.svg | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/projects/template-images/2024_Precipitation.svg b/projects/template-images/2024_Precipitation.svg
index 6e26493f6..ff3eaaa13 100644
--- a/projects/template-images/2024_Precipitation.svg
+++ b/projects/template-images/2024_Precipitation.svg
@@ -1 +1 @@
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-0.25q0.171875 -0.15625 0.171875 -0.359375q0 -0.390625 -0.3125 -0.546875q-0.296875 -0.15625 -0.71875 -0.25l-0.640625 -0.125q-0.34375 -0.078125 -0.640625 -0.203125q-0.28125 -0.140625 -0.46875 -0.390625q-0.171875 -0.25 -0.171875 -0.671875q0 -0.421875 0.265625 -0.703125q0.265625 -0.28125 0.671875 -0.421875q0.40625 -0.15625 0.859375 -0.15625q0.546875 0 1.03125 0.234375q0.484375 0.21875 0.703125 0.703125l-0.75 0.265625q-0.109375 -0.28125 -0.421875 -0.40625q-0.296875 -0.140625 -0.625 -0.140625q-0.359375 0 -0.65625 0.15625q-0.28125 0.15625 -0.28125 0.453125q0 0.296875 0.25 0.4375q0.25 0.140625 0.609375 0.203125l0.609375 0.125q0.375 0.078125 0.703125 0.21875q0.34375 0.140625 0.546875 0.421875q0.21875 0.265625 0.21875 0.765625q0 0.4375 -0.265625 0.75q-0.265625 0.296875 -0.703125 0.46875q-0.421875 0.15625 -0.90625 0.15625z" fill-rule="nonzero"/></g></svg>
\ No newline at end of file

From 9c313caf6dab678c1d44c29035648165ea81b10b Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Wed, 12 Jun 2024 18:58:12 +0200
Subject: [PATCH 27/65] Header update and slides link.

---
 .../2024_Ocean_acidification.ipynb            | 21 +++++++++++--------
 1 file changed, 12 insertions(+), 9 deletions(-)

diff --git a/projects/project-notebooks/2024_Ocean_acidification.ipynb b/projects/project-notebooks/2024_Ocean_acidification.ipynb
index 9e41c0741..5f4f8ac95 100644
--- a/projects/project-notebooks/2024_Ocean_acidification.ipynb
+++ b/projects/project-notebooks/2024_Ocean_acidification.ipynb
@@ -16,18 +16,18 @@
     "id": "avjFjFXwWqyW"
    },
    "source": [
-    "# **Ocean Acidification**\n",
+    "# Ocean Acidification\n",
     "\n",
     "\n",
     "**Content creators:** C. Gabriela Mayorga Adame, Lidia Krinova\n",
     "\n",
-    "**Content reviewers:** \n",
+    "**Content reviewers:** Will Gregory, Paul Heubel, Laura Paccini, Jenna Pearson, Ohad Zivan\n",
     "\n",
-    "**Content editors:** \n",
+    "**Content editors:** Paul Heubel\n",
     "\n",
-    "**Production editors:** \n",
+    "**Production editors:** Paul Heubel, Konstantine Tsafatinos\n",
     "\n",
-    "**Our 2023 Sponsors:** "
+    "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
    ]
   },
   {
@@ -107,7 +107,10 @@
     "# @title Tutorial slides\n",
     "# @markdown These are the slides for the videos in all tutorials today\n",
     "from IPython.display import IFrame\n",
-    "link_id = \"n7wdy\""
+    "link_id = \"n7wdy\"\n",
+    "# or \"hbx8d\"\n",
+    "print(f\"If you want to download the slides: https://osf.io/download/{link_id}/\")\n",
+    "IFrame(src=f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\", width=854, height=480)"
    ]
   },
   {
@@ -383,7 +386,7 @@
     "id": "JsPWOtzV1YBj"
    },
    "source": [
-    "# **Resources**\n",
+    "# Other Resources\n",
     "\n",
     "This tutorial uses data from the simulations conducted as part of the [CMIP6](https://wcrp-cmip.org/) multi-model ensemble.\n",
     "\n",
@@ -422,9 +425,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

From 7a5a5b604f035367199bbd15604e8cc0a29907e8 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 14:09:15 +0200
Subject: [PATCH 28/65] Move Wildfires project to past projects.

---
 .../Wildfires_and_burnt_areas.ipynb                               | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/{project-notebooks => past-projects}/Wildfires_and_burnt_areas.ipynb (100%)

diff --git a/projects/project-notebooks/Wildfires_and_burnt_areas.ipynb b/projects/past-projects/Wildfires_and_burnt_areas.ipynb
similarity index 100%
rename from projects/project-notebooks/Wildfires_and_burnt_areas.ipynb
rename to projects/past-projects/Wildfires_and_burnt_areas.ipynb

From d8efdd8730b12fbca8430afc4c3b907ef220ca62 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:18:21 +0200
Subject: [PATCH 29/65] Moved 2023 project to archive

---
 .../{past-projects => archive}/Wildfires_and_burnt_areas.ipynb    | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/{past-projects => archive}/Wildfires_and_burnt_areas.ipynb (100%)

diff --git a/projects/past-projects/Wildfires_and_burnt_areas.ipynb b/projects/archive/Wildfires_and_burnt_areas.ipynb
similarity index 100%
rename from projects/past-projects/Wildfires_and_burnt_areas.ipynb
rename to projects/archive/Wildfires_and_burnt_areas.ipynb

From b9d08cf1ef972c43fc002b3d97fdd0e7c41279b3 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:20:08 +0200
Subject: [PATCH 30/65] Moved 2023 project to archive.

---
 .../Surface_albedo_and_land_cover.ipynb                           | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/{project-notebooks => archive}/Surface_albedo_and_land_cover.ipynb (100%)

diff --git a/projects/project-notebooks/Surface_albedo_and_land_cover.ipynb b/projects/archive/Surface_albedo_and_land_cover.ipynb
similarity index 100%
rename from projects/project-notebooks/Surface_albedo_and_land_cover.ipynb
rename to projects/archive/Surface_albedo_and_land_cover.ipynb

From 189603edd0761541310b9d97e594a68f197ba044 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:21:13 +0200
Subject: [PATCH 31/65] Moved 2023 project to archive.

---
 projects/{project-notebooks => archive}/Heatwaves.ipynb | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/{project-notebooks => archive}/Heatwaves.ipynb (100%)

diff --git a/projects/project-notebooks/Heatwaves.ipynb b/projects/archive/Heatwaves.ipynb
similarity index 100%
rename from projects/project-notebooks/Heatwaves.ipynb
rename to projects/archive/Heatwaves.ipynb

From 130392621ae67d7ffb02a4d8cdd1c4b5c5845a80 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:22:17 +0200
Subject: [PATCH 32/65] Moved 2023 project to archive.

---
 projects/{project-notebooks => archive}/Sea_level_rise.ipynb | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/{project-notebooks => archive}/Sea_level_rise.ipynb (100%)

diff --git a/projects/project-notebooks/Sea_level_rise.ipynb b/projects/archive/Sea_level_rise.ipynb
similarity index 100%
rename from projects/project-notebooks/Sea_level_rise.ipynb
rename to projects/archive/Sea_level_rise.ipynb

From 2a6ab33a3cf9dfda34072394c50bf48d6c3841c5 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:23:30 +0200
Subject: [PATCH 33/65] Renamed OA project notebook.

---
 ...4_Ocean_acidification.ipynb => Ocean_acidification_2024.ipynb} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/project-notebooks/{2024_Ocean_acidification.ipynb => Ocean_acidification_2024.ipynb} (100%)

diff --git a/projects/project-notebooks/2024_Ocean_acidification.ipynb b/projects/project-notebooks/Ocean_acidification_2024.ipynb
similarity index 100%
rename from projects/project-notebooks/2024_Ocean_acidification.ipynb
rename to projects/project-notebooks/Ocean_acidification_2024.ipynb

From eeaec0afe7acd7730bdfc5c6429a52c562b92085 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:25:36 +0200
Subject: [PATCH 34/65] Archive 2023 project.

---
 projects/{project-notebooks => archive}/Ocean_acidification.ipynb | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/{project-notebooks => archive}/Ocean_acidification.ipynb (100%)

diff --git a/projects/project-notebooks/Ocean_acidification.ipynb b/projects/archive/Ocean_acidification.ipynb
similarity index 100%
rename from projects/project-notebooks/Ocean_acidification.ipynb
rename to projects/archive/Ocean_acidification.ipynb

From ed2bee0f7ac037984108e419869c2b3fd5436940 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:26:34 +0200
Subject: [PATCH 35/65] Archived 2023 project.

---
 .../ENSO_impact_on_precipitation_and_temperature.ipynb            | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/{project-notebooks => archive}/ENSO_impact_on_precipitation_and_temperature.ipynb (100%)

diff --git a/projects/project-notebooks/ENSO_impact_on_precipitation_and_temperature.ipynb b/projects/archive/ENSO_impact_on_precipitation_and_temperature.ipynb
similarity index 100%
rename from projects/project-notebooks/ENSO_impact_on_precipitation_and_temperature.ipynb
rename to projects/archive/ENSO_impact_on_precipitation_and_temperature.ipynb

From 72c5018a5c7c38d8d3d42338c0da6788474f6d1a Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:29:48 +0200
Subject: [PATCH 36/65] Archived 2023 project nb.

---
 .../Regional_precipitation_variability.ipynb                      | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/{project-notebooks => archive}/Regional_precipitation_variability.ipynb (100%)

diff --git a/projects/project-notebooks/Regional_precipitation_variability.ipynb b/projects/archive/Regional_precipitation_variability.ipynb
similarity index 100%
rename from projects/project-notebooks/Regional_precipitation_variability.ipynb
rename to projects/archive/Regional_precipitation_variability.ipynb

From 571088d122407582a27a1f6c94bd87b73340fb9b Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:32:33 +0200
Subject: [PATCH 37/65] New prec project notebook.

---
 .../Regional_precipitation_2024.ipynb         | 55 +++++++++++--------
 1 file changed, 31 insertions(+), 24 deletions(-)

diff --git a/projects/project-notebooks/Regional_precipitation_2024.ipynb b/projects/project-notebooks/Regional_precipitation_2024.ipynb
index c1abced30..835e4d890 100644
--- a/projects/project-notebooks/Regional_precipitation_2024.ipynb
+++ b/projects/project-notebooks/Regional_precipitation_2024.ipynb
@@ -15,7 +15,7 @@
     "execution": {}
    },
    "source": [
-    "# Precipitation Variability and Extreme events\n",
+    "# Precipitation Variability and Extreme Events\n",
     "\n",
     "**Content creators:** Will Gregory, Laura Paccini, Raphael Rocha\n",
     "\n",
@@ -108,7 +108,7 @@
     "from ipywidgets import widgets\n",
     "from IPython.display import IFrame\n",
     "\n",
-    "link_id = \"\"\n",
+    "link_id = \"djpgh\"\n",
     "\n",
     "download_link = f\"https://osf.io/download/{link_id}/\"\n",
     "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
@@ -144,13 +144,21 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f51243f3",
+   "id": "e114dfda-1ff2-4ec9-a338-8c5f5e8e17a0",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "## Data Exploration Notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8af2bd88-1cfe-46cd-82b3-5c58958ae6ad",
    "metadata": {
     "execution": {}
    },
    "source": [
-    "## Data Exploration Notebook\n",
-    "\n",
     "## Project Setup"
    ]
   },
@@ -248,7 +256,7 @@
    "metadata": {},
    "source": [
     "### Indices for Extreme Events\n",
-    "The Expert Team on Climate Change Detection and Indices ([ETCCDI]( http://etccdi.pacificclimate.org/list_27_indices.shtml)) has defined various indices that focus on different aspects such as duration or intensity of extreme events. The following functions provide examples of how to compute indices for each category. You can modify these functions to suit your specific needs or create your own custom functions. Here are some tips you can use:\n",
+    "The Expert Team on Climate Change Detection and Indices ([ETCCDI](http://etccdi.pacificclimate.org/list_27_indices.shtml)) has defined various indices that focus on different aspects such as duration or intensity of extreme events. The following functions provide examples of how to compute indices for each category. You can modify these functions to suit your specific needs or create your own custom functions. Here are some tips you can use:\n",
     "\n",
     "- Most of the indices require daily data, so in order to select a specific season you can just use `xarray` to subset the data. Example:\n",
     "\n",
@@ -401,10 +409,9 @@
     "file_location = fs.glob('s3://noaa-cdr-ndvi-pds/data/'\n",
     "                        + date_sel.strftime('%Y')\n",
     "                        + '/AVHRR-Land_v005_AVH13C1_*'\n",
-    "                        + date_sel.strftime(\"%Y%m\") # or \"%Y\" for the whole Year\n",
+    "                        + date_sel.strftime(\"%Y%m\") # or \"%Y\" for the entire year\n",
     "                        + '*_c*.nc') # the files we want for a whole year/month\n",
-    "# load the files, which might take up to a few hours depending on your device, in the JupyterHub it usually faster\n",
-    "files_for_pooch=[pooch_load('http://s3.amazonaws.com/'+ file, file) for file in file_location]\n",
+    "files_for_pooch=[pooch_load('http://s3.amazonaws.com/'+file,file) for file in file_location]\n",
     "\n",
     "ds = xr.open_mfdataset(files_for_pooch, combine=\"by_coords\", decode_times=False)  # open the file\n",
     "ds"
@@ -412,7 +419,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a453828b-1200-4ddf-b8ab-abc428e9b375",
+   "id": "a03b0a64-4564-4cfa-abed-16a40f63cf57",
    "metadata": {},
    "source": [
     "If the above downloading procedure is not working as expected, please retrieve the following sample to execute the remaining cells of the notebook. Ask for the help of your project TA to download your data of interest.\n"
@@ -421,7 +428,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6d8b70d6-e52c-4131-bd2d-3505f22bd13c",
+   "id": "8dc64b25-c2d7-4e1b-8023-6d551981f77e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -440,13 +447,13 @@
    "id": "658a800d-e83e-422b-ae96-e16bb235e609",
    "metadata": {},
    "source": [
-    "*Note that the downloaded satellite data is quality-controlled and flagged accordingly. Please refer to W1D3 Tutorial 3 and others to decipher it. Choose the feasible helper functions, e.g. `get_quality_info_AVHRR(QA)` depending on the sensor of interest. VIIRS is a state-of-the-art sensor that produces measurements that have been available since 2014.*"
+    "*Note that the downloaded satellite data is quality-controlled and flagged accordingly. Please refer to W1D3 Tutorial 3 and others to decipher it. Choose the feasible helper functions, e.g. `get_quality_info_AVHRR(QA)` depending on the sensor of interest. VIIRS is a state-of-the-art sensor that produces measurements that have been available since 2014. We provide an example of usage in the following.*"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "088d05ba-643f-4886-919c-2bf7fce08193",
+   "id": "b9fb8e19-f723-474d-9b42-f2ce891ffad4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -480,7 +487,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "70a41745-170f-4902-b680-674e25bf7489",
+   "id": "941c3c3d-06ac-427e-9f40-2ae9b3aafc89",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -497,7 +504,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1d07685f-f0a6-4e1f-8d5b-3d852f3b808d",
+   "id": "67f62201-5d28-4715-ad31-7a31ded86682",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -509,7 +516,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f8f5b584-18fc-45af-9c47-c118353d4e88",
+   "id": "24e94e81-f3b8-40be-85d9-635c30bb9567",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -605,7 +612,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09c1aae5-2286-4c4c-873a-c8bcb3e44e13",
+   "id": "45bf5b39-ff2e-4c58-bbf1-93358e8fe5ca",
    "metadata": {},
    "source": [
     "# Q1\n",
@@ -617,7 +624,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "356909bc-d52a-4c4c-9bb0-febf2b5979b2",
+   "id": "9dcfd1a8-3d47-4ca5-aebe-60785c679eca",
    "metadata": {
     "jp-MarkdownHeadingCollapsed": true
    },
@@ -634,7 +641,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81f8a45a-d2ba-4e02-b0a7-036192fdb0e9",
+   "id": "60e16fb0-e60a-4e67-ae55-ceb66bbdee47",
    "metadata": {},
    "source": [
     "# Q3\n",
@@ -645,7 +652,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0607dde-7c65-4ec7-a11b-b8185e33ad19",
+   "id": "86bd1989-70b0-4fa6-9c3c-dca2f61a96ea",
    "metadata": {},
    "source": [
     "# Q4\n",
@@ -676,10 +683,10 @@
    },
    "source": [
     "- Anyamba, A. and Tucker, C.J., 2012. Historical perspective of AVHRR NDVI and vegetation drought monitoring. Remote sensing of drought: innovative monitoring approaches, 23, pp.20. [https://digitalcommons.unl.edu/nasapub/217/](https://digitalcommons.unl.edu/nasapub/217/)\n",
-    "- Zhang, X., Alexander, L., Hegerl, G.C., Jones, P., Tank, A.K., Peterson, T.C., Trewin, B. and Zwiers, F.W., 2011. Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdisciplinary Reviews: Climate Change, 2(6), pp.851-870. [doi.org/10.1002/wcc.147](https://doi.org/10.1002/wcc.147)\n",
-    "- Schultz, P. A., and M. S. Halpert, 1993. Global correlation of temperature, NDVI and precipitation. Advances in Space Research 13(5). pp.277-280. [doi.org/10.1016/0273-1177(93)90559-T](https://doi.org/10.1016/0273-1177(93)90559-T)\n",
-    "- Seneviratne, S.I. et al., 2021: Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V. et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp.1513–1766, [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/](https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/)\n",
-    "- IPCC, 2021: Annex VI: Climatic Impact-driver and Extreme Indices [Gutiérrez J.M. et al.(eds.)]. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V. et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2205–2214, [https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_AnnexVI.pdf](https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_AnnexVI.pdf)"
+    "- Schultz, P. A., and M. S. Halpert, 1993. Global correlation of temperature, NDVI and precipitation. Advances in Space Research 13(5). pp.277-280. [doi.org/10.1016/0273-1177(93)90559-T](https://doi.org/10.1016/0273-1177(93)90559-T) (not open access)\n",
+    "- Seneviratne, S.I. et al., 2021: Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V. et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp.1513–1766, [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/](https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/) \n",
+    "- IPCC, 2021: Annex VI: Climatic Impact-driver and Extreme Indices [Gutiérrez J.M. et al.(eds.)]. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V. et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2205–2214, [https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_AnnexVI.pdf](https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_AnnexVI.pdf)\n",
+    "- Zhang, X., Alexander, L., Hegerl, G.C., Jones, P., Tank, A.K., Peterson, T.C., Trewin, B. and Zwiers, F.W., 2011. Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdisciplinary Reviews: Climate Change, 2(6), pp.851-870. [doi.org/10.1002/wcc.147](https://doi.org/10.1002/wcc.147) (not open access)\n"
    ]
   }
  ],

From 8ec1ecbda33a096311491722f15e5e26b1232cc0 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:34:12 +0200
Subject: [PATCH 38/65] Naming update acc to title.

---
 ....ipynb => Precipitation_variability_extreme_events_2024.ipynb} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/project-notebooks/{Regional_precipitation_2024.ipynb => Precipitation_variability_extreme_events_2024.ipynb} (100%)

diff --git a/projects/project-notebooks/Regional_precipitation_2024.ipynb b/projects/project-notebooks/Precipitation_variability_extreme_events_2024.ipynb
similarity index 100%
rename from projects/project-notebooks/Regional_precipitation_2024.ipynb
rename to projects/project-notebooks/Precipitation_variability_extreme_events_2024.ipynb

From 1785d3aa650900e1873dcaa5cf6e3ddbbd9ab8eb Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:35:23 +0200
Subject: [PATCH 39/65] Renaming acc to title.

---
 .../{Heatwaves_project_notebook_24.ipynb => Heatwaves_2024.ipynb} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/project-notebooks/{Heatwaves_project_notebook_24.ipynb => Heatwaves_2024.ipynb} (100%)

diff --git a/projects/project-notebooks/Heatwaves_project_notebook_24.ipynb b/projects/project-notebooks/Heatwaves_2024.ipynb
similarity index 100%
rename from projects/project-notebooks/Heatwaves_project_notebook_24.ipynb
rename to projects/project-notebooks/Heatwaves_2024.ipynb

From 0ae2b50d2fddc521a694913b23f037754bee4a7c Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:36:26 +0200
Subject: [PATCH 40/65] Renaming acc to title.

---
 .../project-notebooks/{SeaIce_2024.ipynb => Sea_Ice_2024.ipynb}   | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/project-notebooks/{SeaIce_2024.ipynb => Sea_Ice_2024.ipynb} (100%)

diff --git a/projects/project-notebooks/SeaIce_2024.ipynb b/projects/project-notebooks/Sea_Ice_2024.ipynb
similarity index 100%
rename from projects/project-notebooks/SeaIce_2024.ipynb
rename to projects/project-notebooks/Sea_Ice_2024.ipynb

From bd027269e6eb82a690ba9a33046d509d0a08c225 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:37:41 +0200
Subject: [PATCH 41/65] Fix renaming acc to title

---
 .../{Sea_Ice_2024.ipynb => Arctic_sea_ice_change_2024.ipynb}      | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename projects/project-notebooks/{Sea_Ice_2024.ipynb => Arctic_sea_ice_change_2024.ipynb} (100%)

diff --git a/projects/project-notebooks/Sea_Ice_2024.ipynb b/projects/project-notebooks/Arctic_sea_ice_change_2024.ipynb
similarity index 100%
rename from projects/project-notebooks/Sea_Ice_2024.ipynb
rename to projects/project-notebooks/Arctic_sea_ice_change_2024.ipynb

From 1029ce2b134b4378f6eaa038c279dcd1a2d6b894 Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:44:25 +0200
Subject: [PATCH 42/65] updated materials, reducing projects to four for 24

---
 projects/project_materials.yml | 22 ++++++++--------------
 1 file changed, 8 insertions(+), 14 deletions(-)

diff --git a/projects/project_materials.yml b/projects/project_materials.yml
index e99726679..256c20fc4 100644
--- a/projects/project_materials.yml
+++ b/projects/project_materials.yml
@@ -1,26 +1,20 @@
 - file: projects/README.md
   title: Introduction
 - file: projects/keynote.ipynb
-  title: Project Day keynote (W2D2)
+  title: Project Day keynote (W2D5)
 - file: projects/docs/project_guidance.md
   title: Daily guide for projects
 - file: projects/docs/datasets_overview.md
   title: Project materials
   sections:
-  - file: projects/project-notebooks/Sea_level_rise.ipynb
-    title: Sea Level Rise
-  - file: projects/project-notebooks/Ocean_acidification.ipynb
+  - file: projects/project-notebooks/Arctic_sea_ice_change_2024.ipynb
+    title: Arctic Sea Ice Change
+  - file: projects/project-notebooks/Heatwaves_2024.ipynb
+    title: Heatwaves
+  - file: projects/project-notebooks/Ocean_acidification_2024.ipynb
     title: Ocean Acidification
-  - file: projects/project-notebooks/ENSO_impact_on_precipitation_and_temperature.ipynb
-    title: The Impact of ENSO on Precipitation and Temperature
-  - file: projects/project-notebooks/Regional_precipitation_variability.ipynb
-    title: Regional Precipitation Variability and Extreme events
-  - file: projects/project-notebooks/Heatwaves.ipynb
-    title: "Heatwaves: Assessing the Dynamic Interactions of the Atmosphere and Land"
-  - file: projects/project-notebooks/Wildfires_and_burnt_areas.ipynb
-    title: Monitoring and Mapping Wildfires Using Satellite Data
-  - file: projects/project-notebooks/Surface_albedo_and_land_cover.ipynb
-    title: "Changes in Land Cover: Albedo and Carbon Sequestration"
+  - file: projects/project-notebooks/Precipitation_variability_extreme_events_2024.ipynb
+    title: Precipitation Variability and Extreme Events
 - file: projects/docs/continuing_your_project_after_the_course.md
   title: Continuing your project after the course
 - file: projects/docs/past_projects_overview.md

From a94296d7afb1797e3632b2b80ad7f04f19b269aa Mon Sep 17 00:00:00 2001
From: Paul Heubel <paul_heubel@web.de>
Date: Thu, 13 Jun 2024 18:49:54 +0200
Subject: [PATCH 43/65] Small text change in template.

---
 projects/template-images/2024_OceanAcidification.svg | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/projects/template-images/2024_OceanAcidification.svg b/projects/template-images/2024_OceanAcidification.svg
index f338af603..5ce6591fe 100644
--- a/projects/template-images/2024_OceanAcidification.svg
+++ b/projects/template-images/2024_OceanAcidification.svg
@@ -1 +1 @@
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\ No newline at end of file

From 0b51ec050cac403895f8ceaca38fbb7e8c8678c5 Mon Sep 17 00:00:00 2001
From: Konstantine Tsafatinos <kosta.tsaf@gmail.com>
Date: Thu, 13 Jun 2024 17:03:43 -0400
Subject: [PATCH 44/65] update workflow to ignore archive

---
 .github/actions/check-notebooks/action.yaml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index 218a95038..34e641180 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -47,6 +47,8 @@ runs:
       if: "!contains(env.COMMIT_MESSAGE, 'skip ci')"
       id: changed-files
       uses: tj-actions/changed-files@v35
+        with:
+          files_ignore: projects/archive/*
 
     - name: List all changed files
       run: |

From 69de904a03e44dfeb441b93017d7e055f883efac Mon Sep 17 00:00:00 2001
From: Konstantine Tsafatinos <kosta.tsaf@gmail.com>
Date: Thu, 13 Jun 2024 17:09:36 -0400
Subject: [PATCH 45/65] update workflow to ignore archive with proper format

---
 .github/actions/check-notebooks/action.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index 34e641180..a8b238409 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -47,8 +47,8 @@ runs:
       if: "!contains(env.COMMIT_MESSAGE, 'skip ci')"
       id: changed-files
       uses: tj-actions/changed-files@v35
-        with:
-          files_ignore: projects/archive/*
+      with:
+        files_ignore: projects/archive/*
 
     - name: List all changed files
       run: |

From a930479d49ef0c01407cf63f539a2f7f02d6297d Mon Sep 17 00:00:00 2001
From: Zoltan <kosta.tsaf@gmail.com>
Date: Thu, 13 Jun 2024 18:12:11 -0400
Subject: [PATCH 46/65] Update action.yaml - add keys for cdsapi

---
 .github/actions/check-notebooks/action.yaml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index a8b238409..94dd6edf4 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -81,6 +81,9 @@ runs:
       id: process_notebooks
       if: ${{ env.NBS != '' }}
       run: |
+        # add cdsapi credentials for projects
+        echo "url: https://cds.climate.copernicus.eu/api/v2
+              key: ${{ secrets.CDSAPI_UID }}:${{ secrets.CDSAPI_KEY }}" >> .cdsapirc
         python ci/process_notebooks.py ${{ env.NBS }} ${{ inputs.exec_flag}}
       shell: bash -l {0}
 

From cb22e5ac3afb7a15293f6c46704dd0b7200f9f76 Mon Sep 17 00:00:00 2001
From: Zoltan <kosta.tsaf@gmail.com>
Date: Thu, 13 Jun 2024 18:15:20 -0400
Subject: [PATCH 47/65] Update action.yaml - add keys for cdsapi corrected

---
 .github/actions/check-notebooks/action.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index 94dd6edf4..1bc05421b 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -82,8 +82,8 @@ runs:
       if: ${{ env.NBS != '' }}
       run: |
         # add cdsapi credentials for projects
-        echo "url: https://cds.climate.copernicus.eu/api/v2
-              key: ${{ secrets.CDSAPI_UID }}:${{ secrets.CDSAPI_KEY }}" >> .cdsapirc
+        echo "url: https://cds.climate.copernicus.eu/api/v2" >> .cdsapirc
+        echo "key: ${{ secrets.CDSAPI_UID }}:${{ secrets.CDSAPI_KEY }}" >> .cdsapirc
         python ci/process_notebooks.py ${{ env.NBS }} ${{ inputs.exec_flag}}
       shell: bash -l {0}
 

From 5da9d3864d89c06cecda86005d303d88392fa34e Mon Sep 17 00:00:00 2001
From: Zoltan <kosta.tsaf@gmail.com>
Date: Thu, 13 Jun 2024 18:30:13 -0400
Subject: [PATCH 48/65] Update action.yaml - add keys for cdsapi corrected 2

---
 .github/actions/check-notebooks/action.yaml | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index 1bc05421b..9672971f2 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -80,10 +80,13 @@ runs:
     - name: Process notebooks
       id: process_notebooks
       if: ${{ env.NBS != '' }}
+      env:
+        CDSAPI_UID: ${{ secrets.CDSAPI_UID }}
+        CDSAPI_KEY: ${{ secrets.CDSAPI_KEY }}
       run: |
         # add cdsapi credentials for projects
         echo "url: https://cds.climate.copernicus.eu/api/v2" >> .cdsapirc
-        echo "key: ${{ secrets.CDSAPI_UID }}:${{ secrets.CDSAPI_KEY }}" >> .cdsapirc
+        echo "key: ${{ CDSAPI_UID }}:${{ CDSAPI_KEY }}" >> .cdsapirc
         python ci/process_notebooks.py ${{ env.NBS }} ${{ inputs.exec_flag}}
       shell: bash -l {0}
 

From 33a43bb3950a69a5312e6d58ea4c70e65a7add87 Mon Sep 17 00:00:00 2001
From: Zoltan <kosta.tsaf@gmail.com>
Date: Thu, 13 Jun 2024 18:40:57 -0400
Subject: [PATCH 49/65] Update action.yaml - add keys for cdsapi corrected 3

---
 .github/actions/check-notebooks/action.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index 9672971f2..a69cf04f4 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -80,7 +80,7 @@ runs:
     - name: Process notebooks
       id: process_notebooks
       if: ${{ env.NBS != '' }}
-      env:
+      secrets:
         CDSAPI_UID: ${{ secrets.CDSAPI_UID }}
         CDSAPI_KEY: ${{ secrets.CDSAPI_KEY }}
       run: |

From ae1f97cffc99fcb4100d5b33ebe341e7a2eae593 Mon Sep 17 00:00:00 2001
From: Zoltan <kosta.tsaf@gmail.com>
Date: Thu, 13 Jun 2024 19:14:32 -0400
Subject: [PATCH 50/65] Update action.yaml - add keys for cdsapi corrected 4

---
 .github/actions/check-notebooks/action.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index a69cf04f4..3330d2161 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -80,7 +80,7 @@ runs:
     - name: Process notebooks
       id: process_notebooks
       if: ${{ env.NBS != '' }}
-      secrets:
+      with:
         CDSAPI_UID: ${{ secrets.CDSAPI_UID }}
         CDSAPI_KEY: ${{ secrets.CDSAPI_KEY }}
       run: |

From 733600e85a0c8e645f8c2de819481ab5ecba43cc Mon Sep 17 00:00:00 2001
From: Zoltan <kosta.tsaf@gmail.com>
Date: Thu, 13 Jun 2024 20:20:32 -0400
Subject: [PATCH 51/65] Update action.yaml

---
 .github/actions/check-notebooks/action.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index 3330d2161..436f6bf36 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -80,13 +80,13 @@ runs:
     - name: Process notebooks
       id: process_notebooks
       if: ${{ env.NBS != '' }}
-      with:
+      env:
         CDSAPI_UID: ${{ secrets.CDSAPI_UID }}
         CDSAPI_KEY: ${{ secrets.CDSAPI_KEY }}
       run: |
         # add cdsapi credentials for projects
         echo "url: https://cds.climate.copernicus.eu/api/v2" >> .cdsapirc
-        echo "key: ${{ CDSAPI_UID }}:${{ CDSAPI_KEY }}" >> .cdsapirc
+        echo "key: $CDSAPI_UID:$CDSAPI_KEY" >> .cdsapirc
         python ci/process_notebooks.py ${{ env.NBS }} ${{ inputs.exec_flag}}
       shell: bash -l {0}
 

From 465bc3ebb5d3e5f4933a7add7d82163dee92b7c6 Mon Sep 17 00:00:00 2001
From: Zoltan <kosta.tsaf@gmail.com>
Date: Thu, 13 Jun 2024 20:27:48 -0400
Subject: [PATCH 52/65] Update action.yaml

---
 .github/actions/check-notebooks/action.yaml | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index 436f6bf36..012849699 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -14,6 +14,16 @@ inputs:
     required: false
     default: false
     type: string
+  cdsapi_uid:
+    description: 'CDSAPI UID'
+    required: true
+  cdsapi_key:
+    description: 'CDSAPI Key'
+    required: true
+    
+env:
+  CDSAPI_UID: ${{ inputs.cdsapi_uid }}
+  CDSAPI_KEY: ${{ inputs.cdsapi_key }}
 
 runs:
   using: composite
@@ -80,9 +90,6 @@ runs:
     - name: Process notebooks
       id: process_notebooks
       if: ${{ env.NBS != '' }}
-      env:
-        CDSAPI_UID: ${{ secrets.CDSAPI_UID }}
-        CDSAPI_KEY: ${{ secrets.CDSAPI_KEY }}
       run: |
         # add cdsapi credentials for projects
         echo "url: https://cds.climate.copernicus.eu/api/v2" >> .cdsapirc

From a99419be264af26e38307d1a65617fb4412305e7 Mon Sep 17 00:00:00 2001
From: Zoltan <kosta.tsaf@gmail.com>
Date: Thu, 13 Jun 2024 20:28:57 -0400
Subject: [PATCH 53/65] Update notebook-pr.yaml

---
 .github/workflows/notebook-pr.yaml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/notebook-pr.yaml b/.github/workflows/notebook-pr.yaml
index 0ad105738..824cdc8f7 100644
--- a/.github/workflows/notebook-pr.yaml
+++ b/.github/workflows/notebook-pr.yaml
@@ -59,6 +59,8 @@ jobs:
           # exec_flag: '--check-only'
           exec_flag: '--execute'
           # exercise-continue-on-error: true
+          cdsapi_uid: ${{ secrets.CDSAPI_UID }}
+          cdsapi_key: ${{ secrets.CDSAPI_KEY }}
 
       # - name: Add PR comment
       #   if: ${{ env.NBS != '' }}

From ca530ee1ba7793530526a62182afb51a241eb838 Mon Sep 17 00:00:00 2001
From: Zoltan <kosta.tsaf@gmail.com>
Date: Thu, 13 Jun 2024 20:34:17 -0400
Subject: [PATCH 54/65] Update action.yaml

---
 .github/actions/check-notebooks/action.yaml | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index 012849699..ed5db8f1b 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -20,10 +20,6 @@ inputs:
   cdsapi_key:
     description: 'CDSAPI Key'
     required: true
-    
-env:
-  CDSAPI_UID: ${{ inputs.cdsapi_uid }}
-  CDSAPI_KEY: ${{ inputs.cdsapi_key }}
 
 runs:
   using: composite
@@ -93,7 +89,7 @@ runs:
       run: |
         # add cdsapi credentials for projects
         echo "url: https://cds.climate.copernicus.eu/api/v2" >> .cdsapirc
-        echo "key: $CDSAPI_UID:$CDSAPI_KEY" >> .cdsapirc
+        echo "key: ${{ inputs.cdsapi_uid }}:${{ inputs.cdsapi_key }}" >> .cdsapirc
         python ci/process_notebooks.py ${{ env.NBS }} ${{ inputs.exec_flag}}
       shell: bash -l {0}
 

From 74413339efdf7616bd91a8d74d66aa9809586906 Mon Sep 17 00:00:00 2001
From: Zoltan <kosta.tsaf@gmail.com>
Date: Thu, 13 Jun 2024 21:12:10 -0400
Subject: [PATCH 55/65] Update action.yaml

---
 .github/actions/check-notebooks/action.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index ed5db8f1b..12ddb4341 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -88,8 +88,8 @@ runs:
       if: ${{ env.NBS != '' }}
       run: |
         # add cdsapi credentials for projects
-        echo "url: https://cds.climate.copernicus.eu/api/v2" >> .cdsapirc
-        echo "key: ${{ inputs.cdsapi_uid }}:${{ inputs.cdsapi_key }}" >> .cdsapirc
+        echo "url: https://cds.climate.copernicus.eu/api/v2" >> /home/runner/.cdsapirc
+        echo "key: ${{ inputs.cdsapi_uid }}:${{ inputs.cdsapi_key }}" >> /home/runner/.cdsapirc
         python ci/process_notebooks.py ${{ env.NBS }} ${{ inputs.exec_flag}}
       shell: bash -l {0}
 

From 2689b2e58253a584b058c611b203ced63b3c03bd Mon Sep 17 00:00:00 2001
From: Zoltan <kosta.tsaf@gmail.com>
Date: Thu, 13 Jun 2024 23:01:51 -0400
Subject: [PATCH 56/65] Update requirements.txt

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 3ce6b286c..f6187af81 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,7 +7,7 @@ scikit-learn
 ipywidgets
 decorator==5.0.9
 # xarray==0.20.2
-xarray
+xarray[complete]
 shapely==1.8.5
 cartopy
 pythia_datasets

From 38d9f2dfc1694bef1175f71737c9a83d2befe24c Mon Sep 17 00:00:00 2001
From: Zoltan <kosta.tsaf@gmail.com>
Date: Fri, 14 Jun 2024 00:41:06 -0400
Subject: [PATCH 57/65] Update requirements.txt

---
 requirements.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index f6187af81..f3e8c55a3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -13,7 +13,6 @@ cartopy
 pythia_datasets
 pooch
 # climlab==0.8.2
-# climlab # try to run this with no cache
 # climlab-rrtmg
 cftime
 geopandas

From 3acc688d69cdfa1fb8118e2cffa8f645f20bd776 Mon Sep 17 00:00:00 2001
From: Konstantine Tsafatinos <kosta.tsaf@gmail.com>
Date: Fri, 14 Jun 2024 10:32:26 -0400
Subject: [PATCH 58/65] add cfgrib to reqs

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index f3e8c55a3..73c23cd4e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -46,3 +46,4 @@ mystatsfunctions @ https://github.com/njleach/mystatsfunctions/archive/main.zip
 dicelib @ https://github.com/mptouzel/PyDICE/archive/master.zip
 cdsapi
 texttable
+cfgrib

From 9a7f45cc9c8eb5ec801533d435da6e1521746209 Mon Sep 17 00:00:00 2001
From: Konstantine Tsafatinos <kosta.tsaf@gmail.com>
Date: Fri, 14 Jun 2024 23:47:15 -0400
Subject: [PATCH 59/65] add cfgrib install directly to workflow

---
 .github/actions/check-notebooks/action.yaml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index 12ddb4341..eb13d2f23 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -87,6 +87,9 @@ runs:
       id: process_notebooks
       if: ${{ env.NBS != '' }}
       run: |
+        # process notebooks
+        pip install xarray[complete]
+        pip install cfgrib
         # add cdsapi credentials for projects
         echo "url: https://cds.climate.copernicus.eu/api/v2" >> /home/runner/.cdsapirc
         echo "key: ${{ inputs.cdsapi_uid }}:${{ inputs.cdsapi_key }}" >> /home/runner/.cdsapirc

From d2288ad992d2257c35857f27bbe93667b3ff3e80 Mon Sep 17 00:00:00 2001
From: Konstantine Tsafatinos <kosta.tsaf@gmail.com>
Date: Mon, 17 Jun 2024 10:38:44 -0400
Subject: [PATCH 60/65] update reqs for cfgrib

---
 .github/actions/check-notebooks/action.yaml | 3 +--
 requirements.txt                            | 5 +++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index eb13d2f23..e6d8ad1fc 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -88,8 +88,7 @@ runs:
       if: ${{ env.NBS != '' }}
       run: |
         # process notebooks
-        pip install xarray[complete]
-        pip install cfgrib
+        python -m cfgrib selfcheck
         # add cdsapi credentials for projects
         echo "url: https://cds.climate.copernicus.eu/api/v2" >> /home/runner/.cdsapirc
         echo "key: ${{ inputs.cdsapi_uid }}:${{ inputs.cdsapi_key }}" >> /home/runner/.cdsapirc
diff --git a/requirements.txt b/requirements.txt
index 73c23cd4e..7dd229220 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,7 +7,7 @@ scikit-learn
 ipywidgets
 decorator==5.0.9
 # xarray==0.20.2
-xarray[complete]
+xarray[complete]==2024.2.0
 shapely==1.8.5
 cartopy
 pythia_datasets
@@ -46,4 +46,5 @@ mystatsfunctions @ https://github.com/njleach/mystatsfunctions/archive/main.zip
 dicelib @ https://github.com/mptouzel/PyDICE/archive/master.zip
 cdsapi
 texttable
-cfgrib
+eccodes==2.31.0
+cfgrib==0.9.10.4

From 3e7a9bbb74fd54a41fd53b5721f2494b71495de9 Mon Sep 17 00:00:00 2001
From: Konstantine Tsafatinos <kosta.tsaf@gmail.com>
Date: Mon, 17 Jun 2024 11:26:20 -0400
Subject: [PATCH 61/65] retry processing with without eccodes

---
 requirements.txt | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 7dd229220..dba16220d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -46,5 +46,4 @@ mystatsfunctions @ https://github.com/njleach/mystatsfunctions/archive/main.zip
 dicelib @ https://github.com/mptouzel/PyDICE/archive/master.zip
 cdsapi
 texttable
-eccodes==2.31.0
-cfgrib==0.9.10.4
+cfgrib

From 6595e4b6d7f84241f66e7881f58cf9e257e9cbae Mon Sep 17 00:00:00 2001
From: Konstantine Tsafatinos <kosta.tsaf@gmail.com>
Date: Mon, 17 Jun 2024 11:53:47 -0400
Subject: [PATCH 62/65] retry processing with cfgrib install before process
 script

---
 .github/actions/check-notebooks/action.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index e6d8ad1fc..1e32f0f9c 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -88,6 +88,7 @@ runs:
       if: ${{ env.NBS != '' }}
       run: |
         # process notebooks
+        pip install cfgrib
         python -m cfgrib selfcheck
         # add cdsapi credentials for projects
         echo "url: https://cds.climate.copernicus.eu/api/v2" >> /home/runner/.cdsapirc

From d84b28e622d411dcaf973d0af66a3b9b85a0ce4b Mon Sep 17 00:00:00 2001
From: Konstantine Tsafatinos <kosta.tsaf@gmail.com>
Date: Mon, 17 Jun 2024 12:45:57 -0400
Subject: [PATCH 63/65] retry processing with cfgrib, eccodes and xarray
 install before process script

---
 .github/actions/check-notebooks/action.yaml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index 1e32f0f9c..560f36bae 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -88,6 +88,8 @@ runs:
       if: ${{ env.NBS != '' }}
       run: |
         # process notebooks
+        pip install xarray[complete]
+        pip install eccodes
         pip install cfgrib
         python -m cfgrib selfcheck
         # add cdsapi credentials for projects

From e5b08b546a76c63cf137f78d68a5327f84a89860 Mon Sep 17 00:00:00 2001
From: Konstantine Tsafatinos <kosta.tsaf@gmail.com>
Date: Mon, 17 Jun 2024 13:25:01 -0400
Subject: [PATCH 64/65] retry processing installing eccodes and other deps in
 the setup workflow

---
 .github/actions/check-notebooks/action.yaml | 4 ----
 .github/actions/setup/action.yaml           | 6 +++++-
 requirements.txt                            | 2 +-
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/actions/check-notebooks/action.yaml b/.github/actions/check-notebooks/action.yaml
index 560f36bae..c3645eb41 100644
--- a/.github/actions/check-notebooks/action.yaml
+++ b/.github/actions/check-notebooks/action.yaml
@@ -88,10 +88,6 @@ runs:
       if: ${{ env.NBS != '' }}
       run: |
         # process notebooks
-        pip install xarray[complete]
-        pip install eccodes
-        pip install cfgrib
-        python -m cfgrib selfcheck
         # add cdsapi credentials for projects
         echo "url: https://cds.climate.copernicus.eu/api/v2" >> /home/runner/.cdsapirc
         echo "key: ${{ inputs.cdsapi_uid }}:${{ inputs.cdsapi_key }}" >> /home/runner/.cdsapirc
diff --git a/.github/actions/setup/action.yaml b/.github/actions/setup/action.yaml
index 718d02f87..8da5b3919 100644
--- a/.github/actions/setup/action.yaml
+++ b/.github/actions/setup/action.yaml
@@ -133,12 +133,16 @@ runs:
     - name: Update environment
       if: ${{steps.cache-python.outputs.cache-hit != 'true' || inputs.force_env_update == 'true'}}
       run: |
+        # Install python dependencies
         # mamba env update -n climatematch -f environment.yml
         pip install -r ci/requirements.txt
         pip install -r requirements.txt
+        pip install ecmwflibs
+        pip install eccodes==1.3.1
+        pip install cfgrib
+        python -m cfgrib selfcheck
         pip install requests aiohttp
         pip install jupyter-book==0.14.0 ghp-import cftime pyleoclim importlib-metadata==4.13.0
-        # pip install climlab==0.8.2
       shell: bash -l {0}
 
     - name: Build climlab from source
diff --git a/requirements.txt b/requirements.txt
index dba16220d..a3a920ed5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -46,4 +46,4 @@ mystatsfunctions @ https://github.com/njleach/mystatsfunctions/archive/main.zip
 dicelib @ https://github.com/mptouzel/PyDICE/archive/master.zip
 cdsapi
 texttable
-cfgrib
+# cfgrib

From 8f328c22bc95c45050c7a9ed219e82a91d6754ef Mon Sep 17 00:00:00 2001
From: GitHub Action <action@github.com>
Date: Mon, 17 Jun 2024 21:52:08 +0000
Subject: [PATCH 65/65] Process tutorial notebooks

---
 projects/keynote.ipynb                        |   63 +-
 .../Arctic_sea_ice_change_2024.ipynb          | 3504 ++++++++++++++-
 .../project-notebooks/Heatwaves_2024.ipynb    | 1874 +++++++-
 .../Ocean_acidification_2024.ipynb            | 1228 +++++-
 ...tion_variability_extreme_events_2024.ipynb | 3879 ++++++++++++++++-
 tutorials/README.md                           |   24 +-
 .../README.md                                 |   34 +-
 .../W2D3_Tutorial1_Solution_b2906775.py       |   16 -
 .../W2D3_Tutorial1_Solution_bbf54df1.py       |   13 -
 .../W2D3_Tutorial2_Solution_1425c24a.py       |   13 -
 .../W2D3_Tutorial2_Solution_c67833bd.py       |   11 -
 .../W2D3_Tutorial2_Solution_efff427c.py       |    3 -
 .../W2D3_Tutorial3_Solution_2aba4d73.py       |   10 -
 .../W2D3_Tutorial4_Solution_4240ed7b.py       |   10 -
 .../W2D3_Tutorial4_Solution_a4ef74b1.py       |   34 -
 .../W2D3_Tutorial4_Solution_a4ef74b1_0.png    |  Bin 66004 -> 0 bytes
 16 files changed, 10349 insertions(+), 367 deletions(-)
 delete mode 100644 tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial1_Solution_b2906775.py
 delete mode 100644 tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial1_Solution_bbf54df1.py
 delete mode 100644 tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial2_Solution_1425c24a.py
 delete mode 100644 tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial2_Solution_c67833bd.py
 delete mode 100644 tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial2_Solution_efff427c.py
 delete mode 100644 tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial3_Solution_2aba4d73.py
 delete mode 100644 tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial4_Solution_4240ed7b.py
 delete mode 100644 tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial4_Solution_a4ef74b1.py
 delete mode 100644 tutorials/W2D3_TheSocioeconomicsofClimateChange/static/W2D3_Tutorial4_Solution_a4ef74b1_0.png

diff --git a/projects/keynote.ipynb b/projects/keynote.ipynb
index 3bae69ed4..a62f36a75 100644
--- a/projects/keynote.ipynb
+++ b/projects/keynote.ipynb
@@ -3,7 +3,9 @@
   {
    "cell_type": "markdown",
    "id": "347f0ae5",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D2_ProjectsDay/W2D2_Intro.ipynb) &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D2_ProjectsDay/W2D2_Intro.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
    ]
@@ -56,9 +58,25 @@
    "execution_count": null,
    "id": "9656d38e",
    "metadata": {
-    "cellView": "form"
+    "cellView": "form",
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc4d62174a794907b031880e6fb2795c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tab(children=(Output(), Output()), selected_index=0, titles=('Youtube', 'Bilibili'))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# @markdown\n",
     "\n",
@@ -123,9 +141,40 @@
    "execution_count": null,
    "id": "3961e4a9",
    "metadata": {
-    "cellView": "form"
+    "cellView": "form",
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "If you want to download the slides: https://osf.io/download/3tc2h/\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"854\"\n",
+       "            height=\"480\"\n",
+       "            src=\"https://mfr.ca-1.osf.io/render?url=https://osf.io/3tc2h/?direct%26mode=render%26action=download%26mode=render\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7f5df4e47520>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# @title Slides\n",
     "# @markdown These are the slides for the videos in all tutorials today\n",
@@ -140,7 +189,7 @@
   "colab": {
    "collapsed_sections": [],
    "include_colab_link": true,
-   "name": "W2D1_Intro",
+   "name": "keynote",
    "toc_visible": true
   },
   "kernel": {
@@ -163,7 +212,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/projects/project-notebooks/Arctic_sea_ice_change_2024.ipynb b/projects/project-notebooks/Arctic_sea_ice_change_2024.ipynb
index e202e4677..0267e1e4d 100644
--- a/projects/project-notebooks/Arctic_sea_ice_change_2024.ipynb
+++ b/projects/project-notebooks/Arctic_sea_ice_change_2024.ipynb
@@ -3,7 +3,9 @@
   {
    "cell_type": "markdown",
    "id": "ada5523b",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "<a href=\"https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/projects/project-notebooks/SeaIce_2024.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/projects/project-notebooks/SeaIce_2024.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
    ]
@@ -33,9 +35,25 @@
    "execution_count": null,
    "id": "64da422f",
    "metadata": {
-    "cellView": "form"
+    "cellView": "form",
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "428dba8079dd4726934d8fabe46e6179",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tab(children=(Output(), Output(), Output()), selected_index=0, titles=('Youtube', 'Bilibili', 'Osf'))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# @title Project Background\n",
     "\n",
@@ -70,7 +88,7 @@
     "        if video_ids[i][0] == 'Bilibili':\n",
     "          print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
     "        elif video_ids[i][0] == 'Osf':\n",
-    "          print(f'Video available at https://osf.io/{video.id}')  \n",
+    "          print(f'Video available at https://osf.io/{video.id}')\n",
     "      display(video)\n",
     "    tab_contents.append(out)\n",
     "  return tab_contents\n",
@@ -89,8 +107,41 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "0f7b933c-9888-4051-8a62-c7f44246feda",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "If you want to download the slides: https://osf.io/download/y6em2/\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"854\"\n",
+       "            height=\"480\"\n",
+       "            src=\"https://mfr.ca-1.osf.io/render?url=https://osf.io/y6em2/?direct%26mode=render%26action=download%26mode=render\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7f0738066a00>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# @title Slides\n",
     "# @markdown These are the slides for the video introduction to the project\n",
@@ -147,7 +198,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "b68292ec",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# google colab installs\n",
@@ -159,8 +212,19 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "1e541a40",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/esmpy/interface/loadESMF.py:92: VersionWarning: ESMF installation version 8.7.0 beta snapshot, ESMPy version 8.7.0b7\n",
+      "  warnings.warn(\"ESMF installation version {}, ESMPy version {}\".format(\n"
+     ]
+    }
+   ],
    "source": [
     "# Imports\n",
     "\n",
@@ -179,7 +243,10 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "07545499",
-   "metadata": {},
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# @title Figure settings\n",
@@ -193,7 +260,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "c6569502-58dc-43a7-a95f-5fa5f8828179",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# Helper functions\n",
@@ -239,7 +308,9 @@
   {
    "cell_type": "markdown",
    "id": "867ff5a5-6b39-459d-b89f-d224126147b6",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "Let's search the CMIP6 catalog for some sea ice concentration (`siconca`) data from the CESM Earth System Model. We pick a certain ensemble member (`r1i1p1f1`) to reduce the amount of data to download. At first, our scenarios of interest are the `historical` and the middle-of-the-road `ssp245` ones. As we get the data on the native grid (`gn`), we must also download the grid cell area in the next step.\n",
     "\n",
@@ -255,8 +326,75 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "62ca691c-6efe-4598-932f-32f5e9aac31b",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
+      "\t'activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.zstore.dcpp_init_year.version'\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='2' class='' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [2/2 00:02&lt;00:00]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i1p1f1.SImon.siconca.gn.gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r1i1p1f1/SImon/siconca/gn/v20190815/.20190815',\n",
+       " 'CMIP.NCAR.CESM2-WACCM.historical.r1i1p1f1.SImon.siconca.gn.gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/SImon/siconca/gn/v20190507/.20190507']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# create a collection object from the Google Cloud Storage\n",
     "col = google_cmip_col()\n",
@@ -290,8 +428,75 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "5a348976-8faa-4a0f-aedd-c427b212120c",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
+      "\t'activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.zstore.dcpp_init_year.version'\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='2' class='' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [2/2 00:00&lt;00:00]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i1p1f1.fx.areacella.gn.gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r1i1p1f1/fx/areacella/gn/v20190815/.20190815',\n",
+       " 'CMIP.NCAR.CESM2-WACCM.historical.r1i1p1f1.fx.areacella.gn.gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/fx/areacella/gn/v20190227/.20190227']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# repeat the procedure with the grid cell area\n",
     "cat_metric = col.search(\n",
@@ -309,8 +514,754 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "08a37581-9a3f-4650-8811-5cd1aea35f27",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
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+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --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",
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+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
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+       "  --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",
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+       "\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",
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+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
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+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
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+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
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+       "\n",
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+       "\n",
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+       "}\n",
+       "\n",
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+       "\n",
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+       ".xr-icon-database,\n",
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+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3MB\n",
+       "Dimensions:         (member_id: 1, dcpp_init_year: 1, y: 192, x: 288, nbnd: 2)\n",
+       "Coordinates:\n",
+       "  * y               (y) float64 2kB -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n",
+       "  * x               (x) float64 2kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n",
+       "    lon_bounds      (x, nbnd, y) float64 885kB dask.array&lt;chunksize=(288, 2, 192), meta=np.ndarray&gt;\n",
+       "    lat_bounds      (y, nbnd, x) float64 885kB dask.array&lt;chunksize=(192, 2, 288), meta=np.ndarray&gt;\n",
+       "  * nbnd            (nbnd) int64 16B 0 1\n",
+       "    lon             (x, y) float64 442kB 360.0 360.0 360.0 ... 358.8 358.8 358.8\n",
+       "    lat             (x, y) float64 442kB -90.0 -89.06 -88.12 ... 89.06 90.0\n",
+       "  * member_id       (member_id) object 8B &#x27;r1i1p1f1&#x27;\n",
+       "  * dcpp_init_year  (dcpp_init_year) float64 8B nan\n",
+       "Data variables:\n",
+       "    areacella       (member_id, dcpp_init_year, y, x) float32 221kB dask.array&lt;chunksize=(1, 1, 192, 288), meta=np.ndarray&gt;\n",
+       "Attributes: (12/60)\n",
+       "    Conventions:                      CF-1.7 CMIP-6.2\n",
+       "    activity_id:                      ScenarioMIP\n",
+       "    branch_method:                    standard\n",
+       "    branch_time_in_child:             735110.0\n",
+       "    branch_time_in_parent:            735110.0\n",
+       "    case_id:                          966\n",
+       "    ...                               ...\n",
+       "    intake_esm_attrs:variable_id:     areacella\n",
+       "    intake_esm_attrs:grid_label:      gn\n",
+       "    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2...\n",
+       "    intake_esm_attrs:version:         20190815\n",
+       "    intake_esm_attrs:_data_format_:   zarr\n",
+       "    intake_esm_dataset_key:           ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i...</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-495d60a1-23f1-475b-b8c3-fbe5feebdeed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-495d60a1-23f1-475b-b8c3-fbe5feebdeed' 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'>member_id</span>: 1</li><li><span class='xr-has-index'>dcpp_init_year</span>: 1</li><li><span class='xr-has-index'>y</span>: 192</li><li><span class='xr-has-index'>x</span>: 288</li><li><span class='xr-has-index'>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-82484bb8-c3a5-4e05-9380-4b07b32f42a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-82484bb8-c3a5-4e05-9380-4b07b32f42a1' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-b55702b2-1115-4f15-bf0d-f0889ce742be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b55702b2-1115-4f15-bf0d-f0889ce742be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bd502db-8a01-463c-8291-51a1ec5af08d' class='xr-var-data-in' type='checkbox'><label for='data-5bd502db-8a01-463c-8291-51a1ec5af08d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
+       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
+       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
+       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
+       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
+       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
+       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
+       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
+       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
+       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
+       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
+       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
+       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
+       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
+       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
+       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
+       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
+       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
+       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
+       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
+       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
+       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
+       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
+       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
+       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
+       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
+       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
+       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
+       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
+       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
+       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
+       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-fdfd122f-8657-4326-a021-1498c4eb52ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdfd122f-8657-4326-a021-1498c4eb52ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-006334bd-a11b-4040-a820-c3623cdf5bbc' class='xr-var-data-in' type='checkbox'><label for='data-006334bd-a11b-4040-a820-c3623cdf5bbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bounds</span></div><div class='xr-var-dims'>(x, nbnd, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(288, 2, 192), meta=np.ndarray&gt;</div><input id='attrs-cdd96a69-a523-4b73-a772-9229de4fd994' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdd96a69-a523-4b73-a772-9229de4fd994' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-907e2fc6-def3-4208-b515-6f5e0c640354' class='xr-var-data-in' type='checkbox'><label for='data-907e2fc6-def3-4208-b515-6f5e0c640354' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 864.00 kiB </td>\n",
+       "                        <td> 864.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (288, 2, 192) </td>\n",
+       "                        <td> (288, 2, 192) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 9 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",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bounds</span></div><div class='xr-var-dims'>(y, nbnd, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192, 2, 288), meta=np.ndarray&gt;</div><input id='attrs-793716f0-5419-418c-87f2-0a1fcfe3c2f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-793716f0-5419-418c-87f2-0a1fcfe3c2f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-611019ed-ff54-4b5c-8b53-cbc45d879fa1' class='xr-var-data-in' type='checkbox'><label for='data-611019ed-ff54-4b5c-8b53-cbc45d879fa1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 864.00 kiB </td>\n",
+       "                        <td> 864.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (192, 2, 288) </td>\n",
+       "                        <td> (192, 2, 288) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 6 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\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'>nbnd</span></div><div class='xr-var-dims'>(nbnd)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-c4cfab50-9353-4714-b847-c0ae8d209c7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4cfab50-9353-4714-b847-c0ae8d209c7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8f9881f-24fd-4041-9a9f-aeca413a5850' class='xr-var-data-in' type='checkbox'><label for='data-a8f9881f-24fd-4041-9a9f-aeca413a5850' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>360.0 360.0 360.0 ... 358.8 358.8</div><input id='attrs-a4f827bb-abbe-4683-b915-f72a56efafc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4f827bb-abbe-4683-b915-f72a56efafc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3bc264c-d795-4532-a7d5-ad91ac36fc9a' class='xr-var-data-in' type='checkbox'><label for='data-c3bc264c-d795-4532-a7d5-ad91ac36fc9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[360.  , 360.  , 360.  , ..., 360.  , 360.  , 360.  ],\n",
+       "       [  1.25,   1.25,   1.25, ...,   1.25,   1.25,   1.25],\n",
+       "       [  2.5 ,   2.5 ,   2.5 , ...,   2.5 ,   2.5 ,   2.5 ],\n",
+       "       ...,\n",
+       "       [356.25, 356.25, 356.25, ..., 356.25, 356.25, 356.25],\n",
+       "       [357.5 , 357.5 , 357.5 , ..., 357.5 , 357.5 , 357.5 ],\n",
+       "       [358.75, 358.75, 358.75, ..., 358.75, 358.75, 358.75]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-2e5e49fd-ceb3-498d-90e6-3e39a0c5a9ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e5e49fd-ceb3-498d-90e6-3e39a0c5a9ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ab36b65-ffaa-4de5-af56-48ce070c9eb1' class='xr-var-data-in' type='checkbox'><label for='data-3ab36b65-ffaa-4de5-af56-48ce070c9eb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       ...,\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-dbd5ac9e-89fd-4b4d-bf67-9deb8f73ab54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dbd5ac9e-89fd-4b4d-bf67-9deb8f73ab54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9366de5-e122-48cb-b625-ff935fde573f' class='xr-var-data-in' type='checkbox'><label for='data-a9366de5-e122-48cb-b625-ff935fde573f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1i1p1f1&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dcpp_init_year</span></div><div class='xr-var-dims'>(dcpp_init_year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-bb122e9b-0f8a-4a52-a091-256f124b3edc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb122e9b-0f8a-4a52-a091-256f124b3edc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9cbf9e7-8070-4a04-a2d3-160aac1c31b2' class='xr-var-data-in' type='checkbox'><label for='data-e9cbf9e7-8070-4a04-a2d3-160aac1c31b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e79a4c96-6df2-46fb-8268-2fd5c1cd16b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e79a4c96-6df2-46fb-8268-2fd5c1cd16b6' 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>areacella</span></div><div class='xr-var-dims'>(member_id, dcpp_init_year, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 192, 288), meta=np.ndarray&gt;</div><input id='attrs-e3a1e97b-f564-400b-bfed-3ed62a6ad571' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3a1e97b-f564-400b-bfed-3ed62a6ad571' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e0bd76-4bcc-4a72-a9c6-17bf1a9f921c' class='xr-var-data-in' type='checkbox'><label for='data-85e0bd76-4bcc-4a72-a9c6-17bf1a9f921c' 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>cell_methods :</span></dt><dd>area: sum</dd><dt><span>comment :</span></dt><dd>AREA[0,:,:]</dd><dt><span>description :</span></dt><dd>Cell areas for any grid used to report atmospheric variables and any other variable using that grid (e.g., soil moisture content). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>id :</span></dt><dd>areacella</dd><dt><span>long_name :</span></dt><dd>Grid-Cell Area for Atmospheric Grid Variables</dd><dt><span>mipTable :</span></dt><dd>fx</dd><dt><span>out_name :</span></dt><dd>areacella</dd><dt><span>prov :</span></dt><dd>fx ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos land</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>time_label :</span></dt><dd>None</dd><dt><span>time_title :</span></dt><dd>No temporal dimensions ... fixed field</dd><dt><span>title :</span></dt><dd>Grid-Cell Area for Atmospheric Grid Variables</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>variable_id :</span></dt><dd>areacella</dd><dt><span>units :</span></dt><dd>m²</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> 216.00 kiB </td>\n",
+       "                        <td> 216.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 1, 192, 288) </td>\n",
+       "                        <td> (1, 1, 192, 288) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"374\" height=\"144\" 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=\"25\" x2=\"25\" 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=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"109\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"95\" y1=\"80\" x2=\"109\" y2=\"94\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"80\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"109\" y1=\"14\" x2=\"109\" y2=\"94\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"95.0,0.0 109.9485979497544,14.948597949754403 109.9485979497544,94.9485979497544 95.0,80.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"109\" y1=\"14\" x2=\"229\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"109\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"215\" y1=\"0\" x2=\"229\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"95.0,0.0 215.0,0.0 229.9485979497544,14.948597949754403 109.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"109\" y1=\"14\" x2=\"229\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"109\" y1=\"94\" x2=\"229\" y2=\"94\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"109\" y1=\"14\" x2=\"109\" y2=\"94\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"229\" y1=\"14\" x2=\"229\" y2=\"94\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"109.9485979497544,14.948597949754403 229.9485979497544,14.948597949754403 229.9485979497544,94.9485979497544 109.9485979497544,94.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"169.948598\" y=\"114.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >288</text>\n",
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+       "  <text x=\"92.474299\" y=\"107.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,92.474299,107.474299)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-40496caa-39ee-45c1-bd96-8f96816a4b0d' class='xr-section-summary-in' type='checkbox'  ><label for='section-40496caa-39ee-45c1-bd96-8f96816a4b0d' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a52c98ac-1cbf-44d3-b87d-36f52e26107a' class='xr-index-data-in' type='checkbox'/><label for='index-a52c98ac-1cbf-44d3-b87d-36f52e26107a' 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([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
+       "       -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
+       "        -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
+       "       -81.51832460732984,\n",
+       "       ...\n",
+       "        81.51832460732984,   82.4607329842932,  83.40314136125653,\n",
+       "        84.34554973821989,  85.28795811518324,   86.2303664921466,\n",
+       "        87.17277486910996,  88.11518324607329,  89.05759162303664,\n",
+       "                     90.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c72bbd2f-e37e-4848-a189-43fd59eb21cc' class='xr-index-data-in' type='checkbox'/><label for='index-c72bbd2f-e37e-4848-a189-43fd59eb21cc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
+       "        11.25,\n",
+       "       ...\n",
+       "        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,\n",
+       "       358.75],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>nbnd</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-508ffb8a-dd13-4dcc-9351-f91266d44013' class='xr-index-data-in' type='checkbox'/><label for='index-508ffb8a-dd13-4dcc-9351-f91266d44013' 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(RangeIndex(start=0, stop=2, step=1, name=&#x27;nbnd&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b1758d0c-7912-4be5-8187-5070c6ac86ff' class='xr-index-data-in' type='checkbox'/><label for='index-b1758d0c-7912-4be5-8187-5070c6ac86ff' 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([&#x27;r1i1p1f1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;member_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dcpp_init_year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6e0598d0-0200-4fdd-bf08-fd2b859a77f2' class='xr-index-data-in' type='checkbox'/><label for='index-6e0598d0-0200-4fdd-bf08-fd2b859a77f2' 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([nan], dtype=&#x27;float64&#x27;, name=&#x27;dcpp_init_year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e5c42d0-c8b7-47bc-aa5b-54cc944ed0cf' class='xr-section-summary-in' type='checkbox'  ><label for='section-8e5c42d0-c8b7-47bc-aa5b-54cc944ed0cf' class='xr-section-summary' >Attributes: <span>(60)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>ScenarioMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>735110.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>735110.0</dd><dt><span>case_id :</span></dt><dd>966</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BWSSP245cmip6.f09_g17.CMIP6-SSP2-4.5-WACCM.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-03-31T17:07:04Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>SSP-based RCP scenario with medium radiative forcing by the end of the century.  Following approximately RCP4.5 global forcing pathway with SSP2 socioeconomic conditions. Radiative forcing reaches a level of 4.5 W/m2 in 2100. Concentration-driven.</dd><dt><span>experiment_id :</span></dt><dd>ssp245</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2-WACCM.ssp245.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>native 0.9x1.25 finite volume grid (192x288 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos land</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); atmosChem: WACCM (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb; landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC CHEM AER</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>fx</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/0ea8a6f3-80ba-45f0-8c6d-8c28c00fc53c</dd><dt><span>variable_id :</span></dt><dd>areacella</dd><dt><span>variant_info :</span></dt><dd>CMIP6 CESM2 future scenario SSP2-4.5, years 2015-2100, with WACCM6 with interactive chemistry (TSMLT1), interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1).</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>status :</span></dt><dd>2019-10-25;created;by nhn2@columbia.edu</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/0ea8a6f3-80ba-45f0-8c6d-8c28c00fc53c</dd><dt><span>version_id :</span></dt><dd>v20190815</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;areacella&#x27;]</dd><dt><span>intake_esm_attrs:activity_id :</span></dt><dd>ScenarioMIP</dd><dt><span>intake_esm_attrs:institution_id :</span></dt><dd>NCAR</dd><dt><span>intake_esm_attrs:source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>intake_esm_attrs:experiment_id :</span></dt><dd>ssp245</dd><dt><span>intake_esm_attrs:member_id :</span></dt><dd>r1i1p1f1</dd><dt><span>intake_esm_attrs:table_id :</span></dt><dd>fx</dd><dt><span>intake_esm_attrs:variable_id :</span></dt><dd>areacella</dd><dt><span>intake_esm_attrs:grid_label :</span></dt><dd>gn</dd><dt><span>intake_esm_attrs:zstore :</span></dt><dd>gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r1i1p1f1/fx/areacella/gn/v20190815/</dd><dt><span>intake_esm_attrs:version :</span></dt><dd>20190815</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>zarr</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i1p1f1.fx.areacella.gn.gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r1i1p1f1/fx/areacella/gn/v20190815/.20190815</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 3MB\n",
+       "Dimensions:         (member_id: 1, dcpp_init_year: 1, y: 192, x: 288, nbnd: 2)\n",
+       "Coordinates:\n",
+       "  * y               (y) float64 2kB -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n",
+       "  * x               (x) float64 2kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n",
+       "    lon_bounds      (x, nbnd, y) float64 885kB dask.array<chunksize=(288, 2, 192), meta=np.ndarray>\n",
+       "    lat_bounds      (y, nbnd, x) float64 885kB dask.array<chunksize=(192, 2, 288), meta=np.ndarray>\n",
+       "  * nbnd            (nbnd) int64 16B 0 1\n",
+       "    lon             (x, y) float64 442kB 360.0 360.0 360.0 ... 358.8 358.8 358.8\n",
+       "    lat             (x, y) float64 442kB -90.0 -89.06 -88.12 ... 89.06 90.0\n",
+       "  * member_id       (member_id) object 8B 'r1i1p1f1'\n",
+       "  * dcpp_init_year  (dcpp_init_year) float64 8B nan\n",
+       "Data variables:\n",
+       "    areacella       (member_id, dcpp_init_year, y, x) float32 221kB dask.array<chunksize=(1, 1, 192, 288), meta=np.ndarray>\n",
+       "Attributes: (12/60)\n",
+       "    Conventions:                      CF-1.7 CMIP-6.2\n",
+       "    activity_id:                      ScenarioMIP\n",
+       "    branch_method:                    standard\n",
+       "    branch_time_in_child:             735110.0\n",
+       "    branch_time_in_parent:            735110.0\n",
+       "    case_id:                          966\n",
+       "    ...                               ...\n",
+       "    intake_esm_attrs:variable_id:     areacella\n",
+       "    intake_esm_attrs:grid_label:      gn\n",
+       "    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2...\n",
+       "    intake_esm_attrs:version:         20190815\n",
+       "    intake_esm_attrs:_data_format_:   zarr\n",
+       "    intake_esm_dataset_key:           ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i..."
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#ddict_metrics[CMIP.NCAR.CESM2-WACCM.historical.r1i1p1f1.fx.areacella.gn.gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/fx/areacella/gn/v20190227/.20190227']\n",
     "ddict_metrics[list(ddict_metrics.keys())[0]]"
@@ -320,8 +1271,22 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "5d2ffd64-8c4c-4934-b2de-00da88cc5703",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i1p1f1.SImon.siconca.gn.gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r1i1p1f1/SImon/siconca/gn/v20190815/.20190815',\n",
+       " 'CMIP.NCAR.CESM2-WACCM.historical.r1i1p1f1.SImon.siconca.gn.gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/SImon/siconca/gn/v20190507/.20190507']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# add the grid cell area metric to both scenario data sets\n",
     "ddict_matched = match_metrics(ds_dict, ddict_metrics, ['areacella'])\n",
@@ -332,8 +1297,933 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "db90f606-c419-416b-99cb-aecfb810ba2a",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
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+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 441MB\n",
+       "Dimensions:         (member_id: 1, dcpp_init_year: 1, time: 1980, y: 192,\n",
+       "                     x: 288, nbnd: 2)\n",
+       "Coordinates:\n",
+       "  * y               (y) float64 2kB -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n",
+       "  * x               (x) float64 2kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n",
+       "  * time            (time) object 16kB 1850-01-15 12:00:00 ... 2014-12-15 12:...\n",
+       "    lon_bounds      (x, nbnd, y) float64 885kB dask.array&lt;chunksize=(288, 2, 192), meta=np.ndarray&gt;\n",
+       "    time_bounds     (time, nbnd) object 32kB dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
+       "    lat_bounds      (y, nbnd, x) float64 885kB dask.array&lt;chunksize=(192, 2, 288), meta=np.ndarray&gt;\n",
+       "  * nbnd            (nbnd) int64 16B 0 1\n",
+       "    lon             (x, y) float64 442kB 360.0 360.0 360.0 ... 358.8 358.8 358.8\n",
+       "    lat             (x, y) float64 442kB -90.0 -89.06 -88.12 ... 89.06 90.0\n",
+       "  * member_id       (member_id) object 8B &#x27;r1i1p1f1&#x27;\n",
+       "  * dcpp_init_year  (dcpp_init_year) float64 8B nan\n",
+       "    areacella       (member_id, dcpp_init_year, y, x) float32 221kB dask.array&lt;chunksize=(1, 1, 192, 288), meta=np.ndarray&gt;\n",
+       "Data variables:\n",
+       "    siconca         (member_id, dcpp_init_year, time, y, x) float32 438MB dask.array&lt;chunksize=(1, 1, 600, 192, 288), meta=np.ndarray&gt;\n",
+       "Attributes: (12/61)\n",
+       "    Conventions:                      CF-1.7 CMIP-6.2\n",
+       "    activity_id:                      CMIP\n",
+       "    branch_method:                    standard\n",
+       "    branch_time_in_child:             674885.0\n",
+       "    branch_time_in_parent:            20075.0\n",
+       "    case_id:                          4\n",
+       "    ...                               ...\n",
+       "    intake_esm_attrs:variable_id:     siconca\n",
+       "    intake_esm_attrs:grid_label:      gn\n",
+       "    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/...\n",
+       "    intake_esm_attrs:version:         20190507\n",
+       "    intake_esm_attrs:_data_format_:   zarr\n",
+       "    intake_esm_dataset_key:           CMIP.NCAR.CESM2-WACCM.historical.r1i1p1...</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-1f54c94f-c60a-4eff-89d7-9c549fb68453' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1f54c94f-c60a-4eff-89d7-9c549fb68453' 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'>member_id</span>: 1</li><li><span class='xr-has-index'>dcpp_init_year</span>: 1</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>y</span>: 192</li><li><span class='xr-has-index'>x</span>: 288</li><li><span class='xr-has-index'>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-63406f12-2668-489d-9bf1-f3768fd0bce5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-63406f12-2668-489d-9bf1-f3768fd0bce5' class='xr-section-summary' >Coordinates: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-8cf23940-7f85-4fad-80eb-0d41436ff830' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cf23940-7f85-4fad-80eb-0d41436ff830' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c5dfeb3-af55-48e9-87b1-f24d5f591563' class='xr-var-data-in' type='checkbox'><label for='data-1c5dfeb3-af55-48e9-87b1-f24d5f591563' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
+       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
+       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
+       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
+       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
+       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
+       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
+       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
+       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
+       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
+       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
+       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
+       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
+       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
+       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
+       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
+       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
+       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
+       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
+       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
+       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
+       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
+       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
+       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
+       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
+       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
+       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
+       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
+       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
+       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
+       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
+       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-8f5ae203-468c-4d73-9c27-97277ee5a495' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f5ae203-468c-4d73-9c27-97277ee5a495' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef51f436-a475-4918-ab5a-8a0a74bc9927' class='xr-var-data-in' type='checkbox'><label for='data-ef51f436-a475-4918-ab5a-8a0a74bc9927' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</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'>1850-01-15 12:00:00 ... 2014-12-...</div><input id='attrs-c7094241-ea41-4366-b4c1-2bd0ee3a6bb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7094241-ea41-4366-b4c1-2bd0ee3a6bb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dffd9d48-9251-4649-a5b0-595b69f1d74b' class='xr-var-data-in' type='checkbox'><label for='data-dffd9d48-9251-4649-a5b0-595b69f1d74b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       ...,\n",
+       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2014, 12, 15, 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>lon_bounds</span></div><div class='xr-var-dims'>(x, nbnd, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(288, 2, 192), meta=np.ndarray&gt;</div><input id='attrs-49a81682-ad21-48a4-afbd-69d1820ac426' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49a81682-ad21-48a4-afbd-69d1820ac426' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e731fed-e7d6-474c-b88a-74132976b18a' class='xr-var-data-in' type='checkbox'><label for='data-3e731fed-e7d6-474c-b88a-74132976b18a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 864.00 kiB </td>\n",
+       "                        <td> 864.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (288, 2, 192) </td>\n",
+       "                        <td> (288, 2, 192) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 9 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=\"210\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"90\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"160\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"160\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 90.0,0.0 160.58823529411765,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"160\" y2=\"70\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-4b770b58-e2d8-4c88-8ff2-c93e6c03ce5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b770b58-e2d8-4c88-8ff2-c93e6c03ce5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de5fddb9-0613-47ed-9b36-3b611bf2ce67' class='xr-var-data-in' type='checkbox'><label for='data-de5fddb9-0613-47ed-9b36-3b611bf2ce67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 30.94 kiB </td>\n",
+       "                        <td> 30.94 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1980, 2) </td>\n",
+       "                        <td> (1980, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> object 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>lat_bounds</span></div><div class='xr-var-dims'>(y, nbnd, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192, 2, 288), meta=np.ndarray&gt;</div><input id='attrs-5c4021b6-30d5-43a7-8392-2934ef199e54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c4021b6-30d5-43a7-8392-2934ef199e54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26c3c6b4-cadc-4192-b491-680e4723477f' class='xr-var-data-in' type='checkbox'><label for='data-26c3c6b4-cadc-4192-b491-680e4723477f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 864.00 kiB </td>\n",
+       "                        <td> 864.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (192, 2, 288) </td>\n",
+       "                        <td> (192, 2, 288) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 6 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "  <text x=\"23.529412\" y=\"68.942028\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,23.529412,68.942028)\">192</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'>nbnd</span></div><div class='xr-var-dims'>(nbnd)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-bd937803-89f6-40c0-8063-883b33edc26f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd937803-89f6-40c0-8063-883b33edc26f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff2e83a9-05a8-4825-b294-8cffb191a76f' class='xr-var-data-in' type='checkbox'><label for='data-ff2e83a9-05a8-4825-b294-8cffb191a76f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>360.0 360.0 360.0 ... 358.8 358.8</div><input id='attrs-c9cad8ee-4ea4-44dc-b911-47f1868f895b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9cad8ee-4ea4-44dc-b911-47f1868f895b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89960ebd-584a-423b-8654-c161b20f945d' class='xr-var-data-in' type='checkbox'><label for='data-89960ebd-584a-423b-8654-c161b20f945d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[360.  , 360.  , 360.  , ..., 360.  , 360.  , 360.  ],\n",
+       "       [  1.25,   1.25,   1.25, ...,   1.25,   1.25,   1.25],\n",
+       "       [  2.5 ,   2.5 ,   2.5 , ...,   2.5 ,   2.5 ,   2.5 ],\n",
+       "       ...,\n",
+       "       [356.25, 356.25, 356.25, ..., 356.25, 356.25, 356.25],\n",
+       "       [357.5 , 357.5 , 357.5 , ..., 357.5 , 357.5 , 357.5 ],\n",
+       "       [358.75, 358.75, 358.75, ..., 358.75, 358.75, 358.75]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-c31f4232-cdb9-4866-9136-5dcfcfef0888' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c31f4232-cdb9-4866-9136-5dcfcfef0888' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84e54d8e-d8a0-4d1e-ba4f-bb7db26c3367' class='xr-var-data-in' type='checkbox'><label for='data-84e54d8e-d8a0-4d1e-ba4f-bb7db26c3367' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       ...,\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-a71eef4c-d5ea-48dd-98b0-335f53cf6982' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a71eef4c-d5ea-48dd-98b0-335f53cf6982' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e72423e5-b7e4-4a68-b3cf-47475e917dba' class='xr-var-data-in' type='checkbox'><label for='data-e72423e5-b7e4-4a68-b3cf-47475e917dba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1i1p1f1&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dcpp_init_year</span></div><div class='xr-var-dims'>(dcpp_init_year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-07e8ec34-1a96-4f35-a4cc-d5edb4dc4db8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07e8ec34-1a96-4f35-a4cc-d5edb4dc4db8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2d2bf5c-2d08-4431-9455-1be09c8cca38' class='xr-var-data-in' type='checkbox'><label for='data-f2d2bf5c-2d08-4431-9455-1be09c8cca38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>areacella</span></div><div class='xr-var-dims'>(member_id, dcpp_init_year, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 192, 288), meta=np.ndarray&gt;</div><input id='attrs-c5c1f86d-ab04-476b-9a3b-14f7c76694a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5c1f86d-ab04-476b-9a3b-14f7c76694a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6b6d455-afc1-47ba-8515-fe4bcd128836' class='xr-var-data-in' type='checkbox'><label for='data-a6b6d455-afc1-47ba-8515-fe4bcd128836' 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>cell_methods :</span></dt><dd>area: sum</dd><dt><span>comment :</span></dt><dd>Cell areas for any grid used to report atmospheric variables and any other variable using that grid (e.g., soil moisture content). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).</dd><dt><span>description :</span></dt><dd>Cell areas for any grid used to report atmospheric variables and any other variable using that grid (e.g., soil moisture content). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>id :</span></dt><dd>areacella</dd><dt><span>long_name :</span></dt><dd>Grid-Cell Area for Atmospheric Grid Variables</dd><dt><span>mipTable :</span></dt><dd>fx</dd><dt><span>out_name :</span></dt><dd>areacella</dd><dt><span>prov :</span></dt><dd>fx ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos land</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>time_label :</span></dt><dd>None</dd><dt><span>time_title :</span></dt><dd>No temporal dimensions ... fixed field</dd><dt><span>title :</span></dt><dd>Grid-Cell Area for Atmospheric Grid Variables</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>variable_id :</span></dt><dd>areacella</dd><dt><span>units :</span></dt><dd>m²</dd><dt><span>original_key :</span></dt><dd>CMIP.NCAR.CESM2-WACCM.historical.r1i1p1f1.fx.areacella.gn.gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/fx/areacella/gn/v20190227/.20190227</dd><dt><span>parsed_with :</span></dt><dd>xmip/postprocessing/_parse_metric</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> 216.00 kiB </td>\n",
+       "                        <td> 216.00 kiB </td>\n",
+       "                    </tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 1, 192, 288) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "  <text x=\"92.474299\" y=\"107.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,92.474299,107.474299)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-c080bd79-5792-4ae1-9e82-bcc849927209' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c080bd79-5792-4ae1-9e82-bcc849927209' 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>siconca</span></div><div class='xr-var-dims'>(member_id, dcpp_init_year, time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 600, 192, 288), meta=np.ndarray&gt;</div><input id='attrs-94fb65cd-164e-4775-9298-78c69878d258' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94fb65cd-164e-4775-9298-78c69878d258' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-187cd0df-2689-402a-a955-64cb34447718' class='xr-var-data-in' type='checkbox'><label for='data-187cd0df-2689-402a-a955-64cb34447718' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>ICEFRAC</dd><dt><span>description :</span></dt><dd>Percentage of grid cell covered by sea ice</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>siconca</dd><dt><span>long_name :</span></dt><dd>Sea-Ice Area Percentage (Atmospheric Grid)</dd><dt><span>mipTable :</span></dt><dd>SImon</dd><dt><span>out_name :</span></dt><dd>siconca</dd><dt><span>prov :</span></dt><dd>SIMIP [seaicemon]</dd><dt><span>realm :</span></dt><dd>seaIce</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Sea-Ice Area Percentage (Atmospheric Grid)</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>variable_id :</span></dt><dd>siconca</dd><dt><span>units :</span></dt><dd>%</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> 417.66 MiB </td>\n",
+       "                        <td> 126.56 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 1, 1980, 192, 288) </td>\n",
+       "                        <td> (1, 1, 600, 192, 288) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"185.866665\" y=\"129.013868\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >288</text>\n",
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+       "  <text x=\"120.294118\" y=\"93.719750\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,120.294118,93.719750)\">1980</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d3160db8-3217-402f-9520-73d34252a6b4' class='xr-section-summary-in' type='checkbox'  ><label for='section-d3160db8-3217-402f-9520-73d34252a6b4' class='xr-section-summary' >Indexes: <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-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c0ff2d9d-a12b-4a08-af48-626946afd33d' class='xr-index-data-in' type='checkbox'/><label for='index-c0ff2d9d-a12b-4a08-af48-626946afd33d' 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([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
+       "       -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
+       "        -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
+       "       -81.51832460732984,\n",
+       "       ...\n",
+       "        81.51832460732984,   82.4607329842932,  83.40314136125653,\n",
+       "        84.34554973821989,  85.28795811518324,   86.2303664921466,\n",
+       "        87.17277486910996,  88.11518324607329,  89.05759162303664,\n",
+       "                     90.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-09b089ef-c305-4e45-8644-9002484fd956' class='xr-index-data-in' type='checkbox'/><label for='index-09b089ef-c305-4e45-8644-9002484fd956' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
+       "        11.25,\n",
+       "       ...\n",
+       "        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,\n",
+       "       358.75],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#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-362f3c12-a518-42fd-b0fc-e27cd06153a0' class='xr-index-data-in' type='checkbox'/><label for='index-362f3c12-a518-42fd-b0fc-e27cd06153a0' 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([1850-01-15 12:00:00, 1850-02-14 00:00:00, 1850-03-15 12:00:00,\n",
+       "             1850-04-15 00:00:00, 1850-05-15 12:00:00, 1850-06-15 00:00:00,\n",
+       "             1850-07-15 12:00:00, 1850-08-15 12:00:00, 1850-09-15 00:00:00,\n",
+       "             1850-10-15 12:00:00,\n",
+       "             ...\n",
+       "             2014-03-15 12:00:00, 2014-04-15 00:00:00, 2014-05-15 12:00:00,\n",
+       "             2014-06-15 00:00:00, 2014-07-15 12:00:00, 2014-08-15 12:00:00,\n",
+       "             2014-09-15 00:00:00, 2014-10-15 12:00:00, 2014-11-15 00:00:00,\n",
+       "             2014-12-15 12:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=1980, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>nbnd</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cde3578b-b803-4eeb-9747-a1ec846a90a9' class='xr-index-data-in' type='checkbox'/><label for='index-cde3578b-b803-4eeb-9747-a1ec846a90a9' 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(RangeIndex(start=0, stop=2, step=1, name=&#x27;nbnd&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d118d6db-a891-49b4-9c7e-b36ed0bc9612' class='xr-index-data-in' type='checkbox'/><label for='index-d118d6db-a891-49b4-9c7e-b36ed0bc9612' 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([&#x27;r1i1p1f1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;member_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dcpp_init_year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-110b84b0-95a4-49d8-98a4-e1c5f7b09a66' class='xr-index-data-in' type='checkbox'/><label for='index-110b84b0-95a4-49d8-98a4-e1c5f7b09a66' 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([nan], dtype=&#x27;float64&#x27;, name=&#x27;dcpp_init_year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a72fd057-0ea5-451f-86e4-1adcb8592984' class='xr-section-summary-in' type='checkbox'  ><label for='section-a72fd057-0ea5-451f-86e4-1adcb8592984' class='xr-section-summary' >Attributes: <span>(61)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>20075.0</dd><dt><span>case_id :</span></dt><dd>4</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BWHIST.f09_g17.CMIP6-historical-WACCM.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-05-02T15:33:18Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2-WACCM.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>native gx1v7 displaced pole grid (384x320 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>seaIce</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); atmosChem: WACCM (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb; landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC CHEM AER</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>SImon</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89</dd><dt><span>variable_id :</span></dt><dd>siconca</dd><dt><span>variant_info :</span></dt><dd>CMIP6 CESM2 hindcast (1850-2014) with high-top atmosphere (WACCM6) with interactive chemistry (TSMLT1), interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>status :</span></dt><dd>2019-10-25;created;by nhn2@columbia.edu</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/52972bca-08f2-4217-ac15-9ed5fb4c3a89</dd><dt><span>version_id :</span></dt><dd>v20190507</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;siconca&#x27;]</dd><dt><span>intake_esm_attrs:activity_id :</span></dt><dd>CMIP</dd><dt><span>intake_esm_attrs:institution_id :</span></dt><dd>NCAR</dd><dt><span>intake_esm_attrs:source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>intake_esm_attrs:experiment_id :</span></dt><dd>historical</dd><dt><span>intake_esm_attrs:member_id :</span></dt><dd>r1i1p1f1</dd><dt><span>intake_esm_attrs:table_id :</span></dt><dd>SImon</dd><dt><span>intake_esm_attrs:variable_id :</span></dt><dd>siconca</dd><dt><span>intake_esm_attrs:grid_label :</span></dt><dd>gn</dd><dt><span>intake_esm_attrs:zstore :</span></dt><dd>gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/SImon/siconca/gn/v20190507/</dd><dt><span>intake_esm_attrs:version :</span></dt><dd>20190507</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>zarr</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.NCAR.CESM2-WACCM.historical.r1i1p1f1.SImon.siconca.gn.gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/SImon/siconca/gn/v20190507/.20190507</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 441MB\n",
+       "Dimensions:         (member_id: 1, dcpp_init_year: 1, time: 1980, y: 192,\n",
+       "                     x: 288, nbnd: 2)\n",
+       "Coordinates:\n",
+       "  * y               (y) float64 2kB -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n",
+       "  * x               (x) float64 2kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n",
+       "  * time            (time) object 16kB 1850-01-15 12:00:00 ... 2014-12-15 12:...\n",
+       "    lon_bounds      (x, nbnd, y) float64 885kB dask.array<chunksize=(288, 2, 192), meta=np.ndarray>\n",
+       "    time_bounds     (time, nbnd) object 32kB dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
+       "    lat_bounds      (y, nbnd, x) float64 885kB dask.array<chunksize=(192, 2, 288), meta=np.ndarray>\n",
+       "  * nbnd            (nbnd) int64 16B 0 1\n",
+       "    lon             (x, y) float64 442kB 360.0 360.0 360.0 ... 358.8 358.8 358.8\n",
+       "    lat             (x, y) float64 442kB -90.0 -89.06 -88.12 ... 89.06 90.0\n",
+       "  * member_id       (member_id) object 8B 'r1i1p1f1'\n",
+       "  * dcpp_init_year  (dcpp_init_year) float64 8B nan\n",
+       "    areacella       (member_id, dcpp_init_year, y, x) float32 221kB dask.array<chunksize=(1, 1, 192, 288), meta=np.ndarray>\n",
+       "Data variables:\n",
+       "    siconca         (member_id, dcpp_init_year, time, y, x) float32 438MB dask.array<chunksize=(1, 1, 600, 192, 288), meta=np.ndarray>\n",
+       "Attributes: (12/61)\n",
+       "    Conventions:                      CF-1.7 CMIP-6.2\n",
+       "    activity_id:                      CMIP\n",
+       "    branch_method:                    standard\n",
+       "    branch_time_in_child:             674885.0\n",
+       "    branch_time_in_parent:            20075.0\n",
+       "    case_id:                          4\n",
+       "    ...                               ...\n",
+       "    intake_esm_attrs:variable_id:     siconca\n",
+       "    intake_esm_attrs:grid_label:      gn\n",
+       "    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/...\n",
+       "    intake_esm_attrs:version:         20190507\n",
+       "    intake_esm_attrs:_data_format_:   zarr\n",
+       "    intake_esm_dataset_key:           CMIP.NCAR.CESM2-WACCM.historical.r1i1p1..."
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# select the historical scenario and print its summary\n",
     "SI_ds = ddict_matched['CMIP.NCAR.CESM2-WACCM.historical.r1i1p1f1.SImon.siconca.gn.gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/SImon/siconca/gn/v20190507/.20190507']\n",
@@ -344,8 +2234,931 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "bb72400d-8b18-48aa-aedb-8ef0bc28795b",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "data": {
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+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 231MB\n",
+       "Dimensions:         (member_id: 1, dcpp_init_year: 1, time: 1032, y: 192,\n",
+       "                     x: 288, nbnd: 2)\n",
+       "Coordinates:\n",
+       "  * y               (y) float64 2kB -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n",
+       "  * x               (x) float64 2kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n",
+       "  * time            (time) object 8kB 2015-01-15 12:00:00 ... 2100-12-15 12:0...\n",
+       "    lon_bounds      (x, nbnd, y) float64 885kB dask.array&lt;chunksize=(288, 2, 192), meta=np.ndarray&gt;\n",
+       "    time_bounds     (time, nbnd) object 17kB dask.array&lt;chunksize=(1032, 2), meta=np.ndarray&gt;\n",
+       "    lat_bounds      (y, nbnd, x) float64 885kB dask.array&lt;chunksize=(192, 2, 288), meta=np.ndarray&gt;\n",
+       "  * nbnd            (nbnd) int64 16B 0 1\n",
+       "    lon             (x, y) float64 442kB 360.0 360.0 360.0 ... 358.8 358.8 358.8\n",
+       "    lat             (x, y) float64 442kB -90.0 -89.06 -88.12 ... 89.06 90.0\n",
+       "  * member_id       (member_id) object 8B &#x27;r1i1p1f1&#x27;\n",
+       "  * dcpp_init_year  (dcpp_init_year) float64 8B nan\n",
+       "    areacella       (member_id, dcpp_init_year, y, x) float32 221kB dask.array&lt;chunksize=(1, 1, 192, 288), meta=np.ndarray&gt;\n",
+       "Data variables:\n",
+       "    siconca         (member_id, dcpp_init_year, time, y, x) float32 228MB dask.array&lt;chunksize=(1, 1, 516, 192, 288), meta=np.ndarray&gt;\n",
+       "Attributes: (12/61)\n",
+       "    Conventions:                      CF-1.7 CMIP-6.2\n",
+       "    activity_id:                      ScenarioMIP\n",
+       "    branch_method:                    standard\n",
+       "    branch_time_in_child:             735110.0\n",
+       "    branch_time_in_parent:            735110.0\n",
+       "    case_id:                          966\n",
+       "    ...                               ...\n",
+       "    intake_esm_attrs:variable_id:     siconca\n",
+       "    intake_esm_attrs:grid_label:      gn\n",
+       "    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2...\n",
+       "    intake_esm_attrs:version:         20190815\n",
+       "    intake_esm_attrs:_data_format_:   zarr\n",
+       "    intake_esm_dataset_key:           ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i...</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-4057b5cc-acab-40e8-a097-b14ee083030d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4057b5cc-acab-40e8-a097-b14ee083030d' 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'>member_id</span>: 1</li><li><span class='xr-has-index'>dcpp_init_year</span>: 1</li><li><span class='xr-has-index'>time</span>: 1032</li><li><span class='xr-has-index'>y</span>: 192</li><li><span class='xr-has-index'>x</span>: 288</li><li><span class='xr-has-index'>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-77bce696-2f6c-4534-80d3-a28094fe4ae0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-77bce696-2f6c-4534-80d3-a28094fe4ae0' class='xr-section-summary' >Coordinates: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-abe8becf-3657-4502-9719-b44bda1c05f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abe8becf-3657-4502-9719-b44bda1c05f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfeaaf3b-b2e5-4ffb-97f9-5ed9521ff90f' class='xr-var-data-in' type='checkbox'><label for='data-dfeaaf3b-b2e5-4ffb-97f9-5ed9521ff90f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
+       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
+       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
+       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
+       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
+       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
+       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
+       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
+       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
+       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
+       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
+       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
+       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
+       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
+       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
+       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
+       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
+       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
+       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
+       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
+       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
+       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
+       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
+       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
+       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
+       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
+       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
+       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
+       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
+       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
+       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
+       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-39d68c51-9203-484a-ad3a-7b2cdf36ddc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39d68c51-9203-484a-ad3a-7b2cdf36ddc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-585b4d62-22e6-4b8b-8bf3-b593cd49d7c2' class='xr-var-data-in' type='checkbox'><label for='data-585b4d62-22e6-4b8b-8bf3-b593cd49d7c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</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'>2015-01-15 12:00:00 ... 2100-12-...</div><input id='attrs-66251605-fa20-4cc5-87b6-d699cd322562' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66251605-fa20-4cc5-87b6-d699cd322562' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62c9a969-a3b6-4733-9cba-7ab509917587' class='xr-var-data-in' type='checkbox'><label for='data-62c9a969-a3b6-4733-9cba-7ab509917587' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2015, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2015, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       ...,\n",
+       "       cftime.DatetimeNoLeap(2100, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 12, 15, 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>lon_bounds</span></div><div class='xr-var-dims'>(x, nbnd, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(288, 2, 192), meta=np.ndarray&gt;</div><input id='attrs-1804a812-47d0-42af-a4c5-97557a6017a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1804a812-47d0-42af-a4c5-97557a6017a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fce1ac4c-0680-4fc7-abeb-d0983e4fbac7' class='xr-var-data-in' type='checkbox'><label for='data-fce1ac4c-0680-4fc7-abeb-d0983e4fbac7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 864.00 kiB </td>\n",
+       "                        <td> 864.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (288, 2, 192) </td>\n",
+       "                        <td> (288, 2, 192) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 9 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=\"210\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"90\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"160\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"160\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1032, 2), meta=np.ndarray&gt;</div><input id='attrs-ba5af972-ac31-449d-869a-ac70006b250c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba5af972-ac31-449d-869a-ac70006b250c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-844a2d96-bd8f-495c-990f-350f19a45fb7' class='xr-var-data-in' type='checkbox'><label for='data-844a2d96-bd8f-495c-990f-350f19a45fb7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.12 kiB </td>\n",
+       "                        <td> 16.12 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1032, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bounds</span></div><div class='xr-var-dims'>(y, nbnd, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192, 2, 288), meta=np.ndarray&gt;</div><input id='attrs-3a81f8a2-b76c-4cdf-94b5-8071b198213e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a81f8a2-b76c-4cdf-94b5-8071b198213e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f7d37e5-2cb3-41db-b638-61361a74c5a1' class='xr-var-data-in' type='checkbox'><label for='data-8f7d37e5-2cb3-41db-b638-61361a74c5a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 864.00 kiB </td>\n",
+       "                        <td> 864.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (192, 2, 288) </td>\n",
+       "                        <td> (192, 2, 288) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 6 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "\n",
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+       "  <text x=\"23.529412\" y=\"68.942028\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,23.529412,68.942028)\">192</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'>nbnd</span></div><div class='xr-var-dims'>(nbnd)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-cac1681c-129f-4ab5-840c-644407f285d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cac1681c-129f-4ab5-840c-644407f285d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f39ea46d-85ed-4bfc-b704-fa87c78f4ad4' class='xr-var-data-in' type='checkbox'><label for='data-f39ea46d-85ed-4bfc-b704-fa87c78f4ad4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>360.0 360.0 360.0 ... 358.8 358.8</div><input id='attrs-4c07b160-edac-4d3b-bebf-12798657db7f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c07b160-edac-4d3b-bebf-12798657db7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a256cead-47d5-410f-b933-75dd9ee41d1b' class='xr-var-data-in' type='checkbox'><label for='data-a256cead-47d5-410f-b933-75dd9ee41d1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[360.  , 360.  , 360.  , ..., 360.  , 360.  , 360.  ],\n",
+       "       [  1.25,   1.25,   1.25, ...,   1.25,   1.25,   1.25],\n",
+       "       [  2.5 ,   2.5 ,   2.5 , ...,   2.5 ,   2.5 ,   2.5 ],\n",
+       "       ...,\n",
+       "       [356.25, 356.25, 356.25, ..., 356.25, 356.25, 356.25],\n",
+       "       [357.5 , 357.5 , 357.5 , ..., 357.5 , 357.5 , 357.5 ],\n",
+       "       [358.75, 358.75, 358.75, ..., 358.75, 358.75, 358.75]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-0ce457fc-f79f-4825-b122-2fd7d8fa2993' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ce457fc-f79f-4825-b122-2fd7d8fa2993' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c33b30d8-7dd7-44cf-91d4-e111d4fd5bd3' class='xr-var-data-in' type='checkbox'><label for='data-c33b30d8-7dd7-44cf-91d4-e111d4fd5bd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       ...,\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ],\n",
+       "       [-90.        , -89.05759162, -88.11518325, ...,  88.11518325,\n",
+       "         89.05759162,  90.        ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-db1e80ce-c8f0-443b-9afb-7dc776547de2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db1e80ce-c8f0-443b-9afb-7dc776547de2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31e46cfc-6d42-4988-a446-c930c8e56232' class='xr-var-data-in' type='checkbox'><label for='data-31e46cfc-6d42-4988-a446-c930c8e56232' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1i1p1f1&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dcpp_init_year</span></div><div class='xr-var-dims'>(dcpp_init_year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-2d6739b6-20c8-4b39-8433-1899e2c8957e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d6739b6-20c8-4b39-8433-1899e2c8957e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2228e83b-925d-4b15-8403-dff6e54d2cc2' class='xr-var-data-in' type='checkbox'><label for='data-2228e83b-925d-4b15-8403-dff6e54d2cc2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>areacella</span></div><div class='xr-var-dims'>(member_id, dcpp_init_year, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 192, 288), meta=np.ndarray&gt;</div><input id='attrs-12e18bf4-744e-4979-88f6-c14943903fe8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12e18bf4-744e-4979-88f6-c14943903fe8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ed98d2e-ae57-43cf-af40-45b7fefe2370' class='xr-var-data-in' type='checkbox'><label for='data-6ed98d2e-ae57-43cf-af40-45b7fefe2370' 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>cell_methods :</span></dt><dd>area: sum</dd><dt><span>comment :</span></dt><dd>AREA[0,:,:]</dd><dt><span>description :</span></dt><dd>Cell areas for any grid used to report atmospheric variables and any other variable using that grid (e.g., soil moisture content). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>id :</span></dt><dd>areacella</dd><dt><span>long_name :</span></dt><dd>Grid-Cell Area for Atmospheric Grid Variables</dd><dt><span>mipTable :</span></dt><dd>fx</dd><dt><span>out_name :</span></dt><dd>areacella</dd><dt><span>prov :</span></dt><dd>fx ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos land</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>time_label :</span></dt><dd>None</dd><dt><span>time_title :</span></dt><dd>No temporal dimensions ... fixed field</dd><dt><span>title :</span></dt><dd>Grid-Cell Area for Atmospheric Grid Variables</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>variable_id :</span></dt><dd>areacella</dd><dt><span>units :</span></dt><dd>m²</dd><dt><span>original_key :</span></dt><dd>ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i1p1f1.fx.areacella.gn.gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r1i1p1f1/fx/areacella/gn/v20190815/.20190815</dd><dt><span>parsed_with :</span></dt><dd>xmip/postprocessing/_parse_metric</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 216.00 kiB </td>\n",
+       "                        <td> 216.00 kiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "  <text x=\"92.474299\" y=\"107.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,92.474299,107.474299)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-06f3484d-44ee-4350-bd17-0ef9e6b1dc08' class='xr-section-summary-in' type='checkbox'  checked><label for='section-06f3484d-44ee-4350-bd17-0ef9e6b1dc08' 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>siconca</span></div><div class='xr-var-dims'>(member_id, dcpp_init_year, time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 516, 192, 288), meta=np.ndarray&gt;</div><input id='attrs-7cc35e6c-b96d-4c11-9da9-4e390fcc4b1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cc35e6c-b96d-4c11-9da9-4e390fcc4b1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35b32142-aaa1-46d8-a1f8-41afb6f3b8ff' class='xr-var-data-in' type='checkbox'><label for='data-35b32142-aaa1-46d8-a1f8-41afb6f3b8ff' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>ICEFRAC</dd><dt><span>description :</span></dt><dd>Area fraction of grid cell covered by sea ice</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>siconca</dd><dt><span>long_name :</span></dt><dd>Sea-ice Area Percentage (Atmospheric Grid)</dd><dt><span>mipTable :</span></dt><dd>SImon</dd><dt><span>out_name :</span></dt><dd>siconca</dd><dt><span>prov :</span></dt><dd>SIMIP [seaicemon]</dd><dt><span>realm :</span></dt><dd>seaIce</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Sea-ice Area Percentage (Atmospheric Grid)</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>variable_id :</span></dt><dd>siconca</dd><dt><span>units :</span></dt><dd>%</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> 217.69 MiB </td>\n",
+       "                        <td> 108.84 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 1, 1032, 192, 288) </td>\n",
+       "                        <td> (1, 1, 516, 192, 288) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
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+       "  <text x=\"120.294118\" y=\"96.992739\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,120.294118,96.992739)\">1032</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-c5b2510a-146f-4c85-8572-b6e007577e93' class='xr-section-summary-in' type='checkbox'  ><label for='section-c5b2510a-146f-4c85-8572-b6e007577e93' class='xr-section-summary' >Indexes: <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-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-21cef408-efa6-4610-abcd-f90240c5be4d' class='xr-index-data-in' type='checkbox'/><label for='index-21cef408-efa6-4610-abcd-f90240c5be4d' 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([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
+       "       -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
+       "        -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
+       "       -81.51832460732984,\n",
+       "       ...\n",
+       "        81.51832460732984,   82.4607329842932,  83.40314136125653,\n",
+       "        84.34554973821989,  85.28795811518324,   86.2303664921466,\n",
+       "        87.17277486910996,  88.11518324607329,  89.05759162303664,\n",
+       "                     90.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-789e2242-6e43-43e2-982e-0c1d7e9b86d1' class='xr-index-data-in' type='checkbox'/><label for='index-789e2242-6e43-43e2-982e-0c1d7e9b86d1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
+       "        11.25,\n",
+       "       ...\n",
+       "        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,\n",
+       "       358.75],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#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-0e38fc80-cbfc-4f90-bb6f-7ccdfdf53f52' class='xr-index-data-in' type='checkbox'/><label for='index-0e38fc80-cbfc-4f90-bb6f-7ccdfdf53f52' 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([2015-01-15 12:00:00, 2015-02-14 00:00:00, 2015-03-15 12:00:00,\n",
+       "             2015-04-15 00:00:00, 2015-05-15 12:00:00, 2015-06-15 00:00:00,\n",
+       "             2015-07-15 12:00:00, 2015-08-15 12:00:00, 2015-09-15 00:00:00,\n",
+       "             2015-10-15 12:00:00,\n",
+       "             ...\n",
+       "             2100-03-15 12:00:00, 2100-04-15 00:00:00, 2100-05-15 12:00:00,\n",
+       "             2100-06-15 00:00:00, 2100-07-15 12:00:00, 2100-08-15 12:00:00,\n",
+       "             2100-09-15 00:00:00, 2100-10-15 12:00:00, 2100-11-15 00:00:00,\n",
+       "             2100-12-15 12:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=1032, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>nbnd</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a2a87aef-994b-4bdb-b6d6-8a70e2bd2e12' class='xr-index-data-in' type='checkbox'/><label for='index-a2a87aef-994b-4bdb-b6d6-8a70e2bd2e12' 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(RangeIndex(start=0, stop=2, step=1, name=&#x27;nbnd&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-50e1c8e6-9db9-467e-a789-c12ae924c52f' class='xr-index-data-in' type='checkbox'/><label for='index-50e1c8e6-9db9-467e-a789-c12ae924c52f' 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([&#x27;r1i1p1f1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;member_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dcpp_init_year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3315d66e-379c-441c-8b7b-1c10f7ade6af' class='xr-index-data-in' type='checkbox'/><label for='index-3315d66e-379c-441c-8b7b-1c10f7ade6af' 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([nan], dtype=&#x27;float64&#x27;, name=&#x27;dcpp_init_year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-39a7ef74-b0f9-4d23-8405-0c861d1392a9' class='xr-section-summary-in' type='checkbox'  ><label for='section-39a7ef74-b0f9-4d23-8405-0c861d1392a9' class='xr-section-summary' >Attributes: <span>(61)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>ScenarioMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>735110.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>735110.0</dd><dt><span>case_id :</span></dt><dd>966</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BWSSP245cmip6.f09_g17.CMIP6-SSP2-4.5-WACCM.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-03-31T15:33:26Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>SSP-based RCP scenario with medium radiative forcing by the end of the century.  Following approximately RCP4.5 global forcing pathway with SSP2 socioeconomic conditions. Radiative forcing reaches a level of 4.5 W/m2 in 2100. Concentration-driven.</dd><dt><span>experiment_id :</span></dt><dd>ssp245</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2-WACCM.ssp245.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>native gx1v7 displaced pole grid (384x320 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>seaIce</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); atmosChem: WACCM (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb; landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC CHEM AER</dd><dt><span>status :</span></dt><dd>2021-02-07;created; by gcs.cmip6.ldeo@gmail.com</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>SImon</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/64de3236-023c-4a14-ad06-d1c68fa86e49\n",
+       "hdl:21.14100/64de3236-023c-4a14-ad06-d1c68fa86e49</dd><dt><span>variable_id :</span></dt><dd>siconca</dd><dt><span>variant_info :</span></dt><dd>CMIP6 CESM2 future scenario SSP2-4.5, years 2015-2100, with WACCM6 with interactive chemistry (TSMLT1), interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1).</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>version_id :</span></dt><dd>v20190815</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/64de3236-023c-4a14-ad06-d1c68fa86e49\n",
+       "hdl:21.14100/64de3236-023c-4a14-ad06-d1c68fa86e49</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;siconca&#x27;]</dd><dt><span>intake_esm_attrs:activity_id :</span></dt><dd>ScenarioMIP</dd><dt><span>intake_esm_attrs:institution_id :</span></dt><dd>NCAR</dd><dt><span>intake_esm_attrs:source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>intake_esm_attrs:experiment_id :</span></dt><dd>ssp245</dd><dt><span>intake_esm_attrs:member_id :</span></dt><dd>r1i1p1f1</dd><dt><span>intake_esm_attrs:table_id :</span></dt><dd>SImon</dd><dt><span>intake_esm_attrs:variable_id :</span></dt><dd>siconca</dd><dt><span>intake_esm_attrs:grid_label :</span></dt><dd>gn</dd><dt><span>intake_esm_attrs:zstore :</span></dt><dd>gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r1i1p1f1/SImon/siconca/gn/v20190815/</dd><dt><span>intake_esm_attrs:version :</span></dt><dd>20190815</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>zarr</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i1p1f1.SImon.siconca.gn.gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r1i1p1f1/SImon/siconca/gn/v20190815/.20190815</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 231MB\n",
+       "Dimensions:         (member_id: 1, dcpp_init_year: 1, time: 1032, y: 192,\n",
+       "                     x: 288, nbnd: 2)\n",
+       "Coordinates:\n",
+       "  * y               (y) float64 2kB -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n",
+       "  * x               (x) float64 2kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n",
+       "  * time            (time) object 8kB 2015-01-15 12:00:00 ... 2100-12-15 12:0...\n",
+       "    lon_bounds      (x, nbnd, y) float64 885kB dask.array<chunksize=(288, 2, 192), meta=np.ndarray>\n",
+       "    time_bounds     (time, nbnd) object 17kB dask.array<chunksize=(1032, 2), meta=np.ndarray>\n",
+       "    lat_bounds      (y, nbnd, x) float64 885kB dask.array<chunksize=(192, 2, 288), meta=np.ndarray>\n",
+       "  * nbnd            (nbnd) int64 16B 0 1\n",
+       "    lon             (x, y) float64 442kB 360.0 360.0 360.0 ... 358.8 358.8 358.8\n",
+       "    lat             (x, y) float64 442kB -90.0 -89.06 -88.12 ... 89.06 90.0\n",
+       "  * member_id       (member_id) object 8B 'r1i1p1f1'\n",
+       "  * dcpp_init_year  (dcpp_init_year) float64 8B nan\n",
+       "    areacella       (member_id, dcpp_init_year, y, x) float32 221kB dask.array<chunksize=(1, 1, 192, 288), meta=np.ndarray>\n",
+       "Data variables:\n",
+       "    siconca         (member_id, dcpp_init_year, time, y, x) float32 228MB dask.array<chunksize=(1, 1, 516, 192, 288), meta=np.ndarray>\n",
+       "Attributes: (12/61)\n",
+       "    Conventions:                      CF-1.7 CMIP-6.2\n",
+       "    activity_id:                      ScenarioMIP\n",
+       "    branch_method:                    standard\n",
+       "    branch_time_in_child:             735110.0\n",
+       "    branch_time_in_parent:            735110.0\n",
+       "    case_id:                          966\n",
+       "    ...                               ...\n",
+       "    intake_esm_attrs:variable_id:     siconca\n",
+       "    intake_esm_attrs:grid_label:      gn\n",
+       "    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2...\n",
+       "    intake_esm_attrs:version:         20190815\n",
+       "    intake_esm_attrs:_data_format_:   zarr\n",
+       "    intake_esm_dataset_key:           ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i..."
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# select the ssp245 scenario and print its summary\n",
     "SI_ds_245 = ddict_matched['ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i1p1f1.SImon.siconca.gn.gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r1i1p1f1/SImon/siconca/gn/v20190815/.20190815']\n",
@@ -356,8 +3169,39 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "58e3e557-9eee-4116-89e1-cb3ba4d75c53",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'cell_methods': 'area: sum',\n",
+       " 'comment': 'Cell areas for any grid used to report atmospheric variables and any other variable using that grid (e.g., soil moisture content). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).',\n",
+       " 'description': 'Cell areas for any grid used to report atmospheric variables and any other variable using that grid (e.g., soil moisture content). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).',\n",
+       " 'frequency': 'fx',\n",
+       " 'id': 'areacella',\n",
+       " 'long_name': 'Grid-Cell Area for Atmospheric Grid Variables',\n",
+       " 'mipTable': 'fx',\n",
+       " 'out_name': 'areacella',\n",
+       " 'prov': 'fx ((isd.003))',\n",
+       " 'realm': 'atmos land',\n",
+       " 'standard_name': 'cell_area',\n",
+       " 'time_label': 'None',\n",
+       " 'time_title': 'No temporal dimensions ... fixed field',\n",
+       " 'title': 'Grid-Cell Area for Atmospheric Grid Variables',\n",
+       " 'type': 'real',\n",
+       " 'variable_id': 'areacella',\n",
+       " 'units': 'm²',\n",
+       " 'original_key': 'CMIP.NCAR.CESM2-WACCM.historical.r1i1p1f1.fx.areacella.gn.gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/fx/areacella/gn/v20190227/.20190227',\n",
+       " 'parsed_with': 'xmip/postprocessing/_parse_metric'}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# let s print the meta data of the areacella that we added as a coordinate before\n",
     "SI_ds.areacella.attrs"
@@ -367,21 +3211,40 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "59497066-568b-434e-9557-8fb8d9d5ad9f",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "99.999916\n"
+     ]
+    }
+   ],
    "source": [
     "# we also see that it contains the data variable siconca\n",
     "\n",
     "# let's print the minimum and maximum of this variable to check its format:\n",
     "print(SI_ds.siconca.min().values)\n",
     "print(SI_ds.siconca.max().values)\n",
-    "# note that it is formattwd as a percentage not a fraction, ranging from 0 to 100. "
+    "# note that it is formattwd as a percentage not a fraction, ranging from 0 to 100."
    ]
   },
   {
    "cell_type": "markdown",
    "id": "ae442fb9-ddb5-42c3-92ad-fb6f906f2d8c",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "Great, now you are all set to use these model sea ice data to address the questions you are interested in!"
    ]
@@ -389,7 +3252,9 @@
   {
    "cell_type": "markdown",
    "id": "0823d446-6b6a-4684-814a-9892caee2a46",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "# Q1: \n",
     "Plot the annual mean of Arctic sea ice concentration (SIC) for three example years (e.g., 1996, 2007, 2012) using one CMIP6 model ensemble member."
@@ -398,7 +3263,9 @@
   {
    "cell_type": "markdown",
    "id": "b12170fa-fc2c-45ba-9021-c3ebd03f9143",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "*Hint: Select latitudes of the arctic, years of interest and use `cartopy` for pretty projections, e.g. [ccrs.NorthPolarStereo()](https://scitools.org.uk/cartopy/docs/latest/reference/projections.html#northpolarstereo).*"
    ]
@@ -406,7 +3273,9 @@
   {
    "cell_type": "markdown",
    "id": "7be2c8be-21a8-4989-be07-81ab873feb51",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "# Q2:\n",
     "\n",
@@ -416,7 +3285,9 @@
   {
    "cell_type": "markdown",
    "id": "e47283b0-eeb9-47e9-8e39-69fbc7cb278c",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "*Hint: We define sea ice extent (SIE) following the convention that a grid cell is 'sea ice' with a sea ice concentration higher than 15%.*\n",
     "\n",
@@ -426,7 +3297,9 @@
   {
    "cell_type": "markdown",
    "id": "f40e7c26-7588-46bd-ae26-6cb28b470b4e",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "Compute seasonal means of SIE for June-August (JJA) and December-February (DJF).\n",
     "\n",
@@ -436,7 +3309,9 @@
   {
    "cell_type": "markdown",
    "id": "fd1cf207-201b-4c38-8589-e1085b151124",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "# Q3:\n",
     "Repeat the analysis in Q2 for the Antarctic. How does this compare with the respective SIE in the Arctic?\n"
@@ -445,7 +3320,9 @@
   {
    "cell_type": "markdown",
    "id": "3690109a-32cc-4f25-a415-2bdd1aa1f239",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "# Q4: \n",
     "Are there long-term changes (trends) in SIE between 1979-2014 in this model? How do these changes differ across seasons and also between the Arctic and Antarctic?\n",
@@ -480,7 +3357,9 @@
   {
    "cell_type": "markdown",
    "id": "b1fa5d34-263b-4182-ad58-a57122716944",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "# Optional Additional Data Source: Satellite Observations\n",
     "\n",
@@ -498,8 +3377,461 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "ee26e97b-14d9-4577-b3bc-b75fae4c471f",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://osf.io/download/fpr3j/' to file '/tmp/G10010_sibt1850_v2.0.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 090338cc3baa13c91625e6772beb66b5992ba44a864af7ec62c7ec5c22d417d0\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "data": {
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+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 1GB\n",
+       "Dimensions:          (latitude: 240, longitude: 1440, time: 2016)\n",
+       "Coordinates:\n",
+       "  * latitude         (latitude) float32 960B 89.88 89.62 89.38 ... 30.38 30.12\n",
+       "  * longitude        (longitude) float32 6kB 0.125 0.375 0.625 ... 359.6 359.9\n",
+       "  * time             (time) object 16kB 1850-01-15 00:00:00 ... 2017-12-15 00...\n",
+       "Data variables:\n",
+       "    seaice_conc      (time, latitude, longitude) uint8 697MB ...\n",
+       "    seaice_source    (time, latitude, longitude) uint8 697MB ...\n",
+       "    LandRegion_mask  (latitude, longitude) uint8 346kB ...\n",
+       "    Gridcell_Area    (latitude) float32 960B ...\n",
+       "Attributes:\n",
+       "    version:       2.0\n",
+       "    release_date:  February 28, 2019\n",
+       "    Conventions:   CF-1.4\n",
+       "    citation:      https://doi.org/10.1111/j.1931-0846.2016.12195.x\n",
+       "    dataset_doi:   https://nsidc.org/data/g10010</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-d81d68de-eb59-40ee-9991-95647137e12a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d81d68de-eb59-40ee-9991-95647137e12a' 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'>latitude</span>: 240</li><li><span class='xr-has-index'>longitude</span>: 1440</li><li><span class='xr-has-index'>time</span>: 2016</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ecf1fc71-729f-4262-99f5-15e04b0df5a9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ecf1fc71-729f-4262-99f5-15e04b0df5a9' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>89.88 89.62 89.38 ... 30.38 30.12</div><input id='attrs-cf1d8128-7526-492c-8534-f9a795f4c22f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf1d8128-7526-492c-8534-f9a795f4c22f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0538a92-87e5-46a9-b568-7efc9f94e2d6' class='xr-var-data-in' type='checkbox'><label for='data-c0538a92-87e5-46a9-b568-7efc9f94e2d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>valid_min :</span></dt><dd>30.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>array([89.875, 89.625, 89.375, ..., 30.625, 30.375, 30.125], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-556ba142-ce52-4d57-9906-f8b1cfe75ac2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-556ba142-ce52-4d57-9906-f8b1cfe75ac2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22c2fb1a-1adb-44cd-ade6-a7f3f3296f87' class='xr-var-data-in' type='checkbox'><label for='data-22c2fb1a-1adb-44cd-ade6-a7f3f3296f87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>360.0</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
+       "       3.59875e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-15 00:00:00 ... 2017-12-...</div><input id='attrs-44dd8d90-5ea3-4f9f-86d5-ab11454b5442' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44dd8d90-5ea3-4f9f-86d5-ab11454b5442' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ea49a50-25b4-4de7-9232-cd3dcedcf990' class='xr-var-data-in' type='checkbox'><label for='data-3ea49a50-25b4-4de7-9232-cd3dcedcf990' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeJulian(1850, 1, 15, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeJulian(1850, 2, 15, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeJulian(1850, 3, 15, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       ...,\n",
+       "       cftime.DatetimeJulian(2017, 10, 15, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeJulian(2017, 11, 15, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeJulian(2017, 12, 15, 0, 0, 0, 0, has_year_zero=False)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1cbb15b5-0b08-4d08-b8fe-e6a395f368a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1cbb15b5-0b08-4d08-b8fe-e6a395f368a3' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>seaice_conc</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9bb5697d-2477-4758-aa63-e5783db9f8d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bb5697d-2477-4758-aa63-e5783db9f8d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd004b15-8708-4f67-b2e4-e615e2ab9951' class='xr-var-data-in' type='checkbox'><label for='data-dd004b15-8708-4f67-b2e4-e615e2ab9951' 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>short_name :</span></dt><dd>Concentration</dd><dt><span>long_name :</span></dt><dd>Sea_Ice_Concentration</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Values are percent-concentration from 0 to 100.  Sentinel value of 120 in the concentration field indicates land</dd></dl></div><div class='xr-var-data'><pre>[696729600 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seaice_source</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-780a948d-153d-427c-a0fd-f5e865e1797f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-780a948d-153d-427c-a0fd-f5e865e1797f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2b87602-6dcc-4b2c-b0af-ab66743d5a22' class='xr-var-data-in' type='checkbox'><label for='data-b2b87602-6dcc-4b2c-b0af-ab66743d5a22' 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>short_name :</span></dt><dd>Seaice Source</dd><dt><span>long_name :</span></dt><dd>Coded_Source_of_Sea_Ice_Concentration</dd><dt><span>standard_name :</span></dt><dd>source</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>name_source_00 :</span></dt><dd>Land</dd><dt><span>name_source_01 :</span></dt><dd>Satellite passive microwave</dd><dt><span>name_source_02 :</span></dt><dd>Danish Meteorological Institute</dd><dt><span>name_source_03 :</span></dt><dd>Dehn</dd><dt><span>name_source_04 :</span></dt><dd>NAVO yearbooks</dd><dt><span>name_source_05 :</span></dt><dd>AARI</dd><dt><span>name_source_06 :</span></dt><dd>Hill</dd><dt><span>name_source_07 :</span></dt><dd>Whaling Records - Complete Sea Ice</dd><dt><span>name_source_08 :</span></dt><dd>Whaling Records - Partial Sea Ice</dd><dt><span>name_source_09 :</span></dt><dd>Whaling Records - No Sea Ice</dd><dt><span>name_source_10 :</span></dt><dd>DMI yearbook narrative</dd><dt><span>name_source_11 :</span></dt><dd>ACSYS</dd><dt><span>name_source_12 :</span></dt><dd>Walsh and Johnson</dd><dt><span>name_source_13 :</span></dt><dd>JMA charts</dd><dt><span>name_source_14 :</span></dt><dd>Kelly ice extent grids</dd><dt><span>name_source_15 :</span></dt><dd>Landmask correction fill</dd><dt><span>name_source_16 :</span></dt><dd>Change-of-landmask ocean</dd><dt><span>name_source_17 :</span></dt><dd>Analog fill - spatial</dd><dt><span>name_source_18 :</span></dt><dd>Analog fill - temporal</dd></dl></div><div class='xr-var-data'><pre>[696729600 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LandRegion_mask</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f4c0f90f-8f80-4fad-b603-38266ca28b37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4c0f90f-8f80-4fad-b603-38266ca28b37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4ad8679-9209-4689-a8a5-ea5c4ca0c026' class='xr-var-data-in' type='checkbox'><label for='data-a4ad8679-9209-4689-a8a5-ea5c4ca0c026' 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>short_name :</span></dt><dd>LandRegion Mask</dd><dt><span>long_name :</span></dt><dd>Land_and_Ocean_Region_Mask</dd><dt><span>standard_name :</span></dt><dd>source</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>name_regionmask_00 :</span></dt><dd>Ocean, no region specified</dd><dt><span>name_regionmask_01 :</span></dt><dd>Beaufort Sea</dd><dt><span>name_regionmask_02 :</span></dt><dd>Chukchi Sea</dd><dt><span>name_regionmask_03 :</span></dt><dd>East Siberian Sea</dd><dt><span>name_regionmask_04 :</span></dt><dd>Laptev Sea</dd><dt><span>name_regionmask_05 :</span></dt><dd>Kara Sea</dd><dt><span>name_regionmask_06 :</span></dt><dd>Barents Sea</dd><dt><span>name_regionmask_07 :</span></dt><dd>Greenland Sea</dd><dt><span>name_regionmask_08 :</span></dt><dd>Baffin Bay and Gulf of St Lawrence, on polar stereo grid</dd><dt><span>name_regionmask_09 :</span></dt><dd>Canadian Archipelago</dd><dt><span>name_regionmask_10 :</span></dt><dd>Hudson Bay</dd><dt><span>name_regionmask_11 :</span></dt><dd>Central Arctic</dd><dt><span>name_regionmask_12 :</span></dt><dd>Bering Sea</dd><dt><span>name_regionmask_13 :</span></dt><dd>Baltic Sea</dd><dt><span>name_regionmask_14 :</span></dt><dd>Sea of Okhotsk</dd><dt><span>name_regionmask_15 :</span></dt><dd>Yellow Sea, on polar stereo grid</dd><dt><span>name_regionmask_16 :</span></dt><dd>Cook Inlet, on polar stereo grid</dd><dt><span>name_regionmask_17 :</span></dt><dd>Yellow Sea, off polar stereo grid</dd><dt><span>name_regionmask_18 :</span></dt><dd>Baffin Bay and Gulf of St Lawrence, off polar stereo grid</dd><dt><span>name_regionmask_19 :</span></dt><dd>Cook Inlet, off polar stereo grid</dd><dt><span>name_regionmask_20 :</span></dt><dd>Land</dd><dt><span>name_regionmask_21 :</span></dt><dd>Unnamed ocean on polar stereo grid</dd></dl></div><div class='xr-var-data'><pre>[345600 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Gridcell_Area</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1a9343a2-57f0-4699-874d-f7cd5ea60f6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a9343a2-57f0-4699-874d-f7cd5ea60f6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b83ebdb-8528-47e2-8a6a-19236551559a' class='xr-var-data-in' type='checkbox'><label for='data-1b83ebdb-8528-47e2-8a6a-19236551559a' 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>short_name :</span></dt><dd>Grid_Cell_Area</dd><dt><span>long_name :</span></dt><dd>Grid_Cell_Area</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>units :</span></dt><dd>km^2</dd></dl></div><div class='xr-var-data'><pre>[240 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc60d38f-8438-4b07-a9c1-b85fe9aaf722' class='xr-section-summary-in' type='checkbox'  ><label for='section-cc60d38f-8438-4b07-a9c1-b85fe9aaf722' 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>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-66151f07-ea09-45c5-9c5c-579f85ab76bc' class='xr-index-data-in' type='checkbox'/><label for='index-66151f07-ea09-45c5-9c5c-579f85ab76bc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([89.875, 89.625, 89.375, 89.125, 88.875, 88.625, 88.375, 88.125, 87.875,\n",
+       "       87.625,\n",
+       "       ...\n",
+       "       32.375, 32.125, 31.875, 31.625, 31.375, 31.125, 30.875, 30.625, 30.375,\n",
+       "       30.125],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=240))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-60f15ec1-0bb3-4661-a86d-4c9d5d201d0b' class='xr-index-data-in' type='checkbox'/><label for='index-60f15ec1-0bb3-4661-a86d-4c9d5d201d0b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.125,   0.375,   0.625,   0.875,   1.125,   1.375,   1.625,   1.875,\n",
+       "         2.125,   2.375,\n",
+       "       ...\n",
+       "       357.625, 357.875, 358.125, 358.375, 358.625, 358.875, 359.125, 359.375,\n",
+       "       359.625, 359.875],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d9d288b9-49b6-4779-9f26-5986f85b15b4' class='xr-index-data-in' type='checkbox'/><label for='index-d9d288b9-49b6-4779-9f26-5986f85b15b4' 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([1850-01-15 00:00:00, 1850-02-15 00:00:00, 1850-03-15 00:00:00,\n",
+       "             1850-04-15 00:00:00, 1850-05-15 00:00:00, 1850-06-15 00:00:00,\n",
+       "             1850-07-15 00:00:00, 1850-08-15 00:00:00, 1850-09-15 00:00:00,\n",
+       "             1850-10-15 00:00:00,\n",
+       "             ...\n",
+       "             2017-03-15 00:00:00, 2017-04-15 00:00:00, 2017-05-15 00:00:00,\n",
+       "             2017-06-15 00:00:00, 2017-07-15 00:00:00, 2017-08-15 00:00:00,\n",
+       "             2017-09-15 00:00:00, 2017-10-15 00:00:00, 2017-11-15 00:00:00,\n",
+       "             2017-12-15 00:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=2016, calendar=&#x27;julian&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-83102686-0fa6-4557-a715-d0d0e02f7eed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83102686-0fa6-4557-a715-d0d0e02f7eed' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>version :</span></dt><dd>2.0</dd><dt><span>release_date :</span></dt><dd>February 28, 2019</dd><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>citation :</span></dt><dd>https://doi.org/10.1111/j.1931-0846.2016.12195.x</dd><dt><span>dataset_doi :</span></dt><dd>https://nsidc.org/data/g10010</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 1GB\n",
+       "Dimensions:          (latitude: 240, longitude: 1440, time: 2016)\n",
+       "Coordinates:\n",
+       "  * latitude         (latitude) float32 960B 89.88 89.62 89.38 ... 30.38 30.12\n",
+       "  * longitude        (longitude) float32 6kB 0.125 0.375 0.625 ... 359.6 359.9\n",
+       "  * time             (time) object 16kB 1850-01-15 00:00:00 ... 2017-12-15 00...\n",
+       "Data variables:\n",
+       "    seaice_conc      (time, latitude, longitude) uint8 697MB ...\n",
+       "    seaice_source    (time, latitude, longitude) uint8 697MB ...\n",
+       "    LandRegion_mask  (latitude, longitude) uint8 346kB ...\n",
+       "    Gridcell_Area    (latitude) float32 960B ...\n",
+       "Attributes:\n",
+       "    version:       2.0\n",
+       "    release_date:  February 28, 2019\n",
+       "    Conventions:   CF-1.4\n",
+       "    citation:      https://doi.org/10.1111/j.1931-0846.2016.12195.x\n",
+       "    dataset_doi:   https://nsidc.org/data/g10010"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Code to retrieve and load the data\n",
     "\n",
@@ -509,9 +3841,9 @@
     "\n",
     "SI_obs_ds = xr.open_dataset(pooch_load(Sea_ice_conc_obs_url, Sea_ice_conc_obs_fname))\n",
     "\n",
-    "# note the use of the chunks keyword. These data come on a high resolution grid, \n",
-    "# so are potentially too large to load into memory. Chunking (using dask) \n",
-    "# avoids this problem. We arbitrarily pick a chunk length of 100 along each dimension.     \n",
+    "# note the use of the chunks keyword. These data come on a high resolution grid,\n",
+    "# so are potentially too large to load into memory. Chunking (using dask)\n",
+    "# avoids this problem. We arbitrarily pick a chunk length of 100 along each dimension.\n",
     "\n",
     "# we can print a useful summary of the data by calling it:\n",
     "SI_obs_ds"
@@ -521,7 +3853,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "88016832-949b-47c9-9729-478e22716279",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# Code to preprocess data\n",
@@ -532,7 +3866,9 @@
   {
    "cell_type": "markdown",
    "id": "bf33e0f1-657a-40de-ba63-3df6fc4fd1d0",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "We can now visualize the content of the dataset:\n",
     "\n",
@@ -543,10 +3879,38 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "778eabdf-3594-476e-ae69-fda6d6d67001",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f06c5991ca0>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 575,
+       "width": 766
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# We can select and plot the first month \n",
+    "# We can select and plot the first month\n",
     "# using .isel(time=0) and inbuilt xarray plotting,\n",
     "# just to check the data looks reasonable:\n",
     "SI_obs_ds.isel(time=0).seaice_conc.plot()"
@@ -555,7 +3919,9 @@
   {
    "cell_type": "markdown",
    "id": "64abc79c-689a-4e73-91c3-5fe86f7c7d6c",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "Note that the dataset also includes variables for grid cell area and given in $\\text{km}^2$. \n",
     "\n",
@@ -568,8 +3934,36 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "b7bf75ee-8e48-4e26-ac0d-200e940bd66e",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f06c5844d30>]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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m3aTyYkBz/XzP2stnXtpF9YW6x7NgIZp3F9QuPOPAKxx5fXMk9kGbMD+7MaT9SnD3TqCycRDJ5/9TD51Qvz8ap8K+zBL++U/zSEVosRVVx/cZb6uiknN8p1t1zQK8gTrNgShWRVWKg3jHwINPnKX/9333019/7jEiIvp33/736GX/9Ouibb9Lyq2K/D+D//qee+ltn3hYfD9G1tM0v3Hzfbn7znvuOhR1+wAAAAAAdYHiAABjpFXRxULxgtIg8Jq25t7WWdFy/3K+QMizACwpUxV4T5nKjIPtPmmSJDTPCtceU8dawZwovuKgKng4uuJgoT+x7MnwmvoWQeXz/UkD5+LvxAsoX4isOOBqAyKZcUC0s6euq4qCsRQH77nrcfX73tfBWVQc7F+ey33tb1VU/B7E9lbfjZQ1rg8ci6A4qFhjxVAcjMcp/erffoGe/xsfond99jFK0+3hhf/50S/TweNxGnhdU2pVFOE8fO89eoG+rLnqwd8V7AcAAAAAwKyCxgEAxuxbmqOnXLFMV+9ZoD0LA1pdKLcq8ghIHo1TMUmb2aRwL/PhOCUvyooy3lZFfIps31SxijcOPCbe+eeaKU9iFq6JqoOHTzoGJG8Ox+I43FYc5CesN0c+x6BqVcTff8dpU6H6Geg5I2UFlTbwfINBL6G9S0pA9A62KuLXoBhNQ854nNLf3K03Ds5s+BbNyxsHsRQH+eP7qVet5r72Vp+VWhVBceBOWeMgRtF8FqyKPv6lY/S6Wx4kvtxKU6J7HotvYdYFx8qsiiKch0XXmTJFjDUbwxEC2QEAAABwyQGrIgCM+bn/9Wn0c//r09SfzfUT6vcSGk09PXpMO2nFsCwUeMBsMbxsYojKJz3PbgxdbTr4Q2KWcUBENDfoEU3VsjaH9oXrtaKMA+bv7z1pd1yZOp/GU3GgFWxWFwayceOW88HVPwNx/K9t+b3/RYoD3rzwssvhioP9K/OUJAmtLAxy2Q4xrTpiwxuU+1fm6PCUwiOGVdGnHz6R2+Y03kXz8yXnVgzFwfmtkXiNX3XVSs66yfs9KA1HhuLAnbLmXIxg4mqrIv99uOexU4U/i6G6mAW6VhwUBcXHVBzc+fBJ9fvelnUAAAAAAG2A4gCAiCRJQots4t/DqkibMs0UB3ODOEVbomobCM+HRWFVtDTVOOj7N0/WNnSropgT70TlU35ERCccGwfa57u80BfHoFfxduYUBxcURzzjwWvq/QRTk1xxIcdglTevdrDigL82btUWQ3FQ5mHt3jgoub88fuo8DZ0bJ1owslQceNulFb/HMSaddztl59j61ojS1E/1mG1jGn4PiFG0LrMkixEQPQuUhyP7noejcUpbBcrGmIqDWx84qn6/7DoNAAAAANA1aBwAEJkl9tDq8cCgTVZl+Qpi2tvRqqiqIOlVNBuNUxG4Oa044HZNPhkHerFCFG3dFQcVVkXrflZF2ue/PNfvLBx5ab5PyxHf/w12bmfZDrFeP28aZUVzrnqJMXHbFdyK5vKV+I2Dzz1ysvBn3kXzsvvLaJyWFvMs4DZFC4MePYmFsXsff2X3oc3ROFrOxW6lzKooTf0nvtfZNZ5bxcWwqypTdUFx4N+8LmsOlB2f1nzkfr1x4JV1BgAAAABgARoHAERmkeUceGQcaA/iE8VBpKIlUfUDuVcwqPZ39y5dLJbGeA+KrIqWWTiy98RrVePglKviQBbOB/1elHDkNE3FZ7A0JxUH57fGOeswS3hBokhxME7JZR+44uDy1axxsHsyDqRVUb5xEKNoVNYg9VYcbFQUZb0bF0+cOZ/7+uq9C7km7vY+eFsVlV9jPe7B4CJV13fvgGpelL1qNd84iNE4PVuSZXLg6M4PR94YSsuyabwVB2XNqVjT/qfWtwqbyLgGAQAAAGCWQeMAgMjwgOQi39U28AehJLmoNBhwmx6nwtlonFZOUZU9TLfhtFIMyykOuFVMxHBkXrQ961w0qQpH5sVlS7hdU6a2EFY9Du//5kg2BDSrIiK/aT9+HmYNE378Efk0r3jT6PLlXWhVtMmtivJF6xgZB2WqFnfFQcX95bRz0Z5bFV29Z5H2LOaPvy7DkYn8r8G7nSorGO+iMf/7V65yxYH/5182RHHo9PkdXziuGiLxbh6VNQc2hmN3uywioo8/eEyEY2fAqggAAAAAswwaBwBEJo7igHmrD/qUJNsNA2FVVOD72paQB0GvotUplm8w6CW5grFUHNi/B7wYsTLJOOCKg7hWRVeu5ieuPcORueJgeUGfuPcommvn1fL8YPI5TOP1Gchw5AuvfyBDED0K2KJxsFJkVbRzixb8PLy8g4yDssKod+G+a8UBtyq6anWBVhelVdbYSfUT0sD2vgbvdqrOMW+bFn4v4FZFa5sjt+Mvg1sncna6XVFV88jbLqqsMJ+mcRrIRfkGRLAqAgAAAMBsg8YBAJERigOXcGRukXLxVNemvT2mrbQHQf7A7mVVpAUjZ40TojjhyPxBsEhxwIvr1nDFwVOvzAeTujYOCponMTImtAfxpfm+yBgh8pt41Rp4RPIcJPJR/oQ2Dnay4qDKqihG46Ds/e0yHDnG9o+cZoqDvQu0h1kVEflN/YdkmHhfg3c7VUVZb8WBsCpi6xAi/6yhqmvsQzu8cVCVY7G+NXKzDAzZfgzLui8eOlP4s52uOAEAAADApQ0aBwBEZpEVLj0mjbg9xcJgetpeTjt7PLBpvsHX7I3jLcytivayCdcuw5GF4sA9HDlfuPuqq1dyX7uGI7PXlr0HsnFjf/xpxajl+T4tDHrU7+W371U0koqDC1ZFA82qyP49KGocdGVVlKYp/e3dh+j/edfn6ZYvHnHf3nA0FgUjEY7sPGk6HI1Li1JdWxV5Nw6eOCsVB9yqyHM/tOY0v/5AceBLlerF+/3nRVluVUTkP/FeNSTx5R2ecxAyIOM5dV+leIhhFVSmOoHiAAAQk8Onz9MPveE2+sZX/B29/C/vomEE1RUA4NIGjQMAIrM0lz/tqiahmsAf1MsUB0Rehdv8Q9J8v0f7mU2IV9Hs9Hp+23uX8hOuXVjlZEXzFdY48g5HPnEu/x5zxcGJtS03f1/+2rJJdx6O7DFtz9//uX5Cc/0eJUlCyxHswtI0LVQcxMo4OMHUJBPFwby0ivHm0ZPr9KLf+wT91Fs/Rb9365fpx37/k/SxEusGC7RJcn4NGo1T10lXHtDN8VcclB9XWh6MJUeUcGTNLsxLfaY1xa7es5jfNhoHrsya4oDb9RH5HwP873PVw4GjO11xUP0ZezaQKhUHDutwTtmQCDIOAAAxeeOHv0Qf/9IxOnN+SG+97WH6iPN6HABw6YPGAQCRERkHDg8MfLpqepta48Bj6pY/KK8s9HMBxUSOVkVCcZDfboxwZDltfyHjgE97OyoOxuNUfA5fccVy7uvNoZzKtoK/tqxgyI/BDYf3nxejps+B5Qh2UVujVAQhZooD7Ry0tkoYjVMRfJ0VzaVdlm/R7P33HqYXvPrD9JH78w8m7/+Cr+pAKxpzxQGRr11RVWPQXXFQ2bjw3T4PR75qzwL1e4lQvXjtB7/+LQx6QvHgXbje7WxUZUw4F03557tvaU6sAbxVV/w4/Mbr9+W+/vIusyrilp1EvudhlfKqSpFgQZmqBYoDEJu1zSHd/PnDdP/hYgstsHO557HT+a8fPdXRngAALhXQOAAgMvyBKUY48sJcuVWRx7SzNm3Oi0WxwpH3LuW3KxQHQ3+rnC4UB9rD6PWXLYnvedkV8WJMWTiyteqBT/dNh2PziWePSUetEJE1L/q9hJhbkvk5eGp9i/hbesWqblXkGY68ORzT//n2z6oTvd5Fa60YeNmy9Nf3tCuqasp0rTjw3P5onNLRs/lrSzbtLxsHXoqD/LG9ujCQGR/OjbPdTlVjeN35/edrrKX5vmyeOjYO0jQVf/8brtub+3qnZxzw++G+pbloloFE1c0rr+GJacqOsfVN2ISAeKxvjuif/OZH6MVvvoP+yW9+hP7unkNd7xKIDF+Tl1mpAQAAERoHAESH27R4FK1EOPLUNucUf/Whg1WRNm3OJz3dMg5Y42AfsyoSGQcj28LpaJyKKeaijINzm0M3qyDtQfzafYuiaM3tjOy2z5pHBeHIaWqfsyGtoi6+7zwg2WPSUStETJ/73nZZPNuC6GLRPGY48t2PnRKWSRnek978+rI411MnXT0VB1XvrXvjoMOMgxNrm+K8zixa+L3A66GVHwOri7Jx4G0Xt9upOr88rwPD0VissZbn++IY8LQq2hiOacjOg69nioPDpzfc8466RKxJ53q5Zj6R73FQpejzVhyMxmmpqgCKAxCTt3/qID10bDtXZThO6b//7Rc63iMQG7429V6LAgAufdA4ACAyctrdo3FQbNMSy1+dT3quLPRplTcO3KyKWMbBYlXGge+0O9HFwjWfdByn9jY1k/1QCmKrCwPRSPFSHPBJ/qxYox2D1g00XoSYLhgLxYFDwaZMcUAk3wP7xkG+WL9nYTAJSY8ZjnzHgeOFP/NQW02jTZtrwdSuioOKorS3tzqftOXFOt5kteTI6XzzKkmIrrhgFSUaB07qE35sr8wPhOoLGQe+VN3fPAvGWkF2aV6qHz2n3bXj6+uetFd878AODkjW7DP5fdjzPlRl2eadcVDVGEDGAYjJ2z5xMPf1g0/sbMUTkPBhDa/ncQDAzgGNAwAiwyf+oygOpsKRB3zc3GkfRMFGsSo6s+EVjsytirjiIP8eWE8ca4WQiVXRggwG9Xpg5vvRS7an3i9jAbEnCybC2yIVB7pVEZG9XRR/UJ8umHalOChT/mwav36uONg/5e0fc9r2kwdOFP4stuJgpahx4JlxUFGQ9A4n5schD2X1nDJ74mz+GLxiZZ4GF8791Uh5N7wgvLowEKqvnTzpPQtUKw783n+tObk0pykO/K5F2rF91Z4FetK+fEj3gR1sV6TZZ/KsIdeMg4rGQJUyqy1VazzvJjoA08TI9ACzjVQc+K5FAQCXPmgcABAZ70ljIvkQND3p3O8llDj7qxPpVkXdhSNXZBw4T7sTXSxW8yk7ouqp5Ob7IT+DJEmEz7tb40BkHOhWRUREG8Z2UZqvdYYMB/YPKO/3kknRlEhmjXgrDi7PNQ6kv7eHXVaapqWKA+9QVG3aXFW7eFoVVRxbZzeGNDa26ZqGF+yuWs03DjwbF0dOn89ve8/FQqlUHMSxKlpZUPztIxTt0jSlj95/lD724FE3a7pZZdYUB5pVkee0Oz8GB72EFgY9+sorlnPf39mNA2mfuZsUB5WNAygOQES8j3cw24zGqbjvQnkJAKgCjQMAIiP89R2CecV019Q2kySJEg6sBeNyqyIvX+vT68yqaKnKqsi6cZDffr+XTAqWi3M90bjxskngBbGseH5ZJKuiYsWBFtBtbRdVrDgQE8cOx2FZzgiRvA54ZxxMNw648mec+oRDPvjEucJ8AyL/UFTROFvoU5Ikonngqjhgxxaf+E9TX5sU3kTuUnFw9dS2eTM3mlWREo7scf5zfvHP7qJ//bu30w+/8Xb6hT/97K5qHmxWTLd6Tlvz+0ByQXW3GjEcWVM+JUlCf+/Kldz3DxzduY0D3khfmOtHzTiour952UVmVL02b/UdANPAGmt3o605kXEAAKgCjQMAIsOLph42QfwhaJEFggrVw9h+H7j0f1WzKnJaqJziVkWLFeHIxg+NMph3u2BJtN24iTVpxwuzWcGMW/V4TR8VZhwoigPrrI+1rfy2pzMORMHC4SGKe8svsHPQPxy5THEgVS8e00ZlagOiGFZFPGdFP/6sw9Gn4c2za/cuit/xfGCrtiqKl3EwvW1pW+fUPFXuQzLjwPc4PLW2Re/49COTr//8zkfp92894LpNjYPH1+juR09Fb1rw+2uf2SXGVBwszfXVe7DntCW/v2fH/lOu4I2DnZtxoCoOIuZMVFmzeBdSq9Z4KOSCmGiNMg/lOZhNtOsRGgcAgCrQOAAgMqJoHzkcmUixSXHYBz7tuzw/EPYU0ayKKhUHtoUUXizkherpzAkiv2k3XjTLiufeResMXhDKLELmev4BtdKq6OKxF0NxIJp3XHHQ9z0G6igOiHyaVzzfYHofiGKEI4c1rjynTfmxdTUr3BN5Nw7KrYrOOFollSkO9rBmbjyrovgZB0fOnKcRe49/+d2fp3sfP+263Wn+6PaH6dm/9kH6Z7/9UfrXv3s7nTjnozLT4OcXV7y5Ng4K7sVdWhVNGgdccbCjrYrkmlQ08B0beF0rDqqaIsg4ADHRGmlQvewetGdvWBUBAKpA4wCAyPBQVI+iLZ8g50XLgXPhnEibsuuLxsH61sj89Y8V70ZeKJ13Vn3wSX9eqBJWKV6F+y29cB8jZ4NIPixn70Ovl4iQbu+A6rxVUYxw5LqKA+Nw5LVixcGCFhDscAzc8VBecfDsp12Z+zp2OPLqheMvplURbyLuWZQT755T/1WKA0+rpCdKFAcy4yCmVVHcjIOT6/K1jVOin33bndEmjV/zwQcoExrc+sAx+u7/8dFo1jj8/OIZO+tbfgULfo3Jhij4msArZ4hIz9kgIrqSNfE8mxfTnFzbpBe/+Q76tv/2AXrV333RNWMlQygO5pSMg4iWbeLn7oqDCrsuKA5ARLT1pncDHcwOWpPgrFPWWRmnz2/RrQ8cpWNsyAQAMJugcQBAZGIUjauKljEKx/xBaWVhQKsLc8rv2S5WNdslXiiNHY68xIvGzlZJk/3gORMXHtRjbV8Lp82QdjHGzZutbhsHfIJRHoPe4chMcbB8sXGQJP6NmyOnz9NDx/LWG89++lW5r9e3Rq5Fq1DFgWvGAW+eLQyiTdsTyclC3jjw3P6RM/lw5KunwpF54dZr2k1Oe8cNxiXatirSeODIWfqbux933TbRdjDzIRZU/ciJdfq+195K9x8+4759fi3cv5xXHnkW7YvuA/wY8Jy25NOdqxfOf35P8Bjg0PjVv/0i3fz5w/ToyXX6Hx98gG770jH3bfLC/eJcn5YXYioOKsKR3TMOEI4MZoOicyFW4xJ0j3a/G43TqNehR0+u03P++wfpX/3P2+mmV3+YHnzibLRtAwCagcYBAJGJUbTSHtKm8S5aEsnpsRXFqojIvmilPXzzQrn3Z1Bk0TPZfrSJf71oIptX9gWLzeFYfBbT74MM6Pa2KioJR3aYtKqvOLB9/SdKMg607Q+NC/h3PJS3KVpdGNA/+Ir94veqJkHbwK9BWSCqd9Mqtw+8gTovlVfcWs0Srj7jU85Efo2DJ86UKQ7iNE9UxUEEq7JpeObONHc/6m9XtDVKhVUSEdGJtS36f993v/v2KxUHrlZFLOvmwmcfMxxZU18SyWvw5mjsPvF5fmtEb/vEw7nvffSBo67bJJLXoYVBXMVBVWOga8UBMg5ALI4X2NR5NnBnkfNbI3rrbQ/RH97+UGUGyk6j6H4XM+fgN993H524MFRx7NwmveNTj1T8CwBA16BxAEBktIdFazRZeNk+xLAq2vaW7lOS71nYNw6UB0TeKPFXHOjFiqLte008F1kmxWheacX46SlP72NQTHrPTTct8seDddGcKCDjwPkzOMYUB/tZ42Dg3Dy8g+UbPPMr96vZCp52RYXhyBGtivhxuLIgG6heD2ujcSruL8vzffE5eDQuzm0MReNyOuNgb6T3QAtH5oqjLqyKMmL4SpdNER484R/Iy4sylzHFAQ+yt0Q0kC+shWIqDnjw92qB8onIX3VwyxefEN8rKiRaEqQ4cDwXNjpWHFQ1pmJlHBw5fZ7+7p5D9Pip9SjbA7NHYeNgl1kV/eRbPkUv/8u76WV/cTf99B9+uuvdiUrReitm4+BP78g3Cl53y4PRtg0AaIZ8igcAuBJj2lwE0Q3yD2gy48Dfqmh5oU9JktDqwiC3OLF+YNcaMfw99564F/763CoqQs4FkSyITcKJoyhO5IPwdMGO2zRsjmwfnGUoZolNUicB5X6Nk43hSDQP+ZSv93XorkdP5r6+8Sv3i4ItkW/BZBasisQ5OB/Pqkiboluc21Y8TF93PfIFtOLEFasXC8axCrfCX35+ILbt7e1cpjjgzV0PyqaZYzQuhOJgKZ7igOf8ZPcBYVfl+DkUXYf4fZhoe/2iNRSseNfnHhPfW1Kuy9bwwv3iXF+sizzPw6pwZHfFAc+6WRjkGkoxLEK+eOgM/e+vvZXObY7o8pV5+tOf/Ef01Vfvcd8umC2OFTQOPK3CZo2Dx9foQ/ddbKK+794jdHJtUzS1dyrFigM/9es0w4DndADA7IGzFIDIiHDkof2EmbRJ4YVz33BgIs0mZPthea8omtkuVLQiIH8QnxuwwrmzTQ6frIs18cwtOJYmVkX5/fFpHMmF6XTxnhdNNo3PAxGKOV9ikxRB9cMbJZ6F+6HShKjO+bB9/0+v5z//r7hiWWR9EPkWLqVFSEHjwNGqSOSMKCHxXg9rWrFsYa4XRfGgTfBOf/7xmqe68i2/7dTVquB0SePAW+1AVF6YjzHpzM8v3sT0vAYUWdZ1GY68p0D5RGS/FplmbXNIH7j3iPh+DG9z7X64LBp48TIOuAo3dsbBdBOVKI5V0dvvODi53hw/t0n/x598xn2bYPY4AcUBHTh2TnwvhvJqViga1PBU3k1z32GZZ/D3rlyJsm0AQHPQOAAgMjHCkaVNSlx/9TRNxcPocsEDu/VCRXst/PXGDkfmhSpZuPSxJxBZC5NwZN9gXCJZjFia61N/KpDXu3BYpvqIYdXFC5FCceD4+rXGAX/N3KpImwBqg2ZN0eslomDjOWUqps0vXHuE2qVjxYHXw5pWjFqc64vm7WmHxgF/T3tJXunGj8c0JdWHvw3afWh1YaBbZjkWjk+uFRckYhTuy6aZvdUWRNLfnk91rm+N3Lz9pVVRB+HIRZZpirLAs4n5/nuPqMdCDG9zTYEnMg4cPwN+P7psKW7hnh9fPGsmxnXg9i8fz31996On6XOPnHTfLpgtihQHuynj4OjZDfG9MmXgToPfkybfj2RVpF13+HMpAGD2QOMAgMjMO0+7E2kZB+WNA63Q2Hb7vAaUFWtWnaddtSLwoJd/z72bN3xyh4fxyml7J8VBcDiyR8ZBeUC0d84DL5AszxdnHHShOPA8BoZj+bf6FeeA9XtQZNXEz4W4VkV6KKnntKnWQI3l7682DgZxFA/8miJUX4pNi/UxuFEQ0M4nnYl8py3LChIxCvfljQP/YtEG+1z3s8ZBmlZbyTRFWhVlAwz5+9HmcOymejnLzq9sDcSvQ9l+ePGuz0qbIiKZweABv8ZuKw5iZhzkt7+P2WW5Kw5YoY4rDjybZxl8DUZE9Fvv9w9HB7PF8XOyaE4U5140KzxyXGZ8lGUR7TTObuivNVbGwWcfOSW+t5saVwBcqqBxAEBkYoQjSz9ZbtXjWzTUijBZwdDbJoM/eM/3e5SwRGZpF+VrVcQtWrqy6YgZjiwLpuUB0dbvQZFFBVGcnJEqxYFn80ab3ObNM644sFa9iOblhWOOnwu+VkUyGJcozuefERaOHMeqqJdsF+y54oHbSlnA31P+nmtFU/P7kFIQXV0YRLfMKm8cRJj2LtnGxnBsrvSYJk1TcX/Zz6yKiPwaN6GKAyK/iffC61BExcGZ81t0y30yGJkollXRbCkO9rFj0D/joFxxME591SZERMo8Ab3v3iN096OyiAd2LkWWPLFsamaBR07IxsGptd3TOCgq0sdoIhPJDDSi3XX8AXCpgsYBAJHR/LWtJ400m5Bp5nq+GQdFBZvp/88omnxoStWkK1H8cOTY0/YZvGhSNHHtE46sW1VlePrMj8epojiYyleIEo5cpTjw+wy2lEIgVxxI1VEsxQFrHDgVbDaHY3FMdRKOzEPi5/uycO+lOFDuA0mSxFEc8AYus8vjjSsie8sw7eF4ZWFA/V4imgeeD61lk4wxGgdVwauewazadZ0XbYn8lEdFDWStcRAroDtbA/FmLpFfE/OzB08VXueiNA6Ua5G4F2z6Td3z+3F0xcEmVxwsiN85v+m7D0XNOagOdhfHzhaEI0e4F80Kj5xcE98rsxTcaRQN7MUIRz6/NaIvPH5GfD+WTRIAoDloHAAQGW9v59E4FQUYoTjgRUvjYFpesOklF/eBF62sFwv8wVuzxIg/7Z5/zV0pDrJimZy4tn9YL5qyzPAMiOZFCiJuVTQDGQeO5+AoKOPA7xhM01RaU1w4/3mxaN1p0riseRmrcTBSGli64iCOVVF2DO5d4gH1/hkH8/1yqywi++YVL9gmycXjjxeOPTMOysKRZ6Fx4GlRoZ1bvGi7vQ8+70ORVRGfdifys0ooylpJkiTateh0SUEoxqSntM/siXNwOE7dpu75tTB244Dfj65kVkVEvg08bR8y3v+FI5j23UWcKCiQx2ggzgqa4mA3WRUVfdYxivf3Pn6ahkrNY31r5Kp+BAC0B40DACKjFUwsH5Y0yfUCD0eObFW0Mj+Y2AXxaVv7jAPeOFAUB84P62LavsKmxuuhVSoOsnBk/2IFL0ZxX3GZM2C3YNSKUEsdZxxIuzC/fQjLOPB7/7XjOQtoX1KmTD3QCiGTjA9Htcs0WiFIUxx4TXlxX+/MLiqG4oAfA/w9540rIv+smdWp+xBXgXlZ5aRp2nnGQVV+gGfOiHotmOtHC0nnNk1Z8zyW6qQooDsjlm1a2WuL0zjQrIoUyzCn5g0/DkXjoGOrIiL/a8G5gvN8NE7peMEUOth5FIUj7xbFwWic0mMnlcbBbrIqKrjWxMg4+JySb5CBBiYAsw0aBwBERrPOsZw2VgMxxbQzKxpq5qctkKGkFx+U+eS5taciL4JrjQPvwjEvxFTZ9LgpDhSbFCKtaOyRccCaFlVWRYbNC60QNl0k4sWa4Tg1t0jgigPevOs648BTdcML1kTF4chuk8bK382mjD3VLrl9UK5tK/MDeQ2MpDhYuPAZxLBKahKOPDRW/hRNehNpx6GTx/7WqLQpFyMUtVpx4GhVpJxbC4OemPj3al6sbTHV3dR2+cS7x8TtxnAspitzjQNxH/Q5FrRrUUaMSWM9HDlOSPl4LHM2Lus4HHnf0pxo5nelOCDSVZpgZ9J1xsEDR87Quz77WGfWQEfOnFfvyWUN/p1GkbIgxjHw2UdOFv5sN6leALgUkas2AIArWiHbsmioPQAt8GnnXlyrouWp6U7vaVdeAOXe8kR64Xg8TqmneA43QUzXzZfb1HgULst8/nmxwuOhuctwZFVxMNU40Kadt0YpzQ9sPn8iZdqbnYOek6b8oajfS0RAOH8PLIu2WhEie/1cceBVMOQPQMvz/cn5za8JXo0DbcJzeaEv7do2hpSmqfiM2sI/h+x1742gOOCB86Jx0JPnoKaUaYNsYF889ngj86zTpHPVFGOabisC+HlhSVk4MpFv40C7t8wPetuv95z/PvC/O30fWF3o09GzF3/mUbTQCjGrU+cfb6B5qZ+Kps2Jtu8XG8ORaG5boRXuF+f60ULKtWOw63DklQsh7dPHh+c+jMdp6XurNfvBzmM4Ghfek2Ko3z56/1H6N2/6BG2NUtq7OKA3vuhZ9A+feoX7dqfRbIqIdlfGQVGDoMzSzooyxQEaBwDMNlAcABAZTXHgbVW0KKyKfCfOhUVEieLAesKBBx3rigOlcGxYtOIFe/6AHENxUGSTQhQrHJlnHMR7D/hrX5zr5ZpC2rSz9Xsgi7ZVdmG2OSfT8MlGIqk6cb8GZYqDuThWRWWqJ9E4c1P85Pdhrp/QwqBPe9nE/6iiqNMU2bzKFAf+igehOGDXnF4vIX5YWk9bl1nEyIwDnwfWkClGL5ukjMpw5MiKg/l+T6jwvN4D3jSZ3i4/BjymLbXJzjLFAW+4WVFVFPTKdyAqtq7T7KI8Ckfa/ShmxsFwNBZ2YSvzfaEEXncMR+ZZHxwoDnYHJ0oa2Z7XgIw//uTDk7Xu6fND+oE33Ebv+/xh9+1O88gJGYxMtLsyDorudd6Kg63RmB584mzhz60dCAAAtqBxAEBk1KKlZTAse0DpJXKb3oVjOW1erDgwD0fmVkXKFDkv2hLZFm6lv32F4sClcK/YpCzoVi0ejYOqjAPPnAexbR5OrSoObN8DXrQVqh/Hz4BPbnObou3v+W1f81TPpt1FOPKWz4NCHV9xN6siUbTc3gd+DSTyKd5LX/FMcdBBOLJyzeXngLXigKsIVnKNA1649ima8MaBdv/3LNwTVTcOPBsX2nGQJEnOMojI06qINfFLGgcxFAeDXpJTPMVYCxBVFwU9QzH1RvL26+bnoUcDVSuK84B4z2l/rWi/sjCgpfn8Z+9pVVR1bHsrLsBsUBSMTBRHcXDkzIb43k++9VN0c8TmwcHjuuLg1C7JOCgbVPEOR17bGFGZMyMUBwDMNmgcABAZzaLBtGg35MWivrDAkIVj20lPviiZLtp5hyPzB281HFmzizIqHqaptAjiNjXSKsXe11gLGZwoDiJYtVRlHHgWznkRik81etuFEVUrDvjEv2XjJERxwI8BS6sinu+QFQuJSBQMY4UjTxeovMPRM2RI/PY+rKqNA/uH1vOKPQiRvAaf3RiquRhtCMuaidvALs04cHpg5bYQ1+xdFL/jHUpZVZT3DUdm18ELn3ks5VHZvYCrHz2aR1rOxvR6LFY4clVR0HPSVCvcZ3kr3KLLJWdCaWTHVBxoa7GV+YFYl3TZOIBV0e7gWEkIdgzFgXYcjsYpvfJv7nXfdsZuVxyUDQp4hyNXKZu8GxcAgHagcQBAZHq9REwdWj60yClT6SPrXbCR/uLFFhHWD6z8tWhNAs+Jc+2zlIVrf19jXojpJRcbFt6NI6LqjAPP4q1UfLBpf2fFCVF1xoHnOchfi1q07fnZlYn3f+r95oqDaFZF88X2IH7hyDzrZXsf5vo9cU3wCCgW94KBblVEZP/Axq9pWtaMDKmPaFUUySrnNCtG7F+el/YszpOeVdPEMcORM+WVVB7Z78NonIr7cZlVkUfRuuwYJIqTN0RU3RTxPAbLFGi8oe6xFuLFqiSRqqvROHVr2mhr3OUFmfFQlUXShqqiMKyKdgdFwchE/vchouJr7EPH1syHF4ooyzgYR9qHLim7z3lbFVWtRWIFdGdsDsf02YMn6QlFCQMAkKBxAEAHeBYNRcFSKdgMRMEm3qQnf1jaGI4pLdMu1iTIIkOxL7IqHpb5u0/2iX/+DsUCPmG4Mn9x0lEUTke2n4G2fV4wiWnVw7flbRdGFJBx4Pj6gxQHjs2jsualKBh6NQ5KVE/a8e+zD7rigMg/JJ6ouIGmNQ6sQ/F4AVS7DvOAbu8Gdi4cWRSN41gV7Vuai3YOTP5+ZePA72FdHAeZ4oBnTDjsg/a6pyfcee5ODKuimPfBaaoUNZ4FG646SUqGGDyauPw6uDDoiUY+kV/Thh/b84MezfV7MuPAU3FQcX5pzR2w8zh+rrhAqiljrOH2gRmjcUrHzsYp3hY1DsYp0dkIzZOuKRsS8VCfTlN1nYlpVbS+OaLvec2t9D2vuZW+41W30CcPHI+2bQAuVdA4AKADhK/tDlMc8CLM9AO69wNb0LS1o+JAWxiJjIMIhUteOJ0umOiv33jal/u7V4Yjx2seeduFERX7y0/2QYRi2r3+kIwDz4D0smsQt6bwKlpqFiEZ0TIOSlQ3IuslQiho9jmszA9EMLG1RL2J8svSLouovIEtwpGdjsOT6/kJz33Lc+JauLOtirjiIF5Iuva6pgcXVub5OehvVcRtymJdi6oUB74ZB7JwXzTE4NE40a6DvJGv/Z4VRbaNMVQ3F/ehwqoIioNdwbESxcHmaOx2/ckoOw4Pn/ZvHIzGKT12Um8cEO2OnIOqtaav+mx2FAd/+ZlH6d7HTxPRdijzb73//mjbBuBSBY0DADrAs2gqJp2VxoF4WHUu2EwXzLRGhmXhQnpra8GwCbHYB7PifVkQYEYMX+N1Pu1cMnHtsQ+8aMqLNJ4e/1XNo14vEcV0y+ZNmkqLDKk68Svc8wKspjjg4ciWwbTSW7/YqmjdadKxrGgcL+OAh/NOKw78A4p5MSg7Bnu9REw+WyseQpRfXPlm3UDlheDV+emMAz5tHlFxMBenaZFRqThwLFiKvJML12LRQHR4/7V1RZltoovi4HzxdYhIaSB3lHHgOelZ1kiO0TgRzauOFQfZMcjPAc8GXlXjCIqD3UGZVRGR771oNJb5b9McPn3ebdvT2xiWTNTz+/VOpKo475lzUN04iNfAfO0tD+S+/sj9R6NtG4BLFTQOAOgAz8IxtyoK8ZYeWisOSmxqtMaBpb+qmHRVJsuSJJGqC6OJb74wThL5eccoXPJC2PSkpdZMsd4HmXMRz6onaOLe0apn2/op/z1+Hnra5fAHIzUg3DGgu1RxwIqmvMFlhbQIubgPIpzcy6qohuIghlXRwlSxzD2kPsQyrkvFAWtkek3ZnVrP/919S3OK6sb3Ybkq+NSzYFl0HIisE4fmxdqW/Eynz30ZjuyfcbCHZxzEUhxUFGRcw5ELslaItPugvU1G14qDIss0vhb22j4RFAdgm6rGgUdA/MW/XX4MHj7j3zgosinKOLkLFAdV1wJX9VnF/e3sRrz331LlDcBuAY0DADrAMxAvJJTS21taFMwqrIosp534a9GK5ESa6sJHcbA015/I8r23PQ0vxExPO3uGQxNtT9zzYhiftPRsnjSZdrZsnmnns7Cr6jjjgDdTLBUH2oRnRqxwZO08zIgWjlyiOODhnB5TXmUFO964sM44kMov5Rx0DOgmKm9gx5g2J9oOXJzmsqW53HFAFMGqiB0HexfjKR6K1iMy58Eh44Db9c31qTd1zPFjwKN4fqYkZ4OIaH7AVTc+xQx+fIuAbsdJz6KsFSK5PvPJOJDXwbl+IuzaqhpsTSlaC/HPoEurIigOdgeVigPHBmLVMRjDquiRE2ulP+fWgjuRqql+jyGWjKrmqOd9iGP5zAPAbgGNAwA6gD8seSoOtKKpt1URf1BaZf7i3CbI1KoowFubyO8z4A9/IRkTHg/LZf7q2jFh3bziU+/CosGxcB6Sc+HZvNEWx7yB56l4aKa4sLwGdR+OLJRXOXsMGdDuQT3FQbyMAyL/xoW4Dmv3IWeblrKci1g5A6cVqyKuuvEs1hDJe9IVqwv57XtmHBSsR5bmefPC36qIX3v4EINH4ZhfA1YX8uddDNtCIvn+XrM3fwx4TnryafaFMsWBh1WR0rhIkkSoDrym7oXyab6gceA57V1RkOP37Bh87pGT9MEvHHH31QcX6VRxUHGfOxLBqgiKA6KzFY0B3uy2ZJYyDnDdAaA+aBwA0AGeRTs54acUrnkwqrtNzcUiQZIk4oHJ0qooZNJV+77VIoI/pPLXShQnEFCEE1eGI9vtg+ZXvcKKNp4FE64eiG1VpBWgqhQHlotYbvnC1RXb2+eTnn5WRdPXIGHTsjWilPs6GVCmepDFKp+HZTFpOl/8PngUjcomffcuOWcc8PtQkOLA26qoOBjXS3GgZhxEsOmZhh9bl6/Ml/7ckiLFAb8fuDQOKpr4MQrHZZZpRHGGCNI0Feqbq/cs5r729JbWCvcZ3ErSJRy5oHHBG0deU/f82M7Of3EP8FQcVCh6vJrnRbzp1i/TC//HrfRv3vRJ+uE33uayBgCSsnBkIt+sk6przKEIjYOjZ8tVDbsh46CqOeRpVVR1nfHcNqcs6wIAoIPGAQAd4DllxSeHFhRrIOEtbSzZK7PoIFK8XQ2LBjLjQL/MeRXv+cNfyPvvUSzg1g/TzRstHNqyaKc9pC5XWBXZhiOz5pHmr+7YPNMKUCLjwFHxIK2K4l4DygrWy6xgOxqnLlZdYsq1xKrIumCdwc+D5bKA5ogFM6IZzTgwvg/xh9Bp5ZsIR970aWCd5I2DZWlV5Fm4J5KNvP3L+cZBN4oD//eAH4P8XhzjHBQB3Yvl90GXwvmWzNy5iikOXMORS67FooHtcS8oyHrpTHFw4To0SxkHntvWeMW7Pj/57zseOkF3P3o66vZ3K1UDAp7XgVmwKqpS3nBrwZ1I1VrPMxy5StnklTWlod1rx2gmAFAKGgcAdICrTQh/WFYmPT2nrYkUiwhWLPRUHITY1BD5TbyXeatPth2hcMmnSqaLVVo4tGXzQlucL0f0+OfWW5pdlef2eeG830tErohoXIzGZoVLPkkT26qozCKHF2yJfIqGpYqDCBkjRFJ5M22Vw6/LPt7exQ2c2BkHIY0Dy/cgTVPRxJ1uWvFg3NE4NZ+6HY/TIKsiz2INkWxmX7nKGgeORcPNUf5vZxPmvIGoBRm333a5bWEMmxxuC1Fl2eexD1ox5uo9ERsHJfcDEVQfI+NgLq7igL//2VqMrw09G3iz1DjQpr5v//KxaNvvmoeOnaMDR89F3+5onIpjnK8NPY/BKhuaGFZFVXk+u8GqqDIc2dG2ruoaG1NxoD13e6q+ANgJoHEAQAfwB1jLgoUomKkT736BdFujsfh7/GGZ75PlA5ssFujhyOKB3ah4L4t1SuOgg4wDPuHpWTwVk9bz+VBKIt/mSYhVkefrD8nZ4MdfmkqlQFP45LYWjiwzPgytioa8UNOb+m95Png8rIop05xVUf61j8ap2Xs/jSgYzRerHjysIvgE7fR7LxsHvooDNRyZB5QbfgbDcUr8z03fd3jGAZH9cXh2cyj2IbZVkdZAkVZFjuHIBQ088R44WOVUNa940drjHORNdN6wEoVzhyam9t5eszdvVeTra83WRJGbuPJ+tH3sxVIciAZylnEQ1aoo/7f5miCmVdF9h86I7+1dmlN+c+fxm++7n57za7fQc191C/3W+++Pum2taH7VHp51Ek9xwJelx85tuvvO8/OAX4+5QnAnUvUZeyoOZBM3f/2PmXGg4dk4A2AngMYBAB3gWTTlDwCxp621h1S+OFsc+NkUBGccOFnVSF/l6lDQTcNp8wzpr15ukWC5YOfHAJ8uJfJvXuW2FTDtbHkOVllkEOnnpdU+8IwD/l5vf88zHLk4Z0VTHHgs1suyXng4MpFT8054W8e1KirzFp+FcGRhVeSovCPKF2m1a5L11PUpZXrxsuV50bTwtCraGEqbGt44cLUqKijei6wTh32oUh9GURyIjIPuFQdJQnQFOwY8FQdltnExXn9R4yJGODZRceZX3HDk/D7wzz+m4uCLh2XjwFt1NQusb47odR96YPL1b9x8n7nSr2r7HN44qJrIbwP/jJ961ar4nScqMgjawgeqrrss30DV7tk7jU4bB+xecMVKvMbVNEWDQp7HPwA7ATQOAOgAOe1rOO0sipZKOLJj0fCs6m+f3wdeNLC1KgrLOJCKg3hWRVoh19quqCiQr2gfLI+BImn+NJ7hyGFWRfHOQW372nFpdQyGZBxw6yTebGjD+WFxwXqu3xPvvYtVUUnWiPreu0wbF58HQnXmULgR012lGQfOVkUB56CV6kvbPlG+ecQnvbe3b3sM8KDFfi+hlfm+sG2LaRNDRHQFtyryDEcOVBysb43M/YV56Dk/3mIo/+o2DjwyDngxZGV+IBRHvsdgieIgQvNGNi7iZhwUZX5FzTioCEj3smnS+KKiOPAsVs4KR86cF+/zPRGzHbRQ3KtWuWWZp1VR/m9ff9mSOP8PO9sV8evxdZct5b4+ub7zMw64HRBXZPsqDvLH/5WicWW/DtAoWu9CcQBAOWgcANAB8+yBxTQcWQRihkx62t2o+UQHkZx29/SWFdPmHWccaI0brXBpXTDgxQI+YesZyigKpsp0r2fBIMSqKGbOSMi0teU+hGUc+DVOygrWRJq3s/2DSmnGgfJ5bIzsHxjKFAf8uuBi0VFyLeIhrdaFw5AGrq/iQH6e0/sw6CXCKsF64pg3Di5bmqMkScS12NOiRPvbl6/EmzItuhZq9wTLAQIi2Yzn6y6+DhkaW5alaSrznhbiKf8yeMFueb4v9oP/jiVlGQexwqFz279wHHhaZk4jByk6sCriigPWPPRqmmhoigOeBbMT0c7tI2f8ff0z+HV+0Eto3/Jc6e9Ywo/B1YUBXcNC2r1zDviaTDQOdoHigF+PuG2db8YBy1tiDUyiOAHJfG2WgcYBAOWgcQBAB3gW7cKsivikp6HigC0OF+d6wk+VFxItJ622hsyeoEBx4PXAHhSOrHwm1gWDoim3DNdw5Iptq9s3PAZDrIo87cLCgmE11YlR46BB48Ty/S8rFBFpwagxwpGLi1VE9udfmqalypsY085lyg/ZOLWe9i5u3GQMeo7NO6UIOL0PSZKIiWNvxcG+Cz7eXIHn+bCqFUOlTcnYbcpPHgcXgmEjWJZV5R15W5ZtDMeiEcEn/T2zZjL4MMfqwkAoH2IGYnIF2jQxw5G7yjjI1DZdWhXx5mEsxcF4nKoZB7tBcaBN/D98bC3a9jUVsrwOxAtHXlno0zV78kXrQ6e8Gwf5fbheKA62zG1jixiNU3rXZx+jt9z2UFTLKq44uHYfbxxEVBwwxYv39jOKGwc7/zoEQBvkyA8AwB3XYNagcGTHjIOK8CkiokVuVWRYONzg3uZFGQdOhbMmGQfb27d9cONFS/6Q6nkMymBmLePA7xgU3tZK4ZwX022tkvLHQEjOCJFd0UQoDgIyDixVR2Xe+kSKTYmHVVGJ8ipG4+78lvSWXynLODDe/tZIFi3zk75+GSNEYaobvg+mzTvlfObNi4W5Xu56bW0XxacXswBQGQzs97DKz61+L5k0MHK/tzUSU+gWFCl/tKwT6+tAlfqwqIGoNTWaoBVD+XsswpFdMg5YwXBBFgzPb41pOBoLCzsLisKJieKEQxc1LmIpDkTBtCDjwLN4X5lxEElx8OjJdbWAHrNw2hVaUfJAxMYBPwaW5wfiehRTcbCyMBDT7ofP+GYccCsmnnGwORzT+S27e0AZv/zuz9Pv33qAiIj+8LaH6D0/94+ppzyrWMOvR7xxEDPj4PJVRXHQaeMAigMAyoDioCWPPvoo/eVf/iW9/OUvpxe84AV0xRVXUJIkk//dcsstnezXqVOn6Prrr8/ty4/92I91si9A4lk0kgWz6mnvcVocFlSXoiC4aXwVB3zaXF+IeYXz8oc/PeNAsUoxLhjwIkxMiwT+WhYDJv7HqZ1VSYhdlWhcGL7+kGlrT6uikIwDV6uikjBMIv9g1NE4FUXo/JSrvCZYF6y0qanpYql3OLJ2TV0sCSW1blzy16Md7zEVB/P9HiVJ/nPn56X1NVhYFV2whVia81fcZPBG9tJcP8q0fwZfj2RNO74GILK3KODXYX4MatdlS8syrQBSGY4cIeNAKxgS6RPRFvBzscw2LkbGQXYd5MegX8ZBmFXR5mhsateWsa1+K8848AqG5mj5BkS7pHGgTPMfOHYu2vb5M8HyQp9W2DHomXHAr++rWuPA0apoNE7F/fD6y5bF78XIOVjfHE2aBkREXzh0hu5+7JT7donk2vRJ3KrIsXHAhzNWFwZisCiG+qhLxcHB42v03b/9Ufrq//Qeeuk7Phcl0wEAK6A4aMidd95J3/Vd30WHDh3qeldUXvrSl9Jjjz3W9W6AArr2V9cmbrdGY+r32k9ZFD0kTbM0n9++pbdrcMYBe2i0+gyk4qCbjAP+ACDDkf2sSkL8zfVjMCWlntR6+5pVEf+eaziysv1+L6F+L8kV+TeHNp9BWMaB3+cvFQfcqogrDnwLhkT5BmqSJDQ/6OV+z94qrHza2Ltgpk2vTjfw3BsHQZZ5jqqXgKwfaVUSx6qIW7f5WhVJBVyMaf+MIgVkr5fQ0lw/d7+0fh+q7kNq1olhAVU7nvhxGMPjn68FVpSMg+3fG6pqlLYUFe6J4jROihoX8TIOdOtGrYF3fjimVWPVh2aZ1VXGgZZvQLRbrIrka3woYuNAKI/m+9I20rFwym2QVhTLtCOn/RQH2nPmtXsXKUkopw49tb5FT9q3JH7XkjseOi6+9+ATZ+mbnnyZ63bH41TcZ7ni4HTEcOSFQY9WFwZ0futis8azeZXRpeLgdR96kO56dLtJ9Cd3HKT/7Ruupec942r37QJgARQHDTl16tTMNg1uvfVWesMb3tD1boASPH1dg6adlSl8qwdWsThUHo6k4sDSJiasceA1cc0nKrTGgVbItXxgHiuTNSIc2bFwGFI0VG0izBQH1YVzadVkWbSsbhwQ+R2DXYdDC8UBe/3cusp6sa4VQYRNjbO3Nn/46SX5ffDefl3FgYdV0jQhOR+e1yDNso/vk3Xx7BSbXNxXYFW0OfSZNCaSDYHFub467b+25VMsKMtc8rYsE5Z1IVZFjlk7muolhse/UBwoBTsiP2/pspD2OFkv+vZjKA62RmPxmpYLrIqIfBp4mvKFWxXFUhzcV9A42A3hyNpne/TsJp2JpLbQlUdccdCxVZGj4kCzBNyzOKC9i/lmaYyA5I8+cFR8L0bBXGte8SZJzHDkxTktZ8O/iXh6Xd+Gpgqy5o9ufzj39SvedY/7NgGwAo0DA6677jr6nu/5HvqlX/ol+r3f+71O92Vzc5Ne/OIXU5qmdNVVV9E3fuM3dro/QMczmDWkaMktIiz3QVsccngx3dOqqKho6+XxL/105cNhNvE8DQ91boM2WSMUB44TzyKUMjAc2GofOi9aKgUjDa/ifUjGAf+eZeGyMhx5znfiWp/0ZRkfzhP/Qnk1P8gVDXkh23raXSuClXmLe4cj6+cg2wdDyXZZwTpDWBUZF8+EVdGFxoGW+eJlV6RZFWXT/rntR1YcEPmHRFeFIw96CfGequd9ULsOd6U46CvHgF/joNi60FP5d3H7BeHIERQHWiEqK5RpjQPLtfBkH5TzSoQjx1IcFFoV7QbFgf4ePxQp54AfB5ryyMuujEg+G64u9Onqvfnj0LNxoL225YX+xEIwI0bj4GMPHBPfO3rWN9+BSL/Gc8XB+a2xy3WYSM+74cdgt+HI8TMODh6Pl3MCQFtgVdSQpz3tafTOd76TbrzxRrr22msn3z9w4EB3O0VEr3zlK+nee+8lIqJXvepVnTcygA5/gHUNR1amC4usiizgUx3aZBuXaFs+LPHJ8dhF25Bw5Gy/clYplt7KFTYp29uPdwyqGQOOdk2NMg4in4NEfs0rmXFQrbiwLByXFYqIlElj42KJVgDmRSIxbW4eTl6RMdKXU+eW8M9grp/kjgPNIiRNUzER3YTxOBXNK035xgupljkjwqpIKdLFzjjIwpE1Fd765khMPlogGgcXtr08n7cJcrMqEsX7i699mWc9OFuW8XM+a+BPnyuWxwC33tLuQzEm7rVJY6Lta9L0MeA1bVzWSI6iOCi4H8VQHGhrsaxhpl0Tre+FRLIQlyRE+1mxdGuU0micqmsFK7ZGY3rwibPqz86c3zK7/8wqRZaMB46do2+4fp/79vmz2fL8YBLUXfQ7lmgh4VxxcPr8kNY3Ry7hxPz6NuglNN/v0WVLc/TQ1Pe5UtCak2ubap7BE87B0ET6NZ43Doi2cw72r8jg4rZoQfWiedVl48BJeVkGIg7ApQQUBw25/vrr6bu/+7tzTYOu+cIXvkCvfOUriYjouc99Lr3oRS/qeI9AEXHDkcOsiqz2gVsVaX7KfJ8sJ71CMw68LAL4Q7I2VUakHQN2qwdtyo1/Dp7HYNOMA6t9aObxHzfjQPu+VfF+OOZWRdVF26xwbEGV4kCGI9su1rUCED/e3BUH/CF9oeL8M3z/iRR7EFYk0xQ//LxpitYAC2ne8eO2DbwAHJJxsGlcOOSTi/smigMlGDhy0dY7oDxDhCNPfQ58H+ytiqrXAp6Fa7n9asWBpWVeRpHH/mokm5Kyc5GflzHCkRcKFAfWjUMi/d6WKe56vUQMlsSwKlqZH6hKWO+cgy8fPVe4xtkapW4ZE7NC14oDPeOgX/o7pttXhsp444DIT3XA73HL831KkoT2LecL5N6Kg48/eIy0pV6MxgHPElkY9EQTUfs9K8R6ZNCnPZ1YFRU0DiJYFQFwKYPGwQ4hTVP6iZ/4CdrY2KD5+Xl63ete1/UugRK6DkfWHqCtikYh4cj8ocVyykpOu+sTTF5F25BwZG2/LCf++QKZ+6tvbz9e4TykaGi5DyGFe8/GTXjGAbersso4qN84IZJKhSakaSpef1U4srdV0Xy/Rz32HnhPuopiHc8YcfZX1ya7pvE8/7QCXIhdWOycEe/CIT+u9yxuHwPz/Z6Y7PUq3PNCZNbIFpOmTqGYZZlL3teBoMaBaB7FHWKQAwwOVjXKpDER0epi/hiIViwqDUe2b5wUKw74AIvHtL8c5BlMvWY+WOKhONAKxlrmi3fh/v7DutogI5bXf1cUNYUOHI0TkCysihYG6rS35QBD/m/r4chcle7VOODqn+y1X8YC4U86521o+QZEcayK+GewujDYti9kjwjRbOtgVUREPvceADxA42CH8MY3vpE+8pGPEBHRS17yEnrGM57R8R6BMuLapGgZB57hyHxxJgvn/GHJNOMgYNqdyM+qh9ukFFoViYwDy8ZBub86kW84Mm/CaJ9Bv5eI4pldOHJIwSZeMGtoOLKbVZE26aoWjts/MGrFV34OcI936ynLENVV7IyDKsWP9T5UKQ607VvlrGivQzveuBLGMmcjTHHg2zgoCoVNksTdrqvo7y4VKA68rIrKGjiycRDXqohIOwYs1yLVWTOeeVcZfE2WFep486gTqyJxHbY/DoszDrhVkUfGQbl1p2gcRFAcrC7oigPv4tXxc+WF0dM7vHFQdH4dOBarcZDf/pKScTAcp6YDDBmbw7H4u9m2Rc6B0+Q9f/+z+0/sjINbCxoHT3SQcbC6uP1s6FmTmGZDsfLlDexOrYqcBigyita4j5xYd90uAFagcbADOHToEL30pS8lIqKv+qqvope97GUd7xGowteqqNpffXuhwIqWRvugTbVwvMKRNW/t4IwDo9cfrjjw8bcnkp+BZo3hGY4cUrQj8pv6bmJVZGmTIgpWXYcjK69fK2JtGbwHar7AILLigO2DNl3pHUqqTdfltu9o1UUkz8EQxYFX446oQHHg+BmE5Izw71lbdWiy/AxhERFpwm8642CaaOHIOasirnqwDkeuXgt4WuXwIkFIA9ujaKdZdBDJIraXTcn5kmuRd+OkTAHHr4keE/di2p8N0ixGaCAKq6KFgch3IPJpnExzpuIad2p9ZwckF13fDnQZjqw8G3jYtWj3t+z6c82evF3RES+rooI1GVcceGYcPHJirfDzPnpm003tkSGbJxdVkNNYPg9No4Uj8/vQ2QhB6V0pDtYKru8HTyAgGVwaoHGwA/jZn/1ZOnnyJBERveY1r6HFRekZCGYLGYpq5G0+Gotp42CbFC/FwbzWOPB5YNMKn4UZB4rHuAVV/u4ZnhP/IXZRXscgUZi3s/Z9q6IBbwJpQczSIsLu9fNjqbBx4lS0Csk4UK1qDLbPHwyI5Pkupq2drYpCAuKtC3badF9un5RmhmXhpuo6pJ2TXlZhRAWNg57P+U9U7q1f9D2t6dVuH4oLpsvOqpuMouPAe9o/o1RxMOfbvBD3gaCMAcPmlQiGrs5YGI1TE8u4aYosOmJYRIzHqbge5K2KfBsnZQo478Yhka7+nKYLxcHyfJ/m+gnxHGJvxUFVc3SnWxUVXWOfOLMRZcparkkGtKw8G2iB3m3Rri2ZGv0arjiIZFWU3QNjZhzcceBE4c/Wt0auGRNE8j3Icm5E5pnh81DGaJyKNd7iXE9cE6NkHBRca7wVB0Xn+cHjaByASwM0Di5x/vqv/5re8Y53EBHRD/zAD9ALXvCCjvcIhODla6s9dBUVLeXEdbyMAy+rIq1gpRWNiXwK92maNg5Htiwa8mlnbR88VS+hE/fcX9qscBkwaTobVkU+05ZBGQdKM8HiGqCdyyIcmRcMt3zDkbuwKqq6DsRXHPDGgWPGiKY4iB2OzFUnka2KtHvBQoniIHbGgfe0P5FeBM9lHCz4Fk1FAzvyMSCuwwGNCyJ79ROftJ0oDhb9Jz2r1qT8/bdunJQp4HhD27pxSKRZd1Y0DiJkHKwubNuTcNWBd+Og6vg6HWHSuEvKisIx7IqECnK+L5q32u+ZbFspyGYFYx6QfMTJqojf47JJ973sOuhpmXXsXLmawTsgueh65LkWy9AaswuDvrwPOTcOxuO0OBzZu3FTcG6hcQAuFWRFD1wynD17lv7Df/gPRES0d+9eevWrX+26vUceeaTW7z/++ONOe3Lp4yXP1h+S4lr1aItTsU9OjQPtfSy0iREZA+0/g83RmPgzbyeKA/Z+ajkTnoXzkIINkZIz4TZxX21V5BpQXmhV5PMZhGQczA3k9yzef009xK9BfNraPRxZaxz0/QqGRJplWX57auPANBy5vHni2bjQAup5xgqRLKRaZSwQhal+PCeOt0apci+I5++fUXQcek/7E+nH0/R77l005ceAHo7sdx1oEo5MtL3fReuGJhQpDoRVkUPBpqqRPN+Xr3NrNKZ+z+b1lyngoigOCpo2GVyJ5lG8F4qDC5/74lwvd855WxXxoGhOUTFvp1DWGH3o2Bp9/XX7om5/eWFAvd523s709d9DcaCpXnoX1uW8mealviu06XF4Fiyi6vw+enaD/t6VK27bL7JrEo2DCCH1RNvK21X2fOqtvjm7ORRrswx3q6KCc+vgcWQcgEsDNA4uYf7Tf/pPdPDgQSIi+pVf+RV60pOe5Lq9G264wfXv7ya8Cob6dFeRx76PRFzzU+VoBYM0TdXiUh1Ub+2iaXeH168tjIoUB1J1Ytg4KFggT8Mf2E0n7gMsGoj87KKaWBW5vv7IdmFbbFWsqQs0+yKL7WsFa35eu1sViYyDasWN9ZSv8JZn+9DrbefMTDc7Y+aM9HoJDXpJTmViFhAfqjjix7+j4kANxp3zuwbrBcuponmEiX+ibsORtULs9Ofgrfopy1eIsQ+icaBcc/WQclvlS1HGQQyLCG1NND3prjWwN4Z2jZOyxkWMjIOi957vS0accOQL4dCDPhFdLNa7Kw42yhsDZyIrDh49uU6PHF+jf/AV+wvXaJaUFeS/fDSC4oBb9Uxs6wa549Qj44A3jaafCz3t4qaR+Xvbr1+sw538/bf3ofwYj604WJ3PGgf+WTtF1+LVhXzGhLfi4FSJFZW3VVHRa0PGAbhUQOPgEuUTn/gEveY1ryEiohtvvHGiPACXBl4Pq6riQPHSJvKzSQlpHPAHtnG6vf155SGyDrpVUZG/vn3hUF8YhfnbWxYuRSCfGo7sM+2v/a2ihzKvcGR+LId4S1tmPHDrsSLVjwjmNFMc5P9OX1VcyO9ZWBVVWeQQyaKlveKAvf8Bk8beVkXa+zDf79HWyGfik78H2vbn+j0aji/+nlnGSGDjbODavCu2Cbr4Pb9pc+1ePP0ZcCWeV+NAWGYVhSM7FA11xUFx48C6eRcy8c+PC9vGAWvgKmsRz5Byou1jmivQsoYBVyJ6TBpra6LpNam3ZVuZAi6G4kBeC/PbFE30CFZF2SBJjMZJbj+qFAcRMw4+8IXD9B/+8NN0fmtMT79mld75M99uqvLRKGsKeReMiZQm1oXzf2WhT0fPXvy+R+FWNq+mGgeOQ1Rl+5CdB1yR7DFtn7G+Wf7ajp71PQ6Kns/5WsxHcaA8Hw/64j7k3jgoUTa5Kw4KroEPw6oIXCKgcXAJMhwO6d/9u39H4/GY+v0+/c7v/A71lEkmazJ1QyiPP/44ffM3f7PT3lzaeAVzqhN+EW1SxuNUPKRoNjnaAv38cNR66kd7HwvDkR0WqyH+7p7bz1gXIWAh4cgdKA6ccib4xJDmLc2LOJZTnrJxEnYMWEmkQ7y1k8Rn4r3KIoeog3BkpXnnef4RBTYOBr3c9dKzYKZ9DnP9hKafobzCkYsVN34P7GEZB7xw6GcVxfdBNs+crIo29eNQnoP229fez5ziwDmgXBTuAxrItnZV3DKvOmOByNYqQyuEZAXDPREyDrjyJknKm0dEtg2kMgUcf++3Rtv5ClqjvSmyeZX/21EyDgqKhTEaJ9PwgiBXvMUKR37izAb93Ns+M7lH3nf4LH3gC0fou77RV7VfZsESw6apKKibK4887kXSW3/Krsx5iCNDKA7mdcXB0EnxQFR9fns3kKRt3YVw5J6fdW0GX5P2ku3robTM870OlZ1ra5s27gdFFDXnz5wf0qm1Ldq3PKf+3IsHjpylX/u7L9BwlNIv3PQ19HXX7Y26fXDpgcbBJciv/dqv0V133UVERD/90z9Nz3zmM6Ns98lPfnKU7ewGvCxK+IP6XD+Z+EjG2AdtUcQXpUS6fc/5zRHtXWx30+SvIUl0f3sin2lH7fWHT5vbFQtCmjcxw5ELmzcOn8FonFLK3soQb2nXjINAqyI+Jd0UkXFQcA4MevmJd49wZK1gzouWm6MxDUdjMfXUlCqbHqL4VkXa+8CtIiwLNyGKA+ntG7tx0O05KBUHfu8/Uf4ziBaOXGhV5G+VpDYO+sVFY2uPdS1rQ+yP432QX0/DMw7sPgutWHlRceBvVaQ18KaLMurrj2TZpl0TN4YjddCiKVVDFHwfXDIORChsvHDoafjx9aTLFnPe3qfX41gVveJd94h9iTHxW1Y09lZbjMapWJNMLMuE8ihC82rqHPNS3op94ANVBf7+3OrTkqrz29+qSLeM8l4PE8km8uJcn5IkUcORx+O0sHbRljLFwWic0uZoXGjx3JaypsjBE2u0b9k352SaNE3p5952J33+8dNERPT5x0/TrS/9Drf3HewM/MfUgSnHjx+nX/qlXyIiouuuu45++Zd/ueM9Ak1wsyoK9JYm8rEqUh9SVasipXFg8NDCJ/Xm+tJfPUOGE1sUTXmxsHj7wlPSMeOAT7dub99voRhqVcItZCzeg9CAbNeMg8aFUyOrmICMg+3t208ZnRdWRXLbvGhJZGuVsiGmTHWboGmsH1arwpGJfIuWIc0T2bgyahwEWMRo37c6/ok0f/vqxoll4YzfC+b6Sa6BxxvqXoGQonFQZFUUIRy530tyzUF5D/a1KgppHvk20OVaoN9LRGN301lxkDWP+NrMY9JTTvznj7vYIfHTa0/tmmht11Nll+Vpl5ZRZNHCs3+0XBbP/XjSvqXc1zGsiv7unkP07s89Lr5/ssT33ILN4bj0/ubdNNFUBFnhnDfK+PODBaVWRY734WlEMPCFeyBX5HoUzTP4fZ5f+72tivhnO7EqEooDf6ui7FqsDRd6WCdmlDUOiHwyPiZ/u0TNczCyXdHB4+uTpgER0eOnztOXpj3LAFBA4+AS4/Tp07S+vj2h8dhjj9HevXspSZLC/33oQx+a/Ns/+IM/yP3slltu6ehVAP7wME7llHATRLGkxLPTo2ioTapo0+7aA5uFRDvUIofIp2jGF4VFwchEvhMeUpIrF2Yxi3bFigMeyNV+H7RgM82qx6NxlBF6HPJMD6uiVUjGAVGcnA9VcaB8z7Jw2kRxYFmsI1K85QMm/j0LZtq9wOsaIBpnBce/5wO7yLlQGjeuGQd8uo4VTHkz18NfnmhbyZfbbqFVkX84Mj8OvO3CQq7DnqqH4TjwPugYTCksQub7k4lCaRHhYVVU3kjOQtqnsbVsK2kcFCgOLNGGWcq+9ijYFRVtxfXPW3HArLCu27eY+9o7HHk4GtP//Vf3qD87ubbpuu0q+5+qYmZbtOv78lyB4sDhOlAWjtyZ4qAgGNg144Bdj77i8uXc19HDkQsCovm9ywJ+fVm88Llzyzwin2Mwo6pB6dm0KFP1xc45eOCJM+J73jk34NIHjQMAOsArEE4US0oyAzyKhvxmP+gl6mvt9RKxbxYS7ZAJv6Kfmfi7B9iDXNy+X9FEhKApigPPaWcRThwxHFmzW4ltVSSnneNatYRkHOjbNwhH5oUaZdJbU8C4Ng60aX9nxUFoOHJuHxwVB4uq4sCncB96/PPXb5pxENC84IVDy6KhyJhgx2AsqyJZuL1gVcRe+9qW/YO6HGTIvwfe3tYhDeyY98Gi67Cn8oIXLJenCna8cXB2c0gp9/lrSWjWyzSmDdSSxoV2XbIungvFARsWkMefh1URL5hmVkX+NkkZaZrS2U1uVcQUB87F87sfO02HTp9Xf+atOKi6vnurLbShrizrRATER2herZQoDqJlHEz8/f1tejJ44+AG1jg4eta3gSUyDuZ1uyaPz6BQcaC4Eng2EasVB37bLrsOHDwRt3HwhUOyceAdTA0ufZBxcInR7/fpiiuuCP79U6dO0XC4fSFYWFig1dXVyc/m5uKGsICLaIXUzdGYlqidr16ot7r2Mw+ropWFQaFVz+JcP7e/Fg8toRY52s8sFouiaNqZ4iAkHNlv2jd04tijcK4dx1rByHPauWuPd+6trYVyEinvgUnzsrpovzDoUS/ZVlplWBZOpeIgYNrePBy52rLJ1aqokeLAx6qo6Pjnx9/maGwWTFdVtCbytampsmjh12Q3qyKuOJjXH9Z9FAfl9wFvX+UqmxgirWjmaNlXqDzzOw65/dDKVMOKNw7SdPs6rBVymiKKRQWWYdPXf8/rYKVVkXHhvkr1EqNgx4+B7HPn61MPm6SM7dDR/Peu440D5+J5margRMeKA++mCd/+9FCXp21qhlS9TIUj9+Mch7womt2D+TrEIuurCH49vGF//hx44syGbzhvgfJDqC4c3gN+bc3WpHP9Hs0PernjzlNxUNk4cFqLEZW/rum8lxh8UWkceL7vYGeAxsElxg033EBHjx4N/v3nPve5E7uiH/zBH6Q3velNTnsG6qBNwls8NIdOem7vg4PiQEwzFBfOF+d6dGrqPmlhVRRSKCj6mUc4cmnjIKLiICQc2WqxnqZpcOHQIxxZ+xva+RbTqil2xkFoOLKY+DaQJ1cVTImIkiSh5flB7kFu3XDiOUR55TnlStT9pG0Tuyara0DT459o+9gtmsyuQ0jzyNOqqKpxxBUHHoGUaZqWhCPHzzgQigPHe+BonBKvfWjHoTwH7N6H0PWIp/qpbIhAaxCc2xiaNg6EPYXSwPNU/3HFwfQ5nyTbytfp895dcVDZPPOf9l4usCryVBxoBanYVkVl1zhvq6Cq6+u5zRENR+NcBozn9pfm+5PidIxgXGGZVqo48DkOpb+/btMzGqduxXveoOdWRZujMZ1eH9K+ZZ/BzqLPgR93Q49w5JJ7wZ6FAR0bXmzeeU6+n6rIE+mscRBZcaA1DqA4AFXAqgiADlAVBx7TvkqxJEOEIRlsv2iaQYNbJZiEI9fIONAe2NrK9EOmjIu2b1ks4A0czV/d62Fdn/jXF+A+4chhVkWexZqNppOmZoqD/N/h5/rk+2Li2ycgXMOzcBkWDOw7ZRfSOPCceOcFME1x4GWREprxoYV2WxXOhL++GozrN+koMg7muOKAZwzYP7Bpr6cw42BrZG5TU3UceN4DtWNZtavytCoaBlrG8bWIpeKgZIhAGyg4Y1w4kE3cyJZtFddhb7sefj0TjQPn+9BwNBbXgWzam9+bPcORtePqWtY4WNscudrElBXtvBUHIcHjno0ToQafaiB6Nu4m29/kioOSxoHD9sfjVHjXX5y291uHcKqsioiInnAKSN4ajcX1JbsWyOehCOHIg+l7EbPN61Rx4HgeljznHD6l26h5sDkc04NPyCBkNA5AFWgczBC33HJLLrwY6oCdi75QsVcclFn1eEx7l/lYcqRMuv1DC39AqqM4IGq/WKwTjuwpUef7oX0Ofo0DpWATOHFs8cCgSWy1wjn3Gt66YJPSljRNg2xSiBS7KKtwZHYc9wMzDiymjEIK5kTy3DDNOAgomnsG8mmT3ppFh+vEe4DqwsuuKTQcXSukauHmFvugvX5+XvIiYxtExgHbPrcq8phy086prFi4PCdtaqyD8WTzJn8OeN4DtXOJX/OJfM9BfiyHWvaZKg4K7DmItov4XI1mXTivyvrY3o/uLMM8P38iTXFQkXFgXDTVClXZepDfkzzDkfmzwXy/R1euLojf8yyel11jvTMOuKLyipV58TueVk38XrCcswryL9yXhSNrjUPzrJWhtMrKmifa84FXA4tfjy5bmhPhwF4ByWtK8+qi4oAHRMdVHMhBQr8m5qxaFZ3bHEWzCvrS0bNqvYMH2Htzan2LXvk399L//08+Q3c9cirqtkEz0DhowU033USLi4u5/33N13xN5e/cdNNNHe0xmBW0B0gTj/0a4cgeEwZSBllmVWRfOAwN5SXSp+DbLpjrhCN7yoP5dFPMcGTt74RO3Hs0z+b6iSo55sWaNJUWP03QFmOxC0Zb7HVok93a9i3ef3kO6Nv2LNw3sioyDkXlh5IWCO1lFUSkNE+0xoGTTUYbqyKr5kWQVVPf7/2vaqDFCEfW7P8W53WrIiI5FdqWqubNLCgOPM/BWQhHLlMcEPkW7YnCGsmejZMqBZy34qDqWuidcaAVorLmEW/icFsnS3hBanVxQHsXpR3LGcfiedn1bWM4dsuZIZJr8stX5sV572mXxK8D0/cfT9vOyfZLhsr4cThO7T32NcVH9h5o65Chl+KAfQ6L8326ijXQjjopDng4OdFU4yBCQHTZvSBWQDZRdZ5IV4oDIr/PnqPZFBHFVxy84p330Os/9CX68zsfpR94w8eheLgEQOOgBZubm7SxsZH73+ZmXu64tbVV+Ttg99HrJWLKweKBtU44sghDMlgoCG99JZQ3Q8ikXTIOij0qtfembdHqfA3FgdeUz3gsp531cGSfh4VWigMHq6JQX+ntf9v+PdCUM7HDkUMzDvj5YfH6RaGmwC5NFOwMpx3DrIr8HpTUgq0Wjuw6cR2QsaCEE1sQGlKvXZ+tCgYh90L+mQzHqdmkXVUTWVp1Dc2nLLXjsMiqiMg+ILnqM/A8/lXLuoDmkeU+8GOp6F7E98s076hEcUDkP3EfMkzhGhJfYZUUX3FQ3jyzLthpRbAse4zfmz2nfLWhosW5nrgHnK7wH2+DNnE9zcl1v2dzOfE/oL1L+XPR9bWXZJ3EyBgoD0eW10XrwrF6HhRM2xPZKR+nKcocunJPvnHgpThQm4gXrsfzQoHtH45c2jhwtCwrC0kn8lUc8Psx54jTZ8/5wgw0Ds6c36K/uPPRyddrmyP6xJePRds+aAYaBwB0hMeUR51wZB6G1LVVkcW0U51wZA/VhwjiK8k4kL7GNgs1rVgUpDgwsupRLSIKCyb2i1VeeCzy99f2yWKxqiouQsOhjY4BXrAq9NZ2uAbwz79IdeOrOODXwcjFKq1xEHkfqqxyiPyaJyGNG237lvsg74WaVZT8ntVxWBUKy5vq49S+aMmLVYNeMnnPtaa29QNzteIgf10aG6m+iPRraYjiwLN5UXQfFFk/hkUboThgawFPxQWR1kgO+Ax2kOKgKuPAU21BJC1iFga9ydpf2oU6Kg5E4XiOkiShPUx10JXigIjoxLl4216e6wvFhadVkRzqiqs4EI2jnGWaf+OAKw56ycXtaopcD8XB5mgslahzfbqKNw68FAfsM1icu3gtiKE4KFsTiSEmp2vReJwKZQ8Pae/KqojIr2nEKVQcRLQq+sj9R8X3Dp2K8/pBc4qreqCSW265xfTvPfe5zzWfOLPeR2DHXD+h6fuXTThydRBdhodVEV8cl1kVeXic18p4cCgcCxlqieKAFwt4oG5TtIejkIwDou0HBl7MqUsdxYFLOHKwTYqPr6l2DC30C6bunSa+QxUHvHlo8bAkPaXDbKos/eX539IaeDHtMYjCrIo2R5YB0fUtQmKHI6uTfk7hyGrGQUHBYlnaT9dGZkyUWxURbT+wlt0zau+DMt2Y0esltDjXyx2r1hL9qgbSvHJd3ByO1XOlLtq5pN3z+Odia5fEi8ZF4ch+AblVwxwiINy4cC7OQ/Va7Fc0qrJKmrWMA+uCGZ9wXS2xiPFUHBRNnO9dHNDxcxcngF2L5x0qDkThfqFPe5ZY48DRqqhMeSSVp7bHYJqmpdchbY1u3UDj97aV+cHEwlRfhzhY9Wzq60JhVRQp42D6WiDzzvzDkafvPTz/yEtxcPr8lmjeXL9/iR6bCibu0qqo68aBtV1lGe+797D4nle2CLADigMAOoLfKE2mfbfqFM7tF4t8qqPcqogrDgwaB4EFK6KCxkFbq6KSQo3YPp+2N3pg1BowWpEqZuE8dOrfZOK/oT0Ekc1iuZbiwCugOjDjwKNxERqO7FmwC1FeeRZsNNWP7rHP3gPHcNgwxYFPxkHhOahO+rV/D8bjVLwWvXEgj02rwmGV4mBZaeZaP7CKgO553rzI74O3VRF/v9WCkVnWjmyeag1UOXFv9x7w6zmf6pzsg6NtmiiYicaB78R7iHWdZ9FIrAnZ642eccCtitwVB6xgPDXMI8OR/RoHZwoKx1xx4GnXU1UU8wxI5ufh0vyA9rJQXM+mSVnGgbe//PktOWm/WtU4sFYclIRDqxkHxhkLREUWlt0pDlYW4jWPiCoyDpyzXjJOKOf4dZct5b7W8jCs4NeBr7h8Off1kTPnyZvT57fo0ZPr6s88w+mnGY1TuuWLT4jve4fUg/agcQBAR3gU7fjfKLMqkhMGsa2K7D3OuT1BmVWRXjhvt1jkVkVFwbBEStHQSnFQIsnNbd9psc4/g/lBTw0nJtLCWdtvnxf/C216lCKORzgz0exmHHjIk6sKphlxMw40f39PxYEMiNbOAT7xaVW0TtO0dLorg6uLvALSi47/nlLM9bgPEhVYFSnHptVxWPX+a01l68I9L9rybfKvrSX6VaoP7R7slbNRNO3vWTTj9yKtWU3kG5DL1wN8iMDzOkwU1kj2ylva/lvlhXtvxUFV48K7aCvyruaKFQeeVkVScbC9H8LnP6JdD8ezaCWHqvq0VygOPDMOShoHkZtXRExxoDynWR+LXHGxUqK4ILJ5Hhb7oDSulub6dOVqXuLoFZArns/nixUHfPjIgjLbONHAdmsc5FVFC4MeXb6Sf/+9QtI3hiNxb3vKlSu5r2MoDu4rUBsQVVspWfGZgydzSrMMz4B4YAMaBwB0hMcDQz3Fgf3DmvDxLLEc4A+QFjdrUSwoef1JkphP+vHXUKo4cJrwKJPkTuOR8UAkLSLKVB8eUyahvtJeRSv+wFM06UqkBSO2PwfTNBWNg8LmCdu+iVWRsKboIuOg2qbGs2AjJr0L3gOvKavhOBUTfloDx8uuqZ5lXP7YtDgGteKntg96wcLmobGqYNrvJeK4rJKx10ULYpyG35/XrG1qeM7GXHnRlMizcVBgl+d4HZAKyND7gKPiIHY4ckAjmTcwLfeBK0+qFAfWU/dVx6F3xoFooueKdRHDkc8XNA6Ez7+j4qCiKMaLipaIZ4P52BkHXHlSUjQ2Pga1931acTDo94gvkWMqDpLEZ4CBw+/H/V5Cc/1EKg68wpE39XOQSD4jeDROyuwzRQPbybKGByPvX553XwdN/q6iZHjKFXnFQYzGQVEwMlG8cOQPfEHaFBH52sUBG9A4AKAjPBZrUnEQXri2WCjxqZrVWuHIca2KiOzfgxBf8QyvwiWfLCryi9aKNhYP7FUP6tN4KA5Cj4F+LyHeT/E5B+M2TjSJdbFVlH3zKtSqyDOUs6pgSRRZcVBwHfa6BmjvpdbAEY1To+2LKd+ya4CH6kW5l2jnYe/Cg3v+3/p8Btr2xQOrsVXReZ65I6yKePPedvtV12Jfq6Kw+4DndaipVZGp4qCkYEakWMYZWjURha2JPF//cFyuPPHPOChXwXqqLYjKzwPexNGyeazgIc2rE6sipjjw9PmvaMx6TruK/Lf5Ae1jigPP7ZeGIwvbRttjkBcje4k89jytK4lk84Lb9PE1cgyP/+W5PiVJQpexUCWv46DMtsy7eUQkry/T195oVkUsAP2y5TlxLHB1ihVaUf4pV+QVB0ciNA4eOHK28GeeNk3TvP/eI+r3Pa//wAY0DgDoCI8bdcikref261gVxQlHLg/6lQtmW8VB0bQ1kZ+vcZWncYYejmxfOC+yiCBSwpENFuuhVkVJkshzwOCBqU1At8XDElcbEBUXrGTR1mDam9t1FQVji4JVXKsiT3sQYRFT0LzzUl1ok6NqxoHTpHOtrBkH1Y3eOAk7DmMpDojkPdDaJkYqDvLvAT8uza2KRAMvvz0vuziiaouYyfc9i9aBVkUyJN1QcVBiT6Ft27pwLo6BkLwZ04Dq8uaNt10PX1PyNSn/ejSWisE21FEcWOZ7cM5u5AtCKwWKA0+PbV68f9K+xdzXJxTrDCv4s8HyQl/aNLk2DmTGQgZXQnkrDlYWpApaWqbZHotl0/ZEinXv2PY9ICJaZ+HIWSOf34M8mhZEcuK9POPAoXFSQ3EQy6ro8hVFceBkVaT93a/sQHGgWQRlnHFUPWU8cmKtUPWAjIPZB40DADpi9qyKPBoHZVZFbNopok1N0c/bPrDzhVGZVZHXAzufGCjaB20K3WKxKB+Uy4qG9h7rdY4Bj6nz0ElXIp+Jf01x0C+0KvJXHIRaFVkVLcbjNEj1wY/Lcao3XZog/e3jTjtrf0crXHuFI4dM22fw65BNA12xKgq0qrH6DEIsWvi5YV24q7LM4kXk2BkHmuLDqmBQNemdwT+XoWHhVtyLCpVffoXzslBUIv+Je3kMxG3iitwr9nq97XrkIAVXHMj3w/LznxXFAV+XZkoDEY7sadfD9oGHop6MqDhYntOsijxtmkoUB33fa0BV0Z7It3lKJD97fh3kA0YehfMi60D+2ofjlNLUfvt84n116v7vkXfGKVsTxVA8EMnC9P7leWUd5HMealbO1+zNNy+Pnds0bRxrlClazm4MXY69ae58+GThz5BxMPugcQBAR8iipcG0c51wZIdJT/6Qym/I0wirIoMHtrqNA++Mg7JwZK+FEvdnLGreJEni0ryqelAu+5nFe1DnGPAo3POHrlpWTQbv/0g5j4usivjDgsWUlXg4KFQc+DyshgbjaoVkq4JVsF2TU8EsVHHAJw29Mg7qXAMsjkFerOz3EhrEbhwEWLR4F22rMne4dZF10VRcC5XPgH/P7DogjsGCfAGlcGvWvBA2OYGKA8PjoEqByJtX1pOeZRPvGZ6vvypnwl1xUJVxoKhibTMeitcjIt9hOHIrGvGi5UpBOLLnxCsv3InGgWPGgQgnXhgo4ch+r10qHqYUB46KHyKlaaE1DpytajSrqGnEOiRi40BXf9tvv8wRwCPvjMNVJNMWnl0pDi5bnnNXXmZo7//VLN9iNE5LFQEWlBXnx6lvA5moPEfBs3kLbEDjAICO8Ji2rrIGyG3feNIzTdNaVkUejYM6NjHaz7k/f12ERUkXGQcVXp65fXCYuBf+5tHDkfmkabFVkkvOSI1j0MPfmBeriIobB9bHP1H3GQea3UtIsYrIzyqo6DrA98ss44C9B71EPwa8mpdtwpEtjkG+/bIGupj6N3poCrFo4du2LtxXHYfejQvxOWgB3U5Fq9AGsmfOAp92Lwypd1L+bI3G4m/xIomX8isjRIHnZduo/S3+XvP8GctzcKSE1FdlHBAZKw5G+dczP/V6+Wc/Tn0KlkTKtHNROPK6z7RvmqaieH7dZflpX0+bDNE4mO/TXp7vELFpsjxXrDiIYVXE8QypJ1IUB2ygSjwPO1gVFWUO6epv++1L5cfUMRAhHLrs2cBbcZKhKQ5iWRVpqp/LV+ZF1p63XVHVdebMhm/xvuwee2p9y13xANqBxgEAHeHxsLTBFQcRp703R2NhkxK7cRBqT3Dx57bNkzrhyHzfrKxSqqwJpnGxy2ozcR9dceDQOOAP6jW2vzkat1401ck4sLaJSdNUZhwEWhXZBQMHTts7FgzXA2xqiOS0s1XRTrsOcU9hIr+CQZ1wZK4EsFEchG/fq3A6E4oD/qDO7gUe9+BpQmxqvCZNQ9WX2vc3RjbvAz+WCwOaneya1KwP9nrdMw4CzkWxD4ZFI5EzwT4DT8WBdj3lx4CWe2GquCi1KpLnI79uWcGLx4XhyE7Fc+355MlMcXDCs3GgZI1IxYGfVZFUPJQ1DmwLd1KJrtwHnAvHVYoDvg6xUP9yijKHtGuiR+OAB5Qv5zIO7NdhHG5HnM9b6U5xIMKRnayK+N9dnh/QoN+jK1by4dhPnHVuHFRM9XsHJJcpGjaHY3fFA2gHGgcAdIQMZrVQHHB/8To2Ke0Wi9rNRlsgZnALE4ubRZUsnWPtrSutiopfv1awsFgsrSuLkyI8rHraKA5iNw6kRDvutLOmhtAyCupQK+PA+GFB99YPm/T19PfXfbXle+KlOAhVXdhtP+wYtL4HZNQKR/ZooNdRHERSvmjngbAKsQ5HrrAqEq/dePshn4OX8q6N4sDqfeCvpcguy+s6oL2XfFvex0BISLWnTYk8DvLXfU/FgfY5cqWxu+KghlURkf3nn3GW+fevLmZWRXHCkfnEOZG0Kjq1vuk27cotRJfmZcbB+tbIrWCqFS0zvG2CQtbE3g3MssYJkVSDtV2Ha9SxKvKYuBfNq6nGgXj9HuHIZYoD55yNDN4cjKs40JunV+3JK5+OnD7vsn2i7eGuqhwBfq22puoee3Ld16oJtAONAwA6wuNhsU4wKy+qt5Vm8psiUbnigEvm+aKqCbUzDgwn3tM0FRMVdTIOiGyOATHdUxJQHcOqp07h3mKxWM+qyL5xUsuqyGHSaKj8+1hWRVrRoVhxwL21fabticKmXInspsxCrYq8iqahyqdYGQd1rIp8mnfxQ+rFQ7KyD7yAYj3xX1SomOwTVxwYTxvz60FI0djLLquWVZHRPvDiU9G9yCscWPs7C33ePGLXYeOCVcgggZddFVF15pKr4kD5WyFWRZaF27LmnVbAtb4GEW2vjc8WTHxzxcGZ8z5WFXzinIjo+v35xsHWKBXrZyt442JlfkD7WNOEyEdxMRqnYphgeqhLexa1/AxC1gPuGQeK4mOauQjhwKKRP581DpQBIofCvbQLKy7cW98H0jQtXRPFyjjgOSb7V+bEM/LGcOwSUCzcAC5s9yqWc+CpODi/Je0LOWUZBCb7ULHOREDybIPGAQAd4XGjDgmiy7AuGvOFeZKUe/zPQsaB5XuwNUrFYqNMceBllVK0OA3ZB5uMgTpFw24VB9zCJ3Y4sjpt2LJ4r01KFYcj8ymrdq9fWxAWTXv7FWzlhGlf8/dX7Ju8FAdFWTNeD8uhE/fRMg6iKw64r3eZ4oBP/Vs1sKrvxd5WRSKonO1D7Glz1arIado+9D406CXELw9dqx68zkNtW/I67Jxx0LniIL8tT8WBVqDh2+/1EnEftizalU17q4oDB6uitc0R8Tp01jDg+zBOfaa9tSniJ+1bEt874RBMujUai890ab4vmiZEPgHJmvVK3qbGXvk6jbwWK01055ByLWNimi4n7vVwZPvCeZldk7CMNH79WyOZ9zK9HomVcSCtiuZpSVHle9gVFTWvrlpljQPHjAOtKM+vQ96Ng6p1pmfWDGgPGgcAdIS0Kmp/o5YFo+KisfR0tLUqWp7rU6+gYEkkH9gsiha1FQeiedP8PdCKpmWNE22xbrFYrJqsmcajeVVLceASjhy+fQ+rlpBi2WT7yr619dfWJmW0wrm2/bbHn1Z0iZ9xEPb+93qJEszr07yIrTgQjYuC98Br+3UauKJ5FdkuTByHXs0j5TMQVkXGRbsqyyzv7cuMg3gWFaF5R0mSuJwHaZoG74OX4iBEfeXePGqQcWDbOJjtjANtn0wVByUNtL5yD/Twly5TI+vNC/99WJrbDifm9x+PaVetabGy0KfFub44/0472ITwYSKi8nBkIm/Vj1yPuisOeNGcqeFjePzXsSpyaRywZ/TVkuaR9fa15+PFkmPQS/HBr2/7l+dVS2UPuyIRjlygODgSuXHwpH15q6SzHYYjE0FxMOugcQBAR8gOe/sbVZ1JS+uFgihYl9gUEREtzcuCTVt5IC/8l026Esn3oM1i9byy0OhCcVA1WVO2Dxbb559BWdFunvn9WgRE17Eq8rBqqTVtrVoVtVQcsH/f7yVqMC6RvU2MVnSIrTiocw30kmeLUNoC5ZdX0VoGVMdVHJQVqzge09a1Mg544dCgcDZUgjhDwpGti3ZVjQPv7fP3UlNdRFO9DMruA/bXIm1it+he5KU44K9j0JPqK69rEBHReJyK90FtHjkWjbhdED8OomccaEVTh6ylyT5UrEeE4spBcXBGbRz0L2w/jl0SXxevLPQpSRK6bDlvF8Qnki3QC/d6xoOH4kCzX5r2+NdtG/2OwZhZNxncKoo/F3lYJnKKMof6qurN36qoLBzZ+vVXDRXFsCrSzu39y3OqKt+jcVCUM3I1tyqK2DjYsyAt03iItjWVjQMoDmYaNA4A6AgPxUGdSUvrolndxoE2gdn2gYE/JPIgOo5l0UIrupQqDhSrFIsH5rIQNLEPDotFqTgoyxiwfw9qKQ4iBLPWad4RtffZ55NSRTZF29v3VRwsDHqFTQuZceBTMCwNp4408R+qutgapTQ2sAgIVV3Igl37bW9PWjdXHFjsA7cbqmVVZHAM8KwbIr154120q5txYG5TE+Bv7zVpWuVtn9sHh2uRdi0ttCoyVD7m/071echt1CwVB9qacr4vr0Wewag8u4vbE8ZUHCSJrv7zLJpVKT74dSmG4mC+35tc+7T7cwzFQbYuvmx5Pvd9D5sMLV8hK1buZTYhHhkH/Jlg0Ety1xztutxW+TpNyLVYXIMNt08kP4NV9nzqYVvKEffj+eKJe2vFw9ZoLK4F0xkHch3mO0RAxKyKnBUnRLJx0EuI9i7O0Xy/J67LmkqqLTJjIgtHzjcOjjo2Dnhjcu/SnKjVeIcjV13foTiYbdA4AKAjeFG7beE+TdNaVkViodLyYbVOKC+R7r3ftnEgCxXl+2A56aeFO5cVLTWrFIsHJm2yqggPeWqdoqH2s7bvAT+O+QPBNDHCoUsbBw7NIz7hWdY4EHZlxo2DOoobq6JlnWlzr0nXqoLtZPtOway8cF6UdSMsMgy2PRynws+6TsaBFu5dlzp2YTJnwED5p94LNKsiv6Il0SwqDgIyDqysimoov+TUfftjIMTfPkO+BzbXwpB70YJQW9gV7LTjOciqyFJxUGFV5JG1Ndn2UG5ba6R7Ki6qjoEYigNeiFqdKpZr10WrnJlpipS4l7FpWx6earJtNsE73+9NPgeuOIhhlbQ0388dhx7K19zfCrgWexaO0zQtbBxldJlxQOR7DSCSxyBRfriPHwMW67BpVMXB1LnvZV06DW8K7luao94FRTZXoGjP820R16AZsCratzQnmmgeTZNpqu6xJ9ftr8HADjQOAOgI/sDW9mFJW+iVTno6WxWVTboT6UXFtjdrOW1erjiwVF3wm+H8oFea8UDkU7jmkzVln4PHpGWdjIMoioMS1UkMxUXZOegRjMitnoryDYjk+dH2YSnUIoeo+4wDIkdv8YpQ2gyPxpn2N4reA4/GYUgga34fHFQ/vGBdI+OgS8WBtUVHdTiyX9Fwe5ChOuPAy6pI3gdqHAMOeUtEcs2V4WUTEWKb5zlxr10LtDWZ1zEwGqfifsitijwDykMUN9v7FG8f+DnPP38PxQGftJ0eZpnrS5sWF9VDgcd9DMUBn/ifHprau8itiuKFsmZ4KF+nEYqDyFZFG8OxCOblA1XiGhQx44BI3husrYrOKqqXXONAKC6srYrktXD6+ThGODJXHOxfuXju88ZBDMXBSoFV0dmNoUs4M5FsHOxdGojGgXc4ctX1HYqD2QaNAwA6QloVtbtRag/9daZth+N2NhlVUlDOoupt2rJoLKyKyi9xlnZRvOhTZlOU4bFY5j6apRkHDh7/dRQHHo2D0Id1IllE8GicVOds2C7Y61g1WduVhYbyEjlmHARO2xNpVkFGVkW8YFqkOFA+G4tjUHwORYoDEQ7u1DgobR6y5oWDVVMd1ZNF0Tg0JNyzaKvth7Qq8isaDsepKNaoE+9eAd217gMOyptWigOnJqravHK0ydGsiiIWDUPsovh5uTkcU8olU0bbLxpk8Qwmrcr88cx4yJAWHReL5UmSRFE9FHncy4wD/4n/6TBW7i/uYVVU9UzgZZta9LfUBqZj4VgrAgvFQc9fcaApPzK8w4HVgPL5acUBX4cZr0XEmjj/ej2bpxn83N4/1TTkzTQtl6QtvBmwUmBVRER09IzP1D2/vmiKA/fGATsWLl/xb94CO9A4AKAjrB9WQ2XhGWrRtsViQSoOygvng35PPDC1tyqqGY4s7KKabz80EDW3fYdJw3MVIWDTuIQjt7CIsNiHzq2KxIRf+XFgHYxZR3HArYraPizxSevZUBzUKBob7UNRCF7V9omMrIoC3wOPjIPQYmGGsMsyCWivY1VkX7Ti97F+L1Hvt7x5bukvr+0Hb2B55DtkaOdzSCgmD9Zuvn1uE1N8HfSYOteL1nELxyH3Ytdp91CrIiflV0jjwNNjP7SJ32XGgXfzkkg+G6wulKseXPaBF+0uFAr3s8aBh01GWcF47xLLOIgQjrzM3n8v29SMIMs0x3uhFnQrFAfOVj1E5etC74wDXgxemuvnng34c1KaymeJNlTZJnqpf6c5eY4pDqbOfW6d7BKOvKE3EFcXBuJZ6ciZ8+bbJyqwKlqMm3HAB1S44gKKg9kGjQMAOsK6aBn6oD7ZvvKzNoVDXrCuUhwQ2U87cW/gsoIVkcxAaKc4yL//QYoDMfHd7vWPx6loYNQLR7Yv2sVWHLSxKuL5AE2oY1Wk7UP7cOTwjAPrglWdjAN+bRoqthJNaGNVZCXP5hM1xeHI8vsWD0xcdVG0fS3joO20bVurIotzsI3qxOT9D2zc8EI+P27akKbyXlClOLD0FtcKT9rxLq5BRhYNbbJ2LIpmWgMtOBzZTHXB10OK6sVx2jtUfcSVf1bXYW09yy1BtGujVdEytHHgOW1c1TyKoTg4I6yK8mtST7uojCJ/8S7Ckadfv7Aqcija8Uln7ZnA9RgMGOjyaiATyfc/SeRxP8fDgQ2L5hmlGQdiHWK7fVG0Zo0T7TOxPAaqbBO9Mx6IpOLgshLFgYdVEG/eLF+4DiRJQlfvWcz97AmnnANhVbQYX3HA15nX7su/djQOZhs0DgDoCGkTYesvTlRVNFN8LVvcrIWPZkDjQBRO2loVVQThcYQ80zDjoKxommG9WNQyIsoUBzHCgcs+g34vERPxbR8a61hUeEy5CJuUimOQ20W1fWAasXOgXzJpa104FwXrGpPeRFbvf7WveobXpGfVQ1KGdg22sSoKK1xr229buFcVBzWsijwUF6WqJ4dp19B7geeU5dZIWgVVZxz4WaQQ6Q0cqby0KVzWsmxzuA6oReuCJq6Xv3OjSV/PSePCcGD7rCUifT3DrwXatdGqgcfXc0XNK2vLwPw+lN8LvAPaiTTFQf7ZwDOgumgfskIhtyryCEcumzTn4cgeioOiYOhpvIYoiORAl7bu8AxHFirsub7In/NQPnJEI3/qc+D3BuvCucwZKQ+Htt4HsSZiaw9+bRyn9qoPfm6XKQ64SscCfh5OK6+4XdETZ30aBzxDpROrInYsXMOaJrAqmm3QOACgI3g4ctuCYWgQ3eRnioVLmweWMh/PIpbm8/uwk8KRgxoHxg+MfLKGqLyB41EwqTPpSaRNvbd7YJFWRfEm7okUm5QKyyprn3kucdbO88nPjBtHomBd8tr1YOD2i3URjFuyD6JobVQwOh9oVZQkiTLtbPAeBCoeVKukto079u97iXwon4ZL5C0eFkWxrFRxYD9xLcKxC66B/HOxnPbWip98e7xouDkat8o5mkZrgqiTpk5F4zoNbH4MeNwH5/qJWjTX9s1C+UOknAexrYqE4qGggSksI41ef5BVkZ/iQITCFqxHXa2KKtZj3gHtRNL6ghequlQc7J85xYFD46AiHJnIV3HA1/Sa8snzHBCKC+WZiJ+bFspHTlnWROyMAxmQrSkO7N6Dqrwd1bbW+D3g4cjTigOpvrTddpqm4jowrfy5apU1DpwUB7wxuW95TtQI/DMO8u/tNVAcXFKgcQBAR4hpd+Nw5IWBPt11cfu2C4WqiQYNS5n0eJyKxV7dYNo2i9VGGQfGx4AW6FQejswLFvYP65WqD+MFs7Qqilc4J1LssiKHI/NzoDzjwLZxElqwJvLJt9jeh3CrIv4AG9uqiEgW82Ja5XjI0+sUbIk01VdcuyqPolVTxYFlODFvXhHJBpZaNHUKJ97enjwWPD5/bful4ch8iMOggdNG8WDlLx0WjuwXDhwaUq5fh9rvg/Y3QjIOrBQHfD0XbFVlGo5cT3FgeQ3KOFthYyqatzEVB0s848A/nLgs48CjaCYyDpRnAq589czZUBUHjo2DEAUoH2DwsMops7OVGQu2jQtetObnoKo+7VBxQGSfc1AWjsyfBayUjxnrWyPit9Xp5o1QHMS0KmIZB1qQtiVCcbA3/9pPn98yG2AB9qBxAEBHaFNmbQh9SLu4fUWa2OJGHTLVweHywDaNg7qhnES2hWO5OO1AccAe0HpJfKsWbrlV9RlYe4zXKdh4yLPleVh+HFg3L+qEI/Pjr3U4cqBFDlGR4iCyVZFDwWY4GovjqOxa4HEOilDcgmNQOzfavgd1Mk6IpCLG4oG9zjEgrIoMCme88Fj0HnhaBWkFwCrFwfY+2Dww8/dxvmCQwaNxRtQu48BGcRCufNMK57EsuzwnPUPCmYn8QuJDAqp7vUQ2jrrOODD0N696LvBUPWWc3cgXq/hQkWhexFAczBdlHGyaF634unxajS0UB+seGQcBjQMnuzSiMMu0qNtXzkM+RGNduB+OxuI15TIOHCwbp5GDffljQFOFWu5D1TCFeg80vg6UWRV52kYSyWsAUf4z4NfEtu4LRXBFk2pV5ByOzO9J1+7NKw7SlOiM8z6A5qBxAEBHWIcBSS/TioKlYmHCbU7qIKeKqgvnlooD/SGx/BLHF6ttCsd830PCka2Lxrx5szI/KFedOFj1cMstviDmWE/6yJyLsoKN/WK9bjiy9T7wB546jZO2FhG1inXKftk0Duo0L+yn7LSiR5n6yCMUkL8HhRkLxqozompPbbEPDqqfWsdA375wJqyKCu4F0l/c16qIvw/aGsFq4ji0eeNllVMn78gjIJtPa9ZpXBDZFI+DMg7U5pFT46CO4sDBLmrQ0+2i+HtgpjgIPAa8cj7SNK38DDxVTxm8aLZnsQPFQYFdEM84GKcyzLktZeHE+3jGgYdVUZBVj6dVUfX92DPjQJ6HmuKCvf4Wz8Ia2rpwKaJVEQ9H5seArjjwa2CKcGSnQaJpysKRPRtXRPoU/3SzQNYi7K/DRIriQGscbA7NVIecrdFYDLddwxoHREQn1+2zZoANaBwA0BHW3uZ1piyJtiet+DRym3DessVxEeKBrcVDS4gsnWNZtLUJR27bOCiWRGt4LFb5cVxVOLee9GmlODBYqNZtHFh/Brz5V8eqiKidRQa3SSqbsh30e0owtkfGQR2rLvtpf6L4ioNQqx5r1RlRPYsYIp9JvzqNgzhWRYGKA8OiHbfHWBj0RCCkXjS2CoYN+wy8CiYh9hgZHkUD/jd4g3waD+WP9jfUSV+tgevk8R9aONf+bRNCm0de5yG3fiy6FlpaZk6jrYn5PgjFgUfGQYWNaZSMA144nSgO5sTvWgcUl03883DkzeHYPGeiyiaGyLdwzT9P7Tzk6zTLJrpck8h7gbfiQLORnR4uE+HMzoqDVZ5xoNyfPMOR+eftdQ/MGI1T0ZTbv3Lx3PNsXBHJxmW/l+Suex7DC5yt0Vhci/YtDUTjIE3lNcsK7dq2f2VerM+QczC7oHEAQEcIiX5bq6IaNiEZlhPndQvGRHIq31pxUN9f3rJxUP36pbeyseKgwi7Ko2jZNuOgjeqFqGbjwGHKo27hVEyatA5HbmeR0WbinE/ZloXiEvkslus0UD0KNlUPiByP90BaFRUUrBweFusrbuyLtnVs+3jx3EN1Eqo4sAwnDmlkq/7uZoqDsOaVl7d1G6sii8KxUH4NwhsXRE7qt4CCndW2Q7dP5OdvrQVUa0iffx/FQWg4slfOh7YtWbC1L1iJoiVvHAjllYPqoWCwyTPnJaNsXb53Ua7RrVUHIdciOchmVzgPuRZ7Fm5Dcpe8Mw60a8pSiVWRZTAxkZLxwc5BbZDQch+qmlfqEIvhZ3BqfUtkDExnHHhfg7Tm4bT6jb9+i7xBjlaM1xQHRH4ByaqF5qBH+5b8Q+qBDWgcANARfKHWdqFU11uayLZwXqdgmcGLGW18/bT3r+60d5vPIHS6LPc7xoVrLgkvC0Ym8inaiYeEqolj48VqHasij4yD9lYt7fahTsaBpjhoI9Guew3wsCmpZ1Vk/7CoTcrVURx4vAdF2+/1EvEZtc446FhxpO1DeTiyfeEstHGj7ZdV0ZbfS7XmVZIkyvFnlHHQ0KrIrGhd4z4kAoId7oNak27yMwflD5HyGajhyJriwEd1UnQt8Jo2DR1m8Zp4Dw9H5so7n8YNUYhVkX8wMS9U8SKih+qBF+6ynIG+qrz2LRpOX4v3LGqKB9uiXci1kA+yWVgmFm1fVRw4TlyHNC7kEJ2x4kA5pqdfs7dVkQxHlusBvhZsO8Q1TVVAtbYWsTwGjp+T1jfTaiOvrKUMobpiig/ZOLO/BmpKqr2Lc+qAoV/jQH8+2sdC4j1C6oENaBwA0BHWBcM6Fh0ZMmeh+T7UKVhmyEmvNoV7+W/LisZEtoVD4acb0Diwnnhe26rZOPCYuOcPy5WFQ9t9qKM4kFM+DlOedZtX5hkP9Swy2hSs6l4DfBQH4UVjjweV9U05ZVonoLpr1YV1xkFsxRFRzXBkh8J5aONGK+RaFe6qHtQn33fyOA9VfcSzKgpvIMZW3nmFI8uJf71YxG3/zQrXgcMs2j7E/Az4+emmOAhsnnmdA0REC+wYEK/dIxyZBV2u8oyDGIqDjWKff4+sr9y2edNiqmg7P+iJpq61TQdvxmsDHVyFYNG4LPpbscORQ54J+HOIZdGcSCpRF+fy1oHejQOeQagVi61zF6cJUkA6Fu95MPLyfD/3bOCVtZTBrdJ4OLVUv/srDhYGPVqc69P8oCdev1dAsra+Xhj0REg9rIpmFzQOAOgI62lvPiHCb8IavoqD6u1bKg74vidJdeHSsmjGbVqqCmZE9g+Ma/zhqCJnwsOqRQShVRYOZyjjoItwZOOA3lGNjAM9IL1NOHJ404LIadpeeKmGF+xMMg6G1Q9IuX0QRcv2hRte/C17D6yLJnWVbx4Sbd5Er2NVtDFsFxBOFOYpXfR9qwdW/oBW3Lzw8ZYOVf54TZqGFm21fbB4D+pYFSVJIs+DSHk7SZK4TdyHqu+SJHFpoIrPoDDjwOn1d5xxoDYO2PVOvHbjcOQ0Tekst+qZrwhHNm5ejMap+ExXpoZqPNYB0/A1Cb8W87DoM8ZWRSHXQs/CdYjiwbNwG2ZV5JxxUKEA5MpL68KxeDZUGgee+yDtGwOaR4bHAA9G3s8K1d7XoKJw9gzvjAUiWYyfDmbfw/ZHC3O2gD+bDHoJDfo9uoxlvZxaQzjyrILGAQAdwQuGo3HaKpi0TsEsQy4U4k0bEynTpq0yFuRDYsLH2BiWRdutcbhFzsXfMVYclISwhWzfYqEogtCqFAfcqqjlpI8sFpQH9Oa2bfH6eeMkejhyuF2QVsxqcwzWaVoQKRYhHVsVeeQLVDcO7G1SqgoVpdtva5kX6GueYXkPmOyDOAfDrYrStP11IHTaXw0nNircyQnHsJwFq+0HZxxECoYttyqyL1oJBWLFIIVH8TK0iedxHSZSVCc1VBcWhUupAi3KOPApGjXPOPBpHhJVhyNbKw7WNkfCW5wXyvl10Er1dHEfZBFsunBqnTPFqSqc83W69esXzybKtcircZCmqbwWx844YNvX1oRe52BGVePAu3Asc0biBmSHDFOI5pHh9k+wQvR0MLK2bfNwZDHUxz7/COHIp5mKYLpxwBsZZyJZFWX3n328cQDFwcyCxgEAHaHahLS4UdZ5SCv6nXaKA9lJrkLaVDQv2GyOWOOkiVVQm9dfs1BApExbtixY8QekKsWBx2KldsaBKBy2tEqppTiwnbZO07S9VVFbxQGXpdfIeCCyVR3NVWUciHBwe5uYsqJxjHDkqpB0jwdGPt1V2jwRE//ttl8nlJbI3qqMqKbqRPN4b3kcyu2HFc2J7Ap38gEtrGhstf3Qz8Daqi1D2mOEhxOb3AfH4Y0LIi2c1F5xEKr6sCqc17kXxrCLKvoMpM+/k+olsuKA3wsHvSRnj0JkaxeqoU2u8iKV97QvH6ghKlccWBYsiaqPA6/G1WT7YphHXgu9Cpe6hWzAtPmovfIvI6SBx5/X2jyLapzn60JWOPawbJxGTLwrz4ZyiMMxHFlZE3kWz7lVEVcceFplEWkZK+WKA+vGFZEsxu+dKtbz3JlYioPs/rNvOd84QDjy7ILGAQAdoT1EtblZiYfEAMWBfGhvvlDg65wwxYFd0WqTKw4ih0PzqZqygu3F7dvaE5R5qWrE8Piv9ji33Yd6GQe2i0Xt39eeuG75wMILVmUNLK2512b7/EGjX9E849eo2P7+HlNmvGivhdJOY/0eaM2rMsWBdcGyjuKDSF6DLB5W6zTRtf1r+xmIjIOC96DXU8KJjQp30hqgW8VB0WfgVSzgxb869wETxQEv2lasBzwaKKHnotcxWMe2zGPaNNQ6L57iIKxx4RYQrrz/XmqTDL4mJZLTtt52SVoRbHqoxqNpl/975c8mvLHMhw/aIoe6Aib+jYrGmnovJOPAQvmXEWRVJNYhcRUH4ll0aNu4OMc89nmhmEi+B5bFc35Oa4MMXg1UImlVxD31va9B4l7MXr93xgKRDEfeV9I48ApHls9nUBxcaqBxAEBHWAeT1pm0nezDwG6xJBanDax6WlkVBcrCp7GUKNedtFe333Khxh84lmpaFbVdKI7GKfG6c+XEseGkj7b9suPAWpqrLXSrXr+1TUYduyBrb21hk1QVTu5QsJMh8WWKE/uCTV2rIq6MavseaP9+lsOR+fnf9jNI01RaFZUcA9p70/Yz4MdAkeJA275V0VLcC4oyDpwUB6GDDB6Tftv2GDUaBw7NC74eqsx78VDeBE78uxWuazTwZOHMXv1YtBbwUhzwYZbQgHCvjAO9ccBfu23Rml+HeoncplfGRAaf9p3r5xu23hkHVcehDKg3VhwE2Hd6BURrx7K2fbWBb6U+C2igeWQtTcOPQdk48A3o5oVgLRzZU3EQ0sT0nLrnE+z7lyusiqxzTgoK5hne4dhEFY0DkbMSV3HAMw5OonEws6BxAEBHeCsOQgrXvGjT9GY1Vgq2IVZFlmFMdQoFGXKhYjdt3aRx0lpxwBeHka2K1MJ57XBku+bN9t8vKVaIxpFt0ZSofjhszIyD7Z/bNW7q2pXxgqpPxkGZv7/9lFf9jAPjxoFS9Kpj1xQ7HFlO+rUNR0+Fp3adjAMig8ZBjYBsUbg3UxwEhiM7TdqFnoceBePRWB4DpQHZDjkjIcW6aVysegLXhF5WMa2sigw+A76W6F5xEDfjIOTz9y7ar2/l16RLc32RPSZyFowL59JfPL8u9i7aVVlmyZwJ78ZF9bS31Xug/Z2FfrVNDZFl3k2A4oCvg42PAX5M86EuL8s+ou37IX8vtfw7kTdnuA8hn4GnVdF6x+HEVddi78YFkWJVNNUsiGdVVJBxwBo5vMkBZgc0DgDoCG0R36ZwJTraAVZFVvLUkeJFWWVTom2/zUKlrrc8kb5Ya+qrKYP44isO2ocj2xYNieorDlo1b5Sid8yMA+31aw9J01gvlus2sCzfg7oB6T4ZB+FWRWLavwPFgfUDg/YeluUsWGcc1G1gW0+ZqYGgpYqLhFgdq/VxyIv/ZcegsAoyyxiotgbY/r5TxkHgeegx7a7aY9RQHFh8BnUHGTysQkLXRG5WRTXWZB5T9/w9DLUK6jzjwKlxoD0T8Oa9d77AkjLMEltxsBI5mLTqOODT5/aKg+pnE7eMg8BhGm2d7BbSHqA4sM44kNlXFRkHltP+qgq1unnTeePA0iqpwrbP+zpYdS32GKDg8MZBWTiyl1VR0UDLZUt56yhkHMwuaBwA0BHajbPNjbJJODK3Kmp6s+IFQ6JAxUHPbto0NAhvGm0CrGnhWoYhBlglGT8wdh2OHCpLnkZMHLcIBdOO37LCuXnGQSPFge1iXRbv421fNC0qFQf2D6u1wpHZ9c8mHJkVbCs9/m3fA92qKJ7ioOtwZP0huXgfkiQxn7rnD53ligOfollVoaJw+5GLxrJYkdK4ZdFGvw/VmHZ3sMmpGiSwWotNE6r+6dJjv+hnFkWr0In/rjMOPJRvRGHPBLJxaRdKSySvQ9owi3fRjgfDLldNGxvb1FQNc3gHVPPta+eBl12Tdj9Tt++qOKgOqvdWnVRmHPDrv+H2Q59LxPO4YfNEfAYBtmmWDTxRuGdrYm/FQdW12OsePE1ZOPKexUiNg4KBlr3CqigfZg1mBzQOAOiIfi8RE7ltblYW4chNi7baAiMoHNnwYTE0CK/qd5ruA5e2loXSZlgXLMRkVWU4st20//a/b184b9M8Urdfx1e5g8aBueKgpl2Qpce9zDkpf+3W/v5aMHDZdXCeTbnZhCPXyxmx/vy1acVaGQfGzcP6Vl3trkF1Mx62f84LV20/g7Bpf23bVtOmoVZFfoqDMNWFeg9u0Twm0h+6yxrYHgWL2lZFDoW7qmLJ5Pu8cOvUPOLX+7KfmXwGgXkrbs2zgILl9vd9Qkk3R/lzeV75/PkxYRlKS1RdMCXSPP6NFQcb3SkOxuNUPB9VWhUZKw748RRmVWQVjpzfdi8JUzwQyeO3KZvsnqaHI/tN/BNVnweeORsbyvuoqy78michSlSPzLOiv8Vfv7fqqLbiwGCAgnP6fHHjgNsan3XLOCgKRx6w3xub24UBG9A4AKBDZCBTG8VBefiOvn2bxeJI+XdBjQNuU9PiRll30pVIL2Y0bxwYZBy0vFGKyaoKxYGlVRNRs8K59NVscQxoypcajYO2Dwt8cTpQmoMc6weGunZBlpOudfMV+OK57YOCahVVYVOT+/cGDwvCqqjiOiz81Y0VB/P9HvVKPgfZvLWd9q7dOGxbNDZo3rW3Kgo/BrSJXwvCw5F9iqahyh+PSdO6DWzrnBFtH/hah+NRtOHHcajiwMwuq45VEbsPuXwGRa/fS3EgGhdhGQddhiMT2U78S6uiGVAciIwDR2935X7Gz3XvxoFUHAQUjZ2OwaLmnTZIZ3UvDJl2nxPT9rZFy6qMA898gdA1kVfzaPtv1W9eWRbvq6wTve3SxL24X968JGo/QMEpsyri4cj8mmnFhrBy3X7dHlljwAc0DgDoEEt/5zoPaRlWhTNtkRWmOLArWsoFav3CPVHzwq1NOHO7G2WILDy3fbVxYjvxX1UwEZM+xlZFZcfB/EBKc9tMedQNhtV+p33RrN6kK/982rz/snlWUbA1njCqa9PjYY8hMw5qKk6MPf6rpu2tG1c8J6I6HF0+rLZpXmrHQNU+WE+ci4yDGooD/mDVeB/YawjNOLB6WOOvo9CqSLsHOzQOSq2KPKbdRdF6dsORPRonRDWtihyKVtI+Mqxx4pZxEKi6ccs4CAhHJrIt2ARZFcXOOFiIpzjQjmN+HHg3DoRtmvJs5uUvX2egy2viPMSyjK9VrSfORSOfhyOz98VS8RCqwnRtoAV8Bp1mHPD3v+WzIKeqietp1ZVxaq2kccCuiW6KA74uvbD+1dbI1tdBYAMaBwB0CJcOt5n2k7L0kMaBTeGsecaBYdG4QeFee5BrerPm0wFNtt92oXBuo17jQM94sGtezfWT0mlnbR9iNi40O6k2Ux4WAd1dZxy08fetrzgwnrZXrp9l10GPgoGwqanZvDMP5q1sXNiqLuqGI2vKrDbeulrjJOHpxwzrwpWcqirJOOD+1kYPi6Eh3VJxYGQPMQpbj2jHh7Xqhaj8fsybKhZFg5Ap32liWBUFZxyYTRvzKcu412I+SFB0P3JTHIRmHDhZhIQME/HXrv27NoRYFXmpnjLObVQpDnzef6KwYRbPjIM0VaySIhaN66wHPDKviMLOA5m1FteqKKZNUJLo10Jp1+S3D9qzt8x6MWwciHVx9cR/zMaFZd6ixnic0hl2Hdy7ON04yGcM8N+1QlgVXfgctPWp1VoY2ILGAQAdIgNxmj+w1AkFzbBarDTOODCcMus6HLmJVZHllN14nIrFad1wZKJ2i/UmzRvRPDLMuej3yhsX2uK1zWfQJKDcumBUt3gvHphabH/Emi51bZraFmy0f19nws0mHLmeVZF1wayuZZ13zkelTZBhzkyT7RNpGQdtj0N9qkqD/8yqaBbcOHBTHAQ2Dhwm7fg1VLPByO1DX743ba/DIb7iuX3wUBwENm+sMz4m2xfnYryQdu1vFE38e3nsh64HrAdIJn8n4PNXFQeGk55rzPJCsyrSclYsA5q7VRxUK+C8cmaI9GezIMXBDKiO7MKRq89Dobw1zzjI70N1xoHjtHtfH6aQ1smO4cgBORe2hfsqqyKlgWrYwKtaly5oaxDD69CZjSHxS+q+5YvNAs9g6mn4e5o1TbU1suV9CNiBxgEAHWJ5sbaZdm6YcaAuTusX7luFIw+rp2o4lkUrYRETFI5sN+3LmwZEAVZFM1C048dAm0mfEDls1c8t7bKCXr+Ysmk76cqmLCvfA7uCjZyyrTfpbe3vr21jGo8HldBQ2gzrUNC6igPrgl3drBnt+LRs3oU00EXWRosHxtE4FcdRaTiyyDgwsipqqDiwkoeLB/WC7XuEYoYUKar2oW3RoO610Po8HI9T8T4UNw58jsFaRUOHa3FoOPGOVRxsVb//g15CvI5sa1WU/1shVkXWAc1VigPPYFrtb/HJbt644sMHbQi1bfOaeA/NOCDya14E+etz21zrjAM+UMIzDhxtgkKvwzLzzWYf0lSuibTPwKtxRFQ98a+uAYzCuYmqm7ialaHlMXCa5RsQ5a2K5ACZbeMso+j5aK6fEO9lWSqvgB1oHADQIZbesnW9rYnsCvd2ioMWRVt2kw+Zdk+SxCznoW7RmkhOOrZ5YNHCjFYW6oUjE7U7BqW/foDiwLJ5VHfKcwYaJ10rDiytovi2q2ySrL21eaGk30tqhWO3DQcnUkLwamYctM95qKd4sA7EaxuOTNTugbXJfdDSqkgrOpY1L7z81YVlVnDR2GnStCQUk1+iNls2T+vmHalT162bF6yBW/NaaK260LaR4XYM1LgWeEw8hq7J+LnhFcpatH3+2rdGNv7aIQXDJEncFCdEROtbTHGgWRWpdkl+Ac0r8/EUB1oBTloVccWBpeJB235I48CmcBgSTFz0M6+sD015xK/PaaoPxDWlrlWRpU2QVP/p6xFpHWzz+rW/o10LPafeu84YqGriausjy+swD0bu95LcdZBv3zocPEMMtFw4FpMkEc8qlsorYAcaBwB0iG3jwEJx0Gz73KKEKDDjwLDL3aRorf1e4+aJmPSu37hppThQppSqMw6Uol2rcOBwT+MMaVUU7xiwDMfe/rf1X7+1x3zbjIM2jRO+7cqMA+NJ19rBwMqkY9uHRS5Jr1QcWIcj1804EPJ024Jl5Tmo5Yy0saxrkPVjaVWkFf7LFAcynNjmYUkUKgruBV7BnHWOQ+uCUV3Vi0fRoE7BjMi+cKeGhHfdOKjZxG0LX5MVXYt44drqHGgajkxkM/Ec+v5LxZVf0X5Jsc/UmpqW06Z8qGZ5IWLGQUDuFr82W77/uuIgplVR+ECXn1VRtfLIOu+NU9U48AiHz+CfQbFtoO2zSEao6kXUQ1wzBvrsa9/CvVQc5Lff78mJe0/Fwd7FQc6uitcrLI+/acRAy9S9h6+TrbNugA1oHADQIZYLJZtw5Ib+/qzYliRUGYpLZDttLScKqrdPpDywNg5HrmdNQKSEQbVRHLBg5F5SfQxYL5b5pGgzqyJLxUE9e4jtvxG3aGmpOiFqojiwOwbFlG3F+889r62tiiobB8aNIyL50F9UsJ3sg/EDuwgfq1uwNA5Hrty+sUS7SQNdPLC2eGBRFQdl4chORdvG4chOgZRlqgvrglHbfAGi9u+DVBzUVX7ZnodExYVjy+O/bB9KbeMciob8Xl40zOGnOAgr3HtNuzYNx7a1KuK5Wx0oDja6Uxzwa9FAyd3i10arxhWRPoijnQd8gMCqaFnHQtYvpL362VB7T0wbB6KBlt+eR+N08rcCrwNSceBzDBTtg9WzuEbVUNFAKdzbKg7Kt58kiatV0+nzrHGwlA9D9rTKmqZsXeqZ9QLsQOMAgA6xXLA28Xa2WiyKYOCApgGRXKy1mzYPn26bxqp5UteaQNt2m8+fh9CtzA/UAKxpkiQRn1U7u6j6n4HlpENdqyL7xkkT1Y/tgq1+xoGdVZFUO9RVHFg3DuoV7YnaZ0zIwn29fbB+D6oUDyJjw1hxUHUN0K6TrRoHgaG8Zb/T5qG9ruLAq2jE3wfNIoTIUXFQQ/1j2UAnks2vypwN1S7JtnFQdS+yVN4R1bUqsm1eF/2dWjYlDuHQfL2b4aU4CPV3tx5gKNp+6Odva1VUPmm9vX3faV+hOJivUhz4hcJqnzW/P2h5Zc23H9ZA9Cpc86nxonOQSFsL+WStqFZNylrV0ue9qpHvWbgNDmkXQyQ2r187lkIUB1aF85GS98PPOe/Cfci90DMcmq9LhVWWczj4ZD/E88nF7YrnQYQjzyRoHADQIZYFC5tw5Gbbr1swvLh9w6J1jRCuaaS/bP19GI1T4tboIYoHvu3huLm3rZSEVzeOiGwfmnjBRpsm5vDGRTvFQT2rIi3jwnLaOah5Z7xYniXFQd1J39aKA160r7TpUQoWLb3N+UN/WdFY24euVRdtiyYWzbs2dmm8UBEUjmxYOOMFgiQpb54IebbBw2qdgOZZ8LfvWnGQJIn9dbh2OLJtOKe2/0XngpdVTR31j4ddTOi1SDsH22bdbG8/bNraTXEQeC30CscmCluXqgHNhjYVfB+46sFTcRGigpXNW7/tE+nDJF52TXWauNZDFJN9CBhmUBUHhj7v9TMODK2K+JqwYC1gfQ+a/J1Qu6xIAd1E+rXQ2jZ0mpCBFvH6PRUXVY0rp4wDqbwoURzAqmgmQeMAgA6xVRzUD4W0mvaWxcpm0/7Dcdr4gS3UT1buQ3uPb3Vx3kBx0HT7RIrioCIY+eI+8JyJuIoD08ZFg4Bqy+3XmbAs2n7sjANL1c/sKQ4qitYOBZuqyR6Ov1VRXMWDUJ9VnIOa6qnVfbBm80j7nTaFU80qqkz55THtq01MFx0HXhkLdc5F62OwbvOKSN6r2u5D3eaFZd4Tkf45hobzdpFx4DFtGnoc8HMjTW2KRqHrEe19sSiaBSsO2PXPsmATYlWUJImrTcW5jaqMA79p75DCuZfqi0hfz2rDJH4ZB+HXADflU0A4sjrAYFS8T9O0MnPI0raX01hxYBaQ3a3iQLWPVCf+edaI4WfQQHHgm/GQ3xb/PKzDwTOk8mZKceC0FgW2oHEAQIeIG1XDC+U4QIqnYbVgbqo40Bawza2CbMKRmyxWtAnZqoIZUYFVSsPPgGccVAUjZ1hKlJsUbGTBJO72LSet6hRKMixVJ9m/n6a+VVHz119X7cAXitbT9pWhqA4WEVJxULNx0DZjoUQKrGFdNBG+4gENVHENaKM4MDgH2xRO61pFyaJd+4clzeqiSIHGX7tV0bCO+sp62rWOr3aG9bWIK+dqWxUZKw7mSxpYHpO+aSpVL7X8zU0K92xNWLB97RrZ9j0Yj1NxHauTcWDxGYQOE1mGw3NCrIq294E3b/0UByLjIKJFiXY/5O+JleKFSG9eadcBt6JxDSW4Xzhy9ZpA2y+rxsH255n/nlAcDOQ6zOoYCM44MHwWm6Zx48DKLkvL+1HeA3kPsrkOpmmq5P3I66B13tg0VYoH73DwDL6+nF53ea1FgS1oHADQIVYLJdXPtl9dOLbq8PPGQWjGgbpYa/jALGXZzRoHTW6W2g2+ybQ9UfPPYG3LpnFgGQ4cNHEvCiYtiuaBYYi57fPCqWHRsInigKjdgllkHFRZFfXsPv+6yiM55dtuoc5fe9U5qC2W2zysjsfyAaGycMweIEbjtNWkjywWVWQcGBdtR+z6HdJA9WzeBSkOHK2KFivef/5zL8VB0XHooTgYjsbiGI7pbx/qbZ//HduiAW9exMyaIVLOgzqTvgbHgG7fWLwPHsGYomhacC/UrpFtG3ia1UNR0bSvWPVYFG1Cm6jCqsm0aJ+f9i9uYPo0L0Kmva1zfqYRlmWKfadH4yojNHvNa+K965yTNJVDddo+qENsRnYtXHVDVG1VZDnxHfoZeNlVbbJ7Yb+XqMOFscKxt7cVYFXkpLohip9xUPVc4B0OXrQfi6VWRVAczCJoHADQIbyo0fRGpS30QwommlVQE5pnHCg3q4aBTDLjIGwfLIIZtQVmSNFaVxw0e/3cXqOqYJlhOWXSLBzZrmDSpGBkqbho0jixVJ0Q1T8XtUmnJqSpLHhXFcusrn8ZWzUbF1ogmmXGBVG1VZHW4GzzPoiJniq7JuOHxbre7tb70CRnxNLjW05Ulb//Hv7yakBzDZuStpOO2jW0jlVR2wfmJnlH1hPPfE1QGRIuLOPsFQdFeFgV6cMs8Y4BouYZB0QGn7+yjimzz3QJh56FcGRhVaRbaPLPwGradGsk1yX8nuxpExNyDGprda3Y3AT+TFd0P/aaeO/ariw0mFdbJ1sVTjUF4CJvXilrVSvVh5x2L2oc2DavL/6dsGdzL8WJtp4LsgpybVwo23dq3BAp62J2vfUOB8/g1/Xp6z5fo1reh4AdaBwA0CFWC1Yt0DOoaNuzKRpylUC44sBuyoPvQ5W3e4b8DOrfLLUbbJBVkaHioKlVk+VixSKgu3urosiKC8NjgKj+1L9V0VZrOlY1Lax9xUcjfg2obxfWZh/UB8SqcGTjxkFdqxyL6980/PodlvVi98DKHxJD7oPiGGhRtCqbqNLwKNrySa25flLYyNYaK63tsmoOMphbFTXJODAuXItJ30qrIju7LiItJDy8YGfSOAgslmS4KA6GfNo7/BxoO/GuKRfLmqge076hk8ae4cjhVkU+igMtK4HfE7281YnChmm0e4RVxkOoVRDfrzRtfw0iaqk4cLArI9LPwyRJlLw3m8KpqgAccNWLn+JBrIkK1iTWdnkZofdj/n2rwjEvVs/1dcWDV+Mg1CqJ359Mr0MVzSPvcHCi7eEyfl2bvhZzu0goDmYTNA4A6BCrKbe6000ZXlZF/cBpf0t5XJNJVyJl0qZJxoH2/gcUzNSiYePX37R5Y1c4bFa4tyuYmFgVRc44sFSdECnHQaVFhk3jRJNVV50DvKg+bGnTUzffgUgJRze0qSGSi2GOdoy0KZqIcODKgGg7qy4ieQ6GNG/EA2t0qyK7ggW/h1c1jkQgnIniILx5oe1f22lf7UG5TPlhbREgPK1DMg6cQ8Kr1iPWhWN+HJYrDmzzHYr+Rp2ioUXhXHqb659BvyeLhm3PAW3/y4Nh/RUHxRkHPgWzrdFYrCcKrYq48sqqaBhgE+OrOKge6Fmct78GT7YvFAdhRVsin4DuOhkHFpZZqo1skce/sO30KRz3Enk/0NX3PhPvheHI/BrcUP0vth+oRI+VcVC0FolrlSTfA24naNk4qHoPPMPBMzZHMutjunHAFQfIOJhN0DgAoENEh9uoaEwUVjj2mjYOmTIl0hcQTW9WMmchbB8srGp0OWz1+6952zZXHNQv2hMZWxU1mLi3XKxbWBVZbj8kZ8NSdUJUP6DYqnGiKg4q3n8th6XNa29imWZZsFJDaWuGIxO19NivqTiwPP7TVAaCNpn2trUqCigaG0468YmqqsaRCIQzURywY6Ak78Zj2rrutLm18siigWvdvKgMRzaedm0TTGwx7R06ZTn5mYfioEZAtfXEe6hFStHPLIpmja2KjAo22v2wKHvLq3mhqwDL/eUtLUJCbFrm+z3iecVW07a8+Fw0TKEOsBgUjus0cT0C0utYplkOMU2jFW15QLXeuIkbjsztaqwmzkPV8B7NY6LwkPjuFQdsiMmwcF/1HniGg2eoFprTVkUOeVvAHjQOAOgQqw63tsBp0jiwyjgIHHZXF7F2zYtmE/eNFAfKAis058FqsSQnixq+/lZWQfWLhpYFEznlGbdgZJVx0OaBqW7x3EqerDWcisIoM7Rp8Jg2TUS2nz9/2Ncmy6q2T9R24p0XrisUB4bnv6YWCVF9SMu8NlZFDabNDVVX/BioalzIol37hyVeLCtTPXj4u6uewjWKpm0LBvzzC7kXmgc018xZECH1LYtWtTIOlGlvj5yLcn9zO+VXhgyortE88cg4iOzvHhqOLD9/m4JNSChshigaGRXOtWIV/6w9rYpCGohJkghVmFXjgK/niovmtuuQyd+ocR2Mobohih8OHLImU217nQKqizMOfF6/sMtSbJmI/M7D0GESr4wF9RhUzgOv7RPJ+xk/BrV6iaXyansfyi27vHJugC1oHADQIVY3Sq3YGlK4FtPGRo2L0Gl/7WbVtGgkMw7iFe41WwI+UVKE1aSZtKhppvqwLNqFTPvK5lWbxkX998Ay46DJ67dUnRBpYXjl+yCvQU3Pv/YZB0TtihZSdRRXcSKDv+RkGWdg/PnzifWqY1Be/+ysoojCLNtMVT9isqo6HNnSrkk7BsrgD0seGQdlVkUeigOtYNkrORe7nvYnkplIbSzTiOrbpvFrpXk4cg2LkDRtP+3Kt99Lyu/HXH1mMW0ZGspJpE072h6DVdvvVnHgM2m7pjUOOlYcLM7Ja5HlGpATOkzD7wOaUsJi+7UUBw4ZA3XsyiyOAf081PfBWvWVEfJc4GUVRaQUjYusmpxef+j92C0cOdC2z8OyT/s784Oe+lzgqXyqsirSntWsMi4y1Cbu1HWP75NVzguwBY0DADpESvSbXShFwbQXVri28rcfNSzaa4FUVhkHIZOuRDbhoPwhL9QmiEgu4poWreQDSmjOhJ3Hf50H9cn2DaeNm2zfNOOggbe29ntt9oEXnKoVB1ye3Wzb6rR5RdFYm7xq87AoXnuDgPI2BZtQT+lpkiQxLZzyqZ664cht3n/tuG2SM9HmgbWJ4kAWLVu8/yXhbxr8YWk4TlsXjUUgaYlVkYe/e6g1QIZ34yAopN7YpkH665fvg7lVUY0Gmman1bZwXVd9J4+B9kWDUG9tIsUyrGXhVk5al6/J5QCHQeMgOBzZtmmSwRUHvaQsZ8FLcVB9PY6rONCPAam4MJr2Dg6mVSaOXVQvNRrITlZFRQMlXuHAIWsSyyE6sf3A64BX1kfodZhfG4bjlMYOAd1F90LZuLK5Bon1UGjGg2nGQfmazDMcPEMNqi9RHFjdA4AtaBwA0CHzRh1uMWkb2aamadHech+a+Jtvb7+9RF68/lCvJpKvv2nORdOMA9tp3/oFGz6F2KZoJqabGikO7KadQ7av7UObBXv9jAOb19+kaKwrDuK9diLbKTepemqm+onZvLA8/rUHjUaqjxYP7E3symwDsuu9/9rP2xbuRPOoQnVh7e9et3ljblVU0yaISK4XRi0emtM0rW3bZ3kOENX7DNRjsG3hvGbjRJyDDuHIpTYpzoqDys9/lsKRjQo261vD3NdLJQo8PkRgkfWyvQ+siao1DjzDkQOvRXy/zKyKxLNZQeNAWau4BJTXyjlp/x5wu7KiaW8ibR1gpTiobuImSeLSPCQKV5/JZzErxUGY6sTLLivUvtOrcC+uw4Hb91UcVKteLLdPJK9pXIlqrfoDPqBxAECHWIVB8YJViD0Ekd20ddOiPZE28dxtOHIzq6LmigOxWDCyqwp//Z4T//WLdm28naVVUfVxaCmRl8WaapsUbR/aWKXUPRd5waJpIJ6mOKhUO/R74neihyM7Fs5DVT9W1wAizWO/KhzZTnGjZu00yDlp8/qbBKRbPrDVbZ5qE7BtH5hE86Ii50JOetkWres2T1pPuzdRHBjlPRX926p7kVgHGVsF1bEIIXIIqK68DuX3oa1VFFG9JqK14qBu48gyZ2XyN0TBsMAmyMEujUhaFS3NDwp/10txEKLA0wqGbTM+MsSauKh5wxsHRtPOoffDXi8R16DYGQdWg3S5v1GjgSkK50aKg9AGHr9HmE38B94LLNXnZX8nNByZyOZaFLoe8Qjn1rZfdAxaqn/FPgQMtFjVYgr3oaKBYn0PBj6gcQBAh3hlHIRYdBApD8uN8wWaT9ybFc4b7oNF0YgXu9soLpouVprY9Gjbj6444PJgU6uk2IqD+q9f24emqhOi+hPXVhYddcMgM6zs2oiaXQPE+Wdo1dNY+dViwV61OK/cdqvGQX3VibYPrYq2DXJOLL11ZRO/fkB42wemkCnb3D4Ye8vKIL4Kuyxjb+smtn0iY8BQ9UIUMnFvdx8kqtc80YoI1uHQVc0j+f63K1qMx6mS91OWcWB9DNZVHNiGQ6dpKi06Cu4F1oqjDG5VtFximRYv46C6cUBkVzTjDaCi64BXMKi4H5YMFHlkPdRSHDgcA5plWBHWzduM0MK11fM4J/RabJl3l/87YZ+Bdmx42GUFWxUZnYNyTR62fUvlU4jqRZz/xhP/VbZxonmKcOSZBI0DADrEaqHUNJxYK1o3mbRppziwKZo18TcnspEph8qRQ7bf2CqmZihu0e+Zevw3UByMU2rsa1l3yo/IVp7atHFgOXFeNyTcqnCuTYiGXAZ4McNScRBUNLbMl+DnYGhIvOHEfehDUoYWjtx02lJ70G0SjtzmgcnkGmC4/apj0MOqKMTXO7cP5oqDepZtlqo3Inn+FE1aT2M5baf6alcpDgyVd0RKsahGODKRgV1WjYKd9vO0xTqASG+AlysObK1i6iqfrG1KtGMw1Fvcq2hf2jhwUj2sb+b/zpLSPPHy9yeqkXHAjj/edGm+fXY/rFG4jz3x77H9No0Lu3DiMBWol1WMuB8XbN8q74wTqjrxCugOzVzyKtyHroc8C/chA0XWaxAObwTwZqm0KoLiYBZB4wCADrFaKDWfNpe/1+SBuWnjgkhOWsVWHFgUTsWUaY3GgZW3LX/fQqedLUMZLTIOiNpMvdc/D7punGj70HTBOh6nxE/fyowDHkjWWHXEJ9vCAtot5bnaPlRu3/Bh1Uxx0OIYlFZF9Sadt7fftHEn9zukiWw56WdxDWilOKh5DMz3e8RPk7YPTPwBLbbiINTTN0OEU7fdvrAHCTgGDe+Dmlqgak1gqbwjqqc80gLa2x6Dde2itDyYNjkPqgKuZB+sFQeiWFbzOty2aKrtf2E4stO0O7cqKmtg8sK5lU1Fk3BkIj+blqJGslAcGBXN+DlUpoDzKFzXskxzyJqoY5XkZdUSei3mzUW3cOIixYPhAFPp9gOteoiMFAdbYa+fr4O8Mg4Kw6ldFQfVqhPrNQhHXIvZ+y2tiqA4mEXQOACgQ4SvqdG0b3gwsM2CeVRzyjm3D/xm1fBhscuMAzFlWuP1W3nbNm1eWC7Wm1gFae9V06JNk+17Kg6qiraTfbAKCFcmxasmnvnDSuyAdEvFgbBsa5BxYLn90GuQ1Tk4HI3Fw27VtLlWWG98DCgP2kGFe8NJP4trQKvt11SdJEli/sAk7TnqqR5iZxxYT5qKKdOgcOT877Tx2FeL1hXHAT9P2tpUhPoqZwh/Z/OMg6rGibxOtPoMlP0vuxZZKw7qXoesp521zy+0YNaJVZGX4qBBODKRpeIgzKpoad7HpoNnVpVmDDgUrusM01iuBTPq3AtkOLDVxHnY/ZBv36xwH3gt5vcoq4nz0GNQPQ8djsHgkHij62BTqyrTcOQA1YumPrakqonLv0bGwWyCxgEAHWK1UGpiz0BUNGljoTho4/FvlLMQOu3LFQ+NXn/YVFHI9u2OgQ4UB42siuTvNG8cNLApMQwlbJxx4JR1QhSgOLAqWjds3NkqDupfh+SUT5tQ1KbKL5sFu/bZVRZtjZrHRHrjJkR1InI2WikO2lsVjdPmRYO6igMiRaLd8oEpJBA0/3Pbol1duyxrq6I6U6ZF+9DGpkE7f6pUD6Jo1NImou69SBaPjbdfpbhQG5h2yqOqfeg648CygV3074uOAetw9IxaVkVOzYsm4cjb2/dRHIRaFVmFQ8tnkxLFgcPEeZ1rsdUgXe5v1FA+8c/GIqCcKMxfXtu+Wc5GaOFaPAs7nQMF2+/1EvkZOCgOCjMOnMKJQ+/F1s373N8KaJ54WVVlnGevR1gVOVnmAVvQOACgQ7hEv2koal1f8wwzxUGDSd8MWbiOq7qwmPbmExVVfrbW29f+XUzFxeTf8smSIKsipWAQ1arIbqFa154hw0t1QlQ/46B50bjZNUjIg0fNH5jlNaB+wbCdVVH9jAVtH5peA7UpxUqbGC2Qzug+1NQurlXORIPCveWkW91wciJ7j3Fuc1GZcWDt7167aG1cNG1wHRbhvMZF66r7MT9X2kzbE9V/D6ynLbndVLVVkbHiQPm3Zddjc8VBzTWhtU2F2jgozDiwbRplrG0Oc1+XWhU52SWFhCNbPQdphDaQZDCoTeNA2LbVyDqxmPZukzFg0jioZVXkpDgQheu4E+dCfVakeGCvP03b34eI6j2X8ffA4lokGjdFIfEOiheicMWD5RAbRxyDasaBn+Jhex+gONgJoHEAQIdoCyWLcOLQgo0+5VX/ZtF02p/IbrEkC+eBjQODxSovdNdSHBhN2YiCVUObnDYPC40UB0pBpUurolYZBzWnLDOsVCfaIr+u4qDplCffdui0vfDWblEwsFAcmBatIxfOtX+3UBEMq50jsZVvlhYB/GEr5BxUlXfDhtegBsegddGSW4TEVxzUy9kQ14CWD6wWlnltpj1Vq6LKcGB5HW4aUk6kFO4rrgPWdlWycVFxHdLWAW1UHzWtiswzDmoeg9YZB3XUZ14WHTyYuAvFQVUgJ1GWx5T/npW/eOi1iO8Xb3g0RTayu804KG1cGN8HiOopsb0m/kML9x6NE+3v8EGlyfadGmhdB2SHWgV5KQ5CMxbkEJNd4TxEBWqt/ORU5a9pdnVt1kDABzQOAOgQq2nPxlZF2kKhQcGkyaRvhtXNyirnocn7L73NaygOjAqXdULQpvENR64fSklk6bMfMnFut1BtUrAislSdNMg4cAonD1cc2DWueNZK2LS5XSAafw9CP38vuzYion7NgiVR82twU9WJ9Fa2s4sKuRZr78FGQ+WLVDxUHwPWRcuQYtk01o2LuhkHomDV1iZGeCqH3IfsmlfahGWVZZd2rWoz7Vk7oNp54r2qgaddp6xVH2U5Ezs942A7hF0/Br0UB+tbecXB8vyg8HetG1cX96E64yBJEuUaZGUTwxrZhXZRThkHNe5H0qrIYto87PVrP2s6SJfffrjiwW/iP9SqyGaIhxOqALR8Fiv7G7FVL6HWiW6Nm1HY58+b61bbH4/TZlZF5o0DviZhigPlfYFd0eyBxgEAHaJdvJvcLJqGE6vTpk2sitLmhXMuj2yyUEnT1Kxo1ywcub41wuR3jQrXdR4QprFcLMvJkvIpQyJ9sdp00kduP97E+WicEt/tkMYJkV3zQis01bUqsrOpCWyaWCoOGjTwhNqjjeKgac6KWUC1/HfVihO7h8WmqhPToi2fcAy4Fltl/RDVm/DMsC7chRTLpjFXHAR6CmdYF6358RsSUm+rOKh/LdQn7pvvQ+1wZJGzYVu4rvoMrMOR+TE06CXUK7kWumcc1GyetS0a8WJl2ZpUs+iwmPRcq6F8srZryxDXwgLVg7SLtJn2DbVp4ddoK5uO0HDm7Z85hCPXuA55hOPWsioS6xCnjIGCJq6XxzxXbhTdj9VnMYP3oFZAt0POBrfICQ2Jt1IdNVUcmDWOVPVZtWWbdcYBvyfxRoF2f7BqIAM70DgAoENUxUETq5yGwbj9XkL8WarJzaLptD+RfKBqslDRni+Dp10NpmyEt3kLxYGdt3Z8f/FGGQOqVZFN86RZOHKzbeuewtWNEyK75oV27lYdi2bbFmqPphkHbRQH9ZVPsmDQpnHRrHliNXGtFRqrPv/taUujgPaGAdlC9WTobV42ZTz5HeVYbf4e1D8GeNGsbdGI//voGQej8KIlkX0wLC86htwH+HqhTdFaDjHEVd4RNciZEDYNLVUnNYcptPOk3Vqk3iCJdfNOFmzr3ofbFY1C7VGI9KaOReGeW6aVWRV5+VuHBsXz5xCuFGhKaBNRZDw4hTOXrQesJ67TVE4611EcWOxDqL88kVRptw2ozwi2ynGYeE/TNPhe4GVVJJuoNY5BF8VB2PtvZZcWeg54KR604rvWvPJSvGRUKWG1z4XndYHuQeMAgA7RbiBNFuxNC1ZESiBOgwVzG6sevlhrZBXUoGCawR/oGmU8NJz2J7KbNGtqk2M55VC3WEFE1FOaV1bTviGfg9ViTTtuyxbIuX1wVBxUFfD5cTJuGIjWtHkoLQqaLxSb+MvbWlU1bN45NW+Iwj4Hr4DsYMWFgeqs6N+GnIOWD8xNGmi8mNU+Y4A/oHWtOKiYtja2aWlyL7R8aBaql5CMBeNpz9rhyNYe/zUVD9aKg7rXIhnOGzcgnK9DY77/2vSpSeNgK7xxEEtxUHQt9LBIIQrPHvNqnPDzoEz5Ym3VUzfrRTsO2xZP69wL+DXCyq4q1CrHo3CrHcfFVkXKOsgg56FWxoHxEIH2N2I2bojC10NWlrVi+0rxXfsMxPFvHI5cNdCiXZsRkDx7oHEAQIeo0swmjYMW4cQWC+amVklEijyuwWKpacFM236TBxYx5Vrn/bfKOGjYvPFVHIROHNtIJIVlVIjiwVVxEPb6raZs1IyDiiaiVdG0yaQ3kVIwMMw4CLkGmIYjNwxJFw8MDR/WNH//Km91IrsH1qbXIKucldE4Je6wEdJEN1VdNFBdWYeTynDketPW5hkHVds3LhY0uQ9JxUGLaf8mjQtt4r7NPtS0CrIu3NYtnGsDBG2UR3WKVUQeioN6x4B7xkFtxUH7gk0tqyLRPI0XjkwkG8wWFilE4faZ3LbDzKpIrMvihSNr/7624qBLqyIzxUF5KGyGlWXlNOpzSeD2iWzOgzrXQpF5ZhKOzN7/guuQx7aJmisOzMKZlb+jrcn4WrmNZagGV1GFKA6QcTB7oHEAQIeoGQeNJu5bTPxbTNy3sCqSNhXtt0/UwibEIOMgtGCu/W7TxUqdyaJprHw9x+NUFA5DvKWJFIlww31oUrCxCybWFAfNjkFLxUHVqWiWc9KweeiacdAg46JVODKfLgwtnPPj3+jzD89YMFIcNGycWHmrqoWKhs2b5lkf9e2a+ENc22BMLu+utioyVhxwf/UKyzbrSb86gZwZlhkHTa5D5oqD2o0D28J5E/WhLFy0yZmoaVXEFRexw5GNm2d1LFq0IpKFt3Q9qyJ5DbTIWeD7UJT34qU4CD0OFuedGgc1CufyPWj3/tcpWhPZZf5NUysc2eg5hCMmzosyDgyznjLqDDTpOTsWjYPwa7EY5OvUqsiocdAwnDm24sArYyFDKA7Y+9DrJeI9gOJg9kDjAIAOSZLE5IGBLzBCvL0zLCb+22Qc2Cge6lu0ZFgUzdpYRVl52zad+LYKRNOmIxsrDsxsQgImPY2KVq2Klk4ZByET51b+7mLaPDjjwO6BXRTOg/ztLRU3DQv3bqqjuNcA+f6HZ+1M01zxIPc79DOwKl6LBm6IVZEo2tpmHFSHI/sWjasUB1aqr6Lth1yHReOgVdG6/va1e2Wbfajjca/93LpwHdI44NeBNkUrGZJefh56ZxxUrYWs7brqNG604zO+VZFiU2NQOAttolpP2xf9ncLGgSiY2WxfqtHDJ/5dFAd1w5GN70V1itZWE9ehVkXWjZvtvxE2bU60Xbjl12CLrI86KkwPu6DG77/ZxH+o4sSnecmvJYNeol4HvMORQ7K3ZN4XFAezBhoHAHSMRSBPkzC+i79rUDhvoXiwsKlolXFgULhv6m1OZOdtK0LQIk8b150uyu+DzbRnE5sQqThoqHZQXn94zoSP4iDkGNBUEU2OgaZFY1PFQYN9MFUcGFkVNX1gbNrAlQHxNo2L4MahUcFQ91SOew42ad7Iaefmx2Capoo9R4XiwNjfPXTCL0Ozams6bawFcobkXPTZ59/GX19krYQoDpRztY1VkWgcVKg+rO2yGjVvLEPSa078W58DoRY1F39uWzSq0zga9Hvi+LOxKhrmvi61KnKyqTgfqDjwylgIvSd6ZRzUWRNb2fVlaO9h2XHY6yXKcdi2gRkeUi6tiqwyDrqzKtLWEmXXYqu12DS1mpgRrIqKts9VT8Nx2modcHH7zRQPVnZpodu3sgwt3I8A2ziv6yCwA40DADrGQh7XZNI6QywWDYqGbRQPzSb+m2ccWDywtbEqMps2b1q0M7IqalO048U1K7ugkGLF/MDmYYkfN31leqd4H4xUJw2KlmrGQZOA9AbT/kRy+sdScRCUcWCQcZIhigTBOSM2D4xNpt23f8+maNU4HNksY2H2VD8hdmVy2rn5w5J2/FZlHEjFg23BqGjCL4O/92navGij/buwjAF2DlpOuzfIWCBqa1UUVizJ6DrjgMhY9THmBcO450DdY8Bc8VGzceNROJdWRYPg7RMZ2SUFhiN7KQ7kxHtBxkG0xkH4xL2H4qDueWCdNVJqVeQUDhvaxPM4BtVw5BqqD4t9qHMMemw/PJzYPpybKPzz97JLkwrQgmtgz2f7GVz9pa0L+VoVGQezBxoHAHSMiVVRq4yDbhUHFkUj1aooNONAKRzXnXZs4mk82b7RpKv09w9tnNgsltsoDqwmHaRVTEjh3KZw3WTC8uLv2kxZNSmcW2UcNC0aS8VB8wfmJqob03Bksf1m0+6NrboaKg6sHljFpHXjcGSbxqH2t4uwkqk3ybmwVBxoBacqqyJrf/e6RWOrgPaifxdyLZbhyC0yDhooj7SA7jbXorpWQZbKL237IXlH/H1qpfoY1rsXWCsORLGs5jlgbtdV1TjidmUORfvyjAP5M4vieWg4ssekM1ENqyLjnJuL22/uL9/aLov9+ySpXhN4N9BKw5GNBpg4oYVrl8aB0rjqlXwG/Dpp4XNf5xiMY1UUdg2w2n5o3pA2QDY2UTyEKV68FQf8eq5ZZvFmAhQHswcaBwB0DL94WmQcxG4cNA1G3d6+fThznX1QvYVr3qz5dGI3igN+DDQrWjadtGwz7esVjhrSPLGSZ7eyq3LMOKii30tEgLKFVVHo+eeZcdBEcdBm+9JPOLRwbjNxLz//0OYpl0g33H7TaxD7vaYFwzaqJ80up9k+1C8aW/qr82IdUfGEmcf2t/992INqhuUDu3bvDrJsMzoHierZY0wjwoEbngdpmrYOR45plZMhQuLbqD5qqkDNFQd1Mw6ci7bVnz8rXLe0KtoajcV7ENuqKE1TcT1cKmheeCkOpI2s/jnw5u7maGxik1JnoEOsxVr62/N1xFy/V5m5Zd1Aq3Md8CqcynOx6Bi0L9rXVf9ZKdCnqaP6sFqH5f5G4MS9eg0atS9cN1UcELW7BxZtP7RxZZ1xwPdDux/IBioaB7MGGgcAdIzFQqlpwYpIC2VrYFMiLFLqZBy0n7bljYs6+2Ax7djm9VvYhIzGKfFnDG7BU4QoWjZ8WKjrZ5rbB4Nw1DRN/z/23j36trMsD33m+v323rkoEIKYkoCItwINx4EIShUjYkQuUYsyqrVF8XgpHWhrj4N6O4dj7LFqe061tD20imi1o6ekKFCJKNEANVChSasDxAsaJUISyiWBXPblt+b5Y7nWXut737Xm9z7v8831C5nvGAyy955rfd+a85vf5Xnf53nMe1AnVaQ5MNoKv90b9PVQsU5Y5ovi0Gxlksb3OGB8FlSgOeAcUkc2J2affzNz5OrEiebA7K0dtFyTCLiuuQfWEI4/LHmVqlHGwegeB0JTTFa20DIOdMwj2usjwTyy+4EYcCxnnTCsD6FBddTj4GjeaxkfQx4H4krbKGBon3+ufS+BuYtxcHgwM88/67PgyrZtM0ZtxTgw+8I6qSJA4zNhk5j1jIM066UAXU8R+/Hsc0hJFak8DioqrRft65NXtfr+q38fQS5p11yoNiju+766kMG7NwrmlZEN3OI35LHSxjSHbpG4Wo8ac2R1An8KfUyJgymm2HMogDO20hRwtH0V1cYpc+a8vvqsw0465np4m4UoeJjxOLBVPgxo6yVOaqv9NfII5edmXX3VuTUlywPX3vd6oTNFjQEFG9eKkhcWOK8FrvWso1qZHmWlK+OzIGUc7DFxA/CScTKpIpXPCgnals/ucNYNVjguQ+UzYqWKhu9BeYBKMQ4KXfFZN5w8kXsclNIMA4kL98AsYl0Bdc/AmkLqQOvauVCVQHNlA0fWFmc8DpQVjxHAFNgG3I7HeFAyXoD4/bdzUA60LuchYHfiAPASqLkx6Mq2bemD3Qe0qTbfNg48MFkhVxRhHJgxKE5e1fj9lH3IJHC9PkQSJ2zidqgP+5QqGpoH7RqU70PI40C8DnlJ9JBUUYPEyXaPBa+IUcA6qUxcKYo4d4WRjXPug7qIZQp9TImDKabYcyioeUqNfWazmPI4ECQubMV/IHHiLNZRemJGpkZR5cPKM3jtqyruI8kTRaWDnzyJV/uem3O6kuaAWMm2AITmtKzGvYCizs4B9qDAbxSZBKbKHBtwjHFJqS5d4oBrn6/23vxc9fgTmaLm1sFyHHDjsKSWM4yDzDtQSoxccOJgMHmiPqzZCsM9Mw4qnoFSoqFcv6qlilQJNOfeRaVq0triQY8FwM4XmeRN1Cjeuz+Z9yCaOPhkM0e+10kc7JIq8v49m7zwgPdtfdi/x0EbjweTxNyxJyj93vLmyHEGsIJ9vPn5+nmghUzPuaO5mce2VXzbMZhvP5pAVCcwAV+yaluoGQfePLatkOHQkW2VVPyfrXsGyn3QRvu1iSvxu1fGxDj45IgpcTDFFHsOTcV5ArgXVFpabfEAaCqotmYBU8DfSEbvAWOGuAxFtbdXGcD6C/D66ly1MeBJNOTlsgCuyhHggPuMObIKtLFV/7XJowbJu2rGge6gwHg8qMyxAX4eVszBgM6gmmbdmDmAlEuTeZwE5uHiwMKug33xsZp7oKy0LQ9nQzJFXvt5xkFdhdsyvHukkssC6t7D8pqUMS/t9aFJoHn3LmyOmwRtmfWw6TMIAvfed4TaL8Gykc2RoxIlap8Tj/kUTV6l++AA79vMkZXG5MvwJES3Jg4cMNnrfzQMeL9DwrS1QXeZmPBCaVIPeL9/+3pogFOBxrt3D7ea05rE6XjV7i37kGEcpOdB5x3athZ1XSdnPAABxoF4DVpGrVSR9VrTgfYLyaiCceB6HBTr0MQ4OHYxJQ6mmGLPUW5kGNCGlQkBbDUcs1liALtt1zJVZub3JzwGgDjrwshTRKrtBaCdt7mqHQOqAxMLWi6uzVdauvegBjgWbdZSoKXowEYD14rklYxxkEgcHMUTiB5o3pfob237pEm8gnW1aJ97BuVmnTXGtPNw3TugMiVkpZIAjc8DKxmnrLStqeoaav9o3qdo6lGPg67rbAKdrLZkn4EZg0pj3lqvn0bsP2BY4768//uQKlJJNQFxqSJPzipTcXxa4HHArkPLz+/6/jLK5F4WsLnv7LmNP1908nCY+aT2WXCSF9vGQQvGgff+bksknzjozH5hbMZBc38BQjo0bRJumNDjMg68MTyqx0Fltfu2f1e8B5Z9tiN5ZTzHWjAOxmM8APXPwBubisRFrceD3QPpGAeu34zzHAz7dWIcHLuYEgdTTLHnsAe2+GbRVHomgHPmwG4ZB7y+O7NQZyQqFObIVseUb19hTg3wUkXn5j11YGVlUhZ9yAMGrrY1yThg2meAkm19UHkc1DJ/FB4LJWhcOwdoGQfxMei9q+yGmQWuZVJFJOuj1BplK95pj4dZfg5cfC4xDwsSWKxknLLStnx2Q9X+XvsAf2A7mluT+iHQGvAqDdnkFcc4MDI5CdDI+lxwJuHsPOTtIaOAURqwo6SKNq/J6JtH5wLvPc0xDmKFBMp1CIjf/9ZSRUwCMy1VFJBta6Iv7yYOtlc7q9bh9YgwgduD9jVzgJpxUP8etGCduMD1NnNaA5oLpIqC84DS62fVh8A4kDMOgrJ9JfOuReJkV8X/cZJKUjIOvCSotyaU92byODh+MSUOpphiz6EAzo5MpW39q62ocst4HJTgHsc44H//bNaZ/kY3K6baPtC+QmPdlekhQUtA4zGQGgMCqRygzhTTNcQSGFRHpIpUVT501b/AGJD1+VAyDpgEpur5AzzrRnVgZJM3KsDGjAGWcUAChpb5xrN+VJJxNWBJeVjNHJYYqSLXGJbsA6OvD9j5gp0HyjE46xZr/FAo9iHLqDVELcMm0Lh7UN67g5mtZjZti6tty3FQNQaEGstR4N5bK5UG2VGPAyAHGkXvf2upoiFj5EUftIyD+8/Uz4UtKq3dPemOcdBCpiOSQJMbdAdlchZ9KBnw2j7s2peXa4AmcWCf4XZz3DzrsYyoZFyLBFpIqkhc8e99ftc9UBRyrse5o7k5l+xK3qgT+EA9A1QhGVzbh239KFkIU+Lg+MWUOJhiij2H2SxTwD0HWAGaKosM40DRfrm5jIDWiz7kNisGsA0xDvIa66xMD7Ct0i0/BsYeg65ERY05roBxAjjSCCnGwbga9wqDdHYOMIBFYqPIeRwIEweG+VXJ+BBVurHJG5XGvpFqIj02aG33c7p3UOE1BNSNQQXjahmlLnZNpa+ScRABStZDBdyZdYiWy0own0jmjUqyi/EXMObg4mrjcp/jhbLa2MzFFXJZZfspxkE0cSDahywjLFUkNGgH7DxUlzhozzjYFmp9f8B/frveRZVk4K4+7GYcaCvuKbkyQRHRrj5Efn+Lantg+xiwyWt94mCbMfAy1MmjiM8HoE/geTI9uyTTlAxoIOZxATj70AbJq1qPA8X4W/XBk+xy+mGLmHR9mEITU+Jgiin2HIqFMmOOrNgw50Dj/EaFrbQ934cccJMxxi0Nwxht23KB77qAvrrr8aCoOA+wXgRSJSxo5x1mmM1KREt1qA9jMw4k5shktbdSIoGZh5SGZGYeZhM35IadZV4ZwIJM3ljGQd3vL/vJezzwrCdFpZd30KqRi7LV3jqJlJq1SMk4iEgzrIeq4r18d6pBe6FEA+t3U44VFjhnADtlpWnf95RUkSebyIZhfYxukF1vSrv4dzHjIMiAtB4HOcCGkSoy+tZpj4PNz28zRgbaaJt737HLILjsX9l/JiJJTAOaJkFLZh4s+zeuVJGWdQXY9fDkwWwrA24UqaIh5pUwgQ7Ek2em4l8sVTRUxKCWSooyMMt/y3o8AM49qDSIZ5jn28KTnvTeRVvENDEOjltMiYMppthzKKQ6LGDGV1oym6WMVJA1xGMkIvjEBWANqqN9yDEO7L2KHpitpvLuqoqNa72KawlwHDFHzkuVeJTsmntwMOuMruQD1RyZrfpXVLmYd5CUyTmXMGZlPA68uZKlx1vGR6VEicoYtnhvaoF7Sw9mxx/3+1WgbeYdVDAOfGmKOGCZAUyZtejQmQPZMeBWWNYwDpr5vNQmr1qCxpXzQFnxTiYQOYkQXaWpt37UeRzoQKuoVJF3jfIeDHscaPZhyyiB/weGVJGYcVDKtu3ogwFtGzEOIlJFWdCs763fzC7QVrEG7vp8HeNAx77z+rDr/ivOomXUGtMCOubpRvtn69sHGvhceO9AwBw5zTgw+v675yG1XJo3j0aSV9nECVAvW6dm+6xHeR8Ot8gn2jlwYhwct5gSB1NMsedQVLpkTCElwH1CKkhRbWnlCYKJgyRNP+Nx4B4Yg2Og7G8kceFdywD3JWgWk6vKJ69y5sz5dzBjjuxtlhUG1bXAqWKzbgA7UiYH4ECL+bxHectqxqA3V7JVPuU9YE1RadCUfAdVHge0ObJTac2M/zLhUmOOvgxFtaUrl1Zl0K2rdGTW4q7rZGOAMeYFdBR9k0Qnk3fnyDEI8H4/KqmiWjPE9VDKlLDJo6asj5r2hdW2+/Y4KAGnB4RUUcl6yCYvStm2HaBhG6miGAtWLVXktj+iOTKTyFcapC/6UM/8UfrcLMMk8HawXprI1ATnAaVsIuDvpWNSRbl3IJK48drPAvc+46Dea6UJ46DSnLulx8G2cVgaxGfXoSn0MSUOpphiz1FuJKhKx4THQAupollltTug2ahkQGPALpjRZxClpa+Ht5EJMx4SMkGuti5R8cyC1oCmysiANQmpIAXjICRX5SZviGdQ9IFlHFD3n9T3d2VSmOSpJxNTkzhw+slqe1qZlMrEjejAaLxeyOQNW2XFes14/eTGIMe4AESMA+edrUniG231BFjCsl5UPheMMS9gqxBpnxdSttC7jmUdsMwXFXCnkAmSJw4ImZIMcMFIJyqBQ4U5cwY4zHsc5ACre8+c2/gz47WSrbhPMQ4EMjFRFqyK+bet/WUftoXcHJmSTBMzDsy+fMcYKNseEbQFHI15AXC7b3Nk7/lFzJGzz79Wpmf178XzUXssADHGgSR5VMk6UTEevahlPZQeHBPj4PjFlDiYYoo9hzHHFQAWtaAdoNksZmRqFBsVFixQ9cFWGPLV5gCTuOAZJzKpohTroTywjwvcK1g/DFiz61rFPDCmxwHrM+Jt5BnAwAP5avowc4DN0mS3NlQV97xMCzcPmc06zTjg2vfWKwa0ZeRJlqFg3ZSfOZjVyaWV71/f86A1OweoZELKqbt+DtK8A3YvVNm+m0BkEwecSbmq4p4zR9YBNqxUUTlfZKqN7X4gLhmWM0eul4gBFqwfW22aeAbF+ztkTl2uAVmJjlKfv4ZxoDbGLPcRu0DbUy0kQoIJxJIRkU2c+NJ59dXeWeC81EivmYeUknF931upol2MA5HX1HpEKt7V5tSAZ448lMDUsi7CHgdqc+JA4sZrP1vxXivRs2q/gddKbfKkReJqWx+2Mg5M8nRiHBy3mBIHU0yx51AsVGylMeDpaxOgYSmREQGNBRuVrMdBFrTIeBwogPtM4mIBbm3+HQXcp5JXAtZJYgwoDLEiWqqmfY/1IWD+8IwDJnFDehw4G3kGMPDmDd4Ylay4d7xGakIF2rGgsUpb2bKOEowDRi5NOA+PKdnnzdfsGLTzYC3zR1NxzBYRqAyijWRe5Rjwrhvba8Me3EnGQdAQErAsyaN5jzn7+x3Au6YP1utIJ1VU8x6oGA+eOfQuwHIZlnnGgyZRnwu9VNEm4+Cik4eDn1Hri5dSRbsZB/pq8ygT+IKT2sSBqy8/ojGtJIGZYd+5DMB64L6JOfJOj4MG1ebBZ1CuA2ngPsh6UZsTh6WKxMB95Pl7/97GZ8OfB1t4fCyjNoFTngcViZMptDElDqaYYs+hWCitOXIAMDnMg2YZj4Nyo0IlLhJSTYs+5Cq9Usa4gmpzc0AJSCUBGnPWTPJKYcpkASuedaPxGTkGjIMRpXJYg/YFfX/z7yjGgasnzP1+vtqZY92oQDs2eWZ1RVnQNg7WLa5zQFvi0G5lCRKMAwHrqh4w1vx+QMc4YEErI1tIsp7KatX69rl1wLtP3pxSE3YeqJ8L14NNIJaA31CVJeDfJ96c2ZFnIKqNc4mDuFSR53PBRBSwXIYtIuJBk7RUURK0v/dMPWi/DDVoWMpclOvcRtsl87uBOfLojANPvnGXVJGRi+O8tpZRzgM15xLDOEjJlcWq3ZU+N8uwHge7pJL0oHF0HrCFfLk+lL+h63afDVsD94MJVIF09K7Pj524AOrvQVnolJWJ2uxDwYDbyjjQzoFT6GNKHEwxxZ5DQc3LgJblYsEc2DPAfblRUVSaRqrdAYU5sk7fH4gv2Ob5h39//hmkkleCSsNSXiYi12SrPJj2eamiZnJRLONA4HFR23bXdebALGMckBr7KqmieokSDWhn5+G6MWh1RTWMg+rx51XcU+Nfxzpi3gHDuiGlqoBE4oCcA3SMA1YuTQOasMwndx1mmUfF2KmRyVn0QQPalJrGVdrirtcSmbhw5BlqEkiG+SWUS2KkinjmWQywXIay4jla6WqkirKMgzJxUOFxYIGrERkHLWRigmNQ7nHgrGER4Dwjmee1PySXteiDLnkYNWn31oCsVI+p9o4kLlp4HAyaI+f3Qbs+f+JgtlO+US1VVCZuBiv+S9aNOHEQZRywBRTrUWvQbd+9lowDvw/G52ZiHBy7mBIHU0yx5ygnyrHNkRWGUBlzYoVMR5ZxkK04t1JB9e172rbh9hOgvXc9MwaYCr+t7QsYB6HkmUAiwGoa86AlwM0DtFSRU2kWjQzjpNzIyjwOSOBWBRhVa/yLQDvW66SsxGQBC3Ye9KWKFL8/wDgo+sDINLCsJyVozco1GY8DEeOgNnmlkEz02+eTd7Q5Msk4UEnlcObIzhgknwGbRFd4HS2DmQtUGs8uYLtng+qxzZFL0L7G48AwTpKgmTFH3pG8UAOWgOO5M/AM1NW23hqya032JTN1ybsaxoEpJMu8A0GpJmUCfxkRc14vacLKxS3DSpbtfg8NHpAEj6MsUE+qKsP6qJXpWYY8cRFO3GilohZ9qJUq0ieuzveh7j6oipimaBdT4mCKKfYcig07W+kK2M3smNrqgO0rI9Nh9WzH8zjo+97R9s5V/McZDzxg5l3PeQzwyRMrVaSQaeFlSpgqm+gGcT0Ut5PRjQABAABJREFUrBOAZ9600HePzEEKxoHX530bs9ZXnGtAO3YelnkckO+gf2AfN3mpMIZUMk54qSIueVECGixwaJhnlWNQJRfG+wt4c/C4fVBJBTDa4i5oyEoVsYkDoVQRwwJVmSNHActlqN4BID4GrDn6+FJF6opXY468K3HQQCYmyn4yjIPkM/B+w06ZGFEBy+qzJWg9sjmyN3/uZBw4a0BeY79eqsabI7LAfa1EzDJUyetlRItZ1KyXSOIGsPPgvs2ZNR4HlVJFo3oc+H1QMV+naBdT4mCKKfYcCk07pb47U+nJGiJ67S/6ELsHco+DwILp69nm2k8zHqKJg/LALDBHzkkV5YHjkFSRoNo4U+2sYJ0ATtV/tcZ+LnEF8JXGQEPGAamvzgB2fd+bPtSOAZU5Ngucl4mD0+e4Ki/aHFmUOMvMAZZ1lJ8Dqs25haA1uxa28jig30ESLGDb965jAQs6gSQCThng3tsz0IkL0muk3Lcq9c1r3kWVObP37taMATsHce3P57aYJartzTKOlsFIFZVjMKuxHemD0l9iGdE9Ydm/sv/RKOehkwMyMSrJzNVnS8YFIdeVYjw4z3DXe+gn8NWJg10G3VrGByCQKkr+/ug7oGa9RD0O7DyQewdN4ibIuGjhcbBtDCjZPrYPZQLLfw/K9+No3kv7MUU+psTBFFPsORQbVlPpObJUUcrjQGAMmTFnBryK//r2/UNitOI/l+k3gGFUqklQbcuaUgKaA+O+q43LZxZhHAAag2L2PbDVdgLGyciMA9/jYDzGhXfPWMYDwAGn5TtYDxrb9rlnwM0BXj8Z0JQ1ZwY0Facs48d7V2iZHBK0buVxUC0VJJIqUiavWODYSHSQzBsV42AILFm0fRykinQyGUzyonxftYmDivZLyUD2/jvtD1W6qqWK7i+AojI57UX5DmYqjQHHHHmnTIweNItKhtkEfg60LNsf2pO5c0DiPjB74rLgpNzTZNoHdq/J3hwh9zjYyTjwzsLiiveoVJCYcTCYuHDkrJTJq7BUkfr+DzAe1O0DNgm87R6U42/eIy2VtQyWcQBMckXHLabEwRRT7DkMNU4BGCb03RWASciUUlBta+URcsB9JMvvAoYjMw6Oh1QRPwYUpoRK1gvzDjLVVeuhYBzw1b4CmZZE8q4F46DreNCSkspyxmxKJoVJXhngmpMqAqyhWk2wc0DXdRJj0DLhW1vpDNjkKfP+sYwD1xyaBExog2wZ44Cbh2VyYaTXipu8YjXuWbkko/HO3QNGNs8bg7RUD5k40MqUxJ+Blengfj+dOBC9Ax7oP+xxoJUqYqSi1OC9MUce2eMgeg/U+t7R9t0ChhF9NhZ90DDPvPZPHOw2aVcy/5ZRa0wL+HOwXCppALhWy9WcMQbZu9tXsz4iiRvv3+UeB4OSce0ZB9ulivLqD7V92DYXuGeRSa7oWMWUOJhiij1HE4mSBGga3SjM5z1KVYuQvr2AmmiqHEf0OPA2NfHERe4ZGC3VqDmyALi3rIdM8mpkxoEAODeVNSOzTgCv2na8ivvykBqZgzSMAz5xVEpVcTI1fAJRBdqV0h61oLFb5UNUO7Lm0IAdLwqD7lDySiEZaJh/db9/NutQdpWt9GMTeDqPg80/184D9h3U/P7aObDrOsk6CPAJtHLfQjMeCMBuNuucd3BcqaIy0ZnR1y59ioaMaQGdOWQJlh0499aLVsmzxXfvbt8yDnKgNeN1opKKWkYJvO9iPZT3fp7UVgfi0nkXFM+gZExEwxYU7W5fJZm47bM1c7FJHirNmYdAawH7vYy9SxUF52LjN5cEjvcuVVQmbkY2ibeMgyFzai3joO976zWyJXnUYvwvo5Zx4P39xDg4XjElDqaYYs+h0NTLGJNmZUq8zX1E31xBj80ARkDOINr7/VHQOCtTU1YF7KPavdxgRpI3ikq/jEG2qTYm2s+YI3vXj5lANIwbKmnBj0GFvnJOLk3AOEgkEGczC1oqnkHtO+ABKsxmPcM8U8iEmENq4B1UzAH2HRh3DC4+R46BMnlHglZmHdizQXlGNpFnHHBJdGuOTFb8k7J5qmrTqK70Msp9Ywa0Yt5FhdcTwHutmGpTcvx5gPfQWlQmj88eWc+eSDD7MbMGJEGzSOLAG6PZat+wx8FJLeMgCpwrE9gAyzjQzQFlAdAg40LI/FtGVqqIlStbtX829gysbK04eeZIEQ39eyZ5lfc4SCYOgokbRRHbxuePbGHndqkij+2hSRzUPgf/LDIxDo5TTImDKabYc2jMkXmpnuyB3T+k8IwHIL5Ypc2RE/fAe17jSxXxiSPAYxwIKv4jY0AAGmbkmky18cjmyN71Esmw2mrfPcokAZoqH8O6SSQvFffe+97dfcizblQeF4DGaycGnOefgZJ1xEkV8ZKB5bNiQUsV44BhnAAJubRE8n5X+yGvHZHGPZvAUyUuorrWy1Alb1QeBxngmpEOLMeqTKpqRK8dYEsxzyDjwAI2GeCcmQfsGqD1OCiB+fVwjYFHBg3L5G0ptRQNhvmkSmADnHSgWQeFjIOhechLnJTsoWhEgGulXNwyovdAlTxdfT54LlIbdJvEzUDFv1yqyPgLxBI3eakqO4ds64O3V1R4LCz6Uce8OXEwM2vFxDg4XjElDqaYYs9RHtjnfazSpu9tZVBIXz4J2HgVGRmpJCC+WKXNkRPApWvKGk0cZBkHCVNQQHNgNdrWEdBMcGBkTUEX1+pBw32YI7OgWQtz4JjHQVntPC7jQPP7vQQin7xgqi0tUFDPeCjHK8c4KKvNI3NAHrCwgF2AeScArjPMN5UxKGsQrWMccPOAqTQVgbaRZ6DS2GcTaKpqW6trPWxMC+TZp8ugEwdC0JBJItoxKHoHqp9/8ftpubB4MY8HJmXkihQyNdlq7xJ4L6WA1sMbo1nQrKzWHhoHZbVtttKW8vkQAsdnivFTZ47czuekKnEhZDwAsYp/pVzcMqLmwKp1eNV+cB725qlMH/bNODhdygQNtS/2efH6vzVx4JqDqxIH9XNBOU9PHgfHK6bEwRRT7Dm8zUxkw+oC1ynQLFftH23/YNahKy6PLlY2cRIFzvnNmqsnm/U4CC6UmWp/QCWTk0le5Q/sVuM+UG0sAA09I7ZIKJ4B63WhqHbPvIPlRvb+kT0OWpiDZ/sgkSoK+UyUiQMB4yDEOsoDBimpIkfjOjoPsfr63rWqauOxGQdzlnEgkOoCnARiKIGcT94BfAJNBZybQzpp0i6TSqpsv6058nga+9bv5xgwDgYTB1pTSkYyTA1aGnPknYwDfbVtWN9dxLpafb5cjyrGoSqBDWiSdzlz5ngxjyp5uuqDAY6HgHvNHLxqP5jEVa0Bq88HzYG7rpPeg6jHwcmDzedTJr+y7Q/df7VJu5s42FJI4D2bsT0OAE0h2RTtYkocTDHFnsOt9AmANt7EnpJoEEgVRaWCLHAe9VngATO//fp7UG4sD2YdZlngPsk4iMrkSKSKEqwPY4ookEoK6atLGAdcleXqegnjoASsyErHsUHrBoyD2PPX//7F92aA43zyImRQbaodBebIIdaRoOI/MQd563D0GWTmIJVMjTVorvU4KKq8ZIyDStBUBJplkui24nncZ6CqdmUr/vctVaRqv+978+zqZFLK+6+RqqotIrB7ANbjwGEBl9U5RXiGmewcADhJVAK0zlS7z+e9GYe7PA68MZrWFy/6PzQGrVSWFrQd0pcH1MB9fB5QJg+txwORPMxWfJdSNVuMaZehvP9APImsmgOXwSRwlQbBtuI/KFUkloqKMg7YNWAZ3llqO+NA6y+x0Y9AAskUMSWTN1NoY0ocTDHFniNLkfU21yGZjsPcRkmSOEgeGDKAGZDbrGWkEba3H0ycqKWKBMa8IW3pBlIxEYNqW+kV36yZQ9I+noFI456rdteBtlS1e0bbXMB48cZMZCoo52HF84+wXkzyRuBzkQLOiQMrC1gCGo3rjM+KPbCrKu7r+lBWeak8DmpfQxVgYpOn4z4DD7SuHQcZ5uPm57j3QCWTwBYyqEBD777VVTtrKl3Z/aiigAGwc+esw2Axi+9zwwM2jEl7+fuPEqCZN39duCNx4CU21FJFw/rumsTl+c/H9+SqBDbAAffmHcwwHgifEzVwHpbKEYLm547mKLs/tBYoEzcA5zWjOAstI5q4MVJFSbmwPOMgyXgo7t2s2342c2Wiks9/GYZxsOM52EIyTfJiCk1MiYMppthzZE3JvM11CLApNkrzPrZZYGjRpg/JjULW4yBTZWCry+LTatYcttxcnwzK5JhqT8acOAFaKg4rGamk8n4xB3Z7SAoyDgSVLtbjgJUqItpO3H8LWhOMg0TyUFHl4+madwNVnpvXKyruedDUJm8IxgFRZbrtWg3jgE8eAgTzSzgH0qBxuR7VMg5OaA7M7DMo5+Ao63Bb+6OvQ4n9kAGtRFJB9YkLjUwHa1KuAk59v5nhZ6AyR6Z9PsrksShxUfMOzmadWQdZjf35vDeAZc09MHNgArTy+r6LcTCbWYmUrEyIlc7bfQ/UoDVnTqxJYAP2HDcEmgN2z+KxZ6rbJxKoaqmgWlPY8+2Xa4AucbNof4hxoE0cMPKRygRatKBLcQ7b9fno81dLFZ06PNh6Lum6zjz/bPvn+1HPfDF+axPj4FjF4b478ECPv/iLv8A73/lOvOtd71r9/0c+8pHVv//Wb/0WrrrqqiZt33PPPXjLW96C3/zN38TNN9+M9773vfjIRz6Cg4MDPPzhD8eVV16JZz7zmfiWb/kWPOIRj2jShyny4W1mIsCxV50fAkycCfzs0RwHszpDPd/jIFdtHd0o5D0O+MU6Y8q77TPRzWq5QQ//fqOtLGAcJOSymMOK0uOA2SxmpYr2KVVjEkcEaFfOQznQelypJCPVNbLHBuBU+zLJu8RcpJAqKu9bJHmjAG0ZWvyua6PzQIp1JQItWZPw8kDLMw64MWDmPxIwyrAPFdWW3mdqx2H5DrCgDW1OLALN2DFQrhksaOn1uwY0VZlTm7WATtyIWEcBn5P1OY9lHLi+awxonQDtSn8DwCZHyzh5MMPZo/OfG9vjwHv/+74PFSCsB8f60CSwASsTw7B+tIyHOOMi63FQvkODwL0wceGdY4faLz2B0owDInkllSoKVLoDThFhEjiPGoQr2O/rEWVcHB50G/N3O8bBdnxJbRI/hTamxAEZt9xyC57znOfg9ttvH73tO+64A9/93d+N//yf/zPuvfde95p7770Xt912G66//nr88A//MP7P//P/xP/2v/1vmAXBjCnax8Gsw8Gs21igQ4wDd5POSxUBi4V6V3XOeigYB1ngPMs4kEoVEYyDrCGS+f1RxsEeZXK8azmpoLgR2jKyVR4eJTjKOMiyThafKQ8pde+wosqKZTsAGsYBY8a4DIW++nF8B2MeB2WVT6x9r8o0Vu2dB+3YSmdgC+NgxHlYBVqy98DMP+RhrXx1xmQ9AUmD6pL1IjDoBiLAsUqqKA7WLNpXSRVxCUxV8swDmupkStok78aUDAQSBumHB/g4zq3+zJoje3NXTR+UoLGX+B4605w4nAFnzn8u63EQBa63SYUwxUgAJxmmYt30fU8V06h8ZgBOPrRcA/IeBzHgWiUXB/jv76A5srn/6uQZ4bORkSoKnomMVJG44n/o/qtZT1GprBMHsw2gPjv+t/Vjp8eBYb9OjIPjFFPigIy77rprL0kDAHj/+9+P//gf/+PG33Vdh8c97nG47LLLcHBwgD/6oz/CBz/4QQDA/fffj5e97GV497vfjVe/+tV09cIU7eLkwQz3zc9PjhHgzDVHjkhEeIyDUOKCOyTs6kP0wMxWuJ1vnz+wmypficdBkHGQ0PcHPNAuD5iEEgcCjflMxbmt8ojef3u/amjZ66EA78vKjKFDyjKaeBwEDrsKxgFbZQnYd5aRKkoblDdJ3tT3IWtQ7TLfQqBtHjTNPANvzczOw5F1WKUtza6FCo8Lr/0hbfVlmPHPShUlWC+WccAw77z9GClVJDNHZoHrcVkvCp8TYIvHQcV6rNgHee3XroUqqSjrc1KbOChBMyXjoEaqSMM6AoD7zmz2/XDWDa4HSn15IJ5A8/p37qhHZQ1Xun2vDzzrpkdPFNMoWScmgVolk5NPHq9HVqooY47rgc5jexwwezIp66U8Ew38/vLfj+Y9juZ9GFNYhqm0D7avlsoaTlxoGQ/LiHgcWPbrxDg4TjElDgTxqEc9Cl/4hV+IpzzlKbj88svx4he/eJR2u67DM5/5TLz4xS/G1VdfbeSI3vKWt+AlL3kJ3vOe9wAAfuEXfgFPecpT8NKXvnSU/k1RHycPZxvU2phUjr02BJp5m9WEx0HX1YMFy7CgSQ64jSYuMsAxo+E41H60ysCC9lGZlBI4FYCWEdBOoG1rPQ7GYxx416c9DojNUikvUssayiZOjpxDYgq0ZhgHCY8FqyubBwzHZD2t+pBInmUZL75kXUaqiHkGcWmGZSw1rtfXkjjziB+DKtCYlauyjAMNaMhWW6tkWjIG3ZRcXIKBqaq4ttV9deuA4h0EeNk2lb6212/KmHVkg25VpS27DpRgDu1z4vmu1RjziqrdAbuH2GWMvAwV42b1+aAxrDdPLJ4llzlgzgWqBDYvF6YDja0xb03ySjMHLiMqVVQqAGTadxkHQ2OwKCzNStVwiQPNXgiIJ262eU5eeJJ7B6PAvZ7xEGNcqBkn5/vBMw4Y2dQp2sWUOCDjcz7nc/D6178eX/iFX4jLLrts9fe33npr87Znsxle8IIX4Ed+5EfwhCc8Yet1X/ZlX4abbroJT3/601fJg5e//OX4ru/6Lpw4caJ5P6eoj4w5bxawcbWdA4tVFjAD8pU+tsItCpzzoG1WJsltP6ytvX+ZlIzXg6LKyNDCR2QceM8r73HAMA64xEG2yoSVJlhGC8ZBJHGhkSrKMQ5aGDSHWB9JjwNvzOSAc4ZxwEm0rF+f0bjOjAEVYGLlqur6oGMckD4romrfMumckcxjgGt3LqwcB+U7wFZcs347Mrkock+oSp55/a5h/9h9iIZxULsXUZkjq3xOxpYq8hgnrMb/fWfKCtfhvZBa3zy6HnjrZSZ5YpLINaybRqwnoNYcWVfxzoDWlv2c1PgPAsdmDRB6HBzMusG1yPhspH9/nvXBvod931tz4iGpKNdz8ohOHFjGQYxxkk1eRhkP6va39mPyOHjARrw0dgoAwOWXX47nP//5G0mDseLJT34yrrvuup1Jg2U89KEPxf/z//w/qz9/5CMfwVvf+taW3ZuCiEy1cXlImQUr/l2pokTigqH0ZatcrK5xDjiPgBZZiRJAwDiY5wAzBUW/PGBEkjfl85r3C830UPsJ5kd5v6IHZl9TOcd6oRgHxQbrAhIwWtJza8N7X2P6+nqPg1DytJTJEYDW8eRdHjSy9yDA+ijpwcHEgZfsC5kDC4Bz6zcTfAZp5hc/Bq22NAvacvfAK15QmANXexyIKPpltXfMoFqwDnoyOaQ5Mq3xT1TaLq5rI5VTmzgpJXX432/3pDV7Ygtcjwvct/M4qLv/MqkiMons9ZOteC7NkS88SQCWWdA0mMh25fISFb9WOo9h3YzrM6KsNi/7UAVam70g337f9+GKd5Vc3OKz9VXey7AG9WrGwXhjkGFceP+eSSCWc+gg40Aslxb1OFCbg5/vR3EfdjEOTAJ7Yhwcp5gSBw+C+Iqv+ApceOGFqz+/973v3WNvpvAiQ0/LSMQA/sE+pPGfAKuWkc1ypz0OUlJFOcAQEHgcnMsBZgqpIlYiA/DHbHTDngKOxfff+86hUGwYWcaBt4mL3APvcBH5/RrGAT8HSIyJxVJFY0v1GEOypDEwMD7rKJvEzcrl2PsfSZ5qDuzsPOjNFcyBudyP1BYxqGR6MskbhUyFa47MStWIQLtaxkHphUCbI5Nj0MokaGRSWHNm3pyam4dU1Z5llXA940Ajk8FKFXnPia04L/dCNVJFiuKN9bBSRbufg+dFkau4L/fkDOtGxzh4IJgjq5K3gL+Hj0oVpYyBz8ZAa8Bh3ZUapMFQSBWx86A3f3GMAx3rY/j5a+egcgxEGQ9Zxs0yQh4HRqpoYhwcp5gSBw+CODg4wEMf+tDVn+++++499mYKLzJSOSXAGjXn7bouBZgoGAdlpU204jcP2vEHRlPpvgfGgdG0DfZBAlomEkje84puWDKgobn/SVNUgJBJSY4Br7qpWqrIMc/MzAFAbB5QSCRkEngtGDfReUBR7ZhJnhlDsijjwDXE5FlHCp+TNPMsyfyKJU40gImq2hjgtGXZMaiSKmKNYQGNTEZGOlKhL+yxxWokQhbtt2F9VJsji0BLdi9QzlcsaKuS61LJldV7HBTrMOtx4Mp11TA+cgUM61EyDmr2QiqPi22fH2LBeoyDlFSRORdwclFMuAblhMdB5vfvE7QGOOB634yL42COnJWOXYZXqT6o8e/JhSmTV0HGgdoceWj8HQ+Pg3IdmhgHxymmxMGDIO677z7ceeedqz9/2qd92h57M4UX5YY94zGgkAqKVRuX0gBE4iBZZcHqOi+jPFhH2jdgkYRxEQXNS0p07BmUBzbKFDJR7ewadCfvQUpf/Fys7XJTdFgpjbDZh9yG0TuklFXktW0D0TnIq7LNVLvHN4oZnxMFYHAspIoSBuHWkCxXbQ9Eq71L0C7++43PSTB5k9W4NnNgSKpJA5rSUkUOsMYk8Nj3UKavn1iPFcmbjHSkQl/b1fenK95FGv+sVJMItKxN4quAY1Y6U2fOrGEcsKaUbhK5RiZHUECyjBJsqkkcyBkHRiooJhPifUeq/SrGgeYd9O5dzXtgpXJ01d5MxX0GNPYSb8PmtJo5GIhXuy/at4mDPsE6iMp1eX2gpYrc+88k73TJo6H2bdKkD8v2ZtpX7QHW42jem/do19nUJE+SyasptDElDh4E8brXvQ7ztcX3aU972h57M4UXp0qN9QRwz1S8ZxYLicdBcrOYNSguf3/k/mdlggCBx0ECsAIaAacJiQiAkCpKJC7UjIOoMbLbh+AY8A75rDkykJMrA4LGvCXjgKh0VEpVMYBBBjT2+sBQ1Fl9ecAzJBOYIyd8JhSgaZ71EZ2D+ORRO3NkDjQEOOCQ9jhQ/f7imUX2I4rkTYb9p1iHvTHLShWxwCmbPFKYUwO814nC4wLgi3lUwLX1myLbF5ljH8y6KoNjhWTlMjjGgbbatwRNPWbnenhzpVaqKP4MVKyfw9oxINgHbOsDY46cuf9+xfsQcKyZg73P1qwDXpI7NQYJv52mUkUD92A261DegjGTN65sbCJ5Vo7BKONCYY7sypYdbO+Hyu9rijZxuO8OTNE25vM5fvInf3L15yc+8Ym48sorqe+67bbbQtd/8IMfpNp5MIbxOAgc2DNgxTIyC3UWtPfbjy3UxhAxKVFx9miOvu+rNrqKxE3WYyA7BhQbtRRgIqBos1WOgFPhEDVHNpvj8VknXoV4rURF2uOA1DReRll9cuZojvm8D7E2rCkqnzigKo2zBu0CenZ5wBjX48BeHwNtc8Dx0bxH+djGNijPjAFVpSVbce/NFQzjgE1clHPQ0bwPzwFe+2PLRWX2Qwrg2j+kc1JF7BjkPQbyjAu/fS5xQbN+yL2QSqaC3Q+WBUyqxEW1VJcnE0KOgXI/dGEF+1Ll8bCM6Djsug4Hs24DqM2AlpxUT5sEdu0Y9PZitWexMgzziKi4l0sVhSu+de0z1f7A4hkM4M1bIyrX5V3D3oNy/jqYdVVz8eHBbOOzGdaLfQYD5tjO/Tlzbj4I+G9tv5gHo4wLBWjvrSO7JJOUrJ8p9DElDj7J4yd/8idx8803r/587bXX0t/16Ec/WtGlKZzIVPrYTfq4wLUxYqMSF7nNWlauqdys9f0CuKjZ7GYA62WoGQdRfX0FPbGsSol4bSgo2hnWhUnchdvmZZK29SE6BrzqpnrGgb3/mTkIyDEOgMWG+8KT9Ztl9qDqXcvJBOXm4Sw9dz7vUTLKR/U4cObByGHf+kzk3v9FH7LMq9gzyIwBBePC60PtWth1HU4dzjYOupxkGNe+y3qaz3FqFjsw299f/wxspVveZyOWwPRlIiLvkSsRUplAtqyncTX+rTGqJnFRnThpBZqO7HHAMj5KMIcFzln2qcLrahnHgXHA+J8dFomDDGjJSBeqkofmt1eOQW+uqD2LlWFB2zjjIsU4OGvXwqFnoChiWQYjVbTt/rPBzMU2gUpKFRX7l2rJulmHM2t/Zp9B3/fmHBX1uAByycOox4GRjRZIFXn7yF3PosSQsj4bU2hjkir6JI4bb7wRP/RDP7T68wte8AJ83dd93R57NMW2yGgrSxgHRfuRzXrGFHfVfnLDnu2Dt6GqXTCzMkFA/vfbSuNjIFUU6IPCkMpWmkYkInzGSW2U96u20n89sqyTDONgISWw+Xdpc+QA2OXpXUZBy4zHgeKgkp2HTaVbuNrdSd4kWB/3J31mwlJNSV3brDkz4CXv8smT2lDou8+TrAurcT6eZJgHbisS2BmvHYpxkJDL8s1ho8w7nnFgQQMN66X2PSzH6rwHpe9sJGJGNkdmE4iqSlv2HSzHCZs4sO8gx3gBElJFZzbn7gv34nGQ13dPAccGtK0BzjUJbNbzyVuveK8TLnGz+R06qaKa/bhCNnPVvjmXDL8D3jt4pPQ4GJH1EQXNV+0LvIa89oHhOWAb44CNsFSR0GPkfB9ijIMyyaiQS5pCFxPj4JM03vve9+IFL3gBzp07BwB4zGMeg3/7b/9t6jvf//73h67/4Ac/iKc+9ampNh8skdmwstIA65FZqCUeB8nNarYP28xhayqeFYmbcjOxf6miPGASAmwEhlSsPAJg37++X7xXtd9Rboz2wTopK8RPHc6qK1W7rsOJgp4bqTbNGIIC/mY2ClpmPDYUlYYlwBGudk+CRm7yJuFxEJHLAzywLJg4SWore4mWsFxU1qQ+kcDV6Ovnx8Dd959b/ZlhHJRAbzXjwLnu7Lk5cCrWfmYeKPvKGHSXTKEQ88mViZjjZKDGKyNVlN2HLIOdC7yxcm7e42RwLmF9p0zyUiQXxvt8sMA9x/qReSyQz98FjUXVxvvxOCCA64MZgPN9z2j82z35eMA5K9e1bQ5ggvEesz4r/BhgPAZUcwDbvss4GFsuS5RAjRoDr9o3eAQpleR8bih54a3VucRBUKpIxLrb1QdggHEg8jqaok3sJXHwiU98ArfddhvuuusuXHrppXjMYx6DkydPpr7z9ttvx6//+q+v/vx3/s7fyXbzARvvf//7cfXVV+MjH/kIAODSSy/F9ddfj0suuST1vVdccYWie1M4kan0YTWNNz6T8jjIAUaAADhP9iEj1VIeLqMyQYCzUdq7VJEAMAk8g6Uh1fpXxGVCeMbDNo3/WvBXYY6cfQZl4qBWpmgZJ4vEQUiujKwwXIa3mY4zDjiwBPASl4xUET/+AQFo7flMZAyqw3NQknGRrLj3QL7oWmiYf2m5tEjytAQt89X2QCyBVb6HCsZBSqqIeA8zc5Hi0Gy9VnLMu2gfyvf2cNZVJ3FVHgNsIYc3BhjQwFRaV67HqkpTtpBDVfHPJi7KNYBOHJHJM0/jn614LRkHNfshPeMgnsBSgZaL9uPMG8u6YZNHnHSp10cauCW8x1RyZQAHXCtBUyPVQyROgBzrhUkcqLxeyuKXWp+Ach1k9mIAl8R3GQcZnwvjcRBjHLQwRx6S7FKwb6doF6MmDn7+538eP/MzP4N3vOMdmK8tRqdOncIzn/lMfOu3fite8IIXUN/97ne/G9/yLd+yqrB8sCYO7rjjDjzrWc9asQM+9VM/Fddffz2e8IQn7LlnU+yKFOMgUemtaL/c2MwIE6s04yBbce/KJNTdAwXjwJjSJTX2s4kTZqNuAJsgaJc1pDIb1MA98DZrZ8/1QGU+W2GOnE2eldIynvzPrshUOmUTd96BKgpaZkxJNVJFuTGQlaryDpiRd9CCxkHGQVYuLXlg8Z5Zrbb7MrLAkZUqGptxYD8TYhyY5BHjcUCCxqIDs2F9pBgH8Xkgk0Dz3tcs8y6SxFZJFbG+T95YOTuf40LEkuCm0pb0OODNkVUeByzjgHsHjddTcA1YRsZ3zWj8y8yRKxIHZg+WA6zKAqCa9cD6fChB24rEhSCB7bV9LBgHhMdDDriPgbaL9oVSVcQ86CWZUx4HBBtbVfVuPC6qE8iadcCX6Nk9Bg6cIrpMAjOaRFdJla1H1GtC+Q5OoY84ukHEHXfcgS/6oi/Ci1/8Ytx00004OjpC3/er/91///24/vrr8cIXvhBPfepT8T/+x/+g24roUn+yxUc+8hF85Vd+Jf7wD/8QAHDhhRfiDW94A77wC79wzz2bYihMpU/G44Co+M9o+2Y0hbe1P7bHwbaK85pgN8jrUYImccCq2JwFAbNstXPf99YcOZq8SVKkzSEpApg4z+z0Uf2hWcI4SLJOsoyDHOsoJxW2NGZdj6zHQcYcmQMstXJh0cOKd31kLShB47Q5cliqKHdgyf5+wNEXzjIOEjI1CmPeaB9aMA4yMiUSj4MI80ig76s0R170IXYPGHmK1bUiqSLWb8a7V4xMBnsPjEyLyuOg2uMhn8AGEh4HScbVqv1E8syeRTQyITVj4Hh4HGh+/6J9puI+n8AGeNaJL1tKVnwT+voqg3aA8zhQmjMbc+SKYqJtcnl0H5h3QJRALftd7fUjmodZ2cBynGaq/qNj0BRRCTwOouPQ7oXzfZhCF80TB3fccQe+9Eu/FO985ztXoH7XdeZ/wAJ8ete73oWnPvWp+Kf/9J+27tonVdx99934qq/6Kvze7/0eAODEiRO47rrr8GVf9mV77tkUNZHZsLNGdBuf2bPHQXahzgKXGZkE5nBQRvbAbir8osak4vaB+DjMHlhSFedJY07mgFZGlnViEgeVtNxlZDarmUrrZWSNWTPJwyxo730mbA4sngOBGGBjzJGz9z/6/icP7N54jbM+kgncxBg0EgmMMW+SddKGcVB5WBdU2y8+o/MYoJI3Kakifh+yDEZXfdu1tDFq6fdSK5Xk/X4CuCjvQa22tZVq0iRO6oF7y3hhgEP2HTRSbSNLJQE6qRiq2l6oL9/3vX0XD4f7oJTKYZ6DquKYldD15ms6eVSsX4w5cqbi2sjEEMD92IwD1+NAyXohDKLpxAG5F8juRZfh7Z9q5iFVAh+Ij8E2jIPYOFSyrqbQR/PEwbd+67fij//4jwEsEgZLlsEFF1yAT//0T8dsNlv93TKJcPbsWbzsZS/D3/pbf2tl7jvF9rjnnnvwnOc8B+9617sAAAcHB/j3//7f4znPec6eezZFbeTMka1+3Ljt5xkP2YU6K9d0MOvMfavVl82aggItTDmz1b5RwExR7ZvbLGTAa9fjIvAORDdGbh+Sh/ZygxiVKrKgaf39V8xBxpw3yjg4KsESvtKXOayz8hzbrs8apANB0Li4//efOwoxONXmyFmPh1lHJJCTlV52DuKr3RltaXceHplxYEHLus/NnDWYOTDbuaj+HTgo7hUDmCgTF973DUXG78iCluNK5ahAK1aiIrsHWQbLPlP5fLCsF1XFvfV6iUgVaZJXzD04ecAzv8tg/WasOa8QOK4BbUX67uyZzJUqopNH8fXYFDCNLFVk5fLGk6kBtrC+Rvc4KPYBgbPIerAFhSqvG+/96yqknJXMp+gYVDGO1iPMOBDsw6ZoF00TB294wxvwa7/2axuMgq/7uq/D7/zO7+Cee+7BBz7wAdx333248cYb8bf/9t/G7C8XrGWC4T/8h/+Aa665Bvfdd1/Lbj6g4/7778c111yD3/7t3wawuHc/+7M/i6//+q/fc8+miERGpiMLWC0+w4M2bRgHscVK0wcOPFQwPsqNQqTS7Gjeo8T3opW2+zZmBXKmTG51l8AcuTYM64SQKsp7HBSU1D1KFUVlaoA8aJnRNldUOGUl2/JybUnQuBizfR9k3SR/v5pxIPEZSZrUj23M6yePImNgf4wDQGVOzM9FCtAs4/XhGoMG34OMVNG+Kz1dqSaBVFG1KaYIMCrBvmrWjcwgvGh/ZHPmlGygkYvSyJQcVLyHKo+PxWft2KlJ4qneQYBjwJlqX9kcwL8DPOMgD5znEgeEVFFDxgMj1QRok1d1zJ8yeTMu80nFPGLVCJQGxdExWO6Xsj4viz7EihlaGDRPoYumiYOf+qmfAnDed+BHf/RH8Z/+03/CU57ylNU1h4eHeMYznoGf//mfx3/7b/8N/8v/8r+s2Ad93+NNb3oTrr76atx9990tu/qAjLNnz+Lrv/7r8Zu/+Zurv3vFK16BF73oRXvs1RRMZDbs5SEhKpEBZI1ReXmG7e3nZDqiUj2LPnCLlQWsCcaH0GOB6UMWtM4aswI5iqTbfuAedF1nK74D7yCzOS4jm7xJexwkDs2m2p/4/VmZlEzyUHFYz0iUKPrgy9REpIrseCmTUTvb37NUk02g5xO4YdZHArTWmCNnWSfFPoTyONhvAq8cB5F5QFHtaWUDY+2XBYnR9yDDflOZw7JzocoYlTZHFklUsGPATxzkJcvGZhywoPHiWg1wyjwDpUSI91nKHDkB3DGeOzrWDSdX5l02qjmyyBgX8Kq9a56/LSJjg2EcdJ1l/rH3fz7vzWfH9Dg4Kj5XzTgQ/X6WdVOexVLJo+AYbMI4OCqTF8MG0esxMQ6OVzRLHHziE5/AW97ylpX80DXXXIMf+IEf2PmZJz3pSXjHO96Bb//2b99IHtx000348i//cvzP//k/W3X3ARdHR0f4pm/6Jvzqr/7q6u9+4id+Ai95yUv22Ksp2CirbSMb9nKDLDFHDnkc5GVKjgPjoFxQa81pjTGxALAC6pNH3r2K+wskpYoEyYtMlYm3sYsmjzKHRmuOHAPtF5/hWSeArdC/IG2QHbn/+eRlOQdGQcuMZFo5Vud9fLOanYdLmZy4ObCdA2to0ctwEwcBg2QDmifl0uL3X5/AHZNxoDisekB3hnEQSRyd7wO/FhtjPgK4tNXO/DNQMB7CcllJ4CjDflOBZqzGv7duMMmbEqiolyqyycuIXNsyzBggK/4XfVDIhdW1X77/tDlyai0ux7+K9VAhkSKUCPH35XGpnAxoVu4JGNBWxzioGwNeEQ9vjlzsCcZmHJT68hXngpYeB7XMq4NOA9x6bCFGquisSKqo9mzezCCc9HtKGXQbj4PdY8CewwWMgyDzRfkOTKGPZomD//pf/yuOjs5r5P7wD/9w1edOnjyJV77ylfipn/qpVdKh73vccssteMYznoEPfOADrbq897jxxhs3DKNf/epXu9f1fY9v+7Zvw3XXXbf6u5e//OX4vu/7vpF6OoU6MqBlViID8Mxx6ydqicdBgiLe97aqQXEPTlc+g4ym8fm27WdqD4zevcqC9lHg1JN0iAImGdDGe1/CUjGJQ+O+WSeAgHGQAE0Vc0AWtCxZD1mJkLi+PQ8aA46u68jJU88TI5K88RIXkTCHpbBBez6Bm2Z9CPXtFdX2s27hHVAbCsYBCxoD3j6EuQc840EB2mXHYdZnIMN+U8mkmCRq5T1QMC4AXq5JJdNhxkAtYOSY5zLgNbseG8nUQOJ4s/3EOyhI3i0+F38PlIwDnwlcIVUkkmry+lCVuBCBdpl5UFXxzjCPlBXXTAJTWW3NMB68PvBjwDmbEubI7HvI7okVkomLz3FrsdIgPSxVJDSIX0b5/IZZDxPj4DjHYasvvvXWW1f//YhHPAJPfvKTQ59/6UtfiiuuuALf9E3fhDNnzgAA3vve9+JLvuRLcMMNN+AzP/Mzld2l4uqrr8Zb3/rWjb8rq1OuvvrqlXfDMp7xjGfg13/91+l2X/Oa1+Dnf/7nV3++4IIL8Pa3vx3Pfvazqz7/lV/5lfiH//Af0u1PoY9yQxEDbHKAFZBbLDKmpMvIgGbeoiJhPVQe2AxoTFRbexvK2gOje0AJy4T4iYuDWR347DMOooBJgnHgJk9yjIPIO2Are3Ssk9oEgGEcZM2RI8lDwRyQBS1ZIzTAnzOjh6UyeRauuE/qerLSAMvwqtEiclEZbffF9Um5uhZSRUHQrrwHkXlYAVpb0D52D1p4HEQSFwqKvlJfnQFMskUM2T6cOcdV2wPeGsgd2DNJzMNZt9Eu8x6wiQPXY2HeI0og5AGrVoyDut+flWpbhk0cJSTbZPrmccAyJRPkVEnXANctpYqqEhcqn5NMAnk2w/043y4L3jO/vy3jgJBKylSbl2sBKdnGsn68M/QDwePAGLSz7ZPzoKp9gJAqEpnTb/QhzDjQ7EOmaBPNEgcf/ehHASxoZ49+9KOp7/i6r/s6XH/99fiar/kafOITn0DXdbj11lvxpV/6pfiN3/gNPP7xj1d2ORxnzpzB6dOnd15z9uxZ93OZuPfeezf+fP/99+NNb3pT9ecvu+yyVPtT6COzYTemlCNLBSmq/TNVHllt72XQ5sgSxgF/YPSq/bPGrMv2a0FrxTPIVHm0kEqKyYUpqp151glgK/TjjAM+eaiYA9KMg5S2unPvz82BU/XtpzX+jVRabLOcZRwczBYSAevvXcSg2noMRKWKcoclCeunfAZB5lsGMFNUuTEVpuuRlQsD8oDRekRBK5d9GEre5KV69F4fUeZNKVVUvw6oKj0zsmGHsxnOrlXqMsAd6/OgSCADXhKzFjDy9gBM8opbCw3jgDZH5t8BVbVv+R7WrIdZts96+B4HDGiqkyqqaV/lMZEpaCulvTjZvh7lx046jJ4ypObEJWhbUcyjZByw81B5/9nXwHt/GJ8JWqqILGiSzUFkIYmqfcBLHOzeDyjnwGVYxkHU4yDfhyl00Uyq6Ght48doRC7jqquuwg033ICHP/zhABaJiA984AN4xjOegVtuuSXdzymmOA5htEVD5si5KkMgZ0zJVje1ah9gWRfcgd1sDphKV+cz1YwDt6ojB1YAUeBeL1UU2bD7yZNota2OcaDyuYjMA2qpooi+uMKgfZ+MA29DP6ZMDZCnCCuSV6VBdcjjIAuYJg/sGUPOZRjmW2IdBuKA6XowVWbZ5NG+PQ6ywLW/F9gz4yC6DiaBO2OIGar2zidOgDzjINsHmnHgmTNTJuUlaF3Xftd1TvJyvHnAMJ/JxEHG70cF3DKeO0qponIMdB2XvMhJ5eQr7r29dU1kCgkUVc+sVNSB0GPCysSM63FgvVY4xgFb8c4ahGfY3+vBnktUVfcla7uacSBiPp07mpsxMJS8MucQicdBTi5p8jg4XtGMcfCwhz0MwCJpcMcdd6S+6ylPeQpuvPFGXH311bj99tvRdR0+/OEP45nPfCZ+9Vd/FU9/+tMFPY7HjTfeKP2+q666qirJ8i3f8i34lm/5FmnbU+w3MhIJGU3fZUirjffcPtuHcjGrPTRkK22BhZzD4azb+C217XubunjiIHdg9sZgxJgVyFUcs5TYzev5Q7M1R84nrrzv3RUl0B42R04AFgqD9ixoaYDjkESKc+9HlirKghYSg/gTB/j46XOrP0cYB3lz5GL8BQ+risSJAc7S61Cm2p0BS3JyTW08DgLVxoflPiCYPEruBUqAl2McZKWKcgkkU20fWAcyBRzLyLI+FNXGUU3lZXjJRq7inx8DJw9mG/t/Brxm10LvHNL3fXgvl5MLayUTMp5MD2CrpE8czKruoxI4Zt7D8hq22teyXhKsE6IP3n2r2RMpPSYYj4FyDUoxDkj5ShXrwfU4IN5DmUk76THAz0Fs4kJT9e+dYYcYH1auLV/tX+6jh94DpVzbFPpoxjh47GMfu/rv22+/fSVdxMYTn/hEvPWtb13JHnVdh7vuugtXX3013vzmN6e+e4op9h3lZB7Slm4glRPyODDVVSNLJTnXKvrAegww1dYAnzwqN2e1lU3r4W3mIps1jTl1QqrIS54En0NGpoSp7DLtJ1gngK0OPxVkHGQ8DhQG7VnQkq2uArZIRET17cUSJdmKe4pxUD6DxDoUZhwlgfOMPMr5z/CsG7fCMQDaZj0ugOPKOKj/bNqcOsk8U5jyGa+RZBI/ClybJHagfUXFdZ71ka+2ZJMn3vvCgEapJLaR6RDMA5X336uIphInCZkalTEoA5qqZHoAz/usVq5KU208n/dmHFRJ9cikivg9oQGuGa8b5zM1ezKpObHxOCAYB4kxyLJwD4oEl8ogG6iU6zrUvAPs71ck0AFeKkqVwPQ+N2xMrLn365H1OJjMkY9XNEscXHnllQAWAH/f93jLW96S/s7P+qzPwtve9jZ89md/9qoK4t5778Xzn/98vO51r0t//xRT7CsyjANb6TmeTI/XPlNtnKmycQ+qIyYvzOaAqDb326+7Bxp9ffuZyIahTB4xBtH2wMRXvM+6mCknYDd1kXfwTFFdxpgjL1knG9+b8DiI9kHLOor//qwxK6vrDGwZ/0HAiD0kLCOrLZqRalpGOWZijAMeLAI8ffsoYJpfBzNeQ65Be8igO39YL0Hr6DqoYBzYcchXvEer3XywaFyporIP8QRa7uCekc2z5tSaSt/cMxBIFVXeA595yTAO+LlQAVqxe3JXLpEYAxnftYzf2UYfiPdQJZHifbb2XKCqdvYq5RmDaB60LNbjzDpAMQ6c3094TOT05ctinnjiImOMyyZvjMcBK9Vj1qI6JrphfbCMAzKBq5DLA5z9WLXXjipxE2fdKBk3y4gyAJUG4VPoo1ni4FGPehSuuOKK1Z9VwP6jH/1ovO1tb8OVV165Sh6cPn0a//Jf/sswnXKKKY5LlAeWo4AvCGMCVoZNXPDAPVXtb4xBcxIRkj5UmxNzlUVlqBgHTPtZY96MKei2PkQ2S9YUdNzkmaLaGcglEEuQN+1xkGD9cDI5WsZBTKKkM32OzIEAf0hYRglanJv3mAfeAct4YFgfvMdBdh7KHhYyiaNlpOYAF6jg5RkYwLI84IfNkU3yTiFVFJNpWY8I6wnwn0FkLlJQ5NOSXUngiNX3X7S9ee28j1f7HQfWB3sPPGB1bJ8LBXDL+o55CZZSn5ppPyRTI5LqsSzICtA8sf8qgy3oUUkVuTIxFe+Bqn3j85FgHCjeQaDuPVQaszJSRU09DliNfxHjoPYdMO+hyOOg+veLDMrZxIVKLojxfzwejIPN+zTvEToLTdE2miUOAOArvuIr0Pc9+r7Ha17zGnz84x+XfO8jH/lIvOUtb8FTn/rUVfIgY8A8xRT7jgw9UiETYkHbcRkHpto0yTjgpGI4irQCtF60zzIe8u13Xa7a3cqU5BkHmcRFRJ5h9ZnEoZGtcCwjU+0kN0dOMA6YxEna4yA5D2YBu4w8BbDF4yIzD1NyUfwzMEZ0wXcgS9HPAKbbPhNi/nnV7iNre2d0pQGPcRIHDa0xLM+6iIKm7l5g5ErXrHRhtuI147fjrVvhZyBmfTAVv8YgutYcOen1dP4zieSZwKCYTaJ6FdEU4yCRxFZJ5TDFHMpqc1YyTMW48D5Xk8zPSiYuI1NIoJDroT0OlHJVJnEwvCdXSiWxRW1lH+YkvsayQFWsF/ZckmFf7/pc/e/XyLUxUlGq374elnGw+z3wJQMnjPe4RNPEwVd/9VcDWIBS9913H171qlfJvvthD3sYbrjhhpWh8MQ2mOKBHJkMf4YWvK39kMeBADRWmyMzFc/sPVB4TAD8gVEh0QHkDmxW15m4/4n2bfKEYN0k9J1ZWrrpQ4pxUCYOcqBhpOI+q28PNPA4iHpcJA8rWckw16A54fPBzMOZZ5A1yDbmyEmpJmYdzIwBX6qIr7RWgCVxqSIF42Dzz5G5IOMxAfjPa/RK16w5shkHScZBQqoIiAPHvkQK/wwkjIPKe9B1lnnGgDaZYh4FcMOygLM+S9vaDyVQBcm7xefirAeFTNYyyv0TD1qSMjEuaBiXyuFlYvhCAoVUi5fArPv9mmpzgPNaUfpssPOAjvWiSeCy94D//ZrkGW3OrDKId1k3u5+B0iB+GSVrLepxAEw+B8cpmiYOnve85+Hiiy9esQ5+7Md+DPfee6/s+y+++GJcf/31eO5znzsxDqZ4QEe5oPUBalbWlBPwpIIigFW5OIebT9HjXHNkIpHIbtgU1e4Af2BUgPaLz+mSRxKD7hTrZWTGgcBnAsgBl0aqqKK6aT2UBukM6yXLOMhUOQJ2zEalirIV/9lq16zHApB7BlbXO8v4CFZaJ7Tdl5Gp9vWlisaVKMkm8BSMAysPwEsVKRgHGWPafSRvMuxLADidYBy4Uj3Be+A/g3FZHzm5pjx4nGH+SOYB1uPAkyoiEgeZYharbx5/B/veGgPXzMXlODl71NPYgjFnrrwHqopzlgGnkAoDcvuhjN/ZMljZumzidj2Mx0HFPFQmLjKAKe1xIJJrMswvknEQle1cBlvMYs7iLHBPYjM6g/J4El8plbWMrMfBoh/5BMYUmjhs+eUXXXQRfuAHfgA333zz6u/e/va34yu+4itkbZw6dQq/8iu/gv/1f/1fceONN8q+d4opxgxvMj87n+PUbBj8U8iElDI9kSozVkdws32+2rv8/YwxLsAD1wptbYAHrmWMh9QzKCmxjFQRv1mywL3g+Qd+v6Uka1gfkT6UB/x9ShVx+vpqxkFOKiguVZSUKHGuz8yDCtZHyBzZHNJyEi1H8z7EJi0BVk6uTixVlNHXF+g6R++BgnGQ03cv9yFRmZ7sM8iDNlmT8CxwVSbQIoUMCqkilwGaYX2MKFUELMbL6bU/U+bECfZVG4+DuvZnsw4nDrqN38wwDjKeRwqpHnYecOUCj3qcdJg4w33gEgeqamNfpmS4DzpzZP5MkPE7WwabRFYCp8fN46AauBbMwYBXzFEL3DvvYSUesh68x4FKLo1kHRn1A03yrgYbUUmVrUfW40DVjyk00TRxAADf//3f37oJHBwc4Od+7ueatzPFFK3C21jXHlrtJp2RCuI3q+V8TkklJQ7LGSO29WAPrOXmiPU4YDfsWYmUZaSkigRSNRmpElttPS7jQPUMMtVmJR00KlVUylRkEgeUOXLSmDVd6ZuWKsolcPNSRUX7jFRR+Qwy5shJiRZg8Ztqv0dhUK40SD+cdSEJzfJ5cTI5PGAHqBgH/FyQNQX05ssQaKmoNk+bhOcO7iaJHgDNXami6DxMVDluXOskEKPBaItvtn9+3HPJo7KYg0/eRJNnQG4/dOrwAGePzq3+LDFID1WbtwGN60Bzv9L1JCHOULK2x5Yqcs2RK+6BzBw5UdBkz2Ia9l3NelyeH1MeAyVwXsU40FT7A3xRWwkus/eA9zhwCimPepwKIpbsmUAnFcRhA1mvp1X75h2MS2VNHgdTlNFUqmiKKaaoi22gSU0oGAcGtA3IdGSkCZZRbijmfSBxIjAFXfSB26yWla6sVBFb8Z+p7Nn4XKriPJ88yRyYFB4HKcYBSck1fUjIVJSyMlHGQQawsGAZAVqbavcYaGnBimjFe+7Ann0H3MNSqOI9Pw+fSlScZ+WqvDETSl42kCqa9/XrQNqcWyCRkN0LeIyDqFRHJnGQ9bnIJm8UoJnpQ9IkPCqTkPI4cK6N3gNfV5l/BoxMRIYByO4Dd30mlDwrE/gC4D4kF2bk2uLJw4zvmgK4YmXj3ErnoGThMlj5SpVUTbkf6bpac2BNxTEr1eT2QcD8qta3FwKnhgVJ+WwkpIqOmcdB7Rjw1ixmHmYTmKrkHVtEoJDrAzhz7Izf5bYo15BBxoHz/CePg+MTU+JgiimOQbiMg8rFKkOLXkYpVZSp9mba9ysM6vqgSFwAXpUBx/hQmSOzUkVe1WBV+wa0SQDHTPIqoWvKbNDLMObAkfbL6jKRVFHtPTia9+Z5hRkHRqaF17ePSFMsY9+Mg7Q5b7LS2BuzMZ+JfAI1U3GeAYsAv78pnxOBVBFQPw+W72p0DirnzHlf73O0jKxclwewRt/DFGiZlCrKvgNG31tgjBt9BlmZCNaQ0mvb+76h8PobuQdZjfe+75NSRXzyfhmZBJ7G44CXz8zItS3DJJEjJvECY1JfNi4u0wPEzcGXwYKmCnNswGM81CYu8ok7gNeX9/qgkO2rrzbXVNt7n63zWNAxHqz3HDcG5qTPB8t+8/rJvIf0GBDIpQHOO1BtjtwmcVEz/kpZNoVEUDSRr9iHTNEupsTBFFMcg3BN6aor7kvQLi9VFPM4KBYnAeMBqP/9mQ3qZh/iC+bRvEfZTdacWMU4YKWaMpU2Co37jK6ppQTn24+Y4yo8FgC+0scDeCPyDIAHWPAyOczzz8qkyD0OkoBdFLiezbrUgUExD5YV55FnkPV4cFl3CZ+TEoSuCQ9grAXO2EPi6novcRQEbbKSaR5LKZI46Ps+ZVSfBU0zbXvXM6CZ9drIzkPjMQ66rnPWwegzSEp2Jas9vX1T5B60YP5k5LrG9DgAgFNFwQGTOMjI1in07Vm5rEwB09DnqkFTQeIE4KqNAXsP+gD7ez0yDEyFObKVsOVkapjE4eqzxLlAyTgoxwAt1UPLZXF7sm1SRdFgZZxlcl3kO1DOFWzigmH9KPZAZZR7yKFEfka6e4r2MSUOppjiGIQHMrBSPdFKV2D/xqgZmQ57SBJJ9VQsmN59YoF7034lcJ0FKra2H2Kd5JMXucRFvn3D+AgxDnhphPVg9a09gDdujszff1PZREkV5RgHnkxJJDIG8YBGMi3zDmalggCxOXIUMHWlihJzkEAuDagfB1mpKlfXNVlxr2Ec1CePvC1LTKYly/rJsf+y1e5uH5IJtChwFT2kl5FO3iT9jqxUSy5xAuRYF1HAqu97M24iCTyJx0FiLSrbV3gcRPbkZg/E+Ix4clmkVJHKGLUWNDWJK1YmhtDXB7YVsY2bPNqnZJxhnWWkgoiKf8UatO2ztXOxqg+sfKQvGTYi48CsARrGQXXyzrB+NIyDmjlQZQy9HgrGweRxcHxiShxMMcUxCH+i5KRqFB4DkcVCAdy7Mh21v1+QuPA+V3MPvMWMlamxwHUdYKPQ9gZ40BrIV9sCuSqXrDHr4jNl4qZ+s3i6mTlyXR/ud/oalSpKmUMb0PSBxzhISxUJ5LIyybMWjIMIaJxNYPpSRZk5IJ88BAKJg6xUlQC0ylbcl9XGAHA6kDzKmhNnWT9slenqeqfaPerxkPa6SFbbZlknWX1xK9MTbD8JGqYTB0ng2utv7B3Ig1aZPXlmH7CMjFG9ef4iqaI6c+T9SxUpDNoXn+PWgizz7/xnEnJVAvCS3Q96UkXRNWBbH2rWI2X77Fys8zjg2ve9dsZjXmXOwpufY5NXbRgHjFzbucT4W8bEOPjkiqBHeds4OjrCu971Ltxyyy348z//c9x999247777woO26zr87M/+bKNeTjGFPjITZfaguPgMX22b0VNdte8sJLWVXrbSlpUqih8YvQWdYXwAvFSMwpgYyFVctzBHjmwUFXJNLOOg7/uUrvR6sDINGsZBptq9BWi9MGatlbnIgqbWFDMImAm8Tli5MiBvDg14yRue8RBNIPvAOT8HKJKHQH0CMW2K6wE2UdAymTzy5M0iySNvzxLTV8+xfspEU9xfwD6zeQ9EhlKWcaCW6okzDor2R/S48K6PzsNehXyGcRAFLLzrI0nkTAHDMjLFNBmvp1X7iWKmbAIf2GKOXNGHg1mHWbfJnJJJFdGgIQeYsdJ9KtZFJoGqAK5ZJv422VxmP8EU1GzDAqL7yUXCYfi7vZiRBUxl8Abh9j2MSMcug/WaMWsgLVXEvQOqqn+moM/3F+iN90EkLONg99m06xayrev3b/I4OD5xLBIHd911F370R38U/+7f/Tt86EMfSn3XEmiYEgdTPJAi43GgMMVUgoYM48FlXJCVnirGQc2B3TtU0cA9WenF0iHLUMpVMX2wB8b6zZI1iCYSB+Tv9zboNOvDGHRXMg6KxMHBrEtXmu7b4wBYgEC1CZDsPJgZ/3OR10nmGbB6tuthPA4ijIMk62fbgaW+/XwC3dNCr5cqaiDVFK72zkv1nDjoNu57SK7Kma/GBA0zpqyL6/3kzcGsPgmbZhwkEuiA43GQlCoKS7YlE3hZqRaXcRCYCzJz8OJ65x2ISAUl5bqApEG5gHGQSWIrpHoyBt0nDmYbySeVTAlrjEozDkhjXHcdHluqSFBxbZJX5P1fflewDgdz51xQxzjw9wFByzJ33LBSPTzjIME8EryHLPNK4bEBOL+/sv3yOjZxwRR0bWN7nEwI1JTFJzVSvgdF4mBiHByf2Hvi4O1vfzu+7uu+Dh/60Ic2mAURM60ppnighzefsx4HnMfAfkFDt9KTBGyYxAXgZflrGAceJZprn622VABmQK7SSSGXlTkwsUZw61EemGu1fbNmjOvBmyNv9uECInGSkUhgTcjWY5sxa03iwNOVjs5DStB+0T7xDA7jc9AyrM8EkzjgGQfZSmtX4z8wB2Q9FoBFpV0JnNcncLlD4jJcqaa0OTAxBg4PcPbo3OrPYzIOsvr6aYPqLTIdpwInpXwCLVdxnG0/C5xn92NZ0Gbf5sg+44B/BziPAz6JfPKQl6tbRiaJrdAXzxh0nzSApQg0JaWCVMa0tfOAC1yPLFWkMMhmk8jbmX8x5J7dE3oJDgY0ddfiyjFQztlzFXAd9HrJJg7sGBy34t8UtJE+GwzrDOCwAR+L4UH7+bw3n69JHJSJo8nj4PgEn0ISxLvf/W581Vd9Fe68804jSdD3PfW/KaZ4IMaSmrUetQulRqbF0rNrNwsKxsGSmrge9VI9OYmSZZQb+5qFitVS9YKt9FIAZkBSqkgil6VjHIzJuvFMrFmpInbDerpgHERligCecQFoWE8u46DS5yCrrQ7kAAvvOY3N/FKYM19QgkYBn4ls+94aGDmwWWkGVi6Mq7jOzkEKbWmFQXTpcxCTq8q9h+Ua5M2tkfbDHgcC1kdesktbcV9zSF+PLHDO6CpvXp+TCip//4mDzkhv7Ipstae3b8lU/FOVtolCDiNVpGAchBIHeamezFyc8RlS9MHsQ2TV3jxwrqj2jiQwjVyZwCC7dgy6BQwCj4Vt312GyhjWm4eqkycixoFJIgeW45I1TkmmkdiE2YfTPiMcA1RhEA9wc5C/D+VlgtxEfiXjQNWHKbSxV8bBS17yEnziE59YJQz6vsfVV1+Nr/mar8GVV16JSy+9FBdddNE+uzjFFKMFS81SgHbehuLsfI5TFRR9FXDNUhN1Hgdx0IrVUvWCBq7LansRYBYyJ1aMwQRg0UKmpPbAfNoxsdZJFVUyDggqaBlmox4xR25gzAvUsz68+xTWFj8sx38ycSAwiY9Um5aHCyaB6/lMVLcvGAOHB7yuqUqy7uThDPeeOf8+VSdwkz4vPmg9PuOg1J/NMg4y5shhj4MWrJewVE8BmCVN2sMV96VU0UEsiZzxWQH0CbQoaGWMGIPvYRaw8Ct9+WprruKeL6bRmCNzlbaLaxWgMc/+yrKetvahWipo8zpWX559Bt77ylS8W9ZJgPXTIHlUb46cTx4DPnBf8wxUxrCZtbhMdrMyMRmT9qznmNc+LRcm8hio9lggZJO9sGeCikp/0fhfhneGGPI4APIFBFO0i70lDt73vvfhbW97G7quQ9/3uOSSS/Cf/tN/wlVXXbWvLk0xxV7jcNbh9NqfaydrC9rlQcPl99ZQ9DObg40+kBRhmccBkeV39WxFiYPazUpZlclKJWX0pW2FWR40jWxWMgfV1WfISj9vnOoYB3V9MFJFEsZB/f1XsI68ZIdn+uyFN1YiutKA3axHQHs3gcgwDgrmV4SirHgHrTlyhHGQr3Y/MZvhfnD0ZJM8pA3KuXmgHC9RwPhg1qHrsKGLHJaJEUjGlYyD0yGPA23iIAoa5n1O8qCNNcQMJjATFcd931tDyqCp4b6lisr9YzpxkmRchJ+/53s1olwXkFuPTxXtR5LHy7DShZGKf34fuIyMZJkqccAWU+hkUriCKh84ZMZAgvXRwByZNaZdfFf+HQQyjIN88mzbd9f0gWYcJM7m5f51zASqPYuLGAds+6rkYc34c8Y/kzxehld4UrMmK7xupmgTe5Mq+u3f/m0A582MX/nKV05Jgyke1FFuKuoZB/mKe+/AXAtaZfRUd/UhYk67HjRwT2T5y43RyYMZ7c9S/v56U848YOe1H9mo2UrPPOMgJlWUb59lHHjvCZ28YRkHBcB7ikgc5GRy8oyP2awzz6AWtCilGYD4GEj9fq+6jEmeJfqgYH2U4yaUOBCYtGdAkxJgZe4/wM8DLFCzHmWfo6ClMakn3sNSripikO17HAQqTZNSRdkiAt9nIyhVlOxDhnHgJrGjiYu0VFHuPcyCVkayLGkOHU2cyFk3DHCfSKCxXk/rkZGpUVT72gQqz7igq33L5AnpMcAnLjjWxSwhG7vrM5G1SGGOTHsciKSKvD7XvAcqxkFGPrOUdmOrveeFfPgDxSTe7ENouTBuHlQlD5lCCt8cmQftvfWrhhFvz8KTVNFxib0lDm6//fbVf19yySV4wQtesK+uTDHFsQjWnFZRce8dLscG7tnfb2RySMBIwThgZZKAhFSRALBbfI4/MCnkqjKHBXtQJqSKymrvyt/f1By5sg+WcZD//ecCPieKSmeAr3jPVFctIyNV5FaZKjwOAhv2jBHdMow5ckSuSpDAzGi7lgm8aKX1MtgDK2uEtx5Zfe1Mpe8y9sk4yPiseO1L5KKi5sRJ1kMGNGD1hDfbzz0D6/Ew3u8HBIyDpFSRN2dF5gIrFZWX6Eh5HBCAXbkWxCRKFJXGZSEDn7iggXsygZZlvKzaT/k8CKSChAbZEtbJyFJF7FqoMqfOJPHt/VdV3EcSqDwDeBlsQZuVyiJ/P+k5pWM9xQu6vLky4y/gJZ45j4OJcXBcYm+Jg4O/1N3sug6Pe9zj6CrdKab4ZAlW000B2HgLSi1opdBVBnjQ4EgAlgAcaKv67UAmcZCv9gYcmZSRK87LhEdks26NUZnkGafv7pkxsuuZpWfWShUV5sgVGpJl+HNALetFI5dVVrxXMw4UHgcJqSLXY4Eag8UcEPKZyLNuSomro3lffWiQGJQnDguqeZBlvVhNZyJ5mgRMWpiUR1gn+/Y4UIPWAOEzkfRZyJjzevNFNImdBa7ZKstlpBkHSY+DTPIW8PsbGYaG+Ul5DPBzkWEcBBKHyzCsm4Q5MQXaHgOpIvYeWOYvyXhInAutQbNCqohnnimA83qPA6/iP//7Ad7jYMzEhXcdixtnEpgKuSDTPvkO0nJhJD7QzJy6ov2u68x17BwE2PV41tXdh8nj4PjG3hIHj3nMY1b/ff/99++rG1NMcWyCzfJbbeu8RARQf2Ap2y9pjmwfqis9BWAJ274KrAIcjf1KmQa2qsG0L5Rq4cZgpn1F8owbfzZpwY8BdsNYyokwjAM3cVDLekpUGK5HCVrWGrNKPA4yjBePlk7cg0zFeQvQGKhnHSiSqKk5QMQ8swlUkvknSOBHQSvLvssnjyJSJd7hLgSaJuUJ1KA1ED+0W+ZJtuK+/h4oGAdZc94sA7V8b6Kg3ZmjzTXjZDCJnjVH9p5/pJAg+w4AuWdgzJEpxgEvV6Wo9k2ZI5P78ME+VK4HxuNjRH3zZSjkouzZKAEaC4Dz2vsvk2pynhvrccAlLngWrgVt2Yr7Yh4KzINZ5huQAO5N8lj0DpKMA5nPyciME8DuH08e1sk5Tx4Hxzf2ljh4+tOfvjJGvvXWW3Hu3Ll9dWWKKY5FlEBXTYa17/sUJXUZ3saaBu5FFf+1mzVF4mTxufLAWsE4ENz7ZVhTxNpKV021d0bb11Q2UFJF/GFF8RzMgZn0OGBNWQFerklhjuxtauv13TXvoJHKqax2dPVck4BVxl9g1nEJ1H17HHjjplouKllpDeQOC1aiRMN8Yz0OmHk4S89mdZ3XwzIOeObZ4SwImiYr/SxgGvv9XrVdtA9Zc+CMTIg3VrMa/3HgPJfEt9XOOcZBjZ7yRvtJwCL7/LPJw77vU0nUU0Wi5UzA42QZKXNkQbVr+ZlQpbOg2t7rQ+16XLbPVtoqWRecOS8/Bs0cTJkTZ8Zg/Cxo2+f2pLNZh3LJVCROInvS8vzGgrZsxT9gWeMKyTYaOB/ZoDwrWblqn8QGVKwrwFuP686mqgTqFPrYW+Lg8ssvx7Oe9SwAwD333IM3vvGN++rKFFMci2CMURUSHcBiQ1FurGorjWTmxORm1R7U2Ir7+IHFAiUJxgErVWSkclS/n5dJoYxhExuFcqyOaUybMYErg02enC6liojEgdLnhE2glZvKesYBV921HuVBJQJYqQzKMzIhBjSmPA4yiYN8AtP6rGRAaw3zqhY4U3jtlG1HAYsWyaPadxDI7wXs+I8yLvJyUVamIQoc59bjDHDp3a9w+0mpomwhibn/I3scmH1I9Pcn9yJZqShvuKYYBwJz5pDHACnXuB4Zvx+zDyR+P8DvCWwBwx6kipIJ3L7v926QnVkLFcCtJxdWm0RX/H4WNPfap5NXibVA8R6ywL3HeOn7+D1gk2cqg3SWBauSSwPs/rF2PVYlb6bQx94SBwDw4z/+4zhx4gQA4GUvexnuueeefXZniin2Gkylm6utPaLGP5DboGx8jlysVNXOjDGnlHHAShWJQNtMlZHGHJnfrCiMScsNTa05sJFG2IdUkUkcEKClU6FdXW0tYv2Uxqy11c5sddfG9Sl/AV4WYONziQOr4hlc4GzqaY1/gTlwzONAk8Cz5si16xB3SN31maxMDPMMMowDtUxN+PcrJPMSwL2CAapkHHRd/B5kpYqMPEWy4j4KHJfzVZ5xEPz9SfZlttrT62/I44D0eNnoQ6biP5k8BZIyOeT8b/vAzUXZxOW29mNSRbl30LtnGdaJJnlU377iGWTWQkX7JnER+f1def9JxkFf3gM+gadgvVT7XLheR/F74Pnf1YTKY4EtpFCYoy+DZQCqTOKn0MdeEwef//mfj3/1r/4VAOAP//AP8bznPQ933nnnPrs0xRR7C2azpDLlBPiFWsU4sPTQ2vYLSir7+/dsjmwqnklzZBa4tpW2vFQRZY6c0Ha21VWEVJEn1VPRh/I+RSsc14M9sJXgXi0ddD18j4PaxEFJC+fuQWnqXDIptrdv56CoQXVGV7eVz0jMZ0EAmh7MzPxdyzjQeO3wh8UMWLQe5cGmturdAjVE4iRpitfCIHtMxkG20s2wPpjkTQK0kZi0C5l/Jw/q9IQ3289Vemb3AlamJMg4SO6HMpKNgDcPj/f8gbxBeZm8ZxgHttqcr3ZmgPvMvtwyD7lqXxa4zj7/ZWT2JNl52Fu3Q2NAAFxmpJKyc4D3mdg7wO9Fl5FLXBRncRI4ziTyFVXv7Dzg7d24Mci9g2b8iTwmWLk0tn2AZwBmJQuX8ff/wy343B+8Ho//4V/DlS9/E/7lb/0x9T1TnI+9Jg4A4Nu+7dvwi7/4i7jwwgvxlre8BU984hPx8pe/HL/3e79HUYOmmOKBGkyVgbegs5WWdMW/QB4A4DcK5qAqYhyMbo5MyjQowKJF+5lqZ15P9Hz75WYpU21MtO9saKoSBy3NkSvfAWuOHE8ceOOm2mdExLwxjAPSoJ0bf7pqfxa0zsgUmHdQJJdU0wed1w5/WFTNxSxNXJG8zFYbtzDIjlQcZxPp5dg7mlvJi12h8HhQGnQv+hBkHCRAI3NIp1g/OdAqoy0O5GUyslJFdg3OVfyHGR+GeZpL3ACxeSArFwZYoClUbZ702PA+E5Np4Zi/Q32oTSQrQGPvc5E9SdbnxVu3I2Mwm0AHcj4bnwyMg8xarDKmzfi92EK2fAIzwzhgwHPDhCfbZyv+WQaqknFgGYC1HgfFO0AmUM8czXHmaI77zh7h4/efo8bRFJtxuO8OAMA3fuM34mlPexqe//zn4/d///dx7bXX4tprr8WJEyfw8Ic/HBdccEHo+7quw/ve975GvZ1iijbBbJa8xUxV8c9KFenaZ0FDDjBiKt2UUkXsYs3qGJr2E/RkSbUz+fwBDWjoavxXbDKsObJwDFRu2E8bc+T47++6DicPZhtAQTXrRcS8sYyD8aSSMgcVm7xjQfvNfkdAG10CtQPOrn1vRR9UXjsWsBhXLs37HDsPM3NQ2pi3gcdBLeMEyK/F21hPB7O6w6ZmHeJBGwUD1I6/gMdBEjQHcnMQkE/i24rzWPvZe5A1Bs3OQ1mPA6+YKONxULsGr9qf9yhr/kJSQQmvoWVk5DutVBEHNLGJdE+io+/7MHMosyfOysR4c1YoeSOQqckk0RUV1yZxlEqcKH5//f0v54s5WcSbSSIrTOJZ5pMrVUSxTjSMg3OyOYCbg5TmyPWMA1ECVSRlPcX5OBaJg9/+7d/G937v9+K9730vuq5bMQ3OnDmD22+/Pfx90ZdriimOQzBVBt5ixkgkAN6GnQTtVIBN5UKhMIQEOF1BlUSL1/7oUkWkx4LXB4VU0bwH5vMes4rnqUie+ObAw/fAGDMLGQe1Y0DBOAAWz+DM2lexBukscG4ZB7UyOXrGS0yqSDMHZmRCstri2/pQMwa8e0Wxfshn0Pe9uZadB9lqR8U6lAVMFCbdGcaBTRzE2vZYX6H3sIFUUUayDGDMiXnWjWG/EYmDrFRRNnlUMqWi1bbleA1LFSUBi2wSO+9xkEvislJt59tPgsaJApLzn+H3gzKpINYc2Rkv5+Z9eD3NSGZlK56z+wGFMWrmPSyTh4xUT6bavonHQcpjQsM4yJgjx5lvfALVlyoi5iFyP9JqDqj9/ZZ1zCcOSqlL1uNANgYT5/MpFrH3O/iKV7wCV111Fd71rnetEgZd19H/m2KKB2owGVZ3kz5ipeXcqS7igXtOpkPlccBU3KtMUQGeIq6QCFm0z1fZKCQyvE1NNWhX9JV5Dh7IUlN1rkrcAH61WU0Yc2TSZ4GVScgYsa0HyzjQgLY8YFDOFWzyKFPtqPAZAbhDu9dPhTly7RrgSyOwkn0ccGXGAAPaJg/sGbBiGSWVPMM4yOq7AzHgWsE+NBXnIbmwXLX34no+eWTNGIkxeJgDbLJrcUauDBBIFSUYH0A+eWfN2XOJGyDocVC8/1FpB+99iVUa+wUkkciMQUaqzwtaX9wZL1S1s9mTRIDjXAI7ux9QgIZWqiiQPEr+fu8zGY+F8T0OynMIBxxn+lCO1/A8lJAN9KWKmDFY4gMc48D7rpqwcwAJ2kulirgEKm8Sz8+DU/ixV8bBr/7qr+J7vud7VhScdbbByZMn8dCHPhQXXXTRPrs4xRSjBaPplt2kr4fZLJGVpmzVPastqzIoZireFYD5+fa5A6OiyhTgtb0BjVSNt7Gu3SywlND18DYUdebI+SrPZbByVaU5Ms84YJN3mmp3nnGQBwwzgIFuDir6kKh2VjEOag7NvlRRfg6oXQO8uYo9IBjmWeUzYPVs1yNd6SnweillzjIeB2FzZOe9zZgDU6yX0hgyIdnnfd9w+/wYkDAOivajFedldW48cZIDDLLrcdYYNWsSn6149yv+E1JFAXP0RftetTkvkwIsALBTlXJlQI4BqJLpYPcEfgHNHEBsT5e5B2lzZC+RH6p4F7BOlB4PVMU/f/8tFjAy40LA+AByc2EL5lU148AF7uNjsATuWbmy5XddGJwD2PufTRyuB5s4aDUG2XPRFOdjr4mDv//3//4qadD3PR75yEfie7/3e3HNNdfgcz/3czFLyH5MMcUDLZgqC29Cl5kj15gziwAjgAfNVDIpHtg8dGBpaY7MVtuWzIHq9hNSRRkzutVnPF3JygOTAjDbfmDbHU3NkSs3bIZxQCYOWIqq1dfn7kHZ7zE9DlKMA5HPiGH9JKqtVcB5zTzkVvwLDKpZ0H7xXdwzsKABy7oRyMQkq42ZBFpZcXw6xDjg5REA3qD+fPv5Q2L5mYxBN8B4HCQS+BLJwJxUUVYuKlttm02eZPXFswxQL4Ed0bfOMg5s4iDKOLDXZ7TNF9/Z41QArcjsR7Nz8LY+jK2vnql4zwL32XOpQionsxYozGEzoLnGHJrfkyqkkrzPhVgf2b2QuyfNyYWF2k94vXj9jK7DAD8GsonD9aA9DoxkITsPT1JF6thb4uAd73gH3ve+9602Q0960pNwww034NJLL91Xl6aYYq/BULO8xYxNqDKbRW+DSJsjkxRxmceCt1kYOLCoKo0Bu1gfzfsqjX+VMauVCMgAp0y1MX9gUgAmXdfhxEG3sUmqkmkpEixKj4Nqc+Ric8aYIwN2U1cL2qmq3ctqFNbjgBl/BrAaudIZSIKGIr8Vpg++ZB6TPOQqnbxxyks1caDBvvX1AY1kW8n6yTAOavxp1sOVKgoB9/m5OAOaNDFHDrSvYL9lzWEzgCWQM0gH7D04FRwD2UpH67eTYzwAi3F98rBuHGUkOgDH40AgERKSyXELSIIV74m5mJUMLcOwcCv74LKuCNAsY1KelQoq2+66KHCfrzbOyFcqgPvMuUwB3Nt5eNzf730uMhWfOMyNgYyMs1tEFmzfLSKolirKz4FeH1jGQc7joGQc1BW1Gb8/VqpIsCeeYjP2lnq5+eabAWAlTfQzP/MzU9Jgigd1WG3bisSBszlkvT4Yc2Al44DxeADaSXQsvnt3H0xlk5BxANQdGFTGrAYwDBxWWC3F9ch4HLSSiqmSKjraBLdzUkVcpUfJOKjdnJXByCTM5z3Kx8RXu3PgPasnvNk2X2XTTKoo0Ieywoqfh+PrgF/xTzwDErTzKxxFv5+US2NYN2YPkGYcEIkDqcdBMHGQlCrat0m6VxUX9nkgE1dAHjQH9KaU0WeQ1TY25shpqaIc4yDs8eD0N/YO6BkHfVk6uyP85Blf7Q4wFe88C9nuQUjQlASOPY82CjhOnE0MaJeVzAvOgexZcD1sMUng95sirjhwmpGxZVmPu9qPeRxs3qt54P3f3YfAeziLn8V2tQ3U70eWRWTrITGpr5Yqyq0B2/pQnbwsfY6UjIPK97Ad62VKHGRjb4mDu+66a/Xfl19+OZ7ylKfsqytTTHEsggJsRDI9AAccK8wAl8Eu1CrQbpu26q5QmLIuwz8w1siEaKRyMlU+isXZ2yxVy4QITCEB7h1UmVMv2ufomSZxQDIOGNBEOQewY1Ax/pik0TJUCcRSqijUBxHzijHn9K7h5gAOsPBk1XjJPlHyimK95Cr9FH0oK45DyavkezibdQ5oFUlg59fjTMV7ttIW8CQj69s/UwJ2lVXqG59JmlJmKp2BXAEDIJAqcpifkci+Az7rhmd/HsxixUTe/jGURHcrbevbV1TbZhiAMo8Dci72QHZOqihRcZ+UCWG13bddz5kT6xgHjFRLRiIlOwel2xfpy2eeQTqBmzyXZJNX3jxYuyd150DBHFBtzmyq/TOMA+5smimgWA8FC3WKzdjbHfy0T/s0AIvM3qMe9ah9dWOKKY5NlJUmNZs1W9WSAS1VjINxgWtVRplZrG1VSYZx4BwYKw7tCm1twFmoQ8asObAASEoVGX13DXBeAxqpEjeL9rl3oJk5coXPhXIOKMdgLT3VAoaCStsEYDg24wLwaOHjVdx7Y4CpuD8OUkUmgV3NesrPw1lt5axMzKIPPHDWxmskw7ph5gE+eZOttPXa7/t64Iit7ttsPwfYZH02DOMg+A6cKYCK6D1g16DV9UnZRK+/keRNdj98ytk7ZBLYQA60Bohq34RBt8rjwIDntTIhycTRts9ExqHaHDm6DmTnAK8PGdCYkwri7wGjPqBtX1XtzTMAy6R32KQ+4XEAeHhELoG+aD+RPGSSZwYb4OagvTAOBMlDgL8HU2yPvSUOHv3oR6/+++67795XN6aY4tgEA9goq50ZirpbbUxLRHCgkcmqiypNa/qg1M/zK73iFd+8VFGm0lIPWAH1G1bV5qD8XM2BWSUVBXAHhr7vjRfABaRUUTkGT9P69uOOQY1ECX9QUQC2iz7w1d6sGWMZjMdB2c9ZF9e3BzyfFQ60Zyq9l2EOzaRJPbMWZCu9FJJdGX3bcr6mxkACuLPJm3EZB4r2XamW2gSawuMgMQctrs8Vs2T11e09iK2FWcAozzhwEgeZMRhsP5u48ECmGGibr7bNnAmsx4FGIqPaHNn5/ZqK80y1dXQdyu1FsqwjIDcGFKwTpccA8/wze2KFVNKiD/x+xJrER6WCcizY7DqY8f2azTrjVSlhvdQmLhIFTGUYj4NKxoHKZyMrnzmFjb0lDr7kS74En/Ipn4K+7/G+971vSh5M8aAPJsufre5aD+bA5Gf1NZWutQulinHAHFiUoLFbaVWVPNJUvLPV7oAmeZEBTBSMh8Xn4hW/KtAY4N7BM0dzlBKkrDmyqfKpAAy8ezSmvr53ncIcOSSRIhp/ympvnWQc8Q6MLBOkNCi3CfTaBLaCcZCr9LLzsKDiPiFVxMwDBjBImDMrEichxoFEsi/BvBMkD7Pazhltb8ADrYKJg7RUUZb1k1sLtpkj10Z2P+zdr1JuYld4gGlEKsn1GksC1xmpJAY06/ueLurqus48MwY0NCzcVLV1MHmYnAP2DZwr2s/sSbNzIJDbkx4HffmMZKHXtvedkfbjc5DDwg1JpuWleqxkWK1UkiZ5CniMg7pEPlvAU4Zlfk1SRdnY2x28+OKL8Q3f8A0AgHPnzuEXfuEX9tWVKaY4FkExDowpbQK4ZsyZncV0Rpoz06CRSK6JObBozZEdivIAaJI5oJRhKk0DGyVFtbP32Go3zCVYIJMqqrgHKtAYcDbLFb+/lCkChFJFo3sccGNQAVhmKq1tZQ83/my1Iw+aMjIpACvXla+2B/hqYyXzi610UiQvs+bI5fUKr5nIOrBvrxGN1w4PmmiAe2cfUnlozoLmXvtZc+C4TEnuHTAG0dHEgZj1M7rHQXIt9O5XhnEQ3Y963Y1X+/IJVIZxV0ZWvjELWgI5Fm7eHDkH1nlydRGDbuA4SAXx98BKNeXHYKSoUFXtnUmkl55/2TkgyoLNmqR780bIpF3gM8Cei7LFA+tBexwQChheKIs7p1jEXlMv1157LS699FIAwP/xf/wf+IM/+IN9dmeKKfYa5cJec2BVTorMYpHN6u9sv3KhUDEOFiZym383tFlRmDEuwwP6hp6BryfLVvvqGAfMM+i6jq52VZmEMwcmC9YlGAfEHHD6rK0GHDNx4M4BInPqaqkqwfM/WbAt5n2kfY1UVkqmRQAaA1wCWeYzQyZvjDk6AZguw0rVcNXeCn39qL56ttrZ+0yKecbcg0O+fQVwnwFNNFJRDnBMShVFQXMgL9FQVvpG1wK9VFEucRCtts0yj7x9UM7jINZ+WqooWeGZ2QcuI1NMpJAq8sZsqNpYANzmzHlzUkVpg3RnzITfw8RcnB1/QDZxIXj+wvbne2AcZBPY2WKerFxVeg5IMC9XnyGNgdkiTi9MMUOtx4FILkuFDUxxPvZ6Bx/1qEfh9a9/PR760Ifiox/9KL78y78cv/Ebv7HPLk0xxd6CqTIxh7SUTAoj07J5TRfM6m+0T1aaKeQJlmEMggelinT3fzbrTN+Hqi29/rFSRR5gWFvlozCFBLgN83zeO3qyoor3mmprUdveZ2va9xkHqor3CtaTW1mjAs7HSx567241cC0ADAELeNceWOfzHuWrwst1xUErlb8CC9orDwfsgbWFXFuUnq1I4HqVXrXrgMZrhAcMWswDY0s1uV5LJONAIVUUZr0k70EWNM0mDrKVpor9aM7nI9f+bGaB+1Knemf7gmKmrMZ+Ri6LkWssw3teIZkWhUxJORdEQNvkO5hNnrmyscE+mLUg0AcFaJnzmFAkjjIeC6Jq78S+zHoc5MZgdC+QfQbl+3cQlmxrMAYqx6DK5wUAThfn07E9DiZzZH0c7rPxP//zP8fll1+O//gf/yO+4zu+A7feeiue/exn4+lPfzq+4Ru+AV/wBV+AT/u0T8MFF1wQ/u7HPOYxDXo8xRTtgqHIqwArgKPGKY1n2Ip3e0hIVpuuFXAP3QNFhefm52c4Nz/fgWHGgwPaivTVgcXvq/lNVksxkzw6/10lEFXT9qJ9FjiPj8ES3MuAloy2aGmMDPDmyDLW0cjmyKbCkKm0dgCms0fzKvaGSrKMpQi3lIuqkusSgOaAd1iqrLQ2VU38PMzq+yqkiizjoB6w6HsvgZqvuAfq14FyuFKJg1l5aK2/BwrZuAOzD4u0n58HMlI1Vk84zziIggZZ1gXDuluPcrycSjIe4pWuCubRDOsb0ZjHQT55d/JghrNH59sPJQ4kybMOOLv9O4eCrbQF8kkLwB+zkT4ofBZYfXPv2rhkXnIO2HIWiYTVNuer3TXAfSRxkZsDgVwhg8rjoGQqxJJnuXk4i03oGQ+5dZAz6Ob6oJQqYhkH2SKaZSgLS6dYxF4TB4997GM3MnBd16Hve9x000246aab6O/tug7nzp1TdHGKKUYLxhhUCZozwL1KogLgZRpUpqCAc2AZ0RzZb3/3gu1VQ9GgnWdOfNRjCDedz3tjzqsyh61ivSRNqDbbj+t7q2RqAK7a8f5CqujkwYxm/VByaV7iRiQVVXtgk2irE+boq+tECUQD2lUCNlmgYvNzzDuoYV5Zxg8H2iu9ZmrXIYVUkVIuDtBU/AOL31YzniSMg7LiN2UMm0+cROSiWrBOvO/dFtlqeyAPmGQqfQG/gKbv++pqzRLkjksV5UDDvTMOBHvSUycOcM+Z8/uKmFRRvpAiDdoJQWPKmNj5zOhSNZmK96RUj90P5d5BIA4cZoBjy3wUgLYZxgExBjNroWL8eZ8LMX/Sc0BuHsy/Azn2X0aycRnsnlQqVUSux1aykJQqEp4NpljEXhMHy1jfFC7/P2qEM8UUD/RgQDuVMTDALRZKiQhaqkh4D6LmuFk91zKi5qiujqLI42Dx/XMAuzMHHrjO6vwzwLGfOBC1XyXVo2OdlBv2msRFKVVUSwX1gqk08Q7J7CvIVtm0AGsi7StAY68PtaCF/w5qEog1fdAZtHOHBattn1gHzTq8P6miyGHJTR4JkhdAIIGzZ9BUMQ4yFe9WHkTDOGCZVwqporA5cgK0Bfx5q5bxAgg8DtKMg/w8UDKmxjYIL/ehpcHlrpAYtCeB0wxYVHodRZImq/aTLFgF68HsS0PGrGUBTfT+54pp3DUoaVI+ulRQ4j3MsN6WUd6viFSVinGQmYuMVFHwPcwWEWTfweye2MgmK8yRK/ugSJ4ug00i6pJXOnxoikUcm9RL3/cb/5tiigdbMJVuCrBiGVaqaL+MgzGrjZcRPbAopaKA+ILt6ssrpYrIin/2PjDVfq5UEdl+CTLUgAbK5FH5DvT9sDFZyThgjZEBDePjxEFMy3NX+7UHFltdRgBmnlQRKVOjAs6rf7/3DormAUauSuVxwRoTs4lDgK80a2HMGzmwubralFySxzzjmC8PTI8DHjhWtN91nU0g13ocNGAcREHbtETEFuZjTfR9L2AcaKutqeRZuQ8ZWSqovGcR8FxRSBEt4LF9UMrU5PXtF9/LV/wzwF2GCZutuDe+X8H9mHd91qQ8Ahxnq829z0TugSJxUe4JQzJBMmPa4hmENP5zyatsUaVNoCeTl2lz5jzzqXZParCgBOOA3ROxZ6EylBjZFIvYK+PgRS960T6bn2KKYxU2y89U/I/NONgv42HRBx3rIcw4EGmbr9oPGrMpq+29jW2VVI+wD8yBxdvUeCBwTTDAnaraetG+/wxOzbYnA2ziQMj6IRI3GdCWrXQx1V1UpbFT5VYJmKjuAUvP9ZNnGtZDTR+ytOxl2N8/buIGSLBeBEnkcv2IHJZ8xgEB2iWkclpUO0cOzIq9AJu8BPwkKhOHs26jXdbr45QgcXDmaB6SCsquh97cXcN89NoG4j4P5e8PA5aCPWHmHVDIdZXjJsN4YH4/mzjb3ocIaKqQKsoxABWgWWYuyoOmOcaBNwdkpWoia6HCHFiZuNm3xwGZN0jtB9QeB3Fz5Jxcjz0TJNtnEpikdKRJXCcSByxGlCngWMaRI6OcVYWYYs+Jg5/7uZ/bZ/NTTHGsgql0UmlLA1yVRdNqf7baOAMaBTfMcnPkIGjlbSZUoB1QOQYbGjTTUkUijfWazaKi2n0Z3vszdGi4vwC3WWNkwJuD4omjVPKQrHQqk6wqqaLa9lX3oPzc/C8ZJ0OeFUqDagY0UCXPDD2brnTXvYPV5siC5ElGJsZ7TlTFfQK0kVT8J+6BopAhU+2ZMSRdjxMHs43KeZZxoBiDQEwqyM7F0Wrj+Bq4DA/gTksVBQEbBfsq5XGwZ8aB5B1MVnxnKt5LqaKoRAqQl43L6rt7n4m8hzaBnZMJioO23FlkPVKgdQuPg1TFP8E4EIL2POOA3xeadyD4Hso9DsIJ5Fz72eSd14faNVwpVVQyX2r990pvJCZ55hYVToyDdEyplymmOCZhdQ3jwH1OqihODVQyHvhqYyHrIZi8UYKmQJyeWd6jrktom7sSFfExAOiAU8bnA1CCpjXAea66arN9T2d/IHEglSqKV1qVvz8nE8PJZGQPqsvPlB87c46cg2jGjUPRr5BulMqFMcwzmTkyd1jSrkPxPvR9L0meZGRS/OTRJ4HHQQAwMPIMxDMo38EIaKSS7LLAKcc4UEgVAeMC197czSZOgLw5ct8HfS4Ee/Ky4jNW8Z/3GCjvWSn/tCsU7K+svndmT+YxbqJRjtdZVw+YAfuvOM8C11kGpps4GBG4Zc7itn1+T8j4HZr2EwncbQUskZi71d71zyDDugIEknnJ9rPnonK8MMlDdgxkfY42+8A9hxbJMyBX3DrFIqbEwRRTHJNgJkqlxj5zWFUc1DPtL65Tsh6CUkUiwHAZWakittIe2HJgrziw+cA9KxXEeAzoQFNGV7OlOTIw/B6Uh3pGnmIZ1BykZPyQ9FQVcMxW+lkjQk3iDOCTd7w5chw4V91/m7gaj3GyrQ81h3Z/DmJAex4wc+dhlVTRiMwPCxgE7kFSWxvgvZYApUk4J5VhPA4oxkE8eb0eGWNawH9vqhkHzn7p1EEske6t3zGfC15bfRmlOfLYjAMjVZRgHDC/P8s4yJwJrFSfALRNgobR39/3vXlnIsBllvGQLajyDdKjFec8cJ41h160r2QcEKBx4hmwBSxD10fmAnMWns9D/qdZ2cIs6yS7F8l67Xif4c2RH5geB0oZ5SnOxyflHfzQhz607y5MMUU4mCoDpcY+k2FX+guwh2VpH4LVpkrQePH52IJtpRF0oC1QK5elA+6ZSisrz5Aw5yWqTVuaIwPD9+BIyHhgkndN5dJobXVeImSzfQ4w5RkH3IHZe04qua4xWTdl0qKacdIweVV3/70DClNtz9PTVXJVM4d5w7L/mNegvAdnUvruHPNoPSIHdtVcyFb8GYNuinGQBc5zoGFGKkvBOPDnYD5xQvmMZFg3Cp+RQu4wZY4s8HrJgsYx0NzuQaOgVfYemOR92Bw6tyfPJ25y1dZd16WB08w8xPgNlpFiPAhAUzMPRNp3zk/RPrjV3oFzWZb5VY7B6DyYNWfO7omz4H3f9zQ2okieLqNMINX2QWIO7Xq/TYyDbOw1cfCqV71K/p0f/vCH8RVf8RXy751iitZBARZCjX2mfaXHAXtYlvosBLU9FdVV6xHdLCilkrzNOlvxTxs0Mx4HwsQRA9xJNf69as+Be2DnAB3joE7f3iZu2PCMKWuqjCxQwDIOCtCyUqpIdQ+8scNUvHdBaYT1YA4NqjFokhZk4iYzD1PvgOvzEu+DkckJABau18zoCbR8+6btkM9D/j3MVLqp9mNs8kLCOHCA9pjXRg6wYXx+lnHm6Mj8XTRx4M1dJbN2V5RjgGHhZmQyFFJB5bg5fc7e122hkE9lWG/rkan2zkp1AfmCrixwnPU+M2tgWKooPwayGvPlmImxTvKgZWZPYs5BFGjKV7z7BvW5d3Db924Lb+0KzYNqj4Ms6yY4B2SYj9uur2cclOegBOOAfA9tIa3Ga2YyR87HXu/gd33Xd+HXfu3XZN9311134Su/8ivx7ne/W/adU0wxVpSTeh1gpAOuGXqs1F+A1jcXApfBAwtrPrS1/WC1pVJfHuA2694hhR0GDGDS0mOg5sCoMsQEtgDHQdaLVqalRqpKU20P8Nq2quQhC5iqkkdstatSsoxJ4KrGIFvp2FKyr4755yRPqUrjRLW7kzxSvQe1B3aFvnoJXGeq3Tlz5kTyRiSbyFb8mcTBYbx9D7DJeG1kKy2B+t9fyvYdzDqJMWuk4tskjwTJs5jHQT6JeuoEL1WUMeVdfUYM2mVkegAicbBnfXWXcRDyOMiBdgrmVbYPGRaowmPAJLETUkEc44Bfi9kClo3rk6yXUqoIyM6D0XUgV3VvfD7CzDu+gALIGbQrpYpMIr3yPhj2seAdBHL4wBSL2Gvi4Ny5c/iGb/gG/Lf/9t/S33X33Xfj6quvxn//7/8937EppthDMPriClr06rMEYCSt9id1HbWMg9iBRVntDsSrLZX68oDdWNRs1r3kiUoqqKb9cjOZM+eNA3flBlktFzUEWlhKrg64ZxJHmftPG3SLgGN2w6zyOmH1vW0CWTcGGakiWirKeJyMB1gvg/JZEfm8ZA6rWuYTB9xJzJGLz0SkijKA4TJSUkWyeYDbC5X36mRQ399re/G94wHH3pCp/v0CxoX3zDJyVZxUEF/xKfE4KBkHGakqAeMiXm3OF5Owe5CN6/de7Zxjn2WlihQSrlmpkkxRm8IcNsM8UhjD5jwO8okDb87OeI0Asfcgm8DNjoFSXi6eQM+N/wxorhh/y2AlwzL7sF2fmRIH+dg7Z+Oee+7Bc5/7XPzJn/xJ6jue85zn4J3vfKewZ1NMMW5QHgdS4D4vUZECK0h6sgHPU9W2sXugrHb32h+WKtLpywPcZl1RYbatfSp5pmQc1FTcCyoMl+GbIw+MwZZyZVXPX9c+a9Ct8zgg5yDRPfDmD4ZxkfO5iDO/zDugkmipZXwo1yGCceBJOUjkGRIHZW3ypDaBs3kdI5eVqXZTJNCU5shsIQcrVXKmkJSJyvQs2y7z/imPg+B7kNE3t4yL8T0eFOtx5h1QJFHL+3b67LiJi7LPUdAsU0yUff5Afl+eBe58+VAetI0zLvLnIltEFLwHCfaXwmMgA1y3aD+SxPaZv8GKd0diNJS8ce5XyPMpmcBN+3wk5TstHpNjXS2+s7Lav2R9JqSK2D0Zsw+3bXv3YO+w9wM+9noHZ7MZuq7DnXfeiWc/+9mUqfF9992H5z73ubjppptWVa6f93mfp+7qFFM0DwY4V5ojM6BFS3+BWtCkJethbHPkskJuqNpS3T4DGipBS0qqSGhOzAAWyuSRC5oEfTZyc0B8s9qy0tn7fi9s8pAF7kWMA3IMupVeRPIm8wzKOahG31glmcZK9ajMqQE/gT7ks6Eyp86ZI5fUeKFcFWsSPqJU0XzeoxyqzHqY8ZkonwE7DlmpEsu+i//+ruscc94I6yI/Blh98/L3nyITJ2XEmD959lUueZa//2XiYEzGifeZaMW99TrJSRVFfv+i/dy+3P7+fLVxxuMgnLgw+xFmDPAV354xbOj3k7K5m5/RSQVFQePFZ/bLOHDNkZNSRal5MPsOJg3Ko/NwmvWT8N2K4hDbou/tnqyecZC7/95nMvKdU5yPvSYOfvqnfxp936PrOvzxH/8xnve85+Hee++t/vzp06fxNV/zNXjrW9+KruvQ9z0++7M/G29+85sb9nqKKdqErTKoAO2E1caUMawQtLdgxXD7fd83rraNgbZ5c+TYZlkJGgOOxwOjcZ8aA3HWizJ5whzY1T4X0Q1jW7kyInGTkSpy3p86k3aNVI2VCqvbMCsMKQG+0ksKnBMJ5AxQs9k2m7hpnLwamIeymtLLyByWjBlkah3UJHC4in/NGFi0nwesIgd21VrI7AW99pmKe8BLHo7LfGH1zSWMA28N2rNUTyyBmF8LyoRLRCrJzAHU79dW3OfNkaPt5/ZENnEmqDZOMA6Og1RRpA/ecNk3cB+5B+U7M7bHAcN8rrk+K1UUSeBl16GsVFC2qJOV7VyGawxMSxVxjINM8qg8x1KMA6H32xTnY6938SUveQn+0T/6R6vkwbve9S688IUvxLxG1/rMGfyNv/E38OY3v3mVNHjsYx+LG264AY961KNG6P0UU2hD4XGglSqqAKwEeq5b2ycYD4vvETIOBqWKdKAt4ABnAwc2ZbU3YDesVMV/ArS0jIPxQMvFZ+ObxRJUySaPopJhBjBMjAFGLk2ZuGEZBzqPg83P1R5UVGOQrfQyBt1C5hdjjsyYsgK237WHhZass5p+eGulQt8/ZUqrlOwbMYFzkjywq55BhiKvkOgAuHXoaG6LKJiKe8CRLKwEjr1CDmYuZEFDReJgNuuMz0JGrkrxDmQAMwXjoDSd3hUKua6MQTngFXNEGAfeHiTXfrjaOC1VlGPAWamorFSRIIGbmIe972vV9uozCeaRov3Mvvw4MA7cIpoR90NZc+KsfKeVDc4nD6vNkUVSRRmpoKxcndf+xDbQxN7TL//X//V/4Zu/+ZtXVPDrr78e3/7t377zM+fOncMLX/hCXH/99aukwRVXXIEbbrgBj370o8fo9hRTyIPRNSwBIyVoWgcaK8GKzfbn/UJ+INJ+tg/lZmGo0s2asGUZB1HQWPf8AbuxqNksKZMniuTZ2FI9CnmOzT7EkifSin9is6r0uGCNCVUV96pqZ3YMdF3n6PxX/H6hQTcDXqvGgDf/DckE+e3rEife95fhAUWMQbydfyKApe4emGo3MoEzpseBuxegtLXjzMdVHwQSHQC3F/SqwnnJMK7a0Wd95J8BK1XEmCMDOakSxVpkpaISFf9U4mLTVLv0ztjdfn4/kkmgAl4xR/098OQio1JFeXPkrFRTMQ93sbk4qy+uOBNkkkdZxkVWJgbIMY+YAp6h9mOJC3ut51mwK7Jn867rLPMtkkBNJu8s+z7JOAibM2elkuz19dX+3P6vDG/c1t4GiceB2INyikXsPXEAAK961avwrGc9C32/OCS++tWvxv/+v//v7rVHR0f4pm/6Jrz+9a9fJQ3+yl/5K7jhhhvwmZ/5mSP3fIopdMFsVrSVlnF6ekuPg5o+uAfVEUEzpUwSEK80yx5QyjBVDlUV5+009usq3vO6zuc/G9uo9n3vVPZkGQcx0EQpV8aA1lbfXtc+UDcPqYB7VqZEJVUEcKCh1OeDMQiXSRU5VWY1iROlZB7FONA8/4w5slayj+uHIonKylV5XiBjyuQAunmISZ54e4WxpYq894SRB2DWIcBWxrOMC0aycdu1Y/p8ABr23akTvFSRgnFhq23rQaP5vEeJcUb3JBmpKEABGuaqbbPFNFlzZMWZgGUfAnnQOqtvD+Qq/jP+DsvIrEXepQrGQXQqKOeBGPOqSB6OnLzL7gnTc4C3Flf2ofSXOHs0ryrgqelD7XvY5h08FpD3Az6OxV08PDzEa1/7Wnz+53/+Khnwj//xP8a/+Tf/ZuO6vu/xohe9CNddd93qukc+8pF485vfjM/5nM/ZU++nmEITTJWBtNpbwjjQSkQMAvdeZYnwHgyaI5egbdrjIFZpZgA7tUwOIRellYoa22Mh9vwzOpLbIipVlD2k7vosJ1WUaZ+jJ6uA4xJoG9scefHZ+KFROQ9TzLNyDJBSRb6++LjrkJu8GpwHNPNwptJLOQ/S4H2iynEZRqqo0pjXWyuY55CpNs3qGi9DxTjgK+7LZ8BJtgFcIQdrjquQKgLEjAMJ6yYCmublQ6055rgJzEy1uWsKmgbux5UpYd7/zfbL9SgImqalkhRjgAdO1TI5jFRQZi3MPn8gV23tsW6irAtvHoyyMDPMq+yeMG1OnDybpxMXjoRt7f0v73vfa1gvy37UhJUtVryDE+NAEccicQAAn/Ipn4I3vvGNeMxjHrNKCvy9v/f38IY3vGF1zbd927fh3//7f7/690svvRS/8Ru/gcc//vF77PkUU2iC2awoq72Zw4q00pOQKVHpGq8+GzywWB3L3MIUrTQzYFEStGZM6cpDkrTamvBYyAHXsc2aW9WRTN5EfR6U1e7MgVkpleQdWCi5JFpbnANslMkzinEg9LlQVDuz74D33KqYbw3lyoCadaC4/8eCcZDYC5D6sjaBlgdNa0FL95BKGVTzoFEWMFxGNHkM+Pep3E/UhmoeBFiZEg3jgE4cEF5L56/Nr8fHzuPgLF/pyyTR2fkHyMvUAHwBwfk+5O5BttpWzTgYGzQFkswvby4MSRXlfj+QS960SVyMm7xSYANR6V5l+xnJQiC/DqSlkhJ7MbeAR5Q4qO2HtweIsh6yCdQp/Dg2iQMAuOyyy3D99dfjkksuQdd1ODo6wjd+4zfi7W9/O77ru74Lr371q1dJg4c97GF405vehCuvvHLf3Z5iCknYhXpkqSCzWSGqzcXGqEOgkb8wjQOYzOc9yuYzMi3R9gFdheO2z9dVGys17pnkmbDiPdi+qsJyPaL3wCRu9gxa5w3C431QzYMsRVxpwkX5fBjAUsl4iCewpVJFDwTmWyOprHk/7POzDAVov/qsSKpIAxZUvoMJM8D1YFhX5/ugWQuZak+vGpNlHCh9JhQeB7XPwDAORPNQxpiVGQMZ4LpMojKsn1LiKZK4UDCfyj5nEjeLPuSA+7xUULTSmk+cLNrPyUdaydJs4iI/B8RYN1rGAVfxz6+FEsaB2ZPE3gGbQI9K9SiYT4kEqjmXJJN3SYPw6J5Y7XMSShw4jOGozwuQK+bwxmv0NTBr4eRxIInDfXegjL/6V/8qXv/61+Mrv/Ircf/99+Pee+/FVVddhXPnzgFYyBU95CEPwfXXX48nP/nJe+7tFFPoQlHtnQGuKdDYbA4yoD3DONBUGS4jsli7lGixOfKgKaeRSkqCtsRm0WzQpBr7Ne1rqs0Bh6I/QI31waoscB57DxVmhMtgKk3lrJfZDPfj/G+qGgMqbfFDbrOuNCfmJOt0zCfmwGKSd1KpopoEtvL3O4mDYPKOTly5bIceJyu+T8s84ir9FAk0w7qrlCpqBVqHQNtGXhc1RSSuxwG5Fhi5qNrEgQC09T5T275MqihR8a3wGMhozCtkYsr7FvE40JhD6xI3iz4EgftS4zvw+4G8z0XWHNcWk0RB083+LqVKaudTBQtVyb4DYvfAJg7zoGkEOFaYM2f3hMeBcWAkE0PzUM7jgJHsXI9sAjU7BjJFhYz6g9sH5705qJRL8u7X2aM5DmYHdPtZNYApFnEs7+LTn/50/NIv/RK6bqHJdfbs2RVF5eKLL8Z//s//GU972tP23MspptCGrbKpqbRtV+1dJVMiBI19wCYO3CqrTXcdWDI0vG0RlyrSgeYABxxrq60ZjX2hVFHwwOqaP8nNkWNyVSmfEyZxI0xcAHaDz0imsfNgee/OVEu05Ks8l6FgHGTmIWuMGn8HVZW+1e0rPR6cZzcE3KrWYZftUFtxr0zik1I1JTuCShywrB/nOqramWA8LUPlM8HIJJTg7uGsw0yUwGKlqhbfxVSck4yDo6ONP588rAcZ1iNT8W2BewY455JngCaJWjIOSgmoXWH2pCNKVW27NgoYsaynZWSl89IeC+VcEGUcuFIlPOuEkisj5NrOt5+bhxT66mUfYoyDXOII8Fio0TGQSxwo9qSZeTi7JzRrYJh1kzsXZfyuAC9xFWAcOO8/Y9Ct9DjY9n27Ql3UNsUijmXiAAC+9mu/Fv/iX/wL9H2/MvS44IIL8PrXvx5f8iVfsufeTTGFPpiFupwYM6Ato+2r0rYGOJkKN6OtlGjYcQ+8TUwGtPY+PyxVlD+krkdUXx/QAseURIMyeRX2ONAAJbs+P/gOCOWqTKUrI1OTZr0wzCfNBpGt9Dzq84ek1WeJamMl68aaEtYwDjQyOX6lU9zjIAOaz2YdyqKo6DzMrkHe768HbdslEFmpmjE9Dsq2Zx0o4Nwc2BPGtOxcXH6uyhy5TN6R1fYAD1x6czUnV8UBdzqpIl7jXMH+ypgTK97BU0XCJcc4EIDGIakkAeNgVt7/XMV/HLTNaeznZVLiZ7Fd7UsYBwl9e6C+0hnIaetv60NkDDIFPEPtxxkHueRFxhx6GZkEnt0T5hInYcZBdg4o1+Aw64kvpnKlioLtA1sK60iPg23ftysUY3AKG8f6Lv7dv/t38QM/8APo+x4XXHABfuVXfgVf/uVfvu9uTTFFk2BkWpQeA+UifVRhRiOVRyCqXNRV/5EDi1vhmASNo5qOavMfjcdAInlFtK/QFF7GyZKiHgQMAYU5cmyzrNV3j1fZ2KqObPKsrLYdT6qG1RVt6jVDafzrQMOlTMGusFJFXPvefWNYTwq5rI3vj3ociCRqFm1zoK3SIHtUjwNS312VRGe8prb1gTdHjgP3KpkeQOtxwBRTsAbVOqkiHriTJM/MPoQH7qnERYpxsN9KY4U5claqKG2OnADNgfwzyEqVKObBTMV7NonMFFCVYYsp6seAwuPAjoGcXFUWtFWYtEfA6+w8nJGLAxRzQI71lElg+4yjfPIs0g+vD3HGQZlAnRgHimjicXBwwNFDd8Xp06fx1V/91VXXdl238kSYYooHSmwzRty14WltCnn2qDdg6ua/62RiZrMOs27TAGcQuHUOSV2gsqSMyIElk03fFlYiICbVlGU8MNrCUnNkgp6sTF5Ff7+iuq2MMvkzKJck1dcnQGvhHABwrAeFrjTgJe6G2+77XmaOC5AG1ULg3Ht/h7RFVWOAleqxUkG5d/Bg1gFrqidjJe/85DnJOBAyj2pp8hKPAxI0VJlD5/TVNckbZg5Q7sWMXFlCqoi5BZZxUelxUNyDUnKnvv0EcC2Yi6UeB5LEwdGWK732FdXmCY+JpL49IAANk2MgW3GefQbuWTAkVZQ/E7A+J4C3H8zJxOyfccC0n2OhHguPA5NAj7BOku9AMnmXNkhP/Hbv+pBUkScZKpAqiuAz7lkgOw8nzwVTLKJJ4mCoSjkay4Gm/t4ppjhO4Wq69T1m2D7Z2UpbXZUZsNiAntxBTFJWmwOLxX29qiBa6Zmpsly2vx67Nuw+aKyVKhpTpmbxeUW1sxK4rqi0FOmrA/HNYpvkUewZKKWiPIr2ulygF0qDdsA7tNXIZWkoqQzjIFNV4/aBOLQqExceW2CoD2WChR0DXdfhcNZttMfMQfl1oAPObv/+MlSUaM/Aj9X4l47Byj6UHgcUaMnKhYnegYy+tUmgsV4rBHCvkukBOLkywK80Zgo5WMCorIxXmSPXJi78JHJeoiMjFcTMRUYqKSNVJAAMMzI5XRefC837l6x0zeqrZ41Z4zIpScaBIHmW8Zqxe+IcaM4A9xmNfQXjIHsuy94DTRHB/hKoGbk0IH8uyvqsZM6FXdfhxEG3MY8wUkUZfMbbC2fHYKaocYrz0ewuZqp+y+j7YckUdZtTTDF2MNQsVaUt4G+uhg5syio3wMmyDxrDaitNIwcWtTEzENcWloO2hLantuI/vlmVymUlGR9ZxgvAsB6KMSCeA4bnIB3jxOsDZ47MVhsTjBtBheN6KKqNc6wfZx0YODS0NCiveQbKxAlAJHBF7XufqwUNsmaI6xGVbNvWB4WucS1YoJJMs6Bx/WFVZcbHyCWVB3u22h5IeL3skXEB6JInPOvFWQuIMcAyPrw+UB4HJ6zHQm3hnmJPngIMzX6ImYOK5x8EzWy1eVYmJipTkgVNcxXHCqmiTMV1dh4q+1sj2zvUh8ieLGMMva39rMZ/RLLPu17BfMqMwewYiFb8y+XKgu1n98SKMWj2gxGfEYFUkdL7bYrz0YRx8IxnPGMC8aeYIhhMhlWrre1VmQxJNGgr3qPVfmrGQeTAotLzXY+oKZ4ctCUq/qXmyIQppNLjIJw4EEukAI5UzyDrQVlp7M9BhzvUB5WJGyDu8eBdw/ahlCqqqTR0GQcjmxObeTDVviNVFEzgZubBE7MZ7sf576uaA8pDonodGvTa0byDKXNkoceBPTSOV/XPysSo9gKWdTauvrzbhxrGQUOponqDas1abCu+K6WKGjEOWI+PxXflgfOIObJiLiqff98v9gE1AHAWNAdy1c4mcTGyVNTi+lwxRxY0TEsVEQUku9pnEsisXBmQ93jw3pna8Q/4zKNI8kTCOEjK2B5PxsF4CdS8x4CWcZCdg8JSSQcd7ltj3XJSRfz52Pc7SyZQk+eCKRbRJHFw4403tvjaKab4pA632jBqypiRqCASF2fObf57CbzF+xA7sCl1nYHYZklBiS4jW/GukIra+P6qamdhxbupMqqptNTdgyhF3lLC8xuTKOtC6XPhUtQH2tezXvKMA5U5ck2loVquimMctGWdDEumCd9B4vmrE3jGZyQ4D2RkcrpuAdSt2q4G7XUJVPMMztUd2Mu+Rgwpl1GClkfzBQgz9E6X4DL7DjBeU1v7IPL6qFmHlebIrFyTKols5sDK9lt5HNSCZir5yijzdD0Uz8AbO6fPzavGcwtz5IhMyL4lUoC8hKjZB4elkpKgpeu1k2F9EAlkIeMgev+9cX4073Gi0r7Tmy8i4zAz/s/3IZdAPJ4eB5kEatTnIpe8K/cCedbRuIyHDNvjfB/4gqYM+3bV/uRx0CSm9MsUUxyT8KscYsB5qsrQrXYcl3EQNWXUMw4CiQOhTNTqO4KAjdHRlIO2NdXOOp8NzhhWWO28ReN/a9sNqJC22nGIcaAbA94Bb+jQUoLretZLzRjgaeHrwQAGeo+D+IFB+Q568h5Dz0BZ7cxUerX3uhlvHTIeH5UHVuVawFZ8l68LxzjgQKsSXGZZN0zydHWdaE/AMD5UMkmLz3JyTTKTcBK41zEONKwX77tqovSZiUjlKOaiUw7FsFbjWmKOnJDJUBRSZKSigPyejPXYWLWfBA1ns86YmkfugWV9MIwDnvmVLSTIVjt7a3ZkHGZB+/m8R/mReMW9NnFAMX8yUkXJgiZTRJStdk/6rMQTF8nfbyRz46yXTGGrNwflkydT4kARU+JgiimOSTCaborFefVZFzQcqrTUehxEKeJ6j4P69pW64ssoDyyDiSNxH5hKQ6VBNgMYKOmIbrX1TtZJ++TRmGNgG0V7Z/tl4iZR6QpwFHWVTAs1/pz+jc04UOrbe30fksrQShXlE0dp5lVQsk2ZvCuf/9j68gCXvFv0IS+TwlaamXmINUgnCjiWYeUpRIwDgnXD/n6Ar3hXJXAZuTpAmDggZVI8cEVjED6eRAfg37fqxIEgeZaR6lGwz1hz8G3XZ0HbuFRRfj3MGDQr1sOM10x2P5itdvYS7ZE+ZPXlfa+VqFSRtg8RfftlsJJ5gMDjgUxeLyO7Jy7bj/x2ADhKngtPlD5XjDlyn30P+TkA8JInE+StiAfdXbzpppvwuMc9Do973OPwWZ/1WfvuzhRTrMKrkItX3OuqrYHhA5sSNPY+P3Rg0lea1m/YlWDRMkrQdeiwpr//8UpTlb484GxWKzZLZoOWGANetfWuQ2OL5FG02lElkwJsqbaNyjWJWT9jStUYbemKzbKecRA/sCn17SnmW0O5LMrjQWzQPcw4aCcTVHtYsvOgknnFAsd50A6onQM07wCTPF1G+QxUkmlcAl04B1VLFYl+PynTUIIrY5sj+4yDfLV1LePH6wMzF3oST6fPHVV9VrEvZhlPgGYtykoVZYxxveuzoCUzBo05cUqqiGGdcO8g4CVOoqCtdw6ob9+TVou8B2qZIIBgXRSX70OqyEo28cmjcMU/uQaurhezjqJyVdn2ze8Psp7cPgTnAbaIZhkq77EpNqOJx8Fxjvvuuw+33norAEwGzlMcq2A8DpQVz56+8jBoKWYcRCvuxRXfkQOLEizb2v7g8xdLRTGVjmaDlgDNBNXWasbBrmegZPyc/45YtWNr1tHYcmVMtWk7Y9T4+Ae0rI8qxoWwsqbrOpw46DbGfVyqKJO8Ylgf2nUgOgaV7bOGgEqDbBVwyngceO8uY5DNzkOuMWgtcC6aixngXDkPs8C1ivnDMl6amSNXMw48j4M866bv6302JIwDZ+zUMg6yoPniMzxgqEjeZUxZvevjMikxxlsZiiQi63MCePruRPsJqZzsfpBdg5bhzZeR98DbA/V9X41ZefNVtto7yzjgEqgZqaKcfGcmeQvkmeCZpAmQLygzRVSMVNFRuR+MfZ7BA9ZDWUwxxfl40DEOppjiuAZT6anOqEaz7Gqd/+iBIWuClWlfbcjpfUfUHDl9/4lKUyVoxhmj6lgX3uZut1yVPnkUrbRSejy4oNmQVJGa9UJUmqmA8weqx4Gq0ncZ0fuglSqKPwP97w8y34SMD/v8SdBWyDzipWoI0C44ByvbXnzOqTYlgWNaqogADqXMM/LA3iqBW9t+CW7LzJETjANOrosfg4ozwWzWmTnwdGXiQCEfavW9ecYFZQ6dlCrKJvHYxO3qesGekGWdHc17lLZgzJ6Q2QcsI3s28/obM+bVMg4AGM+CXeEWswSfgTWoz0nlMPOQlWxLzAPJMdD3weRR1qA8wboCnMRFdA46jO9BTB+yzKu0XFSe+TSFjekuTjHFMQlPA3AQtBNXWkaz7OVCzlZ4LSMKWLVmHOxaLFuYI5f3b8icV834oEBbgRHaMhiKrjJ55hrD7vQ4aJA8CoImSsDIBe2Cc0DaHDkI2sydg6rK46CmytLVkxUmz2qqndXMl0jyygcKxp0D5OtgsA9Sg3JSW9omT3TMr9o+zAXAsceUYAzC6Wp/Inm67To+eRE/MKvaBpwCglqDbtFawFb8l+D22ObIXj+ZudD1WmJZLyrZvtrkoaDK055DItXeebBo38ao5dydrrQd0WfCZd0I5LoyHgdZ0DzbPhB7BhmfncW1eRasuf/BgnONZFm5Hw88A7FUEJBMXiU9Dsb2ObHmyITHQZr5k5OLMtjAxDiQxJQ4mGKKYxKMi7xc4z8IGDQHDQernTfbnyXlx4ym3o72zW9vIFW0aGcXcC0GDI1MzvBCbQ2IhKBZxWZZq/FvP7sLPFYaM6++IyqTYsYh//tnfylXth5hjwX5GBxIXAho2au2iQN7eU3XcRIt2/pQAxqW7+CYCVTvQCE1SCcSJ/l1MFbxmqXFb7QtMqZVzsM1fej7XiJP4DMOKt4B0TrggjbVyZv87wc84D7OulEalLMG3exawMqkmMTBwQHVPsO8XFxn+8kMQ5f5ODbrg2SdKOYA+/wj+vr5/VBeqihZ8U6yzlbXC84mbPLON+ZlWDc86yLr88EU0GxemwPus4kLBfOJkcxcj3nSGBfgWS+A3hx50T6fPIoyQO1v311EaNpP7kmtMXUwcwRrjhw2iU/OgzaJPCUOFDElDqaY4hhFFLhSbNLXI0oRlpvzRgGbsv0k4yFS5dLEHDlIkTVgQfr5xwGDrJbjepgDIwHcKkG7oT4oaPmmD2GpIvEcEJ2DhPr2i/Zj1YbZ6q5dn6urdteOAQXrRp3A3fUMvEpUqTnyyB4Piz4EE9hir6FI26vrlHJJBEXcu4R5DIxkI6DRVt/efmW1rYj1wMwB5TuaMkgnq31VLEzaHLkw8GUZBxa45X7/iYOO8tLzGQfjVfwDvEGwrbQVAIYZU1RiEiqlimrZFqs+JCVULeMhmLgQJJFZuSRvnHKsGx40NvNwcB7yCmhioLHHuogwDmIFZPbaXPve9VmPA09RYbAPCeZPtqjRG7O1Xkdu+9E5ILEPAfKJi6zPCpBXA8iwjgCtGsIU52O6i1NMcYwiChrIKy2DFGFllZvb/lC1c6nlOaK2t5EJEujneVI5u/pQVgGoJUooc2ShOW9V+0qZkKC+9jjmyEHQMl3xH0se2UNq7j2w9NRY8jDTB3tY3Ue1e/zApmTdAEHGgcPIGVuqSP0MohXPSo8DkzxnZXIyFedMxbvL/GEqXbkDsz2ok4Ap6bGg0vb2PlcDGinXIqMvzlabq+bhStCiBHhpqSKyfQOWCVkvvM/EyD4PgmKiDGgp8VlJeCx4fYjeA2YN3GhfUMzDy5VpCjkyxqgK2bao39/GtUX7syALtQXjILoelOz9CGjuXc95HBQJvEqfFUDAOHCuj/gMZM9l3h46ZhKvbZ+RKsoWs2RYR4C2oGeK8zElDqaY4hiF3Sxtn6z7vm+gbR3bMJfAdT5xEJVp2R9wPgZoDOyudlJn1A/2bo7MSMXoKr5ns85sbnbdf4WebhkRg2xvDsgD97EDm5JxAsRBM28zT+ubExIJasYHI9NgDklZ1kfg0OCBOl4CtL7t+GHBjkHx7x9ah6RzIFfppWQ/Mc/Au0VMH1zJxhHlqthqU1XixPtcFeNAWMjAjkErjzA246CUKhoXNFcxQL19HJ28Ecn21VZ8l3Ml8w5kJEoUDMBsxX92T8Qwfzfbb5G84eYAgJVK4u+B4lzIMv/89hXV5vv1OMgyDrhnEF8Ht7YfrnbPAve5tcC7X5HEhfV8yjEuonPgog+5PVk+garHaKaYEgdTTHGsogR8IsA1kK+0jAIGClryRvvBDLPcHDhwYGmhb+9LFW2/B6ayKfv8iQx/1gRq47PEYUUp0eH2Ydf9F/oLbPuOXZsl7/nk54DYhlFxSF0Pe2AhGAcimZK9MA6IA5ua/RSp9PGqwHKsI6bave0cMFRtp0wis5We0j4Qxng+cE6ClpIEdkbjP/4MlJJpVq4tvg6Ozfxz+0A/f451VF528lDDOmFBezaB6zIOqucBUfKGlKpRVHt7bdfqe1v5VCZxoQXus8asUdDWeB4JDKrZOQDg9oRlEVMMNM4nrzJSLdnklTdvZH4/EPdayRp0K5hP5asbwa6z+6GobPB69H1v56HgWuTKVQUYF1nfs+wcCAiSN2TyehlKGeMpzsd0F6eY4hhFxOPAryrIglaxibpcyNRSRUObFcUhYVf7uw4sWR1NL3x6Yr1cUjZ5wQA2Sp8LBrht7vMxevKoHrj33o8saGyfwZBUkXgMBkE7V8+VBWwIwEoFli3jOEj1ROYB3xxZmTwcfw4IS/ZJQes4aLy4TjcOjUE1MQaBcSvOle+hff5x4B7g3wNmDmjJ/Ks9sKvkshiJAleeQ5VAZv0FWMZBQiZDxziIj0EPMOOMcR198WqfiXwxRylxFQXNjIRq2OPA3vuQMaqgoIpmnLj6+kTyJiFToiio2qdclss4SPh8MF4r5v0PjD9AMw+Vcknz1DOIsj6cszgpF8e0752jMz4bWY8HJnGgNqiOJq9sQdXEOFDElDiYYopjFObAtGOibME4iFaaKUy4NtoPaouqJSoiBxY1YAj4G/zd5shaKp4BjAiZkJS+d7lRGFnb2evDrgNLi+RRBLhXyvQsI2oMqX4PoqClm0ClKz3jm2W9x0G82lDN+ii1ZSOsp2z7TKVj62ewSzLQ+/eUMS1Z7a00yOYYBw0TBxTzbdyKe1+ig6x4J1gnStlCFjRTrYdM4kK5H2YSZ4vrNL8/o3GumgsZ4EiVvMmAZgrA0s5/QdBUrO0NxEDDNqwL7v5731UTGZkShb4+y/wD8gVdao8D5vdnZWLm/X77kD2XunMQ6XWz7ft2tx/DAkwfkmeCjEH9Miz7LCfZFe2DynNpis2Y7uIUUxyjsAtlTFtaX20dq/ZNSwUFEidu+6QZ3jIiG3b1bwcWz99o7J/bBdqpJUryh8VMH0oqYVWlpxi8t/q22/tQHlAUiYNIpZ8rjyFP3m1vv+97xwhQzHoJJg8BnVRRXaWvdvxF1oBlyFkfgXeglCo6nMWr23a1PQQa+j4f4ybQldXe0TV4GYpK322frTmwedWAYwLH+664VzJAo6wvr/2UOTKpb6xaD5lqd2XihjZHFr2DXdfRGu8q9hMzB6jeAdfjYUTWR1amI7seZY1RJT4PhGQgYO/VAbkfyFQ8S6SaSI8HIC/Xlal2X1ybLyJIg7Zl8oYYA2YvHGHdJMdA19mzeO066LOg8x4HGYPurFTTLq+/baH2OEj7bEyMA0lMiYMppjhGEdmsN2EcBHQdF/TZzb8bm3FgzWm1vx/Yvli1MEcGPJr+LpkQbR8YaqAUsGIAA7HHgam438k40ILmQOz5e+9HWqYlINPgbaTVBuXDycPNf591C4NTJhQSLVlj4n3LtLh92PUOiBOo0d8/xjoY9fnItM9qK2eru9ajNJVlgfNRGQctK+4r2leABZn2lYdkJnkJaAA7ILYGLmPfUlnedVqT9HEZB2URB508E4C2QP3vVzCPSqkiz8dnV1ij+nzFecgcVyChyZrjtvI5iYCGCvYd+/5510bvgXd5rNo+Pw+za8C265l9cZlwish1KeZB2iDc9V0LAvdu8oj32Yjq+yukirLeY+W8FR2DLTwIp5gSB1NMcawiUvHqVlilQdN6Uzi1tjXgHRiHABttpW3EY0Ctq72MErSJAMd5c+h4hYXUHJkwxdPrm/P3X2OOnPQ5kZsTx+agcvyG2w9WuinH33Hw2GBYD61N4iPJy3y1f/T565l30QOLwhD0/Gc5xoFStq/8/TXAmVJjnqs4b8n64DwOdIwLInGS8dmgQWtNHxiZEOU8wIIm0r0QMQ947CtaLotYCz1pSU5fv76ApwyNTA83B2+7PmyOnKw2LquDmTWZ8RkBHBa0KHkaMwfOrwUZg+qsRIrHOBqT8QAcj2rvkqWQGQMMC5ll3vkSsrH2Z7POJJAikmlmHkz6C3BSRTm5KHYOWkYLD8IppsTBFFMcq4hUOrkVVmrQMGDMu/h8FrCJbZb0oJX9/LbFylYVabLZpdzSbo39xqDd2BIRzmeH9opqffNI1XmL5FFks+RXtoiB+53GuCMkLgbNkXWsI8+cfihx1d6YOA6ajfoO7Dl56Va6j8x6yVZ3rQcrk6EcA0y1p3cNexsiydtlKBmAUcYJoJNpAdjkoW4vYCQLyUpLlVQRbc5NSqbxPiMtGQcjsy4I4MgbJwp9eSBS7auQ6eHm4G3XR9dEbw9X+w4CmiQiW+2rYiAzsqnLkHgcJIB7xRhM6fsfA48Djc9C8Z2BLkgYByT7UyUhm/HZyBZVlgVgjFRRFhvIjkG1jOwUi5gSB1NMcYyiBBzC+uZq0CoKWsqrTYdAQ7FMRuDAojYmXkYEOFJWGS4+z0hEtKu2BYY37Ba4zIKmkWprbduABxzvaL+BOXIENGshlRQ2R1ZKlDj3bmizqtbRNJV2hDmwWjLu7I6Kc1vdOC7jwrs/+WdQrMMDz0ALWnOHReV6VAJXjEE1MC7jwABWKa8dhvGgAU0BLnlkq/sSv5+sNlQlr1QeE2wC0dz/ffiMCFg33vfUBsO6UBUy+Mak3BhknoEp3glKFWWl67xxm6q4Z+4Ba44smofLe5D7/fE+KIF7jcdCpH29x0EctM3vi2dF4tfzUfJCxbzi2Z+iedAUsESkinJnghbmyHGDajHza0ocSGJKHEwxxTGKNONArO++a6FypYrEoGFUW1oNWnptLKO8Nwp9e+97th3Y5vPeVOPLAcORKz1dbddgxfGY+uZqfwUgJhPRJHkYkksbIXk4lDgSguaMREKWjlsGQxHPaiqXEQHOTKXzoTZxN/z8dYDtMswcEByDStZVdbW3SCYGiP9+wJ8n2Ntg269JXOiqy4xkIwGanjjgTcK5xIVuHjSJo8oDu90LsFJB8cSFMoFomWe1PiO65I1lfZCsCxHrpY515MzFAsAOSMhFEc/AVtvmqq2j5yJX3zwjUyIBTWt9TjTFNHYOzgDnTOIkUe0tmAdSiQuJx0F9EaMXJcg/5j1Q+S2x7E9VQVekgKeM7NnUnoPjjIMsAy/rs6GQrZvCxnQXp5jiGEVkomyj7Vx/YPMOMvlq59hmVbVJXbUfOLCoDUmXUZs4UOgo2s/vF7SMmFNv+/c8aFqvsW7BmvySGjmw2wMKD1YtI6IxP4Zc2dCBUTn+fFPCaPJSnbiJg5Z5g+rAO9DYHHno+XuHybxcVwy4VMpVMYwTQFPluIxyHe37GuaNBWt44DwO3JqDslCqiDMnzshVWeB+SDJNKZ3ISjSo+iADrUeU6QG0snkMaKI8E0S9ZhbXaJI3bgEPWe3LVdtzoPkyyr25wuMgAtxppIpI1pFoL5IBDTUyOeUcxIO2VOImkbhoYwycq/ZmtoVlv+eV5sg++5F5Bpp3gG0/yr7evDbHxFaYI5fPYTayx4G6qGuKRUyJgymmOEaRZRxk50W7WdkBGjrZ77S+dRCwUWeUIxXHquq6MsrNwrZqpxbV5gqJCrVU0aBUiVrfPVBtqjQkPf8dvFyZpP3AZtX1OBDLxAya4yolWpx3OCpTsw/GgcqQcNvnd/XBSBVlzaGDEgVNPA4CBuWAWK6NpGcr++CNn6GDY/aQuNl+/MAoZX1Q5si6tcCbQ8LrYGIO4M2RNXMxA1p7oBLrccCCJsq1gHkHlIwDBrTyGYjxcdh1nel3dbWv4ExgihcClb4LX6TNv4sCx97ckTKGZZInZMW/Sj6VnYMADeMiB9rqgfvI8zfedyMzHrzrGcZBWXhQmzhwE6iMSTrLunH2AkwRRQa8zyaxWamy9di7x4HYA3OKRUyJgymmOEYRmSiVFX7LsFJF9e0Deo+BoYXS6pvnfv9s1pnky1apolbmyJXJG99jQlztzZgjZzTmXami7X3wtCzzck31Gyblb199R+DArAaMgVilk/ds1GNwmHGiuwfeAS8uUzO+z0g5RtXmyGd2ACdyqaKgTMwYcl1R0FwJ2tISHSmvj3jiQAncKzwGUsw3Si5MmMB0vX6CRRSp+1/K5HCg7ZjmyOU1s45PXrHVtkrPLcag2duT08+AqHj22meHIWtQ7bEwo1FKFUVAM5eFGZyL/MQJ3wdmLjQ+J9VSRaI5IKFvrgCt2fG3uDZfTJExZ1ZUWmcYF971lEF0F98HADrZuij7eRkq9ifLeFj04fgxDqLvYdbjwDC/kmezKRYx3cUppjhGYSfresaBgoYVYRycOWcPampt6fBhuQVwOro5ckaqSAsY9v2wIVU5RjJjwKsU3i3Vo6uwW0bInLiJOXL9ZlENGAMOaLrz9+vngGiliwqs2vbZsRkvJehcc1hqnTyLvQPjyeUBWomSbX0Yegb2gKJjXdHV3imNfy+Bu7sfVtNYB5wzMinKeYABTTOJm+g6qG6frXY1PhNkHyipKKHfFQvYaBkH8WpXl3FAa8wTRSRCnw/DPq72eskn0Mp7f+ZoPigVtq19gNuXZ4BjiVwTmTyzxVysVFbi9wvWwgxoqQCOU+bQEo8DNeOA6cPmn2u74M1VHOuDYxwoEkfe51JzQJhxkE8cZJNHNnkX60OZQMoykadYxOG+OzB2XHLJJfiyL/uyfXdjiincyHgcKCreI6Cd8qC6aj9YbdoCuD0x63Bmo40tjING5si1xmwu4yBd7exUms7nODU72PoZ5Tj0+r9rw+prWebuwcm9myPXH1jUgDHggEYjzwFRY8rWldZhj420v0D8wKw2R46A16pD0vm2Y4CFSs921+eHjEmbSpSMXO0NbGEcjGgQzch1KaUDmUOzArDc9dmwZJsQtD57tPBYGAKBVdXGCtA85zNCAkbC9ViRPAH4Z8AxHoTP4GAG4CjU/uK6/H7Ae25H875qbffeU8pjYNbh9Pr3JvTNObmmePJ20bbmHfCqvWvmIEDDRM+AlopzgZ2D+MTF2FJJXh+YeWjGMg5Ee0JWrkfFPmQZD0D+XKKQKsquyVmfDYVk1xQ2HnSJgyc/+cn4rd/6rX13Y4op3Mh4HGiqjflq6xLwptpPVpsqqGj2wFLJOBBJFdXqq/rGtNpKW2DxO0/tWClUusbAFqmYgDkxINA3j3gcmENSfvxFKMJqwBhwQKOAx0EGLFtGtNpXoWe7rW2gxuNAa8DFybRokxeRQ0OZ2MxKFUX15b1D8pgG4Yt/bweas4flzBjYtg5E2s+BhnnQUmoOXPMOCqvbKJP2xs+/Bjg1FX4q0JqZA0cef951KfZlwOto1zW8QXS8fWUSmdWYb2GODCzWwMPt9TPn21cxDsjECaBJIsvMkUVyYcCi4rzmVirmAiVwz5wLMhX/NoktkGraC+OAuwcqJjprUK0qZmIT2ICTPEvKBCmkiqLvYZT5W4ZaynqKRUzplymmOEZhFspAtbOk2jchk6KYlKOgkRI03PYd45sj1y3YqgPKxuc9beUdY6Dve6nGe1Qqxns/suB15B1Q6HiWUY6/2ByQbz9SaaWipe/6jqFKZ6VEi8bjQM042L1Z9n0+cs8hcmiQSxUFD4stEuhR0EoLmnOAhTKJ7mrsDybxlRXfecaBsv2xQVOX+TR0/4VJZNdriADv2bmAMUbVmnNzoIlKqgkgpYKEDEzGa8XMAakkPvcMFKwXb9yeqa24FzGB7RqUAA0FxrBDrLtV241AUyBjUi7wGEh4HCgq/kMeDw3a34dUUck4qJULc8+FDOuGlUsTFVRlqv6z2Ij1WowzDrJ7wkzixGtfVdz5YI8pcTDFFMcoIpVubQCTeo8FNWAEOIelwWrj9lIpWz0GDFCgWZRqpWpcY9qsTIizudh1YHUpoYnDomdOHZHLAvLvgTHG2yUX1iB5FzGmbJG4iFR5KKUZtrUfBszE5tyDwLVYripaZeW9H+nkRSCBawBTceJgTFPeZZTv4CDjQChbF5EK2+iDcBx67/EQcFl6HOQ05uMyGXYvMC5oaQBLMfMpyrxRS1XVJQ407Cvm/ivXQtocuWESm2UcsF1gvFaOh89EPoHmzn+1iQsREzhjzmskOijGQ30ByUbbomIub+6YVwLHCglbuw/mPRaY9iOyxUPtcx4LPOPC7QOTOCjvQbXPiOZcGMFDNq9Tef3wVf9ZfIhJHKv7kJkDvetVxZ0P9jhWUkXz+Ry//uu/jptuugm33HIL/uf//J/42Mc+htOnTw9/eC26rsP73ve+Rr2cYop2EaEnNwENAxl2VVZ9o/2oTEUDc+TaxUpZXbUeJwupjzNbpYqKCrsuV2UHxCtN/Qq3POthvborAlwvP59qP8S60XtsRCqd7AFBkTirT96VVXgSqSQDGsWA88wz6LoOB7NuY8xFK63l/gKDEiUaTeX1iCRwjVRRWi4tBlgpzdnP92F/rIeIVNkyPOZXZi2MSsYBn2SMA6rivRyHOtC0pg9K9hnjsQDo2F8K0FzqcUAblCt9Nob7YKULedk2I5lZ47VjEqg61kdttaliP3DiMM74WV2nkirao0zJ4jNctbOK+eQWMY1oUh71OVoPm0Ad7/4v2s/Pwwekv8C265l7YPtQ9zlXqoiYB9nkmUo2MMV6SRazeAbx0ciOQzXrZWIcaOLYJA5+6qd+Cv/sn/0z/MVf/MXG39dSk9Yjq287xRT7ir17HARAm3Ih8Tbb0YhS1JuYI1eyPizjQJPNZhkPLaSqdrUP6LQkyz6cOS/tujt51cLjIFDloQaNAZ9xss0UroXPRqzavDikJ/XtF+3HKq3UPicmcRCUSlKM/13fX0YTybJAxXdrqaKwtnuDBPrQoV1a7S0AzYHcOOy6DicOuo3fPexxoEvgUPrqAl3n1WeDcmmA2mslXvGvZL34iSMOuGYisgauX6NoG+BAc0D3+wHOnLWlXFdV8kzIepIB1wKZnkX7tTIlmkQ+y7iYz3uUUwVXcV/OwRxoqmQcsBr3FHBOeIxsa58pqst4HDSRatoL42Dzz7WMk/JdYYvqWJ8LFTaQ8RnI+pyw/g7rYdeD2H1gvKbWo1y3J48DTew9cXDvvffia7/2a3HDDTeskgTLzeHQRnE9ltcyiYYppjguEaEntgBMbKVpQKZFAtjEFsoW5rC1wGkLfXkg4nGgl0pyKx2jHgNJ4DBiyuW1nx2HkWprW12VfwbbDkw1SZ0WcmG77r9S13v1HeHkobbi/cSsw5n19kcGrqO6qt79GdMcWS5VFKw0VRyS9X3YL2gOCJgfsxnOHp3P4O6TcVAD3LZkfdTIRakNsrsOWD/OMCbhbHhgK1X1T0s0+PuQXWO6lMrKFBCU7fT94vuHwCelQTljDtxSrqpuHmqXvKrWF5d4HNjPVHscFO133bga995cySRP6PZVjAM3ecolb6jkUUKqRSEbmDFnViQwyz6X8+tQKAoJWNaDivnFjgEVNsAaxC/6kHsHJFJFfe4+GMnQAOvqaN6jhIMV+NAUxyBx8Hf+zt/Bm9/8ZgBYAf+z2QyPfOQj8cEPfnB13SMf+Uh0XYePfvSjG9JFy8TCIx7xCFx88cXjdn6KKcRx7BgHO9pvAVpmq23HlIpRmYCVUUoVbQPtrDm1XiYGGKg2buAxEJPq8aq7sqBpfbW1ipK62b4PmhwejNN+RN9dqeu9rf1Bc2KhrvKi/dgcpKy09j4fleoB8gmcmDmyOHET9bhosAZEkmeAV92l8zhgTEkBEfPl7O421kNJC2d8HpRzIXNgVxrjAot7EGF8WNlG3e8HKn0eRPKV27xmTjhr4DKU7D/XmHU+x6nZjg4gX+W5qw+MQXTK50QwD+V8VsrERd3nFKCdl7hjQcOxq43dQgKJvvu4oGmGcaB4D5QV/8xaYJPXvL49JZWVrPYuu6t4BtWMA1GlOVt1LzMIJ7yezvchtx8wXn8Cj4PS7HoolFJN3vdNwcVe0y+ve93r8NrXvhZdt9BhvOSSS/AzP/MzuOuuu4xk0S/90i/hAx/4AO677z780R/9EV75ylfi8z//81cMg4ODA/y//+//iz/90z/Fn/7pn+7j50wxRToioFEbmZT6w0KTaudApSvgHZb1UjHbK/41h2S6fTFgB/jmxJHkFSCouA5UurUBzAJSRU3MketZHy0YDxHQTKVnu/kdUeBYS0eNGpKpkzfRSjNPezd/D+rHgJGsG5FxBOgTN24fRpTpofTdXbAo9xyiB8emHgM11c5CrwvKHFnMwAzPA8K5mJFKAnRjwFtH9zn+gFrgXpc8KhkTNVIxymKWsv26d1DLulmPeqmc/HvQdZ0B/GtBK5U8Bgtce/1k1mRWKsn6XLCMA+4dBFSsk7jHyPlr8/vyDHCvkWri3r9t1zPzcak4Ust6UK0FJnnK+qzQkn0xPGQ9sklcew6KJY68PsTlkvjkXQsm9hSL2Gvi4Cd+4icALGSGLrjgAvzmb/4mXvziF+Oiiy7a+bnP+qzPwrd/+7fj5ptvxite8QqcPHkSd955J57//OfjuuuuG6PrU0zRJDxt123RAjCJgHbqSlMgTk2zOv8K1kXdhk1BR/WiNnFgDiiNPBYi5sCAptJyo42Axv6iUkxb8bzLFM0ckiTAfT3ro0XyMHJgbJG82qZvvS3sPKQdf2Ma43qfH2RdiTSV18OCJjsSyOe0iQM7/0QZJ/oxGGW+ZdYhRiLCWyfVXhtRnwepOS0hVaTUuK8BrNQMRDsPjQicu9W+8Ypzdi7wzZmD76Cw2r2mfe+aVB/MPmhsxkE8eaY0aOc9DjR7kvJz1VJFojHAVjv7UkUEcG6Yn9z9VxmkA3zyRFLxHzKn1rBeNtuPgKYlNvDA9Dgw96CScaBK4rPJMysTxDLvOKmevu/NeImbI8cZZ2VkMaqUQXkDGeUpFrG3u/ixj30M73jHO1Zsg3/wD/4BnvSkJ4W/5yUveQle85rXoOs6nDt3Di960Yvwx3/8xw16PMUU7SPkcdBAYz9kjNpAqsdIRITNkfVyPduAqxZtA/WATSuPhYgxYAvGQURfWgkWnf+OgFRRA8aBW2lVmbxqIlW04/nb6jp94mLRhwjrIVvpGzswqZM39rASYzx43xGN0uT6TID5lp0Hw6yzBpJ95XdEq53H1hb3DtTyivcRK76jjA9ALVUTB01UVYarPgTeg77vpe+hxzysAW5VrI8I624ZLVk/QF21qTKByJizKlmwTLWnkvXDAqeqgpoThwVwfq42caDZE7HVzi5gRtwDxpzbu47+/V7yrjZxYMaAoOI/Ue1NMT4SwL1CvrGUlYkkLrzrFR4Htbkble8VKxWkWotp1o9ArixawOVF9nxq18CIVJO+mGaKRewtcfD2t78dfX9+IL74xS+mv+t5z3sevuM7vgMAcP/99+OHfuiHJH2cYoqxY98eBxHQ5kyxkS6lDZgIy4Q0YT1UAvcN9N0Bex+3VTqpTUmXEfMY0ANWEdaN4oBg2j8M/P4GUkGuTMQ2nwuhGeHqOyKso3MN7r/z+yNyVdnkhZFoGJKpETOP4oyD/YLGVqpo5N8/ButmxD4wlbbePRoych2KbAInV/EdB02sMa8ueUMxHtLvQU6yTy3Zxmjss3OBN4dHTeKV1fYAZw6cMWpnKj7NWqR8Byqef1kRmlkLWKkWMw5oxkFMKmzbdaqK++rEicsCzgPn9YkbzRjwbtuYHgc54D6fQFN6LFC/P5E48a5nxmA5fdYyDlTnQob1BegYD5b1xY1/gGEceHKB4yaPon5nG9d692AyR5bE3u7iuofBIx7xCDzucY/bef3999+/89+/93u/F8Ci8uaXf/mXcffdd+c7WRF/8Rd/gV/5lV/BD/3QD+GrvuqrcOmll65YFF3X4cYbbxylH7fccgte+tKX4q/9tb+Ghz3sYfjUT/1U/NW/+lfx4he/GG95y1tG6cMU+YhsFpQH9fPfEWEctGA8xDZrLeR6ajfMLfTdAUeqaEulk/KQtqv9SMU90MAcNjAGJcmzWV3ixmu/xTuwaKfO50JSbR3xOCj+7eRh/h3wDvoxr5UkYBqsclHPw9EDq6enq5br2tUHtVSRBWyGKt31kn2ROXDRh3bV9jVVVmN4zQw9h1J/OHcP4pV+yiRylPm4uEa7H4iYZDd5/kTFc3kNC5x7c/iwz4hu/HlrcJVUj5B1wgCHygQmU/FeGrgq54B6j4PyGWgq/qulikTFHKzGviqJyJsja+bBruucvRCpMc8kTkiZmMW1+Xkg6nGzHgrgunz/ao2Jl6Go+meTJ6okPs04ELFuLOuwcvwLqu29dyYqV1QmeqLjMJM8UyVQp7BxuK+GP/KRjwBYLA6XX365e82JEydw7tw5AMOJg8/+7M/GFVdcgdtuuw3nzp3DTTfdhGc/+9naTq/FLbfcguc85zm4/fbbm7VRE2fPnsX3f//34//+v/9vQyP6gz/4A/zBH/wBfu7nfg7f/M3fjH/1r/4VPvVTP3VPPZ2iJvbNOIhUGagBI8CTCRoCzfTArdkwbwNtxwLuR05cWKmi+op/hcdARGO+iVRQROO/QbWzK9NQyXqReAwEtI3VhqCL9mOgkdXUTTIOgsC9GrhOV9wrkleBMaCWKopW3I/BOts1Bjw92cwzUFTbAwKPgyB4LgUtBcasOeA4DpqV66TaoHzXPDCG1xBj0q3UN4+zfrSJA0YuK1PxzwDHx00uLLMWmORdNeNAoy/OShWpziRRycJd1zHJE7biXrkeH8y6jXZ5xoHg90eqnQUeAxmPBbsnHd/joASNS9mhmijlkoDFfmvojKlaC3iDcM07EC2iWV0nMAZmfX7Wo3wPvee5KzIeBy32xFMs4ljwNi644AL37x/ykIeswPAagP6v/JW/svrv1j4Hd911196TBgDwbd/2bfhn/+yfre7TRRddhKc85Sn4oi/6IjzkIQ9ZXfeLv/iL+Nqv/dpVImaK4xmmymZk0DJSZdKi4j2ir+8BNhK5pMoNYwvQGqinqLcAzICYTEkLY9KItnMLj4PIZk1d7e61v6sPLZJHEcBKVVkz9B275gEr1yRmvIwo0bL4fExb1EiWKdaBEjQZkfERlUhoItkXqPZTH1BstX0csMv2AbDsoWGfh81/z0gl7dvjgKl0s2vReAk0X9c8yzraX/LEm8PGZP247TPm0MIxODrrhnj+yrUw4nW10QeZOTJZ8a8yZiWB27Kfs46biy3rjgNNMwlUOnkhGIeWcceDlpRU1J49DjLV3t71zHzsAc11XiuatYA1CLcsYA3zrn4OyicPvXNoLevqfD9y70HG48C7V5M5sib2dhcf+tCHrv774x//uHvNJZdcsvrvP/mTPxn8ztOnT6/+eyypIgB41KMeha/5mq/Btddei1e96lWjtfuv//W/xr/7d/9u9efv+I7vwG233YZ3vvOdePvb344PfOADG34Pv/mbv4kf/MEfHK1/U8QjItXTQqIhsllpYkxc/P6+375RcA2AJH2oA85b6Ot77ZdeEstoYUy7aD8AWDQA7iObhSYeB5Hf3yB5tGBtFO1sY500qTYPsI5GSB4O9UFtEB3VNlYZsZ3/vO3/ri6U4zNT5bqMiFRLOT/lGQdBn5sGCfRItafa54Wp9C5lggCuwm9XP8b0OGBAEylwHGDdra5Rm7QHTLJbJI6YakvVM/DMmaPzcGYemDlr8Jjm0EA8gepdM3b7ikrrbe1XA+cyuSyy2lckVWQLeGpBc825ICIZunGdsJiFBa8l5sgJ4F4jFcQlzoBGHg9h0DjP/PH6XeNzYJ+/6B2gpYpYxgMplSSQK3PlAgPAPZA/G+U8Djb72nUT40AVe5Mq+szP/EwAi6rhD33oQ+41T3jCE1bMgZtuumnn950+fRp/+Id/uKIwXXTRRcLe2vicz/kcvP71r8cXfuEX4rLLLlv9/a233tq03WXcc889ePnLX77689/+238br3zlKzeuufjii3HttdcCAH70R38UAPDTP/3T+O7v/u6t8lBT7Dci9MQWoG2kyqRJtbPzHWeP5jiYHbh/bz/fgvVQl7holTioZRyo2o8AFrayRiFXVb9hVxnhrUdMpqVN8uhw1m2Mr63myC3MmQMH5hZzQNwcWduHKEVcnbzZtmH35kBAX+kMxKRa1AnksDGxEKxb9aH4jnm/AOe9yk01aMsANmUfOrLKdD326XHAmBNbwC6TuCA8FsQJzIi+t/dveaki4hkI54LD2WyjwnFMjwNgsQ5F2ge0awFT7apN3sUrrpXSiRFz8F3X0cakRd+3FfCUYRL5KpmUan3/zetYFjaTwF60rxsDjEE34EimCcyhI8C54lzEJs4Azdmw3D/sg3HgFT/UvAYq6Ui24t+8AyKflVrGgytVJGAcnD0XGwPZ83FUNnbj2kb4yBR7ZBw88YlPXP33hz70oZXnwXo8+clPBrBILvzO7/wO/uiP/mjr9/3CL/wC7r///hWl/4orrhD3eDMuv/xyPP/5z99IGowZP/dzP4c777wTwCJJ8s//+T/feu0P//AP49GPfjSAhVfErmun2G+EPA4aGKNGzHjKxfHkYaNq460a//bvFeastaBBC5kWoH6z0oLxAQQBiwbAdUSmYpxqY7/9vu+beGwA9Yf2FombDOtI8fu9eSwyD6WlcoKV1mrAyq2y2vUMxFJNQOzQqk6clGP/aEiqqQXrxpUL2/IOCg5pm23nK43HZl4BbRkHY1dbM5VuqkrjZYS8dlxa/viMA6PxL6x4HzLnlUvGEcBt22rr4faljAOzDu63/RrQyNuTjW1OXD4n2hhVJFUkq3Zmq/0T8yDPOMizQHOMg/y5KCMVpHgGWY8DxXzsPbY6xoFmLWAKGLzrxmYceGulwuMgKlWUXQ/Y5CXQBhuYYhF7u5OXX345PuMzPmP159/5nd8x13z91389gIWB8nw+x4te9CLcdddd5rp3vOMd+L7v+74Nw5Qv/dIvbdDr4xOvfe1rV//9whe+EA9/+MO3Xnvy5El867d+6+rPv/zLv9y0b1PwkQJNRzaFVOn4rYdvyFMn1bPog77adCtw30CmBajXlm7x/L3viUgVNTHojjAeBO2XFVrbAAuvWzKD6kqKrK1saZE83DUHaCprNr4jkDwE9Am8KHDb2uMAiMplNXgHdhwYykrMrM9MJHkMtPE48J7hVp8RwSFts+04YJU1oavpR1RjPsN4iIKWbhI3lbxhqu2184DxugiwTxefVzMeiIrzzDMIggZq6U6FXFbOYyBe7aoESxSJI6nHAZE4AZTVxhzjgN2T0TIlonlQ5TGhlawjgVuFx0EANFWczRjGzzIk1f5JxsG8NEcW9MH7Xi9Ue0LWINv6rJDJQ1Kqx2ccxKv9rWRuMHGQNMhmWI+raxsUtU2xiL2mYJ71rGet/vuNb3yj+fcnPOEJqwRA13X4r//1v+Lxj388/uE//If4N//m3+AVr3gF/ubf/Jt4xjOegbvvvnvltv785z8fn/7pnz7a7xg7Pv7xj+Ntb3vb6s/PfvazBz/z1V/91av/ft/73oc/+IM/aNK3KXKRkYnJamsDQXPkBhXv3uJWW3EPiCquK4HDFhIhQESqSJ+48b5n14a5vAeaMcB7HGgSF3X33/t7mUF1JWjQxJw4cf9PCFhHXdeZ57hbLqotcD+sra0dg1HGQQtzZG/8bav6V68DvjHqfllHXjvLcMEqKWA2LmC3DFN1P2IC7SCwDwL8JK5WLoqots9KloUk+5y1KPkeUOa4Uo3/pNeJ2OumyhxamMRlpHqOhPc/6vUDeM9flzhi3kEgIRNCPH9AB1jRMikiBiLPeNDtB1SMA4W+fq7amUkcxBNny1DI6JpzQAVgvx6KojKvAMLzczJty8yJuTlAJWPMmjN7ibOOKCYxvz8oVWRUMaLmyCmPA/2eeIpF7DVxsGQU9H2P/+//+/9w5swZc80rXvEKnDhxYvXn22+/Hf/8n/9z/N2/+3fxPd/zPXjNa16Dc+fOrV6KT/mUT8FP/MRPjPMD9hTvec97cO7cudWfv/iLv3jwM09+8pNx8uTJ1Z//x//4H036NkUuYrq2+okxkmG3WXU9aAvsqPT0pIpamCM77fd9L9NRLKOa8dDg/nvfs2uzUm6kWlT7jl1tXVvl4RtQiZI3lcDZcZsD2jEu6sdgFriOGqKpK40j1e6AfT8172C9z4RS1xrwDxe7fn8L1pH3+yPzgFQmpwIwKKvwsv4Gi35s3oMhjW+lx4HR9h2USdKyDxnQzsxDcq+VeubXrMuPgShwNp/3JoGTY97EQAM162af5tAAV/GuTJ4w1d5tGQdc4oBN4LFSRXY9FFX8k/rm7BjQyQQpx+BwH+bzHiXGzYxDNnEDaIBrA5omGAfMuaR8bn1fB9oDumfgzeHMPKDzGeGA+xOkhLIp4Kn0WTHef+Tvr2Xfb+1Hcj3IyGXZeXCSKlLF3hkHf+Nv/A0897nPxVOf+lTccsst5porr7wS1113HU6ePLliFAAL4G5ZAdd1Hfq+x0Me8hBcd911+NzP/dxRf8fY8fu///ur/z558uTKv2BXlNe9973vbdK3KXIR2aypK12BOtB8GSVgpQBtXZmSEONgHODa+zuZOXK1VFEbj4UM46AFcD8268bKtNRV+wMaqSDAGYNbZVJa3P/6zWK75FVApqO5KWkMsMqOQQ/oiKwDEoPyQMV9eZjJMw7q1wCvXxqDdK8P/hj0GQe6KssqiZAGRnBRuZ59goZKiRIgLtO0uKZ1xXv9OtjCIH1oHLqJdCFouG/JuBrg3FZ7jzsPSD0GCGNa0740cRGvNAb4PZlKqoiXSSH11UUJTNac2Rqkj5s88iVsGeA8zvjZdi0nVZRhPAikmtwCFi55BHD7Ypd9S0gV0awfYh+wuK44F6nkykipLpVUUm3iYhl5jwP+HTRr8cQ4kMXhPhs/ODjAddddN3jd8573PPzu7/4uXvayl+GNb3zjBjOh73ucPHkSL3jBC3DttdficY97XMsuH4v4sz/7s9V/X3HFFdUUpMc85jF43/veBwC49dZbw+3edtttoes/+MEPhtt4sEdks2wAE4Wu8RaJCm+MqQEjwN/g1Wr8Lz7fADBx2vcWcJU5ca2+uGpzUkak0s4kjxpU+0beAYnGf2WlnQfkKJJ3gJM8qZUqaiDTskvbtlnyKjEGs5W++/c4iAHnLZI324zRLjhx4Py9psJyGRFjYsB7B/Tr4K4+qBkH3vy3bQ1ehtUTFiSPkhXfSpmUqEwNkJsLy3doH3JREY338pAsKeKoTF4vQ51Ai0oVqRO4+2YcRGRLl9HSoJxhPGT2Y8bjg5CKAvg9SXT8LcMk8mmZFK7iXVXQVT7/+V9WnA8xmaQ+G4TOv/eeMO+hlYqLSAUJGAcJjwNFxXlUMnPoOsog2pUqGv6czhyZfAdFErIn6fZFiROSdbWM7Fxg1+D6dzArkzTF9thr4iASn/M5n4PXvva1uOeee3DzzTfjjjvuQN/3uOyyy/AFX/AFuOiii/bdxdHi4x//+Oq/H/rQh1Z/7iEPeYj7HbVRw2yYIhcxxkGLSktfosIHUvSJg0ilaymdcOKA0/Eb6oMn1eMfUNpUW9dKFTVrf6dMiE7T9vx31G8WWrBuzIFxS5WFX+XaiPVRmbyQyIUZwGgXaNsoeZXQ984zDmKAhdqcN+5x0KLavD55YdcB/e+PSBW1kmra+vs95puw2h5YgDa7bqul5tPNryJ6aFb6zYQZB+IkbrmO1VD01UnUCHDYYg6IVrzLTcKjHhuNJePqKt5163Et63A9tImL+O83YI3SnLkqcaIbg+VephY0Ve3J2Ip3VTGJ9+zOzXucHBhTShYyY5DtS4jmgfOcVFC+4j9jzsyMwe37MFs84l83/H1D4Q3dGsaBymuFr/hXJe9Y1pEmeRctHigjK1+ZeQfNuUQkozvFAyhxsIyLL754ZZj8YI177rln9d8XXHBB9ecuvPBC9zumOD6RqbZuYYi4bOfQ2SvYStN8+0tj1PWNVy3jYExzYHV13XrUUqTbSRUFKs6b+FzUH9haJE9qPR6amiNXanxbsE7P+tl1YDnTwJzZ68NOc2SxlmV0s6rU9AW2gNY714EGUkUucL4leXWuLWC6aHvcdbAmUb6t/a7L6cv7a/AcB7PtB3ZVlevGdwQPzeXwUFb8D72DvjyFEDTdg1xUJHnSZA4ImuN690hZ8T7UvmHd7MUcWfceMlIlyj1x+fuPKphPSulEyuNAOAbZim/Vu8gwPgDdnty7b2PqywOaeRjg3gMz/wWqrRXmuBlzZgX7in3+gM8KGNUcWbQnjLIul6HyO2LGP6CRqgIc2eSgVFF2Lkh5HDQoKJpiEQ+4xMEUwNmzZ1f/fXhY/wjXr/WMqIfi/e9/f+j6D37wg3jqU58abufBHCHGgUhLcz28Te7ZLRIVLWRSAJjEQW2lp8ycuGKxVleZrkftgdVuTtr8/p0V502qfesrLVt4HNgDQ0QqS/cOrMe2eaD8e41Mzf6q/bd9z26pIu17EJUqUrNevI9HkmctknfArgTa5t9nDeqjiROFEaDtQz3zTW3O7Fb6HfU4tWOrV05RimkoalCsfA8saBeXyckAx+VBv4air2Zg1kgmnv+39smzQZP41pJdg+1rJcso0FIInDMyEcp3cNscuGt9UbKOKMaHcE8WNefedh27HynvXZkY2xZWtoxlHHAV56bKOMEC5wyyNWczW0ASkEkR7EkyoKlNYGoYB/UG2RrGgc++rJkHNUlkIxdX+Q6q/I6Y5HXb9uvHIJDfG2c8DiZz5HYxJQ4egLEuy3T//fdXf2792osvvjjc7hVXXBH+zBSxqNVXX/xbA8ZBQF/bANeHmon5xKzDelprq1SRMeFqU3Hvte+BaC2lirxKL1ttPx7jYvVvDcagpWjXt69InpQH43m/2LDUSCjtO3k0tkRFef+zoPEycubIWeA6xzjIPoOu63A46za+d/fv17NOvLnUqzbq+97Ow4dZxoW3Bu2aA/TvgMd82y7VpGW+RRMnQBvGQdZnYMxKU+/fM9R0mzyskMgQF3LE/K7a+00Nz4OeTMx4rBO9ZFxcJsLsiYXmyIxMS47x4cu1OTVEo7S/i/m6al/IAmXmAEBnjsxWnKuSiN55ZnTGQUA2cxmqJHK0gGQ9FHsSJeOAk0ryZYuZ9hffp2EcMGOQT94V38tW/ItYRyzriTZHTiTPvOvDjAPnHRxivS3DrsUT40AVUwrmARif8imfsvrv++67r/pz9957r/sdUxyfKDc4uzbLLfTlXcZBrb66rOK9suK7QaWt277z+/0DShupIsDP9I9W7R2pOJcAVvXtN/E4qDToLp/JrMtJlGz0ofLQ0qKqwpMo2BYtEhfe94zJeoiaMjbR2A8cWtskzzzwuk6yLTsPzmadYV3EWEetxuA2rxPtO8BU+pVDtAX7cMz3IHpgVhlibm2/AjAwJu1qr5Wdc2B75h+TvFF6fUTbz76HUcaN14ecQXh8DCpZyD7rbGAOaNh+DWjmgfas7xkDWgPOeiCq+K+VuFclr5gENqBdB8w9qDHoFiWRrVxmJHGQn4tYxgugYUF6yZZ64FqzHssYByKfD9rnhJUKIuWyFFJZAHCyKAYtfSV3xXzeo7xd0X74rCcueTVJFeni2DEO7r77brzpTW/C2972Nvz+7/8+PvKRj+Cuu+5C3/f4xV/8RXzxF3/xvru497j00ktX//3BD36w+nO33367+x1THJ8IGZOOpe1cyzhoBJxvWyhsVUcrjwGHcSA+JG+274N2J4s8rwEqVL8/oC+vNEJbRgQwaOJx4DBnvDHYymMCqJ8Hyr9vwTjYBRa0ooOGxoD4PYhWuqpowRvfMZsBOH/fI6Ch6h3sOmD9jFY7DyrmocOD2cYhJeRzIkwcnN7RzjIMWJjWVo8xLgCnsishD7GtH0PAWctqY0YmJ+dxUK61NYCVdj8SAY6UEjnbvmOo2lBlSnr+szm5qmwhDVNxrDRk5Ay6dePArTgfNMguk2djMy50SVwDGtaCdqIEYlltXStVpBoDrFSN8RpRrgOkXBXThxTjQLAnOwgU8JRRzhXMXOjtIXKMg3gffNnO4c+pCpqMz0z1OyhiHBTjpmT3bgsVaB71Odrog3Ovov3w1s+j+W7W2zJa4SNTHKPEwUc/+lH8yI/8CF71qlfhE5/4xMa/Lakp26rr/+bf/Jt4zWteAwB4zGMegz/90z9t3t99xud93uet/vvDH/4w7r333g35om2x7lGw/h1THJ8IeRw0yKj6ppi11c4qwKYOuD5zrhHjoAK0UGk4elFm+QHg7LkeOFn04RjoyzdJXgWqTZt4HFRKpbSo9F5GLXCmqm5bj/I96ntfqgnQV9me/5765JWecRA7MLYYgzG5qDYJrBOz2cZBxeuDd5DJShUBizlgXa5uF2jW4v4Dy/t4tLWdbX/fxONgZJkWIA7eG23rEauNy3cga1DNVHuqk+gR4F5lxrjxHUF9Yw/YlRpkB0Hr7DTI6Eu3BO5rgGvlPOCNoUHGwTHzOMjMxdECgtV1ogQiLVPSqP3aPrSUrKsBrv15KF/xX6sv713LPIOMTEw5V6g8DsZmHHTdgn26/nVjehyo5MJ4g/LYHmwZOtYR/w4oWKAp1ksjfGSKYyJVdNNNN+HzP//z8dM//dP4+Mc/jr4yq7eM7/u+70PfL7Sv/vzP/xw33HBDo54ej3j84x+/8ef//t//++Bn/uIv/gIf+tCHtn7HFMcjao1ZgfEYB9ulijb/XqVvXgtct9D1ddv3qs2djQFLiTbtO8/RA+hsH/ahL99eLivicaB5B+xvqLn/UsYBKVUkMcZ1Kerb5MLasH5iclnaexAx5wbGSp7tAg3bJLDK++i/A20k21LV1o1+/7a1uEye5bXt49rCY8hlhYHbxHqYlclJy9Q4lXZD5xIFWLT5+UwCvQXzbzdoUBZyHMy6vRpkpxkHhFxWOUQyaxEjlaP0W3FBmxHXQsoYV7gnY81xVfuRMvFZJma3hdkPKBkHFckrpTmyInnESohmjFkV83HG40CRwPTe3VqfD6V0YMm8YTwO2HkougYuQ7UXOHkY2wetrhMlsDNSRYoxEPU729X+JFWki70nDt71rnfh6quvxm233bbx913X4RGPeERVEuELvuAL8OQnP3n15+uuu07ez+MUj3vc43D55Zev/vxf/st/GfzM2972ttV/nzp1Ck996lOb9G2KXKQYBxLQsP6wUCYUZJWulcB1K+C2RqpIWVVj2vcYB57GfiPALAKcjgGa7jowq/XFgXq5rnIDozRfqn0GarAK2FJpVNu+inEQqLQx81BaoqI+ceX9ewvQbqfHgXkGyor77e0A7aSKIslLy7rRjEELHG1jHKjH3/FgHNQkjnb2IQWaxkAby/rIPQNGpkMtFxTxWmmRQI5WXKv9bqIeA2qT9KjHh7dHy4xDBrhUrkUR9vG2fx+dcSDck7EV/2ZfzgL3xceqq50bGbMCnFyW1OOASR6xjAtn/qvBo/q+l0h2sePPu5Z5BrO/lKtcj9qCc+85scPAJNBqGAeiMcAmb1T7MaO+UJ281DAQlWNw8X2xfmRYLzaBune4+5Mm9nonP/GJT+Caa65Zmfb2fY8v+qIvwute9zrcfffduOOOOwCgqpL3BS94weo7fv3Xf71dp49BdF2H5z3veas//9Iv/dLgZ9aveeYznzmZIx/TCJmCNgBtu66rpqedbSYVVHdgLIEMVfs1B3bVwuyFx9zwnoE9JI0HGJ7/Ny1YAjgH5pE9Drz77x2YbeJGyTioA69bJLBcj41KubJWrJ+dySvxGIhulvct1zVaxb3TB29eUkgVhaSamnkccAns7DvoVWgOGhP3+nsQNeaTehwYf4WYvn6ecRA/sKrB+whw36KIo3b8r/ogZqBGZUrUBuFZ5hmglSqipJKE7yBQI1WkZBzEEyfKPZnZg9VW+4pYuAa0rNVXFzEQaY8DpVSRwOOAbZ/9/apq+xTjoJlcViVw7SRxWUZ+uR+iGAe0VBD3DpbX0YmDIOtzdZ3oHTByfSMzDrZ5HNSEfQcmxoEq9po4+Mmf/EncfvvtqwnlpS99Kf7Lf/kveP7zn1+l2b8ez3rWs1b/feutt24YAX8yxote9KLVf//u7/4u3vCGN2y99uabb8b111/vfnaK4xUxxoFeJgaolwlpJxXEATYyqZ6KZ9ACsF617yYOvIr3cRI3Y4OWEZmaNtredQdme0Acn3HQRKrIBc22ybS0mQNq50FVddeutoc2601kQgL61rbCsA1w7gFXrseBxGcjwjgo7/+4ySu1x8Fs1pnqvCjjIKPvv4yoOa0FjDLV1pv97/vdUh1q9qUHvA8Bt8agWsx6iCTP9iFVZQo5HOZkqP2gvrOafWiT90OJI+1caEArArRUVvzX9EH5DjDVrkrGBS1VVO5HaOCak8pRnU26zkqNVXm9CM2RGYNsFQPRZdwQoDXA7cuZpMkyVH47+6649z5bwziw89C4v78cpuwywEgmArpiqnINr5HLW/VB4DXi7eNqfRbUDNApzsdeEwevfOUrV0mDZz7zmfipn/opzMiNxpOe9CQcHJy32n7Pe94j6eOYceONN6LrutX/Xv3qV2+99ou/+IvxnOc8Z/Xn7/zO78R73/tec90HPvAB/K2/9bdwdLQw+bvyyivxwhe+UN73KTThbRa2LRStKi1rZUJKvTudxn4laNpApgawB0YXNG4k0wQsNitlcYYvVdRKoqT+sNDCnDdyYFNq+m5rH9jC+DCAtTBxUJs8a1JpWl9t28og3VS6VDIugDxo5m3Wd0WLMRg5NKqo+WWUzIGa5OWi/XEZB2NJtm31GWlwQMlL9QgYB0mpmMxtcIH7gNeOWiYHqAGONYDhMiLAdYu1KFpxr9JWX7VvWCexeTi9DhjJxCHGhXYtijJ+AG0hR4R9vAylQTgDGivHoMycmNyTlR8bW6po0Yd8tffYclUmccKCxqQxq888ij8DqceBSCqHNUfOzEPlR2tw42YeB+Q7qGIdAZtG0dtCVdBl94BJxkFwPmaS18uw+NAkVaSKvd3Jm2++GXfeeecKFP2RH/mR1PedOnUKV1xxxerPf/qnf5r6vpq4+uqrccEFF2z87/M+7/MGr7n66qsl7f/0T/80Hv7whwMAPvjBD+JpT3savv/7vx9vfOMb8aY3vQk/+qM/iic/+cmrhMIFF1yAf/tv/63MxHUKfUQWilbmL7WVlq0yurXGiK007GqAczVIUIYBTmvMeWUSJfWVpqpN+q7viLBuVHJdpcxCzRiQmiNXblhbjEPvd2xr3xrzit7ByjnA+/tsH6KAxRjGpLveATVgt/qeigRyOS+pTOJjrKM2zJ+aOXjRvp7xEJUpaWOOHGMc2Kp/XbWx9/3roQaN/eTxEHCuTeJGQNsWJvVRr5dSxiC7H9t38ixuDu4wDjLmxETFe8lMG5P1ArSVSqpKnAiZNyxwq0rilaawNZXWgLcnUwL3w2OwZIaVvyMS5hnUMB5E67F332qMWb1rmPeAlQlaXKt5Dyxo/8BgHNh5kE2cbH5uiPm4ar9kHJDvgF/EVTMGNfOwXKooeB8iRWzmuoaqEA/2ONxXw+uMgEsuuQRf/MVfnP7Ohz3sYfizP/szAMBdd92V/r6hOHPmDE6fPr3zmrNnz7qfU8RnfdZn4Zd/+ZdxzTXX4K677sLdd9+Nf/JP/gn+yT/5J+baU6dO4ed//ufxtKc9TdL2FG1i20JxMDtw/r5RpWUlYKDWtM2238qYtcocWbwonTyYbTA6XMZBK4+DQKXhGFJBuwGTNs/h8KDDmaPdfWjFePG+q9acuJVU07YDUyuZHFttWSeVpOhDpMqt73tpleX2Puxi3YzDPPIqfq3PjEourg60B9odEGqBoxbrcBS0auN1cnxAQyC2DqkN0oF44iQ9DwU0/luMQVtxH0ucpD0OgtWOdk+mHQNDoKHLvkqMgfL9m/8laLVLhkxdzHPiYIbTa/vQcPJQKhVUAdgVoGJmPWIYH4DOJJxOXAgTmIxclnIMRgoItl3DJm5YY1bfFDbeh4hc5Xp4e1L2PbBz4PiJA5NAo8YAW/HvaOz3PWbY/XvK5Aa7HWF9NmQeD0H29XooPA7cd5D0eZg8DnSxN8bBnXfeCWBR3fkZn/EZku+84IILVv89BOh/ssQznvEM/O7v/i6uueaaDammZXRdhy/7si/DO9/5zkmi6AEQkYWiFeOgPDB6E3Xf9810/qulipqZAw+Ddi3NkQF7D86cs/egGeMjUGmq1pcH7IF/bMDE+x7fGLYN4wWol6mwYFUbqaIxPRaAepkKNVgD1CdtAJ0R3tB37HwHxjIHrmA9je1x4f2bipJce2huwXiISjQ0kSoqfr/nZ7EeSlPM6IFZD5jGD6xqySwDnEfmAME8XIImQ8B52b804yCYuDL74STzKXL/gW0SJZl3ICbXBejnougzsKD52B4Pm+0rq92rPQ5MMYvIHJmViUkB9zHg0AN1M+8hJZUkKqZxk8cE6wXg3gNWKku5J2Wq/b0+ZJhPZaK0xqBYNQ96Y5fymqEZB6TPhmgePlniEAGpIkUCzfVZqZ6H28lJP9hjb4yDpeY+ABfwZuKjH/3o6r8f9rCHSb5zV9x4443S77vqqquqjE/KeMxjHoPXve51uP322/G2t70Nt912G46OjnD55Zfji77oi/CZn/mZ0n5O0S4ilXbjAcfDoDWgrHivrDZuJdFRcVgyIIGYcVAD3reQJwDqEzdeHySVroENcytj1BqK5rjmyNsq7vXvQAS0KzeSqjFYXe3tGVJm5RkSiSugjVzXboPyseaBOqmiFm3vNIYdKXm4DbRrItcWNGY15sgKuag9ypT4xpS7DLLFgClxYFevx1aqZ1zWUYTxADhSRYe5Z5D3mtGOgWHGgXYt4syJ1cmT2DPQmjPHpNIW7W/+eWx9fe863hyZSxyYNVkolxUdf953RILR2Nfp+3uMgwqZGBELNirVdr593Z5U9Q5k5uJyDqMq7tl3kPS5UBVRuGcxxuuGNUdWSxWRkl3r31U9D4rm4Sls7C1x8MhHPhLAonL5jjvuSH/fmTNnVjJFAPCIRzwi/Z0PtLjsssvwDd/wDfvuxhSJiDEONAY8Zdgqo+FKU0BXdW9Aoy2/X1ndtOt7xgTtz3/f8GapVeIkcmBuIdMS0dUtn41qc2DNeYeTR1pz5Lp70GIcLk0R19+7bYmLVqyfWqka77moGQe7qjxbMQ4iFPHRDKqdPrSSKqoxqF9GM6+fStBk33JtXh8UY6BWLmxbH1KMg2DFvzp5E5FrAxZAQTk85ObAI/5+IFZAsPh37Vxg9wHjVtvX7kOXUd6fWWerZSMRlesC9GtBVi4qBRgSUi1GXz91/1ngVmSOXAKm1R4HOvnIKHDsVaRrfSZqkkflO8DK9MRZZ9uuoQBTQqZp23W8QXf8/nvXje1xoPI6Yc15VdKRrMa/KnmYkSryngHjf3Y467CuH1PNvBH7/UxxPvZ2Jy+//PLVf992220r6SI2fvu3f3tDnuiJT3xi6vummGIfEam0a1VpaUA7Z6L2KGvZCrNllODnVtCygUwOUAdct5JoCfVhJG3zXQu1Wp4h2r415FSxboZBi6OGyaOaA5sHVulYR1z7raSKahMniz5otbXjjAOBXFfgwNYqgcUkkPchVdTM66YicQK0Yf5FKz2bMA4q5cKWcbqoRsto3Hv7oIhcVfaQOJt1xhhyJ/NOCNasPh+oOC3nB4k5crDiW504iAJnctA8aY6sfv4AYRC9Z7moMUFrwILrmSEQ8fpaD9XZgPc40K3J8eSdmnEQHwMqjwlaJsYzSSeeAfv8vQSbjHFAvgOZeaB8DDW5C5V0I+sxYOYhIeOBkkoi739GqqhM8LDst0gh4XqUxUZqjObBHHtLHPz1v/7XcerUqVUG6pd+6ZdS3/eKV7xi9d+PfOQj8YQnPCH1fVNMsY+ILBRKTeH1qAOMHDpmM8ZDHeNAJ5Nhs+ylhFgLM8r1qNkwmwNCK33/nRrvetZLRKKhRaUl4IGGnjlym0rvxXdVsF4ayeQAFrjznoFvTNxGKmq7Qbr+HkTAkmaMg4hUUTPJsuFqo3agfT1o04pxUJu8aTMHBkHTFgncoFTLPafPbfz5olM8odl7hjsNspsksCNeP95+SFvtvev+t2AfRhgPAHBG3IdyDYpWO6vlqoYTJ9ox6PtsDPlMaEDT832I3QP7Ho4nk9O+/TrQTMWEpqWKhHNhdh0C1IwDAjRNgMYl1lknVWT7yHTB3vvK8edcx94D4y9Qm7wQSqaVRRA1zJtWrB/vu70wzCfa44Br3+wHyOcfZZ1uXKtiXQTXoNV1jZjYU+wxcXDhhRfiy7/8y9H3C1Dux3/8x/HhD3+Y+q43vOEN+JVf+RV03YIK87Vf+7Xazk4xxUjhU+QrKx1liYNhmQzvEJmpMNxov1pbug1o5VJUi3vdqtp/GTUbZvv7G5mC7gJMGiQvMua0MrmuimqzlubINT4PqsomL8oEpvf7fRO4Nsmr7dXeukPS+c/vFzBc9CHAemgmWTZcbVTOQbo1oD552MocuTZ5odK0Xo+oTIYFTfP3IOIzAQD3njna+PPFJ3nvsqhEgKm4V0g1BUAzN4GprnjfyXzS++1ENbb3LVWk3g9HpZpsAje7Dg3vQ8tQm6RnTdrH1re3UkV082Gpqm3XqUBbz3jYbV94LogC92pzZIZxYAt6dD4jjFTRiQNOooVmHDjX0VI1xP0HdBX3gB0/Ne+BLHnEVvyrGAesVJJoDjCSoZXV/oDHgtQkT+jk1SRVJIu93skf/MEfBLDQVP7Qhz6Ea665Bh/72MdC33H99dfjm7/5mwEs/BIODw/xspe9TN3VKaYYJWIeB20qLcsDswfce9V/zcyZt8qUtAFuXbmooq1W/gLLqPJZaFTxbgCTgESDQioooqtoPA6asU5qfC50Y8CA1y5oqwerVt9j2q9LHLTSuN8GGqkOaRvfETgsuYwDxTsQYRw08rqx1Ua2D2dKQ9RWyeM9mCPXUqStlqr++Q9qi5s+pLtgnuUumnrf97jnTME4OMkzDrJSQS2Sd1HmUTaBlWlf8R7WME/Xw5gjJ/sQBW7VQEHNGryr/fzvH96HlmFYD8m1KCoToSzm2We1+eKz9T5Du66jpYpIjwNlIUE0eaNnHBTJSwK4T/lsCFgvvL69BW1L5ntN+wC/Jy3vf42/AKB9Dw3roaIPKvYX43Ewn/cou6gaAwCbPGMZB7F96EYfyj0p2QdWLsv6PEyMA1XsNXHw1//6X8fXf/3XrybDd7zjHfhrf+2v4Wd/9mfxiU98Yuvnjo6OcNNNN+Ebv/Ebcc011+DjH/84+r5H13X47u/+bjz2sY8d6RdMMYU2IguFkpa78T0VlYbeAiID7isPrK0qvl1TugKca22OXLNhtdX+4xuTtpHIqAcsmiXPKqoNDSV8ZHPkpnJhFTRpD0jUJS7qQKMWBumZav9FHxSgZX21b7MEYkXyxvrMtGF9hVhHjRLY21gPx8HjoAXjIFLxfvrc3ByWLz7FMw4W7Uc0/vXrQISm78/F2Yr3eubTcTRHzrKPosC9EjAECHNk8TzgynUNyDSoZdNqJBs321cyDob3IGWU82DG64WtdFWxkdn2rbb3eHJNanNkxiBYacwblUsD7DzFnsu8ftcMAeVadBw8DkwCbWS5qqHvNv8ufAe8j1WxXkTn8uj8vx4ynwmS+dXqXDAFwJcEieLVr341/uRP/gQ333wzuq7DBz7wAXzHd3wHXvKSl+BzP/dzAWCVFPie7/ke9H2PP/uzP8O999678W993+Oqq67Cj//4j+/z50wxRSq8uXV0xgGh7774XKNq52rQsA1oBdjFuhVgtq0PZXt93xvgtpkx7q5KzxGA24hERCu5Lu8daGFIef67hu+BX+U63hhwpZJG9jlpAdoeS4+DHX1oxX6qMccdSy5tF2BhgJpmzLvadXgfHgclWJLugtkHHM371X67jNLfAAAuTEgVAYt7cGbtz7tAU7sO6BMnu8yhvX+TV3vvTGDq38NI8hJo4HGQZhzk2o+CZuq9EFPtqpZNCxtUCwtJ9s04sL+d09ZmnwGrL69MoGXNsQG1x0HcYyAzD5VgI+WzIaq0Xnz3HAez3euqkvWhkkvK7ImMZBfjccAmb1yPA+IdIBOYXdfhcNZttFnlsyFaiywWVPf8AR3zpsZvz4tyT6Y6m06xZ8YBAFx00UW4/vrrcdVVV20kAc6ePYt3v/vdq+v6vsd73vMevOc978E999yzYiksr3/Oc56DX/7lX8ZsGhxTPIBjuVCsx7aFwh4Wx6v2LSUqDmd5iZDVd1WDhm1AK1eqqGirRaXz5vft3rC5OpYyqaL6zcIYwG2s0rQV62WY8TG2OXILsGoZNca4rlTRYav263xOJBIdIY8DL3kjAO3MO1Bf7axjnRTzwDnbh3IdaOdzU8/60Jkj1wGnakNSoF6ubxktfB6837FtLSj9DQDg4oRUkdd+xOOgjcZ/jHEgl+oZWaqpRrJy49/Fc3EYuFcnDoKmjOoEbtd15jfEJcuSyZtgxbVyHmKqzVsmLliJDNqYlWUcCN/DKOtBnTigPA7MO6D0OKgBbTXts/r2yoIi1uNA+x5u/rkGNzbzEC3VFH8GatYNMw+o1qJa9QcvjM8DiQ9FCig2rmuEj01xDBIHAPBpn/ZpuOGGG/BjP/ZjuOSSS1Z/vzQ7Xv5v/e+ARTLhoQ99KH7sx34Mb3jDG/CQhzxkL/2fYgpl1C4UrRgHNfrireQxACfLvRU03OzDSVEfPEpbuRlsTYMbYl20AiyBWKVVG8Cqnh7ZivlRw7oxmzNh8qgucdHQHLkieen6nIxoTg3o5Sm874gkrgCfNZbtw26PgzbvwMnD4T40kyoKyJS0kuyr1Xdt4XEQrbQcQ6oH2L4WlP4GAHDhCa1U0djrgGEcBIB7ID8XheTKGiSxo8krA1geJqWKgsC9OnkUNkduUMiSBW7TyZsAcNT3vQywA+xv7/thY1RjjpySKorL1HjXsfeg7Htl89KzWfQe+PuhcUHTpsmjqvY1vmsu870KNLbzIFvUZ1kvdcCxmYsTY6AcPzUeB6ox4CVvmeRZeR8jwSRvVJJ11ueqnnFg9sWi5E21XFaDYoopFnEsEgfA4gV92ctehve///341//6X+P5z38+LrnkEvR9b/53wQUX4FnPehb+6T/9p7j11lvxspe9TFbtPMUU+45q0KzRxFhzYFLpOHpRW2nWgp4PkObI4kXJbFaK39rSnDpU6dhALiqyUWtRaQrUbdYsUKAbAzUHFtccudXvd6WK2rFeaqs97SFNL5W1GzBsw7yKvAP2oNiIceAlz0rmWSufm0DyZmzJtlF8XoISIZKKe8/r51wd4+CikwepwzIQXAdGMKjevQ5qqwwBz6S9Xq5Lw7yKVRvaQo4k4yIwDwP699B4LAyAZmMksaM+E9kxmDUIVxrjAhXAtfF6GRe0BnTjgPF4WLSvYyBG74E6gcpo7FsJUV3ipCpxIVqL3Gr3qt+vewdYxoHUHLlMoI34DLzPDpsjO98xcvKsHCb78DhQFfTYAg5yHhSdTabA/j0Oyrjwwgvxnd/5nfjO7/xOAMAdd9yBD3/4w/jYxz6Giy66CI94xCNw2WWX4fDw2HV9iikksXfGQcWBqdTXz1aXbbRfLVPSJnFCmSOLF6UhmrYrEyOrNuelWjSVroFq67E8DhyZllbjD3AkAlyPBT1YtWq/QqpIWdlURu2B0TyDBpW2Y5uyArED2z6Bc7Uh6jIigFUrj4PaassWYyAyBgF7mG7FONjG/rv3tE0cpNsPjQE96yRmTqxPIIY8DkZIHA2aAxuJlFwfou+A+j2MFFAAnkSNah44/25F74HaoHuXXFVr0BiIz4OZ5KXdh1YCVip99aLvlc1LJbOic4DeHDmWvAO0e0JOLkuTxPXGDSPXlSlqo5Nnwj2p6QPFOEgkz7rYGPDGqPQdIJJn7DwYYZzZPmjW4/LZ1XhcABPjoGXsDX2//fbb8Tu/8zurPz/pSU/CYx/7WHPdp3/6p+PTP/3TR+zZFFPsN2oOrH3fN5NoqDJGbQqa1lVZGdBKlLxwTalGNkce2rB5B7gxq823/ZvisJwDTDRjoARAPY31lsmjOsZBO58RC9w6cmUN54DaA9sYVZ6RxFmrxNWuDXsryayaaiO1Ieq2tiNyac2SN1uegV0L9NXe0WrrTIXbMmqYd8sopYouSvobAJbavnsdaOBxkDBJH1sqqM08GJMpae1xMFRpqJZNzMq0aJJXMeDGAnbj+UwoTVm3ffbsfI4LsT0puW+Pg/m8N5JC7JrISLQAHhNWxzgYHH/FPZp1SO1JNQbZSsZF3JxZyjgYudo+KpW27brUPEAwDpRMdHsPBtahY+BxYO4/2bw5B4fMkTUFPbTPRkMPwgd77C1x8NrXvhYvfelLV3/+vd/7vX11ZYopjlXULBTe3CmrNq4wx21hSrqMWtCsVeJkaVC9/v3l7x3bHLmGcaCTCbEH5qVxfRmq6qpd33G0o/3xqq2HK/615sgVybuG7Ru5sorEhXIOqGFcAONIFUUAw30wDlpJZtWMASNZ18gcedsz6PveYf6IWA+VJu0t5NKOg8eBy7zb8h7eaxIHCsZBxudC8QwizLsW62B9ArPFXFzr8dGqD9FqX/U7EK22LOdHRRLVyOYNVbuKpYpqmIfL8GRUlObI29rY+HeRISfgJ4627UOX4Rb0CM2Rh9oH7FqZqTiPgnZlNXD6HSRAQ2XFfTSBD7STqgLqGBfaxJHI5yMjVVSaI1d0QVnYZ/eBu69XSxUxkmXWg1HlccAzDlivE5710hajeTDH3u7kxz72sZVnwWWXXYYnPOEJ++rKFFMcq2D01b3P0e1XUHSVdNgyaqVqWlDDt31X2YdWgOG27yvb84A0lUxIxJSrTaVlfaXNWDIlrr67OaAozZGHQauWPiNDHhtAG5mgbd+1nXWk78O2xJkXtqqljVRPxBh1TINiOwe3Ae23zT+jJtC3zkFjsF6i1c5t5uFt/Sg9Di4+JWAcROS6WswDAdCoxTy07wRmtOL6zDntPBTRF3cZuMn2w+bIDYpJ7D0IMg6S4yBSce6eSYTGvIs2BoBroVSR1/chzMot6BFWnNdgZso1OWsMq/bYqKu41yXPIgb1q/ZF8xDLOFDuBY4D48AwbyjWyXjAvcc4yCwFGsYBmbwSShWx70GE+bnrOrUqxIM59pY4uPTSSwEsqnsvv/zyfXVjiimOXZgMtzNRjqlvPjrjoJKirjbjW48SNCoPbC0TJ8AwTd6vbGoDmAHbF+sWOv9+pU0dcKt6B04eDoMGVqpINwYYc+Ry3shETaVhi2r/ZdRq21ojPD3jANh+YD8OjINW7Kca4KyUKjrZaA7atgaMmkDf0ocxPA6GDkumukvQB38dGM/jIKJv3UKyLnJotlWmLTwWticwW1S7l+0PARb7lCpqkUCs8fra+Pcmfjux5IW6kCPC+vDGh1IqaFsbm/9efIew0hcYZt34TOBxgWNlAi0q16WWzIu2712T+/0E40G0H4ucg3ZdI5XrIhkHSqmeGo175XocTVx4UkpjG1S3M0eue/6Abl/MJO+ANnvCKRaxtzv5qEc9avXf99xzz766McUUxy5qNkuuEZms0nT4wKykQ5r2K0GTpsDtwIGt5e8HhqUqlJVN5nsqzKHP96s9aAbsSBw0kquqAQ1s8kjJOBiusmihqXz+uypYT0JK/GD7lYkrBWAWoYi3YrxENsstjFEB+x56oEkzqaJqmaAR18HK5OVeGAe9vg+zWYfya7ZR1a3HgSBxkKi4V/z+iEyLTaLqK/6BHcy7FtXuRqpsz1JFAYN0ID8GIuMPsPenhddJmHWRHAe1CVzAfz+UgNmijd1jsAQVMwlUBrj3xyEpVeSA7gxommGiRiXzlPffb5+puNfJZTEeB0rGCeVxoDQmrnToLsHzDGjLMA6UnkfRvVhrr5e6d0Bjjmw9DuoZB6r3gPc4aIcPPdhjb4mDL/iCL8BsNkPf9/izP/sznDlzZl9dmWKKYxU1C4XHQmgFGLnVzgUtXVVpCtRpawNtfQasTEe/889K0HTR/u7FsqUxritRsS15M8JhedF+XeKincfBcMW9Uq6qRl/bAhUN2x9RpgaIgLbtAbNFO3XA9diMg77vTR9U4H2NXFarMVCrLewm0FXJw2qvnXIe0LNe9uFxADhjYMtzuK+UKhKYI0eqnZuYIwcOrC2Yb958PqpkYPEb+n43aGESB4dJ0Dqir+/8G6upvIyaffDGvzcYAzWSictokUStTeAC24pZdKD1UPuANpHvraMMaEibIzu3rgY0K/eFqYr7oM+IOonO6LvvEzQGdIBl13WUQXe5V8gkjmzisu5zLVkPQybhnkF55h0oEy9D5syuGoTYa2UoDPOKHAKW8cZLFalMwmuTVy2lhB/ssVfGwVVXXQUAuO+++/Crv/qr++rKFFMcq6jZLLUETGpM8ezmtCVoXydV1NJnofy9LUFTYFhfuKkxbkCiokW1s/cd2xkHbSq+a0yhWlS5rtqvODC0kEZYtV8hkdDWnLkWtG0BmGUYLyrGQd1muSXzrGYMWLm4VsnrLYnDlgn0StCgBWhb6/Oz+vdGkm01ySPAYRycasA4CICWTcyRA/ruLUzaF+2MV0Thz4Pb74FatszKZMSq3eUyPYMyNfpEfgi4F0sFATGpCu/56BkHQ6Cdrn33/QsahAP82cB7/2o8BkpcNTMODXA8AFyrzZFrZHvLUK4FDHBvjXnH85jw2le+A7Ua9+V+VelxMES68RILOZ+HGHDvsYK0jIPhZ6BifJRjdz5QPLAeKvYbyzho7UP5YI69pmD+wT/4B6v//v7v/37ce++9e+zNFFMcj6jZrLT0OBiqtgeAs+faVTvXUtTbaqzHgHv1ojR0YGxqjOtKFW0BzRokb7zfsl2ioRVgNjwGW/p8lL+jRiZGyrip0Lc2v1/afi3rSF9tH6l0HI1xsFWuzalwbCXX5YzBMqEmkyqqPDC7Xi9jP4MGyaMoYNFCqgjwKs78fliPAwHjIADcm/ewQfJmN2iqv//eu7QNOGuSQA0wrwBvT5hlHNSDhi0SiLXm6MtoIZsYkUtSS2R4n4+8g4AWtN3WxnoYc2Sxx8Eg40EoIeoNn6FqZzdxkWJ9xEBT9X6I0nc3c2Gm4j7vsTCmMS+gPRNFq/1XfZAyDjb/HDXoXrSfkUrKtd91SKkBMObAhnVDtu+d52tZByqjeiZ5Bnjn8ylxoIq9Jg6e+9zn4u/9vb+Hvu/xR3/0R3juc5+LO++8c59dmmKKvUcNaDKqKWQF40Gr714e2OoYBy2r7ssDk/392kVpqNpuzN8ObD+0l89GItNRuVlpoem77Xvcd6DhMygPO95eqVW1u/dd3jvYcgzWgoY2eaIHDHe232j8mUq7CHDeSK6rziC8lVRRnVQZoDMJrzfo1iePooBFC3Nktx9b1oEmHgeJamtF8qwmebpq38yFbRKY2/ZCLRgXrlRLoOI8b45ctw9ctK0FiwCCcdAkeROQa/KSyGqficA7OOty8xAj1VICm5mfH/HaOv/vdoywz8AD+4aAW3VBWcRjw/v3rDmyouI+VW1OeBwoPRYkv1/qcTB+tXfU48DbK2b2hNm92D7egfISdh705q7axMH+PQ70e7IpFrH3O/kv/sW/wD/6R/8IXdfhrW99K57whCfg5S9/Od773vfuu2tTTLGXsKDxuIyDmkq7VpWmQH21ZcuK76GK55bV3t73GcZBU3PqesCihVxPbaWXt38YVaaloYZijUxD0zFQJZXUrv0axgfgPIORJUKUlOz1YI15vc+yUWOM1kouzvoLjP/792mOnPU40Em21SVw7m3hcVDpcwG0Sd6UcnG7gGvPcyjffoB512AtiCQuAD37KwIaq+UhAPsM5/3uiu8WDLxIxbH3bOSsiwDjQLEnjoJmqipXYItU0pBBtvPvbBcoqaLGMilDFe/qBDZX8a8xhl20HwctlWtBxOdlGUamJgFcs4kDJeuifH5DBuGtmU9Rj4N9sG5UvlvR4oH1UJ2NyufPJq+UhXUP9sjv7hPxzGc+c/Xfl156KT70oQ/hIx/5CK699lpce+21+NRP/VR8xmd8Bh7ykIfgxIkT1d/bdR1uuOGGFl2eYormYUHjWo+DRlWGNdXWwknZgoZ1VXbSxMHAM7BtN2YcjGjOHDkwtdGYrwNuW7JurLbvsFSQUq6qptKrpcdHTbVvCdorDdJrkqeAl7zQV3sD9VJZqsRVudGvBQwXfVCZAw8/gzKBfDJpiLpqu/L5q2npG99TmbywMjmKavdgpa1QomM9auZBwCYOFB4H9h0YF7iPANct1sFa5h3QZj8QSVwAwJlz5VyQBK0d0Kzve1f2ocV+2JtHz87nODXzx3aLtaBWKgzwn002eZFh/agMys9stBFkfYilkqLA+YmDjpYp8R7dEGjWO92TSuUMrENqFiwDXCsBQ9N+lTlxuR8YF7gvk0eZxAmbOLAa+4k+BBkHasm2qEG4ZT3tgXEgMkfOSBU1YxxUJi6UJvFTbMZeEwc33njjxqK6/O/+L1+8u+++G7/3e78XWni3bSynmOKBEjUL1b4ZB6a6SgQYAR5FvbLKTgncDtyDlubQwDBw3NKcues6HM66jXHnbRY8I7YWgA2wreK93TtQAxq2lCqq2Sy28ncAnHdwZI+FWlPWJrrSgc3yWB4HtYAhIJQqqkheWEPQRqB9xBx6bMZBg3UoWmXWyqi89tB8z+lNqSIF4yDiMdDC48CuAfVSOS2qzYFdCcQGoG0QNFAzQL32j+a9//cN9gJRjfsWnls1RUTn23fuQdYgOsD6aSEbGAXNDGg3sseB0qSc8XjwGAcZOKRWMnEZZTV4nnEQYzwA2j1Z7Vl0PexaMC7jQSmVEwXtl9EygcfIdWXWYyuVtHsMKhkfAMe6URWSeGtYWSxU2wf2PYwUkCxj7mITexfY+aSJY3knu67b+N8UUzyYggUNVe9KzYHZVDs3BO299vu+b2KMuupDsOJfTYMbAmxaAUW17S/+zqPG55/BMnEx1FYLXeNl1Eh0NDXnrpBJKDdQLc2Jx06ceIm73jkwtDDAcrWFKwG7Vj4ztYwHQGmOPPwOGJ+LRsnr2mr/rhPq+9cy3xqAtrWMk2UoTUHXo1aqpGQcXCjwOIiAhm3MietBmxbsM+83bANuW8jGhc2RxYnkWubh4u/17ENf33nX729TcV/bvjc/qVkXu4BbMw82YJ8OgWZKqSLXYyEoU5K5/94cPiTT4kp2CZMn4QT2MWAcSM2BicSFsuJ/yGcF0CZvoomjbddlzoXlezDkz6xOoEbfAbVcFzMGVQVV0T3Arj6w7yElF9ZQjWCKPTMOALhgwBRTPJijZqJsVela235LfwFTbeyBll5VQUNz2vLA1Bq4HwKOWujplt93P8634W1YW+qLHxSMB2/D6sqUNKq29hMH7ZJH3mbzqO8xw/m/b2tOXDEHNE2c+DIZQwkNxT2YzTrMuk0PjVqpnLE9DlqaI9ccGsv3QiVVVAvYtJCoOf9ddZVeLZLI0UpLy3poJFe15dDYwuPAyCTtuAct9NVrjaG9f5NIFQUqjq3Xi2IMxsxhy/UoK1UUMadtwcD1QZMdwHkL2byEVBAgSBwEZCLaeBzUv4NeHxTGpOvfGfU4yIyBSOJsGZ7+eiaJvO8EdlQmBgDKR6Q0R67SlxfuB/bNODDM90q8Tnk2Kj86KFUkTqA+ED0ObPKIa9vDFWqlilRyVZRBeEMm9hR7ThzMKzJnU0zxYIuazVpLmR6ra+xVG49ozFrBuADUyYvdlV7tzZF3A0ctq72973PHQFPgvsPpwfbbVRVUMS5GZLwAi3ngxFohb1uD7OPlc7LoQ4/Dg/LvWgGmsw3N7mqPD1H71R4HDZN35aFhzASyBW3HlYpa9KEOvG/Rh6jHQflomkm2bbkH957ZlCrSeBwUB8adHgPtQdudxsANkqi1koFAmzG4YLFuVnjuSmCVEgZpqSJXsrCO9TPrkGbgeqDJTtZJE7mqgFxWA7mmWsnAxb+NwfqJeQwopJoiHgvKSmvv2Q2Blt4/a82BY78/Ow+X7ff94h7s+k1lRXYmeWGA84pq6xK0VY6BKn374pLMNFQmHWpAa8B5BlLWR4z14n1Hpv29exwQPhvsuWQ2s3uQWqmiZh4HhFzY4nuOpcDOAzKmOznFFMcsaqp8mgImFRINZ8rqMiFwXXNY8RYvbcX17gObOSQ2Nkce0+Ng0f7wMxjTmLRGrgvYr89HS48DwBsDDYH7PbOOvN9fI5Wjeg/rQeM2BtnVjANHIkUlWVdDkx9NqqhaKmpcua5FH1pom8cOS2UfZPNg8T3lug8sgJw2jIP6e9AkeROptm70HtRUnPd975iyiiT7Kiu+F9KR7RMH26R6mlS7Bz0exjBH3p240K8FEYPwFmvhPj0OAAc4HmjfJhD59hdSzZt/N6jv7vx75jFEGC/ev+cZH7HknduHFGgc15e37yHdPKVvr2R9lL9/KHG1DGVRUZl0GJLr8s+l4yUu1O+AxUMI1klmDAaLWLb3gesEI5flsk4mxoEspsTBFFMcs7AT5bgyKTWblaaMgwp9c29haMk4KO+BWs+3jCHgyEgTiPX7akCbFpq6277Ha6tp4qLCkKmFtvmu7yrbU+rp2vYJn5OGyUOgzpxXNQ/VHhZazcO1uqYtx0ANaGXAwlZSRbWAYcvk8YhyVVGJiFbAtZUNtOPwvrNH5u8uauBxsKvatUUCqyZ5vWq/0Ti0Y3B4Hl58rs08tIvxUOI5eXPkujUAaLMWHwdz5Ei1a4u1wBRQ7Pr9LRIn0WpftUxIBft6o33xPYia03rAbs4cOFdtrTZHBgjgdkTGhde+0iC7ymNBWPFeTmG1jAPLukj0IfgOqP0fo8lD5W/3Ps/4fGSSR1aBopZxoFHFUDEO1HLOD+aY7uQUUxyzMIZoziSopMSWUWrTrkt2rPrUsNrYW2DKW9B6YRjSN24tFTS0Wfj/2fvzeNmuskwcf3ad4c4DuTcjGcgEAQIGAglTZLCFMBn7C40DiSJDq4o/6tQAAQAASURBVGhoUUDUEBBsBoVu0wg00j8ZtIlxAIEGRBEIKMQAhkGSyBRMDJlDQpJ7c885Vfv3R6XqVK31rmFXvc+7K6fW+/nkk3Pq1N1r7b3XXnut532f52Ho6caOl+szoWGIB+RtmLUpqaORY87q3gN2xX1qDKg+gxOcP1OuDAiwPkgJtBy5NsAHCqwZB5YG3VkeB1qVzpkSGUzJvknNkSmAXWKzpg3YDCJnHnDZBoBO4qCJxwBjPeTOJfFq8/Y8JrjMvzzQQpofp50L5HdAHvNL5xmczhxZpQ+Z5uSs9htJJZHkskajKWg3rcZ+c5kU3Xlg2vMHbD0O2ObI/TaaykXZVZtrtz9JtbdmxfvEjAPFceB5DCS6oJ08bMo6cafIaV/FE/lc1O71n7wTk7QP6N0Hfw5qbg4N6GETJUrioESJmYucxYot40BIHBCleqSXnLthkpIZqlIxCW1Vlrb6+vHiC0amvj4gSRTkmSNrJW/c85HNkZmMh7Qxp+UzCAhSRdRq6+aJG6Y5eb89aQzOGOOAlDjLBey4cm1S4oAzD+VuVpiSfTnJQ1YfpqXHsxgH0jzo+hsAwLZN00sVNdmwMuYifw3QoNqbJpkmJU95a6HcBKrUh6WpzZHTrLtBuMsTDZBg4PEw3n4MONd/FzWRivHHoH77UYNwwhzUVKpFey72JRvjoJV28mba9wCgq/GfSmCzzZGBHLmq8d+nGQO5a4Cx77ScuPDuQQuMA83n0B1DqeSF+y6a/hkc/z39DLKTh81ZL1P5XDRMHg5CYn5MEk0ZH/22edhAiZI4KFFi5iLP44CjaQzkbRZXHeDevNpaqrLTlErxFszx9tUZBwngXHtx5IZvDCkBx/7LWWsc5kgkuPekU9lW2jKTN5MwDnR9TnIkMnjJM9EcOeMe8AC7XFNSu0pj6XPNe5AjFcPy+ZiN888DrRg+C21LdAT7IdyHuw6MMw46FbBJQbLKm4MioBVjPTRNtbdWAj1PNpK3FsqdB931IDD9+1C6hiHQhMU8yvV4AFgV//F16GgwrkETc2RtwAxoDpqpVxs3BI7V23dB04S+u/SamKYLUyewpzZHnsTjwHkXGDIupO9YSkUB/hhQZRwkxt8gVD0OnH+a9PnQliublnEw5TTcxOcGkD2PVJNnuckjj/UwKeOgeeJAek9qF1fOc5QrWaLEjEUONYsplZMjk8AEzqUJ3q+4FzaqqsaYCXNkor47kN6w+/dfm/GQBm20Tahix5EZBzzQ0GMciDI5tubIPuuFA1YBeebQzDko2xyZdA1yNfZ5Hgd5G1ZmAtEdA73ar/ZiSRX5z1+eTJBq8iwTNGUk8KaViNCTbEvfB5dxsHV5UcWgexrGgY7HQf6G1TPEZEn2iWsxqbqOxPwJMQ4IG/WOUPGfa45sWcAwCIZ8pO+zkW/OzGA+RZ9BC3NoY+C+KWioLeHqFsKkzl8Cdk09DpQZB+I6ODkG0sfIjRzZYL99vXfRRFJJnmzhxM3DfYQnN8bVuwYpxoFm0mKS9jU9JqR/n5Yr8z/THIOxAo7R0BoDE0k1icyriZovIcT0fGJCfPe738Vll12Gm2++GbfddhsOHDjQ+Bjnn38+oWclSvAjx5CMK9GQBgtYgBGQB5q6G8iFTqVWbQ6ks+xsqaKmHgfaiYsciq5pxX+GVI5uxX280k0yg9SVKsphHNgB9zn69qrm5BlSTQAHrAGkSssQYMVhfWQzHoyTN2u9Gsv3fF7XNU+qKBO0ZWibh/qQzThQuAeNPQ5oiYN0AtH1ONDwNwAayqR46wEG4yG/2lzLayXrPchkHGTOg9L41FgTLnU6WBk5dn4CV3EeWg23MxruM6iRxM5N3Ijta1z/Bs/gTEi2KVZbA81lMvQTF077ScaB//dpkrjNrz/3/IG0XJTmmkxFJmYqqahmiStAVy7KY77nMg68ezD5XOTuKZsyDqYtqmsKXKvLdU0p1zZtHyb1ONAq5piMceDjIxrFLCX6MTOJg2uvvRZ/9Ed/hPe85z248cYbpz5eSRyUuLdGTrU3VV89CzS2q3QF/PNltt8/Xjx5wkzc9NuPv6x9jwllxkGGtjWz4t+XCZHa51X8pzbMzCpPIFeuyw64l+Ygz2NB8RnodCp0qvHqmTzWQ8seB6RK13bMkePJG6ZcnLRZquvaW/wzmW9uH+q6f/6psTELHgfTAmaDmIRxoOFvADTbsDI8Hvxq0wYyLSyvj4w5EFCUSso0CF8RPteqOB/NS+UmUFmMm0ZSRYyK+8gz4D6bGtcgl/kFcNYDU4N2ytW+SY8Db10+Xftu/1O4tXriYkrGB8Mc2TJ50yRxN/wOlXGQkbhQTN6464hcfXv3Mk11DRr2QVuyzUtcWMulue1PwDqayhw5w29QCv+dPFkfdJ7BIq6jGTOROPizP/sz/Mqv/AruvPNO1JFs4mDTGPpOVVXi5rJEiXtTuKZysseBfoXdINxKMQm0YUrlyMaocdBUk/HQ70MKOOYBVkBaKsWrtCYzHsTkUYvAvfQZ05zYPVdmlScgA3+ezwUJLAFyK115oHW/D50xE3TZpN3pg3XFv1Gld2izwNDVHh5LTOD2ACzc0zanyjjcdu1fF+J7UAJ9Vrs9LHTGK+oZkmFtV7oOYmkxDRy6Hgd6jIMGMi2Ed1GTDSvrXeQn0PKkiljMp5BUhzQuNK5BfgKVM/5z3wPS3zTWxDkFFINgGHQ3MmemSLY1BQ11n8PGwLnyNfBA00S1tfvnaW9BU8k8bakimXGQAk7Hf58qcTABaOomr6aRrZvI40CxiGASY9r+9/SeAxe4T5EetCXbppXrmnoOati+9PdplqRNDdJD/Zj0fZTye5SCyUQuMQOJg/e+9714/vOfLwL+o7/Xde0lDFJ/L1Hi3hgu8CBVc3EZB/5bZrVbY3kkocGUKhIXi057jOqu0YhJlfTIMjX948U3jL5EiPb5pxcLTNZFzobRBws0k1epxI1Q5an4DAz0nUfHmXu+DKBgEFmJI/YY7FRYibQn9Uut0jfXnJfGOMg15uVUOgOheXi9H6trvGcgJFW15GDSzORhbrUjYx5MJS69PrhGdErPovtez/M4UEocTME4UDFHzpTpAYRqa5JJeM57ENCbi91xGEreuHPB8kJHpYArt+Kfl8DNZxx4vlcaY9ADbSLtUyr+8xkHHJP4ZjId2owDf/w3A+2mfR9Oaw6tDdynGBfaBVWTMA48mRxjmRZvX6IpFZVRbe2NAUXQPjdxoCmj2pxxwGW9NPU40GY9NZ0DpGNM137eGHCZD5PORU3nIICPD817tJo4+M53voNf+qVfGiYN6rrGGWecgec+97m43/3uhzPPPHO4+Hzzm9+Mk08+Gbfeeiu+8Y1v4FOf+hS+8IUvAOgnEB70oAfhjW98I3bs2NHmKZUoMXXkyAOwtLX77cuVlssj1YfMiVl60bvteVJFytXOMYqytHjUBK2l9lMyNdovxpwNk28QzQMtpUpHZvJMAo1Gk9sSkKefPKrGxr3POHCr7TWrrdPAJRO0Xj/eejWzKJdE8vrIrbTTouO6kVvpxUzehDwOhm1Lz4DW9Q+8gzYvudX+zPegwHxzrndd15QEnnv+McBASmTrSRWlgUvf44AkVdQINNXwOHDbbyBVZGhOLEnUaLGuc+4/4Be36CUu8kADbXmK4XEaeI34DEx9n41GBt2MZ6CJXJjGPNgUNHNlWtSB83ZBy1SxrxZYN4imFefa+u5VVWGhU41LJKakWhTNkZtKVQHKUkkNnr9BuF+ZZghOqm+veQ3cfzrrHgfaspHTyrUBuqybe4PHAVMRo0TLiYM3vvGNY8bHr33ta3HeeeeJ3z3llFPwpCc9afj76173OnzlK1/Bi1/8YlxyySW44oor8MpXvhJ/93d/hyOOOILe9xIlWJEjVWQJmkp9YBqjZum7O/3RlipyK73WIgAuwAGNx9pPnL+2VFHKHLr/GQ+08xIXGckzC2PYwXWRpYr0k0exxEHb5sjMam/peDkG2SzALlTxzZIsy92watOyR0Maz6PjQALR9BgHcbbD8DPie1CeA+JeN6F/1zQaVdsLG2lWxbUoVeQkDrZt0mEcuBT1qEwM4Tlocg9YCbycTTuT+ZdrEO2tBxftEicA7xrkSiUBnPdhE5kIv5BDg/HQgHFBmItz1qGj4cm0TDkMmwLHbnJx2uRNU9DMTRyoexw0rLbWkisbPe/GjANN4H6Civ/pPA4mkEpSHAO+x8aEiYMpwPOmfeB7HDR7BqdnHEzHegKUWTcZzwCgV9w6GeunSBUxo7U0TLfbxZ//+Z+jqvrVMU95ylOCSYNQnHLKKfjHf/xHnHPOOajrGldccQWe/vSnY3V1ldTrEiX4kSNVxNwsSuCP2wefkqvXflVVyQ2TzzhQBi0jgAlTU3h4vIREAVsmJoeibmrQbQxch6RSBsHSdB4/Xhw0Yi6OcjwmmMlDIHcMcOaB3Eo7FvPLbb9Xyxsmt31N1onM/FpvT5Iq0vM4SPvcAOT3oHAt/WeQk0RuAhixkhdApjnyAVeqSKceyUveN6i2NpdpUQYM14+TBi69tQAxeRYCTlnvgtxqR4Y5NiAA59ExqJ9EbuIxwPB58BgXDTwWGB4HSY8B7Yr7hsCx9rrcvYSS8en438d/n5Z45EkmNjRHnhY0BZpLlbjT9HTmyJPoqysyDiYATTXlqiaRiel/T+8aeGuhhoyDtj0OpmYcNE2eKheSTGLQDegVtukwDkriQDNaSxz8y7/8C+66666hL8HLX/7yiY7T6XTw7ne/G6eeeirqusbXvvY1vPnNb9bsaokSppGjL8+cGMOmmOsxaloK6IOGqZcF0xQUiL+sLRgHqQ0rXyYmvWFy5TOoGvsZ1eZMc2Bg/DkUx4Dy4sSj6CZAS119+zRoRk/eeZvWDNCMpC1u73HgH0faMFEl44RjjSXPhA2EnlRU+h0EkOXSEnNAv08c0D4HsGf3od+P8eOsCMkil3Gg5XHQBDSk+ExMAdpqPQc5CVw/ecozCM9lHLBM0kNyUawEYhO5Ku8+EEzSrSv+3WPUgQR2v2+M828mk6G9L2oKHGuPwyaMF0b7bYOmQLusC41q52mSyJOAtp5c11QyQX4BS05oypa5YyjVB+2CMvf+JT0OlBPITce/diFJ23JVTd8B0ne0FRnmPVq7mldeeeXw502bNuHxj3989PsxFkGn08Hv/d7vAehrzv6v//W/ilFyiXttuDRvEbT1JmW9R1na9LnVpWzg2teYdwEbbuLCBe7HGQc8oCZ0vJRMjWaVIZBniulXefKA6xxTSNX2peTZyDWXQEzNau9+HxLPADF5llNx74FlZLks0RyZNAazPQ5YgFWC8TIIrjlyvOJemhNYgDWQ+x7kJS+l9iTvAY0xOC3jQOs5yElg7PfMkZUYBw20dbVNSaVjRI1pSXOxb5KeZp/qvgfy5KJoCVxPMi43gUtiPDQxR1ZhHOTLdblyjjo+H8JeIFO2j8E4SAGnbI3/lEm9+/fpzZGbgZba5z+tMasK66YhcOmzvzSrrTMq/p2vTDMfTwLaavpMTMo40MQnmksVzVbyTN+g3Dhx0MBnZzS0TMInegaJ+4ISLSYObr31VgB9WZJjjz0WHWFiGTX4uvvuu6PH+0//6T9h586dAIAbb7wRX/rSlxR7W6KEXcRA60FQZWIkiQpnwcCWyklVG/rt21XcM4GacPspqSZu+zJoazcGcwzCNautJRB89Blwr0en0qFlj4b7DLhrdqZUUEyqa/0z5xlc5J5/ls+CVqVvJmBAYxyIrK8040AzgSgdanTj6CaTlxc6aqasqaRF6DP2ezDF/AJa8DgQ/qb1OprI44DGOJCfQdEcmiGTEvNYcKtMSV4f4hxAAOuGx/Lew3mMAzWpIi+Ba8s4yH0PSH/TWJM18lgwAG2BcPKEkcCauuJdueI/ZlIv/X369p3jpxIHzvBsG7Sctn2guca7++epgPMG5uiDcOeoafYFTVhvg9BkXKT2IKHQXJelmNdu+PvC6ebhph4HmowP6d83Td5N24fcNYAbWuNwkuRV8TjgRmuJg7vuumv4865du8TvbN++fcgcuO2226LHW1hYwDHHHDP8/V//9V+n72SJEi2EC8CtiJWWPGPahU7laWOmPAbUK/4Tun7WHgejL2ORcUBOnKRlarjnnyMTojoGMxbM1Ir7FOPAA6z1X6WpBZOlVFOWv4A64yA9BlnzUC5gwGJ+SWNZ6gPzGayqKipV4gNlZNBelOzjvgfd8BgHwnOhkURvAhhSGQeL6cTBPpdxsEmLcZC3YZfAXIoxa2TDzErg5cg1MefhXOCKNQ/nsM4A3nqoiVwWYz3SZAxyzImFBK7hPWgK3Htg1ZTAdRPWk/T3ad8Fbv9T+u4+42Cq5pvLpBDMkRszDhRBQxV99alA2+YyLe4reppb0NQcfL0PRNZH4hnQngebAteajA+x/cQcqG2O3LrHgbsGyUhcaJvElxiP1hIHO3bsGP584MAB8TsDBgEAXHPNNcljbt68efjzjTfeOEXvSpRoL3K0vZmVllVV+Rr7XsU/2Rg1pfHvtq9d8R+pNJPlKZSB+8Ri2bv+yuefJ1XES17kVFpSgXOp4jkGmhIWJu6CM5U80mT95GzWmO0D7bJecg3JLD0OJIDUM0c29JpZJfrcdDqVt+HNuv+KY7CqquSmzcrjoNurg/KbYoWZEvNjOeMZvOsAy+PAeQdlVpv3/60GYJUvE8OSDMsBLpnPgC/VE5AqcthHbsJp0sgF7r0qX6Xxn+s1Utc1RS4rR6pqEIwkdo7fmWX7jSvelat9U6Chr2+vXW1sW+3cFLh290YaLNzGclUtmyMzK/5jHiuh9qe5B/61z/t3mnszz2chKVXFrviPf983556q+QkYN7rroVQRZyi03gcT+YwQC6pKtJg4OOyww4Y/33777eJ3jjvuuOHPX/7yl5PHvPrqq4c/a1HmS5SwjpzNClvDzV2wr7imkGRz4tSCiS2TEgOMWPIUseOlPA70jWnTgJFHCVVMXngb5qzkGdnjYDR5REzchfqQSp4xPSZkY2Iu48BfMNuZ4+aChizml8g4MJYp6fcjfB2YyWsgT1uV6fXTP158HmZV++ewHQYhvo/UzHlzPA44iQPPZyWz0hlQkmlpUu1Nkm7MAY6YazFfqke+Bu76UItx4c8BtpJxuYwLWgKxQbWp/z7Wr/YGYkl0/fabAuf8auNmoOW074Km5+8ml/U9DhomLhRwmCbzsNgHRZmWSYxZrdvXNKietNpcE5+YNcZBWipId180vVzYdHiovxfPSxxovQ+Kx8HsRWuJgwc96EEA+i+6q6++WjQ//pEf+ZHhdz7zmc9g3759weNdeumluOGGG4YPyN69ewm9LlGCH8sOCJ5Taak9MXqVbiN9kKqrlpUqzNbbT4CmRNC6334YMGFWeA5CqvQZ3RSwzalzKg3dcakKWGRUOXhmgGyplNFqazLjBpD0Re2SR+6xerVt+4AAWgnzoFuFzALug2AJ6RpIz3MbclGxTTtdLi3C+gr1QdskvqlcGMDxOAi1Bci6w1pzcZ7HwbhU0TYlc2SPcRQ4f5ZBdeod7P5tNNTmoRzJPiLrKCeBD7QvVaStLT+I3IprlmSZLxkZBk0oxrSSOXJgDDCqPJtWvHsyIdOChg1lMrRBS6/aOgGaejI1Uz6G0vwTmgMHfx8NjQR2E+Cwrmtd0HoCjwEqaJ7TPpHxkGuMq1lY5e6tU13wi3l0k3dJ4F6R8SG1n/Z40GYdaTEOJuvHRHJdBMm0EuvRWuLgAQ94ALZv3w4A6Ha7+MY3vuF958lPfjKAfrbshz/8Id7whjeIxzpw4AD+23/7bwDWM+6nnnoqo9slStDDnSjbYBz4G7awMSxgwTgYb5Ntzhyr9nZfXJ2Kb4wLjC+YXN8LbcAsR6KCOQazjHGJ7UsyJaPPABs0BzLkqoiA0STGsHyD8vH2+pvY8X/DqvTN9zggMg6kxAl7How8h/SkRcYcwPT6kY7nM784+vq5iaP+534f9KRa0u+BfS7jYBPHHLlJtbfOPWhQbU16FnKYf0xafq5MgZc4UJMqymN+sZKYsXVw6nONYg6f+RoxZyYUcjQxR2awPhobgypXvPvjL8U40C3oaeKxAeh7POQyH0N/U2EcNAAupT9NZY4c2QOEQhM0nwS01dTY10ocTCVVlCigckPdZ6QxcO/8+ykfgaYG3doG5U0ZF4Pw9ucT9kODcaC1Hi7Rj9YSB4uLizjjjDOGv3/iE5/wvnPmmWfikEMOAdBPCLz+9a/Hi1/8Ynz961/HysoK7rzzTvzt3/4tHve4x+Gf//mfh2yD4447bshWKFHi3hY5VX5sqaBYH6T+6JsjxzeM5h4LEcaBNmDWP6YEWowC17aMA+meM+9BFmBCl0qJVVtzQVMg7XHATNyIpojeM0i+/olNuwjWaAF2mZWelh4HokE50aBY6sdo1Tc7aZH1HmSzHhLjwP29Ukoi5yaOALkKlcU4WFnz78FdBziMg1x9eZZUk+QblJu80HoWcqR6mOzDXJkQdy5wCw8mjRyvI0DSFSe9B0KgufC5RjFHE41/TyJDYRzkrAPW29evNm8KnGrq28vtpzwOuKBlinGgb8yan8AGOBKeTTTepftjbY6s6bPhnfsExrBTJQ4amnMDsiLBNOty9/K1XXHf1GPAnHGgXG2f4zUnhTsXTfo+aDoHAxz2XYn1aC1xAADPeMYzhj9/8IMf9P6+uLiI//7f/zvqukZVVajrGu985ztxyimnYMuWLdi1axee/vSn48tf/vLw71VV4TWveY3hWZQooRuuVJG0KdGm47kRe1mImySyKadnSulVfHOlksbO30A/L6Vx7b4YLRkXoT6omhNnACZsffOYvjsbNAXS94Clqw2EgOt4tbV+8ipx/lKlsRZgl7lYZY1BqTpHZN0oayq74ZsDhhOo7Gp/6b3DNkFL9YHldZKbOOp/7l8XLQZcCrzv9moccJIJeubImYwDEgNSmktyZVq05kJfriuDdaT5Hs6s+HcTSlrrwdyKa5rHQYZcWuhzjTHQRCaDcQ2kQ+Qm0aXEW9No7nGgy0DL9bgYtk83Zk2BluO/Tw1aNmBdAfrm1EBDxoHweE5nDDuBTIom42ACY1pNc95JEifSVzT7kEqeaRc0NTUn1mbdNE1csA3ic1knWu+jwjiYvWg1cfCc5zwHCwsLqOsal156KS699FLvOy94wQvw3Oc+d5gUAPoZzdH/Ro0/fvEXfxHPfe5zzc6hRAntcDdrrvEcwK+29j0O1vsg9UcfuG7IOFCvNg8vFnzzIz5oDMQ19rUBw+UJqn01x0DMnDr0GT15EkmecZJH4/fAreBgjgFx/LlyYezk1QQyMSzAKrfSWGsu6HQqD7QRPQ6MWTfjCVQu68s9Xo5cGVsuKeVxwJSqyvbZIDKP3Dlnn+NvAABbNykxDjJN+UTPIXOZFs6awEvcGUsG5jD/+p+zPA4m8xhgPYdBg24peaXBesnwmgr1QeMaVFXly1UFn0N92bgmoHFd1x5oqV1tnKq21X4nNq349vTVp7wFTZhvUvuMBG5MKkW6PuYeA4pz0STmxJoV75Ocv8gAnIpx0BQ41/Y4GP+9qTnxtO+BpnJp+oyL5skzzX5Mkjxj74vmPXRW+BPGnj17cNNNN6Hb7Wukbtu2Tfze+973Ppxwwgn4/d//fezfv9/7e13X2LFjB171qlfhZS97GbXPJUqwI4eiT/c4cH0WRtoT6aDqUkHxl4VXVUAGLcdlgnQoeLEQGQdtGpNmyGUxPQ5kxgNX3zy2YGKDpv32m1Xca44BqVrQBS34GvfxeZDptZJtykkcg4udzliSVmYccFkfsY0jew72ZJLE9yA3iZvaOLvta1TZAvJ9zJdp0bsGLvvRveeuvwEAbCMxDpowLjTug2gMG6o4JyVvfINoY8nAjOQdIBURaD0HLmidyfhQu/6ZknWiVFG7jAO1e9DpYLW7/pxns25UQON8qRbp0kxbadq02lg7keyCvimpGte4ePrzz2e+Ae0zDqTrM809mMTjQEvbXWp/EsaDZvu9Gl6xrhvS8NCUi0oxDrRB42kZB/oeAwnWkfL5a3kcTLo/dNuv6/45xuYWthrBvEeriQMA2L17d/I7VVXh1a9+NX75l38ZH/rQh3DJJZfghhtuQF3XOOyww/CYxzwG//k//2ccdNBB/A6XKEEOF7Tq1f2JcPQFSmccuIDBCBV9dc1/cbkV6tNGquKcbswaqTRiyVOMHzMOWvCr7dPVnj5oZwtYuBXv+qyb8KbBwhzZqzZzq52JwH2Kot7r+dV9loyPfn94CczczQJzHl7oVMAILittGtmsD6/qezR5xk4cZVQaeZt0ss9CSi5Mq/0mlZ5M6bxUEYPrbwAAW9U8Dvx1kLRhdJ9NNZ8J4V6G5gG/ytCO+URNIGcaM7pSRW7CadLIBc5ZCdxsc2SxylaBcZDJugF412BxoQJWR/qQ+S7UmAubJE4YzKOmwK128sQD7VKMAzJoKbUR+5uGOXKTqnfp+libA3vA7RTPwWRSQXrrAek92u3V0XOSGQdT9MG5f6lLoF3x31gqSLmQI7eAYv3vysnDTMaZGz54P2H7AZ+V5UaJg8naLiFH64mDJnHIIYfgRS96EV70ohe13ZUSJWghgeCr3R4WOuuVfAwjstGI6ruLgJ0tYEM3R45QxGfB48DXcrWXKvKAe6JUUR7rhgxcGpsjpzbN9oyDkWpzElAyGknGhei1wgFurSUypD6Ixqhk8N7XGA/Pg+qJuwyKNDuJm2YccNpvUunpmdCZShWNMw4WOxWWFzmgOSBvGFkyQak5cDR82TitPqQ37cxnINdjgCZVlAmc8yTDJpfL0ngXNQGNaKyLzDHAeB80McaUKpGn1hdvIJMD6M9FLnDrzvVuaEs15XhdjQZjPZT7DAKBYpKpGAf+GiRVce/PRZPPhZMwDjTNkUOSiYsRUqE0F05zD9x/2pT1o/0MNq34t2YcqJsjTzAGAb39edPkJcCXMJ33uFclDjRiZWUF119//fD3o48+usXelCjhx5Kw8U6ChsqAiQscr4yChgKIywZsXICMJQ0wCK/SbOR6+xt1vkwN4ADXRENEQJBIsNZWzjClY7MufOCyXXNkX+PfHQOKjIMEcGlhkB5L3gGheahliQxNg+qMSh828yVW9e/LJNm+A9z+9P+NrWTeTHgcUBMHfrXfKPvRTRxoGSP325bWQT0sIw4kaZ2/dJygTAspievrG6elijTXI7keA6zEgT//5JrUK1bbj0RYqsqv9o+Bi7mRy7oBeGMw5Tc2CG1t8f4x8kEzaWxOW1DV2ONA2SS9qceBD1pO1bw4lzRiHCg8h02SZ9KfpmGfSc9QrwZiw0qTddFUJkf6zjS3QLp/TaWCQseZtA9pg3LdeahJ8hLQfwaasK4An5k6LfvS81maMHGgZY4MDO5BeK3JLKYpMYeJg8997nN48pOfDKAvgbS25lOtS5RoMyQQeHWtB2xa/52xSB+NGHAmAbYamyT3mGPte6Cps1FVqnIchF/lYss4SMkkeGABG7QVq515wHVOlQPb48DXWA+DphasE3ch5oIYTIkKwAGNpaoiddZRM9BW+jeTxuSMA9tngG6OHBmDLE3t9ePlJE6sPQ5cc2QOaFtVFRY61fj1zqx2ZpojA+Psx7scc+RtSsbIQH61K6u6LDUHDqKua+8e8DT+bZOHuaAJy+8kF7hlzYO51Z5WHgtAWKaBxULNYb1In2u8i3PZDkBAqqhlj4Pp9dWbgYaaxrhS+6k+UMyRm8hVCaD2dIwD+TkbZf+n+qDpsZDFOHCmaE3QPqcP2pJhHuumqcfB1ObEfvI2FtrrMa+ALJG81JZKmsTnA5DeR5P1Q4NxUBIHujGX/I26rof/lSgxaxHarI8Ga6MS6sNo+ytkmSAgTZH3Kr7JjINBpRfAN+TsHzPOOmGbIy9nMA68hYFmtXVOpSUbNI34LLBBU8Df9LoeA+7rS/P8B8DlaIx7PPCq/QeRqjRjVXn2jzUjHgeRtvqfkROIkQQqm/WVY0zKZD0BaeDK0iA+V1+fKVUEjL//9x3gMQ6kd1qWVI/Se0iaA0XgXlmaIXYc6+RhjtcR4K8JtTyv/HdwLuOA035YpofEuMhIEAw/J62J3WsQAo4o1eaZ5tyALOMz7TBIFS+4of0+9kDLhjIlHI8DW3Nk7z0YSx4pmyOLwHmkfSmJrOtxkAZtvTGgfP6pMajOOPA8DpolLvR9ThKMA/XkXR7rbxDa1fbuvj7X58P9mi7jIDUG+IV98xxzmTgoUWKWQ6qed0FSNmDim8KFKx8pwHmi4t264r7fh3rs/4NgvJSkQ8aqT/UrjdObRSZgkVNt7G4StFk3UXNkctICiAOHss8I+/zjjAN1c+SEti3zGcg1xWQuUH2KsCRTol/lORqx5AWb9ZYjUeAn8NiMg/h7WBW0dRN3uaCpIvtPLGIYMcLd5zAOtIyRgVDyXBoDvER+DmggvZv0JNNymH+8tVButeHqGgk4zzRmZD2HLvgePH8W4yKT9QLwfM9yTOqlz609DqRq8+kZB+74S3gcKL+PPamiBG7s3pppz7/Tqby9SEyuifEuyh1/gL45cqqAy2tfSl5N0/4ExrSqUknCv20KXAPTvQ/9NVj8+96acMp5yPM4SFX8Kz8DTT0OPHxiyvZzvMbcEOfiiRkHzeTS+n93j1ESB5pREgclSsxYSJOcuzGjG1NGqozczZOWGeJoNNWWVtdXj1R6sZM2QL/aMbZpYpvz5ixY3QWUZh/yqq25oGFsDDLBmvVjhheMMlilPAdEq835iYv0HMBjPuVWe3OrfTOqncnmyLExyE5axBJXUn8AhseB8x5MvIe45ti2gCEgV46P3gemx8GkjAOuObTfvpzEJTEOMhgPms9hjtcQQPQ48JJntqybHNYTICWxiQbhIYNqI7mmcOKgXY8D2ZRVW9+8IWinDFqmqq1d0FYjh9xE45zjcdCkfV3/Pan/0faVpZKa6vsD8JQ0pvJ4EJ6fSRgH0zyGbuIlyThQLihrOgew5cJSjAe3fX2PhQzWiyLrRFrPNmUcaDCfSqxHSRyUKDFjkSNVRJdoiFC0LaSKYhIZgLRR1T5/IXlzzzVgG5IOIrRolGiA2ufvAkYSUMzU2J8Fj4OYOa+FOXJsw2RjTpzPOgIMpGpSBunG4w/gzsM5m0a2ZFlMY9xaJkkCBdjMK18mIf4eVq32zpZJITIOFqX34Po1YHoc5GrbskBbIK/iWKpAZFWc58iVqSZOEomz4eek92HOHADwWCe5zDNvLaTGeBAqnjPZb2qsl8RafP1zC8ZBw2rvKS9BTK5SCu1Evvs6sQYNpWM0uQca7TcbA/5n04CGss9NvlQToOtxkGWOrChXJQ3fiTwOpumD80/TFfe6+1Lf4yA1B3AZB706nrxRl2pSYL1M0w9xHdjQ56EwDnSjJA5KlJixWOhI+uLxalt9xkF4w+TTce0ZB55UEVmqB1jfFLCrXNf7IF8DC5maHF1brkxHhkwLnXXTADQljAF3vTv6DEhjgJ04GZdq8ttnm+N61d5EwDCVtBh+7o1BW3Nkts+An7yyk+vKMUZlgqZAGri19LiYFY+D0XmA6nEgPEs5knm655++B9JcLAG+k0SWx0HLiRNA8DhQYqHmsI4AHusmx+MCENiXWu03qLZkFbTkyEYCrGrz6RgH6tW2Kaki5feBvw6Of9/zGNCQCvLuQT5wzhkD+e1rm1NLbYyGur7/JDIxnjnyxM1PJBMjAeuaPhNNGQ/Texw09BhwQWvlxAUgM1sGwT7/nOSV7Ps02UCUPQ4SPhOEebDEepTEQYkSMxgpbVl/o6QM2nka9yOgJbnaH0hnub3khbJcklxpUo/9fxCsbHZo02RT7T0JYKGYOJgBj4OYQbOFz0cMtJUWb2zWyej5SwCO9iVImeK5gJ3m+c8CaJtD0feqTMnJm3G5LnLiIEeqyTh54XscMD0u3MRZoNLYq67iJa+A8Wd//+p44mDLkmbiIA+08SuteQlsqeJeGpdq+vIZ1d5M9ltutaHbL60ksvcOzk7gajE+8kATlmyezDjIYx6xfDZyvVY0xmET0E4CLLXNgZNSRcrJG0+mJdG+ewlUzIkbVBy77yJ7xoEDGCqDpkD8/GV9f1vGgdsHbcZBWl9+/O9V1ZfenbwPzjVoaI48ffLIOX5Dj4Pp56BmyRttc+RcucDRUJUqEq5fmvlVGAfMKImDEiVmMGKgXf93crW1I1EQ8zhgMA7cY3qMA3eToq7tHa52ZEvkhPoweBmK2Xw2aJxV7duux4H2NXBNtcZAU3LbgLRpWP/ZxGOggVTR0kI11eZACn8OcObAlqWK6roWpGps++DdB7JUz9rYGLA1qJcqDdmSfamKP0uPi9CGlannWlVVlH244ngvbVrSGwO5xpzMudgHzvPMkdUYBxmMB6ZJeWoOHoSfvOAwDnIl47QqDD1z5Mzz10tc5FdbsuaiSf1+ZsHjYHqZjoZSRcrJG1+qKAFaejI1UzUPoBl47YKWGmOw0RhwExfK5thA/B7I+v5TJA68+S9DX14xedPUHBrQNwd2/31TqaDpEwfNGAfucmDaV2FTjX9t6comjJ/1Puh5jUjrwKbzoKbvV4mSOChRYibDXTCsrLmVjlzAxK/0CYMFjMRBiiLsmkWztb37fZgVxoH0UuaCxivdnme6xQVNJ6i2Nqx4tjBHdhc73QjjAeBXW4+dv4FUU9Ic2U1gUqWKpPGnD1TEjiXLdbEZB+FNk5e8Zfsr5CROyAbd7j339GyJ1e5h0NT9d9wE3ophEUHee0AfsFxvP5w8HYS4SWYxDkSpJCLjILPal8VCzTGHlj5nMQ7C1f6ceVhchwalijgFLZPKRWncAz9x1FBfXplxEPK4GIS+vvr4OEqaIysD5/0+5Ff8u39jSCXFGQe6ewKZ9dZsDGqaI+foy3sV54r+AkDzMahtDpzCrbXVGLw1YEODcm3WExBnPWhffy2PA02T7qasF03frxIlcVCixEyGuwF3F6xsKpa7YB9NXFgYw6Yq7djGnBIAMliwspM2g/AXzL2xfoyGPmiXpkcy9dVzNmxMbWtAAg1GADOP8aI/BtzFzjjjwcJjIOZxwGdcpDZNnscFFTBLjz/p3+n2IQ2ca9+HWPLClwojJ68zfFbUGQdNPQ6Iclkh0IzJOACEeWAkae9p27NZT1LyjCqZl96wMueBHI11bUPWsWNlggasceCCPuFnQBcsGkS2OTJpTVxVvt9ZrscALXkVvAb6iYuUXOHY3xzArKr0QcskYKVe7ewcP6Wv7snEaAD3+aAdx+PAaT82BpyhOe39b1rtrG+O3NzjQBM4bjL/DEIbtHX/eQq41zaqbzoHaPtPNtX4107gTuRxoFzY1iR5Kf29SBXpRkkclCgxg5GiiLMBk1j7FlJFqWpT98W0rAyYSZczBNxbMw4sZGpiHg9ufwahWm2bIdFAT55FxqAJ4yCyYHWBiqrizwFty5X5Pi88wCxns2DNOJAW42xz5FjFte8xYeevsP4ZmXWUYH0wZWJyJSLosoXecxgrItD2mcl5Du18JqQNuwSYasm2ZT0DVMm2XKkilsZ/HmDg+3yQEjfBan/eM5ibQGR5HEwqF6VxDZoAp4wq06YeB9oMQPccktXOzp81HsMmoB1jT5DjtxZqnzIGo4kL3TVhU9AakOSqpgSuG0oFeawX6+Sd8hiU2nfZ96PhTs/aHgODPoSCff6TJK+m7UcTg3ZA8nkoULdmlKtZosQMRqziH+BLhXiGaBHQcFnZmBiQstzjbboVbtrnL2k7DxaMTGmE0QhtWixkaqSNv3vNmeBxzoaRbRAeYxxYmC/FgENf257wDLZojAukNwxM5lMeaC09h8Rq3yy5Lm7Fd2wMao+BnMQJm3WU2jRZehyEAAtNaQIp3KT8KDjmSgZasp4GwbwHsaTJIBhg1SAmeQbaGIOrzvpUrdo9WyqIMw/kVlsy1+O5yROWVJFnEJ5pUM1gHETNkZWrzYE8xlHs79M+i54xrLExKyCtASLVzgRz5CYa69rmyFL7TT0ONAHTHI8DH7ieuPl7/n0z4Nj987RDUFrPxEzC9T0OGgL3ZKkmoFnyTN8gPsfjQJlx0FAuyX8XTdx0CSHK5SxRYgYjtWH2K0vIYMHIRO0CyCaMA+dFwZbo6PdBvgcWoCkQ8TggS6QA8vV0z5vJOMiSaHArnsnVxtbmyL7HQbjam9G+m4wYS5yQq837x2zGOtJkfXiGaK14HKTNgSWTas2IJfCs5eLakGpKaaxrS1OMhn//bUHTQSw5hQGjyQIvebzITdzkMH+oclHCe4jJush6BojPYU7iRvpcS6oot+Kb5XeUK1XEnIdypULca6P1LoitQ2Kfa4zDHHPyYfsEff8cj5NB1HXtMwCnvAdN9dXdSmiV5EmDa+BX+tqyPizGQHOPg8mfg4kYB8rAcdsV/9IYjrEetOchseK/QfvT+6ykZYPH/qbMvvMShxN6HOgyDhLJq8I4oEa5miVKzGCkpIroEg0xfXMTj4O4rqp7/trayv0+yAtWTxqBcP5AJHEhLFzVwSLhRevfg9mq+Nb3OAhv2C3MgaMeBwbJKxcEXIlIlFgk7lJzgLm/APk5zOkD2xw5Bpyyx6AP2ud4HGiffxy8ZyYQfbm+PH137WcxVnXP9jjIqbb2x6EmcJ7TPk8qKUemhsm8yq029YpJlFioucAxy6S8bXNkQNgLBK4Bq5DDk2zMvQaUavMYYGfRvh1oDPigaazSGtCXqQHyE9iAsCYnmDM3GQMUqaSGUkXTdKGJTNOwDy1LBWkzIKV/HwPuLTwGYteAbQ4NJJJnXd32PRwiYwxK7+Np5Bun9TgojAPdKJezRIkZDK/SyaGCe8aUZLBgbQw0tGccuC+ClTUuYAaEq+3a9jiQEjdamsrDYwobfxcccBdomqBdlkQDvdo45vNhkDyLLNhtnsF8qSaGQXjK54JZ6ZtTZcZmHHh9yHkGyMmzMakiMmCdMiaWPuOzjlJeQ/YeBwx5ilg/LL1Ocijq/ntI8xlMg7amCfQM5pPm+Xuss8AYZI2DXI17XyKG5bGQaY5MTR6F7gHnfZgq4hkERd++SbU5Qaoo198h9LepQctpPQ4UhmGTe8BgHOSwrgbhjgGKOXN0DOj63bjXvq7TySOXdTI1cN+UcWAAnMfUcrQLypoC99prctnjIJ95ZW0OLX1Hm3WRmget5KTnNcrVLFFiBsNdLPmgLRcw8jwWRtpnVxkC6SoPC6mYkEwF25h62H5g0+Ju0hnV7tJ48u4BEbTM0RZmLw58mYLRin8Lc+TwNfDGAEOqKOZx4MlE8RMX7jn790Bx/OUAlmSvkVS1WV3XAmip7HEQAQ08sFD7+cvyOeGBxtLxkowD5v3PlCjRXgu4HkYr3VgClTsGRLkuy3uQkbzSNWlPvwfdeVDzOcyt+Gcl0nP1lVlrMp95G3gGiUUMKfbxIFhzYQ54zpDpAZpWm+uPgRzJwuHfpMTBlNdg2mpvHcZBPuvCwueiyRjQMUd29gHRxMn479OD1gLzu6FU0LRFZVNLFU3ZvvTPo4wDC4+DmFyXMuOj06m8a9AkeTdt4sYvXsjxONB9F3ljsKHXC6u4c16jJA5KlJjBSNGT2eB1rH2WEd7YMVOAjfPiYCQv3GsQ8hhgvZRC18DipSgBQL5cFm8c+IBNCx4HMXNkg3vQxJzYgnEQkyujzAEJxoElYJdjzt3/d5qVps1kcrTbl/ow+syxJXJyQEt2AjlV8c2VqckDDLU3im7EdO5d5p+bZJg23OspmhMT2U85+t7MMZADnDNl49z2e0LFa69Xe3OBmsfBhMkzrXuQ43HRb59XSJB9DUiFHDlyWRKOpQPa+u9ht6J6vQ/6oHkTbW0JzJp2TdI4cWDgcdDIY8CcdaJ//s0SJ867QNmYtt9+02pra9YLv+LfMoEoegw08DhgzENNEpjajJMcuSx3DGizLpomz7TXxPMeJXFQosQMhrtZ8MyBiaBZ/3gNKk0pjANXV2+9zbquTcxhfdCi36YVDS5E07fQl1/oVJ4254orl8Ws9MwALNjJs1ilkc/60L8H7oJzdMNiYk7smqJGzJEZjIsUaOfPQ7zxJ1b6EqQJYsdy25P6pC2Z5Ul1jLFuuMmzPNCWOxc39jhQlYnJ2yxZsw+j5sjqyaOMinsi68Zvf/Y8BpjvQWled/sgeW+wpIpCFf+stUiuVM0sSBX5SVSSXFSGXBigMxeLMiUBzIgxD+YUsAxCeg6sGQeeXJPCMGyf9ZEP3FPMkafwOGBUu8fGICDIVU35GDa5/v2/j/8+7TQg3cOYXJP2eqBx4oKQPGv0DKqD9vlSYcPvKEtpu8B/ah5ke4DOe5TEQYkSMxhuxZa7KGWDpp48QRQs4IOGo+fb7dVwE/4UuZ7AptHqpeTT82RzZAZoKx03bdDN8zjot+cCx+SKZxc07I6PwfG2bX0+vHNnsE4ioDETKBlEcvx5959X5SmBAnzGQXzD5s7DgP44dBf8Y3JZ5HkoB7Rls6+aJm+o9z8AWGhX+LkR05hmyxbG/BUGQdX4TxRwSO2rzkPOuUga1+5zyPR6Afx5WLomLHPkEGjF8rnIBU181omiOfLEPg+c5EnOM6DVfs74GwRDXz+ngGUQjGvgFo+4c70bjHfBNMA5hXEQbZ9gjjxN4mTKPYkG42Daind/HRr/vrZUkVQtHnsOtMdAU48DhlxYE/Be2xxZw+NAn3GQSF55ybOSONCMkjgoUWIGwwOunGpvNngde1F5YIGyPAEQZ1xIL02GOa27YQszDjgvpWDiwgC0lY7rsV6IBt0S+OFedzbzI1ZtZmGO7I6r0cWyScV/I48HQuIgsWD1ADNitb8E2Gkb4aX64M570gaabQ48yrqxZhzI+vKz5XGgW+0+WaWxehFBxG+JXUSQkisDyB4HGXJRzLVYDmjBfA6l++kxv9b8a6L1PswxhwZ8MIsFmncDUjlMg+xcuSQWCzeVwO9/JoDmBI8DIAxc+fIUUzffSGOekchvKhPiAqoa65Fcc2ApoaLxHDYxJ2aYIzcCbckyMan2Af3kkb8OTzEelIFr4RpaehyIyZsGUkE6z8DkjINpx2DuO3A03Gugn7xKJc90JcNKjEdJHJQoMYMR0xUGLDwOwpV+FqCpb44bBiv6fbAzaGYDZoMIvSx9Y1zONB6TqgG4gI00nt2NsbdZpz8DEeDegvESq/ineAyEAQO2KS+Qlqph+ixI99Oy2lw6XipxAhCq/iPzMNtfIFVt3uvVHi1fXSonUenENGfO9Tiw9jsaLWJwCxq0Ks0HkWNMaeoxIAAGTObbJAl0zfZF4Mq53tJ6TIt54j4D0pwH8J5DaT4VJdOI0pk5yZOeyMLVYl2k25eANEa1OxBhXDBkaqZIXIT+fZPwikeagsYMffXMBDZA0ndvUO3NkImJVTtrJ/FFff2GY2Dae+DLxMS/b8I4iPRB+x6IycsIcG7BfIqNQa99Zbm0fvvNWC/2HgfOvyfsj+c5FtvugHUcd9xxePWrX912N0qUiEZKqohvChnWNXU3jhagYQw0BUgVzwHgVJuOGmw/8LK0MEfuHzeubev3g1dtC/ibUyZoB8SrTS3MgX2PA7tqb0CYgyLJQ445dNwg3mc9sKWyelgeqbVga8s3TZwAhGcgAlp511+d8ROvNJQ2b+oeBylzZOIYyPU40N6oe/2IJJDpHgeeXJytQXYO64P5HIQ1rheGv/t+O4oyOdJ72LkGLgMV0JuLc2VCWGMgJJWzjPjaiDkGpDEoJVS05sIc1o2YxFZoXwROQ5JtHuOAk7hY7fWwZeT5G34umiPrVlunjGkZHgf+XiwgFSW+j+/dwD3QUKpJWV9e2ls2NoZVNmhOeywoJy6Ef97EnHhqg3KJ8dDAZ4Nj0J3PeJjeoFtOXi35U+DY38f6YO71Mn5/NBKYJdZj7hIHxx57bEkclJj5iEkVMauLBuEmAyzlCQB/47k6BtoKjANCxXdIqocNGA4iyDggJ40G4VXcJzTWrSu+2eB9jHHABGvWjxkGDCxYJzHgmgnah9r3TTl5jJOcSke6OXcicWJhjhwDrZjyHEA6ccLS1Y4dbxY9DpgV54D0Hlwfh3SPgwzgnpvAjj+DUp+YJu1AhkG3auJEqLh352FJMk1Nqsg3R67r2pNgYckW5la8MxmAOVJBzLkwBzhl+f3IiZvM5BEBsAPC87BsEK0LmMVAawCehAgjedKEcdA2cK+RRG8kE6OsL99UJqeufRbmtPdgWp8NbY19IGWOrLsv7XQqVBXGMJe4XNX475asH4AgVTVB8sqfi6d7Hzc1aLbCaOY1ZjJxcNNNN+Fzn/scrrjiCtx66624/fbb0ev18IpXvAInnXRS290rUYIeMakieYFG3rCPgQXj7WuDBYBQaTYK2kqAmYHPwmDByNbWH7YfeFmyK30H4UtUxIFLvrZznHWjXm3sMS7W22eDdf32wwt2dqUvEAfNvOQVhXEQXywzkxc5lY50jf/E+YuaysrPQMyYk508SwFm0vnry0XFWQ9Uff1MwMZaqsiyiCCn2prp8eAlTWaCcZCYBzUTJxNKFWkVcoQ07lPPhp5Mj1DAkCVVxLsHcvtC4oDkMyE/A5zEhVSpGtTYV650BuRxHDbI9sfgtB4DLuiaAm21q82BfI8BVvKoGXCvz0LONSeX/qYtVdVvIyJTI3Rt2qmoSeIGkHwmpmtfGsOh50CSr9QyaR8vnMoH7lW8VlwswtCgPGcNwO6DX0STYL2Q18TzHjOVOPjQhz6Et7zlLfinf/on8e9nn322mDh45StfiUsvvRQAcMwxx+Dd7343tZ8lSrDDkwrqhUFLgM84GKu2XrMALcObFcmMjwFchpI3bGPqQfgL5nvaN9C3B3KAW16lp3ROaakkLnDbHQNN+fcgKhPjVbkSGDeRZ9ClgjIYB+45DUy5BptxrseBfyyXccNOIMZMaQF5A8tmHHQj7yFtybaUtnc7jIO4XJbmGMiVaNCmpruxtBhhHDjv4mV1j4N0xb83F6kmsJszDtjMO1PGgTQPOs+hOwYWO5VKpTMQWAf0aiw6MgkMwDTYvjQG3PWAagIxw5yYKFuX9wxyEhchqSgpGAnUJokLxnOYK9U1CO1qc6kPIeBcqgJntB83htVvv0niQrvaW5bJyQeNAQ2poIaMA+X1SBODdBbrZcFJHMSuASOR36Ti3sLjIV3xr7smyy2iCf29JA50YyYSB7fccgue97zn4WMf+xiAdbpdVVVjP4fiUY96FH7/939/+L2XvexlePCDH0zudYkSvHA3LCsjUkUMSmyq/ZiuMQM0jC3WpPNnAKehakd2hecgQgt2f4NCYhy4xoQecMk0Bs2otCQaEgLtmyPHPQ747ccYJ0ygZBChMTC4L75UkSJomwEYsBOIKcDGfR4XFCocvT40MehWZ/wkEpekTeJoxJKH0u9M0Dhc6clLXgBhc+SuUN2n/R7OAa3cuUgzgeUlrzJkalQTF5LGuuf1QpwHM0AbJutEWtu496Cua9pzmCPVBEhzsWIRRRbrhsf6yPL5EJO407cvm3PbMa/kxFmeQbfG+fugbfz7LqCpsRyIFQ+Mhvg+JjAerM2RvSK2SLWzNmDZ6VToVOP3vYlMjUYfGhvTaksVNWAcyMUkCvNQlX8N/OTd1M03Sp5pPwPSOzDtMaCbyG/ucVASB8zgIE4N4uabb8ajH/1ofOxjH/P0+SQtSynOOussHHXUUcPf3//+96v3s0QJy2ibceC+bGOJA4ZUUUyqyd2odyrOiyEE3LXmcTD0WOCaAg/CrzQdqbgQQSN2peX6ebMoqbE+WJsjx9pvhfEQYVwwzl+UCYj6LHArbd17rl1d5kbs+ZP6Q5GLimza2Ym7VOJEqnBUl0tqmLzQHAM5EiWAvjSAG55J+j33XQJQ6B4HGTItVLkoUd/dTqam3148gak5BquqihaR9H/nvYtyNL5ZEimh9nMq7jXnwhyde/EaqBlET8Y40LgFOV5XoT7oyPTkVzsz1kRNjIEBATAz1FdnmSM3AU0Z7+NGUkmEJH5uAQEg34OpGQeuXFZT0HbK9qV/HsrdiEWVhOewCeuDMQ/F5gGPdaNsjg3Ek2eAPvuqcfLKWxO2DnVvqGj1atZ1jZ/4iZ/At7/97eFn27dvx0tf+lJ8/OMfx7/+6796yQQpqqrCs571rOHvn/jEJyj9LVHCKpp7HOiCNssOaNWr1ydj1+PABbg0IrZYtGA8AOGKczPGQQC4tQCNgfiGkT0G+9XT45/FjFn7/0a70jZWbc2v+Pc9Dkb6YiEVFEkeWrQvLfjjyROux4G5OXdDxg8jcRDbMLkAnvY8lKr2FucAdamm+Kad6vOS7XFgzDjoDtYBQpWz8logZ8PIlAyLJW8HQWUcJJh37AS+1IeUx4KmXFWOZJxc7c5LHEhm0B77TZX1kiGXJRlUGzIO/PWQDvtNuo1B5lWt/xw28ThgvAvcJU0ib0DxecgFTZkyMePtxDT+9c+/iUyM+2gyDLrj7QuJA2PGgbY5clVV3jzQxGdDgw3t7m/icll81kuT5BXDnDopmUaWS2rMeCh5A9VoVarofe97Hy655JLhAuMxj3kM/vqv/xqHHnro2PdyFiBPf/rT8Yd/+Ieo6xpf+cpXcMcdd2DHjh2UfpcowY6YVBGLjpc63mq3h4XOAt0QUWo/LpHBAu5l0MCi0hcIvyztzJGdxEnPegyGDalaYd30wokTDuMgvGGykApqJlPDYBzEq/6ZwLlc6ZvyOOAC5ymJEkbyxr0OY8wvMmCdqrQVq2y1pZoSfWC+C3JNGV3MkO53dI9kmeQ1pC5VlAOaOuNQVV/eG/8S46E9xoH7DGq3D/TH4d0YZbyOt+kmkOhSRQnmF6A3F+eCJpYG2blSQVrXICbZOAjWu7CqKix2qijTbRAuWKXhs9G5p4BldIiF2mcUk+QaEw9CG7QF7l0eB4z3cSOPBWXQFrhnHb66/nvMmFYamtoeA41lYpSSV70MjwGr5FUTqSCGQXmT5NG0z4D0Pm/qMTC1x0GD8wekJHLJHGhGq1dz4EtQ1zWOP/54fOxjH/OSBrlx6qmnDn+u6xqXX365Sh9LlGgjYlJFFpWWS0LV2FoAuKYkDrwqp1HQ1IhxEACNfFNQTvuhl6VvjMtJXMQ07mXAwq7SUdq8qbfvVnnEzIkNGAfdCHBPkQqKsZ4MnkFZXzomF6XXhwFgMBqpind9c+5w8lRq30Kuatxng3v+sXeg25dBaCdvUhtG5rsgV1uaAVaMRkjjWQIQ2/A4YJp051Qa+u8Cvfarqoom0EwS6AngmMv8irPO+r/zihhypJoAIZGvOgYzEgdCn7TGwUQG4UTWTW61sVYSOV8yTj954p6DC4i54b4iNG6Dl7wInL8d48C22juX+Sf9jZG4aHL+wPTV1rOgL59r0Cw9m9YeB22bpGtff+mfh+ZAVh9y18LDv7u+VyVvoBqtXc7vfe97uOKKK1BVfUrj61//euzcuXPi4+3evRuHH3748PdvfvObGt0sUaKVcDeso5sVaeGmDtpIFO01GTBgeBy459Or1zP5PmBGAu4DC0YLiRBAqriVGQ88qaSIz4bJGHRBmzBoDBAqvj3GwShoytV3B5olTihSRRHGiQXjIlXt6TOfuPc/VfGuXXHvacsnzJEZVTWh5GVd1/R5uKm/gPRvtPvgy6TYsV4koB7gyiUB/jhcGSYOWvA4MJYKmgQ0ZifQx+dA4Rkgy3W558uch6Vz8eZh8logp9rRnReYrJMcn49OpZdAzDFn9j0eiOefybzS87nIq/pnSIi6r/QkaOsC5wrJE38dGEpgC8+hhlRQpIDHDYo5cmAfZtV+E9aJyL5S1ri3lioCmshlWXkc5LGeAD3GxWg0YRxMy3gYsL7G22/mcWBt0O23XzIHmtHa1bz00ksB9Degmzdvxk/8xE9MfcyDDjpo+PMPfvCDqY9XokRbsewu1kakisSXI7naE1hfMDKp6YMQKer3vAz89o2A83vaNfM4CEkVGYDGgL9hHJMNkkA75X64C75k+9pSSZ5UT/+6W+hKA1LyLAKaM6SKIpWGkqaxdkjXdC0yBrTvf1Lbm844iAPHFgbdoeSlWOmsbY6cMKZl6Pm6kdqwMs2RPcZTbqWtkWSW6HFANsiWrgHX4yADtHWug/YmNS5ZJ1Wac71+XOCQKV0pa8zbeRwA4XXA+Ge8d0GsgGMQTDPInPaZCdR8xgHHJDwXNGOsy5sY4wLwPCEtQVuWZFgTmZa2zZG99g3NqQGOXJRX7Z9MHLj/fqrmAfjXMcQ4YDHwfOat/D2Z8UFgHESSVwzpyuasE901UYx9L7ZP8LspsR6tJQ5uvPFGAP1s1nHHHYfl5eWpj7l9+/bhz3fdddfUxytRoq3wZEJGq71JWe3RkECgAXDFrvQF4hR1C8BM6sPqELi3eSllmyOT2o8bdPOTV7FNkwVwGTJHFmWaDKSKRscdUx5ieMxFN3EQvv4W1e6Aw7ogsz78xJ0rlcMBKgYRe/4AG+ZTyKDYptq/mVQR4/xTyYtZ8DhgJi8Afx4IrQOqivEOSLMuqKBlBmjKNMYFEgblLUgVuZv2lTVe4iBHMk5cD2uyHjLGoJdIJ7afZU5MbF8CbZjJy5Rk33ofxn9X87lIvAPW29d/F/jmyCnATrfaGMgHrt3r0qny/Cmbtt+k4l7j/HPfw4DEvLGrdgdk4Hrae5AjlTbWB0LyxD1ECDdnrUvzZSNngXGgL13py4Y2rPifsgvTjkGNeaDEerSWOLjzzjuHP48C/tPEHXfcMfx527ZtKscsUaKNiEkVMWnJgxANcQYSBWv6lTVuxBIXFh4LQLji2opxEKKoWnk8xEAzWUuSCxolPQ7IwOVqIHEFcMZArMrCYgz4jJNRxgEXLAMCycORPqyQ+5Cs+CcD1yHQPtg+YQyEQEtJskW7/aaMA+13IJCWCWC+CybdrNK9JgaMA2cdsLzQUQGKRiOHdcEAawaRJdPiJY/sJLvcpAVA8LtKJPDY74KUZJxYxKA4DnN8LqgV/xkeA1zgXi6gGe8Tbw6atOJdCyyaVKpIYx5yzyHpccCo9s70OHCvP8ugPCaTwhiHucw/6W8qUkmZiSupfY0+5PoLDP9OWJdN6nMi/duJ2s/0OGCxbhr5XFASmPljEPDHwLR7gyaMh7quqWvCEi0mDvbs2TP8+dZbb1U55rXXXjv8ee/evSrHLFGijQhVOwM21b6iVFHAFJHhcRDTN/ekikj6daF7wJZIWT+uvGBuzRw5YdBNN0ceM+flg/ehxZJ07ozkVZRxYDAGvOTl2P3nJy46ncrb+K5Gkifa96BN0Bjwz8cFatnmxFIfhs+AZIxLZxzUY1IMJoyLpFwVz2cilbgaBLu6yvc4kJNHjHWA5zNjbMyaU+3sMtD0GQcR5pf4HHIl23xzZO48HDLnHgTbJN1LoItSPbz3cSqBLH1mDdx7FabU5F1eAlVrGIaYx377+pJlTSqNAQ54n1vty2K+ueO/V4eZFwx9/Sb67gypoiagqdQ1a48Dhs+Gew1cSa5BuO+GhU6lUsyQ77FASlw08blwDdKNn4H+33XHQI7P0CCkP7GKO+c1WkscHHbYYQD6E8BVV12Fffv2TXW8r33ta7j99tuHvx977LFTHa9EiTYjZEgI8KUJBseUQLu6rn3gfpEB2IQZD2ZSRYFKKzvGgbxYcV/arPY9beMRnw2p+pWtrTzOOJAYD9rty8ClqCtNqbh3FoujoKmFMW4DxglNriuyYGYn8JLmvOR5KKUtzQbsgHC1n4XHiXQ9R+8/q8Ixdkxfroo3BnIrXdnVVSHJrJW18XZdSSONyKm2dD/T9BiYxByZLdc0Og/ImsqqzScr7r0EkvI4aOozArSQPCK+j3OqPf13keYzkE7eufOi6vm37PWSy/xiSIi6baf15cf/zgFNMxM3SglsaT4PMS/arrY2MWeOVZsT3gcu8Jwcg4RrkMu8Yc0BvsdBIHFFShzkeC0N/+biAwyD8qZjYMo1aRO5LjYDsUSLiYNHPepRqKp+NrDb7eKjH/3oVMd797vfPfx527ZtOO2006btYokSrUVMqshfpLOAc78PVtXWEvixFgDOraWKLCpdpeMOz58sjTA8bgS4dMcBQ986tmB3Fy4Ufe0AcCk+AwYeB2OguUHyzE1ejksVGT2DEWNOtteKv1iOg8bapqg+aBkHzDjJK7naz8LjRDZGDT8DjMRBqtqSWu2eCRiwGQehim8Lr6Mc0JJ7D9KVbn7ixg44t/C7SiVPvEIS5fP3x0Cc+QVwpXqyzJGZHgNixX+75sRtP4NSv7TexzH2d7x9AmifkInxwfupu5D9HmIl8qWxlFvxbQ3cM9ZkjarNJamclhkHllJFHvNLLXk42RwE6KzH/OSprTmyj4XEx4A+46BB8oy8HijRYuLg4IMPxqmnnjqkHL3+9a9Ht9ud6Fj/9m//hne9613DRMSTnvQkLCwsaHa3RAnTiC1WPcCEVHHvA4e1+MJgSBSIxqj3tM3WNg8dd/CycuUBWNc/rC9udP4RiQKLivOYOaqNTIkMXMr67hbVzutSLSZSRbH7b6QhGau0YVZZSm1bexzEEjeAINVEYZ3Iz4AoVaQt0SL63Iwm0Plz0Cx5HEiAJdDCOFwLJQ4Ykolu4lAC7nkMvJRMjvSZtmRXjHlnsUluyrxim0OnTNK15CkG4cllZQD3ms9CinkGcJOoKY8JwNrrJQDaeRIpnPZDoBVjPnQBt7qOV3y7fzIFbUljUDpOMHlBSKLneJyE/mZpTg3IY2PaPjSRSur/3fn3CsMg12eBxjpyDpM7B2n1YZoxwJgDmiaPpl2TNhmD0t+Kx4FutJY4AIBf/dVfHf78ta99Deeee27jY/zHf/wHfvInfxL79u0bgiovf/nL1fpYokQbEau0swBMAHnT7FaXAZaMg4FUEV+mpd+HEOPA6PoHQFMLfXkgPgYt5LJi9Mg2qo2Be1g3LZkjAxGDbAPQeNxjwsZnJAaasAG71GKVCVgCcdZZ/3eDZyAwBk2kwhKVhh5QZHD+acYBz+Mg1xSU7rVh6XEQSFqMBlNf3j0nUSZGmZbvRhPGQafSkScZjZQ5LzuBlPIZYUtH5iTwPGPcliv+Ne9BjjmtJeMiGzRWq3iflPEwffvSOcRYBxTgfBYZB4GKZ0ZByzRSQQyppHi1uZA4MARtAY7PhLusCtRQ0IqJJp0DAHufC39vPnXzSfa1G+7zOe0YmJpxUKSKVKPVxME555yDhz70oQD6XgfvfOc78cxnPhPf+ta3kv92ZWUF73znO/GIRzwC3/zmN4dsg6c85Sl47GMfy+56iRLUiBljWoC2Uh/WunK1tUW1MxCW6mFoK0t9GDIOjK7/QqDShUXHdCMGHLsLB0q1c6zanAjYDY8pJa+6skwLJXkW2TSyqzylY45WWlqA1kB8DHiAlfI8IHlcxH7XTiCmJBLY2vaAfF9Xez3Z54OcOAHirBfG5iAFGrq/q1a7Z25W2UnUkFSNz/wzSF4Kcy9TMmwyxoE28yk8Dix8PlJyXW4xiXYCKeUxYD4PS8C9m8gnAueSSTpzTZpjTuv7bjE9Hqw19vNAM0YCs4lMDyCAtiqgZTxxOPycVe0t7gXlPnDOP2/8AZx30TSgqYpcVqa/QLAPGvfA7UNQqoizHsv1GhFBa+Pkle8xoT8GpXfQaGi/kz3mb6T9IlXEj8U2G6+qCn/5l3+Jxz72sbjlllsAAB/72MfwsY99DKeddhoe8YhHAOgnFaqqwp/8yZ/gAx/4AL797W/j85//PO66667h3+q6xtFHH40//dM/bfOUSpRQiSb0dFbFvSdV0+vJiQMCcF9VFRY6lVhl7mnqGoGWq0HGgVXiQmZcaGurD6IRcMxIHkWAWwvQVJTLEqqtGf4KQJxx4D4DDNZJI9YTTS6siTmybaUpO4EYMuceVBSvGsh1iT4f3dp7/juVXoXnIFKAgYVkXMjjYXCuTMmu3CozOuNgUQaO3Or/pUX967/sXE83WdHvD28emMzjgM04sGb+xYH7VdckmywZ58/DtsyvHNYJ0xy5bY+Dfns1lkc+nwWvF9ZcnOtxwPAek4DvkEwL4Oubc6qN8xgfWokb6TrmGmTr6Lvng7Z+En/q5htVm7v5FJ1q93zQFrBh3tSBZ4A1D6WS54OQ8lmUexBlHOibIzdpH9Bfk+Zef6AkDiyi1cQBAJx44on4yEc+grPOOgs33njjMAlw6aWX4tJLLx1+r65rXHjhhWO/Axh+/6ijjsL/+3//D3v27DE/hxIltKNtmRjABwxW13reJhHgSBQA8BMHQ+DcBrQMbRjNGAeBBaPV+fuMk5hcloU5sK3HQUguy6v2JyVuYtquFgbh7oa5rvv3faFTeaA1yxzZvQZj8yC54rm5TA03cQD0E3bL9wC07gaOkTwKJ88sElcy42cQviwAfw4atNvBPckbokxLKHHtBns9IHkdSf2x8DoSjWmJwLGXPJekktheKxHgklVlPda+UEAy9rvH/OLOg67HANtvKVTAMf4Zb03mJ5BtpYKkOa1Nr5fcBKqWSXyuvrc3D6kY44bXgFK4gKqGbNmkiRutRL50nFypGB1998nNiVWqvTMTV1L7Gksi9/FPMQ7cPmiMwVzWg78m1hmD7lwSYh3JUlHTt5/zDhqEOzytPRakv0/bhyZyXVLfSuJAN1qVKhrE6aefjq9+9at4ylOeIr74Bv+5nwP9F+VTnvIUfOlLX8LJJ59s1ucSJZjhJw7WjVHdSdNMY79Xix4HrPZD5rx2Gv9yxb0FaC0dd/BCtDDGBSSZhlHGgQFwH6FHWngciObIglyXlTEwsL4wtpAqkoDYIevGwBwbCFea1HXtV9wrX4OUKR5TIgWQr2ksgcwxBw4kzwzk0kJJi0FI+u4WfRhtl2lU7wImllWWo+F7HPRQ1/5agJK8dIsXhPUH8/x90FqQKiKvB2LAqYlUUUQuDpDWAsqScU4iwk3eWDO/3Ge+rmuvD1SPgSypIl77gD/mmeuRNj0GmrTvvQtUQGuBcdBEqsZQX52VxGwi18ToQ45U2frf9N8FTRIXvkH4LFSbT92FfINuUkFbLnA+Cx4HjHmwqceBL5c0JeNgCrmu/r+fCah7w8TMXM1DDz0UH//4x/GFL3wBz3nOc7Bz507UdR38b8uWLXjGM56Biy++GB//+Mdx8MEHt30KJUqohVhtOQSujRgHAuvB3SSyZFqA8MvSXaCzGA+hase2GQd+4oglVZUvVWMhkRAzhbTyOFjt+qApCzQPVTsP+jHeByOPhyHrxYZxEPMZcYt7tK9BWt+eOw6WBQm4aLUxI3kW8vloyZh5LZo4sX0Guj0fMNR8F7nzbz5gpu31IV+DFQfAlcbr1G1ngKZ+tTfP40Bs37jifbRwwCSBnvB68TwO6F4zzjxMngdT7wFJ79laqsdjAGq2H5CrG/vdMHHRDTCvWIwDDzQLeizoS4jG5CqlcP+kU+2c9x5iJTHFxFUm+06jmKSROTEDtG0AmjLMsT3GQSJx4N4aDeDcLRwOyXWx1sRTeRwwxkADjX8VubJIEZ8U2v5j0yROAJ15sMR6tC5V5Mbpp5+OP//zP0dd1/j617+OK664Arfccgtuu+02bN26FXv37sWxxx6L0047DUtLS213t0QJSsgyFT0sLXTMgGup0kqSZtCgIua0P9RWNqh2B8Lankxa+thxAwt296XNkyoKL1YYRnBuxBbMJtrOAZq4zziwSVwBtmNQAkFX13rAJp/1YvYMBHxGAIIpZ2LD5gEF2owHYfytzADjoNurTRJHnU6FTjUOhowZw7Y0BwxAM9Hvh1ppnGkKqnwrQmsRn23B91mRGI+e1wWRceD6jAw+i/2baSMGnLZijpwAzrXXAv4YiIPW6oyHRMWxJBuhKlmWYL5JnzFN2gGBcUB8F+XqW1uBhtmMAw3QWthbxaRiOMDxZNXeatdfugaGclVNQFOKOfEUiQtrqSZAGIMqfRj/PeRPzVIDmHQO6lRKUk2Z7FPpb4wEZtMxMO3eqJHPSGEc0GPmEgeDqKoKD33oQ/HQhz607a6UKGEeIX1rwFLj310w+WABq9pfaj9U7cyTKvIr7ntipbMt48A7f5Y5c8xnw6DSMQaceawLwjNQVRUWO9XYQmS126NLgwxCuq3rz4BB4kaagwIG3TTWS2AOkEBba6kiuseBxDjohZ8Bhkm6LBfUs0scLXTGKttjyUsrg/LBPRATB4rV1pMDZtyKb6BviOsXEViA1hmMA2K196C90XP11yNc4Nw6gZ4CTj2TbOX1WMpngn0N3DnQfd5ExoGqVI+/DneDmcQTGQfuu7Drvov0zn9ymRIl0HBCmRKGKazUzmgwEom5+uqMpAWQLiAY75v+GGhmTKvffiOZFsL99/wFUowDhlxSJuOAtS/1PQ5sWTdTjQEVua481lHo79MzDib3GQE4MqbzHCUNU6LEDIa0CR9sGNhAwXof/GpfC7BgEH7F/T2AjScPYQPc901BJSqijUzLaqDa2opxMCaRYCCXFKv081gXhskbtinvIETGQaDa2UymxdBjAQjrO0sAonriwGnbrXb2q4uUAUOp0nNt9Bm0YRy4p7XWq83k0mLGrH7ihME4CLN+JMBQM5Hunnt2paf2OBSeqxVxLaA/BnxjZmtz5LDPy3r73Ocw6nFAvveAIJmV8DjQTxzExwA7ke9WS3qMA2FMWgPnzLlQloyzS6JnS+UQKp2BPMZHv1/6z0FTqSIXNFRhHHhSTZmgPdHnIgjcEsbANPrqOtX2+cC9i5mqMC4aeDwAHCaoex1zpYK0igonTR7qGbQ38LnwxqBG++Eivpw+TPs+aMQ4EJJHLFWMeY2SOChRYgZDrPYNAcc0qSIXOJOlilgRqjZ0K86szJHXur6mNUAErQP0RLY0wPpxZZmY/s/85IW7WBqvtm4neSbJdbESNzGPg7akiobPoAFoC/jPdrTamy1R4QFm3OcwBNgOwnsGjEy6+wbhRoyDyKbZHQKUxEnE40Aag7qJg1yPA+6zGGK+eNr2hPdwjPW23hfeWIwxP9fb5ybRYptmBlDlte8BR+Pnyx4HTRMH+oyDOGgirQk130Xue0BqzxK4l9pjAXb99vOAUxfPZpnzBvX1CdXO0n2M+ZJSgONM0I6ZxMyVi/LNkadvu4kxLCOJOZ1U0tTNe2MoVO0/7IObvGKwHkKMA9I86O4vg4kLM8ZBkzE4/SBoAtxLfZh2DDSR67KQb5z3KImDEiVmMGKgnUWlJZDLOOBNIaHFYlseA31jXG512WiEzDHNqn0jG3YLuZ4mHgdWPhdrhubIor76ADgny0OE2h+ARO79Z80DIfDeZR0BfKkiHzDiSgXFZHL6P7fDulkTWDe85HVsDhq//hTQNFJt65oDA8DSIhEsyfQ40H4OZK+Teoz9ArA8DuKgCVs6UGZdJUzSla9/rOLUYg5IAVds9pk7/lyPAybjRDqeJ1VEfhf5BRRC4oA4BlMm9VKfNMdhivExCBbjYGKpJIV7IAH/MY8DSWN92sgG7YmAnZ88yvT7IbA+mhjT6nhMuNXmEdDYwOOhKWiskUDykheZrB9rqSArn5VmHgfTt5/L+BmE9vsgR7Iy1DaDhTnv0arHwfXXX4/f/u3fHv7+S7/0SzjttNMaH+ef//mf8c53vnP4+5vf/GYcdNBBKn0sUaKNkBadK8aMAwk4X3HAgmVFTWc3QsC5m7xg+SxIFE2RcWBUcb7ucWAE2kUWzP4miQ9cj1KkLTwO+n3wx6CFTBAgb3yHzwCBFu9GCLiua7uK8xDrRAJR1SUyPJkcF7DiAmZVVWF5oTNW0TsK1jJNYUdjqdPB3RgH7BmyDFLENu1tmUNHWS9EbfFe3d8wu/MCXTJLOCdRqkgxaRJq2x13bCM8qXLc0/j33sfajIMwcOyZgVIYB/EEqj8OyB4HKakibeZXArhnv4tSjAepT5pjsHOPXN3oUPMZB3ZSSdmgsdI8mKvv7a0HCKakgH21rTf/hBgXROaPlzwKAIcMffdc5p/0N401ScqcfTQYPhO5MkHDvxPeSe6jFEzekfbGqTVX6PNWPA4YySuhgC4W/jw03VzYLHHCMcgusR6tJg7++I//GO95z3tQVRX27t2Lt7/97RMd5yEPeQg+8pGP4NZbbwUAPPjBD8Zv/MZvaHa1RAnTkCa7sMcBZ2JcXnQ3bDaGiIMIVVxaAbdStSsbqBg7buBl7YLGZubQY9XOBvriM+BxIIEWVubkADxzZsvkUVVVWFqoxq51WK6LNAYD1a4SXZuurb0WB6xYz8BKd6QPbTAOhE271TPg3oOu8CwMgmIMK1V8BiTjAG2JELnatIPK+4zVB6B/XTvVuDllv4jATeAvqLYLyFJxdV0PNWuluUgVtJQKODxzXm4SO0bTZ4Glo+FLdbiJE1uPg1S1uzrzK3n+5HkgAziky5V1xhPYlh4HuRXvLCZyLvOLYRAtHSKG2TGA49Tzz2x72IdJWR8aMjkNQEtPrklhDE5Xbc5gHDTTt1cxR3aTF4FLYAfc5xmEa72PG40Bbz1oL1Xkv5N1248/A+O/syRc5zlalSq66KKLhj+fffbZ2Lx580TH2bp1K84++2zUdX9T8f73v1+riyVKtBKDatPRGBqDtsg4sALtgXClhRVoJb2s2EBF7LhB0Jh1/i5oPmrMagBch8yxAbvkmUSRZOr5uiGNgbr2Tbp5UkF5yTMrc+ThM+As3KuKoG2dqDS0SF7FkhdsqaRBSJsGP3FowziIyaUx5oDOPaD5aMSYb5ombKK2uABS2iSw0msBBuNAlAoaTSALG3i2x4FXbU2W6mniccC4927ltMu8cqWD1D0OFv3k0WgwdM1HIwUaseUrc0Aja7ks7xoQ2W8x5uloeJXOJI+DEGjG8DyqqqoZcO38ydIcmCkhm5s8YshV+fvQMHDOkElpAtoyGCe51fbrf3f+vcIwyPVZYO2L/GdQ/h7D3wGQWGcNGAcEc+TUGPANsqdlHDRh/Th7kyJVpB6tJQ6uvfZaXHHFFcPfn/WsZ011vGc/+9nDn7/61a/i5ptvnup4JUq0He7CK6QvbuZxIMi0aNPSRyMkU+FuXO3MgX3AFrDTuA95PGibwg6PG5GJ8Kss+VI5o+PeymtDlCpyZVqIVEhRX95QLkvSt5YkWqxYL0PWjauvTngGlgPz7yAsgPsYTb2tBHL/PeBef5vEnbW+e78PMuvhgOczogzWiaB5WqaEsR5wweDVro05suivMJo8EjbQmucv3VNPqofsORST6zJh3SQqrr1iEnWpoPg8zJTpAdIa55LHAzOBmGUQrp5Ej4OXXMZBrkm8bpXr+nHyQDPWesAzhm1QbaxxG3LPn8s4yNNYZ4wB7/wbmRNrVNs75x5p30scKdyCJtXeAFAzzJEnfgZZFf+2cmmuV0cT1onG+9ArHoiMQUAC76drvxnjwAYfm+doLXHwta99bfjz0tISHvGIR0x1vEc84hFYWloC0J+4vvrVr051vKbx2c9+Fs9//vNx0kknYceOHdi9ezdOPvlknHvuubjsssuobd944434gz/4A5x55pk48sgjsXXrVmzevBmHH344nvjEJ+L888/HVVddRe1DCf2QgGvAp8pbGZOurPW8F4YLrmmGX/FrZwwLCNWuvR4dqIgdd51xYKPvL8lEDKJtU0a/usuq2tpWqkgaA1K1iVnySJBq6rdvxPgYVHsbeFyktL3ZHgf9Y7rGoPasG2nTxNYVH0Ss2tmvarJN4NK13SWpIuecJXNgStW5txaQZAsJPjfCuIq9hwBd4Fiq+E2ZA+szn8KbZrZMFZDW2LaWKvIk4wgSMaORqvh1z9/anBmQ1oTaPhvx5InvOUX0eAjJhJAYBxN7HCi9Ez1994g5sgvasjT+3XYGn4//O8XEQabOv78m0pBpya929kFbhevfwOOAkrhokLgCOCbZXh8CzwCLBdy2OfKkUkn9Pkzfvi/X11CuaspOTCPXVTwO9KO1xMEAyK6qCve73/2wvLw81fE2bdqE+93vfsPfv/Od70x1vNy44447cM455+Dxj3883v3ud+Pf/u3fcOedd+L222/HN77xDfzRH/0RTj31VLzsZS/D6uqqevt/+Id/iPvd7354xStegU984hO49tprsX//fhw4cADXX389PvOZz+B1r3sdTjzxRLziFa+g9KEEJySZkNH/h77Han+tJ8gTEGVavA3rUKrJSuN/vP26Bla6Xe97lpW+dS3oixuBtrFKT0riIFJl4W6UWawTHzy2AcyG7QsLJrY8R+y4q8H2jcxxA3JdFqClD5bwgetYH+wq7tOsE5ZkXaza2aLSXjruYNPEngdkY2ZnDAobaE7iQFoL8J9B6ZgxuSpAfy6MbdotZOOaSPa1Yo5MZt74UkUJxoG6TE+82tP9XTuJn2OOymcchMcgu/1c0Ii1Ls73OGCBhpNXG2swX6TzkLrAkMkJHStY8U1InOTKJPX7xWAc5IO2jOSZ134kcQVwqu7dcRwyaGatyT2PhZbbz/X4AJR8Tho8A4D+GJjKHLokDtSjNXPkH/7wh8Ofd+/erXLM+9znPsOfb7/9dpVjxmJtbQ1nnXUWPv3pTw8/27lzJx784AdjZWUFl19+Ofbv34+6rvGWt7wFN910E9773veqtf/KV74Sb3rTm8Y+O+KII3DCCSegqipcddVVuPrqqwEA3W4Xf/AHf4CrrroKf/EXf6FKpS3BCa/KL5A4WCbJBUka95agqU9RHpy/u1GzAe4B4O5Vrp5t6ri92tAc2b3+oxX/BhXHscWCL49gkzySNP6ZVEgJtJAq/q1YJyHGgVnF+T333SJx5MqkpCUy+IBtVOPf0OuFDdYNIgaaMaoLpQhV20oeB7rtpj0OZKNym3HorwUI7SaugVR5p33+SwudMVkq68RFDDQwMUdObNrZiZMY8xHgApb99lOMA3biIg2asJPYqTHAbD9X35tR6Sy2HwTtOGtS9zRC+u79v43/zjDHBfrnutBZGPvMdAwYArd+8UbM40Bfrmoq0JSROEnI1HjJC5XkyfjvoVvAWpPn3gPWu2jS9vv/Vp91k/a50L0PTZJnhXHAj9YYBwNZIQDYt2+fyjH3798//Fmi0mnH7/zO74wlDV71qlfhuuuuw+c//3l86UtfwjXXXIMXvvCFw7+/733vwzve8Q6Vtv/xH/9xLGlw//vfH5/5zGdw7bXX4uKLL8ZnPvMZ/Pu//zu++MUv4uEPf/jwe3/1V3+lmrwowYuQVJGVTIuna2xUZTg8dqja2NWYZ8nUCBvAu1fHGQcLHV0927H2Awt2K8aBa3a52usN51ULffdYpRfbkHEQ0oKNoSEZCve01nq1uHGxkipa7foyNYBdtfugbS9xRTh/r9LXkygx8DiI6Fu36XHgSWOwQPvAOxCwkWkBwhXHK67PRgseB9KzqKktPQi3OGFlrfYkExkFDJLh8tgzYCAd6CePw6wfgFDtHQFu2KC51H6q4l+7D64cZpJxQJcKipsj68sE+fOPu79lr8tT7DvmmtSXyQiAZqRK05hU2GiwxmET4Nb3GdBvHwjIZRHngRTrJ/Q5I3ES95iI/9tJYhqZFgbjIZa4EvtAGIOhPrAk03INohnG0EATjwdhPahwCSTWcSy0k7jTyHUVxoF+tJY4OPjggwH0Af7vf//7Kse89tprhz/v3btX5ZihuOaaa3DBBRcMf3/Vq16F1772tdi6devwsz179uBd73oXzj777OFnv/u7v6uSKPmjP/qj4c+7du3Cpz/9aTz+8Y/3vveIRzwCn/rUp3DMMccMP3vb2942dfsl+CFVOwO+KSPLZ0B6WfhgAbPaOlDp6QA2VtXWgM844FabC8CRYNBsZQxc1yM+Cx49n1HlGl4smEkVicawNm1L7Xd7vikpwDMpzzZHNqr2Xh3OARYeB3HAyqKyxUvejRmjWoH3fgLPwt8BkKptIzItRgncoMcBWVcc8M/ZyihdWouYeBwIYzqWPAv9m6n6EEleWRjFx4BTtr8CEJcM7PeBOw/FWFeAL5vIrrZPeVxor4Wk59mXS2L7PPjrkNFgVFqvt51ZbU6QqQEE5nMANLOSSoozDvRBs5TPzLBt4vs4m3VCWJM18zgYfw5Urn8DxoE7NKzbBwC3GFxjLnLHUugZaJtxwGM9TeazotWHJhX/gP66JDdxCNgVVM1ztJY4uO997zv8+Qc/+MHUBsKXXXYZbr311uHvhx9++FTHS8UFF1yAAwcOAACOPvponHfeedHvDhIKN9xwA9797ndP3f7nPve54c/nnHMOjjjiiOB3d+3ahV/91V8d/v7lL38ZKysrU/ehBDdmzuPACCwItj+sNjZiHAgvnANr3eR31NoXFuxu4gbgJS8kMDCkMc/og79hk+UiAJ5UkQiaetIETMaBu2CTN028in9fLkxanNp5TPSG/RhvX/8e+IyrOGjLkesKPwNWJt3Spsni+QfCPjfuzwCTcSAnL9pIHLhjTjTDo8jVSFJFfNaXNK+MJ88kxoVyH1zgOJK8kr4/bcRACwvGQYxxARjI5CSkinygQNtrJA5asNciOfOA+26yHIOAANgqvotyQSMa4yCSvB7/nFPQ06ji3EDjPtSHWfC5YFQbN/E4YKxJmoCmfvJs6ua9axjS9w/1gXEPcn1O9DwGxu9B2OOAk0DNPX/ZHHn6axBSfwiF9rqkSfLKb7s1mHvDRmtX9NGPfjSWl5eHMh/TSvi8/e1vH/68sLCAxzzmMVMdLxUf/OAHhz8///nPj5o7H3TQQXj2s58t/ttJ46abbhr+fPLJJye/P/qduq5x8803T92HEtwIabt6gAWp2jhH15gr0+KDtv3/G1XczyDj4O4135yZBdrGjCm9KkNjjwMzqSLJHNkFrZnJI6HSQ9q48uTK0owDS7muoVSRNwfwGQfumLcwR/aSFzGPAyPgXDLotmI9jY59b4NgxLwLehwov4dDjLOx30kbRTckg1o3ic1Yh1RVFZXK8ROI+nORB1yP+R1IjA874NrCbyfkNRXqg/Zc7L6DXMZd2/r+fHPmdOLAZ11w56LUPKQJGvugVR5wr1ZtnCtVJKyLNMJNBMfMaT2PA4W5UPSZEe4Bg+0wiDZZJ9L9D0lhMxK5bXsctG2MKx0j9Ay0zjjw2BYqzU+cvAS0DMLzWFehfkzPOMhPnvrJo6maLiFEa5d069ateOxjH4u67k/C7373u/HZz352omNdfPHFeM973oOq6m8aHvWoR2HXrl3KPV6PK6+8Et/97neHv5955pnJf/PUpz51+PPFF1+MO++8c6o+bN++ffhzDntgwI4A+psx5vUpoRMhqQy2KeMgpEozr8qQKFUU1Dc3AK0BedHhehxQGQeZ5sxW1fbASPLKQC4ptmGzkiqSqk2tqq0Bf/PVl0oab7+qeH3wqz3tPDb67bvX/x7Gi4FMT0oig13pCsSlisw8DjIMsnn+CrHzd4AaUvIqtGlhJy8l0Nw9Z5YZnhs+eOsnEFmSiY0MwilSPWHQQNrA86VyIudPeAZirB+An7xoOg/rMx785H2sPwxzbjfcRAE7eRFi/w77w9S3zwTuWX1ISVUNgpVA8/TVG3gcaEwHUkJeugdM6cZcqRQLjX+pnUEw5qImMjGM5E1TxgGjD24xgHXFf67HgdsvNbk0dw4MzAE0c+QGHgOAcB2UGQex5J0/B5TMgXa0ekVf9rKXAehPCt1uF2eddRY++clPNjrGJz/5SfzkT/4keiPGnb/xG7+h3tfR+OpXvzr8edOmTWPmw6F49KMfPfx5bW0Nl19++VR9OO2004Y/5yRcLr744uHPD3vYw7Bt27ap2i/BD0kqCPDlahimhEBInsBOqiiob+5pyhqaI6+55sh2jAvAl0oCeBXvYqXRIHnlVptS9K3DgEV7UkWCObLhM9AVZGJY419qv++xYTkHyNrKFoyDlEQHq8JwNKQ5eBBWC+Qsg3Cj52+0XSvGhft8DSXzvIp7RrV3fMMqbeAYw8ADb9d8rxUe8y8G3POTdzHWj1R5p26SHRkDLHmW0YjpK9e1MBdYexzQ9f3jiRP2+cuMg4RBs/I1CLF/13/ngca51b4eYEnyOAiDxkYyJQ08DhgV74A87zHfx7nAJaMPMuPCrv2UMXm0/RlgHOjIZY3/HuoCK3mXW/HPkGmS288bf/0+6Lef9DhQHodNknfuPSgeB/rRauLgqU99Ks444wzUdY2qqnD77bfjzDPPxC/8wi/gX/7lX6L/9l/+5V/wvOc9D2eeeSZuv/12AP0ExGMe8xicddZZ1H5fccUVw5+POuooLC0tJf/NUUcdNSZndOWVV07Vhxe/+MXDnz/wgQ/gU5/6VPC7X/nKV/DOd75z+PsgYVNitsPdsA4qHN1KR55Ujb9gMU0cCPrqo/9f/54dcO5W/HMZBxLjwY5xIElPDCq+TYwxA/e/3z6f8SD2QUye8cZAjkwMa/wDflJS0jZnth9OHvITRzGZnP7v/AVqrNLTinEgjkFyle3wuIHk+aAfFn0IAbc25sDx5JVUeWbBvlnr2RUReMD9Whi4N2EcRFg/AAM4b5I4sTj/uMeDOuPAfQetJart1UHzeLW9L53JTVxIfWAnUWNjsP+7A5orzgW5wD3rGvjnHpAJIa2LPHPkwPnXde0BqpYeBwzAeL0Pk/lcqFT8ZzIupM91PA7yqt2lv6mcf6Yx8frfnX+vMBVM6nGh53HgPoPy91gMwEnPX/q3Gu03lquaci6U1lS5BtEsJvI8x2LbHbjooovwyEc+Et///vdRVRV6vR7e97734X3vex8OOeQQnHrqqTjkkEOwfft23Hnnnbjxxhvx5S9/GTfeeCMADJMOdV3jqKOOwl/+5V/S+/zv//7vw5+PPvrorH/T6XRw3/veF1dddRUA4Hvf+95UffiJn/gJnHvuuXjrW9+KXq+Hpz3tafj1X/91/MzP/AxOPPFEVFWFq666Cn/913+NN77xjdi3bx8A4BWveAV+5md+ZqI2/+M//qPR96+77rqJ2inRj6BUkUG1t3Tc1Z4PGlLNkQMvK6vz73QqdKrxhZArVWTucbDqMw5oMjXCcQfX3jfGtK229UAro2sgGcMyfT7y2rcD7s3PP2SObHANUoAV0xBy2AdXImZtFLSz8VuRvHbaMmYelUiz0HcHwtWOFsB5asMoVZ+aMA4E2UIrxsH4GOAnsGIJRLfymtGHGGjG1BUfRFwujM+48Hx2nHvetseBn8jXfQ6k46WSF+aMA2LyJsX4GIQVaBhqn3UNcvXdpY81hmIuaGc6BgylgnIZFwDHnLlJxb87BqzNoQGBBacA3LrHCCUvWPNgNuOAJpcWT16H2tfqQ+4cHOrH1IyDBsk7RvKwxHi0njg47LDD8LGPfQxnnXUWvve97w21zOq6xg033ICPf/zj3r8ZSBINPA3qusZxxx2HD3/4wzjssMPofb7jjjuGPzfxCti5c6d4jEnjf/2v/4UTTjgBv/d7v4ebbroJb3jDG/CGN7xB/O5JJ52E3/7t38Y555wzcXtHHXXUxP+2RPMISRWxTRkHIembu/IELF1jQDLkuef8SXREKRYXOmNg3QHX44DYtgTE7BcSB5bmyGs9eQxaV9taGYRLCzbfGJY3BiRtTd/fwQ64X13zzZmZiYtQpaPFHCj5W0h9GYRVpXe4fauK+55ACbd5/tqQKgppDFsYtMfu/2hfRoMi1+PIMK2s9bwEPsvvSJJJGkQrybux95ALkuiD9zHQwoJ1474DYubMgL3HAfsapDTG2aC9dDzf44CbvGjV4yC32pZgjAs0AK09Fh5Jqijz/AFfG36SkG6l1AembFqbYyBHKmz9cwbjII9tAUjnP3XzjRgPYh8MWRcsubBcjwOr5GWTOUDlGYisAcR+KD8HOay70OclcaAfM+Ea8ZCHPASXXXYZfuqnfgrAOosg9NIbTRhUVYWzzz4b//Iv/4IHPehBJv296667hj9v3rw5+99t2bJFPMY08ZKXvAR/9Vd/hQc84AHB7xx66KE499xz8axnPUulzRI2EZIqspIIEKWKjKr9xfbveSGwFuhiH5yXzoE1d2FCBI2FQ7uJC4B3/gv3MC5GIyQVwwDuY4CFVbWrr3MvGMMaSvX0ExeGrB+h2tfy/EPVfjYSHQlTTguPg4jPghVwLrJOjJJH0vM3iDbP3+0LwEkgpqpdhYJ38b0xbUjgrZXHQbPkmUHyJiaXZcw6MTFHjiUuDKSy/OR1CrTWbj/+DLLX49K8lk5ikxkHhnJNudW+7sesat9cY1ytdZFbbR2stCWBhlVVZd0DS8aB+8wNguIxkCnVBHCqnZswDjj+As0SBwyD4FzGAauQYFKfFZZBey7jQasPsTWA2A/l69DI48CIiT3P0TrjYBC7du3ChRdeiNe85jW44IIL8Pd///f4zne+E/z+iSeeiDPPPBPnnnsuTjjhBMOeAqurq8OfFxfzL+Hod1dWVqbux9VXX40XvOAFY4bSRx55JI477jjUdY3vfve7uPbaa3HDDTfgV37lV/Df//t/x4UXXogf/dEfnai9a665ptH3r7vuujET5xLNIiRV5ILXjEpHwN8ArUj67qRKb0AGTbo9X8eTVekIDDat62C9KxXErLYeLNhHFynuvWf3wWVcDOWyDIB7X6bE78d6+yTQUAANWFUtUkj6tm7yzpL1stb1GQdt+Jz414ABGMYBIxZQMBpNjFlpckHeGLCTy4oB5+0xDvrtrrRgEO+bI/ubJI0qU78fktdJOwUMoxX/LnDNSd75438QHvvMQCpoLZI4YEgVudd0XKrIX4+w9fW9BC55Hk4Bx/z206CJ/y5QTp4krgEzeZMrlcJ6FnKNeWkyKQvxd8AgJCxPzyC6is47AFc2rU2N+xypsNDnDMZDzJiWwfqYlnGg0Yfc5BlNLmzC8af5/E3Wvg7rqLHHgfK6TGYchJIn6X9bYrqYmcTBIB7wgAfg7W9/OwDg+uuvx3e+8x3ceuutuOOOO7Bjxw4cdNBBOOGEE3DooYe21setW7cOf7777ruz/93od7dt2zZVH773ve/hcY97HK699loAwGMf+1hccMEFOPXUU8e+98UvfhEveclLcMkll+D73/8+zjzzTHz605/G6aef3rjNI488cqo+l2gWuVJFZpV+hrrGgFxxLlWaWGrMu+bETNC4f/zxBbubuGBII4zGUqfCaIpzcP8tJKsaeRwYSaWs9XzGActfQW7fT1xQn0ERMLQBjaVjD6u9DeTKJF330bCRCWlScc96BgTWi4E5NTAb5tD+M9Ae6yol08JKnrjJ+btXe0IC32gOHk2eGTBfYpJlFubMUcZBbTEH5TMuAIbHgV/AMhrsZyBdbc8dg1VVYaFTjUtEpZLYZLkkP3nDuwYpxscgWFJFKX+HYfsk9tM0jAOtJcFip8KBkd/dtZDUL2uPA8k02ppxwKh4b6IvbyHV1FSmRqcP47+HumDlMZAtlWWcvKSdf+YcPOzHDDEOmPjIvMbMJQ5G47DDDjPxLGga27dvH/68f//+7H83MCh2jzFJ/NzP/dwwafC4xz0On/rUp7C0tOR975GPfCQuvvhiPPGJT8TnP/957N+/H7/wC7+Af/3Xf0WHDHqWmC5CwJUP3ttUW8vyBHag4WrX19YGyFIxzrHvXrNjHAyOP7pgdxMXLMBuEEuLHWBl/ZxDySvriu/2pIpqocKQdw98bU1b4F4+f7sxGNo0WciVpbS125FJsQfOpYo3C215QLr/6/eAqak8GqGKP89ng8E4SGwYLaRqAH8c3rWy5n2Hxnx0TcrHEgcWjANnDhyRyrHwm4lt2i0YByHJSPfnQVh7HFiD5u77z+I94CYO/Ip/rjyDa06ZYl3oehxkgnY00DAPOPVNsknVxiGZFilxYFjxzHwX5YwB1lwkSoVlSyVNPxc0qfZ2/0TxFwiMv2EfCM+h+16TkkSAsC9VYxyM/x6cg0iFBO551HX/GqS8F6wTJ4PQfh+IPiPGJvUl1qMgxxPEnj17hj9fd9112f/u+uuvF4/RND7/+c/jc5/73PD3t771rWLSYBDLy8t461vfOvz9iiuuGJM3KjGb4VP0bc2RfZkMa2NW/6UoVfuwAAvAf2G6Ff9s/Tz3+G77zGp3oEG1LUXbOaxtbSVVJG1YrNoGMvXlDZ/BNSFxwTz/UMW5hTmvD9jZV3tLcnHr7dtoeUrJI4/xwfJZaQBa8hgHch9W1vjPgXtdrY1hh/1wxuG+A77XDi15G5HKsXgGQ3Jp/Z/5CbTYe9AicSSx3oY/G3gcpJlfToWh8jXwQHO32t9AMi4m2yj1QXsuij2DUvvcanM7fft++3Fj6EHbLp6qdQ/cZzoEmtbCZdG6BjGfmUEwk5g5Vfcsj4dpGAcq7WdKVQGcYooQaB0KhlSRex2DyTP3+iutSfx3sDXrKU8uy8og3n3/eP0wSBxY+oyUGI+SOJggRo2Ir7766qx/c9ddd+HWW28Vj9E0RkH/o446Cqecckry3zz84Q8fkxr6x3/8x4nbL2ETuVJFLOBcqvg3lSryXla1x3gAuMCpu/j3Kv7ZwL1zbq7HAZ1x4JnjDjTm+aBZrNKrLX1t0RyZOAZEj4M2gXtDfXsgLBNgkbzxQdv1867r2qTqPsa6aY9x4CdwaUmLyIbNTKopABxazEEpiri/UW6PccCah2IV5z5gqn8PpAKKYfueRAvf5yL+DPAZF32QdJDA5XscuDJZ3V49Bg6w30exOQgQfC5a8TrhzoV+AYVdEj1XKsUHjJTaTxQQAKHnQAu0zDt/CUzVyqHl9IG5HvEZL2lzZunfTRIDqbBUW/3P9ddFklxqKDzQXuEWSInYGOvAfTws5ZJYYzCXdcQCrXOTV6zkXRO5KmlvNL3HwRQ+IyVxoB4lcTBBPPCBDxz+fNNNN2WxDr7yla8Ej9E0BhJFQD9xkBuj3x1lP5SYzfClgvoTogseswyK3eOuiYaIzGprf7MiVrhRpYpmnHFAPPf+8WWpFBOZjshi3SqB5etrC+bIllI9PVvgXqp4t2Q8hGQyfKksAmjvAFarkUpjgATaRSrOrfTtRZ+NluTyYvrqrKkwVHFqkziIgwZWUkWuh82+FYFxwPI4aCDVw3kGw2PQk40zTqC3cf7A+jMgAQj6UkH+uFqNsS6U70EKNLF4H8cMuqU+qSdPAnuRYX+IDMCYVNZo+KApB7ST2peZNxzQLnT+rIp7INNjgGiOnNO+NBexfC6ktuq69qSCGB4HTRgHOowL/1mO9oGQvHANfqWxLvXL3ByZ9AyIUj0ZrB+t849JprohXZpp+yH9c2vJuhLrURIHE8Rpp52G5eXl4e+jskGhGP3OkUceieOOO27i9kfbntRjYdTgucRshl/pZmyOLFTc+qa4djIpq0K1M70PzkvHr/gnMw68xIFNpe+w/YA5q+91YVtt62ncWwGXPd9ngykXJWlYtu0xYJm4CFVb+X1gJK7Gj1nX64tS0RSU0AdXhm41yjiwYZ6tGSaPGlVbk/oQ2jRaSAb6rD8HMCSZ8bnhMQ4O2HkcxMxxPV1xE6mi8Bi0MEgfS5wY3H/p3T70mnErHCt9nwUpIbUauQbqiYsEcO2vRTgeB+N9GG+TnUhPAWdM2bz2PQ4m09fXugeevnsANJXkY9Q8DhJyXQDbHDl9D1jmyP32J0tcaFz/Jh4HjIpzaVkZ91nQ74Mv1yV/jyUbl8844RSz5DIOWIyH1Psn1a9p2VdVVU0sWVc8DvSDYo782te+1vvs/PPPz/qeVkjtacWOHTvwxCc+EZ/4xCcAAP/3//5fPOc5z4n+m/e///3Dn5/5zGdO1f4RRxwx/Pnyyy/Hvn37komAffv24corrxSPUWI2I0TRd8EDnimhX3G74rIdDP0F1gRz5v737CqufcYBN/fqMQ48c2ayVFGg4tmrOKaAZuFq2xWrZyAjeWUK3BtLFUkVz5aJC18mYADa8q+BdMzVbg8LnQUaLd4NSS5uEFaVNZIxphUd2K+0HWV92MjGBT0ODOagWLX7aF8GYXUfRMYBK3kUTSDzK/5DXlNAO14/5owDaR7s9bAFCyYJXHEeXusBm/o/syXLQnJ5w98NgAq3OCFpkq7OOIgzn5jXIAe0BYiJg0DxzFifxH2Jzjj0K87l70mXRSuJl+vzMNa2IvstlbwLfUYDTkW5Kg7rJCSVKIXbhTYYB4zn0DMnDiTPfKkollyY/BD6wD2nfSDvGWCxrprcf0BPripWtDGIIlXED0ri4DWveY1HLZKAfOl7WsFMHADA8573vGHi4KMf/Sguu+wyPOxhDxO/++EPfxhf//rXh7///M///FRtn3HGGcOfDxw4gHe+85146UtfGv03b3/723HgwIHh749//OOn6kMJfkgV992erx/nasCqtS9U3LoV7yx5AkDW1rXQ1I0d22McsCv+25YqWnQXzHK1LaMfsSo3K9ZN2+bIsseBXfLOlShZ7QqJC2biLuAz4M4DjGsgHXO128PmpQVx80YBjAKAjZXHAtBu8irKOCBskqUIbRpXvSS6xf13AMOuzSbJPbd9jsdBp7JjO4zOf24hw/Lignr7UgHD+s/8Z7AR64bwDIigRWAeZowBKSG3Gkne6EslNQPtKQbZCfCQvSbwgVOXdcGbhyaVCWGBxjna4oDeOHTBv6BMi/C51m2Y5BpYj4FZk2oCtEDzdNIm1AcW46CRQbPCPfBYN5mgsbXHgefvoDQNixr/EuuHxL5rxHqRngMlya4DI7/nSraVxIF+0KWK6oiJyr25vec85zk4+eSTAQDdbhfPfe5zRa+DK664Ar/4i784/P3pT386Tj/9dPGY73nPe1BV1fC/z3zmM+L3Hv3oR4+ZK//Wb/0WPvrRjwb7+qEPfQi/8zu/M/z95JNPxiMf+cjo+ZVoPySpIqnahQUcShu2/Q5gwPU4SFd7Ly1UtOTj4Pij0bbHgbU5cqjq30KySkqcDU0ZjaSKRHNkI4mY/rH9BZt37sz2hTnIAiwbRAi0tWB9SOc1TJwZJTC98XePKblMB7ZJIK/1el7FlZ1M0ihoaiPbFqp29MA6BusqUWnJlmkZhCvDdNeBbvTvmuElDsaYZ+57iPAMBpLnAK/CcfyY4WpHi02yJME2OG8bg3i//XHWB7cPSY8DC7mqhEEqm3EgFfHEftd8H6RYV0B/3+9u/bVA45Q5NgCsEqWK3OEf0he3leqx01eX289kHCjdA3d9mStXpWOOPHm1t8ayuDHjgPBOcp/lEOOANQ9mG7SzkpfSXkR4Br2kjRrrKt/jQCqqatMgm1VQNM9BYRwA+QC+dWJBKzqdDt71rnfhCU94Ag4cOIArrrgCD3vYw3DuuefikY98JFZXV/HZz34W//t//2/88Ic/BADs2bMHF1xwwdRtLyws4H/+z/+JZzzjGej1ejhw4ACe+cxn4hnPeAZ+8id/EscddxzqusZ3v/tdfOADH8DHPvax4b9dXFzEBRdcgA5Z4qTE9CFV2klSPVYSAQCwz6t4twMtuz3fY4HZPuAvmtzEAZ9xMN7+AeP2fZmGEOOAL1UE9Km4C5WdXJdsDGvncyEtlkyligTgtlVz5HvO3ZsHjCtdrTwOJHNqgEeLlyLH54JnjhwGzKwSWKFq2zbmQHfuYWgaS+Ge237DdUAoeQZIjAPCM+iynkauuYXfTQw0Y1Z6D9uPJFBNZHoiHgv9n7nPocT6qet6WLDClkoC0sAp+z54RTxe+0yPg8kq/lnGpKJMj8hAJMmkhDwORMYB6RpkVDtrvotypILExIkh44DFQm2kL08wCJeuYZxxkP73TcM9jyBoTFqX5lbcs+S6cj0OWDI9uf4Cob+pJNAykxc+C7dgndpBSRx8+tOfVv3erMajHvUovO9978PP/dzP4cCBA7jhhhtw3nnnid/dtWsXPvCBD+D4449XafupT30q/viP/xgvfvGLsbKygrqu8ZGPfAQf+chHgv9my5YteNe73oUnPelJKn0owQ2p4tqVRwCI5sjCcd21KdfjwN2sCNXObODeYxzYmhN7HgerdqA1EAau2FV+/bb9Y652e+hUHQ84NjNH7vb8czeUyrL2GJCSl5ZSRSFjUgtDSumYA8DEzOMgAJxbMg48n4le7TEuWIvzmDmwR8snVRaF9J0tPA4kj5XRsDKCS73nWYlbIJ48MkneCO+A9Z/578EYcGrBOIjJJPiJCyOPgwjjQDuJKV3TQQEDYJM8ac560L0PvjntCOulV3va6pr3IEdfn5lIl9gWo4kjQGYg6jEOxs8/JNMiehwYapxb+lzkJo94FecC44IkVeSee6/ujwEpMcOQCZLWtaHklfQ3jako1yCclcSdtNqdJRUktQXYmSM3YZxo9WNSyTo2RjKPQUkc5OrnbwSd/ec85zl44AMfiJe85CW4+OKLPQbFwsICnva0p+Gtb30rjjnmGNW2X/CCF+C0007Da1/7WvzN3/wN1tbWxO8tLS3hWc96Fs4//3w88IEPVO1DCV54lU4CaAfwZAJyFv+W1c5dodqbzTjwzJE9c2LrxIWxOfKiOwYDPhsUU0i5ykKs9jY0R/blGZjJM3fDLlTVEMegVG1pAdoPjx2odPEqfa0SV/ckbu08DuRqaxkoIT0DIuvGZnEekwiwAOyAiMeBCWjdTKKElTxJPV9cn5UGHgcGXidxfX0L1sn6PW/THBnwxyPjGayqCoudcWPEmEG1PmguJy4WOn0/DQvJtJhBuIXfTSyBywKKQsfK1bdnV/uOXmOmx4F7K3NlWgCex0EOaKn5LsrR+WeaI8fG/yBYiQtxH1TX6CBdhc6QCQLiBs2cPoz/Hip4Z72PvH1YiPXDkgrKZBx4MlFacm0Jr61UvxgJtBDrwWpNPM9Bkyqap3jIQx6CT3/607jqqqtwySWX4Nprr8XCwgKOPPJInHHGGTjssMOyjvO85z0Pz3ve8xq3/Zd/+Zf44Q9/iC9+8Yv41re+hdtuuw0AcJ/73AcnnngiTjvtNGzfvr3hWZVoO9xN8ErXN0aVvqcVOYAgt9LQ3yy5FGm+VNF4HzwdVTJwn/I4oJsjO+2vGPpsiBIFvRodKXlmJlXkV/wzgXsJtLRge4SOLXk8UJOHAZ8LC9BWHn/3gMZmHgeyvrsp40AYg75Ei1Xibv26W4CmQAOPA4a+e0IiwkuekJ7F1Pzq+gBoRgy0sfCZiLE+LK6/P/5GGA8Ger4x0MLKoH1poYO13nrRROwesI2BASeBacBAjHkcMEHr4fEiCVy5fUWPA2H+dSv+me9DkXHTqzHqw+7OQwsdPe81F3zM1Vcf9EMj/Puf4XGgOBd4kpXZySOd9ieVy9IBTGWPgaUF/7tuF5iJCylYclG5cl0sqR73lVIHWB8s4L6qKix0quh7B7DzeKjrflupd+MgGJJd1qyPEutREgeKceyxx+LYY49tpe2dO3fix37sx/BjP/ZjrbRfQj8kirwLHAM84C6PcWBXbb3W63lSTWwaWur86B4DSY8FcuJESt5IiQMCYCPSM7s9SGsxWsWzUGnlA/e8eyBVerXpMbAqeBww5bpCPhdWEiGdanwzNrj3FmANEK52lqptaB4H0hgwkgqLVTqaJQ4CGxa32t1iDnTnHp+a3k4RAdXjYFF+BgDJHNmg4j8C2nIYB+NjYFSqwpPrMqr4X5+HbDbpSwsV9q+u/x5jfWivh0PA8SAsfEaiPhcG7DM/eRe+/oCubF8InBp950jFp1xjUieBS0weecawDRIHWsmLLI1/KuPAL15wQ/J70Tr/nIprVgItZ/wNgiHfKD9/gWpvKXmj8Cx4UkUhmRrScyitq9Z6NZYT/dJmXqUStqw1sXQd13rrrLtYH7T6kTMHAdx7UKIfxTWiRIkZDVlffPyFXVW8iTGnipQBlgwix5iVzThIXVuGrnmsfdeU0jpxsib4TPS/xwBN5Q2jpc+HZMg0c+bIllJFXclnxG4OAOR5kFXx7t7/IWBG3KSOty9XnIuLc1ryzK82tfKakdqWfgaYjAsZNHDHIAW0TlR6+hXn6l0AkJ7fmczDGOvCLaSgmCNHGAcWrJNYxafVcxgyR7UwZwb8+zq6BvCTyMqgeWAdMmzfIHkSq7a08NuRdP6ln4ffV7wG0rvdPWcRsGRKFaWYX4rnH7v2sc/NDaqJfis5UkGe34t14kKSilKYinLG33q/3MTB9O1LxxByhWL7/X+vkLxwk2eZjAOtd4F0D6REHdMgPEeqh2aOnHgHpj639DiwLGyb1yiJgxIlZjSkDask0cEArID+Sy816XJlSjKMWdnmwInjt23OTGc8eHJZslSRlceBVPEPMBMHPuvHlcti3oO2fT4kY0LLZzC0YLWSa/L11WXgngaYZSYuACZ4L7BujFgvMakitiHo+nGdZ7AbYBxYmCMnADPWNUhKFRkatI/r24+fv0XyZmwMGgDnIakKQKgwpVX8y8kzX1OaNQ/ExgCbcSBXWw6iDZ+JMakmiQGqLVUUSd4xteWBzIp/IgMvdf8B/x5ovg9d4DUEmLk4puYQmIRxoMq6iBQQhD6zTlzQGAcZ42/YB0LyZiCTk9O+hOdrJHDcZyDIOCCNwVxzYmYCMcvrhcY48Mex9AyE+qUxBqS9qNy+03ZJHKhHSRyUKDGjkcM42GSs8e+GpcdBHzC02agOIlXJzH4peR4HHuPA9v6vdWtPHgIgGYNKlWYhqSSWXJfTh9WeYERIHIMy46A9qaKVNd/jwdIcGpCZR6w+SB4PgI0pKCC9AyKMAyOporVujwoSjEao0hmQjOgoXQjKJVkk0HxjXMcc2fmddQ1SzELLAoLRa7CyNv4+5LyH8kFTRvsx4NQDy0hFJNIcILZvlbiIsj74Hgcxg2oLj4NuxOcC0L8PMZN26V2kyQDMqbiWcEw9Y9IMxoMlYJhpjsw0J84B7jXbD/kMxT4zZ1yI1fbTt51rjAvwDKrd8w+ZE8tSRdO370kVyafvFbSwNP4BmfXBNAjPSt6RWDe5XgZSH6pKZy7OYf1In5fEgX7MtMfBgQMHcNttt+HAgQON/+3RRx9N6FGJEnYhJQ5W1pyNKlEqCOgnBiRfhWH7VNDQr7Z0K/w2POPATRxYmyMv+qCNVcW/dO1DlS5WjINVATRl3gN34bfWq9ExbF8CDLxnkMm4EM5tVejDMtGUczSGEh1W+vrBxAWnuk0KCbz3QXObSuOYtrcZ42BgkG1i0B0GrQG7a5A6N4ZE0Hrb4WpPj3FA6IcnkzPGOOAnEGPAqVUCLwRa+IkT23lQ6oO6MXCE8eH+DAALFsmrpFQQN3mS8ljQlErKqfiXAEtLjwOPhap4/rnV1gx9+0HEkqeDYJoj++37+wD//NWaz6p2liRSNNQAcqvdAUGuSesZ8PYhIdCWJVOTbqfXqz3GA5dxkDYI101eTZC8U2pfeq+HxoDHwtSSjEushdf7ZacIMK8xU4mDK6+8Eu95z3vw+c9/Hpdddhn27ds30XGqqsLa2ppy70qUsA1pw+xWe7cOnDNBS+FFaSWRMYgUYMIyo1w/vlPxbc64cNu3q/iXFhzdXi1WmzBNGUfDNacGuGPQ3bRKUkXMMSABl6aMhwBoY/UcuON/MP/4CUwbiZiYxwFrfexeW4lxZGVMPLpZsTJBk0CzXq/2NigM0NoDTJ02WUCBG6n53VKqaMwc2SCRHgONfE1lwnswAlowNZVHw70GMa8XRrjzYFyqyIBxMDYG+MmjGPNKWo9pj8NYAlOqvFXV+M+o+JfAdDXAKqPa2K1y1VyPSHKVUjDfBe46NAc05ZojC8A90evEY79LiQuSREoqcTn+OasPbsV/HuMBIEkVCe0zmVfiHCD0gfkMtikXlmLdxfrAYn20MQ+W6MdMJA5uvvlmvOhFL8KHP/zh4Wd1YGIqUWJeQpQqMtBVbnJ8likpIEsVWQAFY31IvHT4jIP49eV7LPiMA+keMHw2Op0KnWqclrrarb2FAat9wE8MuR4TgL3HgdsaM3knzUEWYNkgpGq/VcmgmVbpKptyWkl0uOe10u2hrgWpJqXqtpw+SMkzq0rjmEyMWfKi6yewAZK+fkKqyOoapM6Nao4cSxy40o0GyZu1CGjNSGDGpCp8oEi9+X4fAgatTMB0NEKSbXXNZz/J19/W4yDGvLKQrfMN4keeAWEutPc44EikAPL9d+cdpudSPmA2/rvmcmAimRTFMRAb/4NgMi5ijJv1zzgJROk6SslCgHcN3IR0EDQWpYr0WRe5UlFa74JcqR7b5Jkd46FR8oo0D+TMQYBdMcM8R+uJg+985zv40R/9UVx//fWo6xpVVQ2TBqMb4dFEgrRBLomGEhstcjwOmBIBUh9GY7FT0SrcBsd3wwVu2YmTVNWKtceBG0zQFpAX7JY+E4sLnbFERbfng6aWcllSMNuX6Knum44qVeRW3Hf9Smtq8jBQ6WI1BrxKy6FERzseB0B/DFglLgD/3CxZN361dxiwo/ksLPjPoCjXRngXx2R6ADvWRbKAwNDraHT+cZPYjPVQ7B4w5TkGIQJHAeCexYAMAfdWiauQSboo06N8D6QChrhkGn8MpDwO2MmTZOKCKJMDSBX/PLmmHJkS5rvI3WPlehwwNf6lynLL9iWpIuY9mKTam8p4CTIOOHJNXgFTy4wDqX2JBcP0OJCSJ8x3QY5Uj1W1PxAzJ26XcWC5N5rXaDVxcPfdd+Oss87CddddN0wGVFWF008/HcceeywuvPDC4WdPfvKTcdBBB+HWW2/FN77xDVx77bXDvwHASSedhNNPP72dEylRghDuZqlXA3c7ZoDMSj+pD+N/Yxvz+sff75oD0yv+22YcpBIHtvd/RfA4YAPXKyO/S/r2ljIZUjAXJu6lXev10KnHP+RKFUkeB3bmU9L1XxOAWztzYmNTUOHeSskb5jzknpsl6ya2WWJWOI71QZBLcpl/AEmuLZI46f/OASvcSIGxTK8lT6ZmLVzxz/GZCDMeLLw+xORhQLKMdf/9ilt5HmQVMoTugZTAYzF/RqvMY+bIjHkoBlxKAJJ2F6IG4V0frNQsKJLeLS7LQZIu0epCVVVYXgjff4DLPHKf6VyJDnNjVuJcNFH7qomL5vryWj4fnU6FqsKYfr+5x0EuaEtiHOT4fEjzIJP1IfosEKUDs1gXJHPkFOsu1i+te5DjMwL416B4HOhHq4mDd73rXbj88suH4P+pp56KCy+8ECeccAIA4MILLxz+7RWveAWe9KQnDf/tt771LbztbW/DO97xDqyuruKb3/wmnvWsZ+F1r3ud/YmUKEEIacO4b6Wb/I5mxIDpNvwV3GpX/vnHz1HTBE48fuKlxzSmBWSpDEvWi7RYcoFrrjlwDuOACNoK1c7uco3ZvlRpamlQLg1vKXnBAi5DlbZmiYtFGTSxrKpxr4GbvAaIiQNhszBgl5rJRQlzkCQVwAAs3WfLPWcWWOFG6tws5yBrxkHc44CfRI1VPLt7Z8tnALCbB937OrjvkkwOjfUxMu1Zm7THJdv4snUhqSr35/53+R4TqXmwU8nKBJPG4kKF0a2XO/8zpRO9av9gtff477oyKc2Bc1VzZGEdnGpf8zGcxBxaO3ESY7oNgpU8mrTaW/q3Gu1LzXM9DoTkvfAc+mNApfl7+pCRPHOTuErnH5INlsKOcZCXuNCcB0v0g4u6JeKCCy4YShMdccQR+OQnPzlMGqTixBNPxB/+4R/in//5n3H00Uej1+vh9a9/PV7xileQe12ihE1IC/B9B1zgnDspxoB5tkySLFVkmzhIHZ/vMdAy42DRXzBbVHmGjr0mGoTbmvO6wbwHImhpKdUkJe8c4Jh5/lVVBQyaneQFiXUR0jdngyXD40qMgzXJ48EueSfhFrTzF8afJNUE2PlMrPX8OQjgPIe+VJidRMZopM6NyXz0DKINjXH7xwy378u2EUBrYeM7uO8WiQsgnEC1kkoKgSZylSn/ObT2OIgBtxZ+PyFzbMBP3mizXqqqSgK37HuQAu2Y83DMGHs0fNBYrQued4q1ObIkmekGM4HnJ87882fKtmVXW7csEyNhuRrzgTv+cj0OtNal0mUUGQckc+r+sTKSZ8Rqe3cMWksF5XocWLEg5zlaSxxcffXV+O53vwugvzB41atehV27djU+zimnnIJPfvKT2LVrF+q6xlve8hb8wz/8g3Z3S5QwDykpcNfKmvOdDSxVJBx//4oLWrIr7hOMA6JMTP/4icQBOXHhArKyVJEdaLnWs20/Z3xZysRI1c5M0FgC5L1nkDwGJWM6F6ygSWQEABMrjwMJkF1r2eNAClYCWxp/a71arPaieRwIGybR44CROBASp25fRoMlVZNi9DDnYPcZGL0GJoyDiFSRy3xizMWDar/RGGyafaBCvfl7jisnUK0k07zExYBxYMT8cZk8qYp/7ZDk0tZ/NmA8RIBDCwZiSCpL6o/0/WkjJlcG+MkTzXkgl3HAlO7LAQ190JJ3DSTgnpk4mcQcmumxEKq29lgnMyFVNH37nlRRpseB1j2oqiorgeeeP1OyTTQI91g3uqyrVPsAb03qjcFMxkNhHOhHa4mDL37xiwDWTY3/y3/5L9Hvx8yPTzjhBJx33nnD31/96lcr9LBEiXZD2gC5UkVtmiO3IRPkehy0bY7M9zhItG8sFyUzDogyFcJiyQdr2gZNiYmTSjp/O6mgWXgGveTVWs+reqcB1w7jZr3S1kgmRzivlbWeWeKi34f2fD4k6Z3Vri/VBPA2CNKGTQIsGWPQq3ZPSRXRgNv4cZnrEPcZGGV7uMwPjr59BDQ2qPbu90EG7nzGAUuyTR6HZh4HizJwKz2HFhX3Y1I9Xf4zGPV6MbgHXgJzLHHAZ/0kK/5J2t7D9hMm9ZbGwCHQ1sVIdKWK0n1gSgV5rNMcqSTVxEW64p8lEwPEE5djfSAlT7IZB6LXiAbjoPn4k/4duw/MRHpO8ozpeZVjUC72Qel9lJu4sEikz3u0dkVvvPHG4c9HHXUU7nOf+0S/v3///ujfn/e852FhYQF1XeMLX/gC/uM//kOlnyVKtBUSYHPXgXHGAdscOfbio3scZEkVkSvuUx4HxtXWbrBfipI5rqVUkbvo6ApSRVyZjPSxmUNA0hZmGvG5Id1b9xm0Tl65iQuANwZ9ivg9lbYtmTP3+zCDjAMWYBnQt5Y2DaxxKG0Y3Ur3pQV9XXFAGH+utrZV4iBxf23l6kYSBwaMA/eYoz4bbiKHx3qRN83u3p1mju0C58aMA88guxuRKqIkDsIJPItnMG6OzE8ix0Aj7xkwSFy47z9mpS0Qn4MAySRdEbBzzZEDNZTuXEDV+Heud11z1yQecJ/hMUCt9s6SSmIyDoyrvb0xGJIqEsB7hT7kMA4kFij1Hgh98OYha4NwQ9aLPeNgfA4Ie73Y7Y3mNVpLHNx2220A+hSggw8+WPzO5s2bhz/fdddd0ePt2bMHxx577PD3Sy65ZPpOlijRYkiLT2tz5NhGnF7tL8mkuKAlGThPVvzTGQfx41snTla6PmhGrTYVgFvPkLFFc2QWYDgI0RzaUMMxZw5iP4PugtGVSgJ4ck2+vvmg0te9Bzbt9/vQMwPsgPTCu1PpAzWDkK7rarembVDFPgg+A1Y+K7FKY8COcdDp+Drjo7FsOAeNzv8W90E673Xg3iaJG5Kq8OQ5SFNxqNrPBVDNGA8RxgEjiRmr9rTwGYl6HBjMAbGKbz9x0b7XC1+qyJ2Hec+B+24NytQQGQd+H5zEjYDhWQPn/hhQaz7L48C9/sxq9xBo678PtEDbzMSFKFXESBwIbZszDtI+H9aMB65U0b3D48CqmGaeo7XEwfLy8vDnxcVF8Ts7d+4cVvZce+21yWPu2bNn+PM111wzZQ9LlGg3pA2QyzhIaQ9PG3HGgX3bs+dx0C7jgO2xIFW8mnoMZEgVtWmOzE9cCYkDU6midPLOOnnlMh4Ajilpv21ZIsMHCjjtS8aQklQPcx5K3V9LY2YgzDigySUJz+CqxzhgAabO+HO1vYkVZm7E3rWWjINxc+Tx82ewz0TWzz3tMiuNR8Nl3g0TmEbss5A5aWseB8PEgT8PMpKYUakgImA4bD8CnPuydfzEyeh9t5CuTIFmdKki7/wTjANNwNA5VNiYlncNmppDA8rV1l7iKge01XsOckBLS3PkXKkglr58UCqJdA3yZIJ85pVmUVfONWAmj1JFJIAt60VK2gM8r5Ps5JVhUdW8RmuJg1Fpottvv138ziGHHDL8+d/+7d+SxxywGIC0tFGJErMenY5vyON5HLSo8c8GDCVTwP2rdjI1QDoxwZdpSUlEkEFbV6ZB8Dho2xy4TXNk9v13N1+SvjpXqsg/P7eoiAkc948/3gdJqojVh5AxqmXFv6SvbKnjmdqAM889xLiQNg080NSXqrFKniYBM6KushtRvyND1tl68s6Xx1he1D9/0WdkMA8YVdyHgCP3MWBNxb5J9/o9GP+eVeJgkLiwWYvEnkP3GeRI9YTbt/DZcK9/d1Suy2Au9CUzbZlXTU3qNe/BgtN2gHDgzQWauRPPoNljHHDfx+5z3av9PvjJI7Xmo4yf9c9474JcxoEnXUeq9g5LFTntqyUu3HZyGCfc5GGWz4dq4iI9BpmMg3zgfnwQaPUhRy5M6ldhHOhHa4mDBzzgAQD62njf//73xe889KEPHX7n4osvjh7vpptuwre+9a1hhnHXrl2KvS1Rop1wgZN9K47HAWGjPHb8Fs2RAX/xecBcXz1+jvyK/wRwTX4pupVTq13f48DSGHO1Ww/1jQdBrbhPSkXZgubmUkUZ49vaIFxiHPAAK3mxalvx74N2VowHoN05SAIfJI8HgCfTkpO8ZEn1hMbfsC+GjIO92zcF/8acB901Tq+W7wEALC8sqLcvjsFQApFmjiwDR0ygYjRCUjV2jAO52tF/DlleK2Hg2q805kv1jLZp8S6Sjrk+BpzkDeEepKp9+VJF48dzJdKYzB8XfJWq7QEu8yVVcS8B2ZqPgfRMpZJHqomLhDl2v33n31ClmgJyVSTgOCVVNfycJJ3nvtekxAX7XeStA4U+MKvdJ/E4sGQdDftAeg5ypKKkfpXEgX60ljg4+eSThyD/HXfcIZoZP+pRjxr+/K1vfQt///d/Hzze7//+76PX6w2rIE444QTlHpcoYR/uhtza4yC2EWYCxqH2rT0OUhtRNmiaNEc2Zpys9uz0vQGp0rJnKlVUVT7rZzT499+vMrECSwCZ9eOGtc+HxDiwAoxWAxIl3Kr78T6sdHsmVaa5x7aUKgP6c4Al48AH74U5kPQuDFXbD4JpCOnGUQdtDf6N6XEg3dfVbg8H1vyN4xKhkEKaW9Y1/o2kigKbZiZYFjvuukm8lcdBnlSRpUG69DNg43EQMydmeDxIxxyct4VcVwo4Zer7A1Lyfrw9pkG0O/2EzJHrFj0O2Pry8jog3gfdxEUatGRVWkvtB0FbbwzotN+6OXOOVJEgW6cZEvPUDT9xwqz4t/VYSLGuQv2iMQ5y5bpK4kA9Wksc7Nq1Cz/yIz8y/F1iFDznOc/BwsICqqpCXdf4+Z//eVx66aVj3+n1eviDP/gD/I//8T+GiYjNmzfjjDPO4J5AiRIG4S5Y73IYB+zEQVSewIBx4E76LmjITl5s3yT7rwyC/VKaNXPkta6xxr6grWwpVST1IfdvjLbXBI8D6+RR079PG+4zdrdkjky6D36lmexxwLwG0jNoyXhIJy95bUuJK0mqCTD2OGjLHDlR6cmcj44+aEvwb1SPA+EdL0m2AZwEojS+1yXLjDwGQswnK3PkQMW3FeMilDiweheG3gOAPwaszYkt2GeuxwYQuQcMj4fEPMiSaBm2n/A4YL6PPWPYTNBW1WNgAo8Dpr47IPj9EO+By7zO8TjQfA5yZWJYPhdexX8maKvGeHDal5SS2Mwn993qStQBvMQJkMk4oPpsZDKfaIyDPJ8Pv5ihJA60o7XEAQA8+clPHv780Y9+1Pv7IYccgnPOOQd1XaOqKlx//fV49KMfjVNPPRU/+7M/i2c/+9k4+uij8cpXvhJ1XQ+/98IXvhBbt4aro0qUuLeEJ1V0wJZxcOjOmDwBf0J2z88zRya/FLZvjicO2mYcsKWSpA27JXAvLZgtpYr6xw+fHxs09xfs/vVnj8EUGGd9/SXGAWtx6Gsrt+BxIFSdt9l+079P3b5Q6cQGKsbaFz0OjCRSIsbAANcQ041jDtoW/BvXoF4AjdZ6WBEZB1aJA5lxYKmxX9f+c2CWuAhIJbHO3y0QWVkbVLs7axFjj4Ner/a05TmMgzBwZ1HIIV3XQR9spIpSHgPcNVHIY2P9d3cc8mRywqDt+O+a70P3+nv+AtL7mGhMC/jALdMceRJ9eWtjXIAH3OeOQRZwnSNVxH4X5TAOmGMgzyC63fb7nzvAvdI8kEoeh/pVEgf60Wri4Kd/+qcB9Cl2f/M3f4Obb77Z+84f/MEf4L73vS8ADJkHl112GS666CJ88IMfxPe///1hwgAATjzxRPze7/2e3UmUKEEM92XlMg7YFffH7d0e/JuNx8H4pO/KE7CB2x2JxMFGZxxIFf+uTAdTKkcyxbOUKgLiVZR8jw2BceBKE7RtEE4HjuOso6WFavj+1w5fW1sGzJjzgDvHrxozDto2CJcqnUSPA1I3RMbBmvMM0qSK4oCBX+3MexajUkXEdYg0v60KCVSA8y6SQMCwx4HROBAAa8DeHHnWPA7MnsNB4kQAsBhzcaza1ELTWTrm4F3oJvAspIpSjANtybYY46TfvnMPFK9BdrW3xzhQ60JjxkX/3+g9i+Ic7I4BojlyjseB9xwaG+MCPI37ScegqVSRsceBZAjOlCrKYRxwk1fpxIn0uVYfshMXhr5f8xpxVIwcp5xyCt761rfi7rvvBtA3ON67d+/Yd/bs2YPPfOYzeNrTnjZmfjyIQTKhrmuccsop+NCHPoQdO3aYnUOJEszY5GyE7l61MWUcxLEHt1NlOIjUpM8+/x0JqSI2aLqQ9FggMw4WfcDAA80MGQdrxowHIH6NGXrCo+Gev6TrzQdu22UcuHOAOwcynwHP48MYMJOOLTMObH0Gmvxdu/1+8syvMGUlj6Tk3QEzc2SZ8TIIjxZOfBaP2RNOHFB9LgIV/xLjgJE46HT6PjejG9V1xoFbyMCqePeBIzl5xmLdOHNQTz5/a48Dq3nY91qSE8jSd1XaDyRuAOka8NsHRhkH/PWgX/HszINEU1BAqDZ2xr3n88Cs9JV0WsCrNpeO5fkLCH1S9RgQ5tW0XJQm4yA+/qT2Nd8F2fruJMkuz+MgOAbHf9dak0lTSq9Xj41Ll4GimbwDJpMK0pWrSieP3HwWVSop6HHAeQ7yzZl5km0l+tFq4gAAfuVXfiX5neOPPx7/+q//ine84x246KKL8MUvfhFra/3K68XFRZx++un4uZ/7OfzCL/wCFhdbP6USJdRiy/JC9O9s0PS4vbHEAX9Cbltffcfmpejf22YcsEFjyRzWr/Lj9UHS9rUw4xuNqMeBcduiPAddrio1Bm0TN65cGfP+h0wRfcDQknXTM9G1zj22tc/OareHXm/8vcg0BZbO/4DHerHSVq/HGLaeER1xkxRnHNg9g0D/+fPYh52KNg4WncTBALSyMKYdtD8afcaBDWgtHXcw/zHBsvHjyvNw214jotcK4RrEpYr4yStpDbTuccBP5EvM19Hwqu2V5wF3fnPb99/HiqB1rscB0Rw5BdpJfdIcB9L48+Wq3MSBWvOt68vnJC6kPmgNgUmrvbXugTSWu3WNDtY/Z/sNSbKxXp8sfUZEjwU7g+7cMah1DXLbt/Jdmue416DsS0tLeMlLXoKXvOQlqOsat9xyC+q6xp49e9AhAyclSrQV25bjjygbtNm9dRkHbVvGrXetmLcNtF/tunmp41UbjrVPfimlQTsycO0CV4JEhLXHgTnjICZVRB5/OYsuevIoyXqxHYO+VJGdvvpKC4wDT6ZjzZerorafWF+xk6fSHCAxDlghHdtPXtkAxkC/qm8wJCzHwfZNi9izbRm3GK8FRKkigXnGlksaTVSEGAdWBt1rAusI4Jkjh8x5rcafy+hZGUoV2bAfpesPyAaZDJ+RGHBpIVsnrTEG7VoUcqSASyZoDEj65uPPPdNnIp9x4Pw7oseAmyhgz0USCJweAzzQNktf3tgYF5CAexZoaytVJCYOejWWRupH2JJtOT4T1ORRgnUlta/5Pg6tAbw+kKSCchInvV7tGWcXqSL9uFci7lVVYe/evTj44INL0qDEho4U44DtcQAAxwZYByaJg8QCnN2HqqqwPSJX1DbjwNocua6Bux3glulxIFU6WicO2jRHzkkKWPssmLfvSRU5BulMxoEg1QXYehx4Ffe9nrdoZrbf6VTRyjX2MyCxPiyvv3Rsq+SVBJiMzn9MeQopjg7IFbGTx+5praz5UkWWckkhqRxWIl/atJsahAdAE6vnMChVZCQVFQLuXAAZYAH3YXNgC88paR4anLuFObLkdTUaTJkaIA0aWpqSBo1xSdXmQI7HBDeBJjIOLKWKMvTdbRkHtsC9NwZzZWJIiQsAHkDMNkfO8TjwknfmrBe3fc0xGH4HjfWhyxoDOVJNdvKN8xwFdS9RYoZj26ZE4sAAvA8lDiySFqnFn0XyImaQzM5mJxkH5PYlIGTfih1wK5oDOwsTa+A692/stgfBngNSFed8c2RHqshNHBjq+1tX2gICaLYmeBwYS5aN/812Dlzt+ubIVHNo4dzdMciS6pHu65gxqjsXshMHAbkiPuvLr/h1K52Z6xE/gV2P/X/4PaOK95DHAY/xIFdcu0l8njmynDjwighI74IQcCcBFYxrEDOntVgPSac0fBcS9f0HEWJ8DIJpjAv448pN1jCTJy7wJgGW0ueWwDV7LpKKF0zNkRPjD7D1OJDMmQFeIYE7/+fKZWmNAek83LbY86CbhJGAe3ce0AStc5JHTAnTSVkvNMZBRvKu/+8KzK0d5YqWKDHDsTUlVUTUFh7EcQGDZAuPg7S+Or8P7TIO2vV4kI5vKRUjLZYsKuxGI3aN2YBZTtVU26yHts2RLT0OBlXOVqaggKxzz9RUliI2z9HnQI+i7YOmVKkiYXxZSRWF9P0HwQSLpDgmkDhgz8Hu8Ve7Pax07ZhvrlTOaqjamuax4Fe8sqt8R8MFg4OMA6PnYHXoceCA1rQEnlxxLN4DY6keCwZmVVU+6+Wea++uxyjmyA0r3q1lSqjmyJ62ema1NxG0TF3/TqVnjDsIN3nj3gOv2p5qjpwGLXWvv1ttnacvryZVlEjaDILGeAhIFY33ibsmzhkD7m1h+nyIwLmhXFm2x4GhXJbV+3jeY+Y9Du644w7cfvvt6PV6OOyww7C8vNx2l0qUMIttLZsjA2GDZIu229b4B4CdEYNkdjY7df58ffs044AqESFU2vhVjmyplAjjgO1xkeNxQGedzFbyav/K2tjvlvrqIcYBcx7yQNOewDhokflClwpzDdp7vscDl3EgJA7MpIr8tkdBKsvrAIQNktkFDFLybGXNkHEQMkk3Yv5Im3ap8pgnVZQ5D7I8DpzxNUzgkg0x148rgyaSXANjLvZ8boylioD+2JLmHgvGg3tfXYDIq7TWBq0FxlOsP5pglTueQowD92NN2TqvD4nrz3gOFzoVMPLadece/12o13YOaMtck+VWe7sf6xnTOoyDwBj0DLrVGAf+ZymfDe15WCpgccM/f732fakgH7hnmsTH3kFj/SLdB199wD9/6blgFVPMc8xc4uBTn/oU/vqv/xqf+9zncOWVV6I7UlX093//93jSk57k/Zsvf/nLuOuuuwAAu3fvxkMf+lCz/pYowYytkWp3wChxcPD21tpOUc8t+rA9IlXUtscBHTQWrr+bOKB6HCy4G0Zf350NWsXuMT1xlLEB5Xs8tD0Gx4/vSRUZapu34XHggaZrtacjal31n/s3jZBkKnxZAl4fRI8DK3NkiXEwsmHyxiF5k3TMnoBsobHPRZ9xYMc8kjwOuqIRnw1wvSYkzwBi4kCQCwPs5sGQOa2FMS8Q1pm3kouK6TtbSTcudTq4G6Nzj5y8YcyF7jrIBc3Ynj8p0IwpmeWCf7kSIZqXwJcqi4O2jGkwlLxb/503BmIeI4Pw2X+aHguTsk6U2ndOJahvb8k4cK43+12UpbFvaNCdI5WkuTfLOX/AzmdDZnzYrYnmOWYmcXDJJZfgl3/5l/G1r30NAFA7k0KM9nbRRRfhLW95CwBg586duO6667B582ZeZ0uUMIoU48DCZ+Dog7aiqvyKFgt/haQxq4F+XaseBy2fv3T993kV33bVvqstSBXFNoFtexxUVftyWXy5pvHzs5UqcipdQ5XGhh4Hay0wDmKbYGvGz1pPSJxQ56AMjwMjU1zAMUZ1DILZ64G2PA4kqZxVw3P3WC/dngcWAnbAdbdbm26SQ1ItLoBKA62DUkU2Btm5Xjf979pK9VhdA3eODclF2Xg8xCve9aWK4tW+M2GMS0ym+6B9ArQlJLBTclGWoO2qVO3sJa/Ump9YX15NqiiTccDyWJCYE25bXkGZusfB+O/eHMQ2CM9IHrnDksk6Co1BFgt2EtaPZvsl1mMmPA4uuOACPP7xj8fXvva1RgmDQbzkJS/BwsIC6rrGD3/4Q/z1X/81q6slSpjGloTHgQV4v3lpAffdvcX73MJfYBakitr0OEgzLsjtC+PLkyoyNKXsilJF81Nt7QbLDHKsjcj9NUlcJM2RDUH7QaWtt0m0k0taMTYHBuLXmP/8+dV+fuLEzmcFAO42kyqSGAf2oOEgDtmxSfycnbBw56AVkXFgyTwyrviXkmem7cvVhlZeK65U0erQa8ZNXLBkekLn798Dxi2I6Uv7rAubuWhw7i6IzrgHKaki9vvQTVy615wJWrqgaciY1v1c02PAZz3YJU7WjxlnPTBZiL45slRtzpsLU+N/EC5+pidVNP67JBPT71f8300aEgDudsEfg9oeB/F7wF4PTMI44CYP8zwOtPbIkzA++v+uJA60o/XEwZ/8yZ/gpS99KVZXV4efLSws4PTTT8d/+S//xZsIpTjyyCPHJIw+/OEPU/paooR1zILHASDLFZlIFSXasOjDjpjHQcvAMVtfXrq+VjIdgAyY+FJFthr7Y22zq+0TCz+L5F1Mt9okceFW/a/xgYrhsYVKY0CgBBuyHmTgfH6SZ2uCvjvz9KVra5U8ldoeBeo89hV5Lux0Kuze6r8P2fOgBNy4SRNmEYXE+nGTh9L3tELaNFvq+XrA6VCmxmYekpKngCQRQ0qcBIBTSRpC2xQWkN8Bg/CThzbXYNDuqoHfj+R1NRps6Tp3jeFec6Yx66T69syKdxcgtkgcJFknROA4VEAyGu4YpHoc5EoFqVV7u+8f+XssrxHpPNzr7bJA1D0OAvPfINieQzkeB+6wUB2DmR4HLJPwrOSdcA/Ye6N5jFYTB9/73vfw4he/GFVVDf97+ctfjuuvvx5f+MIXcNFFFwHIy5w/61nPAtDPuP7DP/wDtd8lSlhF2uPAZlKUDJIt2A5tmwMDcamiNrXFAQuJkgru9HuXI1XEHAcSaGhdZRsDI9qXquKP/9gYtKjmSLXBHH9epWsLUkUSaGFV6TvsQyx5ZOyxYc046AhzoC9VxGm/06m8pMjohm11jQ/auXHYTl8G1Jr5ttrtmco0SRrnklwFDTgXgHtLc+RcjX+rxMEQtLaqtg/IRFgxv2LV1mZyTYFr4EqGWXgcuO8/pikoICUO49XGmvsSd0wFjWlJgJ3Uh7S/AIF1khgDVHNkoYDJDapcVeLcgT725SePdPrgjqVQtbn3HBI9Dty22L5fbnFISioL0H0GcxKIzHkwVzKNlUDLaV8q5iiMA/1oNXFw/vnnY2VlBXVdo6oqvP/978eb3vQmHHTQQY2PdcYZZwx//sEPfoDvfOc7ml0tUaKVmAWPAwA4VkgcsE1pgTQgYcM4iHkc2Oq7e+1bVJw719jdtzDvgadt3auHOvPr7bfH+mAzPlKLLovxHzvHNsafZR9CppyWUkEuKL0qAedtPgPGHhtrPSlxwu7D+PHvdg3imXJtQrX7ILyqe4P1wBGCbCE/eeQDxwfcxIEx80iqeGOtBySPA8kIk1HtDoSBezNjXqf9Xj1gXdgwv0IV71YJ5JjGvJVcU/Y1INwDaR0Y+137PviJw7g5s2b7PmibKVNjKJPia+urNR3sg5s0ZIKm/hpEkIkhAtdZoLEwLLS64Cdt5O+xPBak12pKKkh9Dkj4XEjV7roG3el5gHkNcp4BwAfvtZ7DEOsv9VlJHOhHa4mDlZUVfOADHxgyDf7rf/2veM5znjPx8e5///tj69Z147YrrrhCo5slSrQaW2fA4wAAjjvYTxxYGBOnKlcsgNM2PQ5SL36Le5Ci/zNBS0+ioVt7FE1r0Go0LBgfsbAY/7E5xqL9ZPKM+AyE6LlMoMDrg0CRtvY4iD4D5KSFtGn2tXRtn0OXcUA16I4AJl7VvcHzeNguiXFgyzpZFaSKrD0OJHNkGnCd4XHAfAZCoAVTomU0pHEtGVRbSkWN/n/9ezb3fxS08uTKaGNQvga+XBSBceDJdNiBxkBaJsNPYClKFTn3s1f7SQLABy41q53d6+mOe/f6M9ZkqYpjJmiaShwBXKmirGprYsX7pIwDLckw6Tzc6+1r6+uOQS95vhZ/BgBy8kgoXOAmz9z25THAeg5zngGRhUkqppjnaC1x8E//9E/Yt2/f8AX48pe/fKrjdTodHH744cPfr7322qmOV6LELMS2TbPhcSAxDm7fvyp8UzeSoLVBNjnqcdA6cMw//zYNun3AxF6qaOeWCOOEXek9Ax4HbRrjAuk5gPkM+NX+srb1ApN141Gke+YeB3HGAdtjxN8wWTMOXDDMTxwYMg4iHgcW64EjhMSBtUH6Ws9WqsibB3p+8g7gzcfSppkpTeJGqNrQSqpIuq79xIEN+zBU8cusMo61X9frIJGZVFHI58GAdZKSimnf48AZh0TGARACzcZ/17wNMcYL4J8/Y0mQkouimiN77+A0cG/J+AC4Gvve/ZeLzYVroNK8eC/d5BmdceDJlsbnAIDscSAkb5jPgC+XJQ8Cz+dCawxmyIW5n1WVfhK5RIuJg6uuumr4833ve18ce+yxUx9z9+7dw59/+MMfTn28EiXajq0pc2QjqaIjdvnyBKGMs2bMQsV1m4yDWNsAXyoHaDd5JS2YrcCCQRy31zcGX2+7XamqtqWK2m4/5++aba8nDnhAgRtSxTlbz9WNaPKIzTgQgHPzxIlzjnev2gH2nsfDPede17W5OTIAnHXKfcd+l4oKtMMF7lfW/AQyVy7KT15JG3eWWbwE3Jv6rAisp0E/LPogPV+rAvuQ9S4Iaby7IAmLcSEdN5S8oEkVBQyamdX2w2MmNfbHv6/9PvQThw5oSXwf5xjDAjx9eakPLmjoMW8ISUzfZyXuc8E1qJZkUpiMA5ntMxpS4oBlThxiHLgJBT2pIil5Nv67Ox6054CQbOkgRMaBavKqOetE8xq4a5uQVBGPcZB+BqwKGeY9Wksc3HTTTQD6mpyjTAGt6AUmthIl7k2RkiqyMkfudCqcceLesc+ecvJh9HbToKEF4yDmccBt/4jdW1qv+N6ylEoc8PogbVisGQcnHBJOHLCvf1VVrerLA/H7a/H8tcm6CdGDLWVCJH1xa+C8zeSRlDxkS1Ok+uCGtb4+0B+Dln4zgzjqoK345SccD6AP1v/6j9+f3qZUbeYyDixZH1K1O7O6TQJuWJV9UgTNgY08DsJSRTYJXMmgHWjP46Dfdn/8W0kVhSpeLe6Bl7hypXKIMj2AlDRxPQ54c5FsDOt/jzkfSInL0Ypvi/VQquqeaY7sr8Fqr+LdT1zYMg4kIFWrD37SJlBt3mLyij0Xu4UJK45UkZTMY7JO3HkXIBt0ZyTPAF4Cb5JngMnCnOeIo5LE2LRp0/DnlZUVlWPecsstw5/37NmjcswSJdqMbYnEwaaFOKirGa96xoPw8r/8Kq7/4d148RNOwOECC0E7ZsEcNpY4sNAWP3rPVnz3prvEv1sAx9sSrAcm60WqdJypxIGRxn+IFmrRfpseD/32E3MAUSrH3SwMNsxWpqD9Y8dlEgBepev68cPnx2c7tFttDWQkrxZ57Yc2zC5gCACbjBiIv3nmSXj+Y4/FpqUOdkak/LTC1xfuYcV5BpnnLrJ+PNaRLfOu3eTlPTI5Rh4H0vMlsU6sPA7WpZpsJNOk98s648DmGoQMii3ugeR1NdYXsudQSqrGB615EiGADFK6rwPNSyDd0l69LofEMsUdjZDHxrA/TKkg6R706rHPvTFABc39d7+E47ISBxK7AeCOg4WqQhfrx3f7wH4fpnwumIkb6Vh5jAO15rM8FgBBxpXms5GWKiqMA060ljg45JBDAPTp1hp+BLfddhuuvvpqVPcMrsHxS5S4N8eWpFSR3cR4/0N34EO/+jiz9oA0IGfBuAjJBS10quF8w4zj9m4LJg7YoB2Qlsviehz4mwVrqaIjdm/BpsUODqz5K3OmRM0gYgsvi/EfewZNpIoSYBQVtBfurwQaMoH7kFTNaNAZB5Hjs58BacNmbQ6dGoOWUkVD0HbNHwdWnkcAcPCOTekvKYV3DUTGgV3ybk1IYDPnoRyPA+YcEKo2tOqDLFUkMK/M9P1DMkE2jBNgHTx310NWck1D1sUMeByw2Td+4jCub64qUyN5HEjGqEzQVpTK6mGh098bMGWC1o+ZYH0wzZEDUmGLI1sjb02i+BzkgLZSMklre+rP/4HEATF50ukAGLGWSjEOtNdCaaki/9+oShXleBwQn8NcjwM3gan1PkixvgAu66fEerQmVXTCCScMf77llltw5ZVXTnW8T3ziE+j1ekP62CMf+cipjleixCzE8mInCsxaAgVtROr82MacALA9wDiweikdd7Bc8b60YJO4SLFeLD0OVrv2UkULnSp4DzQ3B6FoW6oqVklr8Qykk4dM0FbeMLuVtlSPA0Hf3Rw4j7FOjH0+JNCUff5teo1IxsCAzDiw8DhoIyS5LkuPA8mYs02PAYlxwKTlZ0v1GJkTA6G1gA1w3w0kTljzUAi4BKSKf5vkzbB9911o4HHggmbMSltAYP057Xv3QHEukpZfoseB85nm3kB6/kYvAVMmKNSHZPKIaAwLpCvOVZNHE+jbA3rAdcocexBM1od7P33GAZf9lZIqkoB8VbmqnDFANEd230GuXNx6v8avA89nI804KIkDTrS2y3jEIx6Bgw46aPhye9e73jXV8d785jcPf77//e9P8U0oUaKN2Boxp93oVKw29c0HsWlxQQQlrK59yHzSImkCxMcfYFvpKXkcWFyH4w+W7wFTnmIQseSEhVRRDAwyaT/FODAE7YEQ48CuDyJoyTYoblGqSJJJMfd4aNXjQAZtpcSBleeRdXjVdt2eqceBxHrxGQeGyaNuz0QeZNi+UO3YEzw2WM9hVVVZ94A1BsLGwDbzkLQGWPcYsJJrkis+2TJBUtueOS8RMAPCibPQ73TGQYY5LlMqBxgHSr1KX0ISMyVVxLwHUmGI2x4zkZuj7+56LgCK5sQZMjGA/xyqymUlEgfseSglVSTJNzF9Ltr2OMg1yNa6D5N4HLAlXOc1WruqnU4HZ511Fuq6rxn8tre9DZdddtlEx3rd616HL3/5ywD6C8xzzjlHs6slSrQaWwPmtMuLHZOK8zYjVsm7aCQVBAA7BdaBGeMgmDiwaX+WGAcraz2464VlA7mukM+BhTlw7D4zActBxO6vhVRT0ueE6bERqDSz9TjwNyyzVHHPBqt94Hy2zh+wZb0MNqyrknTaBmUgunO8VG3OZBxIBtVtSgV1e7UnV0JNHAjAqVt13e+D7XNgNQ+7939oDm0EVIjmyAGpIprHQUAuyMJjwUueu6Ct9yzo9iHk8THsD5F1IRVOSCAl1+MgnrywqPRNAafMRKqUFHbnPyrjIDD/jH1GNOfNqXYH4O3NNO+Bey7ueGePwZRUkFDHQX0G8zwOeIyHkMeBxzgg+WzknP9GL6xtK1rdZZx//vlYXl5GVVVYWVnBmWeeiX/+53/O/vfdbhe/9Vu/hde85jVDAHH37t0499xzWV0uUcI8tgY09i1Aw7YjtgGwAG0HIfkcmDEOAtXuVuefYhwwARt3sbB/tet9xwIsCycO+G3HKpcsxkAMGDZJnCTNkW0ZB2vGHgfuPC9V3LOZL9ExwDZmFjats5c4MARtI+bIG3VNIAH37vkzz12qOHY3zpYJ9LVe3TLjwD///vfs5mJRqsgIuB+C5laMgwhw25Zc0zrzif8chDw2BuE/C7rtx6SS6lry3eJKFeVUnGvKxMiMg/X2LOaiVMVzG+bIsd8XVJNH8fEvtQ/YGtMCwj2gGkTHr78648DZ57oeUz7jRFcuLCQVNxq+xr9a88HkuRss5oe0BnHnPOt9wbxGq7uMY445Bueddx7qukZVVbjpppvwuMc9Dueccw7+7u/+DrfccguA9Rdit9vFzTffjEsuuQSvf/3rcfzxx+P3f//3h6yFqqpwwQUXYMeOHW2eVokSqrEtYE67UWUJRiMGSltWV+7YvOR9ZkWDO3j7JjlxYXT+KcYBFyxIJw4srsPxIZ+JlivuLWSaYm2YmCOnfE4MAUMgYMppWO3br3bm6rm6EZvr2O8hSVvVmpKcGudM1ot7/oMKX1eqZ7FTqZuCzkpI1ebu+Vt6HKwKknnMZ1ACTSyNACXgzL3+oe9phexz4QC2JPZh2BzaZgzIHgcDqSKbBJYPHPXG+jH8HsMcOaVvTwSNAcHjYGTsSwCe5jWQxpR7voCtVI7bB4u5KMR4Cf1ON0dOsE6YUlFS0lZSjtEaA57HgcBukD5XZRykpIq8eUh3Hlx2xp9buMA0J5eOJyWPfNYFL3kV8jhgGdWnWE/S7yVxwIk4ImQQ5513Hr797W/jfe97H6qqQrfbxfvf/368//3vH/teXdc488wzvc+Aflavrmu89KUvxdlnn23W9xIlLGJrALjdqLIEo7F3+3Lwb5bn3ybjoKoqHHfwNnztP24f+9wCtAaArYHE1bAfVMbB+LHvXpEYB/zrcOzebagqeJrOFguTmMeBxbnHq83bTZwAZJkgYeHdTxz4oC0rfJmGHtac9tr1OODOwz5F2k/csB+D1HNO9TgIACaWUj1th1Tx727cuQbV/hj0Khyp85DAeDDQFR+EBJzdvea/i5nvQwm48efhjelx0OlU3vojbI5sdA2GUkVu4oKQOEjo21t7HIyCZhKIq8l8kcBf2eNg/HemvjkwPvYtpIrcdYZ73ZnmyCHJytFgabtLxxL13UWpIp32fZmgPMaB5nPoXk5r0DglVcSXSnLugeRxYOhzEvI4YL0TpeN063oMxC7myDYxEzuNP/mTP8GrXvWqMVrPKItg8N/gs9G/AX1g7Q1veMOYQXKJEhsltgWkYuYhcXD4ri3Bv1nq1+1o0eMAkA2SrRgHocTVICyNQSXGgYU8x+alBRx1n63e5yYV91G5rnY9DmzMkePPGVOmRwJB1gR9cW6lrcs4sJfqibJerD0OerVQ3UVOXqQSB4ZybUOJEENz4LbDPbcViXFg6jNRexV3zHnIlb3ois8AkfEgPON3rwqMA+Zc7ElFCIwDI5mesMeBbfIIaFGqKJC4YIyBpL49+T5IyfthXwQATfOdKPpbZJgja+ZOUtW+PuOAPwY8xgFxTZZzD5jsoxx9d9Gcl8U4CJkjE5k/7jVwz9edE9qWKtJOXrp7rRypIs3HMNfjgMU+kp7nlEF5SRxwYiZ2Gp1OB7/7u7+Lz372s3jyk58susMDGCYQBlHXNZ74xCfis5/9LH7zN3/TqrslSphGCLjdtIErDAdx+O7Nwb+ZMg6ExIGlx8Jxe32pHDNz5ITHgWmlp0gLtxkHks+BxRiI3WYTqaSYObIJ4yEhE0PsQ1VVokE3kxLshiTRYW0Cti3gswMYmCNLjAvyJtGNmTJHNpYomYUQzXldwJRqjuwClz3PFNaUcdDznwHmJll6z+wX2H/UPmR4HNAYBzOROJCBIyuvDV8yLcC6IDwHqWpXfuLABS3XATK2Sb0oVSRgJF4ikVhtDCQYB4THwH8G41IxmowDaR3ozj3M+ThnHyTJV2n1ISdxAXAr3lNSRew5wGW8ufefJdEziJzkjV/tzzNpl8ag9LnWGBD95hLMs2KOzInWpYpG4zGPeQz+9m//FldeeSU+/vGP43Of+xyuuOIK3HLLLbjtttuwdetW7N27F8ceeyye+MQn4swzz8TDH/7wtrtdogQ1QlIxGxkoGMThu2KJA7uXwk7R48CQcSAYJJuZI0cYB53KVttZCqtxcMIh2/GpK28c+6xtj4G2zZEtfD5SY4CdOFpa6GCttw6SrQka+7amoLaJCwB46smH4T2f/574N/YzIBrDOqABW9s/adBtLFMDACvdceB2IxcSZHkcUBPYvkyBZfJKAm5MzZGFa3tAkCoyfQ6Ee8BKHoVAkzYZB91eD3VtJ9nlsk7Wej30hHcho/2Uvj292leSLOz1sKmzIAJoms9Bv2ByXKZKAg25Hgf++Y8xDgzmIt/ryViqZqEaT5YkpJJ0GQeyv8jYZwLjRMucNzdxwPQa8fsw/ne2fKj7DLhMI/b4c4/nti8njnjt57JOtPaoYvLSlQtz9wVE+cZ5jplKHAzipJNOwkknnYSXvvSlbXelRInWI+hxQDKCm6XYuryIXVuWcPv+Ve9vVpXmQLseBwBwnCBVFHpxa0fInBvgJ69yQEmmRMRoHC8kbyySFrEFaNtSRTaMh/ZA22H7I9PParfnLdotpYrWBKki9lx0+nF7cM6jjsGfXvLv3t/YbUvAuXVlUSoxYymTs26ObCPTMgshaYy7wNEycT3kVxv6HgOWJu1rgjkyc5MszW+SVBHV48BJCqys9XzWCal9DzTvDoyBDZNHGe8BgPcucq/tWq8WZXoYa8LG+vYE0NiN1W6NTYt+5XH/+7rXYKGqsDZyjtJ9d0kImtdAOlQ3AqJzPA7iwCV7TbTU6eBujJpixzXuzT0OiMkz91jZjAND4NoHrHWfwZRUEdPfAfDHQH0P62nwnIseF0SDdMmcGeAlMKV9vtsWU66sxHps3BKlEiU2SMyzxwEQZh1YAveSVJFFtfUgJI+Da2/bb9L21ohMCdtfIPXiX+hU9GrjQUhSRRYR24RZ+DvEnrOTDt9h0H78HNkV756++lrP26RbMg4kU1AL9tOrnvEgnHa/g7zP2ckrqdp7ljweAFu5toG2/nyZI/v69gc8xkFcUm+aEOWyDA3SJeDUErSWji2ZI1M9DnKkiljGwAHgzp+HmHJZvs+FmzwDeGsCfwz6jA+AMw5TMh3WHgfASPLI4BrkVPv6gJ1e+5JUz+j8Z8F+SkmluH3QTqS6ibsU44FbbS9JFY3/rnn+XvsBOXE3h8Y0R3aThez3oe81ZmyOnJDqEZPImlJFmR4HrASe5LPk+kxZ+j7Nc2zcnUaJEhskQowDC9BwFuKwQOLAEiiRzJEtExeSxrhU8UdpO8Y4IN+DNGBndw+OP9hPHNzwwwP0dqOMA4MxGAJFzzhxL37+Mfejt59KHrGrStwxdrdg0G0JGFlLJQ1iebGDt5/9cC+Re/J9d1LblYwxLfXdc47P1dcPMQ7mxxxZSh5ZJk6kMWjpMSGBduwq6/H2/XO7W/A4YD6GEnDjgjXW5siWclXeGOz5MkUA0eNAYL1I4BGjfV+mJgHaKYPG0jkNnn+JcaB9DXKAW6a+PODPL62bIyc8BrTXhf572I7xILFO3fDfB2rN+8bomca4WlJJQNrjgL0mlOQSR4OdPJMNukeSd0LiQNUcOdPjwEvgKV0H8fyN3wMl+jGTUkWx2LdvH/70T/8U//iP/4ibb74Z97nPfXDqqafiZ3/2Z3H44Ye33b0SJdQjxDjYyBWGo3H4ri3i56aMAwG4n5dsdoxxQJeJSax8LMGy3VuXvc8eeb/70NuNjXMLqaJDdm7yPnvWw4/EG5/1EJPrn5KiorNenPb3C4kD5lzkSrB0e7UHGlvNRXu3b8KfvfB0/NYHvo7rbt+PX/zR44Pzs1a4Y1zSd7eWS3KDOQ/6gOn8MQ5cqaCVrv8McO+B4DPStXsGRY8Dw02yNP5dxsfSQqUKFLkh3QNPqojGOAh5HDhjgDgGJYNgdwwCvES6BB5KUkU25si2labSOa0FTOoZvl/usy3pmdcG5qwrI7+PSRUZmCO7z1ZqDGgzDny5vLjGvS7jwF8DucFMHLnjWTLnlj7XNYiOJ27YJu0S42002NKBTQ3KAV3GQa7HASuZLidOEnJRc4LRWEeriYMrrrgCF1544fD35z//+bjf/e4X/P4//dM/4dnPfjZuvHHcoPKiiy7Ca17zGvyP//E/8KIXvYjV3RIlWomgx8EGrjAcjaBUkeH5S+bIlomLNmPrUphxQDdGTerb2z4Db/z/HoLf/uDX0auBJ510iIl8UWzxY8G4eNDhO/H4+x+Mi795ExY6FX7lCcfjpT9+fypINBptmyO7gKy1trdY7bvmSnTYMm/+4hcfbdaeVGlsLlWUGGNMnxX33g7O3coUdRZCYl2YMg6kamujancgZBBup+fb6VToVMBoky7ziv0MugniA2s9D7xhXYPQM2gpFyVVHUtSRbTkiZDAFRkHlGrzhEwNW6pIMkdeG4wBvlxWCjSXPmMDlzFzZAb7KaXzb2GOPNZ+Crg29jiwNCbOrTbXvAd+8UDcGFeb9ZKSKrIefwDQHRmDUjJR8xL47x9Z8YCVPJLNkePPYEkccKLVxME73vEOvO1tbwMAHHrooTj//POD3/3Od76Dpz3tabjjjjsAjFOg6rrGXXfdhV/6pV9CVVV44QtfyO14iRKGsTUgFbORzRBHI5Q4sJRqkj0ObK//Lz3+ePzvi78z/P1pDznMpN2tAcYLwK9yTUqEGD8DP33a0Tjt2INw2/5V/MiRu03A87hUEf8ZqKoK/7+ffwQuv+6HuM/WZRx10FZ6m6ORusds0NzdtImMA2a1s/CM+YyDjQsaS4CF9QYhyvoh+6z4oJksVbSRpQvdZ+DAmi9TY21Q7VfW8drPYRwwzZGBPiA6OubcxIF1EcEBYR7mMQ5k0MgygSmBd5JMDus5kK6BbAxMAI0ToJWn724A2q0OGQd8g+4cc1r3I+1uxMBrC8mu0HtwENbJIxeoZlY7S2wjN5jn7zEOAokDZtV9SirIS+STpYrWejXquh7uAemsJyl5GfEZAbisk1DyiLU2H/isjLMsbOeAEv1odafx0Y9+dEivO/vss7G4GM5j/Nqv/RruuOMOVFWfDlvX9fA/AMPPfu3Xfg3XXHONSf9LlLCIcOJg4wIFoxGUKjIEjSWPA/ZG3Y1ffsLxOPQe2ZjlxQ6ee/oxJu0uL3SCGwE2cJ+qXmvjGTju4O14+NH3MVuURBMHRs/A4kIHDz1yt3nSYNB2LJjV3lL7krY31RQ0Y5xtZPaTVG1qTUmOs35sfV4GVcZuxd1Glipyn4H9wjPIPH9JLsozKKcyDgSfE2O5LvceuKwn9jPoPmf7hDHAehcEPQ5MDar9MWAF3EvH7Qbap3gcpKrNyfr+sr72QDLOASwJ85DnLyCAhGzgsgnjwMbjwHgMJMyRmfrqOYwDpjm0ey5hxoHz71Qr3p3EwZpx4ijicwLwq92l442eM90cOdPjgLk2T7F+2vB+m8dobadx44034qqrrhpm6572tKcFv3v55Zfjox/96DA5sGfPHvyf//N/cMUVV+Bzn/sczjrrrGHmb//+/XjTm95kdRolStBDMsYFNjZQMBqH7w5IFRlW2UoeBwfW/I0rM3ZtWcI//MYT8MfnnIpP/NqP4rEn7DVpt6qq1pJXKTBmHpJnscXPPLCOUvMcO3niUqRlc2SmREV6jG/kypocU9Q2PQ7oPi9CtTuwLpVh1Y82w30G7lpZS35HM9y1hmjMa+1x4GqrG8t1+YwDW6mifYbMLwk0qWvBZ8KUdeJLNQG8+yCZM0vgEWMeSulrs98HVVUFpUosmEd5jIPxz7TZsLF74CVRCY9BSq7JnQ/p5siJamfN9nP05T3WjWbiwjmXXMaBZvLE3Qd4iRvy/ZfmtdHEKdtzKJa8lNoHlM2RM+TSAHYCLZ68KFJFNtEa6nL55ZcDwBDwf+QjHxn87v/9v/937Lsf/OAH8fznPx8PeMAD8NjHPhYf/OAH8WM/9mNDBsJFF12EnkDlKlHi3hgh0HYjSxOMRkiqyBIo2SF4HEgVb+zYvmkRT37wYTh27zbTdkPJK+tqW7/9jb8wiIERlsmztmLv9mXE1p70Megc39ocOWcDtJEra7JkWlpkHLAT+L4p46DS1WUchCXl7u3hPgN3HfATB+YeB871Z3qtSFIt7Apbrw8u42DVPX9bxoHEOmG9C6Tnv1cL+vaGHgerXb/in2lQ7a41+okLn3XCaD8kEzIIpr77IEKglWuS20biRPpMuxv+/Q+Dlox1qcsmWnUTB9bmyIlqZ80kou/xk8M4UWveu5ZBxgHxOXTnAJdx6SevtD0O/OON3ge2z4focZBgHKhW+wtzkGvILvVDc12QkqwrUkU20Rrq8L3vfW/485FHHolt28JA2Mc//nEA/Qz64x73ODz2sY/1vvPqV796+POtt946TEyUKHFvj21zbo68dXkRu7b4wL3l+UuMA6nyeKNGW8mr1EZ8HoDz2CVm0OJnLTYtLuDg7ZuCf6dXfCcAM4DscTD3jAO/0syakhxnHLDnQJlx4Jsjb9wx4L5nLE1h5fb9am9rc2SvupPOfIkzr9jv4qXFdPKIdQ+k4671enCVeiw9DiSpICrrRpLrMtC2B5rLdDAug3/+A+YX/x5IiUs3PI8DslTKWvT6WyRPrM1p4+A9s+Lcv/aCxwExkewljQTAmN2H5UX5+Ru2TV4TSs/1yhjjYPxv2tOA9H4dHQfsxIFoTmxs0i4lL2K/zwM+0Ea0dlV/8IMfAOgnAw466KDo97761a8Oqxie/exni9977GMfi+3btw9///rXv67Y2xIl2ouQOe28JA4AmXVg6XEgvTSlyuONGkHGwSIZrEh5HMwBcB67BpvmZA44YrfscwLwF4duJbPMOLAzZpViIy+QfdC0ZyoRkjo+m3EQ0nV1zZE3beC5MKean3n+EmjkVzgyEwd+tTNTEiCnD65Uo7XHgWxSb8c4WOvWHnhpPQb85JWdXFffpN4mcSHKdEQ09hn+YyFzVk8miWEOnSVVw70GMeDeQiKkqb65vjltHLxnvg/cxGWv9u83k3XjLn9CMjXMPniMgzX3+rMTBympIuf+Kz9/0umMJe/I5sjSHiOH+cT0WfBYR8ZM5HmN1nYa+/btG/68ZUsYFLjkkkvGTJCf/OQni9+rqgrHHnvs8PebbrpJqaclSrQbW0OMAzJoO0txmJA4YJuipqINqaK2YlY9Dlz9+Y0YsQ3IiYduD/5tI8V9I4kDe8aB/9wz16cLnSp5fHa1cZvhaatKoC359GNgEHsOdMf3YLPoMw42buIg5xnngqZ+taNldZso10U2Q3XDvQeeVNEsmCMbeRwAcvLGUrKu2+15Ug2mrBdD1o2UELKueA+Z83qsD4oxsJy0GA128sSfg0Z/trj+cX1z9nwoMR8HUde1x/jQlWiJj3+Aew+kpKEoU0O8B6HE3bBtusdBXKqIzT6TfFZSHgdMY2JAnoeYYyDFOrL2PpvXaG2nsWnTuvTAXXfdFfzeZz/72eHPe/fuxQMe8IDgd0flju68884pe1iixGzEliUZtJ2XamMAOHyXDxy2nTiZL6mi2fQ42MiV1oMIXYPtmxbxkPvuMu5NO3FEwCAd4GqLA/4Yl0xBWbrSwzYS57iRF8h+lVHPMwJcYCcwZ0mqqGcnkTErkTq3hU5lalAuV3sbgsaCOTI7ceAe35sH6R4H48cXEwek9UBIKsdSU1mSirFMHkpyOS5wxHoXS++3bgQ0Y7wP/TkgYI7clseB85G+VEq44t7i+ktybYPo9Wq4OLZ2H7znL2KMC3AlWgCf4cBMHEljSSId+DI1al2IJm4A32vEwuNgTKrIIJHfRC6sU+kapItzsNNmXXPfiSmfEd/nY+Pui9qM1nYau3fvBtAfaKN+B2588pOfBLDubxCL0QTE8vLy1H0sUWIWYqFTicmDjQwUuCFKFbUMGks6yxs1ZtXjYB6kikJ0y9OPPYgOms9KxKSK2IyDlESGxeI09Zxt5AWyJFFgaUoKAEsxc2RjbfnVQKUrWzKpzUitdeisI+H4LnDOlMuSnm8XNGZLFaUSqGy5MN8cedzjoFPxpAlCwJ0p40AAjSylinzg1Pc4iM2Tmm0DDnBtIlWUNw8z7kGIdTYabgW4djFDLHnB1DUPtx++/4w+xO6BdD805wK52tsFTcf/rvkoSnO75LPg3gbNe+Cus9z3Hzt5JTF/R599tlQYIBvUr//MBc1zPA6kZBKXcTDeoPs+Yq+J5jVa22mMMgfuuOMOXHbZZd53rrrqKnz5y18evgCf8IQnRI958803D3/etWs+KjFLzEdsE3wO5gE0HYSUONjIZpCzFiGDbnaVYWrRMQ9SRaEF8KOP32Pck/Yinjiw1Zh3ATOLBG7qOZsnxsGaVOlL3iDEQNHZMUfeuOuB1PhnJ7Cl4/vzgB1oDPgaz2w9X38enC2pIipoLtxbmfVh57PR7dWeVBFzPSYxn6wYBxKTxFyqKCCV4upsM+YBKWnjBvudGKt2tkigxTwOpIp/7cKymFyUW+kM8I1pU8awqu0LY0nIG1D74BZGuIxLE6+RiEG2zRyU/wywzcHd9gE5mWQpV+UxDuYAH2gjWttpPOxhD8Py8vIwKfDGN77R+86b3/xmAOuZ9B//8R8PHu/WW2/FddddNzzeMccco93lEiVaC0kqZiMDBW6IUkVzdP5tR1sG3VVVRTchbbNOLCK08JqnxEHM48AasNq/6lKi+YvTHKmWjRrSBvDAmu09aNPjQJIIAYCVtfFN0kY2R04lBqwNqgGfeWTpcQD4zwB7HnRB8buNzZHdMWCZOBCB667gcWCYPFrt9rzkITOBJsllWZ2/BAC5UjWjwUiihTwOvOSNgceBq+0NCBXnyt1wn+9e5PozALuYx4EoFaQt1RQBbSXGgSpgKs4/hlJF4vNnq28fA40tpKoAf35dichVWfisjLF+3GuvnDjMkssSkklM5o3HOCAbVJfoR2s7je3bt+NpT3va0Pj4r/7qr/D85z8fX/ziF/GVr3wFL3vZy/COd7wDVdXXDz7llFNw0kknBY/3xS9+EcB6kiHmhVCixL0tJKmYjSxN4Mbhgsa5ZMxjGXu3b0p/aYNEiHFgkbyJ6ovPwTMgLbx2b13CAw/b2UJv2olo4sDYnPbuFd/jgB0pCYiNnECTNs1utXWbzCf2HBSSyPAZBxt3k9Q240B6vva784BhtTfgPwN0c2TP48B2/PkeB+NSRczrH5JpcAFcS4+DriBVZJu48D0OWB4TKY8DF7hlAEZexXPXzuMglDweDbZUSsxjwOL6R6WSyBX/gOxzM2xfYICwpYpszZHzGAfMMehe/9H1j5y44UuGrbbscbBqmDzNYb1IySTNfrj31J17LT2H5jla3W2ed9556HQ6qKoKdV3jve99Lx71qEfh1FNPxf/8n/8TwHoi4BWveEX0WB/60IeGPx966KGFcVBiQ4WYONjAQIEbklTRzXeumPbhrT/zsLHfX3fWg03bbzNCjAOLMRhLTmxksGwQ0sLrUcfuoUtTzFLs3roUNImn65u3XGkLpMHpjbxAlqrd3Gprhp7saMRAALrHQUCqqJgjrwe7iEKaY1zmEVXffgZZNweM50F3DnSxIqoxsAia2HocSMClW3XMNUeWpJIcmZ5FzvlL13XUDNWttrY057VgfeRIFTErzqU+jIJ0FqakUdBWuB7qUi0xc2Ry4kJKHKdAU837Lx3LmnHgvuNHGZdSXzhyWQ2kiow9DtjvIundkkpeaffDLV5wDbEtTNpLtJw4ePjDH47f+73fQ13XQ4mhAQMBWDf3eeYzn4mf+qmfCh6n2+3iAx/4wJCdkDJRLlHi3hbbNs23VJEk1XTznQdM+/C0hxyO857+QDzppENw/jMehDNPPsy0/TZjZhkHG7jSehDSAvQxJ8yPTBHQXwscIbCOAP4YTJmCWjwDqTY28gJZYlu494Av09KeVFHIlNEDrDYw+yo1z/PlotIeB0zmkzS+zYF75/wOeIkT8nOQGgPE8+90KrivYdFrxZBxsNb1PQYskyeixwFpDKSqXT3Q1NDjwIRxEKn2HwS74jnqceB5fcwA40CbcRFjHJClikRzZFcmxk0ckRkH0jX3JKtUGQfhxBFbKmoQblIwao5s7HHgXXvl9qXDpZJX/X+nyThISRUVxoFFtL7TeOUrX4l3vetduM997jNMGAD9BEKn08GLXvQiXHTRRdFj/Pmf/zluvPHG4b9/+tOfTu1ziRLWITEO5ilxIIW7cWfHQqfCC884Dn/yvEfi+Y87dpjYnIeQxh9gIxUUAwVZFW6zFNfett/77NHHzVfiAAgbJPM9DsaP70qUWCxOQ2wLAKgqvjFqmyEBsubV1i0mDiRTUsCX6tvI64HUPN8K48DT2LeVyrFm3bh9cD0e+IyD+PHZknWSxr0l40ACj12pIkuD7rVebWIMDPQLB2JV9xYJnJBknFvtzBiHPtvDr7B2cVztS+DKhPQiwDmH8REG7l3QVPr+tNEmcC0znsbbdLug+ShK60sJJHaTCZrr0pjHgSgVRZiL3DGwEpMqMpDrWoskz7TnQMlv0J376IwDbwwUqaI2Qi4jNY4XvOAFOOecc/AP//AP+OY3v4n9+/fjiCOOwJOe9CQceeSRyX//b//2bzjrrLOGvz/jGc9gdrdECfOQKr43MlAgxTN/5Ah85KvfH/7+X3/0+BZ7M18hMT4AmzHYpjHpLMRNd/jMmhMO2d5CT9oNyedgsVPRE3g+48DWlBQIJ+6s2m8zZNDUFrSM6eWamyMPPA4c4HgjmyOnQCA660PyODA0R87xOGDPA55km8e8sp2H/b/zz3+1u37OXZFxYGtObMo4EOYhC2Pg9WNXY9d7bUyqaPy7jCRaUDLOTd4YyCS5bQJcqRqpD3HQkpE8CUsFScC9ujlyRF+dnbgYJM5G77H77Ft7HIiJAyefpTkdxRIH0v2nmJQ3kSoyZh5Z+IwsLlRRpo3I/FG8Dj7joEgVtREzkTgAgOXlZTz1qU/FU5/61Mb/9rWvfS2hRyVKzE5IGvMbGSiQ4iVPOgFfueYHuObW/XjSSYfgR++/t+0uzU206XEQWwDOQ+LgrIfdF1/69x8Mf3/8/Q+eK7bLICTGAdsUV2rDlYixqGqRpOos228zJEDQBU7aZBwsk1lP7vwXNkfeuHNhChRmMw46nQqdahyg9BMHLTMOjJlX1qyfdOLAmHHQ65kCFdI8YCtV5DOfLGR61tuvMFpCsRZIIgy+qx0hc1yLe5CS6AD4PgMx0M6XCFFtGkAicWEgVeMxDiLnDzASF1VUnovpcZFjjAtwzZE9j4MIaA+wWEcR1kPLHgcmUkmdDoD1Nt11uOS9wpWrSvh8bPC9UVsxM4mDEiVKhEOq+N7IQIEUJx66A5/89cfj7pUedm5ZnEvwtK1o0+MgzjjY+GPgaScfhvd+/nv49o134qBty3j1Mx/UdpdaCSlxYOFxkRrjJlJFUcbBxn4P5Jwf+xrE5qATDtlh2nZIqohhyjkrMaDJSwANYHPuiwudsSp/r+LfUCam377DujGWKvJBc+49SN1jdhLZrZxc6/qMAyZQIQG3llJFklSQq7POXA9GzXm9SmcLqaKBxwFfqiiUtBgNJmgLpIB7N4lomzyxAI5j+vKSdJT2fOgmzixlWnITB55UkWbiwHkGRt+/7loI4CQP3UK5qFSRgcfB6BiwkM1LrQHc5JX0b5jtF8aBTZTEQYkS94KQPQ7mb1LctLiATYthEK0EJ0JSKWxdYSC+8NjooCkA7Nm+Cf/v3Mfh2zfeiWP3botWn2/kkKSKLDw2UvOsRfJsWyRxsNEZBzmLf/Y0ELrGe7cv4/972H2pbXvmyAGpIotnoc1YWuhgrSf7GlkYQy87iQM3mO/CgTnv6L7crfhnA+dtJ1BnjXHQ7c2Cx4EdcC+B16trdomLaLUvEbAcRMgc2cLnQTLGdsOTa9I2R40C9+PfNWF8jAL3BubIbjJklOUi3A51j4n++Ft//7ljgClVJV1La9ZLk2p/7bYHEZMqYifugFTy1IJxIBexrP9O9vrwrn/iGdjge6O2Yj4RiBIl7mUhehxscKCgxOxECKy2kSqKyYTMxzOweWkBJ993V9vdaDWkxIFrUMqIVHLKArgPeYwAG7+qRpKJcYPOOAgc/3VnnYz7bFs2bXs1pK29wQsJFhcqYFX+m8W5p4B5vsdANXbP3SQG2xw5dX7sxEXq+Gz2mWRM6VdaExMHGR4HzHkwx2uGmTwLgWZ1LXlNEIDrgDmzC14xkjexav9B+MkTbh+6EcYBA7Dzn7+wTIv0/WnDvf/j1d7+PKDNiHffce6Y98yxFc9fSlxLyRomeN7UnJrxHMb6wPR3GEQjnxGSx8FY+wnWi3Y/UvOgZSJ/nmM+UJcSJe7lIWnMb2RpghKzFaGKZxOpoqjHQVkYzEscumuT95mrM86I1Bhr2xx5ozMOgDQgxTdH9o//9Iccjqc+5HBqu4AkVXSPtvYcmSMD8fXOsgELMQXKsucBdwz6XivU5pPPIPv8U2sNeuLC05e2ZRx4Fc/d2kseMv1WpPeg+/5lGAMPIgQaSQllCmgXkAlx7wFjHEpJIzdqMnAYq3a2eA7cY/bqdaDabb9TQR24j1U7WySu3GO6TBcfOFZuv4qDxmIfNBkHzvpmdP0jPQ+MqSjOeuAnkb13kPEYlN6Bsd+rSjeB5fvsxKWSGJJpJUrioESJe0VIwNG8VFuXaD+2BhgHFomDHZtj1dblGZiXaEuirG2JDiBujjwPVTWpc2Tfg2P2bB37fdeWJfzuWQ+mtjkId/x1ezXqusaBOTJHBtr3umlbssytqD+wytcVH430M9i2x4Gtz8lq1zdHpjIOhIprS6ki6dzudsYg8x6EZEJkmRILnwHZa4bBfJGMqd3wQTtl4DxS7etWmlvIxIz2wYRxEkjgS+1zEifNZFr45tgS48D9N3rtex4HEcbHIoHxAUiJg1jyjDAPNBiDFskr95zZz4E3ByXMmUtdISc29k6jRIkNEssLksdBeXxL2MSWpQDjwCB5df9Dw+ajRa6rBDuSlbYGq9PQ8wf4pp0bMZIyKeTEwUmH7cBPPeIoAMDurUt4x9kPx97tPgOGEdK5SfrmG72QILbesWBbJKWKjM15PcYBeRpoW6ppKVFNz5ZNlIAzF6igGmS3LFUkPX93u4wDQ4PwQYWvaMhpCBpajAHJmNoNF8e1BI5dAJEikxIx6LWptg7fA699C5kYt9ra2Bxbkipi+iy465sxqSIXMCa9i6JySW3MA9bJK+8dZJu88p+BRPsFI6NE8TgoUeJeENJLqMi0lLCKhU6FzUsdr8LMwuPgAYeFEwcW7ZeY70hLFRmYIwtSdZbttx2pJDlb372qKrzp2Q/Fbz71JGxdXsDmSCJHO6T7e/dq19M03uiFBLHzszj3tplHMY1xgL9JblsqKNm+MeOi73FgJ43gtr/a9RMXqeTKNCEzDtzEgR3jIVRtDgCM2+Bf/949/TDwOMiQKvKBY90+NDFmZRQzSOOvf+0XjPTdw6wPTyrJOHEB8A3CcxgHVKmiiEyQlWRcrA+rFvNARCrIwhhYegeNBjuBmPI4cMffPLCx24iNvdMoUWKDhETTLh4HJSxDNOg2GIMPPDycOJgH0LTEetxn65J5m2nAir84jZkjz4PHQeocLVgfAHDQtmXTpAEgn9s+wRR8o68HYgk8i3NPSZCw34WpZ4ABlo1G26yftFQRm3EwWx4H3V7tsU6Yz4E0/l2PA+Z6LChVJDEODKRyBlIxKwYm9R5gJyUOXOBYXSYkrC9uwjgQrutgDFhIJfnV5mHQljEGQlJZg7BmnIisF+I4cK/pSsTjgCWZFpMqckF0k3lg1OPAIHkmvYNGw/cY0GYcxOW6LOahEiVxUKLEvSKkRRNb07VEidGQDLotEgdFqqjEIB5z/F7zNtvW1wfi5sjzUFXTdrV3m5GdONjgc2EMlLR4D7Qt1ZMCZenmyG17HCTuMTt5JGmcWxhihtvv+fIYxHsgVZHvX2lPqihkEt//LsPjQAbvXfCK0bYP2AkeB+YV5xHg1kDjH1gfAx5gaOExEGEccMZAHLj3GCfqiYM4aAwIY1CxD+78vtarR8yxfY8DRsSligzmgQjzqI3kmTvu2ZJRKcaBxTUoURIHJUrcK+LwXVu8zzY6UFBitqItxsGOzUu4725//ANFqmje4pVPPWmMgv/ffuxEepspUNKi2r0wDtqttm4zpErffStr/vc2eCHBpqXw+VkwDlKFGq0zDthSPS0zr3ZujrPN+IwDF7jrGTMOfODQM+YlShVJ5+ZKFTHvQQi4dlkXAGdv5FV8D6SKDCqNlybxOCAnDsb01cmVxqFjDoBTG3Pm8D1wQWMTxour7+5VnCu3P4HHAVOqCFiXB7IyqY9KFbUxD8RYPxbmyEmDbt15OPYMAjbXoERJHJQoca+IEw7ZjocdvXv4+5NOOgTbNxWLkhJ2IVU9LxM3qqMRkisqUkXzFUcdtBV/9oLT8RM/cgRe9uT741efdAK9zZREicUYnHfGQQqQ2sgbhMI46McxB20N/s3i3FNJamuPA799avOtP4OblzrRBBGbgStVO1pWOErtu6B56l2l2T4Az/OKa84sS8WsCIwDhll6CLTy7gFhHLqVxi5ABvjguXYu3R3bo+11DcxpJSB23WfCov18jX0LmZqkOTLdHNt/7pg+G1IBT9Cg3CxxMArcO3MhYx5wWS/dMOvHxCA84TGgfQlirB/AH5MbeV/QZhTksUSJe0m89/mn4U+/8O9Y7FT4uUffr+3ulJiz2CYkqqyA+wcctgOfvOJG7/MiVTR/8ZgT9uIxJ9hJFrUtUQLEzZHnYXGcTN5sYOaRBERJiQOmTMgsxAMP34m/+cr3xb+ZMA6SHge2Fe9usA3CU/Mc+/yrqsLOLUu4+c4D4t/ZY0AyB7WQKAkdu9sTzJENK/4B4O41O6mikDmrlDhgjAX3HbQSMEdmAIYpwA4w0BefsWpnYH0MmJgjx87fIHGSNEdmG9Mmqr0Bn/Wi+U4SE0drPWCTMP5I81BMqshjfxnPA37yii+X5Y45F7jXfh82fQbmoaiqjSiJgxIl7iWxc/MSfuWJ/ArbEiWkkKqereQxHnDYTvHzjQ6WlWg/ZkFfPyZVNA+sm3mWKpI2P/sFqaKNbo78wMPldwBgwzhIJxDblgpiV9yn5kH+Pdi1ZTGYOLD2mOh7HLhVvsT2hapzX6qI14GqqrDYqcYAKt/jwC5xMujHgTUXrKrUq62BMHBqkbzxk0bj51zXNRW0BeKgoQvcM+aimMeBx/xh3IOYVBEZMAUkc+R4tbf6/a/ioLHUB8218aYFf/85mP/c54HFvGpbqijmcWAhlxU7/35/xr9PN0f2Ehdc1k2JfmzsnUaJEiVKlFCJzUstShUdJksVbXRd7xLtR2oDYONxMOeMgzmWKpLOzWUcLC1UqDZw8gQATgrI1QE274G2E4htSxUl50GDZ3DXlrDPAVuqKM/jgGhOLLTvJQ7IyRu3D/s9jwNi4sADbmWPA1YSMaQx794DCmgekGkahCA3rz4fuMDxmMeBQQJtoVN58kuDe+BVnFMYBxGpIlcqhwEaJ/Tl3THAlirKkctSZRwIe81B0tBK296bAyI+F5TkUQPGAeN93JRxYG2ObOk5NM9RUJcSJUqUKJEMCTixAu7vt3ebWFFbEgcl2NE2YAgkGAdzwLppu9q6zRhU+o7GXYaVvrMSh+zYjL3bl8W/mTAOklI97Zoj06WKZmAejCUOUh4U04abOJEkciw9Drq92q9yJRdyuGPcNUdmMkBDoJF7H1hzgTu+BtfepNI4BdgJmQPtRLJb7TwAievaZ96w3schqRK2vj8gM34GYSGR4o6rVLW1euLI9biQzJGJjAPRHDlgUM6ah905YCXic8FJHuV7HLQhmcZnHMSTZ5aeQ/McG3+3UaJEiRIlpo42EwdLCx0cf8h27/NSUVCCHelKWwNj1sVOsB/zsDhOgqYbfCXrbphcqaKNbow8iJBckYVkXUoGhp3AS73r2O2nnkGLezBLjANXIgfgrkckqR4LXe3RcK+BC5pbMi5CHgcsybZQxbkLXjHugcsk8YxxBRBXXeM+ABoKhee0NUloDFhUGkdlakyMcRP67mSfh5THQV3XHutBcxxI93RojmwA2gPxMeDOQxTJskjyyOIZ8N5BzhhwJbus5qDQ7/OwN2oj5mO3UaJEiRIlpgqpos+y0vUBh/qJg3kBzEq0FykwxCp5tUWQCrNsv82YheRNm+ECR75U0cY+/0GEEgebDN4DKUDSWibGDTbjIC0Xxr8Hu7fKjBPAInEzfn4H1nyDciZQ4VZ8S1JF7HmwTdk+XybkHqkiI8ZBCDhdNak0TlXa+v9G3+PAN+cGfLCw/10ScOslUPptu4kTxlwoMX6knwGSOXJAKmsQbNZF6P6Hfgd070NVVd47OORxwHoXRaWKDMyJY2PQwuPA91hwxiDboD3wDrBqv0Q/5mO3UaJEiRIlpgppQ2ZpyHmCwDiYF8CsRHuRqjRmGPFJsW2TLFc0D4vjtDHrxr4GLhjlJg42ujHyIE5q0etm26awzwjAnwdSgCTfY6H9BOrOqFTRDDAOjKV63Ip/tudUm6yToFSRkceBV23cs2McuOPKBWklxoE2dure2iFoLyQtWHOBnzzrn7cv08JnHIyCxhZyVTF9e0DwmVA3Rx7/PWXODOi/kzy5uAHrxwXt25AqsvA6ibBObBgHiYp/smRUSrLNwuukREkclChRokSJjBCliozMkQHguIOLVFEJ+0htQqzGYMggeaNX2wM5xrAbex5wK632FamisbA4/52bw6A1wJ8HUlWU7ZsztyxVZHz+B1atPQ78il9rxkHq+MwEXvtSRQHGgQFoLMlUjYYE2qoDx851HSQMJMYBw2MACFdcm5gjux4XI+dtUfEe07cHfMkofWNad/5Js16074P7nl9dGzAObCrNo3JVJl4nYdaJRfJMegfFfmcnjtxr3jUwKS9REgclSpQoUSIj2vQ4AICH3HeX99mWAJhaooRWzIoxb8ggeaOD5kB6A7DRL4GbvJpXxsHxB28XN+QW76FYtbtFH5LAfcvmyBab9DY9Dtzzk6SKmO8CD7js9kzkMWJ98P5OPH9XpmYAGrmJA5Zsmcs8HAC3FsmblFRRLYG26sCxW20sg7bSd/X6ICdQTMyRnbbrev3cPdDYwBzZbZMt1zSJQbf2o+DObyvDZ9AGMI5LFfF9LmLMIwu/Gfe59oB7slRQUi7La38+1sXWUa5qiRIlSpRIhlTVaVnxf9RBW/H4+x88/P1Rxx2Evds3mbVfYj4jra/fNuNgg6PmiG/CFjsVqg1OSXbPf7/rcWDI/Gozlhc7OOEQX65oFhgH7McwtQmna/zPgM9ILHHAlypyPQ5sGQcScLfq6fu3yzphShWF9LUPWEkVdVzQ0s6YNSURIoG22q/EEHAsJQ6szJEHYK2FSbg0ttc19vkV70ng3uuDbvsxY16pfUA/eRGq+LfyOPCTN7aMg9g84F8De48DOuPATVw47RepIpuQS9hKlChRokSJkZAWQtaA3TvOfjguvPQa9Ho1fvb0o03bLjGfkdqEWnkchBIHc8E4iJwjSxZhliLlcTBPXi8PPGwHrrjuh2OfWTAudm4Jb5eWFvjJqxQQQTdHngGfkTjjwFiqSPI4IF4D3xi2BsCvMm1yfCbrI+hx0JY5cq+HXq/2wDLGXDSJx4E2aBa6/mLigDQXhQyq3WeRwTqRxvbgGlj4XHgeCy5o61ZbGzMOagOPA/fZXlkLJO9oHgcy4wGwSV75zKOIx0EbCUzyfWiePNv4e4M2oiQOSpQoUaJEMh5wmKwvbRlblxfxgscd23Y3SsxRpDYAZoyDgDnyPOh4xgCruWBceFJFjsfBHCUOTjp8B3DZ+GdtMw4squ3b9jjYstz+PNimVJFvjuxLFVkzDlyorm2pIpYpKSDI1ASkilhzoZu4W+vWXsUrQJIoCcg0DcKV6gHspGpc8BDgFVOEPA78am++TAuwnjDwKp0tjGkTY0C7oMJNRLTDOJAr/j1te5ZUVkyqyKAPIbkwu/bD5w9YeByE269rP4k7D3ujNmJ+dhslSpQoUWLiOOOEvTj+4G3D389+VKn4L7HxY6FTRWn/Vjqa2+aYcRCrtp6H83c3bJ7HwZyYIwPACYds9z6zGAMxjwOLDWrb5sS7tixH/25xDXZvjUkVGTMOBHNkZvJEkgrxqt3blioizkMhqRQzxoEH3vc84Kz/PQuZmrgxLkAAjhtIFVl5HKwaGmRL5zRIWFgAlm4yJgXcz4LHARs4Xg0kbljvophUkcsAoSSvnGOO3gMftOczn1JjUN/jIJw4EefAIlVEicI4KFGiRIkSyeh0Knzglx+Li750NXZtWcKzTz2q7S6VKGES25YXceeBNfFvTF3n0QiZI1uZM7cZsQ3IPCQO3DE2r+bIAPDgI3Z5n+0IsHE0Y+fmcBsW1fapcc5PHMQ9Htr2OKDL9DjPmLXHQc4Ys74GfvuWjIv+9V/puklUOcE+bfhSMbWYOLDQ18/xONC+FaFqf4tK80GExoAnE2MmVRQw52WYIwuJq9Fwb4O6OXZKLksy6DbyOHBBe1Yxj7vOGrRf13UrrJfVmDlzCwlMtsdASKqs37ZtIn+eoyQOSpQoUaJEVuzauoT/+qPHt92NEiVMY+/25WDiwAq4nmePg9gmbB42By5o4UoVzZPHwaE7N+PHH3Qo/v7yGwAApx97EA7ZuZnebpxxwL/+ScYBuboulTiwmIc2Ly1gebHjVRgD9h4Hd6+OA9adiuv5lJMUYFb8A+l7zJyHfGPMdqWKur0aB7q+XBWjkCCmbQ7IUkX61b6Sx4actGC9kyW5KGBcax7gjAHpvq4NGQcOaDsDPhf6jINwtTsQSF4pXwbP4yBkUG4kVbQaaL//XQvgPsw4YLQfmoNDfVBPXkWSZ2Liag72Bm1ESRyUKFGiRIkSJUoEYs/2TfjeLfvEv1kB19tCHgdzsDiOneM8bA58j4P5lSoCgLf+zMPwF1+6BmvdGj99mg3zbUeEccDUdh9E24yD5cUOti4veGNvEFZ6wru2LOGmOw54n7NZN77HgVvhSW4/4/qyx2EKFGeOAQ+4DHkc0MyR/ePevdKOx0GOObI+cDz+e4xxwJqLfKmS0Bjg67sD68ClC6Ba6Nu7Fe4+aMtt3wXLLZJXnjnxmuxxwBp/IakikXlEeB9IrKf1vvDHYGgOHv7OThxEkmeS38w87A3aiJI4KFGiRIkSJUqUCMTe7WF9byuPgy1L88s4iIFm7ErrWQh3w7jfAW/niXEA9CvPf+7R9zNtc9PiAjYvdXC3pG0/C4wDI3PiYOLAaB4KJQ7Y98DzOHDMkdnXPycpwJ4HUu86ZvIk6HHgVJtvonkc+Nd//6oN48A/9wyPA6OKcxc0ZTJvPHPWwBiwkIsCRpInHmhroW/vjgEu48A9ntt+m+bIbhKDJR8akkqSTdItmEfr7c6Dx4GXPBtpz0o2rkQxRy5RokSJEiVKlAjG3u2bgn+z8jjYtklOHMwD4yBWvZVTiXtvj9SGjW2KWqIfOzfLcj02Hgfx7ZqFEWCbHgOpPrAZD+484zMOyO3nJA7IzKNU8oL5LvQrnvvX370PLMaBdFxXMg4ggcbOuffq8QpvGbTl9mHwDnIBa8vkUXcA3BrIVVVV5T2DgypvFzhmzEWpin+2OXKy/TbMkQeMAzJgHWy/W6Ou7bxOQj4jgGTOzB+DXvLImwu4UkVj59/1kzdWe7N5i5I4KFGiRIkSJUqUCMSeSOLAquJ/S8Ac2Yrx0GbENuLzYA6dOsd5MkduM0I+BxYyPW3KxAwiljiwmgdDfWA/Ax7jwGGesBOYOfNc28kLZoVnSN/byuNAZBwI7BsLjwNgHLitDUDbkDGqCyAzX8dBqSIDc2QgnLzy9OVNpIrijAP1+++Ma1eaSJQq0mYcLMoV/74xsI3PSb/t2rsXoe9OG6HxD/gV94z3cax9QEheqSeO/HfAYO5z5cIAGyboPEa5qiVKlChRokSJEoE4OCJVZOZxEDBHttIWbzNi13gOCBfJMVYo2TaxM+BzYJG8SnocGDAOdm+NGUS3mzjgmyOP3+O7HakiOmif43FAngdSx2feAw+0nQGPA1eqaLFTUWR6pOs+ZowqJA60+xFK3HjGwEzGQQC4dIFbnkF2wBzXQF/elypygfvx72uDtpMwDrT74HkcBK6/FeMA6I89KXHAAK3d9scZB/bJK/e6sxNosQSqxDiYBzZ2G1F2GyVKlChRokSJEoGIMw5sllFbg4yDjb84jm3C5oJxkADk5s0cua1ok3EwKx4HobD0OJDbJzMOnHvs4mTs65+6vp2K34ck44AqU+Pq2/eBIrfanDUXShXEXuKANA9I131UHsdCqiWcOIh/TzNC4Lld8kjWmPcq3g1kajxzZJdxYJQ4GoSXuCAMAzdxMADsLRgfQChxIEsVccyJw6wTG4+DePKKPQ9J139w7WXWR1kXM6Jc1RIlSpQoUaJEiUDEPA6sALOtIcbBHCQOQtrywHwkTlIboLJBsokwaN2+x0H7iYN2PQ7Yesap60tPXMwA6yjVB1PGgblUkcA4MDKpl677qCGvm8RiTAWh6++C5tTEQQA4XXGAW6sxsF7tzE/cNAdtddt3ExFuhTtbKgkAlhwvp8Gz71Xb08yRhQRet+c9A/3vWnsc8A2ifXPi8fNmzwUy4+AenxPBpH0e9gZtRNltlChRokSJEiVKBGJvTKqoZXPkeVgcH7qzfY+JNiMF2BXGgU0EzZENQNvQ8z8IbVkIKXZvDc+DVibl4cRBu8B929X+Fj4nqXfdTHgctMg4oCUOpErbiDkywyg9zDiwkYmJ9cE1R2YlEUOJi1ZA24THgfYYcOf3VOKCMQZ9qaiQOTJpDhDmltVuDytrNqC1z7oKmwNbso7Wf3e+r23QLXlMhOTCSjENLcqVLVGiRIkSJUqUCMQsmCOHpIrmgXFw6M7Nwb/NReIgsQkq5sg2sXOL/Ayyq90BYPsmue1BWMwDIakmq/aB9jwOUmAU+/yrqoq2YZHATl0DZuIgpC9vJVUkXf99KzY+F7FKW4APGkt9WAskDpjPQZB14o2BeJJ14vY9qaIQ64IhE9Os4l97DMSq3QFBKokwDnyPg0G1ueuzQWIcCPd1rVubSFUBwjMYkSri+GykPA64z4HE+hpjh8RnAAB5eUlEQVT6nDhtL83BvqCtKLsNpfjsZz+L5z//+TjppJOwY8cO7N69GyeffDLOPfdcXHbZZSZ9uOGGG/C2t70NP/7jP47jjz8eW7duxZYtW3DMMcfgqU99Kt70pjfh0ksvRU+gVZUoUaJEiRIl/Ni5eTEIzlpJdISkiqw8FtqMeU8cpIBpC+C6RJhxYPEMbg8YMw+CARa6sXuGEwdMfX0g/YxZzEOxNiykikLvIACoyNIQfrXrPTI1TrX5JiL7ygXO2pQqGgXtLGRipD70erVJpfmwD27FdQA4Zr0P3fs7AIxdAJXDOEhJFY1/X18mJmXOzPVYAMLJQyvWi3RfV7o9TyaHBVrHDKp9c2SGVJI8/gfhj0Hl9iXGwUCqaM0meVMCiK9ESyTjjjvuwItf/GL82Z/9mfe322+/Hd/4xjfwtre9Db/+67+ON7zhDVhaCi+8J41ut4sLLrgA559/Pu666y7v71dffTWuvvpq/O3f/i0A4Fvf+hZOOOEE9X6UKFGiRIkSGy2qqsKe7cu47va7vb8VxgE/9mxbxtJC5W3QgPlIHKQ2gawKyxLjEaq4t6huSzEO5sbjYGvgHpDlulLX1+L6L3YqHAj8zSJxcNC2sFQVO3HTtscB0D/Hu7Heni9VRAIMheOOgqUOZggGdi8lR9eExMFM+FywzJEDBsUuaGuRuPGkitwEjnIf/Gvvgsbc9gEhcRDyODBk/qx2e55MkBXjoBuTKjKQy3KvO59xEJEq8uTCSuKAFSVxMEWsra3hrLPOwqc//enhZzt37sSDH/xgrKys4PLLL8f+/ftR1zXe8pa34KabbsJ73/te1T6srq7i2c9+Nj784Q+PfX7CCSfgiCOOQF3X+P73v4/vfve7qF0HpRIlSpQoUaJEMvZu3yQmDqyqvZcXO1jsVCab1FmLTqfCITs249rb9nt/m4fESWEczEaEPQ74139HgnHAqPB0Y3cAtAfa9zhgzwNtexwAA0CqK/7NYg6IJQ7Yz4Bfbd5/Dx4wAo37fYgzDliAoSzREZYpsWIcdIXEQRseB55UkZHXxJBx4IKWBlJFSakgbY+DGZAqcs2RQx4HrOewqiosL3TGxttat/YZB6T2fcZLjbquUVX+voDxPkyPgfHva/chKlVkxDoqUaSKporf+Z3fGUsavOpVr8J1112Hz3/+8/jSl76Ea665Bi984QuHf3/f+96Hd7zjHap9OPvss4dJg6WlJfzmb/4mrr76anzrW9/CxRdfjM9+9rP49re/jVtvvRUXXnghfvzHfxydOZA2KFGiRIkSJbRiT8Ag2RK4l6QirMyZ245DAgbJ85A4SRqjFnNkkwh5HFjQ4rdvCoP2VWVjjhxnHNg8hyG5JHaFYdseB6k2LCos7xMxx7ZO3ARBY+Jc6F5jK48D6bBrY4wDvlyQNL+s9Xp+IQMxgRn0uWjJIHvdmJVvTOuCpi5Y7UkFKV+CkMfFevvj32eMwZDHgecxQJyL3DGw0u35+vqkNbk0rixNyr3ERcLjgM16GW3TN0fe+PuCtqLsNiaMa665BhdccMHw91e96lV47Wtfi61btw4/27NnD971rnfh7LPPHn72u7/7u9i3b59KH/70T/8Uf/EXfwEA2Lp1K/7u7/4Ob3zjG3HUUUd53929ezd++qd/Gn/3d3+H4447TqX9EiVKlChRYh5ib8Ag2UqiAwC2CXIllu23GYcFfA7mIXFQzJFnI4KMAwupogjjwIJtAMQTB1bPYUguit3+LDAOYm1YJK9CyXPAInEjS6WYShU5x77bkyriVTqHQGvABrSVPQ5s/BVCxx6Ahn61sY1UUdAgmiET45kjO1JBzn2oWjdHVm0egJ8QGjz7LmjMHIPuPLu61jMDrUWpnmDFvf4NCM3Bg2AbNHc6lZdEXQ0YlLOl8+Y5ypWdMC644AIcONBXmzz66KNx3nnnRb87SCjccMMNePe73z11+3fccQd+/dd/ffj7H/7hH+IJT3jC1MctUaJEiRIlSoxHKHFgCVxvERgH8wCcA2GD5LmQKkqcY9FztYkQaG2RvIt5HFjNATs2LwX1062ew81L7fh5pMAgizEQe86XDSosY4wD9hzkAafG1eZSH/atrI39zpTHiFV8+4wDfvv9PvigKRW0bdvjINOcmTEXuGOr6zIOWpYqascc2dbjQOrD2v+/vTuPk6Ms9/7/7e7Zt+wrSQgJkIQk7AnBICQSSTAGkINRFAER4cARBBRQQQ+gv+eA6MEoHngePAcEQR8ByYkgeOSBEBDZJIRgAmFJyEIIYcs+M739/him6K66q7tnMvddnenP+/XiRXenuqq6qrp7+r7u67oy2UDGia1B60K9TqIoVxZs0O1iH8xZD+1kHDjDr41uuu+++7zbZ511lmpqwv+g6t+/v0455RTjc7vrrrvu0rvvvitJGj9+fF5JJAAA0HMGhsy2dDlw3WhokFwJA+dSeODAxuzKclM044BSRU60hMz6d1FPtxwCB4l4TM2G/UjEYz0+w7XclENz5ELbcBE8HBBljwP/gFEEpYr837X+5sg2sz78rz+3LIiL+vKl9jiw+fdIWPDIXY15f419d4O2/lJp/vI4/qyTnt6HYo1x/YELG98HYaWqXPbZ8AdoO0oVubn+jDX+Q5oD23gfBnt8ZAvet/I5FJJ5EwjeMJnGGo5sN7z88st64403vPtz5swp+pzjjz/eu/3YY49p+/btu7UP//mf/+ndPu2003r9H+0AAEQltFSRw5ktph4HrpqSRm1on+iPf1SKN0fmT3kXmkNKFbkaNDa9/11tv1Nfw6zzSsh6KjaL2MXnUKHBIBfb71NfHTqb3XWpos5Ba/+gYa3DHgf+5sg2A4hhg+aSlHXQ48CccZB1ErT4eN3+4Ik548DWNRAMXoU0R7ZRqqiLs717ur68f31RzDYPBm7Cjr/NrKNgqaKk7/qz9VkclvUjmcoEOShVFOhxYD/rJCzzKlguir+JbeHIdsOyZcu827W1tTr00EOLPufII4/0bqdSKa1YsaLb2//www/13HPPefdnzpzZ7XUBAIDCwuo7u+wxYGyOXAGDdpI0pDmsx0Hv/zO22Dkm48CNmqq46g2lclwFbsKyDlwO3Jv6HFTCZxAZBx2Dh2HlimxfA6ZBQ/+AsSTVJOyVsgoEDvwZBxa/iwrN+E77exxY2I2wxqwuZhl3Ch6DjLLZbCDrxHXGgYtyTabZ9rkBo0AAx3KPg2IZBzaOQVhzZH9TXpvXYKDXSCYbbM7s6PqTOt6D2WzwfWilz0YXexy4bNAcaFBdAX+TRIVfG92wcuVK7/bIkSNVXR3eMCx3udxyRi+//HK3t//cc8/lfWFMnjxZkrRkyRJ95Stf0ZgxY1RXV6cBAwbo0EMP1aWXXqpXXnml29sDAKCSlUOPg4aQMiGVYEifkMBBBbx8miOXj5b66MqFhTVIdtUcWSqPwEEUn3nFXqOLY1BbXajHgZvPgH4h5Ypsz/A0ZRwYAwdOexy4aY4sdbXHgZuMg3Qmq3Ta3aCtKevCX6ZIsncNhM24djHjPez4d/L3GOjp4JF/goZ/e4HglYXLILTHgX+2ucMeB8l0Jlgqy9L2TddA0pB1FbZsT2+/WNaJjeBFWINmV1kfkMKLZiLUm2++6d0eNWpUSc+Jx+Paa6+9tHr1aknSmjVrur39F1980bvd1NSkmpoaff3rX9evfvWrvOXa2tr0/vvva+nSpbrhhht0ySWX6Nprr1W8m98o69ev79LyGzdu7NZ2AAAoJ6Glipz2ODBlHFTGoHFYj4NKyDgoVvqAjAN3WuqqtWlrW95jrtLiTf0FJMcZBw2GwIHjwFVDTULbWlPFF+xBxQYiXJyDsO8gyd1ASf+QwIHtPh+mHgdt6XRgOZufhf6mp61Jh6WKCvQ4cNGY1lhfPZOVf9zeddaFvzGxZO88BGu8Z/L+38lFmZ6O7WZV9dGfhLaDR8UyDlyUrPK/tzsDhy6zXvyBg/ZUxll9fdN7K50OZhtIdho0h2W9dJZKD5TLstHnIqxUkcNyVZWOwEE3bNu2zbvdp0+fkp/X0tJiXEdXvffee97tpqYmnXXWWbrrrrskSYlEQpMnT1a/fv20fv16vfrqq5KkdDqt66+/Xhs3btQdd9zRre2OHDmy2/sMAMCeqp9hwExy3eOgcjMOmmqr1FRbpe1t+QOGlVEmpfCPIBfNedEhyhn3jeUQODC8ftefQU21Ve4DB8V6HDgIYA5uDg8cuBoo6R9Sqsj29k3fs63t0WYc+HscWG2OHFImR5L844Y2EpBMb/GOHhP556Cna+vnMvU4cJl14h+09JrzBpozu+sx0cl2mZjgbHN/c2YHg8YlNke2+Te5qVSRP+PAdY8DU+DARu8z0/Yz2Y+zfv3BIycNmh03SAelirplx44d3u26OvMsOJP6+nrjOrpqy5Yt3u23337bCxqceuqpWr9+vZYuXapHHnlEq1at0gsvvKDDDz/cW/43v/lNIDMBAACECxsUoMeBO0NaggNnNgcqygXNkctHiylw4ChwE9bjwMYgTZi+ZVCqqD6kSbRN5dDjYFA5BA5Ce/3YzjgIrn9HezB4ZLNkk/9vgJ0OMw4KlQlxMds7FouFNKjOX85umZhgnwtTxoGtayA4aPlRmZRAxoH92d5SfsDCP3bc098JhUplSW7q29dU+c5/ypzxYfNvcmOpIn99fWsZB2HlwoKBAyuD9gWaM3fuSy4bf5sHm4R3lqvyXwO9/3dBVMg46IZkMundrqoq/RDmLtve3t7t7be2tgYe+9KXvqQ777wz8PhBBx2kRx55RNOmTfMaMl999dU644wzSurNkGvdunVdWn7jxo2aOnVql54DAEA5qqmKB2a4Oe1xYBgwq5SMA6mjXNHrm/MnXVTCD4RiP4RrKVXkTIuhz4Cz5sghPQ5cZj0ZMy4cZ7w0GjKvbCv2OePic3hwSIN4yV3WUVQZB6bj6+8x0LEf9o5DjW/dvvF6K+VBwtadO3CbddDjQOo4B2nfLHeXjWlNgYs2lxkHhtnmnfuRy9WgbbLAoK39jAP3pYoCZYK85sjR9TjoKFXkZvvxeEzxWH6QKJkONmeW7Bx/0/d8Kp1V53wGF8chLPPKH0BkMo09BA66oaGhwbttGsQPk7tsY2Njt7fvf259fb1+/vOfhy7f3NysG264QbNnz5bU0avg4Ycf1vHHH9+l7Y4YMaLrOwsAQC/Qt75a72zz1Td3GjgwNGatoDI1Qw19DiohcFLsHPMjyR1TxoGrazC0x4HLjANTjwPHfUa+eex+Ovv257z7x08aan2b5ZBxUBalikKbI9t9/abX5y8VVFMV9+pt21C0XJXDjIPcGbYuaotLHX/r5E55NJVJsflZVHqPAzc15lOZjLJZN6VqjD0m0gWyTiz3OPBfc9GUKvoo4yNtP2jx8T4Er0FXPQ6kjuug3ffeNzVHthHEDOtz0snfZ8NOg+ZguTKpo0l0rkr6XeQavza6oampybu9a9eukp+3c+dO4zp2Z/uSdPzxx2vAgAEFn/PpT39agwcP9u4vWbKk29sHAKDSmAbNXJbKMZXoqIQZ952G9KnMwAHNkctHS110pXrCMg5cfgaVQ4+DmeMH69jxHb9nBjbV6NxjxlrfZrGBCBfXQFmUKgoLHFgOHpmuMX+potoI+izksjpgGDLbXTKUqbG0G6ZZ54HGzA4bRKcymbxB1I+Xs9Uc2R+8yQaOfcf2LQzaGpsjf/za/VknPT1uX44ZB5mseeDcbo8DX/AilVG7gx4XnYKfA5nAoLnkrsdB7rF30aTaVK5MCpYqYjKNPWQcdEPuIP3GjRtLft7bb79tXEdXDRw4MO/+oYceWvQ5sVhMhxxyiP785z9LklavXt3t7QMAUGn6hpRpcMVUu9dGPd1yNcQwcFYJgZNiAxH8SHKnpT7CUkW1IQ3aHb4HjD0eHL8HE/GYfnXG4Xp7a6v61FcbM7F6WrH3YMWUKgoJHPjrj/c00zVmyjiwqdj73OqAYYGB28DgvcVSRf59cJpxYBi4T6byt28z68TUnNeU8WDj89DYGDc348BxqSL/eQ9mHPTo5iWZSzK2pzKR9zgI1Ne3+PeA6Ty46nFg+nzLPfb+a8DF+8DLOHAYvKl0BA66Ydy4cd7ttWvXlvScHTt26P333zeuo6vGjx+fd7/UIETucrn7AgAACutnyDhwqbY6+IOkEgbOOw2t0IyD4jNde/8xKBfGjANXzZHDMg6cNkcODhy73H6nWCymYX3qnW2v2OcsGQe2Z/sH1+/vcWA7cFD0GrBcoiRX7mClv0SIrQwkU/DCZX1506Bhe9p3DVg9B8HZ3qYyMTa+D0zv787B+2w2mPnguseB7ebMkvkYtKeD58BlqaKkIXjmMoCYCulxEEXwyn8crFwDvs/BpBc4cBc8qnQc2W6YMGGCd3vz5s0lZR288MILoevoqokTJ+bdb2trC1kyX26Phfp6d39wAwCwpzMNmrlUW1XZzZEHG3ocRDFo6VqhH0E1Cbt1vZEvyhn3YT0OnDZHNgRPW5PBJrW9TbHSDy4yv+prEgWugd7d48D0HtvpK1VkPeOgyPqrHQ6a5w7S+cvE2GyO7N8Hl4O2wVJFWbUbMg6sbT/hD95kAw1hJTuDlol4LFB+qHPA2FQuqaevgUKBK8l+xoNkHpBPpoN9NlyXKnI5aG2q8e9//R3Luemz4W+Wnre8g3JJaa9UET0OXCFw0A1Tp05VTc3Hfzw9/vjjRZ+Tu8yIESM0ZsyYbm9/1KhRGj16tHe/1LJDa9as8W4PGTKk29sHAKDSDGyOuFSR4UdxRWUcGAIH21qTEeyJW4V+BJFt4Ja5x4GrUkXlkHEQfP3b21KGJXuX4rPN3ZyDQS3mrIOaiEsV2WjGmcs0EBbIOLAcPCkWGLCaceA7v3mlivyzza01Ry4+aGk3cBCc8e/vceBytnfHoLVhtrelfTDNNpeCA7aS/YwD/yZdNMY1Be5ak2n5Nm31b2L/e7yjVJE/48BmqSR/xkNw+x1BJguBA2OpovDAgY2/S8J6vSR970Pb3wWVjCPbDc3NzZo5c6Z3/8477yz6nLvuusu7PW/evN3eh8997nPe7b/85S9Fl9+0aZNefPFF7/60adN2ex8AAKgUX5k2Ou8H0RH79He6fVON10piKtXx7vb2CPbErUKDcjRGdsvU4yDqUkUus44aDA3ad1RA4KDYMXZ1DgY1mQMHrjIO6qoTxmsgmowDx6WKivY4cFcmJ7emt7+2uK04ov9ryFSqyOb7wJ/1k05nlUz5BgytZhwEBy1Ns72tNWc2ZFxIwUF7qeeDR6agTS4Xg8amwWBTtpvN7C9/gDaZCfa5sBm8Cs64d/ceNF1TuZkn/swnK03CQz4HyThwh18c3XTmmWd6tx944AEtXbo0dNlFixZp+fLl3v0zzjhjt7f/1a9+VfGP3pQrVqzQokWLCi7/k5/8RKlUxx/3NTU1mjNnzm7vAwAAlWJonzp9f+4E9W2o1thBjbpy7gFOtz/AMNuzkkoVmQZmNm8vrVTjnqxwxgF/xrtkyjgwzfi0ISzjwOVngGkm44723l+qqNiMeleZX6ZybZLbz4F+DcHvIdvbj8VigevcdamiKHvNBEuUhPc4sPV5YBq4Dg4WWiwTY6hvHsw4sDjb2799Q5kcyV4QL5hx0FmqKLgPPT1uX6zHgf86sFOqKHhcd7W7qe8ftg/JVMars+9t32WPg0w2MNveVsm0eDwWaHpdMOPAwtvA/97q/Bx02SC70nFku2n+/PmaNGmSJCmdTuvLX/6ysdfBypUrde6553r3586dqyOOOMK4zttuu02xWMz7b/HixaHbnzx5sr70pS95988+++y8jIJcv/vd73TDDTd498844wwNHz684OsDAAD5zpy+j174wXH6f9+aockj+jjd9r6DmzRuSLN3/6CRfTUgZAZqpdje2vtnO/ct0JSbjAO3TD0OXA2cN4dlHNDjwrq4ocZ4rqgzDlyVKpKkAU2mwIH97QcDB25LFRVbv90BywI9DhzM9pbMg8fptH+w0G1zZP9sb6vNkf3nIJ0N1PqXLGYchFwDUZQq8gdM/FkvNq7BhGHg2h88lCwP3BtLFbkbtA4OnEfbZyR32y6OQ1jGgb/XCSU87TH/FYqi4vG4brnlFs2YMUNtbW1auXKlDjnkEF1wwQWaMmWKksmklixZoptvvllbt26VJA0YMEALFizosX24/vrrtWTJEq1du1abN2/W1KlTdfbZZ+u4445Tv379tG7dOt19991auHCh95yxY8fqxz/+cY/tAwAAsC8Wi+nWr07RLx55TYm4dOGx+0W9S84dd8AQ/c+KTd79f5m5b4R748awPvWavFcfLd+wJfBv1HJ1yzR4v8sweGFDYxlkHFSyqngsr0SM/99cGBzS48BVqSLJnHHgYvvV8ZhyC9M5zzgolnVi8RgEBm5zrkP/hHNbg/emcjUuMw6CA/cZtTktVeTPuAjJOLAWOAg2Z5YkQ5uFHv9O8L+mbLYjWNB5rfnjJ7beCjVVcbUmP97YLmOpInvXoLlUkcMG3Yb3YLBMj93gWW68NjdwF+i14iTj4KNSRf6MA/4utobAwW6YNm2abr/9dp1++ulqa2vTpk2bdOWVVxqX7dOnj/7whz9o7NixPbb9oUOH6oEHHtCcOXO0YcMGtbW16Ze//KV++ctfGpcfN26c7r//fvXt27fH9gEAALgxvG+9/u3kyVHvRmS+ddw4Ld+wRRu3tGramP6aOX5Q1LvkxPkzxuq8O58PPE6pIrdMx9s/89mWcihVVMlqqxJKps1BIpt1tXOFZRy4/BwwlcyzVR4jV7GMA9s9gIrNZLY6273AjG//4L2tU+GfRZ7JBmc728w4MJZpcZhx4L/Gk+lsYNBWslkqKtgYVwqef6nnZ/ybXlM6m1VcMeM+2DoG1Qlf4MDw3Wtzxr+xVFFgpr27rJuUoceBy+3nZRz4Bu9tfCeGvQdcNqiudBzZ3TR//nw9++yzmjFjhrH2ZyKR0Lx587Rs2TIdffTRPb79SZMmafny5Tr77LPV0NBgXKapqUmXX365nn76ae27b++fnQcAAHqfcUOb9ci3Zuhv3/2U7jx7mhpqKmP+y+yJQzV2UGPgcUoVudfiyzqYM2mok+3WVsWNKfgEDtwoVDLM1ThFWMaBy9IM/QyBAxczPP3bcN0cudgxdlkipWCPA0ulioylehwOWpp6LLSn3PU4MM12DtZWjxnHgnpm+8HGuJ374dfzGQfB45q7XReliqRgYMh1xoGxVJHD2e6ma9Df48DuezA8gOnPfLGxH/51dr4HXDaornSV8YvLssmTJ+vRRx/V6tWr9dRTT2nDhg1KJBIaMWKEPvnJT2ro0NJ+VJx55pl5TZdL1a9fP91yyy264YYbtHjxYq1du1Yffvih+vfvr3Hjxmn69OmqqQn+oQcAALAnqa9JqL6mPurdcCoej+mfjxmrS+/J72VlKl8Eu/7t5AN1ye9fUFsqo88eOEzjh7Y42W4sFlNTbZU+2JnMe9zWIA3y9W+s0foPdhn/zVXGweDm6Jsj9zcGDuxfg/5Bo0CpIsvHIMpSRWG1vaXgoK2tgWvTbGP/tm2+D0rqcWAxeOQfjOyob++uvry/OXPngLWpOXJP74bptBbOerGXcZDLFDiwOWhs6jXiL1Vkc/umGff+PiMJhwHM3OvfH0CxcQ2E9fkIZn0wocYWAgc9aJ999tE+++wT2fabmpr02c9+NrLtAwAAoOeddMhegcDBvoObItqbyjX3wGGaMrqftrWlNGZgMAvEpqa6YODAVX39TlfNO0BX/XGFd/8bFdBnRDLX9u/k6hwMao6+VJEpcOAf1LQhGDhwnHFQZP02G1QXKhHiora4ZJ5tHChPYjGI6R+QTWeyaks6zDgwZDz4My5sbj+sQbF/0NTGfhgzDnKDV65KFVXlr9dUqshqjwPfZ0B7Ktgc2eU1kDZcg1abMxt6LEhSNpsNfA7ZCCYH34MflSryHwMyDqwhJAMAAACUsepEXFefMDHvsaP2HRjR3lS2wS11Gjuoydrs3jBNtcFyOa5LFZ182AhNHd1fkjRuSLNOPWKU0+1HpV/BUkVuzkHf+mrjjFaXAyXGwIGD7ftn0voHDa0HDoqcY5e11fNmezsqE1NKjwOb16HpPeafcW6zz4WpObP/9bssk5PyyrQEMw56+r1gel25QSNXpYr874NWQ8aB3YHzYKki//F3WioobS6XZUtYANNULsvGNRDWJD7QoJoeB9aQcQAAAACUudOm7a1/vLVFC5e+pfHDmnXW9OiyXOFes6FBss2GpCYtddX63TnTtLU1qabaKif17cuBqbZ/J1cZB/F4TAObarVxS2ve4y4HSkzNkV1cA/6shh1t/lJFCavbL/YaXQ6a585yzvrLxNhqzmvoceB04NwwIOzPOnFZpqajObK72urBclUd2/b3eTAt29PblvLLE/mTHmx9HPg/5/znX7LXHFwKK1UUXZ+NVMZt8CosgOmf8S/Z+U40HX9JgfchGQf2EDgAAAAAylwiHtOPTzlI1558oCT3g8aIVlNd8Geb61JFUsd117dA6Z7eqH+B1+sy62NwczBw4LJUkSmA4h+8tsF/jP2zzaNujmy3TE54qSJX9eX9/QtMZVJslioyDQa6zDrxv/5UJhN8/VYzDszXQKDPQyLe45lwprr5+eWy3BwH//n1Bw5sNqc2bb89FbwGrDZJN5UL82c8OA1gdmzb1GfDxjUQzLrpuPbpceAORxYAAADYQ8TjMYIGFajJlHFAc2QnCmYcOJzhOMjQINnl9k0ZB1tbU4Yle5Z/ICpQnsJy4KDYYJTdmcbmmbaSoceBpUvBNGjpNuMguO6dSX/GgcVyUSWVibG5fV+ZnI+OfVvKfoNoU0Aod8DaVbmsYqWKbH8OmkoVRdrjwPE1GNbjwJRxYCVwENrnw12D6kpHxgEAAAAAlLFyyTioRKba/p38s5FtMjVIdlmqqKUu2Otha2vSsGTPKjYgZ7O+fcf2i/Q4sDrT19wUVHJXXz5Y3zxYJsV5jwOHGQf+2c4dg8bRvf50yGxrG4OmpmNfKOvFWnNkf58Tf+DA8uewqVSO/xpwWS7L1KA7ih4H/s8gW/sRaI6c7iyV5C54U+kIHAAAAABAGSuHHgeVql+BUkUugzemwIHLgRLT9bY9gowDP9vBk7rqwj0U/DPCe5KpKWonf5kQW58H/lnn6Yy7meaSucfErqS/z4XbrI8oSxV1btvf48BG8MT0+ZaX9RJRxoE/cGS7ZFy179gmTaWKLH4OmAKI5dCg25RxYONzKOw94DKAV+kIyQAAAABAGWs0BA6YXOdGv8bgTPtOLnsc9G8I7ofrgZL9Bjfl3f/ClJHWt1ksOGO7VNHApmDAJpfV2uYh9e07bucva+tS9Ne5T2cygZnmNgNoxlJFDjMO/IGhjlJF2YLL9KRgc2RzjwMbQUTT51smG16qyFqPA3/gIJBxYDlw4Du/7YZroDf3OAgrFeQu48BcKsllg+pKx5EFAAAAgDJm6nFAI0A3CjVHdplxYOq14LJUkST9YN4BXvbL8ZOGavJefaxvs9iAmO3AgSnTI5fL5sjJTHjGga1B21J6HNjMfiqlVJHLPhPJTLC+vcvZ3umQQVMbnwWxWCy0Ma7kslRR4ebI1jMOAjPeM04HraPucWAqVya5yzgIuwb9PQ4o32gPpYoAAAAAoIyZehzQHNmNvgUCBy4zDkwlk1xuX5I+ud8gPX75TG1rTWlEv3rFHFyDxQbEbAdP+jfWKB4LNiPuZLO2ub9ESTq3x4Fv0NbWuTDVNw/MdrZ4HZoGZP0Dx1abI/vObzbrpr9A2Lo7B0tdNEeWOkpVpWXOMvDPOLf1ceB/bS4DR5K5VJH/88DlNei6x0HYNegPINraD/+x/bg5MxkHrnBkAQAAAKCMmXoc8BvZjZqquPH4S24H7of1qQs8FsUMy74NNRrZv8FJ0EAqoceB5YyDRDymAQXKFZlq8PeUwIBhgR4H/l4EPSXY4yDrLNtBMpdgclmqyBS4ak26yzgIBo/Ms61tDZoGZnvnBa98yzrqcdCadJxx4DsHyXQ2EDxy2Zw4lckonXb3Hgy7Bk2BAxsTGkwZB9ls1tl7AAQOAAAAAKCsmTIO/D/mYY+pTJDkNnCw7+AmTRjW4t2fMKxFg1uCwYTepthsbtuBA0kaVCBwYDfjwFxbXHLX4yDq5sCxWCwwKOsfOK5x2GdCMtTYtxk8CpSr6jjxLpojS8EB8bw+G44CSDVV+ev1B46s9zjwbb89HWyObLdkWXDgPul0+yHNiZ1lHBQvlybRHNkmShUBAAAAQBkz9Thgcp07/RprtPb9nYHHXfaZiMViuuX0w/SL//eaYjHpm7P2c7btKEWdcSB91Odgo/nfXDbGzR2oy/oGbW31GTCVKvIP2tkeuK1KxPJe+872VN6/W22ObPig9QcunM42D22ObOn8F2jQ7S9VZKt8nv8c+AM39nscFL++nDYnTmfzypZJlvtshPQ48Gce2doP/ySJjh4TwW3b/CyudAQOAAAAAKCMNRsyDgy/2WFJ/4Zq4+OuewyM6Neg60450Ok2o1YsOFPrIIJWqEGy3QHD8B4HgQbFlgZt/ftgmu1rO/upYx/CS+S4nG0tuc04CDZHDgscRJBx4CjzJPLAQQnXt81eK/7gjeseB4EG4R8N2vt7ncRjdnqtmEq2JX2BE4mMA5sIyQAAAABAGWuqDQ5c++tswx5TY2Ipmh4DlabYYJCzjIMQVgetC/Y4yF/W1uCpPyCRMQYOrGw6Z/3RXQOmwFVb0l19+2Bj2o5t+5sj11o6Bv7znztg7arXhf895i/TZL85cvHX5TTjIJMtix4Hrs6/qWSbP2gh0ePAJo4sAAAAAJQxU4+DXcmUYUnYUA49DipVWZQqKtjjwOJM4wKlivyDdrZ6VZt7HPjLpFgeuC0yKOsyeCNJuxzW2DeVipLKM+PAVtZLsfd4WZQqslqyLJh5FMg4iKDHQbuzazAYuEj5m7zIbr+ZSkfgAAAAAADKWEN1IvCYf/AK9vQPCRxQGsG+YoOyUWYcxGK2a4ubZ/pKhtm+lgZtgwPXmUBjZtuZN5FmHBje460pf6kimxkH+a+ts0yMq+bIpjI5nfyTvm2NXRdrfm29OXIJQQGbg9b+15dMB4N3VksVBXosdGw7GVGD7mQ6EwhaSHaDJ5WOIwsAAAAAZczU+HQngQNnKFUUnWKDQTZri3cKCxzYbsZpGjDr5K7HgaFMim/Q0ta2P96H6K4B0zkOZhy4zDr5aNA2wtnenaJqjuxnPeOghFJFVjOPDA2qXfWXkMxZR5ICDYqtXYOG128qVcT3sT0EDgAAAABgD+NvEAl7+jeGNUfm57Rt5ZxxYDvjxDRg2MnfHN0UXOwJgR4H2eCgpe3jEGWfi3g8Jv+hbU057HEQUibG1aBtWKkk/23Tsj2l2Guzff2VVKrIcY+D4Pl3Xy6rPZ3/N4itAJ4/eGXKuJDocWATRxYAAAAA9jCUKnKHjIPolEWPg7CMA9tNWX0DZqmC9eXt7EOwTEkwcGA746DYNWD7PPizXlr9GQc2B4192045bo7sL4GVO2CbzjrKOCjy2mxmfHSsv4SMA4c9DlLpjCFo43D7H10D7Sk3wYtgxkEmEDiJWy4bV+kIHAAAAADAHqZvyGA2el5YjwMGKuwrNihcmwj2/+hpzbVVxoFZ2804A2VqcsrTOOtxYCoT4s84sPw+KJp1Yj2Ak799f48Dm4O2pTdHdtXjIjfrxTd4a+k6iLrHQSwWK3h84zF7r10Ka1Du7j1oCh5K7spl+Y99Kh0sVUR/A7s4ugAAAABQ5s45ekze/Ytm7RfRnlSesCANGQf2lUPGQSwW0+CWYNaB9ZnOBRrT+gMHtgYuTWVSAkEL64GDIj0OSqhBv1vb92cc+MrE2Qwg+dftujlyoWswMOu9l/Y4KLYPtjNeTMGblG/Q3mbWS1jJNFeBA39gLpXJBpoj+4N76FlVUe8AAAAAAKCw844Zq3e2tmrVpu2af/gITRzeEvUuVYy+DeYeBzZnmaJDOfQ4kKRBTbVa9/6uvMes1/bvQn15W2ViTD0O/LOdrQcOivU4sJx14h+8b03mD1pabUwbUibG3aCt7/znXoNZ/7JWdqHoe9z2+1CSGmoS2hlSHjCKkmUuMw7820+GBA6sBa8MDcKDgRPmxNtE4AAAAAAAyly/xhr97IuHRL0bFSlsYIiMA/sKzTZPxGPOykWZ+hzYLpETrG2em3GQv6yrHgftqUygMbPt90HRHge2Mw7ixTIOLNaXN5RpkdwFDkwZJ50yjoJXxTMO7A8aN9VW6d3t7cZ/s94k3VCyzGWPg2DGw0c9DnyRI2vNkQ1ZN/7ACY2R7eLoAgAAAABQgGk2JT0O7Cs0KGd74D6XKXDgfMAwU6DHgaVrMeE7xv4SITa33amuqnBGgfUAju8870r6exy4zDjoOO+umiP7gwGFsl5sHYdi59dFmZrmOnPWmeS+ZJnrPiNhPQ785bJsBfBMx7ct5a5cGAgcAAAAAABQUIth4Mj2gBEKD0a6KlMkSYOa6gKP2T7//sGwTPbjWd7+wEHM0mxvU8aBn+3AQVipsE7Vlq8D/zFoS7qrrx42499ZxkEX+mzYug7KocdBU214sRbnTdId9zjwlwFy3ePAFBTZ1e7u9YPAAQAAAAAABbXUBweOyDiwr9BglNPAgSHjwPaAten66hy4DTamdbMP5Rg4sJ9xUDjrwmaZmGCpoo/KxDhqjux/bemcrBdXfTaKDcy7GDRuqisUOHBdsiwTacaB1+PAn3HgKHglBbN+/H0Y0LM4ugAAAAAAFGDOOCBwYNtefetD/y3qUkW2S6SYMhrSXsZB/uO2GnUnfIPB/hI5kv3Miz71NQX/3X6viegGrv1Bi497HLip8R5s0J1z21XGQZGgiIsAbnOBjAPXTdJTmWxZ9DgINEe2dg0G1+vvM0LGgV0EDgAAAAAAKKClPhg4sDVYi4/tP6Qp9N9s1XU3MQUObA9YmgbDOvscuGpM698Hf21xSbI92bdfgYyDqnjM+vuw2KC80/ryIYO2tsrlhA0aS+6uwWID0i5KxhXMOLC8/YSpx4HTUkUhPQ78zZEtfR6bXpvLBuUgcAAAAAAAQEEtBQaOYM/eAxpDB6SiLlXkL5fR00wD0p2Ddv768rYGbUspVWR74LZQqSIXA4bFBmX9WQE9uu2QHgf+82AriObPOMktkePPOLAVPyn2PneR+VWox4Ht2e7+wEQy7bo5srlBt6vglbnHgT/jgKFtmzi6AAAAAAAUYMo4gH2JeEz7DTZnHbgMHAxsCpbL+WBnu9VtFupxEBg4dDRoZ+xxYClo0alvQ3ipIhfXQLEZ5VYHbUNne7upL2+a7d4p47sUImuO3Mt7HJiyPoKlitw36PZ/FtjrcRBcb7DHAdl/NhE4AAAAAACgAFOPA7gxbkiz8XGXPQ5qqxKBxz7ckbS6TdOAWdrxjHN/JoOpx4Htgdu+BYJ25ZFx4HK2t7k5sqseB7kBq1TGXy7Hzj4UbY4ccY8DWzPtOwWCR5lsYLa/zayfUnscuLoGJak16a5UEwgcAAAAAABQ0JTR/aLehYq1/9CQwIHDjAOTbW0pq+s3DZh1Dta5mnFuGrQMLGN54LZfY4GMAwcDhsUGxKPIOAg0prVVqsj32nL7Gvhnvdua9V0sQGizMXCn5gKBY9uluoINqoPNkaPpceDmGjSWKqLHgVMcXQAAAAAACpg5brDG5wxgnz9jbIR7U1nCGiRHHTiwzVR+JB1aX9zWwHHx9drqr9CpUMaBm1JFhV+fzYHrsPry5ZFx4KZcTqEyQVLv73EQuAbKpseBL3DkqEG3RHNk1+jwBAAAAABAAfF4TPee9wktWvaW+jXUaPbEIVHvUsXYvwxKFUXBNBgWNnDscrZvd5bZHX3KvDmyzVI1wdnenWVi8gdtrTVHNsx2926n3cx6r69OqDoRC7xm29vNVSh4YftzyH8OUo57HIRdA0lHwatYLKaqeCwvWBJojkyPA6t69zctAAAAAAA9oLG2SqdOHaU5k4YqZnmWNT62V996NdYEewy4zjj47IHD8u5/4fCRVrdnGgtLZcwDx7YGr4tlE8RiHUE1m2qrEmownH/JzTVQrBSN68a02WzWXXNkw6B17r7kL2tv4LhQj5len3HgW38m6y7jqGPd5mvAVbksKXgdtqbIOHCJowsAAAAAAMpSLBYz9jlwHTi4dPY49fmobE5zXZW+etRoq9vrnGmby6tx7884cNTjwC/hKIAWVq6oHDIObNa4N5WJMc28txU48m8/XaDHgc0B/D4FylW56XFQKHDgtseBFGwO7DLjILTHgdXgRf66AxkHNEe2ilJFAAAAAACgbI0b0qylaz/Me8xWeZYwew9o1MOXHKNl6z7UAcNbNLxvvfVtViXyS3R0Dta2RdQct6v/3lP6NtTorS2tgced9DiIsDmyPyCQzgSzDSR35z+d1+PA3eB1c4HAQdQZB7aaQncyBabaUu5K9UTdZ0MyZBzQ48ApAgcAAAAAAKBsmfocRNHjYFBzrWYd4K6/RcegXW55mIyy2ayzUiXFBiRd1RbvG9LnwMU1EFYmqZPN2c6mwfid7anAY9YyTkKaI2cyWfkSDqxeCy0FZvy7CF4V6nFge9A6Ybi+/FknkfQ4iLBc0q5A4ICMA5sIywAAAAAAgLJlChzYLhFSDkxlQtKZrLK+QVtbg3bFehzY7m/QqV9DjfFxFxkHDTWF59vaLFVkOq/+Mi2SvePgP7+dg8b+/gaS3cHrQqWKXAwa11YlQo+x7c+hUjIaXA7aJ0MadFttEu57j/kDBzbfgyBwAAAAAAAAytj+Q5sCj72zrS2CPXHLVKrGVOPeVtmm4vX93QQO+oRkHLgYNDY15s5lM+PAtG7/oKnkLuOkM2Dg72/Qsay94cWWiHscSFJzSLki29dgKQGZcsg4cNoc2dfjgR4HdhE4AAAAAAAAZWtQU23gsTXv7ohgT9zyD5glM9lAbXEpulJFrnoc9AsrVVRVeFC/JzQUqG8v2Q2emEsVuQscBAaN050ZB8Fr0ObgbaGMA1fBq7ByRbZnu5ey/kh6HDhtjuwLHPjeA1GUraskHF0AAAAAAFC2YoaSOZ8YOyCCPXHLP2iXzmSMzXFtzXouNpvbWXPkenOpovLIOLA4YGo4/jvbTIEDO8fBPyCdzhbKOLDZ46BQxoGjwEFUGQclrN/mNegPCKW8UkXRNUcOlCoi48AqAgcAAAAAAKCsXXvyZO92Ih7TKYeNiHBv3AgO2gUbI0v2yoQkivQ4cFVbPKxUka0STbkizTgwDIj6myPXJOLGwFqPbD8QuKrMHgdSocCB7YyDEgIHVjMO8tedyXY0x/ZnPlVbfC/6j7H/+qPHgV2FPwEBAAAAAAAiNv/wkaqpiuulDVt1/OSh2s/QMLm3CTRHdlyqqNhsZ1fjdWHNkW0P2krR9jgwZRz4Z1vbrS2ffz+6HgfhQ5fOehyElSqyHLgoJSjgsseB1JF54rI5crHX5yp4VKkIHAAAAAAAgLIWj8d08qEjdPKhUe+JO6bmtMaMg4h6HLia6ds3rMeBg8BBQ010GQemQWl/jwO7A7bBUllSSMZBb+9xEFHGQSlBAZv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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 575,
+       "width": 775
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# select only the ocean regions by ignoring any percentages over 100:\n",
     "SI_obs_ds = SI_obs_ds.where(SI_obs_ds['seaice_conc'] < 101)\n",
@@ -587,7 +3981,9 @@
   {
    "cell_type": "markdown",
    "id": "9fc76012-d7b8-4b5e-aa41-0facc05c0fa8",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "Now you can add the observational decline of sea ice to your analysis if you wish!"
    ]
@@ -597,7 +3993,7 @@
   "colab": {
    "collapsed_sections": [],
    "include_colab_link": true,
-   "name": "Example_based_on_CMA_structure",
+   "name": "Arctic_sea_ice_change_2024",
    "toc_visible": true
   },
   "kernel": {
@@ -620,7 +4016,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/projects/project-notebooks/Heatwaves_2024.ipynb b/projects/project-notebooks/Heatwaves_2024.ipynb
index 2201e51d7..aa2d88b30 100644
--- a/projects/project-notebooks/Heatwaves_2024.ipynb
+++ b/projects/project-notebooks/Heatwaves_2024.ipynb
@@ -2,7 +2,9 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "<a href=\"https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/projects/project-notebooks/Heatwaves_project_notebook_2024.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/projects/project-notebooks/Heatwaves_project_notebook_2024.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
    ]
@@ -10,7 +12,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "dbNfwh3vgEcU"
+    "execution": {}
    },
    "source": [
     "# Heatwaves\n",
@@ -30,14 +32,25 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 621
-    },
-    "id": "mUt8EEPe0hKk",
-    "outputId": "3e1145b7-ff68-4475-d51c-88784416b5e8"
+    "cellView": "form",
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd6d4f947c0e4138b8069f6bd309e3b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tab(children=(Output(), Output(), Output()), selected_index=0, titles=('Youtube', 'Bilibili', 'Osf'))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# @title Project Background\n",
     "\n",
@@ -72,7 +85,7 @@
     "        if video_ids[i][0] == 'Bilibili':\n",
     "          print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
     "        elif video_ids[i][0] == 'Osf':\n",
-    "          print(f'Video available at https://osf.io/{video.id}')  \n",
+    "          print(f'Video available at https://osf.io/{video.id}')\n",
     "      display(video)\n",
     "    tab_contents.append(out)\n",
     "  return tab_contents\n",
@@ -90,15 +103,40 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 621
-    },
-    "id": "tX5Bh4Pivakh",
-    "outputId": "7036341a-1ed4-420a-b198-cecffce9cc01",
-    "scrolled": true
+    "cellView": "form",
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "If you want to download the slides: https://osf.io/download/zeyxn/\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"854\"\n",
+       "            height=\"480\"\n",
+       "            src=\"https://mfr.ca-1.osf.io/render?url=https://osf.io/zeyxn/?direct%26mode=render%26action=download%26mode=render\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7fcf2c31d3d0>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# @title Slides\n",
     "# @markdown These are the slides for the video introduction to the project\n",
@@ -111,7 +149,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "o0arQA56IUtk"
+    "execution": {}
    },
    "source": [
     "**In this project**, you will look into the characterization of heatwaves using near-surface air temperature reanalysis data. Since we are talking about extreme events when the temperature exceeds a certain threshold for a continuous number of days, we will first analyze the global spatial and temporal distribution of air temperature. Next, we will calculate the number and timing of heatwaves for a local area, then focus on determining the percentage of a region under heat waves. Additionally, you will be able to explore its relationship with other climate drivers. Also, you are encouraged to analyze the health impact of heatwaves using an available mortality dataset. Finally, enjoy exploring the heatwaves!\n"
@@ -120,7 +158,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "I9_7XVXBgLf3"
+    "execution": {}
    },
    "source": [
     "# Project Template"
@@ -129,7 +167,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "FT_VUme8iNod"
+    "execution": {}
    },
    "source": [
     "  <img src='../template-images/2024_Heatwaves.svg'>"
@@ -138,7 +176,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "raQmOQkWigMy"
+    "execution": {}
    },
    "source": [
     "# Data Exploration Notebook"
@@ -147,7 +185,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "uuozLOtcjL5Q"
+    "execution": {}
    },
    "source": [
     "## Project Setup"
@@ -156,7 +194,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# google colab installs\n",
@@ -169,11 +209,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "uYVofx_Ej0c5",
-    "outputId": "1506c899-d979-4b1e-aac4-1e12e2d2f6e1"
+    "execution": {}
    },
    "outputs": [],
    "source": [
@@ -187,13 +223,16 @@
     "import cartopy.feature as cfeature\n",
     "import cartopy.crs as ccrs\n",
     "\n",
-    "#from xclim.core.calendar import percentile_doy\n"
+    "#from xclim.core.calendar import percentile_doy"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# @title Helper functions\n",
@@ -221,7 +260,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# @title Figure settings\n",
@@ -235,7 +277,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "FICTmBLTkIPr"
+    "execution": {}
    },
    "source": [
     "## ECMWF Reanalysis v5 (ERA5): Air Temperature at 2m\n",
@@ -249,7 +291,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "In the following, along with a small subsample file that was downloaded beforehand, we show how to load, explore, and visualize ERA5 data."
    ]
@@ -258,9 +302,449 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "y_cz1nOyk7fe"
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://osf.io/download/z9xfv/' to file '/tmp/file_sample.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 2236e254419e163ed9ae6ad44b61bfe39d5fb2bd943dbd912093e703c5fa1485\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
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+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+       "  --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",
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+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
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+       "  --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",
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+       "  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",
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+       ".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",
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+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
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+       "  margin: 0;\n",
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+       ".xr-dim-list li:not(:last-child):after {\n",
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+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
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+       "  background-color: var(--xr-background-color-row-even);\n",
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+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
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+       "  display: block;\n",
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+       "\n",
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+       "\n",
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+       "}\n",
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+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
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+       "\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",
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+       ".xr-icon-database,\n",
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+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 25MB\n",
+       "Dimensions:    (longitude: 41, latitude: 41, time: 3653)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
+       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0\n",
+       "  * time       (time) datetime64[ns] 29kB 1991-01-01 1991-01-02 ... 2000-12-31\n",
+       "Data variables:\n",
+       "    t2m        (time, latitude, longitude) float32 25MB ...\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      2024-02-14 07:40:23 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...</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-798a2614-1f76-4295-a694-d8de1b764ace' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-798a2614-1f76-4295-a694-d8de1b764ace' 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'>longitude</span>: 41</li><li><span class='xr-has-index'>latitude</span>: 41</li><li><span class='xr-has-index'>time</span>: 3653</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-da48f57b-da42-4edf-91e0-632335c67bfa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da48f57b-da42-4edf-91e0-632335c67bfa' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>143.0 143.2 143.5 ... 152.8 153.0</div><input id='attrs-b517a9af-7a30-4ebe-ba13-2ccab365e020' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b517a9af-7a30-4ebe-ba13-2ccab365e020' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eb3e407-6402-465f-a343-685b6936ea40' class='xr-var-data-in' type='checkbox'><label for='data-5eb3e407-6402-465f-a343-685b6936ea40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([143.  , 143.25, 143.5 , 143.75, 144.  , 144.25, 144.5 , 144.75, 145.  ,\n",
+       "       145.25, 145.5 , 145.75, 146.  , 146.25, 146.5 , 146.75, 147.  , 147.25,\n",
+       "       147.5 , 147.75, 148.  , 148.25, 148.5 , 148.75, 149.  , 149.25, 149.5 ,\n",
+       "       149.75, 150.  , 150.25, 150.5 , 150.75, 151.  , 151.25, 151.5 , 151.75,\n",
+       "       152.  , 152.25, 152.5 , 152.75, 153.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 4.75 4.5 ... -4.5 -4.75 -5.0</div><input id='attrs-b436ab8c-a94a-4430-b8cf-aaf4ac862553' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b436ab8c-a94a-4430-b8cf-aaf4ac862553' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fe34653-d280-44fc-be78-2bb2288a521b' class='xr-var-data-in' type='checkbox'><label for='data-0fe34653-d280-44fc-be78-2bb2288a521b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 5.  ,  4.75,  4.5 ,  4.25,  4.  ,  3.75,  3.5 ,  3.25,  3.  ,  2.75,\n",
+       "        2.5 ,  2.25,  2.  ,  1.75,  1.5 ,  1.25,  1.  ,  0.75,  0.5 ,  0.25,\n",
+       "        0.  , -0.25, -0.5 , -0.75, -1.  , -1.25, -1.5 , -1.75, -2.  , -2.25,\n",
+       "       -2.5 , -2.75, -3.  , -3.25, -3.5 , -3.75, -4.  , -4.25, -4.5 , -4.75,\n",
+       "       -5.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1991-01-01 ... 2000-12-31</div><input id='attrs-e5715aa1-06d8-4b55-b3e3-34534d817b4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5715aa1-06d8-4b55-b3e3-34534d817b4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba71ab1b-52a4-4b3d-b99b-ab3f1337d698' class='xr-var-data-in' type='checkbox'><label for='data-ba71ab1b-52a4-4b3d-b99b-ab3f1337d698' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1991-01-03T00:00:00.000000000&#x27;, ..., &#x27;2000-12-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-12-30T00:00:00.000000000&#x27;, &#x27;2000-12-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7a7bd655-4703-43c9-a687-62056821d6ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7a7bd655-4703-43c9-a687-62056821d6ce' 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>t2m</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-68e6974a-3f96-418b-b204-57729ff43632' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68e6974a-3f96-418b-b204-57729ff43632' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b2ddb8e-0c0e-4425-b9f2-10e8d36bc7aa' class='xr-var-data-in' type='checkbox'><label for='data-9b2ddb8e-0c0e-4425-b9f2-10e8d36bc7aa' 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>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><pre>[6140693 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f563889-efdd-42ff-9d5f-4e3c2d2fc0ec' class='xr-section-summary-in' type='checkbox'  ><label for='section-7f563889-efdd-42ff-9d5f-4e3c2d2fc0ec' 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>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f3ce39c4-9f34-4234-a1b9-40fdea6d865f' class='xr-index-data-in' type='checkbox'/><label for='index-f3ce39c4-9f34-4234-a1b9-40fdea6d865f' 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([ 143.0, 143.25,  143.5, 143.75,  144.0, 144.25,  144.5, 144.75,  145.0,\n",
+       "       145.25,  145.5, 145.75,  146.0, 146.25,  146.5, 146.75,  147.0, 147.25,\n",
+       "        147.5, 147.75,  148.0, 148.25,  148.5, 148.75,  149.0, 149.25,  149.5,\n",
+       "       149.75,  150.0, 150.25,  150.5, 150.75,  151.0, 151.25,  151.5, 151.75,\n",
+       "        152.0, 152.25,  152.5, 152.75,  153.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-400c7c01-9872-4901-8f01-b775039a5d8b' class='xr-index-data-in' type='checkbox'/><label for='index-400c7c01-9872-4901-8f01-b775039a5d8b' 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([  5.0,  4.75,   4.5,  4.25,   4.0,  3.75,   3.5,  3.25,   3.0,  2.75,\n",
+       "         2.5,  2.25,   2.0,  1.75,   1.5,  1.25,   1.0,  0.75,   0.5,  0.25,\n",
+       "         0.0, -0.25,  -0.5, -0.75,  -1.0, -1.25,  -1.5, -1.75,  -2.0, -2.25,\n",
+       "        -2.5, -2.75,  -3.0, -3.25,  -3.5, -3.75,  -4.0, -4.25,  -4.5, -4.75,\n",
+       "        -5.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5ee442e0-5453-4cf3-a7aa-908f4754acd4' class='xr-index-data-in' type='checkbox'/><label for='index-5ee442e0-5453-4cf3-a7aa-908f4754acd4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1991-01-01&#x27;, &#x27;1991-01-02&#x27;, &#x27;1991-01-03&#x27;, &#x27;1991-01-04&#x27;,\n",
+       "               &#x27;1991-01-05&#x27;, &#x27;1991-01-06&#x27;, &#x27;1991-01-07&#x27;, &#x27;1991-01-08&#x27;,\n",
+       "               &#x27;1991-01-09&#x27;, &#x27;1991-01-10&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2000-12-22&#x27;, &#x27;2000-12-23&#x27;, &#x27;2000-12-24&#x27;, &#x27;2000-12-25&#x27;,\n",
+       "               &#x27;2000-12-26&#x27;, &#x27;2000-12-27&#x27;, &#x27;2000-12-28&#x27;, &#x27;2000-12-29&#x27;,\n",
+       "               &#x27;2000-12-30&#x27;, &#x27;2000-12-31&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=3653, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7e6ff7a-2050-43f2-a96e-5ab64ef7bed3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d7e6ff7a-2050-43f2-a96e-5ab64ef7bed3' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>2024-02-14 07:40:23 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data4/adaptor.mars.internal-1707896415.6796277-19686-8-cf1f5a1a-aa36-4c24-a9fb-29291e38f918.nc /cache/tmp/cf1f5a1a-aa36-4c24-a9fb-29291e38f918-adaptor.mars.internal-1707895649.3473864-19686-6-tmp.grib</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 25MB\n",
+       "Dimensions:    (longitude: 41, latitude: 41, time: 3653)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
+       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0\n",
+       "  * time       (time) datetime64[ns] 29kB 1991-01-01 1991-01-02 ... 2000-12-31\n",
+       "Data variables:\n",
+       "    t2m        (time, latitude, longitude) float32 25MB ...\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      2024-02-14 07:40:23 GMT by grib_to_netcdf-2.25.1: /opt/ecmw..."
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# loading a subsample of the ERA5 reanalysis dataset, daily from 1991 to 2000\n",
     "link_id = \"z9xfv\"\n",
@@ -275,7 +759,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "16xyHYikLmrd"
+    "execution": {}
    },
    "source": [
     "Let's visualize the distribution of the annual mean near-surface temperature for the year 2000 in the given area around the equator. After calculating the anomaly according to the hints included in the template, you should be able to visualize the answer to **Question 1** similarly."
@@ -285,14 +769,448 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 610
-    },
-    "id": "7on_k6PakcVi",
-    "outputId": "7c793a9c-c42a-4c35-80ab-12cacadc460b"
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
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+       "  font-size: 11px;\n",
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+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t2m&#x27; (latitude: 41, longitude: 41)&gt; Size: 7kB\n",
+       "array([[300.1665 , 300.18317, 300.19998, ..., 300.12216, 300.10117,\n",
+       "        300.07745],\n",
+       "       [300.17957, 300.1994 , 300.21912, ..., 300.12817, 300.12216,\n",
+       "        300.105  ],\n",
+       "       [300.19885, 300.2193 , 300.23114, ..., 300.14175, 300.13776,\n",
+       "        300.13687],\n",
+       "       ...,\n",
+       "       [299.88504, 300.1443 , 300.40536, ..., 300.0386 , 299.20694,\n",
+       "        299.89627],\n",
+       "       [298.28378, 298.61932, 298.86646, ..., 299.7144 , 299.8679 ,\n",
+       "        300.66873],\n",
+       "       [294.1361 , 294.42368, 295.4092 , ..., 299.97037, 300.18015,\n",
+       "        300.3619 ]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
+       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0</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'>'t2m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 41</li><li><span class='xr-has-index'>longitude</span>: 41</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7e4520f4-672b-47b1-a55b-d023a7eee127' class='xr-array-in' type='checkbox' checked><label for='section-7e4520f4-672b-47b1-a55b-d023a7eee127' 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>300.2 300.2 300.2 300.2 300.2 300.2 ... 299.4 299.3 300.0 300.2 300.4</span></div><div class='xr-array-data'><pre>array([[300.1665 , 300.18317, 300.19998, ..., 300.12216, 300.10117,\n",
+       "        300.07745],\n",
+       "       [300.17957, 300.1994 , 300.21912, ..., 300.12817, 300.12216,\n",
+       "        300.105  ],\n",
+       "       [300.19885, 300.2193 , 300.23114, ..., 300.14175, 300.13776,\n",
+       "        300.13687],\n",
+       "       ...,\n",
+       "       [299.88504, 300.1443 , 300.40536, ..., 300.0386 , 299.20694,\n",
+       "        299.89627],\n",
+       "       [298.28378, 298.61932, 298.86646, ..., 299.7144 , 299.8679 ,\n",
+       "        300.66873],\n",
+       "       [294.1361 , 294.42368, 295.4092 , ..., 299.97037, 300.18015,\n",
+       "        300.3619 ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-112f8534-3746-42aa-a222-e1df06e57897' class='xr-section-summary-in' type='checkbox'  checked><label for='section-112f8534-3746-42aa-a222-e1df06e57897' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>143.0 143.2 143.5 ... 152.8 153.0</div><input id='attrs-4479345c-1a27-4ba1-8958-a704881e8f6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4479345c-1a27-4ba1-8958-a704881e8f6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74ef0746-f0ca-4bc9-ba86-2391f54e285f' class='xr-var-data-in' type='checkbox'><label for='data-74ef0746-f0ca-4bc9-ba86-2391f54e285f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([143.  , 143.25, 143.5 , 143.75, 144.  , 144.25, 144.5 , 144.75, 145.  ,\n",
+       "       145.25, 145.5 , 145.75, 146.  , 146.25, 146.5 , 146.75, 147.  , 147.25,\n",
+       "       147.5 , 147.75, 148.  , 148.25, 148.5 , 148.75, 149.  , 149.25, 149.5 ,\n",
+       "       149.75, 150.  , 150.25, 150.5 , 150.75, 151.  , 151.25, 151.5 , 151.75,\n",
+       "       152.  , 152.25, 152.5 , 152.75, 153.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 4.75 4.5 ... -4.5 -4.75 -5.0</div><input id='attrs-832d32f0-1f57-467a-81cd-eae1f894b51d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-832d32f0-1f57-467a-81cd-eae1f894b51d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a3531ed-58f3-44ff-8fd6-4f2ba21d737b' class='xr-var-data-in' type='checkbox'><label for='data-8a3531ed-58f3-44ff-8fd6-4f2ba21d737b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 5.  ,  4.75,  4.5 ,  4.25,  4.  ,  3.75,  3.5 ,  3.25,  3.  ,  2.75,\n",
+       "        2.5 ,  2.25,  2.  ,  1.75,  1.5 ,  1.25,  1.  ,  0.75,  0.5 ,  0.25,\n",
+       "        0.  , -0.25, -0.5 , -0.75, -1.  , -1.25, -1.5 , -1.75, -2.  , -2.25,\n",
+       "       -2.5 , -2.75, -3.  , -3.25, -3.5 , -3.75, -4.  , -4.25, -4.5 , -4.75,\n",
+       "       -5.  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-88ecf8eb-6075-497c-9612-a79e66b05899' class='xr-section-summary-in' type='checkbox'  ><label for='section-88ecf8eb-6075-497c-9612-a79e66b05899' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-026d2442-2468-4926-a24e-007b8ebb182e' class='xr-index-data-in' type='checkbox'/><label for='index-026d2442-2468-4926-a24e-007b8ebb182e' 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([ 143.0, 143.25,  143.5, 143.75,  144.0, 144.25,  144.5, 144.75,  145.0,\n",
+       "       145.25,  145.5, 145.75,  146.0, 146.25,  146.5, 146.75,  147.0, 147.25,\n",
+       "        147.5, 147.75,  148.0, 148.25,  148.5, 148.75,  149.0, 149.25,  149.5,\n",
+       "       149.75,  150.0, 150.25,  150.5, 150.75,  151.0, 151.25,  151.5, 151.75,\n",
+       "        152.0, 152.25,  152.5, 152.75,  153.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1c589d39-a65b-4fde-92f6-c765c6240d96' class='xr-index-data-in' type='checkbox'/><label for='index-1c589d39-a65b-4fde-92f6-c765c6240d96' 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([  5.0,  4.75,   4.5,  4.25,   4.0,  3.75,   3.5,  3.25,   3.0,  2.75,\n",
+       "         2.5,  2.25,   2.0,  1.75,   1.5,  1.25,   1.0,  0.75,   0.5,  0.25,\n",
+       "         0.0, -0.25,  -0.5, -0.75,  -1.0, -1.25,  -1.5, -1.75,  -2.0, -2.25,\n",
+       "        -2.5, -2.75,  -3.0, -3.25,  -3.5, -3.75,  -4.0, -4.25,  -4.5, -4.75,\n",
+       "        -5.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-91a35c0f-8590-4e48-a5d3-e428b2e5c655' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-91a35c0f-8590-4e48-a5d3-e428b2e5c655' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 't2m' (latitude: 41, longitude: 41)> Size: 7kB\n",
+       "array([[300.1665 , 300.18317, 300.19998, ..., 300.12216, 300.10117,\n",
+       "        300.07745],\n",
+       "       [300.17957, 300.1994 , 300.21912, ..., 300.12817, 300.12216,\n",
+       "        300.105  ],\n",
+       "       [300.19885, 300.2193 , 300.23114, ..., 300.14175, 300.13776,\n",
+       "        300.13687],\n",
+       "       ...,\n",
+       "       [299.88504, 300.1443 , 300.40536, ..., 300.0386 , 299.20694,\n",
+       "        299.89627],\n",
+       "       [298.28378, 298.61932, 298.86646, ..., 299.7144 , 299.8679 ,\n",
+       "        300.66873],\n",
+       "       [294.1361 , 294.42368, 295.4092 , ..., 299.97037, 300.18015,\n",
+       "        300.3619 ]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
+       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# calculate the annual average of the year selected\n",
     "year_to_use = ds.t2m.loc[\"2000-01-01\":\"2000-12-31\",:,:].mean(dim=\"time\")\n",
@@ -303,16 +1221,43 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 610
-    },
-    "id": "7on_k6PakcVi",
-    "outputId": "7c793a9c-c42a-4c35-80ab-12cacadc460b"
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_coastline.zip\n",
+      "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_0_boundary_lines_land.zip\n",
+      "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 611,
+       "width": 804
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# plot temperature \n",
+    "# plot temperature\n",
     "fig, ax = plt.subplots(1, subplot_kw={'projection':ccrs.PlateCarree()}, layout='constrained')\n",
     "\n",
     "extent = [year_to_use.longitude.min(), year_to_use.longitude.max(), year_to_use.latitude.min(),\n",
@@ -340,7 +1285,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "yPwmRiYClFdf"
+    "execution": {}
    },
    "source": [
     "Additionally, you can calculate the air temperature trend. We choose a longer time period and therefore another subsample from 1991 to 2020, that we load in the next cell:"
@@ -349,8 +1294,450 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://osf.io/download/z9xfv/' to file '/tmp/data_sample_91_20.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 2236e254419e163ed9ae6ad44b61bfe39d5fb2bd943dbd912093e703c5fa1485\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --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",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 25MB\n",
+       "Dimensions:    (longitude: 41, latitude: 41, time: 3653)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
+       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0\n",
+       "  * time       (time) datetime64[ns] 29kB 1991-01-01 1991-01-02 ... 2000-12-31\n",
+       "Data variables:\n",
+       "    t2m        (time, latitude, longitude) float32 25MB ...\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      2024-02-14 07:40:23 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...</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-ec3651de-9361-468a-997b-e2cfbbbb6c94' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ec3651de-9361-468a-997b-e2cfbbbb6c94' 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'>longitude</span>: 41</li><li><span class='xr-has-index'>latitude</span>: 41</li><li><span class='xr-has-index'>time</span>: 3653</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-161d467c-647f-43c1-8eec-b561a142a7fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-161d467c-647f-43c1-8eec-b561a142a7fd' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>143.0 143.2 143.5 ... 152.8 153.0</div><input id='attrs-34fec3f3-c5c1-4587-ab4a-4ad9eb32a6e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34fec3f3-c5c1-4587-ab4a-4ad9eb32a6e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0b46de9-7c0f-4726-b5a6-21f6e6808147' class='xr-var-data-in' type='checkbox'><label for='data-b0b46de9-7c0f-4726-b5a6-21f6e6808147' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([143.  , 143.25, 143.5 , 143.75, 144.  , 144.25, 144.5 , 144.75, 145.  ,\n",
+       "       145.25, 145.5 , 145.75, 146.  , 146.25, 146.5 , 146.75, 147.  , 147.25,\n",
+       "       147.5 , 147.75, 148.  , 148.25, 148.5 , 148.75, 149.  , 149.25, 149.5 ,\n",
+       "       149.75, 150.  , 150.25, 150.5 , 150.75, 151.  , 151.25, 151.5 , 151.75,\n",
+       "       152.  , 152.25, 152.5 , 152.75, 153.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 4.75 4.5 ... -4.5 -4.75 -5.0</div><input id='attrs-caae63c6-4ef1-4209-a836-f22592459364' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-caae63c6-4ef1-4209-a836-f22592459364' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f39d103b-049d-4411-b68b-a52910f18f4e' class='xr-var-data-in' type='checkbox'><label for='data-f39d103b-049d-4411-b68b-a52910f18f4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 5.  ,  4.75,  4.5 ,  4.25,  4.  ,  3.75,  3.5 ,  3.25,  3.  ,  2.75,\n",
+       "        2.5 ,  2.25,  2.  ,  1.75,  1.5 ,  1.25,  1.  ,  0.75,  0.5 ,  0.25,\n",
+       "        0.  , -0.25, -0.5 , -0.75, -1.  , -1.25, -1.5 , -1.75, -2.  , -2.25,\n",
+       "       -2.5 , -2.75, -3.  , -3.25, -3.5 , -3.75, -4.  , -4.25, -4.5 , -4.75,\n",
+       "       -5.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1991-01-01 ... 2000-12-31</div><input id='attrs-4c914c36-aaf2-44f3-b23b-920e492aa620' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c914c36-aaf2-44f3-b23b-920e492aa620' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5e84cd5-4ce7-4b9d-a22d-16a0d843c563' class='xr-var-data-in' type='checkbox'><label for='data-a5e84cd5-4ce7-4b9d-a22d-16a0d843c563' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1991-01-03T00:00:00.000000000&#x27;, ..., &#x27;2000-12-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-12-30T00:00:00.000000000&#x27;, &#x27;2000-12-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de3ac063-c61a-47c4-9ed8-479e0035e89e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de3ac063-c61a-47c4-9ed8-479e0035e89e' 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>t2m</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b70b2ff1-dfe4-418d-aa01-32597497dace' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b70b2ff1-dfe4-418d-aa01-32597497dace' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-341af3c6-76c3-4ee5-90e0-8a75b10f12a8' class='xr-var-data-in' type='checkbox'><label for='data-341af3c6-76c3-4ee5-90e0-8a75b10f12a8' 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>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><pre>[6140693 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ac5a659f-8be7-4404-83c0-b2f76f5ca6c2' class='xr-section-summary-in' type='checkbox'  ><label for='section-ac5a659f-8be7-4404-83c0-b2f76f5ca6c2' 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>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-88159ba2-eb01-463d-87c4-62c67c89917e' class='xr-index-data-in' type='checkbox'/><label for='index-88159ba2-eb01-463d-87c4-62c67c89917e' 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([ 143.0, 143.25,  143.5, 143.75,  144.0, 144.25,  144.5, 144.75,  145.0,\n",
+       "       145.25,  145.5, 145.75,  146.0, 146.25,  146.5, 146.75,  147.0, 147.25,\n",
+       "        147.5, 147.75,  148.0, 148.25,  148.5, 148.75,  149.0, 149.25,  149.5,\n",
+       "       149.75,  150.0, 150.25,  150.5, 150.75,  151.0, 151.25,  151.5, 151.75,\n",
+       "        152.0, 152.25,  152.5, 152.75,  153.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c6f0f2ab-c82c-4e6a-b043-ca569595989d' class='xr-index-data-in' type='checkbox'/><label for='index-c6f0f2ab-c82c-4e6a-b043-ca569595989d' 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([  5.0,  4.75,   4.5,  4.25,   4.0,  3.75,   3.5,  3.25,   3.0,  2.75,\n",
+       "         2.5,  2.25,   2.0,  1.75,   1.5,  1.25,   1.0,  0.75,   0.5,  0.25,\n",
+       "         0.0, -0.25,  -0.5, -0.75,  -1.0, -1.25,  -1.5, -1.75,  -2.0, -2.25,\n",
+       "        -2.5, -2.75,  -3.0, -3.25,  -3.5, -3.75,  -4.0, -4.25,  -4.5, -4.75,\n",
+       "        -5.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bd1f155d-cbaf-4199-a289-74a60d96d893' class='xr-index-data-in' type='checkbox'/><label for='index-bd1f155d-cbaf-4199-a289-74a60d96d893' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1991-01-01&#x27;, &#x27;1991-01-02&#x27;, &#x27;1991-01-03&#x27;, &#x27;1991-01-04&#x27;,\n",
+       "               &#x27;1991-01-05&#x27;, &#x27;1991-01-06&#x27;, &#x27;1991-01-07&#x27;, &#x27;1991-01-08&#x27;,\n",
+       "               &#x27;1991-01-09&#x27;, &#x27;1991-01-10&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2000-12-22&#x27;, &#x27;2000-12-23&#x27;, &#x27;2000-12-24&#x27;, &#x27;2000-12-25&#x27;,\n",
+       "               &#x27;2000-12-26&#x27;, &#x27;2000-12-27&#x27;, &#x27;2000-12-28&#x27;, &#x27;2000-12-29&#x27;,\n",
+       "               &#x27;2000-12-30&#x27;, &#x27;2000-12-31&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=3653, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b257d68-cdbb-4a20-8f7f-54638c75642e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6b257d68-cdbb-4a20-8f7f-54638c75642e' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>2024-02-14 07:40:23 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data4/adaptor.mars.internal-1707896415.6796277-19686-8-cf1f5a1a-aa36-4c24-a9fb-29291e38f918.nc /cache/tmp/cf1f5a1a-aa36-4c24-a9fb-29291e38f918-adaptor.mars.internal-1707895649.3473864-19686-6-tmp.grib</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 25MB\n",
+       "Dimensions:    (longitude: 41, latitude: 41, time: 3653)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 164B 143.0 143.2 143.5 ... 152.5 152.8 153.0\n",
+       "  * latitude   (latitude) float32 164B 5.0 4.75 4.5 4.25 ... -4.5 -4.75 -5.0\n",
+       "  * time       (time) datetime64[ns] 29kB 1991-01-01 1991-01-02 ... 2000-12-31\n",
+       "Data variables:\n",
+       "    t2m        (time, latitude, longitude) float32 25MB ...\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      2024-02-14 07:40:23 GMT by grib_to_netcdf-2.25.1: /opt/ecmw..."
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "link_id = \"3xbq8\"\n",
     "fname_ERA5 = \"data_sample_91_20.nc\"\n",
@@ -362,14 +1749,42 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 502
-    },
-    "id": "k5D4Vu-9k5ZJ",
-    "outputId": "6840837d-eb0e-44b4-b2b5-dfe0195bbad7"
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<string>:6: FutureWarning: 'Y' is deprecated and will be removed in a future version. Please use 'YE' instead of 'Y'.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fcecba63880>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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pnXekFi2kESOkxYulxERp7lzp0Uftjg7wHa4neNu2lSpUsCcWlFyu5YpIHADwNhIHAAAAAAC3HT4sPfaYVL++9Pe/S3v2ZN/nvfeko0eLPzbAF9HfAJ7guuJg504pIcGeWACUDSQOAAAAAAD52rhRmjBBathQev556dSp3Pe9cEF67bXiigzwXYZB4gCe0b69FBRkHVu92pZQAJQRJA4AAAAAADnKyJDmz5f69ZM6dJBmzJBSU3PeNyzMuv2//0knTng/RsCX7d0rxcdbx0gcoDACA6VOnaxjNEgG4E0kDgAAAAAAFsnJ0vvvS61aScOGSUuW5L7v0KGZ/Q02bMhs+polKUn697+9Hyvgy1zr0NeqJTVoYE8sKPlokAygOJE4AAAAAABIyuxL8MQT0v+zd9/hUVRdHMe/m4Tee++IiIhIpCkiIgIqRSB2iu21o2LBjgXsgr0XFAVFQxPpoqgoAVGQjkhN6L0FCEnm/eOazc6mkLK7s+X3eZ487r07O3siISRz5pxTpw7cdpvpoZ2d4sXh1lth9Wr4/nvo3BkaNYL+/e3HvfVW7i2NRMJddm2KXC5nYpHQ5z0geeFCUxkmIuIPShyIiIiIiIhEuOXL4aabzJ3QI0bA3r3ZH1e1Kjz7LGzZYgYgN21qf/6xxyDK47fMI0fgjTf8F7dIsNN8A/El74qDgwdzTvCKiBSWEgciIiIiIiIRyLJg5kzo2hVatIDRoyElJftjmzeHTz+FzZtNRUKVKtkf16QJXHONfe+NN8zFLZFIc/AgrFhh31PiQAqjVi2oXdu+pzkHIuIvShyIiIiIiIhEkOPH4eOPTTLg0kthzpycj+3eHWbPhmXL4MYbTYuiU3n8cXsrloMHTcsikUizaJFJ0GUoWhRatXIuHgkPmnMgIoGixIGIiIiIiEgE2LULnn4a6taF//0PVq3K/rhixeCWW8yd0jNmwCWX5K8ne7NmEBdn33vtNTh8uMChi4Qk7zZFsbHm75dIYXjPOVDiQET8RYkDERERERGRMLZypUkE1K0LzzwDu3dnf1yVKiaxsGULfPQRnHlmwd/ziSfs63374N13C34+kVCk+QbiD94VB6tWqR2ciPiHEgciIiIiIiJhxrJMi6Hu3U1Lok8+gRMnsj+2WTPTumjLFnjqKTMAubBatIDeve17I0fC0aOFP7dIKEhPz9p73vuCr0hBnHMOFCmSubYs0xZLRMTXHEkc/PLLL+6Pg4VMix48eNB2PhERERERkUh1/LgZYtyiBXTrBrNm5XzsJZeY4cgrVsDNN+dtfkF+PPmkfb17N3zwgW/fQyRYrVoFhw7Z95Q4EF8oXjzrrAwNSBYRf4hx4k07deqE678mmXPmzKFz584FPtfixYvp2rUrAC6Xi9TUVJ/EKCIiIiIiEip274b334e33zazDHJStChcfz0MGQJnneXfmGJj4bLLYPr0zL1XXoE77oASJfz73iJO825TVL8+1KzpSCgShtq3h4ULM9eacyAi/uBYqyLLsnx6rowPERERERGRSLF6Ndx2m5lfMGxYzkmDypVNBcDmzaYiwd9JgwzeVQc7dpi2SSLhTvMNxJ+8ByQnJJiWRSIivuRY4iCj4kBERERERETyzrJg7ly4/HIzn+DDD02Louw0bWraA23ZAs8+C9WrBzbWdu1MSyRPL76Y87wFkXDhnThQmyLxJe+vp/374Z9/nIlFRMJXWA1HVjJCRERERETC1YkT8Pnn0LIldOlibwHk7eKLYdo0WLkSbr3V2dZAw4bZ11u3wmefORKKSEDs2QPr1tn3VHEgvlSnDtSoYd/TnAMR8bWQTxwcPXrU/biEGmWKiIiIiEiY2bsXnnvO9Ei/4QZYtiz744oUgUGDYOlS+OEHM18gKgh+4+vQATp1su+98AKcPOlIOCJ+591vvmRJM7BcxFdcrqxVB5pzICK+FgQ/RhbOmjVr3I/Lly/vXCAiIiIiIiI+tHatGSRcpw488YSZD5CdihXh8cfN/ILPPoOzzw5omHniPetg82b44gtnYhHxN+82RW3bQkyMM7FI+PKec6DEgYj4WkgnDg4ePMinn34KmDZFTZs2dTgiERERERGRgrMs+Okn6NnTzCd4/304diz7Y5s0gffeg8REGDEia9uKYHLRRXD++fa9556D1FRn4hHxJ803kEDw/rpasQIOH3YmFhEJT37LeT/77LN5Om7MmDHMnz8/z+e1LIvk5GQ2btzI3LlzOXDggPu5Dh065DdMERERERERx6WkwDffwKhRsGRJ7sd26gT332+GIwdDK6K8cLlM1UH37pl7GzbAV1/BgAHOxSXiaydPwh9/2Pc030D8ITbWVLJkJGDT083XXufOzsYlIuHDZVmW5Y8TR0VF5Tis2PMtCzPQ2LIsXC4XlmVRtGhRVq9eTYMGDQp8Pgl+SUlJ1KlTB4DExERq167tcEQiIiIiIgW3bx98+CG89RZs25bzcTExcO21MGQInHNO4OLzJcsyrTUWLcrca9IEVq2C6Gjn4hLxpcWLoXVr+96ePVCpkjPxSHhr3dp8zWV47jl47DHn4hGR8BIi96dkLyNp4HK5GDlypJIGIiIiIiISEtatg7vvNvMLHn0056RB+fLwyCOwaROMGRO6SQPIrDrw9M8/8O23zsQj4g/ebYqaNlXSQPxHA5JFxJ/8mjiwLCvbj7wck5ePUqVK0bt3b3766Sfuuusuf34qIiIiIiIihWJZ8MsvcMUVcPrp8M47kJyc/bGNG8Pbb5v5BS+8ALVqBTRUv7n88qzJjxEjTIsNkXCg+QYSSN4DkhMSzL81IiK+4LcZBz/99FO2+5Zl0blzZ3eLoldeeYXY2Ng8nzcqKopSpUpRsWJF6tWrV6hWRyIiIiIiIv528qS5q37UKPjzz9yP7djRzC/o0SM82/dkVB307Zu5t3IlTJoE/fo5F5eIr3jf8a35BuJP3ompPXtg/XqTfBYRKSy/JQ4uvPDCPB3XsmXLPB8rIiIiIiISKvbvh48+gjffhK1bcz4uOhquvtrMLzj33MDF55TevaF5c1ixInNv+HCTTNB9YRLKkpJgyxb7nhIH4k/160PVqrBrV+ZeQoISByLiG47NOMiubZGIiIiIiEioW78e7rnHzC94+OGckwblysHQobBxI4wdGxlJA4CoqKyzDv7+G6ZOdSYeEV/xrjYoX97MOBDxF5dLcw5ExH/8VnGQm3Q1sBQRERERkTBiWfDbb6Yd0eTJufeYbtgQ7rsPbrwRSpcOVITBpV8/c0F1zZrMveHDoWdPVR1I6PKeb9CunUmUifhTu3YwZUrmOiHBuVhEJLzonzAREREREZECSk2Fr7+Gtm3hggtMr/6ckgYdOsDEifDPPzB4cOQmDcC0Z3riCfve4sUwc6Yz8Yj4guYbiBO8Kw7+/huOHnUmFhEJL0ociIiIiIiI5NPBgzByJDRqBNdeC3/8kf1xGfMLFi6EX3+FPn3Cc+hxQVx9ddY+3MOH516tIRKsjh2Dv/6y7ylxIIFw7rn2f1fS0kwiVkSksJQ4KKADBw4wYcIE7r77bjp06EC1atUoWrQoZcqUoX79+vTp04f33nuPI0eOnPJcmzZtwuVyFepj06ZNPvm8UlJS+PHHH3n88cfp1q0bdevWpWTJkpQoUYKaNWty8cUX8+yzz5KYmOiT9xMRERERCSUbN5ohxrVrw4MPZh2EmqFsWXjgATPv4OuvoU2bwMYZCmJi4PHH7XsLFsCPPzoTj0hh/PknnDyZuY6K0t97CYxSpaBFC/ue2hWJiC+4rCCcUHzixAkOHDjAiRMn8v3aunXr+iGiTGvWrOHBBx9k9uzZnPT8qSAH5cqV4/XXX+eGG27I8ZhNmzbRoEGDAscUExPDrl27qFChQoHPcfz4ce68804mTZrEgQMHTnl8dHQ09913H8899xzFihUr8PvmV1JSEnXq1AEgMTGR2rVrB+y9RURERCRyLVhg5hdMnAi5jWyrV8/ML7jpJpM8kNydPAlNmoDnfVAdO8LPPzsWkkiBvPyyGYae4eyzYelSx8KRCHPXXfDuu5nr3r3NvB0RkcJwZDiytzVr1vDZZ5/x+++/s2TJEpKTkwt0HpfLRWpqqo+js1uxYgXTpk2z7UVHR9O4cWOqVatGWloaq1evZt++fQAcPHiQG2+8kfXr1zN8+PBsz1miRAm6deuW5xjS09OZM2eOe92tW7dCJQ0Ajhw5wujRo7Ps161bl5o1a1K8eHE2bdrkrmxIS0tj5MiRLF26lGnTpgU0eSAiIiIiEgipqWZmwahRp757s317uP9+uOIKcye95E2RIvDoo3DbbZl7v/xiEgcXXuhcXCL5pfkG4qR27eyJg4QE0/ZNw+ZFpDAc/ZF2z549/O9//+O7775z7wVhAUS2YmJi6NGjBzfccAMXXXQRZT1uJ7Isi++++4677rqLrVu3AjBixAjatGlDz549s5yrWrVqzMzHFLDZs2fbEgeDBg0qxGeSVdu2bbn55pu57LLLqFWrlu25JUuWMHjwYH777TcA5s6dy2OPPcbIkSN9GoOIiIiIiFMOHYJPPoE33oDNm3M+LioK+vUzrYu8h1NK3g0aBCNGgGc31OHDlTiQ0GFZ8Pvv9j0lDiSQvP8N2rnTVHIVormFiIhzrYrWr19Px44d2bFjB5Zl4XK53EkDl0dK1DM8VzapUu/n09LS/Bg1TJkyhe+//54nn3zylG2REhMTadOmDTt27ACgefPmLF++vNAxXH/99YwbNw6AChUqsH379kLf8b93716uvvpqhg8fTvtT/NZz8uRJunXrxk8//QRAkSJF2LJlC9WrVy9UDHmhVkUiIiIi4i+bN8Obb8JHH8HhwzkfV6YM3HIL3HMP1K8fsPDC2jvvwN132/d++00XXyU0rF+fddD3+vXQsKEz8UjksSyoWhX27MncGzcOrr3WuZhEJPQ5kjg4fvw45557LqtWrXInDKKiomjTpg0NGjTgq6++MsG5XFxyySVUrFiRffv2sXLlSvcd/BlJhNNPP522bdu6z51dux0nffjhh9zmUXf777//0qhRowKf79ChQ1SvXp1jx44BcMcdd/CuZz1agPzzzz+cfvrp7vXHH3/MzTff7Pf3VeJARERERHxt4ULTjmjCBMjtPqS6deHee+Hmm6FcucDFFwmOHzcXWbdvz9zr3h1mzHAuJpG8+uILGDgwc121KuzYoTYxElg9e8L332euBw82yXARkYKKcuJNP/roI3fSACA2NpY1a9bw+++/M3bsWCAzMTB06FDGjRvHzJkzSUxMZO3atdxzzz3ExMRgWRb//PMPtWvXZvTo0UGXNACytCZas2ZNoc737bffupMG4Ps2RXnVpEkTmjRp4l4X9vMSEREREQmktDQz6LhDB9Mb+ptvck4atG4NX39t7iC+/34lDfyheHEYOtS+N3Mm/PGHM/GI5Ed28w2UNJBAa9fOvj7VbB4RkVNxJHHwxhtvuCsNatasyQ8//EBj77q+HJx22mm8/vrrLFy4kLp165Kens7zzz/PUO+fMoNExYoVbetDhw4V6nyff/65+3HTpk1t1RaB5vm5FfbzEhEREREJhMOHzR2Yp51m5hP8N7orC5cL+vaF+fNNRcLVV2vosb/dequ5U9vTiBHOxCKSH5pvIMHAu+v0kiXgcd+piEi+BTxxsGXLFjZs2ACYqoInn3yScgW4Zadly5b88MMPlCtXDsuyGDlyJHPnzvV1uIW22WuaWpUqVQp8rg0bNjB//nz32qlqgwyen1thPi8REREREX9LTDR3tNepY9oNbdyY/XGlSpnZBevWmdZF55+vO4cDpWRJePBB+95338HSpY6EI5Inhw6B9yhDJQ7ECa1bQ5THVb7UVPjzT+fiEZHQF/DEwR//1ZpmjFa48sorcz0+txEMjRs35oknnnCvn3rqKR9E6FsTJ050P46JiSE2NrbA5xozZoz7/0dUVBQDBgwodHwFlZCQwHaPBqROVj6IiIiIiORk8WK47jpo0ABeeQUOHsz+uNq14eWXISkJ3ngDCjGWTArhjjugUiX7nqoOJJgtWgTp6ZnrIkWgEL/2ixRYmTLQvLl9T+2KRKQwAp442LVrl/txnTp1qFChQq7HHztFXdUNN9xAdHQ0lmWxYMECkpKSfBKnLxw9epS3337bve7WrdspP9+cWJbFmDFj3OsuXbpQq1atQsdYUC+++KL7ceXKlenSpYtjsYiIiIiIeEpLg8mToWNHcwfmV1/lPL8gNhbGjYMNG+Chh6B8+UBGKt5KlzZzJDxNmAArVjgTj8ipeM83aNXKzOwQcYJ3uyLvr08RkfwIeJfOAwcOAKZNUU7tbYoXL86JEycAc/E9N5UqVaJBgwb8+++/gLkTPi4uzncBF8KDDz7I1q1bAfP5PvvsswU+16+//spGj3pqJ9sUjR8/nilTprjXjz32GCVKlCjQufKb6PGschARERER8XTkCHz2Gbz+uhlknBOXC3r3NheoO3RQK6Jgc/fdpjrkv18dAXjuOZMAEgk2mm8gwaRdO/jgg8z1ggVgWfp3TkQKJuCJg6JFi2a+eQ7TxcqWLcuuXbtwuVzuC++5qVSpkjtxkJiY6JtAC2ns2LG8//777vX9999Pq1atCnw+z6HIZcuWpU+fPoWKr6BWr17Nrbfe6l63bduWwYMHF/h8derU8UVYIiIiIhLBkpLg7bfNxRLPi83eSpaEm24yMw4aNw5YeJJPZcvCfffB009n7o0fD089BU2bOhWVSFbp6Vnv6FbiQJzkXXGwfbuZ8VO3rjPxiEhoC3irIs9WPQdzaDBatWpV9+O1a9ee8pwHPH47OFVro0D49ddfufnmm93r2NhYnn/++QKfLzk5mW+//da9vuqqqwp8h39h7Ny5kx49enDo0CEAypUrx7hx43JMAImIiIiI+NNff8GAAWZ+wUsv5Zw0qFkTXnjBXDx56y0lDULBPfeYft0ZLAsK8SuViF+sXp11bor3hVuRQDrtNPDukK05ByJSUAG/4nv66acDpmf/tm3bsj2mRYsWrFixAsuy+Pnnn3M93+7du1m3bh2u/+quypUr59uA8+nvv/+mZ8+e7lZLDRs2ZOrUqbZKi/yaNGkShw8fdq+daFN08OBBunfvzoYNGwDTTmrKlCk0bNiwUOfNb4XI9u3badOmTaHeU0RERERCV3o6TJsGo0bBvHm5H3vOOaYd0VVXQSF+HBcHVKhgkgfPPZe5N3YsDBumxI8ED+9qg7p1wcFRhCJERZl2RTNmZO4tWGD+HRQRya+AJw6aN2+Oy+XCsiwOHz5MUlIStWvXth3Trl07xo0bB8C6deuYM2cOl1xySbbne/nll0lPTwfMHIHGDv4UuXbtWrp27equpKhZsyZz5syhRo0ahTqvZ5uixo0b06FDh0KdL7+OHj3KZZddxtKlSwHTYuqbb77hwgsvLPS5vf/sRURERESyc/QojBkDr70G69blfmzPniZhcOGF6uscyu67z8yryBh7l55uKkc++cTJqEQyab6BBCPvxIEqDkSkoALeqqhcuXKcffbZ7nV2FQVXXXUV0dHR7gTDoEGDWLRoke2Y9PR0XnnlFUaNGuWuNihevDgXXHCBfz+BHGzcuJEuXbqwa9cuACpXrsycOXMKfUf+1q1bmTt3rns9cODAQp0vv44fP07v3r35/b+fiKKiovjiiy/o2bNnQOMQERERkci0bRs8/ri5k/fOO3NOGpQoAXfcAWvXwnffQadOShqEusqV4a677HtjxsCmTY6EI5KFEgcSjLzbZf31F/zXFENEJF8CnjgA6Nq1q/vxtGnTsjxftWpVBgwYgGVZuFwuduzYQfv27YmNjeW6664jLi6OunXr8sgjj2BZlvu4W265hZIlSwbyUwEgKSmJiy++mKSkJMAML545cybNmjUr9Lm/+OILW0VFIBMHJ0+eJC4uzpa4eP/997nmmmsCFoOIiIiIRKalS2HQIKhf3/S237cv++OqVzftbBIT4d13oUmTQEYp/vbAAyYplCE1FV580bl4RDLs3WsSlZ4030CCQZs29sR5SopJHoiI5JfLsiwr0G+6dOlSWrVqBZgqgS1btlC5cmXbMXv37qVly5buOQgZYWZUF3iuLcuiSZMm/PHHH5TxnKAVADt37uTCCy90D3EuWbIks2bN8lk7oTPOOIM1a9YAcNFFF/Hjjz/65LynkpaWxtVXX82ECRPce6NGjWLIkCEBef+cJCUlUadOHcDMR1CrIxEREZHwkZ5u2iuMGgWn+rG3RQtzUfnqq6FYscDEJ84YMsS0LMpQpAisXw///Vog4ohp06BHj8x1iRJmUHKRIs7FJJKheXNYuTJzPWqU+V4qEtYOHDBlp337QunSTkcTFhypOGjZsiVvvfUWr7zyCsOHD2f37t1ZjqlUqRLz5s2jcePG7ooCl0fK1DOB0LJlS+bMmRPwpMG+ffu45JJL3EmDYsWKMXnyZJ8lDRYtWuROGkDghiKnp6dzww032JIGzzzzjONJAxEREREJT8nJ8MEH0KyZuRCXW9Lg8sth7lxTkTBwoJIGkeChh+x/zidPwssvOxePCGRtU9SmjZIGEjy8q1+8B3mLhI29e+HTT+Gyy6BqVVOuOn2601GFjYAPR85wl3ezymw0atSIFStW8N577zF+/Hj++OMPUlNTATOgt23btgwcOJAbb7yRmJjAfiqHDh2iW7duLF++3B3PN998k+MQ54LwHIpcqlQp+vXr57Nz5+aOO+7gyy+/dK+HDh3KsGHDAvLeIiIiIhI5duyAd96B994zv/flpHhxkyQYMgSaNg1cfBIcataEW24xXysZPvoIHnsMatRwLi6JbJpvIMGsXTv4+OPMtQYkS1jZuRMmT4b4ePjpJ0hLsz8/YQJcdZUjoYUbR1oVFZRlWezduxfLsqhUqRJRUY4UTJCcnEy3bt2YP38+YAYGjx071qe9/1NSUqhRowb7/mvmOmjQID777DOfnT8n999/P6+99pp7fdddd/H222/7/X3zSq2KRERERELfsmXw2mswbpzpvZyTqlXh7rvh9tuhSpXAxSfBJzERGjUy1QYZhgwx7TdEAi01FcqVM9VSGb77Dnr2dC4mEU+rVsGZZ9r3kpKgVi1n4hEptG3bYOJEkxT45RfT3zInpUrB7t32IUlSIM5ceS8gl8tF5cqVqVKlimNJgxMnTtC7d2930sDlcvHxxx/7fGDw1KlT3UkDKHibok2bNrnbPLlcLp5++ukcjx02bJgtaXDjjTfy1ltvFeh9RUREREQ8WRbMnAmXXAJnnw2ffZZz0qB5c1N1vnkzPPmkkgZi5hnceKN97/33YdcuZ+KRyLZsmT1pABqMLMGlaVOT3PKkqgMJOVu2mCFHHTpA7doweDDMm5d70qBYMbj4YtizJ1BRhjXHWhWFqjfeeIMffvjBvS5fvjzjx49n/PjxeXp9//796d+//ymP82xTVK9ePTp16pTvWPNj0aJFDB8+3L2OiooiKSmJSy+9NE+vb9GiBS+r0aiIiIiIeDl2DMaONRUGq1blfmz37nD//dClC3iMNxMB4NFHTULpv+61HDtmKg5efNHZuCTyeLcpatIEKld2JhaR7ERFQdu2MHt25t6CBRCgDtgihffyy/Dww3k7tkQJM+OgXz8zDKtsWf/GFkGUOMinZK/bCvbv38+sWbPy/Pp27dqd8phdu3YxY8YM93rgwIG2wdD+4P15paenM2fOnDy//vjx474OSURERERC2K5d8O675mP37pyPK1YMBgyA++7L2lZBxFP9+uZrZfTozL233zbDkytVciwsiUCabyChoF07e+JAFQcSUtq0yf35UqWgRw+Ii4NLLzVr8bmQalUUKcaNG+ceAg0mcSAiIiIiEgpWrjSDbOvWhWeeyTlpUKUKPP20qUL/6CMlDSRvHnvM3Emb4ehR08VAJJAWLLCv1aZIgpH31+XixbnPFRIJqFWr4Nlnc/5B8YILsvaqLFsW+vc3g5F374avvzaJAyUN/CakhiOLaDiyiIiISPCxLJgzx7SNOVUxbrNmph3R9ddD8eKBiU/Cy4AB8OWXmeuyZc08jPLlHQtJIsi2bVkHzC5fbmaziAST/fuhYkX73sKFp76RW8QvLMsMiImPNwOOV682+x99ZO44yc5tt8E338AVV5gEQZcuplRVAkYVByIiIiIiUiDHj5ue82edBd265Z406NrVDEdesQJuvllJAym4xx+3z8A4dAjefNO5eCSyeFcblC1rEqIiwaZCBTMk2ZPaFUlAWRb8+acZUtSkCbRsCSNGZCYNwCQRcvLCC7Bzp+lRePnlSho4QIkDERERERHJl927TXV5vXomCbByZfbHFS0KN91kbjCbNcskFzT0WAqraVO46ir73muvmQSCiL95zzdo397ePkskmHi3K/JOfIn4XHq6yVA9+CA0bAjnngsvvgj//pv98T/8YMpjslOxovlhUhyj4cgiIiIiIpInq1ebC7RffGGqDXJSuTLccQfceSdUrx64+CRyPP44jB+fuT5wAN55x9zUKOJPmm8goaRdO/tAeVUciF+kpZms6oQJ5iMpKW+vq1UL+vWDEyf8G58UmBIHIiIiIiKSq5QUU1ng2Vc+O02bwpAhpgd9iRKBiU0i01lnQd++MHFi5t6oUTB4MJQu7VxcEt6OHzddNzydd54zsYjkhXdia9Mm2LFDSX3xseeeg6eeytux9eqZZEFcHLRtq5KtIKc/HRERERERydXHH+eeNLj4Ypg2zbQsuvVWJQ0kMJ54wr7eswfef9+ZWCQy/PWXSaRmcLnMdS+RYNWsGZQpY99T1YH43OWX5/58o0bw8MPwxx+wcSOMHKk+byFCf0IiIiIiIpKrTz/NulekCAwaBEuXmva0l12m3/8ksM45B3r0sO+9+iokJzsTj4Q/7/kGzZub4cgiwSo6Gtq0se9pzoHkS0oKTJ9uhlZt3pz9Ma1aQf369r3TTzd9BZcsgXXrzJyDc8/VsKsQox/tRUREREQkR6tXZ23Ncc895nfHzz6Ds892JCwRAJ580r7euRM++siZWCT8eV9wVZsiCQXt2tnXqjiQUzp+HKZMgYEDoWpVU1EwerS9P6Anl8u0HmreHJ5+GlasMD9AjhgBLVsqWRDCXJZlWU4HIZJXSUlJ1KlTB4DExERq167tcEQiIiIi4e3xx+H55zPX1atDYiLEaFqaBInu3WHWrMx1zZqwfj0UL+5cTBJ+LMt8be3Ykbn3+efmuppIMJs2zV6dVaIEHDxoKgdF3JKTYcYMiI+H77+HI0eyHnPeefDbb9m//uRJfVGFIVUciIiIiIhIttLTs842uPZaJQ0kuAwbZl9v25Z9ey2RwsgYKutJFQcSCrzncBw7BsuWOROLBJnDh+Hrr+HKK6FKFVM18PXX2ScNwPRr27o1++eUNAhLShyIiIiIiEi25s+HLVvsewMGOBOLSE7OOw86d7bvvfiifYitSGF5zzeoXNnM+xQJdpUrw2mn2ffUriiCHTxo7gq54gqTLLj2WlNlcKoBQeedB6NGmZIViRhKHIiIiIiISLa++MK+PvNM06pWJNh4Vx0kJpo2MiK+kt18A7XtllDRvr19rQHJEezdd81dIFOmwIkTOR/ncsGFF8Kbb0JSkmlRNGQIVKwYuFjFcUociIiIiIhIFsePw7ff2vf699eFMglOF14IF1xg33vhBdNyWcQXvCsO1KZIQokGJItbv345PxcdDRdfDO+9Z/r+zZsHgwdDrVoBC0+CS9AkDg4dOsS3337LPffcwyWXXEJsbCyNGzemUaNGLFAqVEREREQkoL7/3lSzZ3C54PrrnYtH5FSefNK+3rgRxo1zJhYJL0eOwN9/2/eUOJBQ4l1xsH497NrlTCziR9u3m4qCzp1h5crsj2nSBFq0yFzHxED37vDxx2aQyw8/wO23Q/XqgYlZgprjY83279/Ps88+y6effsoRr+EblmXhcrk4duxYtq+95ppr+Pa/26Dq1q3Lxo0b/R6viIiIiEgk8G5T1KkT1KnjSCgiedKli7mr1vNO2ueeM5Uy0dHOxSWhb9EiMyw+Q0wMnHuuc/GI5Ffz5lCqFBw9mrm3cCH07OlcTOIjiYkwcSJMmGCGU1mW2Y+PNz0ms3P99VC3rhmG3KsXVKgQuHglpDhacfD777/TsmVL3nzzTQ4fPoyV8cWdRw899BCWZWFZFlu2bGHu3Ll+ilREREREJHLs2QPTp9v3+vd3JhaRvHK5slYdrFsH48c7E4+ED+8mCOeco/mgElpiYqB1a/uemnuEsE2bYORIU0pSty7cdx/8+mtm0gBM4iAnQ4fC1KkwaJCSBpIrxxIHixcvpmvXriQlJdn2XS4XlStXzlMSITY2llatWrnX8bn9pRARERERkTz55htITc1cFy9ubkoTCXaXXgqxsfa9ESPsd4uL5JfmG0g40JyDEPfvv/DiiyYD1KABPPhg7n+IK1bAmjWBi0/CkiOJgyNHjtCrVy+Sk5MB05KoXbt2TJkyhUOHDrFz507AJBFOpd9/Qz0sy2L27Nn+C1pEREREJEJ4tynq3RvKlnUmFpH8yK7qYPVq08FBpCDS07Pema3EgYQi7zkHixbZbxKQILRhg8l+t2wJp50Gjz4Kixfn/poyZeC660z7onr1AhKmhC9HEgevvPIKO3bscCcGBg8ezPz58+nZsyclS5bM17m6dOnifrxp0yZ27Njh01hFRERERCLJunVZb2BTmyIJJb16wdln2/eGD1fVgRTM2rWwf799z/sCrEgo8K44OHrU3JQuQWzmTJMN957O7q18edN2aOpUM/V67Fjo00c91aTQHEkcfPDBB+6kQefOnXnjjTeIiipYKC1atCDaY9LVqlWrfBKjiIiIiEgkGjvWvq5cGbp1cyYWkYJwueCJJ+x7y5fDd985E4+ENu9qg9q1NSheQlPVqtCwoX1P7YqCgGXZZxN46tPH/KOWnUqV4JZbYMYM2LkTPvsMevQw/SVFfCTgiYO//vqLXbt2uWcYPPvss4U6X7Fixahdu7Z7vXHjxkKdT0REREQkUlkWfPmlfe+aa6BIEWfiESmovn2hWTP73vDhOV+bEcmJ5htIOPGultGAZIdYlukVNXQoNGqU8x9EjRpw/vmZ62rV4Pbb4YcfYMcO+Ogj6N4dihYNTNwScQKeOPCsCKhQoQLtfVDjV758effjgwcPFvp8IiIiIiKRKCEB1q+37w0Y4EwsIoURFZW16uCvv2D6dGfikdClxIGEEw1IdlB6Ovz2G9x/P9SvD23bwiuvwMaNEB+f8+tuuQUGD4aff4atW+G99+DiiyEmJmChS+QKeOJg165dgBl8XM9HQzqKe5ThnDhxwifnFBERERGJNN5DkZs0gdatnYlFpLCuusp8DXtS1YHkx759Zri2J803kFDm/fX7zz+wd68zsUSEtDRzwX/wYNPjrEMHeO012LLFftyECTn/4zRoELz5JnTsCB6t2kUCIeCJg7S0NPfjaB99we/3mFTkWX0gIiIiIiJ5k5IC48fb9/r3z7m1rkiwi46Gxx+37y1cCHPmOBOPhJ6FC+3r4sWhZUtHQhHxiRYtss7L9f46l0JKTTWthG6/HWrWhE6d4O23Ydu2nF+zZQssWRKwEEXyKuCJg6pVqwJgWRY7d+4s9PlSUlLYvHmze125cuVCn1NEREREJNLMmGHurvXUv78zsYj4ynXXZR0G+uyzqjqQvPFuU9S6tVqJS2grUgTOPde+pzkHPvLnn6atUPXqcMkl8MEH8F/XlRw1aWIy3EuWwDnnBCZOkXwIeOKgVq1a7sdJSUnu1kUF9dtvv9naE5155pmFOp+IiIiISCTyblPUoQM0aOBMLCK+EhMDjz1m3/vtN5g3z5FwJMRovoGEI8058JPVq+GTT07d+6l5c3jqKVi+HNasgREjTCmTSjwlCAU8cXD++edTrFgxXP/9hRg7dmyhzvf222+7H1etWpVmzZoV6nwiIiIiIpFm/36YOtW+p2oDCRcDBkDduva94cOdiUVCR2pq1hYumm8g4cD763jhQtOKX/IgOdl8c8hOz56mpCM7LVuaBMHq1SZh8PTTJoGgZIEEuYAnDkqUKMFFF12EZVlYlsVLL73E3gJOYpk6dSqTJ0/G5XLhcrm44oorfBusiIiIiEgEiI83Mw4yFC1qBsuKhIOiReGRR+x7P/0E8+c7E4+EhhUr4OhR+54SBxIOvCsODh/OOgRcPBw5At98Y34wqlLFzC/ITrly0LVr5rp1a3jpJfj3X9OK6PHHoWnTwMQs4iMBTxwAPP7fhCqXy8Xu3bvp1asXBw4cyNc5ZsyYQf//boOyLIuYmBgefvhhX4cqIiIiIhL2vNsU9egBFSo4E4uIP9x0E3h0zQVUdSC5825T1Lgx/DeyUSSk1agB9erZ9zTnwMvBgzB2LPTta5IFV18N335rKg7i43N+3V13wciRsHEjLFoEQ4dCo0aBi1vExxxJHJx//vnExcVh/TeRKiEhgebNm/PJJ59w5MiRHF+XlpbG77//zrXXXkuvXr04fPgwlmXhcrm45557qF+/foA+AxERERGR8LBpE/z6q31PbYok3BQrZq7feJo9O2srGpEMmm8g4cy7ekaJA0zfxs8/Ny2HqlY1PwxNmgTHj9uPmzQJTp7M/hyXXgr33w+6PilhwmVlXL0PsOTkZDp27Mhff/2Fy+VyJwBiYmJo0qQJK1euNAG6XDRr1gzLsti8eTPJyckA7uMty+Kiiy5izpw5REU5kgeRAEpKSqJOnToAJCYmUrt2bYcjEhEREQltzz0HTzyRua5QAbZvNxdaRcLJsWNm4PfOnZl7l18O33/vXEwSvBo2NDcNZ3jvPbj9dufiEfGlN96A++7LXJ9xBqxa5Vg4ztmzByZPNlUEc+fmPL/AU1SUyTqfe67fwxNxmmNX2kuWLMmMGTPo1KmTLQlw8uRJd9IATIJg1apVrFq1iqNHj7qrFDKOv+yyy5g0aZKSBiIiIiIi+WRZWdsUXXWVkgYSnkqUgIcesu9NmwZ//eVMPBK8duywJw1AFQcSXrwrDlavNjfcR4wff4QuXaB6dfjf/2DWrNyTBjEx0K0bfPSR+QahpIFECEevtlepUoW5c+fywgsvUMGjiWrGsOOMD889MMmEcuXK8cILLzB16lTKli3rSPwiIiIiIqHszz9h7Vr73oABzsQiEgi33w6VK9v3NOtAvHm3bSlTBs4805lYRPyhZcusNwksWuRIKM44csRUGKSl5XxM0aKmLG30aFOqNnMm3HKLmXkgEiEcv03f5XLx8MMPk5iYyHvvvUfPnj2pUKEClmVl+ShevDhdunTh1VdfZdOmTTz88MPuZIKIiIiIiOSPd7VBgwa6q1bCW6lS8MAD9r3Jk2HZMkfCkSDlPd+gXTuIjnYmFhF/KFoUYmPte2E352DzZjPMODtdu0Lp0ln3ixWDK66AL7+EXbtML7sbboCKFf0ZqUjQinE6gAwlSpTgtttu47bbbgNg586d7N27lwMHDlCyZEkqV65M9erViYkJmpBFRERERELWyZPw1Vf2vf79QfflSLi76y54+WV7W44RI+Cbb5yLSYKLd+LAu62LSDho187+tZ6Q4FwsPvPvvzBhgplZsHgxjB9vejB6K17cDEH+6isoWdJUFvTrB5ddZkqMRARwIHGwY8cOFnnUP7Vo0YL62Uwbr1atGtWqVQtgZCIiIiIikWPOHNi9276nNkUSCcqUgSFDYNiwzL34eNPj+4wznItLgsOJE6aNmydVYkk48k6IJSRAerqZ/RtS1qzJTBYsXWp/Lj4++8QBwL33QlwcdO9ukgcikkXAEwcTJ05k8ODB7vXy5csDHYKIiIiISMTzblPUti2cdpozsYgE2uDB8OqrcOiQWVsWPPec6U4hkW3JEpM8yOByme+PIuGmXTv7+uBBM/co6BOolgUrV5qkQHy8eZyTadNMu6LsEgP6iy1ySgHPIx44cMA9s6B69eo0a9Ys0CGIiIiIiES0Q4dMX3dP/fs7EoqII8qXNzebevrqK1i3zpFwJIh4tyk680zz9SISbmrXNh+egnbOgWWZrN4TT5jMxllnwTPP5J40ANOSaM2awMQoEoYCnjioVKkSYIYi16pVK9BvLyIiIiIS8SZOhOPHM9cxMXDNNc7FI+KE++6zz8ZMT4fnn3csHAkSmm8gkcT76zsoEwdTpkDjxtCqlSkNW7s29+OrVIHbbjM9GXfsMK8TkQIJeOKgZs2a7sdHjx4N9NuLiIiIiEQ87zZFl14KlSs7E4uIUypWhLvvtu998QVs2OBMPOI8y8p64VTzDSScebcrCsoByeXKnfobc/XqZvL9Tz/B9u3w/vvQpQsUKRKYGEXCVMATB7GxsURFRWFZFps3byYlJSXQIYiIiIiIRKykJPN7tSe1KZJIdf/99tbXaWnw4ovOxSPO2rIFtm2z7ylxIOHMu+Jg5Uoz6yCg0tLgl19g377sn7/gAlNF4K12bVM69uuvsHUrvP02dOoE0dH+jFYkojhScdCpUycAjh07xrRp0wIdgoiIiIhIxBo3ztxVm6FsWejZ07l4RJxUpQrcfrt977PPzAVkiTzebYoqVdLQeAlv55xjvynfsuCPPwLwxqmpMHcu3HEH1KwJF15o+ihmJzoa+vY1jxs0gAcfNKURmzfDa69Bhw4QFfDLmyIRwZG/WUOGDHE/fvTRR0lOTnYiDBERERGRiGJZWdsUxcVBiRLOxCMSDB580MzPzHDyJLz0knPxiHOym2/gcjkTi0ggFC+edQSA3+YcpKTAzJnwv/+Z1kJdupiWQrt2mefj43N+7f33w59/wvr18Mor0LatkgUiAeDI37LLL7+cu+66C8uyWLduHZdffjm7Mr5RiIiIiIiIXyxbBitW2PcGDHAmFpFgUaOGuY7l6eOPTecLiSyabyCRyK9zDk6cgO+/hxtugGrVzFCljz+GvXuzHjt3Luzfn/15mjQxGQ5l8kQCyrH03FtvvcUjjzyCy+Xil19+oVmzZjz99NOsWbPGqZBERERERMKad7VBnTrQsaMzsYgEk6FDoWjRzHVKirmpVSLH0aOwdKl9T4kDiQTecw4SEuwtDfPt2DGYNAmuv970g+vZEz7/HA4cyP11zZpBYmIh3lhEfM1lWYX6dlAgnTt3dj9euXIlu3fvNsH8lzksU6YM9erVo2zZshTJxwR0l8vF3LlzfRusBJWkpCTq1KkDQGJiIrVr13Y4IhEREZHQkJZmEgXbt2fuPfIIvPCCczGJBJM77jBdMzIULw4bN5qOGhL+5s2Diy7KXEdHw6FD9uHZIuFo82aoX9++t3atuck/3776ypRwHT2at+NjY03PxH79NFBEJAjFOPGm8+bNcycJIDNhkJHDOHToEMuXL7cdcyqWZeXreBERERGRSPLjj/akAahNkYinRx4xHTRSU836+HEYOVKVB5HCe75By5ZKGkhkqFvXtGzz/BlhwYICJg5OP/3USYN27UyyoG9fM+xYRIJWUE0Scblctg8REREREfEN7zZFrVqZrgAiYtSrB4MG2ffefRf+K5CXMKf5BhKpXK6s7YpyHJC8fz+MGQNJSdk/f845WZMBLhd06ACvvw5btpiTP/CAkgYiIcCxxIFlWT79EBERERGR7B09ChMn2vf693cmFpFg9uijpkVNhuRkeO015+KRwLCsrBUHShxIJMl1QPKePfDJJ2awcbVqJsM6fnz2J3K5TNuhqCjT++udd8yk+V9/hXvvNT0TRSRkODLjQKSgNONAREREJP/GjrUnCqKizO/x6t0uktWgQeaG2gxlysCmTVCxomMhiZ+tXQtNm9r3Nm82LVxEIsH8+XDBBZnr6q6dbHxtMsW/j4effjKDkjy1b58125Zhxw7zg0bVqv4LWEQCIqhaFYmIiIiIiO95tynq2lVJA5GcPPaYuWk2w+HD8OabzsUj/ud9/bNmTd0YLZElNhbqRG/jLt7mJzqRZNWk+H23ww8/ZE0agGk3lFO7ourVlTQQCRNKHIiIiIiIhLHt22HOHPue2hSJ5Oz00+Gaa+x7r78OBw86Eo4EQHbzDTR2USLCli3w2muUuKQDW9Jq8TaD6cTPRJOe82uKFYNeveDIkcDFKSKOUOJARERERCSMff01pHv8/l+qFFxxhWPhiISExx+3rw8ehLffdiYW8T/NN5CINHq0mQp///3w22+5H1uihJld8NVXZmL8lClZ+3uJSNhR4kBEREREJIx5tynq188kD0QkZ2eeCXFx9r1Ro0zbIgkvBw7AypX2PSUOJCJ4DjXIxlFKYV1zDcTHm2RBfLwpxypTJkABiojTlDgQEREREQlTK1fCkiX2PbUpEsmbJ56wr/ftg/fecyYW8Z+EBPu6WDE45xxnYhHxqVWr4NlnYc2a7J9v3BjOPtu2dZCyfEF/ejOZyuxmw4ivdMeBSART4kBEREREJEx9+aV9XaMGdO7sTCwioebss6F3b/veq6/C0aPOxCP+4T3f4NxzoWhRZ2IRKRTLgr//hiefhDPOMKVTTz1lehbmJC4OKlSAG2/Emvo9zavsYiBf8B29OU6JLH8/RCSyxDjxps8++6zfzj1s2DC/nVtEREREJFSkp8PYsfa9666D6Ghn4hEJRU8+aVp5Z9i9Gz78EIYMcS4m8S3NN5CQZlnw558wYYJpJfTvv1mPiY+Hp5/O/vX33QcPPwxFiuACYs+DJI/veQsWqFJRJJK5LMuyAv2mUVFRuFwuv5w7LS3NL+eV4JCUlESdOnUASExMpHbt2g5HJCIiIhKc5s2Diy6y7y1dmqUrgYicwmWXwYwZmevq1WHDBjMrVEJbWhqULw9HjmTuTZwIffo4FpLIqaWnw6JFJiEQHw+bN5/6NatWmSqEU3jxRXj00cx1q1YmLyEikSnkWhVZlpXlI2NfREREREQM76HIZ52lpIFIQTz5pH29Ywd88okzsYhvrVxpTxoAtG/vTCwiuUpLg19/hXvvhXr1zBfqyJGnThrUqgX33APFi+fpbby//v/+W+3ZRCKZI62KoHAX+j2rFTyTByIiIiIiAseOmZsQPanVgEjBtG8PXbrADz9k7r30Evzvf2aQroQu7zZFDRuaihKRoPPpp3DrrXk7tl49M7sgLg7atIGovN8zfO65pqVhRjOPtDRTcdCxYwFiFpGQ50ji4Keffsr3a5KTk9m9ezd//PEHkyZNYtu2bbhcLho3bsyrr75KuXLl/BCpiIiIiEjomToVDh3KXLtcZr6BiBTMsGH2xEFSEnz2Gdx2m2MhiQ9ovoGEjB49zD/mOd0426gRXHkl9OsHsbHm2AIoVQpatIAlSzL3FixQ4kAkUjky46CwUlNTefPNN3n88cdJSUnhjDPOYM6cOdSoUcPp0MTPNONARERE5NR69oTvv89cX3yx/aKniORfp07w88+Z63r1YN06KFLEsZCkkBo3hvXrM9fvvAN33ulcPBKhTpww/0hPmAC3326qBLLTsaNpV5ShadPMyoIWLQqcLPB2553w3nuZ6yuugEmTfHJqEQkxITfjACAmJob777+fCRMmEBUVxerVq+nRowcpKSlOhyYiIiIi4qjdu2HmTPue2hSJFN6wYfb15s1ZZ4lI6Ni1y540AFUcSAAdOwZTpsCAAVC1qqkoGD0avv4659fExZmBRc88YwZ0rF4Nw4ebAUY+ShpA1jkHCxbkXOggIuEtJBMHGS677DLuuOMOLMti6dKlvPjii06HJCIiIiLiqPHjITU1c12ihOlcICKFc9FFWS8sP/+8/e+bhI4FC+zr0qWheXNnYpEIcfSoGUB0zTUmWXDFFfDll/begvHxOV+lv/tuWLbMZDGbNfNbmO3a2dc7d556BrOIhKeQThwA3HfffYAZkvzGG29w8uRJZwMSEREREXGQ9x3QV1wBZco4EopIWHG54Mkn7Xvr18NXXzkTjxSO93yDtm0hxpEpkBLWDh82VQRxcVCliplDMH48HDmS/fGJifDHH9k/l48hx4XRuDFUrmzf8060iUhkCPnEQcOGDd197g8cOMBvv/3mcEQiIiIiIs745x9YtMi+pzZFIr7TrRu0bm3fe+45SEtzJh4pOO/EgXd7FpECO3DAZPF79zbJgmuvNfMLjh3L/XXnnw+jRkH9+oGIMkcuV9aqAyUORCJTyCcOAKpXr+5+vHLlSgcjERERERFxzpdf2tdVqkDXrs7EIhKOsqs6WLvWdBeR0JGSAosX2/c030B85vvvYeBA+O47M/g4Jy4XXHghvPUWJCXB/PkwZIhpY+Qw78RBQoIzcYiIs8IicXD8+HH344MHDzoYiYiIiIiIMywra+Lg2mvVekPE13r0gJYt7XvDh0N6uiPhSAEsXQoelxGArBdKRQqsZ08oUiT756Kj4eKL4b33YNs2mDfPzC6oVSugIZ6KdwXOkiWnLpgQkfAT8omD5ORk1q1bh+u/CfLlypVzOCIRERERkcD7/XfYuNG+N2CAM7GIhLPsqg5WroTJkx0JRwrAu01Rs2ZQoYIzsUiI2b4d3nkHOneG2bOzP6ZcOXu5X0wMdO8OH38MO3bADz/A7beDR/eMYNO6tX2kQmoq/PWXc/GIiDNC/v6j999/nxP/lX65XC7q1KnjcEQiIiIiIoHnPRS5aVOIjXUmFpFwd8UV0Lw5rFiRuTd8OPTpYxILEtw030DyJTERJk40Pcl++82U+AE0bJhzP8BrrzVX3vv1g169Qi4zVaaM+R63bFnm3oIFZgyDiESOkK44+Prrr3nsscfc1QbR0dF06tTJ2aBERERERALsxAn45hv7Xv/+uoAp4i9RUfDEE/a9pUtNa3MJft6DXjXfQLLYuBFefdX0sKpbF+67z8wgyEgaAEyaBCdPZv/66683Mw4GDQq5pEEGzTkQEUcqDrZs2VKg1x07doydO3eyZMkSvvnmGxISErD++6btcrm46qqrKFu2rC9DFREREREJetOnw/799r3rr3cmFpFIERcHp59uhiNnePZZMwNBSbvglZho5tB6UuJAAFi3DiZMMJUFf/556uP37YOff4YuXfwfmwPat4cPP8xcL1hg8ib6/iYSORxJHNSvX99dJVAYlmXhcrmwLIvKlSvz4osv+iA6EREREZHQ4t2mqGNHqF/fkVBEIkZ0tKk68JwlsngxzJpl2plLcPJuU1SxIjRp4kwsEgQ2b4YxY0yywLMvT27KlDHth/r1C+usk3fFwbZtJummDuEikcPRVkWWZRX4A3AnDWrVqsXMmTOpXbu2k5+OiIiIiEjA7duXtT1K//7OxCISaa65Bho1su89+6y9m4kEF+/EQbt29iGwEmH+/huGDTt10qB8edN2aOpU2L0bvvzSDDUpWTIgYTqhSZOsXZa823yJSHgL2X8eLcuiSpUqDB06lFWrVnHOOec4HZKIiIiISMB9+629xXKxYnDllc7FIxJJYmLg8cftewsWwI8/OhOPnJrmG0Qgy4K0tOyf69oVSpfO/rlKleCWW2DmTNi5Ez77zPQiK1bMb6EGk6gozTkQiXSOtCoaNGhQvl/jcrkoXrw4ZcuWpV69erRq1YrY2FhiYhz5FEREREREgoJ3m6KePc2NkSISGP37myqDTZsy94YPh4svdiwkyUFyMixZYt9T4iBMWRYsWpQ5s+CFF+Dqq7MeV7y4+Yfzq6/Mulo16NvXDDHp2NFkByNYu3YwY0bmWhUHIpHFke+Ao0ePduJtRURERETCyoYN8Ntv9j21KRIJrCJF4NFH4bbbMvd+/hl++cVcd5TgsXgxpKZmrqOjoXVr5+IRH0tPN1e24+NNwiAxMfO5+PjsEwdgWhBVqWKSBeedZ74wBDADkj399RecOBExRRciES9kWxWJiIiIiES6sWPt64oV4dJLnYlFJJINGgTeI/eGD3cmFsmZ93yDFi1y7lIjISItzWTqBg82fwk7dIDXX7cnDQCmTzclJ9np1g3eeAMuuEBJAy9t2oDLlblOSclatSMi4UuJAxERERGREGRZWdsUXX01FC3qTDwikaxYMXj4YfveDz+orUew0XyDMJGaav6C3X471KwJnTrB22/D9u05vyY5WcNHCqBcOWjWzL6n72sikUOJAxERERGREPTHH7BunX1vwABnYhERM0O1Rg37nqoOgodlZa04UOIgxKxYYf6iVa8Ol1wCH3wAu3bl/prTTzcTzJcsgcsvD0ycYUYDkkUilyOJg4YNG9KwYUMaNWrEgkKmKn///Xfb+UREREREIoF3tUGjRll/uReRwCleHB56yL43Y4ZJ8onz/v0X9uyx7ylxEGL27oVPPjH/zU3z5vD00ybRsHo1jBgBLVvae+5InnnPOVDFgUjkcGQ48qZNmwBwuVwcO3asUOc6duyY7XwiIiIiIuHu5En4+mv7Xv/+uiYi4rTbboMXX7TfBD1iBEyZ4lxMYnhXG1SvDvXqOROL5CJjDkHJklmf69ABqlbNvsrgnHPMcON+/UyVgfiM900JiYmwdSvUquVMPCISOI61KtJFfhERERGRgpk1K+uds/37OxOLiGQqWRIeeMC+9913sHSpI+GIh+zmG+iyRJA4cgTGj4crr4QqVWDs2OyPi46Gvn0z161bw0svmXKSv/6Cxx5T0sAPzjjDzDrwpHZFIpFBMw5EREREREKMd5ui9u2hcWNnYhERuzvvhEqV7HsjRjgTi2TSfIMgc/AgfPkl9OljkgXXXAPx8abiID4+59fddBOMHAmbNsGiRTB0qOnVJ34TFQVt29r3lDgQiQwhnzg4efKk+3GRIkUcjERERERExP8OHjR3MHtStYFI8ChdGoYMse9NmGDarYszDh7M+v9fiQMH7NsHn30GPXqYlkMDBsDkyXD8uP24uXNznmPQujXcf7/6TAWYd7sizTkQiQwhnzjYvn27+3GZMmUcjERERERExP8mTLBfYylSBK6+2rl4RCSru++G8uXte88950goAixcCJaVuS5aFFq1ci6eiLJ7N3z0EXTrBtWqwY03wrRpkJKS82ssy/yhSdDwHpC8eHHuf4QiEh5CPnHw/fffA2ZmQt26dR2ORkRERETEv7zbFF12Wda2KCLirHLl4N577Xvjx8OaNc7EE+m8746OjYVixZyJJWIsXAgXX2ymUN96K8yeDampOR8fE2OSCx99BDt2mH/cJGh4tyo6cQL+/tuZWEQkcGL8deItW7bk6bidO3fm+VgAy7JITk5m48aNxMfHM2nSJPdzrXTLgIiIiIiEsS1bYN48+57aFIkEp3vvhVGj4PBhs7YseP55GDPG2bgikeYbOKBoUfjxx1Mf07Ur9OsHvXpBxYqBiU3yrUIFaNrUnvxcsMB0jhKR8OW3xEH9+vVxuVw5Pm/9VyfY34e/6Vx11VU+O5eIiIiISLAZN86+LlfOtIoWkeBToQIMHmySBRnGjYOnntIs10BKS8s6yFWJAx/ZtAnKlMm+7K1lS2jYEDZssO8XLw7du0NcnPkHrFy5QEQqPtCunT1xkJAA99zjXDwi4n9+b1VkWVaWj1M9n9cPwJ2cuOSSS7jkkkv8/emIiIiIiDjCsrK2KbrySnMNRkSC05AhUKpU5jotDV54wbl4ItGqVXDokH3Pu1+75MO//8JLL5lbzRs0MMOOs+NymeQAQMmS5h+s8ePNzINJk+D665U0CDHef280IFkk/IX0jAPLsoiJieGmm24iPj7e6XBERERERPxm6VJzAczTgAGOhCIieVS5Mtx5p33v88/NjdoSGN4XN+vXhxo1HAkldK1ZAyNGmCqC006DRx4x03EBcrsWc/PNMGGCSRZ88w1cdRWULh2QkMX32rWzrzdtMuMoRCR8+a1V0aBBg3J87vPPP7dVCtTIx7/aUVFRlCpViooVK3LmmWfSqVMnqlSpUuh4RURERESCmXe1Qb160KGDM7GISN498AC8/TYcO2bWqanmhu333nM2rkih+QYFYFmwYoVJCkyYACtX5nxsQgIkJUHt2lmfa9LEfEhYOPNM05kqY24LmD/+K65wLCQR8TO/JQ5Gjx6d43Off/65+/HQoUPp3Lmzv8IQEREREQl5qalZ5xtcfz1EhXT9sEhkqFYNbr0V3ngjc+/TT+Hxx7O/1iq+pcRBHlmWKW2Ljzcf//yTt9dVrGgqEvTFHPaio6FNG5g7N3NPiQOR8ObYrxresw5ERERERCR7c+fCzp32PbUpEgkdQ4dCsWKZ65QUePll5+KJFLt3w7p19j3NN8jGvHnQuDG0amWmeZ8qaVC1Ktx+O8yZY3rVdOkSkDDFed7tijTnQCS8+a3iIDee1QhnnnmmEyGIiIiIiIQM7zZF554LTZs6E4uI5F/Nmqbd+7vvZu59+CE8+qj67ftTQoJ9XbIktGjhTCxBrXZt2LAh92Nq1IB+/czA4w4dzO3nEnG8E29//AEnT0KRIs7EIyL+5UjiILf5ByIiIiIikunIEZg0yb7Xv78zsYhIwT38MHz0kbnIBnDiBLz6Kowc6Wxc4cy7TVHbthDjyFUQh6Wlwfz5ZjJ0vXpZn2/c2Aw+XrrUvl+njkkU9OtnrhirP17Ea9vWvj52DJYvN8UqIhJ+9F1fRERERCSITZoEycmZ6+houPZa5+IRkYKpWxduuMG+9/77sGuXI+FEhIieb5CaCj/8YFoK1awJnTqZ4Ro56dfP/LdBA3joIVi4EDZvhlGj4PzzlTQQACpXhtNOs++pXZFI+NJ3fhERERGRIObdpqhbN9NeWkRCz6OP2ju8JCeb67LieydPmjYqnsJ+vkFKCsycCbfcAtWrwyWXwAcfZGan4uNzfu1NN8Fff8H69WYAR5s24HIFJm4JKd5zDrxbgolI+FDiQEREREQkSG3bZgYje1KbIpHQ1aBB1sHm77wDe/c6E084+/tv00bFk/cFz7Bw/DhMnQqDBkG1anDppfDJJ9l/Ua1aBatXZ3+emjXhnHOULJBT8k7AqeJAJHwFTXe/NWvW8Pvvv7NkyRL27NnDgQMHOHHiRL7O4XK5mOv9m5WIiIiISIj66itIT89cly4NvXs7F4+IFN5jj8GYMZl/t48cgddfh+HDHQ0r7Hi3KWraFCpVciYWn0tOhlmzTAXB1Klw+HDeXnf22bBnj39jk7DnnYBbvx5274YqVZyJR0T8x/HEwZQpU3jxxRdZtGhRoc5jWRYuZcZFREREJIx4tynq1w9KlnQmFhHxjdNOM3NKxo7N3HvzTXjgAShf3rGwwk7YzjeYNQv69rUPv8nNuedmDjhu3Ni/sUlEOOssKFUKjh7N3EtIgJ49nYtJRPzDsVZFaWlp3HLLLfTt25dFixZhWZb7Ocuy3B/ZOdXzIiIiIiKhbvly02rDk3eLExEJTY8/bu8Ic+iQSR6I73gnDsJmvsHZZ2ftweStXTt49VXYuNEMenj4YSUNxGdiYqB1a/ue5hyIhCfHEgf33Xcfn376abYJA5fL5f7wTBLk9ryIiIiISDj58kv7ulYt6NTJkVBExMfOOAOuvNK+9/rrJoEghZeUBImJ9r2QqTjYvx8+/xxWrMj++erVoUMH+57LBRdcAG+8YT7xBQtMCUv9+n4PVyKTd7sizTkQCU+OJA5+/vln3nnnHffF/6JFi/LEE0+wZs0ajh8/bksE/PDDDxw/fpxt27YxZ84cHn30UapWreo+plmzZixZsoT09HTS0tKc+HRERERERHwqLc3exgTguusgOtqZeETE9554wr7evx/efdeZWMKN90XM8uXNjIOgtWcPfPyxGWxctSrccIMZcJyTuDiIioKLLjLTtbduhV9+gXvugdq1Axa2RC7vCp5FiyA11ZlYRMR/XJYDt+t3796d2bNnAxATE8P06dPp0qWL+/moqCj3vII5c+bQuXNn2+tTUlJ44oknGDlyJAClS5fm+++/54ILLgjQZyBOSUpKok6dOgAkJiZSWz8UiYiISBj68Ue4+GL73rJlpq+wiISPvn1h0qTMdeXKsGmT6R8uBTdkiKngyNC9O8yY4Vg42du50/zhx8fDvHkmY+ypTh3YvNne0yrDwYNw4oRJMog4YNcuqFbNvrd0qemkJSLhI+AVB0ePHmXu3LnuaoNbb73VljTIi6JFi/Lyyy/z7rvvYlkWhw8fpk+fPuzcudNPUYuIiIiIBI73UOSzz1bSQCQceVcd7NkD77/vTCzhJGgHI2/dCm+9ZfrO1agBd9wBc+dmTRqAaTn0xx/Zn6dcOSUNxFFVq0LDhvY9tSsSCT8BTxwkJCSQlpbmbjV0++23F/hct912G1f+1xhy//79PPnkkz6JUURERETEKcnJMGGCfa9/f2diERH/atUKLr/cvvfKK6eefSs5O3YMliyx7zmaONiyBV57Dc4/37QRuuce+PlnyK35Q7Fi0Lu3+tNJUPOec6ABySLhJ+CJg82bN7sflytXjubNm+d6/IkTJ3J9/vHHHwfMYOWxY8dy/PjxwgcpIiIiIuKQ776Dw4cz11FRZr6BiIQn7/vfdu6Ejz5yJpZw8OefcPJk5joqCtq0cSiY6dOhXj24//6sZRDeSpQwswu++gp274bJkyE2NiBhihSE95wDVRyIhJ+AJw727dsHgMvlom7dutkeExMT4358qkRAixYtqPZfY7Xjx4/z+6n+MRYRERERCWLebYouvhhq1nQmFhHxv7ZtoWtX+95LL4HuiSsY70sCZ50FZco4EwsdOkDRojk/X7o0XHONmXOwezd8+61ZOxawSN55Vxz88w/s3etMLCLiHwFPHKR6jFkvlcPEpzJlyrhbGe3ateuU5/QckLt27dpCRigiIiIi4oxdu2DWLPue2hSJhL9hw+zrbdtg9GhnYgl1AZtvYFmwciU88wzMn5/9MWXLZs0KlS0LAwbAlCkmWfDVV9CvnyZiS8g5+2xTKONp4UJnYhER/wh44qBcuXLux0eOHMn2mPLly7sfe7Y2ykl6err78YEDBwocm4iIiIiIk77+2j4js2RJ6NvXuXhEJDDOPx8uusi+98ILkJLiTDyhyrKytkvxaeLAsmDpUjPVulkzaN4cnn469yxPXBxUqAA33gjTppkM8Zgx0KsXFC/uw+BEAqtIETj3XPue5hyIhJeAJw4y2hNZluVuW+StadOm7scLT5GuTEtLY926dbhcLgCKFSvmo0hFRERERALLu01Rnz6mk4WIhD/vqoPERHN9WfJuwwZzXd5ToRMHlgWLF8Mjj8Bpp8E558Bzz8GaNZnHTJ5sH6zg6ZprzOCKTz+Fyy4zg49FwoR3uyLNORAJLwFPHJx55pnux9u2bcu26qBly5aASS7Mnz+fbdu25Xi+SZMmceTIEXdroxo1avg2YBERERGRAFizxlyb8qQ2RSKR48ILTUt8T88/n/P1aMnKu01R1arQoEEBTpSebq6APvCAOUHr1mbwxPr12R+/bx/Mm5f9c8WKmVuzRcKQ94DkhQvtlZMiEtoCnjioX78+1atXd68Xe/92BFxxxRWAGaCcmprKHXfcYZuNkGHDhg3ce++97moDgPPPP9/3QYuIiIiI+NmXX9rX1apBly7OxCIigedyZa062LgRxo1zJp5QlN18A4/LBblLS4Nff4V774W6dc2LR42CU7VPrlXLvKZOnQLFLBLKvCsODh+G1audiUVEfC/giQOAzp07ux9Pnz49y/OtW7fmrLPOcq+///57YmNjeeutt5g9ezbff/89jzzyCK1atWLHjh1YloXL5aJjx47uVkgiIiIiIqEiPT1r4uDaayEmxpl4RMQZXbpA27b2veee0x28eVWo+QYzZ0LHjvDmm7B1a+7H1qtnqhEWLIAtW+D118Gj5bJIpKhRw/x18KR2RSLhw2Vl9PgJoIkTJxIXF4fL5aJu3bps2LDBVjUA8Ouvv9KpUycAdxsi72MyEgaWZVG0aFF++eUX2rRpE5DPQZyRlJREnf/u5EhMTKR27doORyQiIiJSeL/+aq5XefrzT2jVypl4RMQ506ZBjx72vbFj4brrnIknVBw6ZGYQp6dn7s2fbwZP58nx46a30eHD2T/fuLEZdBwXZ74557mUQSS8XXMNjB+fub7pJvjkE+fiERHfcaTi4PLLL6dVq1Y0a9aM0qVL87t3PSFwwQUX8O677wImYZCRIMj4yNi3LIsiRYrw4YcfKmkgIiIiIiHJeyhys2Zm/qaIRJ7LLsuaNBwxwn5BXLJatMj+/6hIEYiN/W9x4oTJyNx4I3z3XfYnKF4ceva07zVtCk88AUuXwj//wAsvmJMqaSDi5j3nQBUHIuHDkeLnYsWKZTvbwNttt91GkyZNeOCBB1i6dKntuYzkQfv27Xn11Vdp7/2dSkREREQkBBw/Dt98Y9/r31/XpUQilcsFTz4Jffpk7q1eDRMmwJVXOhdXsPO+H7F9y2MUnzUb4uNNsuDQIfPEkSPQq1f2J4mLg+XLMysLmjXzb9AiYcB7zsHq1XDgAJQv70Q0IuJLjrQqKoh169aRkJDAzp07sSyL6tWrc95559GoUSOnQ5MAUqsiERERCTcTJpjrU542bzazOUUkMqWnm6qjZcsy91q0gCVLIMqRvgHB79JL4ZeZR7mUGcQRT58i31Ps5NGsB5YsCbt2QalSWZ+zLGVtRfIpJQXKljWFPRlmzoRu3ZyLSUR8I2TGrZ122mmcdtppTochIiIiIuJT3m2KOnVS0kAk0kVFmQ45V12VubdsGUydCr17OxdXUDp8mPTvvuf2HyYwgemU5JjZP5nD8cnJ5qpmv35Zn1PSQCTfihY17dU8WxQlJChxIBIOdK+CiIiIiIhD9u6F6dPte/37OxOLiASXfv2ydsp59llzU3zEO3TIZF1794YqVYjqfx29UydkJg1ycv758NprcN55gYlTJEJozoFIeAp44uCrr76iYsWK7o9Zs2YFOgQRERERkaDwzTdw0uOu2OLFs7YtEpHIFBUFjz9u3/vrL5gxw5l4gsqSJTBwoJld4NkfxVtUlCnjeustSEqC+fPhvvugRo1ARSoSEbwTBwkJGuguEg4CnjjYvHkzBw4c4MCBA6SmptKlS5dAhyAiIiIiEhS82xT16gXlyjkTi4gEn6uvBu+Ovao6ADp0gKpVs30qlWiWV+sC778P27bBTz/B3XdDrVoBDlIkcngPSD54ENaudSYWEfGdgCcOihcvDoDL5aJevXpER0cHOgQREREREcetX5+1lF9tikTEU3R01qqDhQvhhx+ciScgtm2Dd96Biy6CTz7J/pjoaOjb171MoQjTuZSb+ITq7GDe43PgttugWrUABS0S2WrXNh+e1K5IJPQFPHFQw6MksFixYoF+exERERGRoPDll/Z1pUrQvbszsYhI8LruOmjQwL4XdlUHiYnw+utwwQXm6uPdd8O8eaafW06uvhp69uTwO59TjZ1cznRGcxN7qZylbYqI+J931UFCgjNxiIjvBDxx0LhxYwAsy2Lr1q2BfnsREREREcdZVtY2RddcA0WKOBOPiASvIkXgscfse/Pnw88/OxOPz2zcCK++aq421q0LQ4aYT8wzIzJ3rpkin51OneC77/il3kAOUMG9XaIEnH22f0MXkaw0IFkk/AQ8cRAbG0uD/26X2LVrFytWrAh0CCIiIiIijlq40LQq8jRggDOxiEjwGzjQXFv3NHy4M7EUyrp18MILEBsLDRvCQw+Zb4g5SUszA5Bz8fvv9nWbNkrCijjBu+Jg5Uo4dMiZWETENwKeOAC44YYb3I9feOEFJ0IQEREREXGMd7XBaaeZi10iItkpWhQeecS+9+OP8NtvzsSTL9u2mSzH2WdDkyamfOKvv3J/TZkycP31MGmSKcfKhXfi4LzzChmviBRIq1b2pJ1lwaJFzsUjIoXnSOLgoYce4owzzsCyLL7++mvefvttJ8IQEREREQm4lBQYP96+178/uFzOxCMioeHGG6FmTfteSFQd7NwJw4bBsmW5H1e+PNxwA0ydCrt3m0EwV1xheg/lIDU164VJzTcQcUbx4nDOOfY9zTkQCW2OJA6KFy/OjBkzaNKkCZZlce+999K/f382bNjgRDgiIiIiIgEzc2bWlt39+zsTi4iEjuLFYehQ+96sWUFyR69lmaxodlq2NG2JslO5Mvzvf+Yb486dMHo09OgBxYrl6W2XLYPkZPueEgciztGcA5Hw4rIsz8lDgTFmzBgADh48yPPPP8/OnTtx/XeLVevWrWnTpg0NGjSgbNmyFMlnc8KBAwf6PF4JHklJSdSpUweAxMREateu7XBEIiIiIvlz5ZUQH5+5Pv98Mw9URORUkpPNNfidOzP3evQwN+kHXEYfkvh483H33fDAA9kf+/DD8PLL5nG1atC3L8TFQceOEBNT4BDefhsGD85cN2kCa9cW+HQiUkjjx9u7i1WsCHv2qKpSJFQ5kjiIiopyJwoyeIbh/Vx+pKWlFfi1EvyUOBAREZFQduAAVK8OJ05k7r33Htx+u2MhiUiIefVVM1PY059/mv7ifpeeboYKxMfDxImQmJj5XLt2Od9evHSpqSaIizNDCKKjfRLOddfBV19lrm+4wbyNiDhj82aoX9++t3atSeqJSOhxpFVRBu9kQcZHxnN5/fA+l4iIiIhIMIqPtycNihaFq65yLh4RCT233246/HgaMcKPb5iWBvPmmYqC2rXhggvgjTfsSQMwzcy99zK0bGlec8EFPksaQNbByGpTJOKsunWhRg37ntoViYQuxxIHnhf8c0oG5PdcIiIiIiLB7Isv7OvLLzdl/CIieVW6NNx/v31v0qRTzx7Ol5MnYc4ck6WoWRMuugjeeQe2b8/9dTNn+jCI3G3bZu5u9nTeeQF7exHJhstlio88aUCySOgqeDPBQhit2kERERERiTCbN8Mvv9j3NBRZRArirrvglVdg//7MveeeM/3FC2X9enj+eZg8Gfbty9trmjY1LYji4qBFi0IGkHfedzGXLQvNmgXs7UUkB+3bm2RmBlUciIQuRxIHgwYNcuJtRUREREQcM3asfV2+vKk4EBHJr7Jl4b774KmnMve+/RaefhrOOKOQJ//001Mfc9ZZmckCh67WZ9emKMrRZswiAlkrDpYvhyNHTLWUiIQW/bMqIiIiIuJnlpW1TdFVV0GxYs7EIyKh7557TAIhg2WZqoNTSk42k9qz06iRmUeQnXPOMW+wZo3pizRsmKO3+Gu+gUhwio2FGI/blNPT4Y8/nItHRApOiQMRERERET/76y9zrc3TgAHOxCIi4aF8eZM88PTVV7BuXTYHHz5s+hhdeSVUqQKjRuV84ri4zMdt2sDLL5sWRn/9BY89Bqef7ovwC+X4cROOJ803EAkOJUvC2Wfb9zTnQCQ0KXEgIiIiIuJn3tUG9evD+ec7EoqIhJH77rO3/0hPNyMKADh4EL78Eq64wiQLrrkG4uNNxUF8fM4nveYak1jYtAkWLoSHHoKGDf33SRTAX39BSkrm2uWCtm2di0dE7LwrgDTnQCQ0KXEgIiIiIuJHqanmLmBP/fubC10iIoVRqZIZlJyhAvuI+nw0yZ17mGTBgAEwZQqcOGF/4erVsGpV9idt1AiGDIF69fwXeCF5tyk66yx72yYRcZZ34iAhwbRTE5HQ4shw5Jzs3r2bX3/9ldWrV7Nv3z4OHjxIeno6Q4cOpWnTpk6HJyIiIiKSb3PmwK5d9j21KRIRX3lg4G4Oj5pMr5PxdOZHilip8NMpXhQdbZqOOzijoDA030AkuHkPSN69GzZsMHlJEQkdQZE4mDJlCiNHjuS3337L9vn+/ftnmzh45JFHWLRoEQD16tVj9OjRfo1TRERERCS/vNsUtWkDTZo4E4uIhJGVK2HwYKr8/DPvpKef+viYGOjSxcww6N0bKlf2f4x+YFlZ255ovoFIcGnQAKpWtd84sWCBEgciocbRxMHevXu54YYbmD59OgDWf3VLLpfL9jgn7dq14+WXX3Yf9+CDD3LmmWf6OWoRERERkbw5fBgmT7bv9e/vSCgiEm4qV4Z583Lv/1G0KHTtapIFvXpBhQoBC89fNm2CHTvse0ociAQXl8tUHXz3XeZeQoJ+BhIJNY7NONizZw/t27dn+vTp7iRBBsuyck0YZOjduzd16tRxr8eNG+fzOEVERERECmriRDh2LHMdE2PmjoqI5MmmTbBlS/bPVasGHTtm2T5GcSbSh0ExY9m+bDdMnQqDBoVF0gCytimqUkV3MYsEIw1IFgl9jiQOLMuiV69e/Pvvv+690qVLM2TIEGbMmMGKFSuyJBOy43K56Nevn3s9a9Ysv8QrIiIiIlIQ3m2Kunc3F7lERHK0bh28+CKce67p9zFqVM7HxsWZ/5YsSXKPK7kuejxV2E0/JjIm9Tpefj/8JgZnN99Aw+ZFgo/3nIO//4bkZGdiEZGCcSRxMGbMGBISEtxVBeeddx7//PMPI0eOpFu3bjT7b0BTXqoOLr/8csAkI5YuXcrhw4f9F7iHAwcOMGHCBO6++246dOhAtWrVKFq0KGXKlKF+/fr06dOH9957jyNHjpzyXJs2bcLlchXqY9OmTT793ObOnctLL71EXFwc9evXt73X008/7bP3EhEREQlXW7fCjz/a91SiLyLZWr0ahg+Hli3NEJRHH4U//zTPTZgAOc0wuPJKU9q0ezclp35D2Vuu4iil3U+//z7s3On/8ANJ8w1EQkPr1hDlcdUxLQ0WL3YuHhHJP0dmHGTMJbAsi8aNGzN9+nTKli3YnRCxsbHux5ZlsWrVKtq2beuTOLOzZs0aHnzwQWbPns3JkyezPH/y5EmOHDnC5s2bmTx5Mo8++iivv/46N9xwg99iiomJoVy5cj45V5MmTfj333/zVPEhIiIiIjkbN87eerxMGdNiXEQEy4IVKyA+3nysWpXzsUlJsGhR1tt3wbQr6tPHvXzkEfjkE0hNNevjx2HkSPjvV/CQd+SIuWvZkxIHIsGpVClo0QKWLs3cS0jItsOaiASpgCcONm3axOrVq93VBM8//3yBkwYA5cuXp0aNGmzfvh2Af/75x6+JgxUrVjBt2jTbXnR0NI0bN6ZatWqkpaWxevVq9u3bB8DBgwe58cYbWb9+PcOHD8/2nCVKlKBbt255jiE9PZ05c+a41926daOCj/pVrlu3zifnEREREYl03m2K4uKgRAlnYhGRIGBZsGRJZrIgr797Vapk5hxklzjwUr8+DBwIn36auffuuzB0qJmlHOoWLbIXX8TEmI5OIhKc2re3Jw4050AktAQ8cbBo0SLAVAeUKFGCXj647apixYruxMH+/fsLfb68iImJoUePHtxwww1cdNFFtuSHZVl899133HXXXWzduhWAESNG0KZNG3r27JnlXNWqVWPmzJl5fu/Zs2fbEgeDBg0qxGeSvXLlyhEbG8u5557Lueeey7333uv+fywiIiIiuVu2DJYvt+8NGOBMLCISBJYuhb59YePGvB1ftao5Pi4OLrzQXCHPo8ceg88/N21BAI4ehddeg+eey3/YwcZ7vsE55yghKxLM2reH997LXCckmByq5pKIhIaAzzjYtWsXYOYXNGzYkKJFixb6nKVLZ/ZwPHr0aKHPl5siRYpwyy23sH79eiZNmkTv3r2zVEy4XC569+7NggULqF69unv/scce80kMn3/+uftxhQoVfJJ8yTB27FjWrl3L/v373XMOrrzySp/8OYmIiIhECu9qg9q1zbU/EYlQDRuawSe5qVkT7r4b5s2DbdvM1baLL85X0gCgUSO47jr73ltvQYDusfMrzTcQCS3ehVI7dsDmzc7EIiL5F/DEgeewYM8L/oXhORC5VKlSPjlnTnr37s1HH31E3bp1T3lsnTp1eOaZZ9zrFStWsH79+kK9/6FDh5g0aZJ7fc0111CsWLFCndPTddddR5MmTfI0mFpEREREskpLM/MNPF1/vX1AoIiEmbQ0c8Hfu9QoQ9mykF172jp1YMgQ+O03SEw0V/gvvBCiowsVzmOP2e/oPXwY3nijUKd0XHq6EgcioaZxY9NtzZPaFYmEjoD/+lLJ4ztGxhyAwtrqcedG5SBr3OjdmmjNmjWFOt+3337LsWPH3Gt/tCkSERERkYL76Sdzs7AntSkSCUMnT8IPP8Dtt5tqgYsuMj2BctKvn/lvw4Zm6MDChebW21GjzBVwH2YXmzaFq6+2773xBhw86LO3CLi1a7NWTShxIBLcXK6sVQcJCc7EIiL5F/DEQUbrHsuy2LhxI8nJyYU637Jlyzjo8dNPgwYNCnU+X6tYsaJtfejQoUKdz7NNUdOmTf06CFpERERE8s+7TdE558CZZzoTi4j4WEoKzJgBN98MNWrAJZfABx/Afy15mTzZJBSy06cP/PUX/PsvvPQStGnj10bfjz9uXx84AG+/7be38zvv+Qa1a5sPEQlu7dvb16o4EAkdAU8ctGvXDpfLhcvlIi0tjWnTphXqfKNHj3Y/LlWqFG3atClsiD612at5W5UqVQp8rg0bNjB//nz3WtUGIiIiIsHl6FGYONG+17+/M7GIiI8cPw5Tp8KgQWZo8WWXwaefwt69WY/dv9+0LMpO2bImkxigtrDNm2cWOWQYNcq0LQpFalMkEpq8Kw6WLAGPRhoiEsQCnjioUqUKsbGxWJYFwPPPP09aWlqBzrV27Vo++ugjdyKic+fORBeyF6SvTfT4zTEmJobY2NgCn2vMmDHu/29RUVEMUM27iIiISFCZMgU8RnoRFQXXXutcPCJSQMnJJgt43XUmWdCrF4wZc+peP2efnXPFgQOeeMK+3rfPzFwORd4VB0ociIQG7+Kq1FRTfCUiwS/GiTe9++67ueGGGwDTamjw4MG8++67+TpHUlISV1xxhbvVkcvl4qGHHvJ1qIVy9OhR3vaoBe3WrRsVKlQo0Lksy2LMmDHudZcuXahVq1ahY3RaUlJSvo7fvn27nyIRERERKTzvNkWXXGK6mYhICPnzT+jY0SQP8uLccyEuztze37ixf2PLp5YtTc7ju+8y9159Fe6+G0qWdCysfNu3D1avtu95tz8RkeBUpoypgPKcHZ+QAOef71xMIpI3jiQOBgwYwKhRo1i+fDmWZfHBBx+QmJjIqFGjOO2003J9bUpKCqNHj+app55i9+7duP5LW3br1o3zg+y7zoMPPuge3OxyuXj22WcLfK5ff/2VjRs3utfh0qaoTp06TocgIiIi4hM7d8Ls2fY9tSkSCUHNm8OpKtnbtzeJgn79oH79gIRVUE8+aU8c7N5txjIMGeJcTPnlPUy1eHGTFBGR0NC+vT1xoDkHIqEh4K2KwFxE//bbb6lUqZL7wv/06dNp2rQp7du3Z/DgwQDutjyffvopd999N927d6dy5crceeed7Ppv+JRlWdSpU4cvvG/vctjYsWN5//333ev777+fVq1aFfh8nkORy5YtS58+fQoVn4iIiIj41ldfQXp65rpUKTMLVUSCzL598Nln8Msv2T9frBj07Gnfc7ngggvgjTcgMdH0zXnggaBPGoApiOje3b73yitmdEOo8L7I2Lo1FC3qTCwikn/eFULeyUARCU6OVBwAnHbaaUydOpXevXuza9cuXC4XlmWxaNEiFi1a5D7Osiy++uor2xpwH1+nTh2+//57KlWqFPDPISe//vorN998s3sdGxvL888/X+DzJScn8+2337rXV111FSVKlChUjMEiMTExX8dv37496AZgi4iIiAB8+aV93bevSR6ISBDYswcmT4b4eJg71zTZ7tfPtCTKTlwcfP01dOpkHvfpA9WrBzJinxo2DGbOzFxv3w6ffAJ33eVcTPmh+QYioc17QPLWrSYHqyYUIsHNscQBQNu2bfn777+54YYbmDVrlu05l+fkFK99y7KwLItu3boxZswYqlSpEohw8+Tvv/+mZ8+enDhxAoCGDRsydepUihbidohJkyZx+PBh9zpc2hQB1K5d2+kQRERERApt9WrTFt2T2hSJOGzHDpg0ySQLfv4Z0tLsz8+YAUePZp/hu+wy8/og+l2zMNq3h4svNjmTDC++CLfcYgosgllqKixcaN/TfAOR0NKkCVSoAPv3Z+4tWKDEgUiwc6RVkadq1aoxY8YMFixYwFVXXUXZsmXdiYHsPkqUKEGPHj34+eefmTFjRlAlDdauXUvXrl05ePAgADVr1mTOnDnUKOREPM82RY0bN6ZDhw6FOp+IiIiI+JZ318zq1c1FOhEJsK1b4a234MILoWZNuPNO+PHHrEkDMMOPPW/D91SsWNgkDTIMG2ZfJyWBx6+aQWv5cpPf8aTEgUhoiYqCtm3te2pXJBL8HK048NS2bVu+/vprLMti+fLlrF69mr1793LgwAFKlixJ5cqVadCgAW3atKFIkSJOh5vFxo0b6dKli3v2QuXKlZkzZw4NGzYs1Hm3bt3KXI/bQgYOHFio84mIiIiIb6Wnw9ix9r3rrjv1bFUR8ZHNm2HCBPPh3dMmJ8WKmcb/Vav6N7Yg0rGj+fAc7fDCC3DjjRCEv2K7ec83aNw4ov7YRMJG+/b2XK0GJIsEv6BJHGRwuVy0aNGCFi1aOB1KniUlJXHxxReTlJQEmOHFM2fOpFmzZoU+9xdffEH6f1P2XC6XEgciIiIiQebXX2HLFvvegAHOxCIScf7800z/zYuSJU0Lorg4898yZfwbWxAaNgy6dMlcb9pk5rPceKNjIZ2S5huIhAfvOQd//QUnTgR/uzSRSOZ4q6JQt3PnTrp06cLGjRsBKFmyJNOmTSM2NtYn5/dsU9SpUyfq1avnk/OKiIiIiG94tylq3hzOPtuZWEQiTsuWUK1azs+XLg3XXmuqEXbvhm+/hauvjsikAUDnzlnb/Dz3nJkjEKy8EwdqUyQSmtq2Bc9xpikpsGSJc/GIyKkpcVAI+/bt45JLLmHt2rUAFCtWjMmTJ/tsBsGiRYtYs2aNex1OQ5FFREREwsHx4+Y6pKf+/e2/GItIAVkWrFwJzzwDEydmf0x0NPTpY98rV86U/UyZYpIF48ZB376m4iDCuVzw5JP2vfXr4euvnYnnVHbsgP/u0XNTxYFIaCpXDs44w76nOQciwU2JgwI6dOgQ3bp1Y/ny5QDExMTwzTffcMkll/jsPTyrDUqVKkW/fv18dm4RERERKbypU+HQocy1ywXXX+9cPCIhz7Jg6VJ44glzhal5c3j6aXj//ZxfExcHFSvCTTfB9OmwaxeMGQO9ekHx4oGKPGR07561u9Nzz2U/P9pp3j3Qy5SBM890JhYRKTzviiHNORAJbkEz48CyLObMmcO8efNYtGgRGzdu5MCBAxw5coTSpUtTvnx593DkCy+8kK5du+Jy6Fau5ORkLr/8chYvXgxAVFQUX3zxBb169fLZe6SkpPC1x20fcXFxlC5d2mfnFxEREZHC825TdNFFULu2M7GIhCzLgsWLTTuh+HhzC7y3H3+EvXuhUqWsz3XqZG5ND+YJv0Eko+qgd+/MvTVrzP/6q692Lq7seLcpatdOg+dFQln79vDJJ5lrVRyIBDfHKw7S0tIYOXIkDRo04NJLL+Wll17ip59+YuPGjezfv5+TJ0+yf/9+Nm7cyE8//cRLL73EZZddRv369Xn11VdJC/BtESdOnKB3797Mnz8fMAOLP/74Y6655hqfvs/UqVPZt2+fe13QNkWbNm3C5XK5P55++mkfRSgiIiIS2fbsgRkz7Hv9+zsTi0jISU83t5o+8AA0aABt2sBLL2WfNABzO/x332X/XHS0kgb51LNn1lksI0aYP5ZgovkGIuHFe0Dyli2wbZszsYjIqTlacbBq1Squu+46li9fjmVZ7v2Mi9zZyTguMTGRhx9+mC+//JKxY8dyZoDqFd944w1++OEH97p8+fKMHz+e8ePH5+n1/fv3p38efqP0bFNUr149OnXqlO9YC2LEiBGMGDEiy/6JEydsx7z44otZjjl+/LhfYxMREREJJuPH2weKFi8O6iwpkou0NPjtN3Nr+8SJsHVr3l5Xu7b5y+XdX0cKLKPqIC4uc2/FCpg82YyDCAYnTphCFE+abyAS2s44A8qWtbd5XLBAPz+JBCvHEgd//fUX3bt3Z+/evViW5U4UWJblTg5ERUVRokQJjh07RrrHrQ+exy5btowLL7yQ2bNn06pVK7/HnZycbFvv37+fWbNm5fn17bzTq9nYtWsXMzxuXxs4cGDA2jKlpqbakgTZSUtLC3ilh4iIiEiw+fJL+/qKK8wvwyKSgxUr4MIL83Zs/frmqnZcHLRuDVGOF8uHnT59zLyAlSsz94YPN/vBMOB9yRJISclcu1zQtq1z8YhI4UVFmb/Hc+Zk7iUkKHEgEqwc+enr4MGD9OzZkz179gAmEWBZFo0bN+aZZ57h559/Zu/evaSmpnL48GFSU1PZu3cv8+bN45lnnqFx48buZIPL5WLfvn307NmTAwcOOPHp+Ny4ceNI9bh9beDAgQ5GIyIiIiLe1q3L2pdXbYpETqFFC2jUKOfnGzeGRx81t5lv2ACvvGKuMClp4BdRUWYGtaelS+H77x0JJwvvNkVnngnlyzsSioj4kAYki4QOl+XZIyhA7rzzTt5//313wqBkyZKMGjWK//3vf3m6s96yLD788EMeeOABjh075k4i3Hbbbbz77rsB+AzEKUlJSdSpUwcw7apqa/qgiIiIOOCpp+DZZzPXVaqYritqsy4R68QJ+OEH04aobVu4/fbsj3vkETPLIMMZZ2RWFpx1VnDc6h5B0tLMBfm1azP3WreGhQud/6OIizPzsjP873/w4YfOxSMivjFjBlx2Wea6eHE4eBCKFnUuJhHJXsATB8eOHaNatWocPXoUy7IoUaIEc+fOzVMLH28JCQl07tyZEydOYFkWpUuXZteuXRQvXtwPkUswUOJAREREnGZZ5sboDRsy9wYPhjffdC4mEUccOwazZplkwdSpmU2r27bNWpKTYfFiuPlmc1W4Xz9o1ixw8Uq2vvgCvIvcZ86Ebt2ciQfM99latWD79sy90aPhhhscC0lEfGTfPqhUyb63aJFJWopIcAl4zee8efM4cuQIYFoUPf300wVKGoCZF/D000+7ZyIcPXqUn376yWexioiIiIh4W7DAnjQAGDDAmVhEAu7oUfj2W7j6alNq06cPjB1rn3S5cCFs2ZL96889F/7+20zmVdIgKFx7bdYOUs8+ay7eO2XLFnvSADQYWSRcVKwIp59u38sp1ywizgp44mDz5s2AaTdUpEgR/ve//xXqfLfeeitFihRxtzjKOL+IiIiIiD988YV9ffrp5lqoSNg6dAi++spUCFSpAlddBd98Y5IIOZk4MXDxSaHExMBjj9n3fv8dnLwnz3u+QaVKcNppzsQiIr6nOQcioSHgiYN9+/YBptqgQYMGlC/kdKPy5cvTsGFDd9XB/v37CxuiiIiIiEi2UlJg/Hj7Xv/+zvcCF/G5I0dgzBjo1QuqVoXrrjPJgGPHcn6NywXnnw+vvWZaEUnIGDAA6tWz73nOcQk078RB+/b6PisSTrwTB6o4EAlOMYF+w3LlyrkflylTxifn9DxP2bJlfXJOERERERFv06eD930q/fs7E4uIX+3cCYMGnfq4qCjo2NEkCvr0gZo1/R+b+FyRIvDoo/aZ1j//DL/8Yv54A807caA2RSLhxbtj+caN5p+datWciUdEshfwioMzzjgDMK2KkpKSfHJOz/NknF9ERERExNe82xRdcAHUr+9IKCK+kVMj+0aN4Jxzsn8uOhouuQQ++MA0ov/pJ7jrLiUNQtwNN0Dt2va94cMDH8fRo2YMhiclDkTCy5lnQunS9j1VHYgEn4AnDs4//3x3hcDOnTtZunRpoc63ZMkSduzYAUDp0qXp0KFDYUMUEREREcli/374/nv7nqoNJCRt2wZvvw0XXZR7PxrPdkNFisCll8Inn5jbQmfPhltvNW2MJCwUKwYPP2zf++GHwPce/+MPSEvLXEdHQ+vWgY1BRPwrOhratLHvac6BSPAJeOKgWLFi3O5R//jII48U6nwZr3e5XNxxxx0ULVq0UOcTEREREcnOt9+aGQcZihaFK690Lh6RfNmyBV5/HTp0MLeVDx4M8+ZlHdrhKS7OzDj4/HOTLJg+HW66yUyqlbB0881Qvbp9L9BVB95tilq2hJIlAxuDiPifBiSLBL+AJw4AnnrqKU4//XQsy2LOnDnccccd7uHGeWVZFnfccQdz5swBTIuip556yh/hioiIiIjw5Zf2dc+eUKGCM7GI5MmGDfDKK9C2rZl8O2QI/PabvT3R6tWwalX2r2/SBKZMgYED9cUeIUqUgIcesu/NmAGLFwcuBu+Lh2pTJBKevOcc/PEHpKY6E4uIZM+RxEHJkiWZM2cOzZs3x7IsPvzwQ84991xmzZp1ygSCZVnMnDmT2NhYPvzwQwBatGjB7NmzKVGiRCDCFxEREZEIs2kT/PqrfU9tiiQo/fMPvPACxMaaOQVDh8KiRbm/ZsKEwMQmIeG226BKFfveiBGBeW/L0mBkkUjhnTg4dgyWLXMmFhHJnsvK763+PjBmzBgAjh49yssvv8zmzZtNMC4X1apVo0OHDjRv3pxKlSpRsmRJkpOT2bNnDytWrGD+/Pns2rXLnWCoX78+Q4cOpWQ+ahcHDhzo+09KAiIpKYk6deoAkJiYSG3v6V0iIiIifjBiBDz5ZOa6YkUzE1ZdMiUo7N4N770H8fGwfHneXlO2rGlDFBcHXbuaW81F/vPSS+DdVXjpUjj7bP++79q10LSpfW/zZqhb17/vKyLOOO00+PffzPU778CddzoXj4jYOZI4iIqKwuVy2fY8w/B+riDH5SbNc9KShBQlDkRERCTQLAvOOMNc0Mpw++3mOq1IUNi5E2rUsLcgyk6FCnDFFSZZcPHFZhquSDYOH4b69WHfvsy9uDgz68WfRo82YzQy1KwJSUlQwF/9RSTIDRwIX3yRue7f374WEWc50qoog3cSIOPD8/mMj7wel9OH9/uJiIiIiOTF4sX2pAHAgAHOxCIRzLIgOTn756pVg44ds3+ucmW49VaYNcskGD79FC67TEkDyVWZMmYkhqf4eFi50r/vm918AyUNRMKX94DkhARn4hCR7DmWOPC8mJ/bxX7P4/Ny3KneT0REREQkP7zvfGvYMOsvuiJ+kZ5urqI8+KD5wnvssZyPjYvLfFy9uun18OOPpqfWBx+YdkRFivg/ZgkbgwdDuXL2veee8+97ar6BSGTxnnPw77+m+56IBIcYJ9509OjRTrytiIiIiEi+nDwJX39t3+vfX3fAih+lp5urp/HxZmhxUlLmcxMmwKhREJXN/V99+5orLnFxJrMVHR24mCUslSsH994Lzz6buff11/DUU3D66b5/vwMHslY0KHEgEt7OOgtKlrQX1C1cCD16OBeTiGRyZMaBSEFpxoGIiIgE0rRpWX95/ecfM8xPxGdSU+HXX02yYNIkUyWQkwULst6iKeIn+/ZBvXpw5Ejm3sCB8Pnnvn+vmTPh0ksz18WKwaFDGkIvEu46dYKff85cP/aY/6ubRCRvHJ1xICIiIiISzLzbFLVrp6SB+MjJkzBnDtx2m5kA27kzvPtu7kkDMK8RCZCKFU3LIk9jx8L69b5/L+/5Bueeq6SBSCTwbv/o/b1ARJyjxIGIiIiISDYOHYIpU+x7/fs7E4uEkW3b4OabzRyCrl3hww9P3dC5aVN44glYutT8VySAhgwxrUQypKXBCy/4/n0030AkMnkX0S1aZL7PiIjzlDgQEREREcnGhAlw/HjmOiYGrr7auXgkTJQubW7Z3rcv9+POOgueecY0fV+9GoYPh7PP1oANCbgqVcysbU+ffw6bN/vuPdLSzBxwT0ociEQG78TB0aOwYoUzsYiInRIHIiIiIiLZ+PJL+/qyy6ByZWdikRBz9GjOLYfKljWVBtlp1Qqefx7WroVly2DYMGjWzH9xiuTRAw9A8eKZ69RUePFF351/xQr7HAXI2r5ERMJTtWrQoIF9zzuRKCLOUOJARERERMRLUhL89JN9T22KJFeHD8PXX0NcnLlF+7HHcj42Li7zcZs28PLLpmn8n3/Co49Ckyb+j1ckH6pXN+M4PH36qfle6QvePc0bNjQXE0UkMmjOgUhwinE6gAyWZbF27Vq2bNnCoUOHOHbsGJZl5fs8AwcO9EN0IiIiIhJJxo4Fzx9Fy5aFnj2di0eC1IEDMHUqxMfDrFlw4kTmc1OmmAHIRYpkfV3PnvDaa9C3L9StG7BwRQrjoYfgvfcgJcWsU1JMzuvNNwt/bs03EIls7dvDuHGZa1UciAQHl1WQq/M+NG3aND755BNmz57NsWPHCn2+NE1QCWtJSUnUqVMHgMTERGrXru1wRCIiIhJuLMu0l1+5MnPv5pvh44+di0mCyN698N13JlkwZ45JDuRk1qyc2xKJhKA77zTJgwzFi8PGjaYioTAaNzZFNxnefRfuuKNw5xSR0LF4MbRubd/buxcqVnQmHhExHGtVtHv3brp160avXr2YMmUKycnJWJZVoA+gQNUJIiIiIiLe/v7bnjQAGDDAmVgkSOzaBR9+aJIA1arBTTfB9Om5Jw2iozXdUcLOI4/Yi2iOH4dXXy3cOXftsicNQPMNRCLN2Wfb56gALFzoTCwiksmRxMG+ffu48MIL+eGHH9wX/F0uV4HPp6SBiIiIiPjKF1/Y13XrwgUXOBOLOGzLFujczaIhfQAArMJJREFUGWrUMA3e58yB3CqcixSBSy+FTz6BHTvg/vsDF6tIANStC4MG2ffeew927y74Ob17mZcuDc2bF/x8IhJ6ihSBc8+172nOgYjzHJlx8OCDD7JmzRpcLhculwvLsihXrhxdu3blrLPOolKlSpQsWdKJ0EREREQkgqWl2XvsAlx/PUQ5VqcrjqpWzQwsTk/P+ZiiRaFbNzPwuGdPqFAhcPGJOODRR2H06MwcWnIyjBoFL7xQsPN5zzdo2xZigmYao4gESvv2MH9+5lqJAxHnBfyf43379vHFF1+4EwYul4unnnqKRx55hGLFigU6HBERERERt7lzzY3intSmKMxt3Gj6rZxxRtbnihWDXr3gyy/t+8WLw2WXQb9+0KOHmZ4tEiEaNoT+/eHzzzP33n4bHnwQKlXK//k0GFlEANq1s68XLjQJyuhoZ+IREQdaFf3444/uAcYul4unn36ap556SkkDEREREXGcd5ui2NjsrydLiFu3Dl580fRFaNgQnngi52Pj4sx/S5WCq66Cb74xfVkmTIDrrlPSQCLSY4/ZK7GOHIE33sj/eVJS4I8/7HuabyASmbwTB4cPw+rVzsQiIkbAKw4SExMBM5egRIkSPPjgg4EOQUREREQkiyNHYOJE+17//s7EIn6wejXEx5uPZcvsz02fbr4ASpfO+rquXc0XRrduoHaqIgA0aQLXXGNv7fbGG2asR/nyeT/P0qVw4oR9z/vioYhEhpo1zRyVLVsy9xISNPNExEkBrzhISUkBTLXBaaedRokSJQIdgoiIiIhIFpMnm17dGaKj4dprHQtHCsuyTIJg2DA480xo1sw89k4agGlVNGNG9ucpUQL69FHSQMTL44+Dy5W5PnQI3norf+fwblPUrJnGhIhEMu+KI805EHFWwBMH1apVcz+O0cQjEREREQkS3m3su3Y1s3ElhFgW/PWX6aNy+ulw9tkwfDisWpX76ypVgv37AxOjSJho1iyzk1eG114zCYS80nwDEfHknThISHAmDhExAp44aN26NWBaFWW0LRIRERERcdL27TBnjn1PbYpCzObN0KiRGUzxwgtmjkFuqlWD22+HH34wE7FvvTUwcYqEEe/xIPv3w7vv5v313okDzTcQiWzercpWrYIDBxwJRURwIHFw5pln0qxZMwD27NnD4sWLAx2CiIiIiIjNV19BenrmunRpuOIKx8KRgqhdG44dy/2YmjVh8GD4+WfYuhXeew8uvhhUCS1SIC1aZP1eOXIkHD166tcmJpq/hp5UcSAS2c45B4oVs+8tWuRMLCLiQOIA4JlnnnE/HjZsmBMhiIiIiIi4ebcp6ttXLe2DTloazJsH8+dn/3x0tPmD81a3rpnY+ttv5krlm29Cx47meBEpNO+qgz174P33T/0672qDihXN0GURiVxFi0KrVvY9zTkQcY4jiYN+/fpxyy23YFkWs2bN4sEHH3QiDBERERERVq6EJUvsewMGOBOLeDl50vSQuv12Uy1w0UXgcRNSFv36mf82bAhDh5rbFDdtMrdAn3ceRDny649IWIuNhcsvt++98sqpC4C8Ewft2umvqIhoQLJIMHHsn+UPPviAW2+9FcuyeO211+jUqRPz5s1zKhwRERERiVBffGFfZ1yfFoekpMCMGXDzzVC9uplS/cEHsGuXef6nn2Dv3uxf27GjGY7877/w0kvQujW4XIGLXSRCPfmkfb1zJ3z0Ue6v0WBkEcmO95yDhQvt7SRFJHBclmVZTgYwfvx4hgwZws6dOwGoUKEC55xzDlWqVKF48eL5OpfL5eKTTz7xR5gSJJKSkqhTpw4AiYmJ1K5d2+GIREREJJSlp0O9epCUlLn34IPmblkJoOPHYfZsiI+H776DgwdzP/6TT+CmmwITm4jkSdeu9iHzNWvC+vWQ3a/1yclQrhykpmbu/fijkrYiYn4m+++yj9uqVXDGGc7EIxLJHJ0CduDAAf7880+OHz9ORv5i3759/Pjjj/k+l2VZShyIiIiISL78/LM9aQBqUxQwyckwc6ZJFkydCkeO5O11LVtCqVJ+DU1E8m/YMHviYNs2GD0a7rgj67GLF9uTBtHRpkBIRKR2bahVyz48PSFBiQMRJziWOEhISKBv377uSgOXSohFREREJMC82xS1aGE+xM+2bDFXAJKT83Z869YQF2dmGDRq5N/YRKRAOnSATp3MDPMML75ouo4VLWo/1rtNUYsWULq0vyMUkVDRvr25ryDDggVw443OxSMSqRyZcbBmzRq6d+/Ojh073JUGlmUV6kNEREREJD+OHbP/UgrQv78zsUScOnVMH5PctG9vhhpv3GiGHA8dqqSBSJAbNsy+3rIla4IWNN9ARHLnPSA5IcGZOEQinSMVB3fddReHDh1yVxlYlkX37t3p1asXzZs3p1KlSpQsWdKJ0EREREQkQnz3HRw+nLl2ueC665yLJ6zs22f+B5crB336ZH3e5TLVAy+9ZN+74AJTWdCnj+lVICIhpVMnOP98+O23zL3nn4dBgyDmv6sPlmXuHvakxIGIePIekLxiBRw6BGXLOhOPSKQKeOLgn3/+4aeffsLlcmFZFhUrVmTixIl07Ngx0KGIiIiISAT78kv7+uKLTU9dKaDdu2HyZFPG8eOPpoF527bZJw7AJAheecVcacxIFlSvHsiIRcTHXC5TddCtW+behg0wbhwMHGjW//4Le/bYX6fEgYh4atUKihSBkyfN2rLgjz/Mz2oiEjgBTxz89t+tBxnDjN9//30lDUREREQkoHbvNnN5PalNUQHs2AGTJplkwbx5kJ5uf37hQtOrpG7drK+NjTWvr1IlIKGKSGBccgm0aWM6jGV47jm4/nozBNm7TVH16lCvXmBjFJHgVrw4nHOO/fvIggVKHIgEWsBnHGQMQwYoX748/fr1C3QIIiIiIhLhvv7a3BCfoUQJ6NvXuXhCSlISvPkmdOxo5hTceaepMPBOGmSYODH7fZdLSQORMORywZNP2vf++Qe++cY8zm6+wX9djEVE3LznHHi3OBMR/wt44qBMmTIAuFwuGjZs6J5zICIiIiISKN5tivr0gf9+TJXsbN4Mo0aZK3x16sC998Kvv5reATkpVgyuuAKaNQtYmCISHC6/3Nwt7GnECJNf1HwDEckL7zkHCQm5/9ghIr4X8FZFtT2GnKWkpAT67UVEREQkwq1day99B7UpytXWrVC/ft6OLVnSXDHs1w8uu0zZGJEIlVF14FnJtWoVfPaZGXLqSYkDEcmOd8XBvn2wbh00aeJMPCKRKOAVB61btyY6OhrLsti0aROpnjXiIiIiIiJ+5l1tULWq6cktOahVy0wpzEnp0nDttTBhghke8c03cPXVShqIRLjeveGss+x799xjv2O4aNHcv72ISOSqW9fMQPGUkOBMLCKRKuCJg5o1a3LJf7+ZHTlyhBkzZgQ6BBERERGJUJaVNXFw7bUQE/A63CBhWeb236efhvfey/k477lk5crBwIEwZYpJFowbZ24tLlnSr+GKSOiIioInnrDvHT1qX8fGmq5mIiLeXC7NORBxWsATBwDDhg0jOjoagKFDh3LkyBEnwhARERGRCPPbb7Bpk31vwABHQnGOZcGSJfD443DGGeaW4GeegTfeyLl5cFwcVKwIN90E06bBrl3w+efQqxcULx7Y+EUkZPTrZ77N5ERtikQkN96JA1UciASWI4mDdu3a8eabbwLwzz//cPnll7Nz504nQhERERGRCPLFF/b1GWdESJsMy4I//oCHH4bGjc0n/fzzZuBDhrVrTRPy7DRpAjt3wiefmNkFRYsGJm4RCWnR0SZHmRMlDkQkN94DkpctA917LBI4jiQOAO644w6+/vprypYty/z58znzzDMZNmwYy5Ytw9KYdBERERHxsRMnTPt9T/37m1L4sJSeDr//Dvffb4Ybt2kDL78MGzbk/JoJE3J+LmL7OYlIYVx9tclXZsf7bmIREU+xsfYfP9LTYfFi5+IRiTSO/PTfsGFD92OXy4VlWezbt4/nnnuO5557jiJFilCxYkWK57Ps2eVysX79el+HKyIiIiJhYNo0OHDAvnf99Y6E4j9paaYfU3y8SQJs25a319WubdoR9ejh3/hEJOLExJiqgxtvtO/Xrw81ajgSkoiEiJIl4eyz4c8/M/cWLIBOnRwLSSSiOJI42LRpkzth4HK5cP13m1dGpUFKSgo7duzI93ldYXu7mIiIiIgUlvdQ5AsvhHr1nInFb/buhYsuMrfknUqDBiZZ0K8ftG5tJpmKiPjB9debUSqeM2bUpkhE8qJ9+6yJAxEJDEd/O/C+0J+RRCjIh4iIiIhITvbtg++/t+/17+9MLH5VtSp07Jjz86edBo8+an4DX7/etC5q21ZJAxHxqyJF4IUX7HveFQgiItnxnnOQkGBGN4mI/zlScVC3bl1d7BcRERGRgPnmGzh5MnNdrJi52T6knDgBc+aYNkR168Kzz2Z/XFwczJuXuW7WzOzFxUHz5mE81EFEgtk115gc5Zw5ZsZ6ly5ORyQiocB7Fsru3WZcU6NGzsQjEklcliYRSwhJSkqiTp06ACQmJlK7dm2HIxIREZFQ0KGDaf2f4corsw5KDkrHjsGsWSZZMHUqHDpk9mvVgi1bsq8U2LbNXJXr1898NGsW2JhFREREfMSyoFo1kzDI8OWXYTinSiQIOVJxICIiIiISKBs22JMGEORtio4ehenTTbJg2jSz9rZ1KyxcmPU2PICaNWHpUr+HKSIiIuJvLpf5cee77zL3FixQ4kAkEJQ4EBEREZGw5j0UuVIl6N7dmVhydOiQGcIwYQLMmGEqDU4lPj77xIGIiIhIGPFOHCQkOBeLSCRR4kBEREREwpZlZU0cXH01FC3qTDw2x47Bt9+aBMCsWZCScurXuFxw/vlmXkHfvv6PUURERMRh3gOS//4bkpOhZEln4hGJFEociIiIiEjYWrQI1q2z7w0Y4EwsWZw8CbfeaoYe5yYqCi680CQL+vSBGjUCE5+IiIhIEGjd2vw4lJ5u1qmp8OefcMEFzsYlEu6CNnFw+PBhDh48SHp6OtWrV6doUNwWJiIiIiKhxLvaoHFjaNs2wEGkp2c/xLhsWejWzV57nyE6Gi6+2Aw3vuIKqFrV72GKiIiIBKNSpaBFC/sIpwULlDgIB2lp5l4az4+UlKx7p3oup/1LLoFzz3X6swxdQZM4+PHHH5kwYQK//vora9asIS0tzf3cnDlz6Ny5c5bX/Pnnnxz9b1hc+fLladGiRcDiFREREZHgdvIkfP21fa9/f9Ptx++2bYOJE00bosaN4eOPsz8uLi4zcVCkiPntJi4OevUywxhEREREhPbt7YmDSJ9zkJ6e88X1glxg98dzeXmNZfn3/1OpUkocFIbjiYOEhATuuOMOli1bBoDl9RXjyuU3u/HjxzNy5EgAypYty/bt2ylevLj/ghURERGRkDFzJuzZY9/r39+Pb7hlixluPGEC/PZb5v7ff8O772Y/WKFnT9N+qG9f6NEDypf3Y4AiIiIioaldO3jvvcz1ggXmonN+bwixLNPqKNAX0X19Md/jfmvJxcmTTkcQ2hxNHLzxxhsMHTqU1NRULMuyJQlcLleWJIK3e+65h9dff53U1FQOHTrEhAkTuP766/0dtoiIiIiEAO82ReedB40a+fhNNmwwiYL4eDNQITsHDsBPP5m2RN7KlzeVCSIiIiKSo/bt7esdO+CiizLvvM/rxffUVGfiF2cocVA4jiUOPv30U4YMGQJkVhVER0cTGxtL3bp1+fbbb095jtq1a9O5c2dmz54NwHfffafEgYiIiIhw8CBMmWLf81m1wT//mERBfDwsWZK310yenH3iQEREREROqXFj08Vx797MvZ9/di4eCZwiRbJ+FC2at32f3zQUYRxJHGzatIk777zTnTBwuVw8+OCDPPzww1SsWBGAb7/9Ntc2RRn69evH7NmzsSyLuXPn+jVuEREREQkN8fFw4kTmukgRuOqqQpzw0CF4/XVz4uXL8/aacuXMrIK4OOjatRBvLiIiIhLZXC5TdfD9905HEjqio3O+wJ7bxff8XJj39/miowM0n0yy5UjiYNiwYaSkpAAQFRXFuHHjuKqAv8ld4DFCff/+/axfv55GSieJiIiIRDTvNkWXX17IWcPFisHIkSaBkJuKFeGKK6BfP7j4YvM6ERERESm0O++E6dNNeyJ/iopy9mK5r86nC+5SWAFPHKSkpDBx4kR3NcGtt95a4KQBQJMmTShZsiTJyckArF69WokDERERkQi2ZQvMm2ffy1ObIssyiYFy5bI+V6yYqR7wzkgAVKliBhzHxUGnTuY3NRERERHxqUsvhYQE+OUX82Obvy7MR0U5/ZmKBIeAJw5+++0390V+l8vFQw89VKjzRUVFUaNGDdavXw/A1q1bCx2jiIiIiISusWPt6/LlTcVBttLTYeHCzAHHsbHmcXbi4jITB9Wrm6qCuDjo0AFiHBsdJiIiIhIxWrc2H/9n777jo6jWP45/NwkJhN57702lCoJIj4ggmIioYAP1p2JHsVdswFWuiqgXCyAoEHpVpCOgqFRpIiAJvfeSMr8/RiaZbMom2d3JJp/365WXM2fOnHmy92ZI5plzHgC+5/e/cHbv3m1tV6xYUdWrV8/2mMWKFbO2T2c0fRwAAAC5lmFI48fb2267TcqfP1lDQoK0apWZKJg6VUr+4smhQ9LZs1KhQu6Dd+0qPf20Obvguut4HQ0AAABAruX3xMGRI0ckmbMNypcv7/XxE3290BkAAAByrHXrpK1b7W39+0uKjzfntUdHS9OnSwcPpj7AxYvS/PlmtiGlAgXMOgcAAAAAkMv5PXEQlqxA3JUCydl17Ngxa7tktqreAQAAIJAlL0EQojjdWXax2o6Nlm6dIR096tkgy5ennjgAAAAAgDzC74mDMmXKSJIMw/BKPYKTJ09q7969VrHlK+MDAAAgb4mPlyZOlIrqpD7UU7pFM1Xi0AnpSw9Orl/frFcQFSU1buzzWAEAAAAgJ/N74qBWrVrW9rFjx7Rt2zbVq1cvy+P98MMP1vJELpdLLaiQAgAAkCf99JNZoiBIhXWjFqiETqR/wlVXmYmCyEipQQP/BAkAAAAAAcDvFd2aN2+uEiVKWDME/ve//2VrvBEjRljbderU8UndBAAAAOQw585Ju3fbmq4sU5SoYE3Tramf17Sp9M470vbt0oYN0iuvkDQAAAAAgBT8njgICgrSLbfcIsMwZBiGRo0apXXr1mVprLfeeku///67JHO2Qf/+/b0ZKgAAAHKS06el774zZwiULi09+KB16OxZs+bxFdGKStq59lpp+HBp1y7p99+lF16Q6tTxY+AAAAAAEFhchmEY/r7oP//8o7p16youLk6GYah06dKaNWuWrr32WqtPUFCQNSth4cKF6tixo3UsISFBL7/8soYNGybJrJdQvHhx7dmzR4ULF/bvNwO/io2NVeXKlSVJMTExqlSpksMRAQAAnzp5Upo1S4qOln78Ubp0KelYcLB08KBUqpTGjZPuuSfpUGhQvI4O/UyF7+opVani97ABAAAAIJD5fcaBJFWtWlUvv/yyDMOQy+XSkSNH1LZtW/Xv318//vijjh07JslMCEhmouDo0aNas2aN3nnnHdWsWVPDhg2zZi24XC7997//JWkAAACQGxw7Jn31lXTTTVKZMmZGYPZse9JAkhISpJkzJSUtU3RFl24hKvzCIJIGAAAAAJAFjsw4uOLee+/VuHHj5HK5rATAFcnDSt6e/NiV855++mlbrQPkXsw4AAAglzp82FxrKDpaWrLETApkJDhYev557X9kqCpXlhITkw59953Ut6/vwgUAAACA3CzEyYt/9dVXqlq1qt5++20rGZA8KXBFytxG8kTDO++8oyFDhvgvaAAAAHjPqVNSr17S8uX2J/9pyZdP6txZioqSbrlFKllSE0fYTy1cWOrZ02cRAwAAAECu58hSRdbFg4L0xhtvaPny5eratatbguAKl8vllkjo0KGDli9fTtIAAAAgkBUpIu3bl37SICzMzASMHSsdOiTNmyfdf79UsqQk92WKIiOl8HAfxgwAAAAAuZyjMw6uuO6667RgwQJt27ZN8+fP14oVK7R161YdO3ZMJ0+eVHh4uEqVKqXq1aurQ4cOuvHGG9W0aVOnwwYAAIAndu+WjhyRWrZ0P+ZymbMH3n3X3l6ggNStm3mse3czwZCKTZukDRvsbf37eyluAAAAAMijHK1xAGQWNQ4AAAgQO3ZIU6eaNQv++ENq0UL69dfU+/7xh9SsmVSwoHTzzWayoFs3cz8DQ4ZIw4Yl7VeqJP3zjxTk6LxaAAAAAAhsOWLGAQAAAHKBLVvMRMHUqdLGjfZja9eaT/SrVnU/r0kTac4cqWNHc6aBhxISpAkT7G133knSAAAAAACyi8QBAAAAssYwzLWCoqPNr61b0+8/bZr01FPu7S6XuRxRJi1dapZHSI5ligAAAAAg+xxJHHTs2NHa/s9//qMmTZpkeaw//vhDgwcPlmQWUV60aFG24wMAAEAaDMNcWuhKsmDnTs/OK1lSio/3aigpiyJfc43UqJFXLwEAAAAAeZIjiYOlS5fK5XJJkk6cOJGtsU6cOKGlS5dKkjUmAAAAfODMGenqq81ix54oW1a69VazZkG7dlKI9371PH/ezFsk16+f14YHAAAAgDzNsaWKDMPgQT8AAEAgKVxYKlEi/cRBxYpJyYI2baTgYJ+EMnOmdPZs0n5QkFnfAAAAAACQfY4lDkgaAAAA5DDx8dKKFeYT+R49Uu8TGSn9/ru9rUoVM1EQFSVde61fqhOnXKaoc2epfHmfXxYAAAAA8oSAL44cn2yt3BAvTn8HAADIE+LizCrD0dHS9OnSkSNS3brSzTebRYtTioyUXnxRqlFDuu02c79589T7+sihQ9IPP9jbWKYIAAAAALwn4J+0HzlyxNouVKiQg5EAAAAEiMuXpZ9+MpMFM2dKx4/bj2/fLm3ZIjVs6H5unTrSn39K9ev7NVmQ3PffSwkJSfvh4VLv3o6EAgAAAAC5UsAnDhYvXizJXPqoYsWKDkcDAACQQ128aL6mP3WqNGuWdOpU+v2jo1NPHEhSgwbejy8TUi5TdOutEu+PAAAAAID3OJ44yEqtgwsXLmj37t2Kjo7WuHHjrDGuvvpqb4cHAAAQuM6fl+bPN5MAc+bYqwmn55prpMqVfRpaVm3bJv32m72NZYoAAAAAwLt8ljgIDg5O97hhGJKkzp07Z+s6V8ZxuVzqzRx1AAAA09mzUoUK0pkznvVv0cIsbhwZKdWs6dvYsiHlbINy5aROnZyJBQAAAAByK58lDq480PdWv9S4XC65XC4ZhqGmTZvqlltuyfJYAAAAuUqhQlKzZmbh47Rcd52ZKIiMlKpW9VtoWZWY6J44uOMOKcTxObQAAAAAkLv49M+sKw/1feXK2B07dtT48eMznOUAAACQaxw/btYqiIuTHngg9T5RUfbEgcslXX+92X7rrVKA1YdauVL65x97W//+zsQCAAAAALmZzxIH7dq1S7N+wbJly6xjjRs3VvHixT0eNygoSAULFlSJEiXUsGFDdenSRddcc403QgYAAMjZjhyRZswwaxYsXizFx5sP/wcMkIKC3Pv37i099ZTUrp2ZLOjVy1zbJ0ClnG3QsKFZjgEAAAAA4F0+SxwsTWdafFCyP2w/+OADdezY0VdhAAAABLYDB6Tp06WpU83ZA4mJ9uP79km//CK1bu1+boUKZrKhaFG/hOpLFy9Kkyfb2/r1MydRAAAAAAC8y7EVYQ3DSHNGAgAAQJ4WGytNm2bOLFi5Uspo6cfo6NQTB1KuSBpI0pw50qlTSfsul3TXXc7FAwAAAAC5mSOJg9dee83arlGjhhMhAAAA5Cx79pizCqKjpTVrPDsnf37pxhul9u19GVmOkHKZovbtpcqVHQkFAAAAAHI9xxMHAAAAed7581L9+uZ6PBkJD5e6dzdrFtx0k1SokO/jc9ixY9K8efa2fv2ciQUAAAAA8gLHlioCAADAv8LDpa5dpVmzUj9euLDUo4eZLIiIMPvnIZMmSXFxSfv585sfBQAAAADAN0gcAAAA+JJhSJs3m0sQuVzS66+n3i8qyp44KFZM6tnTbO/SxXxankelXKbollukIkWciQUAAAAA8gISBwAAAN5mGNL69WayIDpa2rHDbC9WTHrxRSk01P2cHj2k8uXN5YeioqSOHVPvl8fs3CmtXm1vY5kiAAAAAPAtEgdAADAMaf5880XU0aPNF1YBADmMYUhr15qJgqlTpV273PucPCktWWIuN5RSsWJSbKwUFOTrSAPKhAn2/VKlUv/4AAAAAADeQ+IAyOHWr5cGD5YWLTL3e/Qwa2ICAHKAxERpzZqkZMHevRmfEx2d9pNvkgY2hiGNH29v69tXypfPmXgAAAAAIK8gcQDkYPHxUu/e0p49SW3PPms+bwrhpxcAnJGYKK1YYSYKpk6V9u/37LzKlaXISOmOO3wbXy6yZo3099/2tv79nYkFAAAAAPISHj0COVhIiPTWW/aHJFu3Sl9+KT30kHNxAUCeFhdnVuc9dSrjvtWrm/UKoqKkFi1Yay6TUhZFrlPH/BgBAAAAAL7FfHggh7vzTqlpU3vbq69KZ844Ew8A5HlhYVLPnmkfr13bLID8++/m6/LDhkktW5I0yKTLl6Xvv7e39evHxwgAAAAA/kDiAMjhgoKkESPsbYcPm8+hAABedvGiNHu2dO+90v33p90vKsq+36CBmdXduFHavl16+20z68tT7iybP186ftze1q+fM7EAAAAAQF7jMgzDcDoIwFOxsbGqXLmyJCkmJkaVKlVyOCL/6dnTfJZ1RYEC0o4dUh76CADANy5ckBYsMIsWz56dNKUrf37pyBGpUCH3cy5elDp1krp1M+sW1K/v35jzgNtuM/8nuaJtW7O0BAAAAADA96hxAASI99+X5s2TEhLM/QsXpFdekb7+2tm4ACAgnT1r3lSjo83/njvn3ufiRfNYnz7ux/Lnl37+2fdx5lEnT9qT5RKzDQAAAADAn1iqCAgQ9etLDz5obxs7Vlq/3pFwACDwnD4tTZwo3XqrVLq0dPvt0pQpqScNrkj+yjv8ZsoU6dKlpP3Q0NTzNwAAAAAA3/D7jIOEhASdS/YHeoECBZQvXz5/hwEEpNdfl779NmkVDcOQBg+WFi5kGW0ASFVcnJksiI6WfvzRrLibEZdLatPGrGNw662+jxFuvv3Wvn/zzVLx4s7EAgAAAAB5kd9nHIwdO1bFixe3vlawWC3gsTJlpOeft7ctWmQuzQ0ASEVwsPTCC9KcOeknDYKCpA4dpFGjpH37zMX0n3hC+reuDvxnzx5p+XJ7G8sUAQAAAIB/+T1xcOjQIRmGIcMwVLRoUXXs2NHfIQAB7ckn3QsiDx4sxcc7Eg4A5Axp3QSDgtKeNRAcLHXtKn3xhXTwoLR4sfTII1L58r6LExmaONG+X7y4dNNNzsQCAAAAAHmV3xMHhQoVkiS5XC5VrVrV35cHAl54uPT22/a2LVsokgwgD9q3T/r4Y6l9+/SfLEdFJW3nyyd1727eNA8fln74QXrgAbPmARxnGNL48fa2Pn2ksDBn4gEAAACAvMrvNQ7K8xYfkG39+kkffmgvjPzKK9Idd0j/5uYAIHfau1eaOtWsWbBqVVJ7cLB05EjqCYDrrzdvnBERUo8eUtGi/osXmfL779K2bfa2/v2diQUAAAAA8jK/zzioX7++JMkwDMXExPj78kCuEBQk/ec/9rZDh6Thw52JBwB86u+/pWHDpJYtpapVpaefticNJCkhQZo5M/Xzg4PN19j79SNpkMOlLIpcvbp03XXOxAIAAAAAeZnfEwcNGzZUw4YNJUknTpzQL7/84u8QgFyhY0dztY3khg83V+4AgIC3fbv0zjtS06ZSrVrSkCHS2rXpnzN7tn9ig0/Ex0vffWdv69dPcrmciQcAAAAA8jK/Jw4k6cEHH7S2X3vtNSdCAHKFYcPM2QdXXLggvfqqc/EAQLZcviy98YbUuLFUr5700kvSunXpn1O0qLmWzcyZ0qRJ/okTPvHjj2bZieRYpggAAAAAnOFI4uCRRx5RmzZtZBiGFi5cqMGDBzsRBhDwGjQwa3om9/XX0saNzsQDANmSL580YYK0eXP6/UqUkO6/X5o3z3zSPG6c1LOnlD+/f+KET6Rcpujaa6XatZ2JBQAAAADyOkcSB8HBwZo9e7batm0rwzD04Ycfql27dlq6dKkT4QAB7fXX7QWRDUN69lnHwgGA9BmGWcQ4NS6XFBWV+rHSpaWHHjJfSz94UPryS6lbNyk01Hexwm/OnJFmzLC39evnSCgAAAAAAEkuwzAMf1/0zTfflCTFxcVpzJgxOnTokFz/LmBbtmxZNW/eXNWrV1eRIkWUL1++TI39Kuu05GqxsbGqXLmyJCkmJkaVKlVyOKKcYehQ6ZVX7G0LFkgREc7EAwA2iYnSL79I0dHS1KlSmTLSr7+m3vePP6RmzcztcuWkyEgzmXD99WaRY+RK33wj3Xdf0n5IiHTggFSqlGMhAQAAAECe5kjiICgoyEoUXJE8jJTHMiMhISHL5yLnI3GQuvPnzeUc9u9PamvUSFq/nudsABySkCD9/LOZKJg61b1y+549UtWq7ucZhjmVqksX6brr7IVckGt17iwtWpS036OHNGuWc/EAAAAAQF6XY/4ad7lc1ldWOJD/AHKM8HDp7bftbZs3m29wAoDfxMebT38feUSqWFG64Qbpo4/ckwaSmUxIjctlFkhu25akQR4RGystXmxvY5kiAAAAAHCWY3+RG4bh1S8gr+vfX7r6anvbK69IZ886Ew+APCIuTvrhB7NSe7ly5qvjo0dLhw6lf94vv/gnPuR4331nTjS5okgRc8YBAAAAAMA5IU5cdMmSJU5cFsjVgoOlESPM1T2uOHDAbHv9dcfCApBbxcVJDz4ozZwpnTjh2TkNGiTVLGjc2LfxIWCMH2/fj4qSChRwJhYAAAAAgMmRGgdAVlHjIGM33STNn5+0Hx4u/fWXVKGCczEByKWaN5d+/z39PlddZT4Jjow0EwdAMhs2SNdcY29bskRq396JaAAAAAAAV7B4MJDLDBtmXxb8/Hnp1VediwdAADt3Tvrzz7SPR0Wl3t6smfTuu9KOHeaT4VdeIWmAVH37rX2/cmWpXTtnYgEAAAAAJCFxAOQyjRpJAwbY2776Stq40Zl4AASY06eliRPNGQKlS0u33mpfgD65yMik7WuvlYYPl3btkn77TXr+eal2bf/EjICUkGD+Xy25u+6iJjYAAAAA5AQsVYSAwlJFnjlwwHxed+5cUltEhLRggXMxAcjBTpyQZs+WoqPNQseXL9uPb9pkZiVTM3as1LGj+ao4kAkLF0pdu9rb/vyTySkAAAAAkBPwTheQC5UvLz33nL3thx/MLwCQJB09Kn35pdStm1S2rHTPPWbyIGXSQDITCmm55x6SBsiSlMsUNW1K0gAAAAAAcgoSB0Au9cwzZgIhuWefNZeGAJBHHTokff651KWLVK6cNHCgORUpLi7tc4KDpcOH/Rcj8oRz56SpU+1t/fo5EwsAAAAAwF2INwfbu3evW1uVKlU86uctqV0PyIsKFpSGDrXXO9i0yVxV5P77nYsLgAMSEsz1ypYskRITM+6fL5+ZXIiKknr2lEqW9H2MyFNmzrQvpxcUJN1xh3PxAAAAAADsvJo4qFatmlwul7XvcrkUHx+fYT9vSet6QF51zz3SyJFmwuCKl1+Wbr/dTCwAyCOCg82EQXpJg7AwM7kQFSX16CEVK+a38JD3jB9v3+/a1ZwEAwAAAADIGXyyVJFhGNaXp/289QUgSXCwNGKEve3AAek//3EmHgA+tGuX9NNPaR+PinJvK1BAioyUvvtOOnLEfA28f3+SBvCpgwelH3+0t7FMEQAAAADkLNQ4AHK5rl3Nl4iTGzbMTCAACHA7dkjvvis1aybVrCndfXfaswp695ZcLnO60e23S1OmmMmC6Gipb1+pcGH/xo486/vv7f83LVhQ6tXLsXAAAAAAAKnw6lJF99xzj1f7AfCO4cOlhQuTHtScOye99pr0xRfOxgUgC7ZsMR/2R0fb1yGTzIzg6tVSmzbu55Uvb9Y4aNnSnGkAOCTlMkWRkSyfBwAAAAA5jctgfR8EkNjYWFWuXFmSFBMTo0qVKjkcUeAYOFD68suk/aAgacMGqVEj52IC4AHDkDZuNBMFU6dKW7em3/+pp6QPPvBPbEAmbdkiNWxob/vxR7MWNwAAAAAg5/DqjAMAOdebb5rLmJ8/b+4nJkrPPSfNm+dsXABSYRjSH38kzSzYudOz80qV4tVt5GjffmvfL19e6tjRmVgAAAAAAGkjcQDkERUqSM8+K73xRlLb/PnmEka86QnkIAkJUuPGGc8suKJsWenWW83ix+3aSSH8046cKTFRmjDB3nbnnVJwsDPxAAAAAADSlquKIyckJDgdApCjDR4slSvn3saPDpCDBAdLdeum36diRenxx6Xly6V9+6RPPzVf2yZpgBxs+XJp7157W//+zsQCAAAAAEhfrkgcbNiwQU8//bRf17s/efKkpk6dqkGDBqlt27YqW7asQkNDVbhwYVWrVk29e/fW6NGjdfbs2QzH2rNnj1wuV7a+9uzZ4/Xv8a+//tILL7ygJk2aqFSpUgoPD1ft2rV1++23a86cOaI8RuApVEh66y1728aN7oUqAfhQfLxZpHjcuLT7REa6t1WtKj3zjLRqlfn09b//la6/nte1ETBSLlPUuLF09dXOxAIAAAAASF/AFkc+evSovv32W40dO1YbN2602n0962Dbtm0aPHiwfvzxR8XFxWXYv2jRoho5cqTuvffeNPvs2bNH1atXz3JMISEhOnz4sIoXL57lMZIzDEMjRozQSy+9lO73GBERobFjx6ps2bJeua4nKI6cfQkJ0jXXSJs3J7VVqCD99ZcUHu5YWEDuFhdnJgumTpWmT5eOHJGKFpUOH5ZCQ937nzollS4tValiLkEUFSU1aya5XP6PHfCCCxfMGW+nTye1vf++WWsHAAAAAJDzBNSaBvHx8Zo9e7a++eYbLViwQPHx8ba33l1+eKCyefNmzZ0719YWHBysWrVqqWzZskpISNDWrVt1/PhxSdKpU6d033336e+//9ZbKV/1/leBAgUUERHhcQyJiYlauHChtR8REeG1pIEkvfrqqxo6dKi1ny9fPjVs2FAFCxbUtm3bdOzYMUnSDz/8oE6dOmn16tUqXLiw164P3woOloYPl7p1S2rbv1/64APp5ZediwvIdS5dkhYtMosbz5ghnThhP37qlLR4sXTjje7nFi0qbd8uVatGsgC5wpw59qSBy2XWNwAAAAAA5EwBMePg999/19ixY/Xdd99ZD+SvhH0lWWAYhlwul89nHERHR+u2225TSEiIbr75Zt17773q0KGDihQpYvUxDEOzZs3So48+qn379lnts2bNUo8ePbIdw48//mhLNEyePFm33XZbtseVpHnz5ql79+7W/i233KJRo0apYsWKkqTLly/r888/19NPP634+HhJUp8+fTRp0iSvXD8jzDjwDsOQIiLMwshXFCwo7dzpXgMBQCZcuCD9+KOZLJg920wOpGfAAGnMGP/EBjioZ0/zR+KKTp2kn35yLh4AAAAAQPpybOLg0KFDGj9+vMaOHastW7ZISj1ZIEnh4eG6+eabdccdd+iWW27xaVwzZ87UnDlz9Morr6hKlSrp9o2JiVHLli118OBBSVKjRo20adOmbMdw1113aeLEiZKk4sWL68CBAwoLC8v2uImJibr66qu1+d81bNq3b6+ffvpJwamsn/3ll19q4MCB1v7atWvVvHnzbMeQERIH3rNhg9SkiZlEuOKhh6TPPnMuJiAgnTsnLVhgJgvmzJE8qG0jyfwBvP9+adAg38YHOOzIEXNJvH/fN5Akff21lM4qjgAAAAAAh+WoxMHly5c1c+ZMffPNN1q4cKESEhLcliK6sh8aGqqIiAj17dtXt9xyi8Jz6OLsX3zxhR566CFrf+fOnapZs2aWxzt9+rTKlSunCxcuSJIefvhhffrpp9mOU5LmzJljzYhwuVzasmWL6tWrl2b/Vq1a6ZdffpEkRUVFacqUKV6JIz0kDrzr/vvNhzdXBAVJmzZJDRo4FxMQUBITpcqVzfW+PNGypVmvIDJSqlHDt7EBOcSoUfb8WIEC0qFDEqscAgAAAEDOFeR0AJL0yy+/6JFHHlH58uXVt29fW/0Cl8tlq13QqVMnjRkzRgcPHtTMmTN1xx135NikgSS3pYm2bduWrfGmTJliJQ0k6Z577snWeMlNmzbN2r7hhhvSTRpIsiVE5s+fr4sXL3otFvjHW2+ZD3CuSEykUCWQKUFB5por6bnuOrOIyJ490i+/SM8+S9IAecr48fb9Xr1IGgAAAABATudYceT9+/dr3LhxGjt2rHbs2CHJvhTRldkFV5IHVyQvChwISpQoYds/nbwyYBaMHTvW2q5Xr56uvfbabI2X3Lx586ztG1Mr1plCt2TVdc+dO6dly5ZlqsgznFexojR4sJlAuGLuXLOea0bPQoE84fhxaeZM6cAB6cUXU+8TFWV/MupySe3ambMKbr3V/EED8qgdO8x8WXL9+jkTCwAAAADAc35NHFy8eFHTp0/XN998o8WLFysxMTHNZEGhQoXUu3dv1ahRQ2+88YY/w/Sqf/75x7ZfunTpLI+1a9curVy50tr35myDQ4cO6dChQ9Z+69atMzynXLlyqlatmvbs2SNJ2rBhA4mDAPTss9IXX5jLRlwxeLD0++/my9RAnnP4sDRjhlmzYMkSc2H2/Pmlxx+XChVy79+1q1SsmNSsmZlE6NWLKuPAvyZMsO+XLm3+yAAAAAAAcja/JA5+/vlnjR07VlOmTLHeuE+eMLiSLAgJCVHXrl111113qVevXipQoIAWLVrkjxB9JvnyPyEhIWrWrFmWxxo3bpz1uQUFBal///7Zju+KrVu32vY9rcNQs2ZNK3GQ3WWY4IzChaU33zQLI1+xfr307bfS3Xc7FhbgXwcOSNOnm8mCZcvMdbuSu3hRmjdP6tPH/dz8+aV9+6QcvGwe4ATDMP8tSe6OO6QQx+a7AgAAAAA85bM/3WJiYjR27FiNGzdOf//9t6S0lyJq2bKl+vXrp759+6pUqVK+Csnvzp07p08++cTaj4iIUPHixbM0lmEYGjdunLXfuXNnVfTi8hfJZ0aEhISofPnyHp1XpUoVa/tKAiEzYmNjM9X/wIEDmb4GMnb//dJ//ytt2ZLU9tJL5svTPAtFrhUTI02bZiYLfv7ZfMqZnujo1BMHEj8oQCpWrZJ27bK3efGdBwAAAACAD/kkcdCpUyctW7bMSgxI7smCmjVr6q677lK/fv1Uq1YtX4ThuMGDB2vfvn2SzO//zTffzPJYK1as0O7du619by5TJElnzpyxtgsXLqwgD9eoKVKkSKpjeKpy5cqZPgfeFxIiDR8ude+e1BYbK40cmfay7kBA2r1bmjrV/FqzxrNz8ueXunVLO2kAIFUpZxvUq2eu6AUAAAAAyPl8kjhYsmSJtZ08WVCqVCndfvvt6tevn1eL+uZEEyZM0GeffWbtP/3002ratGmWx0teFLlIkSLq3bt3tuJL6dy5c9Z2/vz5PT6vQIECqY6BwNOtm1kQOfnqYO++Kw0YIJUt61xcgNcYhtS2rbR/f8Z9w8PNTFpUlHTTTanXNgCQpkuXpEmT7G39+pm1wwEAAAAAOZ/Pliq6kjCQpLZt2+r5559XRESEgoODfXXJHGPFihUaMGCAtd+sWTO98847WR7v/PnzmjJlirXfp08f2wN7b4iLi7O2QzKx+HDyvpcvX870dWNiYjLV/8CBA2rZsmWmr4OMuVzSiBFS06ZJK7acPSu98Yb06afOxgZ4hcsl9e4tjRqV+vHChaUePcxkQUQEyw8B2TB/vnTihL3trruciQUAAAAAkHk+LU93JXmwatUqvf/++9q/f79uu+02FS1a1JeXddSGDRvUo0cPXbp0SZJUo0YNzZ49W6GhoVkec/r06bZlgLy9TJEkhSd7QHbx4kWPz0vet2DBgpm+bqVKlTJ9DnznmmvMgsjJJrjoiy+kxx6T6td3LCwgY4Yhbd5s1iE4eFD6/PPU+0VF2RMHxYpJt9wiRUZKXbqYyxIByLbx4+377dpJ1ao5EgoAAAAAIAs8W8g+C5LXNkhMTNTKlSv10EMPqVy5coqMjNT06dNtb7nnBtu3b1fXrl116tQpSVKFChW0cOFCjwsNpyX5MkW1atVS27ZtszVeagolW4bjwoULHp93/vz5VMdA4Bo6VEo+oSUhQRoyxLl4gDQZhvTHH2Yhjnr1pKuukt58U/ryS+nIkdTPuf56Mws2YID5SvShQ9I335gzDUgaAF5x4oQ0Z469rV8/Z2IBAAAAAGSNTxIH8+fPV58+fRQWFmYlECQzmXDp0iXNmDFDUVFRKlu2rB566CEtX77cF2H41e7du9W5c2cdPnxYklSqVCktXLhQNWrUyNa4+/bt06Jki87ffffd2RovLSVLlrS2z549q7Nnz3p03sGDB1MdA4GrUiXpmWfsbbNnS8lKlwDOMQzp11+l556TatY0K62++660Y0dSn4QEaebM1M8PDpb+/FMaM0a68UYpG7PBAKRuyhQp+eqFYWHSbbc5Fw8AAAAAIPN8kjiIiIjQ999/r4MHD2r06NFq1aqVbQaCZCYRTp48qTFjxqhDhw6qVq2aXnrpJW3ZssUXIflUbGysOnXqpNjYWElm8eIFCxaoQYMG2R57/PjxSkxMlGR+dr5KHNStW9e2v3fvXo/OS16jIOUYCFzPPSeVKWNvGzxY+vf/ioB/JSZKP/8sPfWUVLWqdO210vDh0u7daZ8THZ32MaqzAj6VcpmiHj3MVcEAAAAAAIHDZ0sVSeYD9IceekirVq3S9u3b9fzzz6tixYqpJhH27t2r9957T40bN1aTJk30wQcf6MCBA74MzysOHTqkzp07a/e/D7DCw8M1d+5cNWvWzCvjJ1+mqH379qpatapXxk2pTp06tsLV69evz/CcuLg4bd682dqvzyL4uUbhwuaKL8n98Yc0caIz8SAPMgxp2TKzwEalSlLbttLIkVJGBdUrVzYTDK+95pcwAdjt3i2tXGlvY5kiAAAAAAg8LiP5WkJ+YBiGFi1apK+//lozZsyw1tO/UkjZCszlUlBQkOrUqaOtW7dabQkJCf4MN13Hjx9X+/bttWnTJklSWFiYZs+erS5dunhl/F9//VXXXnuttf/NN9/4pDDyFdddd51Wr14tSXr44Yf16aefptt/zZo1at26tbX/119/qVatWj6LTzJnd1SuXFmSOduB4sq+Ex9vLhn/74+fJPOZ7Pbt9hoIgE8Yhlm3IPkSRGmpUcMsehwZKbVowYwCwEFDh0qvvJK0X6KEdOAAq4IBAAAAQKDx6YyD1LhcLnXu3FkTJkzQwYMH9cUXX6ht27a2WQhXkggJCQnatm2bNTNBklamfI3NIadPn1ZERISVNAgJCdHkyZO9ljSQ7LMNChYsqMjISK+NnZqePXta21OmTNHl5AsUp2LChAnWdv369X2eNIB/hYSYq8EkFxMj/fe/zsSDPMblMpMBaalTxyyK/Mcf0s6d0vvvSy1bkjQAHGQY7ssU3X47SQMAAAAACER+TxwkV7hwYQ0cOFDLly/Xzp079dJLL6lKlSpuSxld2TYMQzfccIMqV66sZ599Vr/99psjcZ8/f17du3e3rh8UFKTx48fbHrxn1+XLl/X9999b+1FRUSpUqJDXxk/NnXfeqdB//7o/evSoPv/88zT7xsbG2hIbvpwJAefcdJPUsaO97Z13pCNHnIkHucTFi2bF7Xvukbp2TbtfymRpw4bmEkSbNknbtklvvy01aUKyAMgh1q51nyTUv78zsQAAAAAAssfvSxV5YsmSJfr66681ffp0nTt3TpK9HkLy/Zo1a6pv377q27evV4oRZ+TSpUu6+eab9dNPP1lxfPnll7rvvvu8ep2pU6cqKtnbtosXL1aHDh0yPc6ePXtUvXp1a/+1117T66+/nmb/Rx991FqiqFChQlqwYIHatGlj63P69Gl1797dmv1RtmxZ/f333ypYsGCm48ssliryv3XrpGbNzDdJr3j0UemTT5yLCQHo/Hnphx/MosWzZ0tnziQd271bqlbN/RzDkHr2NIshR0ZK1FEBcrTHH5c+/jhpv2ZN6a+/yO0BAAAAQCDKkYmDK86dO6fJkydr3LhxWr58uQzDcEsgSElJhEaNGmnDhg0+jWnYsGEaMmSItV+8eHG1bNnS4/P79eunfh5UCezZs6dmz54tSapatap2795tm4HhqcwmDo4dO6aWLVtq165dksy6DQMHDlTXrl1VsGBBbdiwQZ988olVDNrlcmnq1Knq3bt3pmPLChIHzrjnHmncuKT94GDpzz+lunWdiwkB4OxZad48M1kwd66ZPEjNf/4jPf20f2MD4FVxcVLFivYZaa+9JqXzKwcAAAAAIAcLcTqA9BQsWFD33Xef7rvvPu3Zs0fffPONxo8fb3toLSUlETZv3uzzmM6nePB14sQJ/fDDDx6f36pVqwz7HD58WPPnz7f277777iwlDbKiZMmSmjNnjjp37qz9+/fr0qVLGjVqlEaNGuXWNygoSCNGjPBb0gDOGTpUmjzZXGFGkhISpCFDpBkzHA0LOdGpU9KcOdLUqdL8+Un/p0lPdDSJAyDA/fij+zJ2HrwnAQAAAADIoRytcZAZ1apV0+uvv66///5bS5cu1b333qtChQopB0+YyLKJEycqPj7e2r/77rv9ev369etr06ZNuvvuuxUWFpZqn6ZNm2rx4sV66qmn/BobnFG5svtz3ZkzpWXLnIkHOYxhSGPHSj16SGXKmE8Lp09PP2ngcknt2kkffSRNmeK/WAH4RMqiyK1bS7VqORMLAAAAACD7cvRSRRm5cOGCpkyZonHjxmnJkiWSpISEBIejyl1OnjypJUuWKDY2VhcuXFCFChXUtGlTv9STSA1LFTnn9GnzIVDyN0qbN5d++UUKCpgUJHymRQspo4L1QUFShw5SVJTUq5dUrpxfQgPgW6dOmT/OyXOFo0ZJjzziXEwAAAAAgOwJ6MRBcnv37tX48eP10ksvOR0KfIjEgbNGj3Z/EDRhgnTnnc7EAz+7dElKYxaS3n9fev559/aQEKlTJzNZcMstUunSvo0RgN99/bV0//1J+/nySQcOSCVLOhcTAAAAACB7ck3iAHkDiQNnxcVJV10lbduW1FalirR9u5Q/v3NxwYf27ZOmTTPrEBw+LG3ZYi4zlNLOnVLt2uZ2aKjUtasUGSn17CmVKOHfmAH4VceO0r8TPyWZOUJq4AAAAABAYMvRxZEB5Cz58knDhpnPgq/Yu9dcpv6555yLC172zz9mceOpU6VVq+zHNm+WGjd2P6dWLenJJ831q26+WSpa1C+hAnBWTIy0dKm9jaLIAAAAABD4SBwAyJSbb5bat7c/KHr7bXOZilKlnIoK2fb332aiIDpaWrs27X7R0aknDiTpww99ExuAHGviRLM++hVFi5r/TgAAAAAAAhslTQFkissljRhhbzt9WnrzTWfiQTZs325mfZo0MWcMDBmSftJAkhYs8E9sAHI8w5DGj7e33XYbS9cBAAAAQG5A4gBApjVr5r4UxejR0o4dzsSDTDAMaehQc9ZAvXrSyy9L69enf07RotLdd0uzZknLlvklTAA53/r10p9/2tv693ckFAAAAACAl5E4AJAlb78thYUl7cfHS88/71w88JDLZVYx3bw5/X4lSkgDBkjz5plFkceOlXr04FViAJZvv7XvV60qtW3rTCwAAAAAAO8icQAgS6pUkZ56yt42fbq0YoUz8SAZwzArlqYlKir19tKlpYcekhYulA4elMaMkbp1k0JDfRMngIAVH2/WN0jurrukIH6zBAAAAIBcwWUYyUvaATlbbGysKleuLEmKiYlRpUqVHI4obzt1ylwa/+jRpLYWLaQ1a3h45HeJieYHHx1tFjmOi5NiY1P/H+LgQalCBTPBUL68FBlpJhPatpWCg/0fO4CA8+OPUkSEvW3rVnMFNAAAAABA4AtxOgAAgatoUen116VBg5La1q6VJk+W+vZ1LKy8IyFB+vnnpGTB/v3246tXS23auJ9Xrpw0cqRZrKJ1a7I8ADItZVHk5s1JGgAAAABAbsKMAwQUZhzkPHFxUqNG9sLIVatK27axHL5PxMebBYqjo6Vp08z6A2l58knpww/9FhqAvOHsWalsWen8+aS2kSOlJ55wLCQAAAAAgJfxmimAbMmXTxo2zN72zz/SJ584E0+udPmytGCBNHCgOVugc2fps8/STxpI0saN/okPQJ4yY4Y9aRAcLN1xh2PhAAAAAAB8gMQBgGzr2VNq187eNnSodOyYM/HkKg8/bL7a262b9OWXGX+o9etLr75qJg1++sk/MQLIU1IuUxQRIZUp40wsAAAAAADfoMYBgGxzuaQRI6SWLZPaTp2S3nrLXL4C2XDwoHTyZPp9rr7aLHAcGSk1aOCXsADkTQcOuOck+/VzJhYAAAAAgO8w4wCAV7RoId15p71t1Cjpr7+ciSdgnD0r/fpr2sejolJvb9ZMevdds7jE+vXSK6+QNADgc999JyUmJu0XKiTdcotz8QAAAAAAfIPEAQCvefttKSwsaT8+Xnr+eefiybFOn5YmTpRuvVUqXVrq2tWsY5Cam2+WQkPN7VatzKkdu3ZJv/1mfri1a/svbgB5XspliiIjpfBwZ2IBAAAAAPgOSxUB8Jpq1aQnnrAXS542TVq5Umrb1rGwcoYTJ6RZs6ToaOnHH+2JgosXpUWLzDoGKRUtKk2aZM4wqFzZf/ECQAqbN5sTnJLr39+RUAAAAAAAPsaMAwBe9cILUsmS9rZnnpEMw5l4HHX0qFnQuFs3s3LovfdKc+akPrsgOjrtcXr1ImkAwHHffmvfr1hRat/ekVAAAAAAAD5G4gCAVxUrJr32mr3t11+lyZMdCcf/Dh2SPvtM6txZKldOGjhQWrDAXLcpLcHB0vnz/osRADIpMVGaMMHedued5u0LAAAAAJD7uAwjT74HjAAVGxuryv++eR0TE6NKlSo5HBFSc/my1KiRvTBytWrStm32Ggi5Tu/e0syZnk2vyJdP6tLFLH7cs6f7NA0AyEGWLJE6drS3bdwoNW7sTDwAAAAAAN9ixgEArwsNld5/3962Z4/0ySeOhOM/RYqknzQIC5NuuUUaN046fFiaO1e67z6SBgByvJRFka++mqQBAAAAAORmJA4A+ESvXu4FkYcOlY4dcyQc79i1y6z2nJaoKPe2AgWkyEjpu++kI0ekGTPMaqLFivkqSgDwqvPn3cuw9OvnTCwAAAAAAP8IcToAALmTyyWNGCG1apXUdvKkmTz48EPHwsq8HTvMJ2bR0dK6deasgSNHpMKF3ft26SIVKmRu33yzmUi48UapYEH/xgwAXjR7tnTmTNJ+UJBZ3wAAAAAAkHuROADgM9deK/XtK33/fVLbqFHSo49KtWo5F1e6DEPassVMFEydKm3aZD9+6ZI0b550++3u5+bPby4E3rChOdMAAHKBlMsUdeokVajgTCwAAAAAAP9gqSIAPvXOO2bNgyvi4qQXXnAunlQZhrRhg/TKK1KDBmZl59dfd08aXJFyzY7kmjcnaQAg1zh8WFqwwN7GMkUAAAAAkPuROADgU9WrS48/bm+LjpZWrXImHothSL/9Jj3/vFS7tnTNNeY6Stu2pX9eqVJSpUp+CREAnDZpkpSQkLQfHi7deqtz8QAAAAAA/IOligD43IsvSl99JR0/ntT2zDNm8sDlciio66+Xfv7Zs77lyplPyqKizPNCuHUCyBtSLlPUu3dSKRcAAAAAQO7FjAMAPle8uPTqq/a2NWvSX/HH55o2Tf94xYrSE09IK1ZIsbFmcYYOHUgaAMgztm+X1q61t7FMEQAAAADkDSQOAPjFww9LNWva255/3qw17HXx8dLixdLIkWn3iYx0b6ta1ZwKsXq1tHeveX7btlJwsA+CBICc7dtv7ftly0qdOzsTCwAAAADAv3h1FoBfhIZK779vrvZzxa5d0qefSk895YULxMVJS5aY0ximT5eOHpWCgqS77pJKl3bv37atVKaMVLiwdNttZiKhWTMH104CgJwjMdE9cXDHHUy6AgAAAIC8wmUYhuF0EICnYmNjVblyZUlSTEyMKlGkNqAYhvm8Pnlh5OLFpZ07pRIlsjDgpUvSTz9JU6dKM2ZIJ0649/niC+mBB1I/f/9+qXx5kgUAkMLKlWZJl+R+/z3jVd4AAAAAALkDSxUB8BuXS/rPf+xtJ05Ib7+diUEuXJBmzpT69zdnDNx8s/T116knDaT0CylUqEDSAABSkbIocoMGUpMmzsQCAAAAAPA/EgcA/KpVK6lPH3vbxx+byxal6dw5MwHQt6+ZLOjVy1xD4/Tp9C/WpInUqVN2QwaAPOXiRWnyZHtbv37kWQEAAAAgL2GlWgB+9+67ZhmCuDhzPy5OeuEFadKkNE64/npp3TrPBm/Z0iykEBkp1ajhlXgBIC+ZN086edLedtddjoQCAAAAAHAIMw4A+F2NGtJjj9nbJk+WVq9O44Ru3dIfsE0b6YMPpD17pF9+kZ59lqQBAGRRymWK2reXqlRxJBQAAAAAgENIHABwxEsvmYWRS+iY7tXX+lBPavBgs4Cym6go+77LJd1wg7nGUWysWcXzqaekqlX9EjsA5FbHjklz59rb+vVzJhYAAAAAgHNYqgiA/x0+rBIzZmhd6WhVPLFYIUqQJI1c9aSmTaumyMgU/a+5RqpTx3zlNSrKrHFQtqy/owaAXG/KlKRl5CQpf3733C0AAAAAIPcjcQDAPw4ckKZNk6ZOlZYtkxITlXJ+QKSmasiQZ9SjhxQamuyAyyVt2pSiEQDgbSmXKerZUypa1JlYAAAAAADOYakiAL4TEyP9979mceOKFaVBg6QlS6TExFS7Rylaf/8tjR6dykGSBgDgU7t2SatW2dtYpggAAAAA8iZmHADwrt27zVkF0dFmoWIPXHLl11yjm6boNkmG3nzTpbvvNmsgAAD849tv7fslS0o33uhMLAAAAAAAZ5E4AOBdffpIv/2Wcb+CBaXu3aWoKK0r3k2RXQpZh44fl955Rxo+3IdxAgAshuG+TFHfvlK+fM7EAwAAAABwFksVAfCu9KpoFi4s3XWXNH26dOSINGmSdNttatW5kNtpH31kTl4AAPje+PHSzp32tv79nYkFAAAAAOA8EgcAPGMY0saN0quvSj16mPupiYy07xcrJt1zjzR7tpks+PZbqVcvqUABW7d337W/2Xr5svTii179DgAAKRiGObvrnnvs7bVrSy1bOhMTAAAAAMB5LFUEIG2GIa1bZ9YriI6W/vor6djmzVLjxu7n1Kolde4sVatmzj7o0MGjwsa1akmPPiqNHJnU9v330pNPStdem91vBACQUkKC9MQT0qhR7seefFJyufweEgAAAAAghyBxAMDOMKRff01KFuzZk3q/6OjUEweStHBhli798svSN99IJ08mtQ0eLC1fzgMsAPCm8+elO++UZs50PzZokPR//+f/mAAAAAAAOQdLFQGQEhOllSulp56SqlaVWrWSRoxIO2kgmYkDLytZ0kweJLdypTRjhtcvBQB51pEjUseOqScNhg83a8wE8RsiAAAAAORpLsNIa6FyIOeJjY1V5cqVJUkxMTGqVKmSwxEFuOXLpcmTpWnTpAMHPDunShWzjkFUlNS6tdenAly6JNWrZ89Z1Kol/fmnRyseAQDSsXOn1K2beyHk0FBp3Djp9tudiQsAAAAAkLOwVBGQl73xhrR4ccb9atQwEwVRUVLz5j5dNygsTHrvPalv36S2nTulzz+XHnvMZ5cFgFzvl1+km2+Wjh61txcrZs4+aNfOkbAAAAAAADkQE9GB3C69SUVRUWkfq1tXeuklszjyzp3S++9LLVr4pdhAnz7uBZHfeMNe+wAA4LmZM81a9SmTBlWqSD//TNIAAAAAAGBH4gDIjS5elGbNku65R2rY0KxhkJreve2JgEaNpNdflzZvlrZulYYOla65xu+ViV0us8RCcseOSe++69cwACBX+PRT6dZbpQsX7O3XXCOtXi01aOBIWAAAAACAHIwaBwgo1DhIx/nz0oIFZtHi2bOls2eTjq1YIbVtm/p5Dz1kFkSOjDRnGeQgkZFm+YUrwsKkbdukatUcCwkAAkZiovTii+aEsZQiIqQpU6TChf0fFwAAAAAg5yNxgIBC4iCFs2eluXOlqVPN/54/n3q/J5+UPvzQr6F5w19/mW/Cxscntd15pzRhgnMxAUAguHRJuu8+6bvv3I/dd59ZNyZfPv/HBQAAAAAIDCxVBASaU6fMJ+e9e0ulS5tVhKdMSTtpIJmv7QdgjrB2bemRR+xtEydKa9c6Ew8ABIKTJ6Ubb0w9afD669KXX5I0AAAAAACkjxkHCCh5esbBd9+ZCYOFC6XLlzPu73KZ1S6joswkQ8WKvo/RB44elWrVMvMlV7RrJy1d6vfSCwCQ4+3dK910k/Tnn/b24GDpiy+k++93Ji4AAAAAQGBhxgEQKKZMMZcjSi9pEBwsdeokjR4t7d9vPl0fNChgkwaSVKqU9NJL9rbly83azwCAJOvXS61buycNChUy//kgaQAAAAAA8BSJAyBQREWl3h4SYq5JMWaMdPCg9NNP0v/9n1SunH/j86HHHjPrNyf33HNSXJwz8QBATrNwoTkba/9+e3u5cmayNSLCmbgAAAAAAIGJxAEQKG6+WQoNNbdDQ839b76RDh+W5s+XBgwwX8/PhfLnl9591962Y4e57AYA5HXffGMuT3TmjL29fn1pzRqpSRNHwgIAAAAABDBqHCCg5OkaB5L59LxqVTNpUKSI09H4lWFIrVpJv/6a1FaqlLRzp1S0qHNxAYBTDEMaOlR69VX3Y+3aSTNmSMWL+z0sAAAAAEAuwIwDIJC88IJ05515LmkgmYWQR4ywtx09Kr33njPxAICT4uKkBx9MPWlw++3Sjz+SNAAAAAAAZB2JAwAB4/rrpd697W0ffijt3etMPADghLNnpZ49zdI2KT37rDRxohQW5v+4AAAAAAC5B4kDAAHlvffMetBXXLokvfSSc/EAgD8dPCjdcIO0YIG93eWSPv5YGjZMCuK3OwAAAABANvGnJYCAUqeO9PDD9rZvv5V+/92ZeADAX7ZuNWu9/PGHvT1/fmnqVGnQIGfiAgAAAADkPiQOAAScV191L/MweLBZKBQAcqMVK6Q2baR//rG3lywpLV7svowbAAAAAADZQeIAQMApVcp9eaKlS6U5cxwJBwB8asoUqUsX6cQJe3vNmtLq1VLr1s7EBQAAAADIvUgcAAhIjz8uValib3v2WSkuzpl4AMDbDEP64AOpTx+znktyLVtKq1ZJtWs7ExsAAAAAIHcjcQAgIOXPL737rr1t+3ZpzBhn4gEAb0pIkJ58UnrmGfdjPXqYyxOVKeP3sAAAAAAAeQSJAwABq29fqXlze9trr0mnTzsTDwB4w4UL5iyDjz5yP/bww9L06VLBgv6PCwAAAACQd5A4ABCwgoKkESPsbUeOSO+/70w8AJBdR49KnTtL06a5H3vvPWnUKCk42P9xAQAAAADyFhIHAALaDTdIt9xib/vgAykmxpl4ACCrdu2SrrvOrF2QXL580oQJ0pAhksvlTGwAAAAAgLyFxAGAgPf++/Y3cC9elF5+2bl4ACCz1q6VWreW/vrL3l60qPTDD9KddzoTFwAAAAAgbyJxACDg1a0r/d//2dvGj5f++MOZeAAgM+bMkdq3lw4ftrdXriytXCl16OBIWAAAAACAPIzEAYBc4bXXpMKFk/YNQxo82PwvAORUn39uLrd2/ry9/aqrpNWrpUaNnIkLAAAAAJC3kTgAkCuULi29+KK9bckSae5cZ+IBgPQYhnnP+r//kxIT7cc6d5ZWrJAqVnQmNgAAAAAASBwAyDWeeMJc2iO5Z5+V4uOdiQcAUnP5stS/v/Tuu+7H7r7bTHgWKeL/uAAAAAAAuILEAYBco0AB6Z137G3btkljxjgTDwCkdOqU1K2bNGGC+7FXXpG++UYKDfV7WAAAAAAA2JA4AJCr3Hmn1LSpve2116TTp52JBwCuiImR2raVFi+2twcHS//7n/Tmm5LL5UxsAAAAAAAkR+IAQK4SFCSNGGFvO3xYGjbMmXgAQJI2bpRat5Y2b7a3FywozZ4tDRzoTFwAAAAAAKSGxAGAXKdDB6lHD3vbf/4jxcY6Ew+AvG3RIun666V9++ztZctKy5aZSxcBAAAAAJCTkDgAkCu9/765/McVFy9KL7/sXDwA8qbx46Ubb3RfLq1uXWn1aqlZM2fiAgAAAAAgPSQOAORK9etLDz5obxs3Tlq3zpl4AOQthmEWa7/7bik+3n6sbVtp1SqpenVnYgMAAAAAICMkDgDkWq+/LhUunLRvGNLgweZ/AcBX4uOl//s/6aWX3I9FRUkLF0olSvg/LgAAAAAAPEXiAECuVaaM9Pzz9rbFi6X5852JB0Dud/as1KuX9MUX7seeekqaNEnKn9/vYQEAAAAAkCkkDgDkak8+KVWqZG979ln3pUMAILsOHZLat5fmzrW3u1zSyJHSBx9IQfzmBQAAAAAIAPz5CiBXCw+X3n7b3rZli/TVV87EAyB32r5dat1a+v13e3v+/NKUKdITTzgTFwAAAAAAWUHiAECu16+fdM019rZXX5XOnHEkHAC5zM8/S9ddJ+3ebW8vUUL66ScpMtKZuAAAAAAAyCoSBwByvaAg6T//sbcdOiQNH+5MPAByj6lTpU6dpOPH7e3Vq0urVklt2jgTFwAAAAAA2UHiAECe0LGj1L27vW3ECGnfPmfiARD4Ro6UbrtNunTJ3t68ubR6tVS3riNhAQAAAACQbSQOAOQZw4bZC5NeuCC98opz8QAITImJ0tNPS089JRmG/Vj37tLSpVLZso6EBgAAAACAV5A4AJBnNGggPfCAve2bb6QNGxwJB0AAunhR6ttX+vBD92MPPijNmCEVLOj3sAAAAAAA8CoSBwDylNdflwoVSto3DGnwYPe3hgEgpePHpS5dpClT3I+9/bb02WdSSIj/4wIAAAAAwNtIHADIU8qVk4YMsbf99JP0ww/OxAMgMOzeLV13nbRypb09JEQaN0568UXJ5XImNgAAAAAAvI3EAYA85+mnpQoV7G2DB0vx8c7EAyBn++03qXVraft2e3uRItKCBVL//s7EBQAAAACAr5A4AJDnhIeby4ok9+efZr0DAEhu3jypfXvp0CF7e8WK0ooVUqdOjoQFAAAAAIBPkTgAkCf17y9dfbW97ZVXpLNnnYkHQM7zv/9JPXtK587Z2xs1ktaska66ypm4AAAAAADwNRIHAPKk4GBpxAh728GD7m0A8h7DMBOJDz4oJSTYj3XsaNY5qFTJmdgAAAAAAPAHEgcA8qzOnaVu3extw4dL+/c7Ew8A512+LN17rzR0qPuxfv2k+fOlokX9HhYAAAAAAH5F4gBAnjZsmBSU7E54/rz06qvOxQPAOadPS927S+PGuR978UWzPTTU/3EBAAAAAOBvJA4A5GmNGkkDBtjbvvpK2rjRmXgAOGPfPun666WffrK3BwVJn31mFlR3uZyJDQAAAAAAfyNxACDPe+MNqWDBpH3DkJ57zrl4APjX5s1Sq1buCcPwcGnmTOmhh5yJCwAAAAAAp5A4AJDnlS/vnij44QfzC0DutmSJ1LatFBtrby9TRlq6VLr5ZkfCAgAAAADAUSQOAEDSM8+YCYTknn1WSkhwJh4AvjdxohQRIZ06ZW+vXVtavVpq0cKZuAAAAAAAcBqJAwCQuVTR0KH2tk2bpLFjnYkHgO8YhvTee9Jdd0lxcfZjrVtLq1ZJNWo4ExsAAAAAADkBiQMA+Nc990iNG9vbXn5ZOnfOmXgAeF98vPToo9ILL7gfu/VWadEiqVQp/8cFAAAAAEBOQuIAAP4VHCyNGGFvO3BA+s9/nIkHgHedO2cmB0aPdj/2xBPS5MlSgQL+jwsAAAAAgJyGxAEAJNO1q7nmeXLDhpkJBACB6/BhqUMHafZs92MffCCNHGkmDwEAAAAAAIkDAHAzfLgUlOzueO6c9NprzsUDIHv++susXbB2rb09NFSaNEl66iln4gIAAAAAIKcicQAAKTRuLN13n73tyy+lzZudiQdA1q1ebSYNdu2ytxcvLv30k9SnjzNxAQAAAACQk5E4AIBUvPmmFB6etJ+YKD33nHPxAMi86dOljh2lY8fs7VWrSj//LF1/vTNxAQAAAACQ05E4AIBUVKggPfusvW3+fGnhQmfiAZA5H38sRUZKFy/a25s2ldaskerXdyYuAAAAAAACAYkDAEjD4MFSuXLubQkJzsQDIGOJiWbS7/HHJcOwH7vxRmnZMvefawAAAAAAYEfiAADSUKiQ9NZb9raNG6Xx452JB0D6Ll6U7rxTGjHC/diAAdKsWebPNQAAAAAASB+JAwBIx333SY0a2dteekk6f96ZeACk7vhxKSJCmjTJ/dibb0r/+5+UL5//4wIAAAAAIBCROACAdAQHS8OH29v275c++MCZeAC4++cfqW1baflye3tIiPT119Irr0gulzOxAQAAAAAQiEgcAEAGIiKkLl3sbe+9Jx086Ew8AJKsWye1aiVt3WpvL1xYmjtXuvdeR8ICAAAAACCgkTgAgAy4XOasg+RvLJ87J73+umMhAZD0ww9Su3buSbzy5c3ZB127OhMXAAAAAACBjsQBAHjg6qvd31z+3/+kLVscCQfI8776SureXTp71t7esKG0Zo10zTWOhAUAAAAAQK5A4gAAPPTWW1KBAkn7iYnSc885Fw+QFxmGOdtnwAApIcF+rH17aeVKqUoVJyIDAAAAACD3IHEAAB6qWFEaPNjeNneutGiRM/EAeU1cnJkweOMN92N33CEtWCAVK+b3sAAAAAAAyHVIHABAJjz7rFS2rL1t8GBz9gEA3zlzRurRQ/r6a/djQ4ZI334rhYX5Py4AAAAAAHIjEgcAkAmFC0tvvmlvW7/efGgJwDf27zeLIP/wg709KEgaNUp67z1zGwAAAAAAeAd/ZgNAJt1/v9Sggb3tpZek8+ediQfIzf78U2rd2kzQJVeggDRtmvTII46EBQAAAABArkbiAAAyKSREGj7c3hYbK40c6Ug4QK61dKnUpo20d6+9vVQpackS6ZZbHAkLAAAAAIBcj8QBAGRBt25Sp072tnfflQ4dciYeILf5/nspIkI6dcreXquWtHq1dO21zsQFAAAAAEBeQOIAALLA5ZJGjDD/e8XZs9IbbzgXE5AbGIY5o+eOO6TLl+3HWrWSVq0ykwcAAAAAAMB3SBwAQBZdc4109932ti++kLZudSQcIOAlJEiPPSY995z7sVtukRYtkkqX9n9cAAAAAADkNSQOACAbhg41i7RekZAgDRniXDxAoDp/XoqMlEaNcj/26KPS1KlSeLj/4wIAAAAAIC8icQAA2VCpkvTMM/a22bPNwq0APHPkiNSxozRzpvuxYcOkjz+WgoP9HxcAAAAAAHkViQMAyKbnnpPKlLG3DR4sJSY6Ew8QSHbulK67TvrlF3t7aKj03XfSs8/aa4kAAAAAAADfI3EAANlUuLD05pv2tj/+kCZOdCYeIFD88ovUurWZPEiuWDHpxx+lvn0dCQsAAAAAgDyPxAEAeMGAAVL9+va2F1+ULlxwJh4gp5s5U+rQQTp61N5epYr088/SDTc4ExcAAAAAACBxAABeERIiDR9ub4uJkUaOdCQcIEf79FPp1lvdE2vXXCOtXi01aOBIWAAAAAAA4F8kDgDAS266ySzwmty770qHDzsTD5DTJCZKzz8vPfqoew2Qrl2l5culChWciQ0AAAAAACQhcQAAXuJySSNG2Au5njkjvfGGczEBOcWlS1K/ftL777sfu+8+ac4cs14IAAAAAABwHokDAPCiJk2k/v3tbZ9/Lm3b5kw8QE5w8qR0443Sd9+5H3vtNenLL6V8+fweFgAAAAAASAOJAwDwsqFDpfz5k/YTEqQhQ5yLB3DS3r1SmzbS0qX29uBgacwY6fXX7bN0AAAAAACA80gcAICXVa4sPf20vW3WLPcHp0But3691KqVtGWLvb1QIXNpogEDHAkLAAAAAABkgMQBAPjAkCFS6dL2tsGD3QvCArnVwoVSu3bSgQP29nLlzCLIN97oTFwAAAAAACBjJA4AwAeKFHEvivz776mv8Q7kNt98I910k1kcPLn69aU1a8xaIAAAAAAAIOcicQAAPjJwoFSvnr3txRelCxeciQfwNcOQ3nxTuu8+KT7efqxdO+nnn6WqVZ2JDQAAAAAAeI7EAQD4SL580rBh9ra9e6WPPnImHsCX4uKkBx6QXnvN/VifPtIPP0jFi/s/LgAAAAAAkHkkDgDAh26+WWrf3t72zjvSkSOOhAP4xNmzUs+e0pdfuh8bPNhcoit/fv/HBQAAAAAAsobEAQD4kMsljRhhbzt92lzOBcgNDh6UbrhBWrDA3u5ymbNrhg+XgvhtAwAAAACAgMKf8gDgY82aSf362ds++0zavt2ZeABv2bpVatVK+uMPe3v+/NLUqdJjjzkTFwAAAAAAyB4SBwDgB2+/LYWFJe3Hx0vPP+9cPEB2rVghtWkj/fOPvb1kSWnxYql3b2fiAgAAAAAA2UfiIItOnjypqVOnatCgQWrbtq3Kli2r0NBQFS5cWNWqVVPv3r01evRonT17NsvXOHTokEaNGqUuXbqoZs2aCg8PV4ECBVS1alV169ZN77//vn799VclJiZ68Tsz7d69Wy+99JJatWqlsmXLKiwsTBUrVlSbNm00fPhwHTx40OvXBHKzKlWkp56yt82YIS1f7kg4QLZMmSJ16SKdOGFvr1FDWrVKat3ambgAAAAAAIB3uAzDMJwOIpBs27ZNgwcP1o8//qi4uLgM+xctWlQjR47Uvffe6/E1EhIS9N///levvvqqzp07l2H/v/76S7Vq1fJ4/Iyu/eKLL+rDDz9M9/srWrSoPv74Y/Xv398r1/VUbGysKleuLEmKiYlRpUqV/Hp9IDtOnZJq1ZKOHk1qa9FCWrOGNeARGAxD+vBD6Zln3I+1aCHNmSOVKeP/uAAAAAAAgHeFOB1AoNm8ebPmzp1rawsODlatWrVUtmxZJSQkaOvWrTp+/Lgk6dSpU7rvvvv0999/66233spw/Li4OEVFRWnWrFm29lq1aqlChQoyDEP79+/Xrl275O2cT2JioqKiojRjxgyrzeVyqWHDhipdurSOHj2qzZs3yzAMnTp1SnfffbfOnTun//u///NqHEBuVbSo9Prr0qBBSW1r10qTJkl33OFYWIBHEhKkp582Cx6n1KOH9N13UsGC/o8LAAAAAAB4H++4ZlFISIh69eqlGTNm6Pjx49q2bZuWLVumlStX6ujRo5oxY4YqVqxo9R86dKhmz56d4bj9+vWzkgb58uXTkCFDtHfvXv31119atmyZli9frp07d+r48eP67rvv1KVLFwV56VXlt99+25Y06NWrl/bs2aNNmzZp8eLF2rhxo/bu3avIyEirz2OPPabVq1d75fpAXvDgg1KdOva2F16QLl50Jh7AExcuSH36pJ40ePhhado0kgYAAAAAAOQmLFWUSTNnztScOXP0yiuvqEqVKun2jYmJUcuWLa16AI0aNdKmTZvS7D9+/HjdfffdkqTw8HDNnTtX7du391rs6Tl69KiqVatmLY3Uq1cvTZ06NdWkhGEYuu222zR16lRJUps2bbRy5Uq/xMlSRcgNZs6UevWytw0bJj37rCPhAOk6elS65RazdkFK774rDRkiuVz+jwsAAAAAAPgOiQMf++KLL/TQQw9Z+zt37lTNmjXd+p05c0Y1atTQ0X8XP//iiy/0wAMP+C3OTz75RI899pgkczbFnj17bDMmUjp48KCqVq2qy5cvS5KWLFnilyQHiQPkBoYhtW9vL4xctKi0c6dUqpRjYQFudu2SbrxR+usve3u+fNLXX0t33eVMXAAAAAAAwLdYqsjHevToYdvftm1bqv0mTpxoJQ3q1aungQMH+jy25JYsWWJtt23bNt2kgSSVK1dOHTp0sPajo6N9FhuQ27hc0ogR9rZTpyQPyqAAfrN2rdS6tXvSoEgRacECkgYAAAAAAORmJA58rESJErb906dPp9rvyy+/tLb79esnl5/Xffjnn3+s7auuusqjc5L3S1nMGUD6WrSQ7rzT3vbpp+4PaQEnzJljzoo5fNjeXqmS9PPPUseOjoQFAAAAAAD8hMSBjyV/IC9JpUuXdutz8uRJ/fbbb9Z+8jf5/eXUqVPWduHChT06p0iRItZ2TEyMjh8/7vW4gNzs7belsLCk/fh46fnnnYsHkKTPPzdrGpw/b2+/6ippzRqpUSNn4gIAAAAAAP5D4sDHpk2bZm2HhISoWbNmbn1+++03JS810bhxY0nS8uXL1b9/f9WoUUP58+dXyZIl1bRpUz377LPavn27V+NMniw4c+aMR+eknD2xZcsWr8YE5HbVqklPPGFvmzZN8lOtccDGMKQXX5T+7/+kxET7sc6dpRUrpAxWsQMAAAAAALlEiNMB5Gbnzp3TJ598Yu1HRESoePHibv02btxobRcqVEihoaF64IEHNGbMGFu/S5cu6fjx41q3bp0+/PBDPf3003rvvfcUFJT9/E+lSpW0bt06SdKmTZs8Oidlv927d6tt27aZum5sbGym+h84cCBT/YGc7oUXpC+/lI4dS2p75hnzzW4/r1iGPOzyZen++6UJE9yP3X239L//SaGh/o8LAAAAAAA4g8SBDw0ePFj79u2TJLlcLr355pup9juW7IlhoUKFdP/992vixImSpODgYDVu3FjFixdXbGys/vp3AfSEhAQNHz5cBw4c0Pjx47Mda9u2bTV79mxJ0sqVK7V//35VqFAhzf6HDx+2FVSWPJ+pkFzlypUzfQ6QmxQrJr32mvT440ltv/4qTZ4s3X67Y2EhDzl1Srr1VmnxYvdjL78svfkmSSwAAAAAAPIalirykQkTJuizzz6z9p9++mk1bdo01b7J6wscPHjQShrccccdio2N1bp167R48WLt2LFD69evV/Pmza3+3377rdvMhKzo06ePgoODJUlxcXF6/PHHbcsnJWcYhh5//HFdunTJ1n727NlsxwHkRQ89JNWubW97/nkpxY8Y4HUxMVLbtu5Jg+Bg6YsvpLfeImkAAAAAAEBeROLAB1asWKEBAwZY+82aNdM777yTZv+LFy+6td15552aOHGiypUrZ2u/+uqrtXjxYjVo0MBqe+ONNxQXF5etmKtVq6b777/f2p86daqioqIUExNj67dv3z7dfvvtmjRpktsYWYkhJiYmU1+//vpr5r85IIcLDZXef9/etmePlGylM8DrNm6UWreWNm+2t4eHS7NmSQ884ExcAAAAAADAeS4jrdfKkSUbNmzQDTfcYM0iqFGjhlauXKny5cunec4TTzyhjz76yNovUKCAYmJiVLJkyTTP+fHHHxUREWHtz5s3T926dctW7OfPn1ebNm20fv16q83lcqlRo0YqXbq0jh49qs2bNyvx36qZTzzxhP773/9afT/++GMNGjQoWzFkJDY21lreKCYmRpUqVfLp9QB/MQypXTt7YeRixaSdO6V0bgVAlixaZC5PlKLGvcqUkebOlZJNbAMAAAAAAHkQMw68aPv27eratauVNKhQoYIWLlyYbtJAMusaJNetW7d0kwaS1KVLF5UpU8baX758eRajThIeHq5ly5apV69eVpthGNq0aZMWL16sjRs3KjExUUFBQRoyZIheeukl2/nFihXLdgxAXuVySSNG2NtOnpSGDnUkHORi48dLN97onjSoW9csyk3SAAAAAAAAkDjwkt27d6tz5846fPiwJKlUqVJauHChatSokeG5pUqVsu2nVQshOZfLpSZNmtiu7w1FihTR9OnTtXLlSj344IOqV6+eihQpovz586tmzZoaOHCgfv31V7333nu2os6SVKVKFa/EAORV114r9e1rbxs1ypx1AGSXYUjvvCPdfbcUH28/1qaN9PPPUvXqzsQGAAAAAABylhCnA8gNYmNj1alTJ8XGxkoyH74vWLDAVocgPfXq1bPtZzTbILV+x48f9zBaz7Rp00Zt2rRJt8+WLVus7aCgII8SHgDS98470rRp0uXL5n5cnPTCC9KUKc7GhcBy6ZK0a5e0fbv5tWOHtGmTtHate9/ISOnbb6X8+f0fJwAAAAAAyJlIHGTToUOH1LlzZ+uN//DwcM2dO1fNmjXzeIyGDRva9i9duuTRecmLKhcoUMDj63nL2mRPoBo2bOi25BKAzKteXXr8cfuyRdHR0qpV0nXXORcXch7DkPbvN5MCyRME27dLu3dL/5ajSddTT5n/Xwti/iEAAAAAAEiGxEE2HD9+XF26dNH27dslSWFhYZoxY4batm2bqXGqVKmiatWqac+ePZI8X3boSn9JKlu2bKau6Q3R0dHWdt+U66sAyLIXX5S++kpKPpHomWfM5IHL5VxccMaZM2ZCIGWCYMcO6ezZrI3pckkffCA9+aRXQwUAAAAAALkEiYMsOn36tCIiIrRp0yZJUkhIiCZPnqwuXbpkabzevXvrww8/lCQtXLgww/6HDh3Sxo0brf1WrVpl6bpZNX36dO38d+H1kJAQ3XfffX69PpCbFS8uvfqq/aHumjXmzIPbbnMsLPhQfLy0Z4/7zIEdO8xZBd5UrJj0v/9JUVHeHRcAAAAAAOQeLsMwDKeDCDTnz59XRESEVq5cKclc33/ChAnZeut+06ZNuuaaa5T479oSM2fOVM+ePdPs/+yzz2rEv2uZhIaGavfu3apQoUKWr58Zhw4d0rXXXqt//vlHkvTkk09aSQ9fi42NVeXKlSVJMTExqlSpkl+uC/jb5ctSgwbS338ntdWoIW3ZIoWFORcXss4wpCNHUl9a6O+/zXoW3launFSnjlS3rvlVp47Uvr1UuLD3rwUAAAAAAHIPEgeZdOnSJd1888366aefJEkul0tffvmlV96479+/v7799ltJUunSpfXTTz/pqquucuv3/fffq1+/fkpISJAkPfDAA/riiy9SHfObb76xxbZkyRK1b98+1b47duzQwYMH1a5duzRj/PPPP9W3b19t3rxZkrnM0pYtW1SwYEGPvsfsInGAvGTqVPe3wj/4wFyXHjnXhQvSX3+lniA4edL71wsPNxMCKRMEdepIRYt6/3oAAAAAACD3Y6miTPrvf/9rJQ0kqVixYpo0aZImTZrk0fn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+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 575,
+       "width": 775
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# find the last year of the dataset\n",
     "last_year = ds_long['time.year'].max().item()\n",
@@ -406,7 +1821,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "## ERA5-Land hourly data from 1950 to present \n",
     "This [ERA5 Reanalysis dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview) of hourly data has an increased spatial resolution and focuses on the land variable evolution over the last decades. It is updated regularly by ECMWF and accessible via the CDS API (cf. [`get_ERA5_reanalysis_data.ipynb`](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D2_StateoftheClimateOceanandAtmosphereReanalysis/get_ERA5_reanalysis_data.ipynb)). A similar dataset of lower temporal resolution, i.e. monthly averages can be found [here](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview).\n",
@@ -419,7 +1836,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "import cdsapi\n",
@@ -455,7 +1874,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "As you can see in the request code block, we downloaded the 2m_temperature / $\\text{t2m}$ variable from the *reanalysis-era5-land* at noon on every 15th of the month in the last 50 years. In other words, the requested data is not averaged over the whole month but just a sample. To reduce the resolution, we chose a grid of 0.4° in both spatial dimensions. As we want to calculate global trends over the whole time period, this choice should be adequate and save us a few computational-intensive averaging calculations. Furthermore, it helps to emphasize how the downloaded data looks like. \n",
     "\n",
@@ -470,15 +1891,66 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": true
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://osf.io/download/6d9mf/' to file '/tmp/reanalysis-era5-land_1974_2023_04x04.grib'.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The file reanalysis-era5-land_1974_2023_04x04.grib does not exist locally and has to be downloaded from OSF.\n",
+      "Downloading the data ...\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 91297515da329e89e4035e8e94734046ea2003259a29f10bc13069cce3654cc7\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset> Size: 974MB\n",
+      "Dimensions:     (time: 600, latitude: 451, longitude: 900)\n",
+      "Coordinates:\n",
+      "    number      int64 8B ...\n",
+      "  * time        (time) datetime64[ns] 5kB 1974-01-15 1974-02-15 ... 2023-12-15\n",
+      "    step        timedelta64[ns] 8B ...\n",
+      "    surface     float64 8B ...\n",
+      "  * latitude    (latitude) float64 4kB 90.0 89.6 89.2 88.8 ... -89.2 -89.6 -90.0\n",
+      "  * longitude   (longitude) float64 7kB 0.0 0.4 0.8 1.2 ... 358.8 359.2 359.6\n",
+      "    valid_time  (time) datetime64[ns] 5kB ...\n",
+      "Data variables:\n",
+      "    t2m         (time, latitude, longitude) float32 974MB ...\n",
+      "Attributes:\n",
+      "    GRIB_edition:            1\n",
+      "    GRIB_centre:             ecmf\n",
+      "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+      "    GRIB_subCentre:          0\n",
+      "    Conventions:             CF-1.7\n",
+      "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+      "    history:                 2024-06-17T21:46 GRIB to CDM+CF via cfgrib-0.9.1...\n"
+     ]
+    }
+   ],
    "source": [
     "# specify filename and filetype\n",
     "filetype = 'grib'\n",
     "#filetype = 'nc'\n",
     "fname_ERA5 = f\"reanalysis-era5-land_1974_2023_04x04.{filetype}\"\n",
-    " \n",
+    "\n",
     "# check whether the specified path/file exists or not (locally or in the JupyterHub)\n",
     "isExist = os.path.exists(fname_ERA5)\n",
     "\n",
@@ -493,7 +1965,7 @@
     "        raise (\"Please choose an appropriate file type: 'nc' or 'grib'.\")\n",
     "\n",
     "else:\n",
-    "    _ = print(f'The file {fname_ERA5} does not exist locally and has to be downloaded from OSF.\\nDownloading the data ...\\n')    \n",
+    "    _ = print(f'The file {fname_ERA5} does not exist locally and has to be downloaded from OSF.\\nDownloading the data ...\\n')\n",
     "\n",
     "    # retrieve the grib file from the OSF cloud storage\n",
     "    if filetype == 'grib':\n",
@@ -502,15 +1974,15 @@
     "        link_id = \"8v63z\"\n",
     "    else:\n",
     "        raise (\"Please choose an appropriate file type: 'nc' or 'grib'.\")\n",
-    "        \n",
+    "\n",
     "    url_grib = f\"https://osf.io/download/{link_id}/\"\n",
     "\n",
     "    # The following line is the correct approach, however, it sometimes raises an error that could not be solved by the curriculum team\n",
     "    # (cf. https://github.com/ecmwf/cfgrib/blob/master/README.rst & https://github.com/pydata/xarray/issues/6512)\n",
-    "    # We, therefore, recommend to download the file separately if this EOFError arises. \n",
-    "    \n",
+    "    # We, therefore, recommend to download the file separately if this EOFError arises.\n",
+    "\n",
     "    fcached = pooch_load(url_grib, fname_ERA5)\n",
-    "    \n",
+    "\n",
     "    try:\n",
     "        if filetype == 'grib':\n",
     "            ds_global = xr.open_dataset(fcached, engine='cfgrib')\n",
@@ -519,14 +1991,32 @@
     "    except EOFError:\n",
     "        print(f'The cached .grib file could not be parsed with Xarray.\\nPlease download the file to your local directory via {url_grib} or download its NetCDF equivalent.')\n",
     "\n",
-    "print(ds_global)\n"
+    "print(ds_global)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 575,
+       "width": 768
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# plot the mean 2m temperature of 1974 as example\n",
     "t2m_1974 = ds_global.sel(time=slice('1974')).mean(dim='time')\n",
@@ -536,7 +2026,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "YFB352Tmjil7"
+    "execution": {}
    },
    "source": [
     "## Weekly Mortality Data (Optional)\n",
@@ -548,14 +2038,205 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 652
-    },
-    "id": "qAGgrbtLljHg",
-    "outputId": "45bc3215-d89f-418c-b9bd-c7db6d3bd82b"
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 16168 entries, 0 to 16167\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column                    Non-Null Count  Dtype \n",
+      "---  ------                    --------------  ----- \n",
+      " 0   STRUCTURE_NAME            16168 non-null  object\n",
+      " 1   ACTION                    16168 non-null  object\n",
+      " 2   REF_AREA                  16168 non-null  object\n",
+      " 3   Reference area            16168 non-null  object\n",
+      " 4   FREQ                      16168 non-null  object\n",
+      " 5   Frequency of observation  16168 non-null  object\n",
+      " 6   MEASURE                   16168 non-null  object\n",
+      " 7   Measure                   16168 non-null  object\n",
+      " 8   AGE                       16168 non-null  object\n",
+      " 9   Age                       16168 non-null  object\n",
+      " 10  SEX                       16168 non-null  object\n",
+      " 11  Sex                       16168 non-null  object\n",
+      " 12  UNIT_MEASURE              16168 non-null  object\n",
+      " 13  Unit of measure           16168 non-null  object\n",
+      " 14  TIME_PERIOD               16168 non-null  object\n",
+      " 15  OBS_VALUE                 16168 non-null  int64 \n",
+      "dtypes: int64(1), object(15)\n",
+      "memory usage: 2.0+ MB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>STRUCTURE_NAME</th>\n",
+       "      <th>ACTION</th>\n",
+       "      <th>REF_AREA</th>\n",
+       "      <th>Reference area</th>\n",
+       "      <th>FREQ</th>\n",
+       "      <th>Frequency of observation</th>\n",
+       "      <th>MEASURE</th>\n",
+       "      <th>Measure</th>\n",
+       "      <th>AGE</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SEX</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>UNIT_MEASURE</th>\n",
+       "      <th>Unit of measure</th>\n",
+       "      <th>TIME_PERIOD</th>\n",
+       "      <th>OBS_VALUE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mortality by week</td>\n",
+       "      <td>I</td>\n",
+       "      <td>SWE</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Weekly</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Mortality</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>DT</td>\n",
+       "      <td>Deaths</td>\n",
+       "      <td>2015-W01</td>\n",
+       "      <td>1927</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mortality by week</td>\n",
+       "      <td>I</td>\n",
+       "      <td>SWE</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Weekly</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Mortality</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>DT</td>\n",
+       "      <td>Deaths</td>\n",
+       "      <td>2015-W02</td>\n",
+       "      <td>1966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mortality by week</td>\n",
+       "      <td>I</td>\n",
+       "      <td>SWE</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Weekly</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Mortality</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>DT</td>\n",
+       "      <td>Deaths</td>\n",
+       "      <td>2015-W03</td>\n",
+       "      <td>1935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Mortality by week</td>\n",
+       "      <td>I</td>\n",
+       "      <td>SWE</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Weekly</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Mortality</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>DT</td>\n",
+       "      <td>Deaths</td>\n",
+       "      <td>2015-W04</td>\n",
+       "      <td>1946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Mortality by week</td>\n",
+       "      <td>I</td>\n",
+       "      <td>SWE</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Weekly</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Mortality</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>_T</td>\n",
+       "      <td>Total</td>\n",
+       "      <td>DT</td>\n",
+       "      <td>Deaths</td>\n",
+       "      <td>2015-W05</td>\n",
+       "      <td>1975</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      STRUCTURE_NAME ACTION REF_AREA Reference area FREQ  \\\n",
+       "0  Mortality by week      I      SWE         Sweden    W   \n",
+       "1  Mortality by week      I      SWE         Sweden    W   \n",
+       "2  Mortality by week      I      SWE         Sweden    W   \n",
+       "3  Mortality by week      I      SWE         Sweden    W   \n",
+       "4  Mortality by week      I      SWE         Sweden    W   \n",
+       "\n",
+       "  Frequency of observation MEASURE    Measure AGE    Age SEX    Sex  \\\n",
+       "0                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
+       "1                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
+       "2                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
+       "3                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
+       "4                   Weekly       M  Mortality  _T  Total  _T  Total   \n",
+       "\n",
+       "  UNIT_MEASURE Unit of measure TIME_PERIOD  OBS_VALUE  \n",
+       "0           DT          Deaths    2015-W01       1927  \n",
+       "1           DT          Deaths    2015-W02       1966  \n",
+       "2           DT          Deaths    2015-W03       1935  \n",
+       "3           DT          Deaths    2015-W04       1946  \n",
+       "4           DT          Deaths    2015-W05       1975  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# read the mortality data from a csv file\n",
     "link_id = \"rh3mp\"\n",
@@ -569,7 +2250,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "YHJ5l944lstE"
+    "execution": {}
    },
    "source": [
     "# Hint for Q3\n",
@@ -580,7 +2261,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "hU7ut6Zgl6Ml"
+    "execution": {}
    },
    "source": [
     "# Hint for Q4\n",
@@ -590,7 +2271,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "vJwFdRc3l7pv"
+    "execution": {}
    },
    "source": [
     "# Hint for Q5\n",
@@ -601,7 +2282,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "1T2EcomF7U9M"
+    "execution": {}
    },
    "source": [
     "# Hint for Q6\n",
@@ -612,7 +2293,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "GvVxbZFYfLLK"
+    "execution": {}
    },
    "source": [
     "# Hint for Q7\n",
@@ -622,7 +2303,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "d5GaD8vdmOfO"
+    "execution": {}
    },
    "source": [
     "# Further reading\n",
@@ -652,7 +2333,16 @@
  ],
  "metadata": {
   "colab": {
-   "provenance": []
+   "collapsed_sections": [],
+   "include_colab_link": true,
+   "name": "Heatwaves_2024",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernel": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
   },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
@@ -669,7 +2359,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/projects/project-notebooks/Ocean_acidification_2024.ipynb b/projects/project-notebooks/Ocean_acidification_2024.ipynb
index 5f4f8ac95..554b266d7 100644
--- a/projects/project-notebooks/Ocean_acidification_2024.ipynb
+++ b/projects/project-notebooks/Ocean_acidification_2024.ipynb
@@ -3,8 +3,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "execution": {},
-    "id": "x1WZhSN61YBE"
+    "execution": {}
    },
    "source": [
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ClimateMatchAcademy/course-content/blob/main/projects/project-notebooks/Ocean_acidification.ipynb) &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/ClimateMatchAcademy/course-content/main/projects/project-notebooks/Ocean_acidification.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
@@ -13,7 +12,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "avjFjFXwWqyW"
+    "execution": {}
    },
    "source": [
     "# Ocean Acidification\n",
@@ -34,10 +33,26 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "umnBeSiJeqYo",
+    "cellView": "form",
+    "execution": {},
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a5e84792d5843b8b86763755987168c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tab(children=(Output(), Output()), selected_index=0, titles=('Youtube', 'Bilibili'))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# @title Project Background\n",
     "\n",
@@ -100,9 +115,40 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "TB8_jibV1YBM"
+    "cellView": "form",
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "If you want to download the slides: https://osf.io/download/n7wdy/\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"854\"\n",
+       "            height=\"480\"\n",
+       "            src=\"https://mfr.ca-1.osf.io/render?url=https://osf.io/n7wdy/?direct%26mode=render%26action=download%26mode=render\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7fbc9c75eb20>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# @title Tutorial slides\n",
     "# @markdown These are the slides for the videos in all tutorials today\n",
@@ -116,7 +162,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "DZbqRlQKXhnc"
+    "execution": {}
    },
    "source": [
     "Human activities release CO2 into the atmosphere, which leads to atmospheric warming and climate change. A portion of this CO2 released by human activities is absorbed into the oceans, which has a direct, chemical effect on seawater, known as ocean acidification. When CO2 combines with water in the ocean it forms carbonic acid, which makes the ocean more acidic and can have negative impacts on certain marine ecosystems (e.g., reduce the ability of calcifying organisms to form their shells and skeletons). The degree of ocean acidification is often expressed in terms of the pH of seawater, which is the measure of acidity or alkalinity such that a pH below 7 is considered acidic, and a pH greater than 7 is considered alkaline, or basic. Additional background information on ocean acidification can be found [here](https://coastadapt.com.au/ocean-acidification-and-its-effects). In this project, you will explore spatial and temporal patterns of and relationships between pH, CO2, and temperature to assess changes in ocean acidification and the impact on marine ecosystems.\n",
@@ -127,7 +173,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "B2hG2V_2veFC"
+    "execution": {}
    },
    "source": [
     "# Project Template\n",
@@ -140,7 +186,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "-uS_n9-3YJrZ"
+    "execution": {}
    },
    "source": [
     "# Data Exploration Notebook\n",
@@ -151,7 +197,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "drh-swqCsb9k",
+    "execution": {},
     "tags": [
      "colab"
     ]
@@ -167,7 +213,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "9nDg6MeVY1CX",
+    "execution": {},
     "tags": []
    },
    "outputs": [],
@@ -188,7 +234,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "8GNKCquN1YBV"
+    "execution": {}
    },
    "outputs": [],
    "source": [
@@ -209,7 +255,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "iYblWn8cZAGT"
+    "execution": {}
    },
    "source": [
     "## NOAA Ocean pH and Acidity"
@@ -218,7 +264,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "W43m6TQBZrpe"
+    "execution": {}
    },
    "source": [
     "### Global surface ocean acidification indicators from 1750 to 2100 (NCEI Accession 0259391)\n",
@@ -251,7 +297,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "xqiXjDKC9G2M"
+    "execution": {}
    },
    "source": [
     "We can load and visualize the **surface pH** as follows:"
@@ -261,10 +307,497 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "mZMo14eJs26r",
+    "execution": {},
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://www.ncei.noaa.gov/data/oceans/ncei/ocads/data/0259391/nc/median/pHT_median_historical.nc' to file '/tmp/pHT_median_historical.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 5a88450b240954e1db5771e06c54278d0dd3e1edbdfcd7b05f25bec3b2c47f1f\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
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+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 10MB\n",
+       "Dimensions:    (time: 18, lat: 180, lon: 360)\n",
+       "Coordinates:\n",
+       "  * time       (time) float64 144B 1.75e+03 1.85e+03 1.86e+03 ... 2e+03 2.01e+03\n",
+       "Dimensions without coordinates: lat, lon\n",
+       "Data variables:\n",
+       "    pHT        (time, lat, lon) float64 9MB ...\n",
+       "    longitude  (lat, lon) float64 518kB ...\n",
+       "    latitude   (lat, lon) float64 518kB ...\n",
+       "Attributes:\n",
+       "    title:               Global surface ocean pH on total hydrogen ion scale ...\n",
+       "    comment:             This gridded data product contains pH on total hydro...\n",
+       "    reference:           Jiang, L-Q., J. Dunne, B. R. Carter, J. Tjiputra,\\n ...\n",
+       "    Fair_use_statement:  This data product is made freely available\\n   to th...\n",
+       "    created_by:          Li-Qing Jiang\n",
+       "    institution:         (a) Cooperative Institute for Satellite Earth System...\n",
+       "    contact:             &lt;Liqing.Jiang@noaa.gov&gt;\n",
+       "    creation_date:       August 14, 2022</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-303e42c5-d217-4904-8a7d-c53d067912a1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-303e42c5-d217-4904-8a7d-c53d067912a1' 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>: 18</li><li><span>lat</span>: 180</li><li><span>lon</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a4dee663-54bc-45c2-8375-e785db2b8095' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a4dee663-54bc-45c2-8375-e785db2b8095' class='xr-section-summary' >Coordinates: <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 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'>1.75e+03 1.85e+03 ... 2.01e+03</div><input id='attrs-125ab974-f268-41de-8ac2-bac074ff5267' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-125ab974-f268-41de-8ac2-bac074ff5267' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e862f2b1-ba2d-482f-b77f-5e1c0a527ed4' class='xr-var-data-in' type='checkbox'><label for='data-e862f2b1-ba2d-482f-b77f-5e1c0a527ed4' 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>The decade of 1750, and all decades from 1850 to 2010, i.e., 1750, 1850, 1860, 1870, 1880, 1890, 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010</dd><dt><span>CF_name :</span></dt><dd>time</dd><dt><span>unit :</span></dt><dd>year</dd></dl></div><div class='xr-var-data'><pre>array([1750., 1850., 1860., 1870., 1880., 1890., 1900., 1910., 1920., 1930.,\n",
+       "       1940., 1950., 1960., 1970., 1980., 1990., 2000., 2010.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-503c2e06-1411-4c29-a994-de026dcd9000' class='xr-section-summary-in' type='checkbox'  checked><label for='section-503c2e06-1411-4c29-a994-de026dcd9000' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pHT</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b375bdcb-32ad-42d9-b522-0f0d02c71798' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b375bdcb-32ad-42d9-b522-0f0d02c71798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36130497-25d5-4ec6-8527-ea22d9dcd85f' class='xr-var-data-in' type='checkbox'><label for='data-36130497-25d5-4ec6-8527-ea22d9dcd85f' 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>pH on total hydrogen ion scale</dd><dt><span>CF_name :</span></dt><dd>sea_water_ph_reported_on_total_scale</dd><dt><span>units :</span></dt><dd>N/A</dd></dl></div><div class='xr-var-data'><pre>[1166400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-be25df68-9030-4bd3-b366-e44d61fd8da9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be25df68-9030-4bd3-b366-e44d61fd8da9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20566251-2334-4824-b820-e34a06071081' class='xr-var-data-in' type='checkbox'><label for='data-20566251-2334-4824-b820-e34a06071081' 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 on the 1 x 1 degree global grid (20.5 to 379.5 degrees).</dd><dt><span>CF_name :</span></dt><dd>grid_longitude</dd><dt><span>units :</span></dt><dd>decimal degrees</dd></dl></div><div class='xr-var-data'><pre>[64800 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-675b3935-8bce-4a24-84ee-775a33150c75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-675b3935-8bce-4a24-84ee-775a33150c75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b49a5bd-e5d0-43cd-b240-bcc243817a99' class='xr-var-data-in' type='checkbox'><label for='data-3b49a5bd-e5d0-43cd-b240-bcc243817a99' 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 on the 1 x 1 degree grid (-89.5 to 89.5 degrees) at this depth level.</dd><dt><span>CF_name :</span></dt><dd>grid_latitude</dd><dt><span>units :</span></dt><dd>decimal degrees</dd></dl></div><div class='xr-var-data'><pre>[64800 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5872eeb1-c390-45ec-940f-8e569967b1a9' class='xr-section-summary-in' type='checkbox'  ><label for='section-5872eeb1-c390-45ec-940f-8e569967b1a9' 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-be13c959-a16f-4dd4-9253-1fb88ed14833' class='xr-index-data-in' type='checkbox'/><label for='index-be13c959-a16f-4dd4-9253-1fb88ed14833' 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([1750.0, 1850.0, 1860.0, 1870.0, 1880.0, 1890.0, 1900.0, 1910.0, 1920.0,\n",
+       "       1930.0, 1940.0, 1950.0, 1960.0, 1970.0, 1980.0, 1990.0, 2000.0, 2010.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f935230f-d4f7-4ae8-af88-700f3df138a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f935230f-d4f7-4ae8-af88-700f3df138a5' 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>title :</span></dt><dd>Global surface ocean pH on total hydrogen ion scale (unit: N/A) on a 1 x 1 grid\n",
+       "   in 1750, and all decades from 1850 to 2010.</dd><dt><span>comment :</span></dt><dd>This gridded data product contains pH on total hydrogen ion scale\n",
+       "   [median of 14 Earth System Models from the sixth phase of the Coupled Model\n",
+       "   Intercomparison Project (CMIP6)] on a global [20.5:1:379.5] degree longitude\n",
+       "   x [-89.5:1:89.75] degree latitude grid in the global surface ocean in 1750 and\n",
+       "   all decades from 1850 to 2010, i.e., 1750, 1850, 1860, 1870, 1880, 1890, 1900,\n",
+       "   1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010.\n",
+       "\n",
+       "   At each grid point, temperature, salinity, DIC, and TA were estimated based on\n",
+       "   their temporal changes out of fourteen Earth System Models of the\n",
+       "   sixth phase of the Coupled Model Intercomparison Project (CMIP6):\n",
+       "   (1) ACCESS-ESM1-5, Ziehn et al., 2020, https://doi.org/10.1071/es19035,\n",
+       "   (2) CanESM5, Swart et al., 2019, https://doi.org/10.5194/gmd-12-4823-2019,\n",
+       "   (3) CESM2, Danabasoglu et al., 2020, https://doi.org/10.1029/2019ms001916,\n",
+       "   (4) CMCC-ESM2, Lovato et al., 2022, https://doi.org/10.1029/2021ms002814,\n",
+       "   (5) CNRM-ESM-2, Séférian et al., 2019, https://doi.org/10.1029/2019ms001791,\n",
+       "   (6) EC-Earth3-CC, Döscher et al., 2021, https://doi.org/10.5194/gmd-2020-446,\n",
+       "   (7) GFDL-CM4, Held et al., 2019, https://doi.org/10.1029/2019MS001829,\n",
+       "   (8) GFDL-ESM4, Dunne et al., 2020, https://doi.org/10.1029/2019MS002015,\n",
+       "   (9) IPSL-CM6A-LR, Boucher et al., 2020, https://doi.org/10.1029/2019ms002010,\n",
+       "   (10) MIROC-ES2L, Hajima et al., 2020, https://doi.org/10.5194/gmd-13-2197-2020,\n",
+       "   (11) MPI-ESM1-2-LR, Mauritsen et al., 2019, https://doi.org/10.1029/2018ms001400,\n",
+       "   (12) MRI-ESM2-0, Yukimoto et al., 2019, https://doi.org/10.2151/jmsj.2019-051,\n",
+       "   (13) NorESM2-LM, Seland et al., 2020, https://doi.org/10.5194/gmd-13-6165-2020,\n",
+       "   (14) UKESM1-0-LL), Sellar et al., 2019, https://doi.org/10.1029/2019ms001739,\n",
+       "\n",
+       "   with bias corrections based on three latest observational data products:\n",
+       "   (1) the Surface Ocean CO2 Atlas (SOCAT, version 2021, Bakker et al., 2016,\n",
+       "   https://doi.org/10.5194/essd-8-383-2016),\n",
+       "   (2) the Global Ocean Data Analysis Product Version 2 (GLODAPv2, version 2021, Lauvset et al., 2021,\n",
+       "   https://doi.org/10.5194/essd-13-5565-2021), and\n",
+       "   (3) the Coastal Ocean Data Analysis Product in North America (CODAP-NA, version 2021, Jiang et al., 2021,\n",
+       "   https://doi.org/10.5194/essd-13-2777-2021).\n",
+       "\n",
+       "   Once temperature, salinity, DIC, and TA at that decade were calculated,\n",
+       "   they were then used to calculate the rest of the OA indicators. The CO2 system\n",
+       "   calculation was conducted using CO2System.jl (Humphreys et al., 2022) with the\n",
+       "   dissociation constants and parameter as recommended by Jiang et al. (2022),\n",
+       "   i.e., dissociation constants for carbonic acid of Lueker et al. (2000),\n",
+       "   potassium bisulfate (KHSO4–) of Dickson (1990), hydrofluoric acid (HF) of\n",
+       "   Perez and Fraga (1987), and the total borate concentration equations of\n",
+       "   Lee et al. (2010). The values reported in this NetCDF are the median values\n",
+       "   among all 14 ESMs at each of the grid point</dd><dt><span>reference :</span></dt><dd>Jiang, L-Q., J. Dunne, B. R. Carter, J. Tjiputra,\n",
+       "   J. Terhaar, J. D. Sharp, A. Olsen, R. A. Feely, R. Wanninkhof, P. Hogan,\n",
+       "   S. Alin, T. Ilyina, N. Lange, S. K. Lauvset, T. Lovato, Julien Palmieri,\n",
+       "   Y. Santana-Falcón, J. Schwinger, R. Séférian, G Strand, N. Swart, T. Tanhua,\n",
+       "   H. Tsujino, M. Watanabe, A. Yamamoto, T. Ziehn. (2022). Global surface ocean\n",
+       "   acidification indicators: past, present, and future. Global Biogeochemical\n",
+       "   Cycles, under review.</dd><dt><span>Fair_use_statement :</span></dt><dd>This data product is made freely available\n",
+       "   to the public and the scientific community. If it is used in scientific\n",
+       "   publications, please cite the paper on GBC, so that readers can access all of the\n",
+       "   detailed information about this data product. If these data are essential to\n",
+       "   the work, or if an important result or conclusion depends on these data,\n",
+       "   co-authorship may be appropriate. This should be discussed at an early stage\n",
+       "   in the work.</dd><dt><span>created_by :</span></dt><dd>Li-Qing Jiang</dd><dt><span>institution :</span></dt><dd>(a) Cooperative Institute for Satellite Earth System Studies, Earth System\n",
+       "   Science Interdisciplinary Center, University of Maryland, College Park, Maryland\n",
+       "   20740, United States, and\n",
+       "   (b) National Centers for Environmental Information, National Oceanic and\n",
+       "   Atmospheric Administration, Silver Spring, Maryland 20910, United States</dd><dt><span>contact :</span></dt><dd>&lt;Liqing.Jiang@noaa.gov&gt;</dd><dt><span>creation_date :</span></dt><dd>August 14, 2022</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 10MB\n",
+       "Dimensions:    (time: 18, lat: 180, lon: 360)\n",
+       "Coordinates:\n",
+       "  * time       (time) float64 144B 1.75e+03 1.85e+03 1.86e+03 ... 2e+03 2.01e+03\n",
+       "Dimensions without coordinates: lat, lon\n",
+       "Data variables:\n",
+       "    pHT        (time, lat, lon) float64 9MB ...\n",
+       "    longitude  (lat, lon) float64 518kB ...\n",
+       "    latitude   (lat, lon) float64 518kB ...\n",
+       "Attributes:\n",
+       "    title:               Global surface ocean pH on total hydrogen ion scale ...\n",
+       "    comment:             This gridded data product contains pH on total hydro...\n",
+       "    reference:           Jiang, L-Q., J. Dunne, B. R. Carter, J. Tjiputra,\\n ...\n",
+       "    Fair_use_statement:  This data product is made freely available\\n   to th...\n",
+       "    created_by:          Li-Qing Jiang\n",
+       "    institution:         (a) Cooperative Institute for Satellite Earth System...\n",
+       "    contact:             <Liqing.Jiang@noaa.gov>\n",
+       "    creation_date:       August 14, 2022"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# code to retrieve and load the data\n",
     "# url_SurfacepH= 'https://www.ncei.noaa.gov/data/oceans/ncei/ocads/data/0206289/Surface_pH_1770_2100/Surface_pH_1770_2000.nc' $ old CMIP5 dataset\n",
@@ -277,7 +810,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "S6fZBOG00xW6"
+    "execution": {}
    },
    "source": [
     "For those feeling adventurouts, there are also files of future projected changes under various scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, recall W2D1 tutorials):\n",
@@ -293,7 +826,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "OcwO6XpY3rOS"
+    "execution": {}
    },
    "source": [
     "For the advanced questions in red you can use **sea surface temperature** and **CO<sub>2</sub> concentration** from [NOAA Global Monitoring Laboratory](https://gml.noaa.gov/ccgg/trends/gl_data.html)) which were previously introduced in the tutorials.\n",
@@ -304,10 +837,193 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "rF3Z4CwmwE1K",
+    "execution": {},
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_gl.csv' to file '/tmp/co2_mm_gl.csv'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 4013d5c4fbd276601a05f148072ceda2ad49f4e80294a3f307b9e277cf656a53\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "data": {
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+       "      <th>1980</th>\n",
+       "      <th>5</th>\n",
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+       "      <td>0.07</td>\n",
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+       "      <td>0.07</td>\n",
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+       "      <td>0.07</td>\n",
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+       "\n",
+       "[526 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "filename_CO2= 'co2_mm_gl.csv'\n",
     "url_CO2= 'https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_gl.csv'\n",
@@ -320,10 +1036,456 @@
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    "execution_count": null,
    "metadata": {
-    "id": "ZkjKVTMN1YBc",
+    "execution": {},
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://osf.io/6pgc2/download/' to file '/tmp/sst.mon.mean.nc'.\n"
+     ]
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+     "name": "stderr",
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+      "SHA256 hash of downloaded file: 577705b952b71abf66baf8710c38baaa87f00922b3f6a80109fdecb62b3e27c8\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\n",
+       "Dimensions:  (time: 499, lat: 720, lon: 1440)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 4kB 1981-09-01 1981-10-01 ... 2023-03-01\n",
+       "  * lat      (lat) float32 3kB -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
+       "  * lon      (lon) float32 6kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
+       "Data variables:\n",
+       "    sst      (time, lat, lon) float32 2GB ...\n",
+       "Attributes:\n",
+       "    Conventions:    CF-1.5\n",
+       "    title:          NOAA/NCEI 1/4 Degree Daily Optimum Interpolation Sea Surf...\n",
+       "    institution:    NOAA/National Centers for Environmental Information\n",
+       "    source:         NOAA/NCEI https://www.ncei.noaa.gov/data/sea-surface-temp...\n",
+       "    References:     https://www.psl.noaa.gov/data/gridded/data.noaa.oisst.v2....\n",
+       "    dataset_title:  NOAA Daily Optimum Interpolation Sea Surface Temperature\n",
+       "    version:        Version 2.1\n",
+       "    comment:        Reynolds, et al.(2007) Daily High-Resolution-Blended Anal...</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-59eda453-4f96-44cb-8d78-5f9e9410056a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59eda453-4f96-44cb-8d78-5f9e9410056a' 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>: 499</li><li><span class='xr-has-index'>lat</span>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bb35101c-1d2e-473e-a6e3-0f77110dbb28' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bb35101c-1d2e-473e-a6e3-0f77110dbb28' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1981-09-01 ... 2023-03-01</div><input id='attrs-16166363-d8cc-4f0c-936c-82b4d3fd1a8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16166363-d8cc-4f0c-936c-82b4d3fd1a8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6670206c-84c8-4a0c-9e3c-20e5db3bca36' class='xr-var-data-in' type='checkbox'><label for='data-6670206c-84c8-4a0c-9e3c-20e5db3bca36' 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>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[66352. 81508.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1981-09-01T00:00:00.000000000&#x27;, &#x27;1981-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1981-11-01T00:00:00.000000000&#x27;, ..., &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2023-02-01T00:00:00.000000000&#x27;, &#x27;2023-03-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-49ad5eaf-2a7c-4b2a-9a7a-be4441e372d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49ad5eaf-2a7c-4b2a-9a7a-be4441e372d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f50db655-1f69-4220-902f-f39f19922ed7' class='xr-var-data-in' type='checkbox'><label for='data-f50db655-1f69-4220-902f-f39f19922ed7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[-89.875  89.875]</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-2641f17e-adfc-4dc3-ac15-1dcc96295737' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2641f17e-adfc-4dc3-ac15-1dcc96295737' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-579ebe94-179e-4d1a-a4c7-e33f466bc7d2' class='xr-var-data-in' type='checkbox'><label for='data-579ebe94-179e-4d1a-a4c7-e33f466bc7d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>actual_range :</span></dt><dd>[1.25000e-01 3.59875e+02]</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
+       "       3.59875e+02], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a7f1e906-bb02-4655-a113-4ecfc4b34fe6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a7f1e906-bb02-4655-a113-4ecfc4b34fe6' 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>sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e169cbe5-c8fe-4456-8cd7-1e400e8010e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e169cbe5-c8fe-4456-8cd7-1e400e8010e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20fa3434-798c-42c3-9ce2-0b2d6e3af2d2' class='xr-var-data-in' type='checkbox'><label for='data-20fa3434-798c-42c3-9ce2-0b2d6e3af2d2' 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>Monthly Mean of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_range :</span></dt><dd>[-3. 45.]</dd><dt><span>precision :</span></dt><dd>2.0</dd><dt><span>dataset :</span></dt><dd>NOAA High-resolution Blended Analysis</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Monthly Mean</dd><dt><span>parent_stat :</span></dt><dd>Individual Observations</dd><dt><span>actual_range :</span></dt><dd>[-1.8  32.14]</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd></dl></div><div class='xr-var-data'><pre>[517363200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2bfedd1-e651-4024-bc8a-91eccc7345b9' class='xr-section-summary-in' type='checkbox'  ><label for='section-a2bfedd1-e651-4024-bc8a-91eccc7345b9' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eca79452-6373-4042-a900-eb92df547204' class='xr-index-data-in' type='checkbox'/><label for='index-eca79452-6373-4042-a900-eb92df547204' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1981-09-01&#x27;, &#x27;1981-10-01&#x27;, &#x27;1981-11-01&#x27;, &#x27;1981-12-01&#x27;,\n",
+       "               &#x27;1982-01-01&#x27;, &#x27;1982-02-01&#x27;, &#x27;1982-03-01&#x27;, &#x27;1982-04-01&#x27;,\n",
+       "               &#x27;1982-05-01&#x27;, &#x27;1982-06-01&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;,\n",
+       "               &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;, &#x27;2023-01-01&#x27;,\n",
+       "               &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=499, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f761df8f-09f8-489b-9644-230a73098bbb' class='xr-index-data-in' type='checkbox'/><label for='index-f761df8f-09f8-489b-9644-230a73098bbb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,\n",
+       "       -87.875, -87.625,\n",
+       "       ...\n",
+       "        87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,\n",
+       "        89.625,  89.875],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ffeb3546-17ee-486b-a8a6-7c25c0334874' class='xr-index-data-in' type='checkbox'/><label for='index-ffeb3546-17ee-486b-a8a6-7c25c0334874' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.125,   0.375,   0.625,   0.875,   1.125,   1.375,   1.625,   1.875,\n",
+       "         2.125,   2.375,\n",
+       "       ...\n",
+       "       357.625, 357.875, 358.125, 358.375, 358.625, 358.875, 359.125, 359.375,\n",
+       "       359.625, 359.875],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0fb92b8-a87b-460e-b097-ded69ebe064c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0fb92b8-a87b-460e-b097-ded69ebe064c' 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>Conventions :</span></dt><dd>CF-1.5</dd><dt><span>title :</span></dt><dd>NOAA/NCEI 1/4 Degree Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1</dd><dt><span>institution :</span></dt><dd>NOAA/National Centers for Environmental Information</dd><dt><span>source :</span></dt><dd>NOAA/NCEI https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/</dd><dt><span>References :</span></dt><dd>https://www.psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html</dd><dt><span>dataset_title :</span></dt><dd>NOAA Daily Optimum Interpolation Sea Surface Temperature</dd><dt><span>version :</span></dt><dd>Version 2.1</dd><dt><span>comment :</span></dt><dd>Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H.-M. Zhang, 2021: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1 (available at https://doi.org/10.1175/JCLI-D-20-0166.1). Anomaly is based on 1971-2000 OI.v2 SST. Satellite data: multi-satellite sensor (AVHRR+VIIRS) data. Ice data: NCEP Ice and GSFC Ice. Data less than 15 days old may be subject to revision.</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 2GB\n",
+       "Dimensions:  (time: 499, lat: 720, lon: 1440)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 4kB 1981-09-01 1981-10-01 ... 2023-03-01\n",
+       "  * lat      (lat) float32 3kB -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
+       "  * lon      (lon) float32 6kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
+       "Data variables:\n",
+       "    sst      (time, lat, lon) float32 2GB ...\n",
+       "Attributes:\n",
+       "    Conventions:    CF-1.5\n",
+       "    title:          NOAA/NCEI 1/4 Degree Daily Optimum Interpolation Sea Surf...\n",
+       "    institution:    NOAA/National Centers for Environmental Information\n",
+       "    source:         NOAA/NCEI https://www.ncei.noaa.gov/data/sea-surface-temp...\n",
+       "    References:     https://www.psl.noaa.gov/data/gridded/data.noaa.oisst.v2....\n",
+       "    dataset_title:  NOAA Daily Optimum Interpolation Sea Surface Temperature\n",
+       "    version:        Version 2.1\n",
+       "    comment:        Reynolds, et al.(2007) Daily High-Resolution-Blended Anal..."
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# from W1D3 tutorial 6 we have Sea Surface Temprature from 1981 to the present:\n",
     "# download the monthly sea surface temperature data from NOAA Physical System\n",
@@ -341,7 +1503,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "tiOBsy9wF-59"
+    "execution": {}
    },
    "source": [
     "**Hints for socio-economic question (dashed boxes):**\n",
@@ -352,7 +1514,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "z9W0HPU01YBh"
+    "execution": {}
    },
    "source": [
     "\n",
@@ -364,7 +1526,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "eQkNW-g61YBi"
+    "execution": {}
    },
    "source": [
     "# Further Reading\n",
@@ -383,7 +1545,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "JsPWOtzV1YBj"
+    "execution": {}
    },
    "source": [
     "# Other Resources\n",
@@ -398,6 +1560,9 @@
  ],
  "metadata": {
   "colab": {
+   "collapsed_sections": [],
+   "include_colab_link": true,
+   "name": "Ocean_acidification_2024",
    "provenance": [
     {
      "file_id": "1UrzuI288izT-KofHfuuvFa1qDvOP0NyL",
@@ -410,6 +1575,11 @@
    ],
    "toc_visible": true
   },
+  "kernel": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -425,7 +1595,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/projects/project-notebooks/Precipitation_variability_extreme_events_2024.ipynb b/projects/project-notebooks/Precipitation_variability_extreme_events_2024.ipynb
index 835e4d890..f5ac34e5a 100644
--- a/projects/project-notebooks/Precipitation_variability_extreme_events_2024.ipynb
+++ b/projects/project-notebooks/Precipitation_variability_extreme_events_2024.ipynb
@@ -3,7 +3,9 @@
   {
    "cell_type": "markdown",
    "id": "ada5523b",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "<a href=\"https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/projects/project-notebooks/2024_Regional_precipitation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/projects/project-notebooks/2024_Regional_precipitation.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
    ]
@@ -33,9 +35,25 @@
    "execution_count": null,
    "id": "64da422f",
    "metadata": {
-    "cellView": "form"
+    "cellView": "form",
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a10a6ba095a404584a17e51d0b3733a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tab(children=(Output(), Output()), selected_index=0, titles=('Youtube', 'Bilibili'))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# @title Project Background\n",
     "\n",
@@ -98,9 +116,25 @@
    "execution_count": null,
    "id": "b52fef8b",
    "metadata": {
-    "cellView": "form"
+    "cellView": "form",
+    "execution": {}
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79181962c56a48ee96b0c3a4980d5cf6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# @title Slides\n",
     "# @markdown These are the slides for the video introduction to the project\n",
@@ -166,7 +200,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "b68292ec",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# google colab installs\n",
@@ -178,7 +214,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "1e541a40",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# Imports\n",
@@ -200,7 +238,10 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "3692a9ca",
-   "metadata": {},
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# @title Helper functions\n",
@@ -208,7 +249,7 @@
     "def pooch_load(filelocation=None,filename=None,processor=None):\n",
     "    shared_location='/home/jovyan/shared/Data/Projects/Precipitation' # this is different for each day\n",
     "    user_temp_cache=tempfile.gettempdir()\n",
-    "    \n",
+    "\n",
     "    if os.path.exists(os.path.join(shared_location,filename)):\n",
     "        file = os.path.join(shared_location,filename)\n",
     "    else:\n",
@@ -221,7 +262,10 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "07545499",
-   "metadata": {},
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# @title Figure settings\n",
@@ -235,7 +279,9 @@
   {
    "cell_type": "markdown",
    "id": "02a3777f",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "## CHIRPS Version 2.0 Global Daily 0.25°\n",
     "\n",
@@ -253,7 +299,9 @@
   {
    "cell_type": "markdown",
    "id": "3f36c1fa",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "### Indices for Extreme Events\n",
     "The Expert Team on Climate Change Detection and Indices ([ETCCDI](http://etccdi.pacificclimate.org/list_27_indices.shtml)) has defined various indices that focus on different aspects such as duration or intensity of extreme events. The following functions provide examples of how to compute indices for each category. You can modify these functions to suit your specific needs or create your own custom functions. Here are some tips you can use:\n",
@@ -272,7 +320,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "e9697620",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "def calculate_cdd_index(data):\n",
@@ -322,8 +372,656 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "ad439e00",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1981.days_p25.nc' to file '/tmp/chirps-v2.0.1981.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: abf5aa47db1a47b1bb0358591d02adb7cdc765af1de83834aa0a36157828f0cb\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1982.days_p25.nc' to file '/tmp/chirps-v2.0.1982.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 5264a039135cbe00fff7c2b8296123ddc652db67a3086ac1d84bc7cfd91d1b3c\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1983.days_p25.nc' to file '/tmp/chirps-v2.0.1983.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 8083ed549a4027fddad74e4a3be75c8587ad0830de3be0200c56b295d81cbe26\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1984.days_p25.nc' to file '/tmp/chirps-v2.0.1984.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: e1c3edab18b4bd69a659a42e95ca9a97051b7055738fdf4b63796ff199bbf2a4\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1985.days_p25.nc' to file '/tmp/chirps-v2.0.1985.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 795f6ef239d5a88b0afc682875ad5567f61d694980346c656db5445af428b3ab\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1986.days_p25.nc' to file '/tmp/chirps-v2.0.1986.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 3ad6497e71ee565d2ecdb895993f2ca9b4db6ed0efb702eb000b934b7ea4438f\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1987.days_p25.nc' to file '/tmp/chirps-v2.0.1987.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: a47dd1314d10589213b7d807fb8f1486972f2a0fcf081d899b414304c09aad8d\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1988.days_p25.nc' to file '/tmp/chirps-v2.0.1988.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 07b7a89ac7e7f1e16cad1db1100217a60a124964b77693e6323b0c1316f813e3\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1989.days_p25.nc' to file '/tmp/chirps-v2.0.1989.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: a81c7a0916bf81e4047cda8d6320cc5c94720f758f232c50b80502a840702ab8\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1990.days_p25.nc' to file '/tmp/chirps-v2.0.1990.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 10a95daa84df0e1934c462c3687cff0accff907557f92c6c1e7ee0d2077ed0d4\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1991.days_p25.nc' to file '/tmp/chirps-v2.0.1991.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: f549808c69601cf288b4f0f42b8ac5337c348f06d1fb55aa86b67e9bbd3837f9\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1992.days_p25.nc' to file '/tmp/chirps-v2.0.1992.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: ae26310b18369c1015e6b70db6dfb7ad1c138f846bd7c322e0b6794a26423f56\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1993.days_p25.nc' to file '/tmp/chirps-v2.0.1993.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 292ff168ef7f4420d36606f7ed84aa5380dd979a90351762926ef4c569e90f56\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1994.days_p25.nc' to file '/tmp/chirps-v2.0.1994.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: f856963801db2a62fa5c35537810f1649f19d32c84f1c4f2bb3fd3dcc82ba7ec\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1995.days_p25.nc' to file '/tmp/chirps-v2.0.1995.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 54c7a408d49112465479a06a3d1e5b37cb10b526c2cf20215f2b75579c8c2a0e\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1996.days_p25.nc' to file '/tmp/chirps-v2.0.1996.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: b0a4604e5a213671e6d591ad959c2156685ee026b54fbc8ad87860e35f3db4df\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1997.days_p25.nc' to file '/tmp/chirps-v2.0.1997.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: a04019a26e20988e35d8d14e88f1083aabb8104434c556c21e3d29e1c30ae731\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1998.days_p25.nc' to file '/tmp/chirps-v2.0.1998.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 15f3e33707c14b3ef26b525cb116766b847f2295c60aa41c6ae73df10e748e98\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1999.days_p25.nc' to file '/tmp/chirps-v2.0.1999.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: dbb0a6a0ee1ff5e3ba5439101bedc24dad0951810a03a6f5cdc96e4b08c06716\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2000.days_p25.nc' to file '/tmp/chirps-v2.0.2000.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 57014c9ff72c8997b5b141793c06ffd1a8d8f5c6d1308789ac8e12c3860e5790\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2001.days_p25.nc' to file '/tmp/chirps-v2.0.2001.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 8550c96c7f8c29cbc6a1be3be56d7f769125d15b9ac7bc6c4f0fc4a76034f0fa\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2002.days_p25.nc' to file '/tmp/chirps-v2.0.2002.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 8027aaf7e005d74073d2c744d9c8a0343ffade272c1d1f8c420972bcbade2231\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2003.days_p25.nc' to file '/tmp/chirps-v2.0.2003.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 5b5a538e187ca651bc1fcc25eb445d31a05067b5a109bd8bb4651db11346cf6f\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2004.days_p25.nc' to file '/tmp/chirps-v2.0.2004.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 5e671526f27567e9218d275f7a47b6c0d7e0acf718edfee64c5843f5ee497e93\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2005.days_p25.nc' to file '/tmp/chirps-v2.0.2005.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 2185afd5c3d110da52a9a81eb97f25b37f30db5c0478e536a84c957351e44b53\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2006.days_p25.nc' to file '/tmp/chirps-v2.0.2006.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 3f78d5e733ea38bef25e47af9b3a907a96e27b8334cc04e4898837239e6539e6\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2007.days_p25.nc' to file '/tmp/chirps-v2.0.2007.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: c4594aeaafd7b9e7065577563799bca461582723e6b32df332206b43cb853138\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2008.days_p25.nc' to file '/tmp/chirps-v2.0.2008.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 6fe5dc8facd7750b6c08ecd0cadaaf19570acccd6a15f20efcbdd960230b5551\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2009.days_p25.nc' to file '/tmp/chirps-v2.0.2009.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: e6ccdc38aa146793f2c92858412122ef14a7a34332940bec03bd520cc77dc235\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2010.days_p25.nc' to file '/tmp/chirps-v2.0.2010.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: f6b6bd962626c038959a743b5f6e6c22b0bd6647b89b4a08b0f2305ac77f7277\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2011.days_p25.nc' to file '/tmp/chirps-v2.0.2011.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 45f437d8f1c7237b7043ebf4fec800461252af02e7cbcc87a844ddf0d1a911d2\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2012.days_p25.nc' to file '/tmp/chirps-v2.0.2012.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 27792b3d028ca5e745a8fe45c4f133a4183469512d2e55ead950d54a5a5a890b\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2013.days_p25.nc' to file '/tmp/chirps-v2.0.2013.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: d531a704ed1a40be2d509b1d984913194015f84721edc555f2de8c000bcab638\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2014.days_p25.nc' to file '/tmp/chirps-v2.0.2014.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: c3225de85da99422bc5fde9dbd1bdabff206fc2902d811944169348c8352d521\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2015.days_p25.nc' to file '/tmp/chirps-v2.0.2015.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 0c770adf07884a25610ea00d66d5e221529159746d3d76d9affea69c77666233\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2016.days_p25.nc' to file '/tmp/chirps-v2.0.2016.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 8ff4f6284c1d37b50e8b1e47dbd9f6e108a9ee3b17a6752bb18cc0b7384526d3\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2017.days_p25.nc' to file '/tmp/chirps-v2.0.2017.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 9692ca1fee495b8091084b1e5d29ea618483e67886a281f82b4a03555861d6b8\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2018.days_p25.nc' to file '/tmp/chirps-v2.0.2018.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 05b0850b8b8c16a2d5d33ddc6ba89d503084dc7374845214289be1b411a30d3f\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2019.days_p25.nc' to file '/tmp/chirps-v2.0.2019.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 311723f2f8d2af3307da198ebafece0c081200f336444a4261516539348fed38\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2020.days_p25.nc' to file '/tmp/chirps-v2.0.2020.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 2ab3b142141c5cfc708bc666dc89900005b7452b66324515fca1baa8dff00402\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2021.days_p25.nc' to file '/tmp/chirps-v2.0.2021.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 1b2db33508896ed1932b9db7e6dad6136e90687d3fe630f08066dbd279e0dabb\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2022.days_p25.nc' to file '/tmp/chirps-v2.0.2022.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 5f9b88f0623083759ba351a263bf23afd7fb26b13e23d051d5bb22a55aa0bcb9\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2023.days_p25.nc' to file '/tmp/chirps-v2.0.2023.days_p25.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 8430cc8afd1a69205ecf757a2d8d95f3e6fbc44544326b27b4d534a82ee9c14c\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    }
+   ],
    "source": [
     "# code to retrieve and load the data\n",
     "\n",
@@ -342,7 +1040,9 @@
   {
    "cell_type": "markdown",
    "id": "660f8525",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "We can now visualize the content of the dataset."
    ]
@@ -351,8 +1051,585 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "114e4a72",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
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+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 36GB\n",
+       "Dimensions:    (latitude: 400, longitude: 1440, time: 15705)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float32 2kB -49.88 -49.62 -49.38 ... 49.38 49.62 49.88\n",
+       "  * longitude  (longitude) float32 6kB -179.9 -179.6 -179.4 ... 179.6 179.9\n",
+       "  * time       (time) datetime64[ns] 126kB 1981-01-01 1981-01-02 ... 2023-12-31\n",
+       "Data variables:\n",
+       "    precip     (time, latitude, longitude) float32 36GB dask.array&lt;chunksize=(61, 67, 240), meta=np.ndarray&gt;\n",
+       "Attributes: (12/15)\n",
+       "    Conventions:       CF-1.6\n",
+       "    title:             CHIRPS Version 2.0\n",
+       "    history:           created by Climate Hazards Group\n",
+       "    version:           Version 2.0\n",
+       "    date_created:      2015-10-07\n",
+       "    creator_name:      Pete Peterson\n",
+       "    ...                ...\n",
+       "    reference:         Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros,...\n",
+       "    comments:           time variable denotes the first day of the given day.\n",
+       "    acknowledgements:  The Climate Hazards Group InfraRed Precipitation with ...\n",
+       "    ftp_url:           ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CH...\n",
+       "    website:           http://chg.geog.ucsb.edu/data/chirps/index.html\n",
+       "    faq:               http://chg-wiki.geog.ucsb.edu/wiki/CHIRPS_FAQ</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-fb82c958-0c51-426a-b721-6083fb48dcb5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fb82c958-0c51-426a-b721-6083fb48dcb5' 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'>latitude</span>: 400</li><li><span class='xr-has-index'>longitude</span>: 1440</li><li><span class='xr-has-index'>time</span>: 15705</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-008f05ed-9f8c-4599-91ef-c1146318ffc9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-008f05ed-9f8c-4599-91ef-c1146318ffc9' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-49.88 -49.62 ... 49.62 49.88</div><input id='attrs-4d8ac5f4-176c-4748-8e0c-13d920475c7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d8ac5f4-176c-4748-8e0c-13d920475c7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20b884b0-edb9-4db6-b692-753bec9d2bba' class='xr-var-data-in' type='checkbox'><label for='data-20b884b0-edb9-4db6-b692-753bec9d2bba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-49.875, -49.625, -49.375, ...,  49.375,  49.625,  49.875],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-be60d2ac-544d-4ece-858a-826ba1d2d17b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be60d2ac-544d-4ece-858a-826ba1d2d17b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-246bee16-7932-4586-8905-7471c4b9a8b3' class='xr-var-data-in' type='checkbox'><label for='data-246bee16-7932-4586-8905-7471c4b9a8b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1981-01-01 ... 2023-12-31</div><input id='attrs-f7919ec3-f8b4-446b-9431-f205efe86402' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7919ec3-f8b4-446b-9431-f205efe86402' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04e2ba79-307f-42b6-ad02-7bb2c4711ab9' class='xr-var-data-in' type='checkbox'><label for='data-04e2ba79-307f-42b6-ad02-7bb2c4711ab9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1981-01-01T00:00:00.000000000&#x27;, &#x27;1981-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1981-01-03T00:00:00.000000000&#x27;, ..., &#x27;2023-12-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2023-12-30T00:00:00.000000000&#x27;, &#x27;2023-12-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a8e7920d-da72-4e17-a6d2-b72fc87f5a67' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a8e7920d-da72-4e17-a6d2-b72fc87f5a67' 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>precip</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(61, 67, 240), meta=np.ndarray&gt;</div><input id='attrs-0dfb463b-ec6e-4465-a7db-ced80d2ba8d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dfb463b-ec6e-4465-a7db-ced80d2ba8d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ad19811-9a6f-498c-8604-8360910fa2e2' class='xr-var-data-in' type='checkbox'><label for='data-4ad19811-9a6f-498c-8604-8360910fa2e2' 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>mm/day</dd><dt><span>standard_name :</span></dt><dd>convective precipitation rate</dd><dt><span>long_name :</span></dt><dd>Climate Hazards group InfraRed Precipitation with Stations</dd><dt><span>time_step :</span></dt><dd>day</dd><dt><span>geostatial_lat_min :</span></dt><dd>-50.0</dd><dt><span>geostatial_lat_max :</span></dt><dd>50.0</dd><dt><span>geostatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geostatial_lon_max :</span></dt><dd>180.0</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> 33.70 GiB </td>\n",
+       "                        <td> 3.74 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (15705, 400, 1440) </td>\n",
+       "                        <td> (61, 67, 240) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 9288 chunks in 87 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"168\" height=\"151\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9173d87e-9f86-4ef6-8506-a0726f52799b' class='xr-section-summary-in' type='checkbox'  ><label for='section-9173d87e-9f86-4ef6-8506-a0726f52799b' 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>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ba5a41c0-8694-4fc2-8d83-fced6a057f6d' class='xr-index-data-in' type='checkbox'/><label for='index-ba5a41c0-8694-4fc2-8d83-fced6a057f6d' 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([-49.875, -49.625, -49.375, -49.125, -48.875, -48.625, -48.375, -48.125,\n",
+       "       -47.875, -47.625,\n",
+       "       ...\n",
+       "        47.625,  47.875,  48.125,  48.375,  48.625,  48.875,  49.125,  49.375,\n",
+       "        49.625,  49.875],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=400))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f8dd92d2-9a0a-41c0-9b76-3e8cabcff4a9' class='xr-index-data-in' type='checkbox'/><label for='index-f8dd92d2-9a0a-41c0-9b76-3e8cabcff4a9' 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([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625, -178.375,\n",
+       "       -178.125, -177.875, -177.625,\n",
+       "       ...\n",
+       "        177.625,  177.875,  178.125,  178.375,  178.625,  178.875,  179.125,\n",
+       "        179.375,  179.625,  179.875],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-42042ef8-8253-4997-b6a6-26005d8491f3' class='xr-index-data-in' type='checkbox'/><label for='index-42042ef8-8253-4997-b6a6-26005d8491f3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1981-01-01&#x27;, &#x27;1981-01-02&#x27;, &#x27;1981-01-03&#x27;, &#x27;1981-01-04&#x27;,\n",
+       "               &#x27;1981-01-05&#x27;, &#x27;1981-01-06&#x27;, &#x27;1981-01-07&#x27;, &#x27;1981-01-08&#x27;,\n",
+       "               &#x27;1981-01-09&#x27;, &#x27;1981-01-10&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2023-12-22&#x27;, &#x27;2023-12-23&#x27;, &#x27;2023-12-24&#x27;, &#x27;2023-12-25&#x27;,\n",
+       "               &#x27;2023-12-26&#x27;, &#x27;2023-12-27&#x27;, &#x27;2023-12-28&#x27;, &#x27;2023-12-29&#x27;,\n",
+       "               &#x27;2023-12-30&#x27;, &#x27;2023-12-31&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=15705, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0fba95aa-83bb-4521-b318-ba1ab24aec18' class='xr-section-summary-in' type='checkbox'  ><label for='section-0fba95aa-83bb-4521-b318-ba1ab24aec18' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>title :</span></dt><dd>CHIRPS Version 2.0</dd><dt><span>history :</span></dt><dd>created by Climate Hazards Group</dd><dt><span>version :</span></dt><dd>Version 2.0</dd><dt><span>date_created :</span></dt><dd>2015-10-07</dd><dt><span>creator_name :</span></dt><dd>Pete Peterson</dd><dt><span>creator_email :</span></dt><dd>pete@geog.ucsb.edu</dd><dt><span>institution :</span></dt><dd>Climate Hazards Group.  University of California at Santa Barbara</dd><dt><span>documentation :</span></dt><dd>http://pubs.usgs.gov/ds/832/</dd><dt><span>reference :</span></dt><dd>Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p., http://dx.doi.org/110.3133/ds832. </dd><dt><span>comments :</span></dt><dd> time variable denotes the first day of the given day.</dd><dt><span>acknowledgements :</span></dt><dd>The Climate Hazards Group InfraRed Precipitation with Stations development process was carried out through U.S. Geological Survey (USGS) cooperative agreement #G09AC000001 &quot;Monitoring and Forecasting Climate, Water and Land Use for Food Production in the Developing World&quot; with funding from: U.S. Agency for International Development Office of Food for Peace, award #AID-FFP-P-10-00002 for &quot;Famine Early Warning Systems Network Support,&quot; the National Aeronautics and Space Administration Applied Sciences Program, Decisions award #NN10AN26I for &quot;A Land Data Assimilation System for Famine Early Warning,&quot; SERVIR award #NNH12AU22I for &quot;A Long Time-Series Indicator of Agricultural Drought for the Greater Horn of Africa,&quot; The National Oceanic and Atmospheric Administration award NA11OAR4310151 for &quot;A Global Standardized Precipitation Index supporting the US Drought Portal and the Famine Early Warning System Network,&quot; and the USGS Land Change Science Program.</dd><dt><span>ftp_url :</span></dt><dd>ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CHIRPS-latest/</dd><dt><span>website :</span></dt><dd>http://chg.geog.ucsb.edu/data/chirps/index.html</dd><dt><span>faq :</span></dt><dd>http://chg-wiki.geog.ucsb.edu/wiki/CHIRPS_FAQ</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 36GB\n",
+       "Dimensions:    (latitude: 400, longitude: 1440, time: 15705)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float32 2kB -49.88 -49.62 -49.38 ... 49.38 49.62 49.88\n",
+       "  * longitude  (longitude) float32 6kB -179.9 -179.6 -179.4 ... 179.6 179.9\n",
+       "  * time       (time) datetime64[ns] 126kB 1981-01-01 1981-01-02 ... 2023-12-31\n",
+       "Data variables:\n",
+       "    precip     (time, latitude, longitude) float32 36GB dask.array<chunksize=(61, 67, 240), meta=np.ndarray>\n",
+       "Attributes: (12/15)\n",
+       "    Conventions:       CF-1.6\n",
+       "    title:             CHIRPS Version 2.0\n",
+       "    history:           created by Climate Hazards Group\n",
+       "    version:           Version 2.0\n",
+       "    date_created:      2015-10-07\n",
+       "    creator_name:      Pete Peterson\n",
+       "    ...                ...\n",
+       "    reference:         Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros,...\n",
+       "    comments:           time variable denotes the first day of the given day.\n",
+       "    acknowledgements:  The Climate Hazards Group InfraRed Precipitation with ...\n",
+       "    ftp_url:           ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CH...\n",
+       "    website:           http://chg.geog.ucsb.edu/data/chirps/index.html\n",
+       "    faq:               http://chg-wiki.geog.ucsb.edu/wiki/CHIRPS_FAQ"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# code to print the shape, array names, etc of the dataset\n",
     "chirps_data"
@@ -361,7 +1638,9 @@
   {
    "cell_type": "markdown",
    "id": "c3fb296a",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "## NOAA Fundamental Climate Data Records (FCDR) AVHRR Land Bundle - Normalized Difference Vegetation Index\n",
     "\n",
@@ -395,8 +1674,1593 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "3e66216a",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010101_c20170608112729.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010101_c20170608112729.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: bea1b2cfeead6742dd8e6006533d447a1cfd6f7311fbc8a662f5f0b58ac65500\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010102_c20170608114253.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010102_c20170608114253.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 3b8037793f3699a79d3d2c6d5413966a356d4b1e27d853555231961616fe40b6\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010103_c20170608115841.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010103_c20170608115841.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: a92fd5e6188556cd35511394312fb4f5ecf4d7e7153fc4002f29d23316463340\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010104_c20170608121434.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010104_c20170608121434.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 21c0f5e7b361eeed45bad648c12b3d88c670da77e81ed36d0c83e7daa59e0ece\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010105_c20170608123036.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010105_c20170608123036.nc'.\n"
+     ]
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+     "name": "stderr",
+     "output_type": "stream",
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+      "SHA256 hash of downloaded file: fc7d244edf3dd0eec1609ed9aeca5d94c479f8115a38c9cf879efa889046a66d\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010106_c20170608124647.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010106_c20170608124647.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
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+     "text": [
+      "SHA256 hash of downloaded file: 260c939a2b10f35eafe6ebd67c9b9d260062e7c455a1b7268f6bafaec4e9edf0\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010107_c20170608130258.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010107_c20170608130258.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 85f0e58ce20cf1fcacdb8856b2bc805a073e7d349de2816c4c71d18c35f88de7\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010108_c20170608130658.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010108_c20170608130658.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 3dc1a63efd5439b5c7c30421dfc3f19918d03719ee824c5bba9f28842a133748\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010109_c20170608131430.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010109_c20170608131430.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 5c1ddfbe3f562b8fcd84a5f2c294aa5ae804f6a9c686ea21e7a019c2434b2ce1\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010110_c20170608133019.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010110_c20170608133019.nc'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 4c270d4cd8c43a3e8830477f681fdc7f492acdbeb517fbb9fa002246955d6468\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
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+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010111_c20170608134807.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010111_c20170608134807.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+     "output_type": "stream",
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010112_c20170608140615.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010112_c20170608140615.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010113_c20170608142322.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010113_c20170608142322.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010114_c20170608144105.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010114_c20170608144105.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010115_c20170608145913.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010115_c20170608145913.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010116_c20170608151715.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010116_c20170608151715.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010117_c20170608153425.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010117_c20170608153425.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010118_c20170608155135.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010118_c20170608155135.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010120_c20170608162614.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010120_c20170608162614.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010121_c20170608164215.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010121_c20170608164215.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010124_c20170608173542.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010124_c20170608173542.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010125_c20170608175403.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010125_c20170608175403.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010126_c20170608181206.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010126_c20170608181206.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+     "text": [
+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010127_c20170608182926.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010127_c20170608182926.nc'.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010128_c20170608184655.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010128_c20170608184655.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010129_c20170608190436.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010129_c20170608190436.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010130_c20170608192209.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010130_c20170608192209.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+     "name": "stderr",
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+      "Downloading data from 'http://s3.amazonaws.com/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010131_c20170608193958.nc' to file '/tmp/noaa-cdr-ndvi-pds/data/2001/AVHRR-Land_v005_AVH13C1_NOAA-16_20010131_c20170608193958.nc'.\n"
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+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
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+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 11GB\n",
+       "Dimensions:    (latitude: 3600, longitude: 7200, time: 31, ncrs: 1, nv: 2)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float32 14kB 89.97 89.93 89.88 ... -89.93 -89.97\n",
+       "  * longitude  (longitude) float32 29kB -180.0 -179.9 -179.9 ... 179.9 180.0\n",
+       "  * time       (time) float32 124B 7.305e+03 7.306e+03 ... 7.334e+03 7.335e+03\n",
+       "Dimensions without coordinates: ncrs, nv\n",
+       "Data variables:\n",
+       "    crs        (time, ncrs) int16 62B dask.array&lt;chunksize=(1, 1), meta=np.ndarray&gt;\n",
+       "    lat_bnds   (time, latitude, nv) float32 893kB dask.array&lt;chunksize=(1, 3600, 2), meta=np.ndarray&gt;\n",
+       "    lon_bnds   (time, longitude, nv) float32 2MB dask.array&lt;chunksize=(1, 7200, 2), meta=np.ndarray&gt;\n",
+       "    NDVI       (time, latitude, longitude) float32 3GB dask.array&lt;chunksize=(1, 1200, 2400), meta=np.ndarray&gt;\n",
+       "    TIMEOFDAY  (time, latitude, longitude) float64 6GB dask.array&lt;chunksize=(1, 1200, 2400), meta=np.ndarray&gt;\n",
+       "    QA         (time, latitude, longitude) int16 2GB dask.array&lt;chunksize=(1, 3600, 7200), meta=np.ndarray&gt;\n",
+       "Attributes: (12/48)\n",
+       "    title:                                  Normalized Difference Vegetation ...\n",
+       "    institution:                            NASA/GSFC/SED/ESD/HBSL/TIS/MODIS-...\n",
+       "    Conventions:                            CF-1.6, ACDD-1.3\n",
+       "    standard_name_vocabulary:               CF Standard Name Table (v25, 05 J...\n",
+       "    naming_authority:                       gov.noaa.ncei\n",
+       "    license:                                See the Use Agreement for this CD...\n",
+       "    ...                                     ...\n",
+       "    PercentValidDaytimeData:                33.79\n",
+       "    PercentValidDaytimeLand:                33.79\n",
+       "    PercentValidClearDaytimeLand:           4.54\n",
+       "    PercentValidDaytimeLandInCloudShadow:   0.85\n",
+       "    PercentValidClearDaytimeWater:          0.00\n",
+       "    PercentValidDaytimeWaterInCloudShadow:  0.00</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-02b74567-d16d-4e3d-bbe1-9a892e2d9470' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-02b74567-d16d-4e3d-bbe1-9a892e2d9470' 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'>latitude</span>: 3600</li><li><span class='xr-has-index'>longitude</span>: 7200</li><li><span class='xr-has-index'>time</span>: 31</li><li><span>ncrs</span>: 1</li><li><span>nv</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-67990c1b-37c8-452d-86e7-33e6fbdcbfc7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67990c1b-37c8-452d-86e7-33e6fbdcbfc7' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>89.97 89.93 89.88 ... -89.93 -89.97</div><input id='attrs-47768310-e81c-41e1-98f2-7932922fd7a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47768310-e81c-41e1-98f2-7932922fd7a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6dc559e-12ac-4dcf-bd83-0253aceca4c8' class='xr-var-data-in' type='checkbox'><label for='data-e6dc559e-12ac-4dcf-bd83-0253aceca4c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([ 89.975  ,  89.925  ,  89.875  , ..., -89.875  , -89.925  , -89.97499],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-180.0 -179.9 ... 179.9 180.0</div><input id='attrs-006d75e2-7161-4d3e-bac6-2c6575955c39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-006d75e2-7161-4d3e-bac6-2c6575955c39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcdad24d-7a6d-447b-bf94-9e4b85d39755' class='xr-var-data-in' type='checkbox'><label for='data-bcdad24d-7a6d-447b-bf94-9e4b85d39755' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([-179.975  , -179.925  , -179.875  , ...,  179.875  ,  179.92499,\n",
+       "        179.975  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.305e+03 7.306e+03 ... 7.335e+03</div><input id='attrs-657b5b82-9ecf-4aa4-9ace-e7d829690f51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-657b5b82-9ecf-4aa4-9ace-e7d829690f51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4ffd09e-a79f-478f-8e4b-3f468b23b107' class='xr-var-data-in' type='checkbox'><label for='data-b4ffd09e-a79f-478f-8e4b-3f468b23b107' 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 1981-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>calendar :</span></dt><dd>gregorian</dd></dl></div><div class='xr-var-data'><pre>array([7305., 7306., 7307., 7308., 7309., 7310., 7311., 7312., 7313., 7314.,\n",
+       "       7315., 7316., 7317., 7318., 7319., 7320., 7321., 7322., 7323., 7324.,\n",
+       "       7325., 7326., 7327., 7328., 7329., 7330., 7331., 7332., 7333., 7334.,\n",
+       "       7335.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3bf3f38-0c7e-474d-9aa5-706539fb1a8c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3bf3f38-0c7e-474d-9aa5-706539fb1a8c' class='xr-section-summary' >Data variables: <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>crs</span></div><div class='xr-var-dims'>(time, ncrs)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1), meta=np.ndarray&gt;</div><input id='attrs-a54eb2ec-0d51-459b-bf86-ed115fdb3041' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a54eb2ec-0d51-459b-bf86-ed115fdb3041' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94a15b5d-d168-4fa4-9cf4-d55afda818ff' class='xr-var-data-in' type='checkbox'><label for='data-94a15b5d-d168-4fa4-9cf4-d55afda818ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>epsg_code :</span></dt><dd>EPSG:4326</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</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> 62 B </td>\n",
+       "                        <td> 2 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (31, 1) </td>\n",
+       "                        <td> (1, 1) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 31 chunks in 94 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int16 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\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>lat_bnds</span></div><div class='xr-var-dims'>(time, latitude, nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 3600, 2), meta=np.ndarray&gt;</div><input id='attrs-532fa5c2-361a-456e-b8c4-88da95f7c6e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-532fa5c2-361a-456e-b8c4-88da95f7c6e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e9693cc-ea88-4ff0-a3ca-784d17600963' class='xr-var-data-in' type='checkbox'><label for='data-8e9693cc-ea88-4ff0-a3ca-784d17600963' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 871.88 kiB </td>\n",
+       "                        <td> 28.12 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (31, 3600, 2) </td>\n",
+       "                        <td> (1, 3600, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 31 chunks in 94 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(time, longitude, nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 7200, 2), meta=np.ndarray&gt;</div><input id='attrs-dcfe9465-068d-4b69-bb57-420df8a6e1dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcfe9465-068d-4b69-bb57-420df8a6e1dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9f91741-bb0c-4f23-886b-ee9eff048c0f' class='xr-var-data-in' type='checkbox'><label for='data-e9f91741-bb0c-4f23-886b-ee9eff048c0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.70 MiB </td>\n",
+       "                        <td> 56.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (31, 7200, 2) </td>\n",
+       "                        <td> (1, 7200, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 31 chunks in 94 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NDVI</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 2400), meta=np.ndarray&gt;</div><input id='attrs-6b0c3a5e-001e-4c92-9a7d-7e859001b766' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b0c3a5e-001e-4c92-9a7d-7e859001b766' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b10daa8b-c54d-40d9-b798-b58229ebc86d' class='xr-var-data-in' type='checkbox'><label for='data-b10daa8b-c54d-40d9-b798-b58229ebc86d' 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>NOAA Climate Data Record of Normalized Difference Vegetation Index</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_range :</span></dt><dd>[-1000 10000]</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>standard_name :</span></dt><dd>normalized_difference_vegetation_index</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> 2.99 GiB </td>\n",
+       "                        <td> 10.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 1200, 2400) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 279 chunks in 63 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TIMEOFDAY</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 2400), meta=np.ndarray&gt;</div><input id='attrs-d011d5ba-7ce5-4260-ab13-d5bc5d97b5dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d011d5ba-7ce5-4260-ab13-d5bc5d97b5dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4c35834-9868-4a3d-bb04-485e7a8e94f0' class='xr-var-data-in' type='checkbox'><label for='data-c4c35834-9868-4a3d-bb04-485e7a8e94f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time since Start of Data Day</dd><dt><span>units :</span></dt><dd>hours since 2001-01-01 00:00:00</dd><dt><span>valid_range :</span></dt><dd>[   0 2399]</dd><dt><span>grid_mapping :</span></dt><dd>crs</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> 5.99 GiB </td>\n",
+       "                        <td> 21.97 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (31, 3600, 7200) </td>\n",
+       "                        <td> (1, 1200, 2400) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 279 chunks in 63 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",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QA</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 3600, 7200), meta=np.ndarray&gt;</div><input id='attrs-06902f7c-1b45-4b40-bf3f-794b65030aa2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06902f7c-1b45-4b40-bf3f-794b65030aa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-717145a9-b08d-474b-add6-7d6d9db43190' class='xr-var-data-in' type='checkbox'><label for='data-717145a9-b08d-474b-add6-7d6d9db43190' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>[     2      4      8     16     32     64    128    256    512   1024\n",
+       "   2048   4096   8192  16384 -32768]</dd><dt><span>flag_meanings :</span></dt><dd>cloudy cloud_shadow water sunglint dense_dark_vegetation night ch1_to_5_valid ch1_invalid ch2_invalid ch3_invalid ch4_invalid ch5_invalid rho3_invalid BRDF_corr_problem polar_flag</dd><dt><span>long_name :</span></dt><dd>Quality Assurance</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.50 GiB </td>\n",
+       "                        <td> 49.44 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (31, 3600, 7200) </td>\n",
+       "                        <td> (1, 3600, 7200) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 31 chunks in 63 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int16 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-c021c163-7d06-4c83-a52d-b8f5944b53e6' class='xr-section-summary-in' type='checkbox'  ><label for='section-c021c163-7d06-4c83-a52d-b8f5944b53e6' 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>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b245e03b-7844-469b-8938-5f009d57e5a6' class='xr-index-data-in' type='checkbox'/><label for='index-b245e03b-7844-469b-8938-5f009d57e5a6' 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([  89.9749984741211,  89.92500305175781,             89.875,\n",
+       "        89.82499694824219,   89.7750015258789,   89.7249984741211,\n",
+       "        89.67500305175781,             89.625,  89.57499694824219,\n",
+       "         89.5250015258789,\n",
+       "       ...\n",
+       "       -89.52499389648438, -89.57499694824219,            -89.625,\n",
+       "       -89.67500305175781, -89.72499084472656, -89.77499389648438,\n",
+       "       -89.82499694824219,            -89.875, -89.92500305175781,\n",
+       "       -89.97499084472656],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-28af8989-6f51-47a2-a7c2-6b76e99c6d32' class='xr-index-data-in' type='checkbox'/><label for='index-28af8989-6f51-47a2-a7c2-6b76e99c6d32' 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([-179.97500610351562,  -179.9250030517578,            -179.875,\n",
+       "        -179.8249969482422, -179.77499389648438, -179.72500610351562,\n",
+       "        -179.6750030517578,            -179.625,  -179.5749969482422,\n",
+       "       -179.52499389648438,\n",
+       "       ...\n",
+       "        179.52499389648438,  179.57501220703125,             179.625,\n",
+       "        179.67498779296875,  179.72500610351562,  179.77499389648438,\n",
+       "        179.82501220703125,             179.875,  179.92498779296875,\n",
+       "        179.97500610351562],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=7200))</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-3552f8f7-fd54-4643-a1c3-2b2aac1d4470' class='xr-index-data-in' type='checkbox'/><label for='index-3552f8f7-fd54-4643-a1c3-2b2aac1d4470' 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([7305.0, 7306.0, 7307.0, 7308.0, 7309.0, 7310.0, 7311.0, 7312.0, 7313.0,\n",
+       "       7314.0, 7315.0, 7316.0, 7317.0, 7318.0, 7319.0, 7320.0, 7321.0, 7322.0,\n",
+       "       7323.0, 7324.0, 7325.0, 7326.0, 7327.0, 7328.0, 7329.0, 7330.0, 7331.0,\n",
+       "       7332.0, 7333.0, 7334.0, 7335.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f45d9407-52e8-4246-b35f-01e6d151d785' class='xr-section-summary-in' type='checkbox'  ><label for='section-f45d9407-52e8-4246-b35f-01e6d151d785' class='xr-section-summary' >Attributes: <span>(48)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>Normalized Difference Vegetation Index parameters derived from NOAA-16 GAC data for day 2001/001</dd><dt><span>institution :</span></dt><dd>NASA/GSFC/SED/ESD/HBSL/TIS/MODIS-LAND &gt; MODIS Land Science Team, Terrestrial Information Systems,  Hydrospheric and Biospheric Science Laboratory, Earth Sciences Division, Science and Exploration Directorate, Goddard Space Flight Center, NASA</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v25, 05 July 2013)</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.ncei</dd><dt><span>license :</span></dt><dd>See the Use Agreement for this CDR available from the NOAA CDR webpage</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>time_coverage_start :</span></dt><dd>2001-01-01T00:00:00Z</dd><dt><span>time_coverage_end :</span></dt><dd>2001-01-01T23:59:59Z</dd><dt><span>product_version :</span></dt><dd>v5r0</dd><dt><span>platform :</span></dt><dd>NOAA-16 &gt; National Oceanic &amp; Atmospheric Administration-16</dd><dt><span>sensor :</span></dt><dd>AVHRR &gt; Advanced Very High Resolution Radiometer</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>platform_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Platform Keywords</dd><dt><span>instrument_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Instrument Keywords</dd><dt><span>keywords :</span></dt><dd>EARTH SCIENCE &gt; BIOSPHERE &gt; VEGETATION &gt; VEGETATION INDEX</dd><dt><span>source :</span></dt><dd>NSS.GHRR.NL.D01001.S0000.E0048.B0143233.GC, NSS.GHRR.NL.D01001.S0043.E0237.B0143334.WI, NSS.GHRR.NL.D01001.S0232.E0426.B0143435.WI, NSS.GHRR.NL.D01001.S0421.E0615.B0143536.WI, NSS.GHRR.NL.D01001.S0611.E0756.B0143637.WI, NSS.GHRR.NL.D01001.S0751.E0945.B0143738.GC, NSS.GHRR.NL.D01001.S0941.E1114.B0143839.GC, NSS.GHRR.NL.D01001.S1110.E1255.B0143940.GC, NSS.GHRR.NL.D01001.S1250.E1444.B0144041.GC, NSS.GHRR.NL.D01001.S1439.E1616.B0144142.GC, NSS.GHRR.NL.D01001.S1611.E1741.B0144243.WI, NSS.GHRR.NL.D01001.S1736.E1922.B0144244.WI, NSS.GHRR.NL.D01001.S1918.E2112.B0144345.GC, NSS.GHRR.NL.D01001.S2107.E2301.B0144546.GC, NSS.GHRR.NL.D01001.S2256.E0037.B0144647.GC, NSS.GHRR.NL.D01001.S0000.E0048.B0143233.GC.txt, NSS.GHRR.NL.D01001.S0043.E0237.B0143334.WI.txt, NSS.GHRR.NL.D01001.S0232.E0426.B0143435.WI.txt, NSS.GHRR.NL.D01001.S0421.E0615.B0143536.WI.txt, NSS.GHRR.NL.D01001.S0611.E0756.B0143637.WI.txt, NSS.GHRR.NL.D01001.S0751.E0945.B0143738.GC.txt, NSS.GHRR.NL.D01001.S0941.E1114.B0143839.GC.txt, NSS.GHRR.NL.D01001.S1110.E1255.B0143940.GC.txt, NSS.GHRR.NL.D01001.S1250.E1444.B0144041.GC.txt, NSS.GHRR.NL.D01001.S1439.E1616.B0144142.GC.txt, NSS.GHRR.NL.D01001.S1611.E1741.B0144243.WI.txt, NSS.GHRR.NL.D01001.S1736.E1922.B0144244.WI.txt, NSS.GHRR.NL.D01001.S1918.E2112.B0144345.GC.txt, NSS.GHRR.NL.D01001.S2107.E2301.B0144546.GC.txt, NSS.GHRR.NL.D01001.S2256.E0037.B0144647.GC.txt, NCEP_REANALYSIS_SURFACE_2001001.hdf, NCEP_REANALYSIS_SURFACE_2001001.hdf, TOMS_2001001.hdf, GLOBAL_LAND_SEA.4KM, NOAA-16.OLE.txt</dd><dt><span>date_created :</span></dt><dd>2017-06-08T11:27:29Z</dd><dt><span>spatial_resolution :</span></dt><dd>0.050000 degrees per pixel</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>metadata_link :</span></dt><dd>https://doi.org/10.7289/V5ZG6QH9</dd><dt><span>program :</span></dt><dd>NOAA Climate Data Record Program for satellites</dd><dt><span>cdr_variable :</span></dt><dd>NDVI</dd><dt><span>Process :</span></dt><dd>LTDR_GAPS</dd><dt><span>PostProcessingVersion :</span></dt><dd>2.9</dd><dt><span>PFIIVersion :</span></dt><dd>3.5.45</dd><dt><span>Satellite :</span></dt><dd>NOAA-16</dd><dt><span>Instrument :</span></dt><dd>AVHRR</dd><dt><span>InputDataType :</span></dt><dd>GAC</dd><dt><span>summary :</span></dt><dd>AVHRR GAC data from NOAA-16 for 2001, days 1 to 1, processed by the Long-Term Land Data Record (LTDR) project (v3.5.45) into normalized difference vegetation index (NDVI) and quality-control flags. </dd><dt><span>ProductionDate :</span></dt><dd>2017-06-08T11:27:29.000000Z</dd><dt><span>ESDT :</span></dt><dd>AVH13C1</dd><dt><span>LocalGranuleId :</span></dt><dd>AVHRR-Land_v005_AVH13C1_NOAA-16_20010101_c20170608112729.nc</dd><dt><span>id :</span></dt><dd>AVHRR-Land_v005_AVH13C1_NOAA-16_20010101_c20170608112729.nc</dd><dt><span>RangeBeginningDate :</span></dt><dd>2001-001</dd><dt><span>RangeBeginningTime :</span></dt><dd>00:00:00.0000</dd><dt><span>RangeEndingDate :</span></dt><dd>2001-001</dd><dt><span>RangeEndingTime :</span></dt><dd>23:59:59.9999</dd><dt><span>PercentMissingData :</span></dt><dd>16.33</dd><dt><span>PercentValidDaytimeData :</span></dt><dd>33.79</dd><dt><span>PercentValidDaytimeLand :</span></dt><dd>33.79</dd><dt><span>PercentValidClearDaytimeLand :</span></dt><dd>4.54</dd><dt><span>PercentValidDaytimeLandInCloudShadow :</span></dt><dd>0.85</dd><dt><span>PercentValidClearDaytimeWater :</span></dt><dd>0.00</dd><dt><span>PercentValidDaytimeWaterInCloudShadow :</span></dt><dd>0.00</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 11GB\n",
+       "Dimensions:    (latitude: 3600, longitude: 7200, time: 31, ncrs: 1, nv: 2)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float32 14kB 89.97 89.93 89.88 ... -89.93 -89.97\n",
+       "  * longitude  (longitude) float32 29kB -180.0 -179.9 -179.9 ... 179.9 180.0\n",
+       "  * time       (time) float32 124B 7.305e+03 7.306e+03 ... 7.334e+03 7.335e+03\n",
+       "Dimensions without coordinates: ncrs, nv\n",
+       "Data variables:\n",
+       "    crs        (time, ncrs) int16 62B dask.array<chunksize=(1, 1), meta=np.ndarray>\n",
+       "    lat_bnds   (time, latitude, nv) float32 893kB dask.array<chunksize=(1, 3600, 2), meta=np.ndarray>\n",
+       "    lon_bnds   (time, longitude, nv) float32 2MB dask.array<chunksize=(1, 7200, 2), meta=np.ndarray>\n",
+       "    NDVI       (time, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1200, 2400), meta=np.ndarray>\n",
+       "    TIMEOFDAY  (time, latitude, longitude) float64 6GB dask.array<chunksize=(1, 1200, 2400), meta=np.ndarray>\n",
+       "    QA         (time, latitude, longitude) int16 2GB dask.array<chunksize=(1, 3600, 7200), meta=np.ndarray>\n",
+       "Attributes: (12/48)\n",
+       "    title:                                  Normalized Difference Vegetation ...\n",
+       "    institution:                            NASA/GSFC/SED/ESD/HBSL/TIS/MODIS-...\n",
+       "    Conventions:                            CF-1.6, ACDD-1.3\n",
+       "    standard_name_vocabulary:               CF Standard Name Table (v25, 05 J...\n",
+       "    naming_authority:                       gov.noaa.ncei\n",
+       "    license:                                See the Use Agreement for this CD...\n",
+       "    ...                                     ...\n",
+       "    PercentValidDaytimeData:                33.79\n",
+       "    PercentValidDaytimeLand:                33.79\n",
+       "    PercentValidClearDaytimeLand:           4.54\n",
+       "    PercentValidDaytimeLandInCloudShadow:   0.85\n",
+       "    PercentValidClearDaytimeWater:          0.00\n",
+       "    PercentValidDaytimeWaterInCloudShadow:  0.00"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# we can access the data similar to Tutorial 3 of W1D3\n",
     "# to access the NDVI data from AWS S3 bucket, we first need to connect to s3 bucket\n",
@@ -404,7 +3268,7 @@
     "fs = s3fs.S3FileSystem(anon=True)\n",
     "client = boto3.client('s3', config=botocore.client.Config(signature_version=botocore.UNSIGNED)) # initialize aws s3 bucket client\n",
     "# choose a date of interest, we pick its year or month while defining the file_location\n",
-    "date_sel = datetime.datetime(2001,1,1,0) \n",
+    "date_sel = datetime.datetime(2001,1,1,0)\n",
     "\n",
     "file_location = fs.glob('s3://noaa-cdr-ndvi-pds/data/'\n",
     "                        + date_sel.strftime('%Y')\n",
@@ -420,7 +3284,9 @@
   {
    "cell_type": "markdown",
    "id": "a03b0a64-4564-4cfa-abed-16a40f63cf57",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "If the above downloading procedure is not working as expected, please retrieve the following sample to execute the remaining cells of the notebook. Ask for the help of your project TA to download your data of interest.\n"
    ]
@@ -429,7 +3295,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "8dc64b25-c2d7-4e1b-8023-6d551981f77e",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# uncomment to get a subsample of AVHRR-Land_v005_AVH13C1_NOAA-16 NDVI data if data retrieval above is not working properly\n",
@@ -445,7 +3313,9 @@
   {
    "cell_type": "markdown",
    "id": "658a800d-e83e-422b-ae96-e16bb235e609",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "*Note that the downloaded satellite data is quality-controlled and flagged accordingly. Please refer to W1D3 Tutorial 3 and others to decipher it. Choose the feasible helper functions, e.g. `get_quality_info_AVHRR(QA)` depending on the sensor of interest. VIIRS is a state-of-the-art sensor that produces measurements that have been available since 2014. We provide an example of usage in the following.*"
    ]
@@ -454,7 +3324,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "b9fb8e19-f723-474d-9b42-f2ce891ffad4",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# AVHRR: function to extract high-quality NDVI data\n",
@@ -466,7 +3338,7 @@
     "    Bit 7: 1 (All AVHRR channels have valid values)\n",
     "    Bit 2: 0 (The pixel is not covered by cloud shadow)\n",
     "    Bit 1: 0 (The pixel is not covered by cloud)\n",
-    "    Bit 0: \n",
+    "    Bit 0:\n",
     "\n",
     "    Output:\n",
     "    True: high quality\n",
@@ -488,7 +3360,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "941c3c3d-06ac-427e-9f40-2ae9b3aafc89",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "outputs": [],
    "source": [
     "# get the quality assurance value from NDVI data\n",
@@ -505,10 +3379,560 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "67f62201-5d28-4715-ad31-7a31ded86682",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;NDVI&#x27; (time: 31, latitude: 3600, longitude: 7200)&gt; Size: 3GB\n",
+       "dask.array&lt;where, shape=(31, 3600, 7200), dtype=float32, chunksize=(1, 1200, 2400), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float32 14kB 89.97 89.93 89.88 ... -89.93 -89.97\n",
+       "  * longitude  (longitude) float32 29kB -180.0 -179.9 -179.9 ... 179.9 180.0\n",
+       "  * time       (time) float32 124B 7.305e+03 7.306e+03 ... 7.334e+03 7.335e+03\n",
+       "Attributes:\n",
+       "    long_name:      NOAA Climate Data Record of Normalized Difference Vegetat...\n",
+       "    units:          1\n",
+       "    valid_range:    [-1000 10000]\n",
+       "    grid_mapping:   crs\n",
+       "    standard_name:  normalized_difference_vegetation_index</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'>'NDVI'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 31</li><li><span class='xr-has-index'>latitude</span>: 3600</li><li><span class='xr-has-index'>longitude</span>: 7200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-aa9d5a12-c927-46d1-99ec-5ab64f7453e1' class='xr-array-in' type='checkbox' checked><label for='section-aa9d5a12-c927-46d1-99ec-5ab64f7453e1' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1200, 2400), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
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+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 2.99 GiB </td>\n",
+       "                        <td> 10.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (31, 3600, 7200) </td>\n",
+       "                        <td> (1, 1200, 2400) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 279 chunks in 139 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"136\" y2=\"6\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"136\" y2=\"6\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"19\" y1=\"9\" x2=\"139\" y2=\"9\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"142\" y2=\"12\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"50\" y1=\"0\" x2=\"64\" y2=\"14\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"104\" y2=\"14\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"34\" x2=\"144\" y2=\"34\" />\n",
+       "  <line x1=\"24\" y1=\"54\" x2=\"144\" y2=\"54\" />\n",
+       "  <line x1=\"24\" y1=\"74\" x2=\"144\" y2=\"74\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"74\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"64\" y1=\"14\" x2=\"64\" y2=\"74\" />\n",
+       "  <line x1=\"104\" y1=\"14\" x2=\"104\" y2=\"74\" />\n",
+       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"74\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,74.9485979497544 24.9485979497544,74.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"94.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7200</text>\n",
+       "  <text x=\"164.948598\" y=\"44.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,44.948598)\">3600</text>\n",
+       "  <text x=\"7.474299\" y=\"87.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,87.474299)\">31</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-c0f9c598-8504-4b41-8f5c-c8579f9c7f85' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c0f9c598-8504-4b41-8f5c-c8579f9c7f85' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>89.97 89.93 89.88 ... -89.93 -89.97</div><input id='attrs-b8508037-7c45-4617-b6a1-5495a4789275' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8508037-7c45-4617-b6a1-5495a4789275' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b36ae28-5eb5-4b27-bc6e-20bf86cdc2f1' class='xr-var-data-in' type='checkbox'><label for='data-7b36ae28-5eb5-4b27-bc6e-20bf86cdc2f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([ 89.975  ,  89.925  ,  89.875  , ..., -89.875  , -89.925  , -89.97499],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-180.0 -179.9 ... 179.9 180.0</div><input id='attrs-6576b0e6-54ff-4fd1-86d5-41a11f6d3b03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6576b0e6-54ff-4fd1-86d5-41a11f6d3b03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a20c900e-fb66-48f3-9a6a-c527af74e275' class='xr-var-data-in' type='checkbox'><label for='data-a20c900e-fb66-48f3-9a6a-c527af74e275' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([-179.975  , -179.925  , -179.875  , ...,  179.875  ,  179.92499,\n",
+       "        179.975  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.305e+03 7.306e+03 ... 7.335e+03</div><input id='attrs-84c0e9f3-0c1f-478e-943f-9af56215a8d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84c0e9f3-0c1f-478e-943f-9af56215a8d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea174805-bafe-427f-ab37-310f245c57f6' class='xr-var-data-in' type='checkbox'><label for='data-ea174805-bafe-427f-ab37-310f245c57f6' 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 1981-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>calendar :</span></dt><dd>gregorian</dd></dl></div><div class='xr-var-data'><pre>array([7305., 7306., 7307., 7308., 7309., 7310., 7311., 7312., 7313., 7314.,\n",
+       "       7315., 7316., 7317., 7318., 7319., 7320., 7321., 7322., 7323., 7324.,\n",
+       "       7325., 7326., 7327., 7328., 7329., 7330., 7331., 7332., 7333., 7334.,\n",
+       "       7335.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da5b2e0d-f820-466c-a196-d1d626a1f9f0' class='xr-section-summary-in' type='checkbox'  ><label for='section-da5b2e0d-f820-466c-a196-d1d626a1f9f0' 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>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1a71d123-9f44-4699-adf5-bd6ed6c1fb18' class='xr-index-data-in' type='checkbox'/><label for='index-1a71d123-9f44-4699-adf5-bd6ed6c1fb18' 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([  89.9749984741211,  89.92500305175781,             89.875,\n",
+       "        89.82499694824219,   89.7750015258789,   89.7249984741211,\n",
+       "        89.67500305175781,             89.625,  89.57499694824219,\n",
+       "         89.5250015258789,\n",
+       "       ...\n",
+       "       -89.52499389648438, -89.57499694824219,            -89.625,\n",
+       "       -89.67500305175781, -89.72499084472656, -89.77499389648438,\n",
+       "       -89.82499694824219,            -89.875, -89.92500305175781,\n",
+       "       -89.97499084472656],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1b43135e-93a1-4293-9b81-af1c3f3ae051' class='xr-index-data-in' type='checkbox'/><label for='index-1b43135e-93a1-4293-9b81-af1c3f3ae051' 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([-179.97500610351562,  -179.9250030517578,            -179.875,\n",
+       "        -179.8249969482422, -179.77499389648438, -179.72500610351562,\n",
+       "        -179.6750030517578,            -179.625,  -179.5749969482422,\n",
+       "       -179.52499389648438,\n",
+       "       ...\n",
+       "        179.52499389648438,  179.57501220703125,             179.625,\n",
+       "        179.67498779296875,  179.72500610351562,  179.77499389648438,\n",
+       "        179.82501220703125,             179.875,  179.92498779296875,\n",
+       "        179.97500610351562],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=7200))</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-5e3f9356-9536-441a-ab4e-7c4a7fa3adfa' class='xr-index-data-in' type='checkbox'/><label for='index-5e3f9356-9536-441a-ab4e-7c4a7fa3adfa' 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([7305.0, 7306.0, 7307.0, 7308.0, 7309.0, 7310.0, 7311.0, 7312.0, 7313.0,\n",
+       "       7314.0, 7315.0, 7316.0, 7317.0, 7318.0, 7319.0, 7320.0, 7321.0, 7322.0,\n",
+       "       7323.0, 7324.0, 7325.0, 7326.0, 7327.0, 7328.0, 7329.0, 7330.0, 7331.0,\n",
+       "       7332.0, 7333.0, 7334.0, 7335.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f79da8e-de06-412a-a74b-f2e0bb676cb2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f79da8e-de06-412a-a74b-f2e0bb676cb2' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>NOAA Climate Data Record of Normalized Difference Vegetation Index</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_range :</span></dt><dd>[-1000 10000]</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>standard_name :</span></dt><dd>normalized_difference_vegetation_index</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'NDVI' (time: 31, latitude: 3600, longitude: 7200)> Size: 3GB\n",
+       "dask.array<where, shape=(31, 3600, 7200), dtype=float32, chunksize=(1, 1200, 2400), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float32 14kB 89.97 89.93 89.88 ... -89.93 -89.97\n",
+       "  * longitude  (longitude) float32 29kB -180.0 -179.9 -179.9 ... 179.9 180.0\n",
+       "  * time       (time) float32 124B 7.305e+03 7.306e+03 ... 7.334e+03 7.335e+03\n",
+       "Attributes:\n",
+       "    long_name:      NOAA Climate Data Record of Normalized Difference Vegetat...\n",
+       "    units:          1\n",
+       "    valid_range:    [-1000 10000]\n",
+       "    grid_mapping:   crs\n",
+       "    standard_name:  normalized_difference_vegetation_index"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# mask ndvi data \n",
+    "# mask ndvi data\n",
     "ndvi_masked = ds.NDVI.where(mask)\n",
     "ndvi_masked"
    ]
@@ -517,8 +3941,36 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "24e94e81-f3b8-40be-85d9-635c30bb9567",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f963dd488e0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 575,
+       "width": 789
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# draw data to an example plot\n",
     "ndvi_masked.isel(time=30).coarsen(latitude=10).mean().coarsen(longitude=20).mean().plot()"
@@ -542,8 +3994,239 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "a2359308",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from 'https://raw.githubusercontent.com/Sshamekh/Heatwave/f85f43997e3d6ae61e5d729bf77cfcc188fbf2fd/data_cereal_land.csv' to file '/tmp/data_cereal_land.csv'.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SHA256 hash of downloaded file: 0d71645aeeb9e1cca8abe179c525c496f3b2b02867119069679762c0f9f1da47\n",
+      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country Name</th>\n",
+       "      <th>Country Code</th>\n",
+       "      <th>Series Name</th>\n",
+       "      <th>Series Code</th>\n",
+       "      <th>1972 [YR1972]</th>\n",
+       "      <th>1973 [YR1973]</th>\n",
+       "      <th>1974 [YR1974]</th>\n",
+       "      <th>1975 [YR1975]</th>\n",
+       "      <th>1976 [YR1976]</th>\n",
+       "      <th>1977 [YR1977]</th>\n",
+       "      <th>...</th>\n",
+       "      <th>2012 [YR2012]</th>\n",
+       "      <th>2013 [YR2013]</th>\n",
+       "      <th>2014 [YR2014]</th>\n",
+       "      <th>2015 [YR2015]</th>\n",
+       "      <th>2016 [YR2016]</th>\n",
+       "      <th>2017 [YR2017]</th>\n",
+       "      <th>2018 [YR2018]</th>\n",
+       "      <th>2019 [YR2019]</th>\n",
+       "      <th>2020 [YR2020]</th>\n",
+       "      <th>2021 [YR2021]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>AFG</td>\n",
+       "      <td>Cereal production (metric tons)</td>\n",
+       "      <td>AG.PRD.CREL.MT</td>\n",
+       "      <td>3950000</td>\n",
+       "      <td>4270000</td>\n",
+       "      <td>4351000</td>\n",
+       "      <td>4481000</td>\n",
+       "      <td>4624000</td>\n",
+       "      <td>4147000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6379000</td>\n",
+       "      <td>6520329</td>\n",
+       "      <td>6748023.28</td>\n",
+       "      <td>5808288</td>\n",
+       "      <td>5532695.42</td>\n",
+       "      <td>4892953.97</td>\n",
+       "      <td>4133051.85</td>\n",
+       "      <td>5583461</td>\n",
+       "      <td>6025977</td>\n",
+       "      <td>4663880.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>AFG</td>\n",
+       "      <td>Land under cereal production (hectares)</td>\n",
+       "      <td>AG.LND.CREL.HA</td>\n",
+       "      <td>3923100</td>\n",
+       "      <td>3337000</td>\n",
+       "      <td>3342000</td>\n",
+       "      <td>3404000</td>\n",
+       "      <td>3394000</td>\n",
+       "      <td>3388000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3143000</td>\n",
+       "      <td>3182922</td>\n",
+       "      <td>3344733</td>\n",
+       "      <td>2724070</td>\n",
+       "      <td>2793694</td>\n",
+       "      <td>2419213</td>\n",
+       "      <td>1911652</td>\n",
+       "      <td>2641911</td>\n",
+       "      <td>3043589</td>\n",
+       "      <td>2164537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>ALB</td>\n",
+       "      <td>Cereal production (metric tons)</td>\n",
+       "      <td>AG.PRD.CREL.MT</td>\n",
+       "      <td>585830</td>\n",
+       "      <td>625498</td>\n",
+       "      <td>646200</td>\n",
+       "      <td>666500</td>\n",
+       "      <td>857000</td>\n",
+       "      <td>910400</td>\n",
+       "      <td>...</td>\n",
+       "      <td>697400</td>\n",
+       "      <td>702870</td>\n",
+       "      <td>700370</td>\n",
+       "      <td>695000</td>\n",
+       "      <td>698430</td>\n",
+       "      <td>701734</td>\n",
+       "      <td>678196</td>\n",
+       "      <td>666065</td>\n",
+       "      <td>684023</td>\n",
+       "      <td>691126.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>ALB</td>\n",
+       "      <td>Land under cereal production (hectares)</td>\n",
+       "      <td>AG.LND.CREL.HA</td>\n",
+       "      <td>331220</td>\n",
+       "      <td>339400</td>\n",
+       "      <td>334040</td>\n",
+       "      <td>328500</td>\n",
+       "      <td>350500</td>\n",
+       "      <td>357000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>142800</td>\n",
+       "      <td>142000</td>\n",
+       "      <td>143149</td>\n",
+       "      <td>142600</td>\n",
+       "      <td>148084</td>\n",
+       "      <td>145799</td>\n",
+       "      <td>140110</td>\n",
+       "      <td>132203</td>\n",
+       "      <td>131310</td>\n",
+       "      <td>134337</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>DZA</td>\n",
+       "      <td>Cereal production (metric tons)</td>\n",
+       "      <td>AG.PRD.CREL.MT</td>\n",
+       "      <td>2362625</td>\n",
+       "      <td>1595994</td>\n",
+       "      <td>1480275</td>\n",
+       "      <td>2680452</td>\n",
+       "      <td>2313186</td>\n",
+       "      <td>1142509</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5137455</td>\n",
+       "      <td>4912551</td>\n",
+       "      <td>3435535</td>\n",
+       "      <td>3761229.6</td>\n",
+       "      <td>3445227.37</td>\n",
+       "      <td>3478175.14</td>\n",
+       "      <td>6066252.82</td>\n",
+       "      <td>5633596.78</td>\n",
+       "      <td>4393336.75</td>\n",
+       "      <td>2784017.29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 54 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Country Name Country Code                              Series Name  \\\n",
+       "0  Afghanistan          AFG          Cereal production (metric tons)   \n",
+       "1  Afghanistan          AFG  Land under cereal production (hectares)   \n",
+       "2      Albania          ALB          Cereal production (metric tons)   \n",
+       "3      Albania          ALB  Land under cereal production (hectares)   \n",
+       "4      Algeria          DZA          Cereal production (metric tons)   \n",
+       "\n",
+       "      Series Code 1972 [YR1972] 1973 [YR1973] 1974 [YR1974] 1975 [YR1975]  \\\n",
+       "0  AG.PRD.CREL.MT       3950000       4270000       4351000       4481000   \n",
+       "1  AG.LND.CREL.HA       3923100       3337000       3342000       3404000   \n",
+       "2  AG.PRD.CREL.MT        585830        625498        646200        666500   \n",
+       "3  AG.LND.CREL.HA        331220        339400        334040        328500   \n",
+       "4  AG.PRD.CREL.MT       2362625       1595994       1480275       2680452   \n",
+       "\n",
+       "  1976 [YR1976] 1977 [YR1977]  ... 2012 [YR2012] 2013 [YR2013] 2014 [YR2014]  \\\n",
+       "0       4624000       4147000  ...       6379000       6520329    6748023.28   \n",
+       "1       3394000       3388000  ...       3143000       3182922       3344733   \n",
+       "2        857000        910400  ...        697400        702870        700370   \n",
+       "3        350500        357000  ...        142800        142000        143149   \n",
+       "4       2313186       1142509  ...       5137455       4912551       3435535   \n",
+       "\n",
+       "  2015 [YR2015] 2016 [YR2016] 2017 [YR2017] 2018 [YR2018] 2019 [YR2019]  \\\n",
+       "0       5808288    5532695.42    4892953.97    4133051.85       5583461   \n",
+       "1       2724070       2793694       2419213       1911652       2641911   \n",
+       "2        695000        698430        701734        678196        666065   \n",
+       "3        142600        148084        145799        140110        132203   \n",
+       "4     3761229.6    3445227.37    3478175.14    6066252.82    5633596.78   \n",
+       "\n",
+       "  2020 [YR2020] 2021 [YR2021]  \n",
+       "0       6025977    4663880.79  \n",
+       "1       3043589       2164537  \n",
+       "2        684023      691126.7  \n",
+       "3        131310        134337  \n",
+       "4    4393336.75    2784017.29  \n",
+       "\n",
+       "[5 rows x 54 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "filename_cereal = 'data_cereal_land.csv'\n",
     "\n",
@@ -562,8 +4245,73 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "396c635a",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1972 [YR1972]</th>\n",
+       "      <td>22703928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1973 [YR1973]</th>\n",
+       "      <td>23721606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1974 [YR1974]</th>\n",
+       "      <td>26240014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1975 [YR1975]</th>\n",
+       "      <td>26238419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1976 [YR1976]</th>\n",
+       "      <td>31143200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      0\n",
+       "1972 [YR1972]  22703928\n",
+       "1973 [YR1973]  23721606\n",
+       "1974 [YR1974]  26240014\n",
+       "1975 [YR1975]  26238419\n",
+       "1976 [YR1976]  31143200"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# example\n",
     "country = 'Brazil'\n",
@@ -581,7 +4329,9 @@
   {
    "cell_type": "markdown",
    "id": "73f4f21b",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "We can now visualize the content of the dataset."
    ]
@@ -590,8 +4340,36 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "dfbed1e8-0dec-486b-b2bb-2c8506c1a9bc",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Cereal production (MT)')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 575,
+       "width": 775
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# example\n",
     "plt.plot(np.arange(1972,2021,1),ds_cereal_land_brazil[1:].astype(float).values)\n",
@@ -605,7 +4383,9 @@
   {
    "cell_type": "markdown",
    "id": "3543b122",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "Now you are all set to address the questions you are interested in!"
    ]
@@ -613,7 +4393,9 @@
   {
    "cell_type": "markdown",
    "id": "45bf5b39-ff2e-4c58-bbf1-93358e8fe5ca",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "# Q1\n",
     "\n",
@@ -626,6 +4408,7 @@
    "cell_type": "markdown",
    "id": "9dcfd1a8-3d47-4ca5-aebe-60785c679eca",
    "metadata": {
+    "execution": {},
     "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
@@ -642,7 +4425,9 @@
   {
    "cell_type": "markdown",
    "id": "60e16fb0-e60a-4e67-ae55-ceb66bbdee47",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "# Q3\n",
     "Repeat the analysis in Q2 for a selected region of interest. How does this compare with the respective mean precipitation in the northern and southern hemispheres? How does this compare to the global mean?\n",
@@ -653,7 +4438,9 @@
   {
    "cell_type": "markdown",
    "id": "86bd1989-70b0-4fa6-9c3c-dca2f61a96ea",
-   "metadata": {},
+   "metadata": {
+    "execution": {}
+   },
    "source": [
     "# Q4\n",
     "Examine the time series in your region of interest and identify extreme values. \n",
@@ -694,7 +4481,7 @@
   "colab": {
    "collapsed_sections": [],
    "include_colab_link": true,
-   "name": "Example_based_on_CMA_structure",
+   "name": "Precipitation_variability_extreme_events_2024",
    "toc_visible": true
   },
   "kernel": {
@@ -717,7 +4504,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/README.md b/tutorials/README.md
index 7f9576ae0..ba001516d 100644
--- a/tutorials/README.md
+++ b/tutorials/README.md
@@ -2,7 +2,7 @@
 
 <!-- DO NOT EDIT THIS FILE. IT IS AUTO-GENERATED BY A FRIENDLY ROBOT -->
 
-[W1D1](#w1d1---climate-system-overview) | [W1D2](#w1d2---stateofthe-climate-oceanand-atmosphere-reanalysis) | [W1D3](#w1d3---remote-sensing-land-oceanand-atmosphere) | [W1D4](#w1d4---paleoclimate) | [W1D5](#w1d5---climate-modeling) | [W2D1](#w2d1---future-climate--i-p-c-c-i-physical-basis) | [W2D2](#w2d2---projects-day) | [W2D3](#w2d3---future-climate--i-p-c-c-i-i&-i-i-i-socio--economic-basis) | [W2D4](#w2d4---climate-response--extremes&-variability) | [W2D5](#w2d5---climate-response--adaptation-impact)
+[W1D1](#w1d1---climate-system-overview) | [W1D2](#w1d2---stateofthe-climate-oceanand-atmosphere-reanalysis) | [W1D3](#w1d3---remote-sensing-land-oceanand-atmosphere) | [W1D4](#w1d4---paleoclimate) | [W1D5](#w1d5---climate-modeling) | [W2D1](#w2d1---future-climate--i-p-c-c-i-physical-basis) | [W2D2](#w2d2---projects-day) | [W2D3](#w2d3---the-socioeconomicsof-climate-change) | [W2D4](#w2d4---climate-response--extremes&-variability) | [W2D5](#w2d5---climate-response--adaptation-impact)
 
 *Warning:* The 'render with NBViewer' buttons may show outdated content.
 
@@ -144,24 +144,24 @@
 
 
 
-## W2D3 - Future Climate- I P C C I I& I I I Socio- Economic Basis
+## W2D3 - The Socioeconomicsof Climate Change
 
 [YouTube Playlist](https://youtube.com/playlist?list=PLnpWOQFK76goJrAoOgA7CcrFiEf8gPsYa)
 |   | Run | Run | View |
 | - | --- | --- | ---- |
-| Intro | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Intro.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Intro.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Intro.ipynb?flush_cache=true) |
-| Tutorial 1 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial1.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial1.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial1.ipynb?flush_cache=true) |
-| Tutorial 2 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial2.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial2.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial2.ipynb?flush_cache=true) |
-| Tutorial 3 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial3.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial3.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial3.ipynb?flush_cache=true) |
-| Tutorial 4 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial4.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial4.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial4.ipynb?flush_cache=true) |
-| Tutorial 5 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial5.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial5.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial5.ipynb?flush_cache=true) |
-| Tutorial 6 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial6.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial6.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial6.ipynb?flush_cache=true) |
-| Outro | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Outro.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Outro.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Outro.ipynb?flush_cache=true) |
+| Intro | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Intro.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Intro.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Intro.ipynb?flush_cache=true) |
+| Tutorial 1 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial1.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial1.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial1.ipynb?flush_cache=true) |
+| Tutorial 2 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial2.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial2.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial2.ipynb?flush_cache=true) |
+| Tutorial 3 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial3.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial3.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial3.ipynb?flush_cache=true) |
+| Tutorial 4 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial4.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial4.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial4.ipynb?flush_cache=true) |
+| Tutorial 5 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial5.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial5.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial5.ipynb?flush_cache=true) |
+| Tutorial 6 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial6.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial6.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial6.ipynb?flush_cache=true) |
+| Outro | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Outro.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Outro.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Outro.ipynb?flush_cache=true) |
 
 
 
 
-[Further Reading](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/further_reading.md)
+[Further Reading](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/further_reading.md)
 
 
 ## W2D4 - Climate Response- Extremes& Variability
@@ -188,7 +188,7 @@
 
 ## W2D5 - Climate Response- Adaptation Impact
 
-[YouTube Playlist](https://youtube.com/playlist?list=PLkBQOLLbi18PTzOU6g-giQCy3OVYqZL8r)
+[YouTube Playlist](https://www.youtube.com/playlist?list=PLnpWOQFK76gqeFuNocGeynrhuCZ4Rbf69)
 |   | Run | Run | View |
 | - | --- | --- | ---- |
 | Intro | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D5_ClimateResponse-AdaptationImpact/W2D5_Intro.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D5_ClimateResponse-AdaptationImpact/W2D5_Intro.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D5_ClimateResponse-AdaptationImpact/W2D5_Intro.ipynb?flush_cache=true) |
diff --git a/tutorials/W2D3_TheSocioeconomicsofClimateChange/README.md b/tutorials/W2D3_TheSocioeconomicsofClimateChange/README.md
index 73c11a8e7..0f520efdc 100644
--- a/tutorials/W2D3_TheSocioeconomicsofClimateChange/README.md
+++ b/tutorials/W2D3_TheSocioeconomicsofClimateChange/README.md
@@ -1,29 +1,29 @@
-# W2D3 - Future Climate- I P C C I I& I I I Socio- Economic Basis
+# W2D3 - The Socioeconomicsof Climate Change
 
 ## Instructor notebooks
 
 |   | Run | Run | View |
 | - | --- | --- | ---- |
-| Intro | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Intro.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Intro.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Intro.ipynb?flush_cache=true) |
-| Tutorial 1 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial1.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial1.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial1.ipynb?flush_cache=true) |
-| Tutorial 2 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial2.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial2.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial2.ipynb?flush_cache=true) |
-| Tutorial 3 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial3.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial3.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial3.ipynb?flush_cache=true) |
-| Tutorial 4 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial4.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial4.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial4.ipynb?flush_cache=true) |
-| Tutorial 5 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial5.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial5.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial5.ipynb?flush_cache=true) |
-| Tutorial 6 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial6.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial6.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial6.ipynb?flush_cache=true) |
-| Outro | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Outro.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Outro.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Outro.ipynb?flush_cache=true) |
+| Intro | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Intro.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Intro.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Intro.ipynb?flush_cache=true) |
+| Tutorial 1 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial1.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial1.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial1.ipynb?flush_cache=true) |
+| Tutorial 2 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial2.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial2.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial2.ipynb?flush_cache=true) |
+| Tutorial 3 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial3.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial3.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial3.ipynb?flush_cache=true) |
+| Tutorial 4 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial4.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial4.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial4.ipynb?flush_cache=true) |
+| Tutorial 5 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial5.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial5.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial5.ipynb?flush_cache=true) |
+| Tutorial 6 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial6.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial6.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/instructor/W2D3_Tutorial6.ipynb?flush_cache=true) |
+| Outro | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Outro.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Outro.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Outro.ipynb?flush_cache=true) |
 
 
 ## Student notebooks
 
 |   | Run | Run | View |
 | - | --- | --- | ---- |
-| Intro | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Intro.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Intro.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Intro.ipynb?flush_cache=true) |
-| Tutorial 1 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial1.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial1.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial1.ipynb?flush_cache=true) |
-| Tutorial 2 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial2.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial2.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial2.ipynb?flush_cache=true) |
-| Tutorial 3 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial3.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial3.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial3.ipynb?flush_cache=true) |
-| Tutorial 4 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial4.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial4.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial4.ipynb?flush_cache=true) |
-| Tutorial 5 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial5.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial5.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial5.ipynb?flush_cache=true) |
-| Tutorial 6 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial6.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial6.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial6.ipynb?flush_cache=true) |
-| Outro | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Outro.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Outro.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Outro.ipynb?flush_cache=true) |
+| Intro | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Intro.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Intro.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Intro.ipynb?flush_cache=true) |
+| Tutorial 1 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial1.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial1.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial1.ipynb?flush_cache=true) |
+| Tutorial 2 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial2.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial2.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial2.ipynb?flush_cache=true) |
+| Tutorial 3 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial3.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial3.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial3.ipynb?flush_cache=true) |
+| Tutorial 4 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial4.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial4.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial4.ipynb?flush_cache=true) |
+| Tutorial 5 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial5.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial5.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial5.ipynb?flush_cache=true) |
+| Tutorial 6 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial6.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial6.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/student/W2D3_Tutorial6.ipynb?flush_cache=true) |
+| Outro | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Outro.ipynb) | [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/climate-course-content/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Outro.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_TheSocioeconomicsofClimateChange/W2D3_Outro.ipynb?flush_cache=true) |
 
diff --git a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial1_Solution_b2906775.py b/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial1_Solution_b2906775.py
deleted file mode 100644
index 254d0948d..000000000
--- a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial1_Solution_b2906775.py
+++ /dev/null
@@ -1,16 +0,0 @@
-
-'''
-3. One scenario to try based on the goals of the energy transition is the combination of:
-(1) full electrification of the energy supply, i.e. 'very highly taxed' fossil fuel emissions (Coal, Natural gas, and Oil),
-(2) 'highly subsidized' renewables, and
-(3) a 'very high' carbon price,
-as well as the 'highly reduced' emissions of Methane and other Gases
-which gets us to a maximum increase of 2°C by the end of the century
-(cf. this example scenario https://en-roads.climateinteractive.org/scenario.html?v=24.3.0&p1=100&p7=85&p10=5&p16=-0.05&p23=-1&p39=250&p59=-64&p67=2&g0=2&g1=62).
-
-Nevertheless, other scenarios are possible, either by involving more parameters or by focusing on carbon removal.
-There are (at least!) two important observations to make. First, actions vary in leverage, in other words, some actions are more helpful than others.
-Second, to make a difference and reach an ambitious goal like the 2°C degree target, many actions in many sectors are required.
-Sometimes one refers to this circumstance by calling it a 'Silver Buckshot' instead of a 'Silver Bullet' approach (cf. e.g. https://www.washingtonpost.com/archive/opinions/2006/05/27/welcome-to-the-climate-crisis-span-classbankheadhow-to-tell-whether-a-candidate-is-serious-about-combating-global-warmingspan/26b2ac5a-a4a3-46ff-b214-3fc07a3a5ab3/).
-Furthermore, people might be surprised by the fact that some actions may be much lower leverage, while others like carbon pricing and energy efficiency might be higher leverage than people expect.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial1_Solution_bbf54df1.py b/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial1_Solution_bbf54df1.py
deleted file mode 100644
index 6bef8614b..000000000
--- a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial1_Solution_bbf54df1.py
+++ /dev/null
@@ -1,13 +0,0 @@
-
-'''
-1. At first, En-ROADS is a world model, not an optimal planning model so it is unlike any IAM used e.g. for the IPCC reports.
-Controls are somewhat limited (DAC/CCS, soil, BioChar, and mineralization are not covered).
-It allows only for single parameter changes -  in reality, there will be correlations, e.g. between carbon pricing and renewables due to market pressure.
-Some feedbacks are also missing, besides the above-mentioned damage from climate change on GDP, land use, etc.,
-climate change harms the human population by shortening lives.
-Although this happens already in the current 1°C increase reality, this harm is difficult to quantify and hence not implemented.
-Furthermore, it is a fully aggregated model, no spatial/regional or income resolution, and corresponding interdependency exists.
-An action/ policy is assumed to be executed globally, which is a utopia so far.
-It hence remains important to consider the implications of heterogeneity across different countries and their interactions.
-Last but not least, tipping points such as the thawing of the permafrost are represented in a very simplified manner only, although they probably have strong implications.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial2_Solution_1425c24a.py b/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial2_Solution_1425c24a.py
deleted file mode 100644
index 6435f43d1..000000000
--- a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial2_Solution_1425c24a.py
+++ /dev/null
@@ -1,13 +0,0 @@
-'''
-1. There are two options. We could change one boring parameter and a more exciting one.
-The former has a strong impact. By turning off the  'Climate change slows economic growth' button, we make the strong assumption that there is less severe damage as asked because there is NO damage at all.
-In other words, we decoupled GDP and temperature increase which results in even more GHG emissions than in the baseline scenario.
-To get a more relevant and exciting result, it is better to change the 'Economic damage formulation' instead.
-
-2. The default formulation by 'Burke 2018' serves as the baseline.
-In order to have a less severe damage formulation we could have a look at the 'Reduction in GDP vs. Temperature' plot.
-However, all other formulations show a more or equal severe damage function than 'Burke 2018' with respect to temperature.
-Another view, i.e. the 'Gross World Product' in contrast shows that the 'Howard & Sterner' formulation results in less economic damage,
-which might have been covered in the 'Reduction in GDP vs. Temperature' graph as the scale was going up to 5°C,
-which we do not reach until 2100 in our simple baseline-like scenario.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial2_Solution_c67833bd.py b/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial2_Solution_c67833bd.py
deleted file mode 100644
index 46db77624..000000000
--- a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial2_Solution_c67833bd.py
+++ /dev/null
@@ -1,11 +0,0 @@
-'''
-1. In En-ROADS the population growth has a small effect on the temperature increase by 2100. On the left, the global population graph emphasizes the development until 2100, resulting in an expected population of 8.8 billion in the lowest growth case and an expected population of 12.56 billion in the largest growth case, respectively.
-The former leads to a 3.2°C increase and the latter to a 3.5°C which is only a $\pm$ 0.2°C change due to population growth.
-In contrast, economic growth has a much larger impact: high growth (+30.000 $/person/year in 2100) leads to a temperature increase of 0.4°C by 2100 and low growth (-20.000 $/person/year in 2100) decreases it by 0.3°C, making discussions of overpopulation rather irrelevant as the decisions around family choice are personal decisions and efforts to shift these decisions have many ethical implications.
-It is instead raising the question of the necessity to end economic growth or at least to discuss its current coupling to resource exploitation.
-Note that lower population growth takes a long time to affect emissions because global population shifts do not occur quickly and instead play out over many decades.
-
-2. All possible combinations of pop.-econ. -- low-low: 2.9°C, low-no: 3.2°C, low-high: 3.6°C, no-no: 3.3°C, no-low: 3.0°C, no-high: 3.7°C, high-low: 3.1°C, high-no: 3.5°C, high-high: 4.0°C.
-In terms of an anticipated minimal temperature increase, the best (worst) case is to have a low (high) growth in population and economy. It is better to have a decreasing economy and a growing population than vice versa (increasing economy + shrinking population).
-Having high population growth and economic growth add up to a larger temperature increase (+0.7°C) than their individual contribution (high pop. growth +0.2°C, high econ. growth +0.4°C) which indicates that these variables are not fully independent in the En-ROADS model.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial2_Solution_efff427c.py b/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial2_Solution_efff427c.py
deleted file mode 100644
index 9fcb077f6..000000000
--- a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial2_Solution_efff427c.py
+++ /dev/null
@@ -1,3 +0,0 @@
-'''
-Other potential examples are human health, extreme weather events, sea-level rise, desertification, flooding, species migration.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial3_Solution_2aba4d73.py b/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial3_Solution_2aba4d73.py
deleted file mode 100644
index 1cc5f7ad8..000000000
--- a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial3_Solution_2aba4d73.py
+++ /dev/null
@@ -1,10 +0,0 @@
-
-'''
-3. The earlier the carbon price starts to phase in the more effective it is regarding the temperature increase.
-However, the earlier the larger the disturbance of the market price of electricity, emphasized by a strong increase in the price at first
-and a strong drop in the price after the first few years.
-This might lead to economic disruptions as energy supply becomes very expensive, therefore low-income households,
-which usually spend large parts of their income on energy, would need compensation like a 'carbon dividend' to avoid precariat.
-In summary, actions and their temporal implementation always need to be evaluated in various aspects to be a successful and
-societal least disruptive action against climate change.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial4_Solution_4240ed7b.py b/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial4_Solution_4240ed7b.py
deleted file mode 100644
index 278c4e8b0..000000000
--- a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial4_Solution_4240ed7b.py
+++ /dev/null
@@ -1,10 +0,0 @@
-'''
-2. We choose 'Emissions' and 'Land Cover|Forest' as variables of interest to contrast ecosystem health between the two scenarios.
-First, note that the land model components from the IMAGE and REMIND-MAGPIE estimate different initial land cover areas, if we assume however that their trend is reasonable, we can conclude the following:
-For high emissions, forcing rises and hence temperature, we know that precipitation patterns change with higher temperatures such that forests,
-here our indicator for ecosystem health, are dying.
-In turn the land cover by forests is decreasing in the SSP5 scenario.
-In contrast, the SSP1 reduces emissions and also exploits resources like wood only sustainably due to afforestation.
-As afforestation is also used to capture carbon, while land use is reduced in general,
-the scenario shows an increase in land cover by forests within the current century.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial4_Solution_a4ef74b1.py b/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial4_Solution_a4ef74b1.py
deleted file mode 100644
index f6f1f4a52..000000000
--- a/tutorials/W2D3_TheSocioeconomicsofClimateChange/solutions/W2D3_Tutorial4_Solution_a4ef74b1.py
+++ /dev/null
@@ -1,34 +0,0 @@
-
-# put two variables of interest in a list
-vars = ['Emissions|Kyoto Gases', 'Land Cover|Forest']
-# create new names for structured data series and plot labels
-val_name = ['Emissions\n(Mt CO$_2$/yr)','Land covered by\nforest (million ha)']
-# choose scenarios of interest and a color for plotting
-scenarios = ['SSP1-26', 'SSP5-Baseline']
-colors = ['darkblue','darkorange']
-
-
-# init figure and axis
-fig, axs = plt.subplots(2,1)
-# loop over all variables and new names
-for var, val, ax in zip(vars,val_name, axs.flatten()):
-
-    # loop over scenarios and their color
-    for sc, col in zip(scenarios, colors):
-        # retrieve SSP for the respective variable from rich dataframe
-        ds_unstrct = get_SSPs_for_variable(df,sc,var)
-        # restructure dataframe for plotting
-        ds_strct = pd.melt(ds_unstrct, id_vars=["MODEL"], value_vars=['2010','2020','2030','2040','2050','2060','2070','2080','2090','2100'], var_name="YEAR", value_name =val)
-        #print(ds_strct)
-        # plot variable vs. time, add label incl. scenario and model
-        ax.plot(ds_strct['YEAR'],ds_strct[val],label=f'{sc},\n{ds_strct.MODEL[0]}', color=col)
-        # altern. plotting procedure w/o the color distinction
-        #sns.lineplot(ds_strct, x='YEAR', y=val, hue='MODEL', ax=ax, palette='flare')
-
-    # aesthetics
-    ax.set_ylabel(fr'{val}')
-    ax.set_xlabel('Time (years)')
-    plt.setp(ax.get_xticklabels(), rotation=45)
-    plt.setp(ax.get_xticklabels()[::2], visible=False)
-    ax.grid(True)
-    axs[0].legend()
\ No newline at end of file
diff --git a/tutorials/W2D3_TheSocioeconomicsofClimateChange/static/W2D3_Tutorial4_Solution_a4ef74b1_0.png b/tutorials/W2D3_TheSocioeconomicsofClimateChange/static/W2D3_Tutorial4_Solution_a4ef74b1_0.png
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