Repository: earth-env-data-science/earth-env-data-science-book
Branch: master
Commit: c279e1b1b74a
Files: 49
Total size: 10.7 MB

Directory structure:
gitextract_bf1j6390/

├── .github/
│   └── workflows/
│       └── deploy.yml
├── .gitignore
├── README.md
├── binder/
│   └── Dockerfile
├── final_projects/
│   └── final_project_list.py
├── requirements.txt
├── scripts/
│   ├── clean.py
│   └── templates/
│       └── jekyllmd.tpl
└── src/
    ├── LICENSE.md
    ├── _config.yml
    ├── _toc.yml
    ├── assignments/
    │   ├── basic_pandas.ipynb
    │   ├── basic_xarray.ipynb
    │   ├── cartopy.ipynb
    │   ├── cmip.ipynb
    │   ├── files_markdown_git.md
    │   ├── more_matplotlib.ipynb
    │   ├── more_xarray.ipynb
    │   ├── numpy_matplotlib.ipynb
    │   ├── pandas_groupby.ipynb
    │   └── python_basics_and_functions.ipynb
    ├── intro.md
    ├── lectures/
    │   ├── basic_scipy/
    │   │   ├── more_matplotlib.ipynb
    │   │   └── numpy_and_matplotlib.ipynb
    │   ├── core_python/
    │   │   ├── functions_classes.ipynb
    │   │   ├── organization_and_packaging.ipynb
    │   │   └── python_fundamentals.ipynb
    │   ├── dask/
    │   │   ├── dask_arrays.ipynb
    │   │   └── intro.md
    │   ├── data.ipynb
    │   ├── environment/
    │   │   ├── binder.ipynb
    │   │   ├── intro_to_git.md
    │   │   ├── intro_to_jupyterlab.md
    │   │   ├── intro_to_unix.md
    │   │   └── python_environments.md
    │   ├── introduction.md
    │   ├── mapping_cartopy.ipynb
    │   ├── models/
    │   │   ├── cmip.ipynb
    │   │   ├── intro_models.ipynb
    │   │   ├── regridding.ipynb
    │   │   └── xgcm.ipynb
    │   ├── pandas/
    │   │   ├── basic_pandas.ipynb
    │   │   ├── pandas_groupby.ipynb
    │   │   └── pandas_intro.md
    │   └── xarray/
    │       ├── xarray-part2.ipynb
    │       ├── xarray.ipynb
    │       ├── xarray_intro.md
    │       └── xarray_tips_and_tricks.ipynb
    └── projects.md

================================================
FILE CONTENTS
================================================

================================================
FILE: .github/workflows/deploy.yml
================================================
name: deploy-book

# Trigger the workflow on push to main branch and PRs
on:
  push:
    branches:
    - master
  pull_request:
    branches:
    - master

# This job installs dependencies, builds the book, and deploys the html
jobs:
  build-and-deploy-book:
    runs-on: ${{ matrix.os }}
    strategy:
      matrix:
        os: [ubuntu-latest]
        python-version: [3.8]
    steps:
    - uses: actions/checkout@v2

    # Install dependencies
    - name: Set up Python ${{ matrix.python-version }}
      uses: actions/setup-python@v1
      with:
        python-version: ${{ matrix.python-version }}
    - name: Install dependencies
      run: |
        pip install -r requirements.txt

    # Build the book
    - name: Build the book
      run: |
        jupyter-book build src

    # Deploy the book's html to earth-env-data-science.github.io
    - name: GitHub Pages action
      if: github.ref == 'refs/heads/master'
      uses: peaceiris/actions-gh-pages@v3
      with:
        deploy_key: ${{ secrets.ACTIONS_DEPLOY_KEY }}
        external_repository: earth-env-data-science/earth-env-data-science.github.io
        publish_branch: master
        publish_dir: src/_build/html
        full_commit_message: "GitHub action book deploy"


================================================
FILE: .gitignore
================================================
*~
._*
*.lock
*.DS_Store
*.swp
*.out
*.py[cod]
*.pid
*.ipynb_checkpoints
_build
secrets/


================================================
FILE: README.md
================================================
# Earth and Environmental Data Science Book

[![Binder](https://img.shields.io/static/v1.svg?logo=Jupyter&label=Pangeo+Binder&message=GCP+us-central1&color=blue)](https://binder.pangeo.io/v2/gh/earth-env-data-science/earth-env-data-science-book/master)

This is the source repository for <https://earth-env-data-science.github.io>.

To deploy changes to the site, push to the master branch of this repo.


================================================
FILE: binder/Dockerfile
================================================
FROM pangeo/pangeo-notebook:2020.11.18


================================================
FILE: final_projects/final_project_list.py
================================================
import pandas as pd

url = 'https://docs.google.com/spreadsheets/d/e/2PACX-1vQZuGOVK_afpqjadVaI9wd29iXRC78Ofvp-hL3IQV7SbJN11LIW0SbcRtjhpdreHjqh6Efhh8UMlkqH/pub?gid=1496620424&single=true&output=csv'
df = pd.read_csv(url)
df

for idx, row in df.iterrows():
    repo = row['repo']
    repo_base = repo.replace('https://github.com/', '')
    github_badge = f'[![github](https://img.shields.io/badge/view-github-green?logo=github)]({repo})'
    
    binder_badge = f'[![Binder](https://mybinder.org/badge_logo.svg)]({row.binder})' if row.binder else ''
    name = row['name']
    
    print(f'- _{row.title.strip()}_ by {name}\n  {github_badge} {binder_badge}')
    

================================================
FILE: requirements.txt
================================================
jupyter-book==0.13.1
sphinxext-opengraph

================================================
FILE: scripts/clean.py
================================================
"""A helper script to "clean up" all of your generated markdown and HTML files."""
import shutil as sh
import os.path as op

path_root = op.join(op.dirname(op.abspath(__file__)), '..')

paths = [op.join(path_root, '_site'),
         op.join(path_root, '_build')]
for path in paths:
    print('Removing {}...'.format(path))
    sh.rmtree(path, ignore_errors=True)

print('Done!')


================================================
FILE: scripts/templates/jekyllmd.tpl
================================================
{%- extends 'markdown.tpl' -%}

<!-- All code cells -->
{%- block codecell %}
{% if 'remove_cell' not in cell.metadata.tags and 'removecell' not in cell.metadata.tags %}
<div markdown="1" class="cell code_cell">
{{- super() }}
</div>
{% endif %}
{% endblock codecell %}

<!-- Add class for input area -->
{% block input %}
{%- if 'remove_input' not in cell.metadata.tags %}
{%- if cell.source != '' -%}
<div class="input_area{% if 'hide_input' in cell.metadata.tags or 'hidecode' in cell.metadata.tags %} hidecode{% endif %}" markdown="1">
```
{%- if 'magics_language' in cell.metadata  -%}
    {{ cell.metadata.magics_language}}
{%- elif 'name' in nb.metadata.get('language_info', {}) -%}
    {{ nb.metadata.language_info.name }}
{% endif %}
{{- cell.source }}
{% endif %}
```
</div>
{%- endif %}
{% endblock input %}

<!-- All output areas -->
{% block output %}
<div class="output_wrapper" markdown="1">
<div class="output_subarea{% if 'interactive' in cell.metadata.tags %} output_widget_view{% endif %}" markdown="1">
{{- super() -}}
</div>
</div>
{%- endblock output %}

<!-- Remove indentations for output text  -->
{%- block stream %}
{:.output_stream}
```
{{ output.text -}}
```
{% endblock stream %}


{% block data_text -%}
{:.output_data_text}
```
{{ output.data['text/plain'] }}
```
{% endblock data_text %}


{%- block error %}
{:.output_traceback_line}
```
{{- super() }}
```
{% endblock error %}

{%- block data_html %}
<div markdown="0" class="output output_html">
{{ output.data['text/html'] }}
</div>
{% endblock data_html %}

{%- block data_png %}
{:.output_png}
{{- super() -}}
{% endblock data_png %}

<!-- Markdown cells -->
{% block markdowncell %}
{% if 'remove_cell' not in cell.metadata.tags and 'removecell' not in cell.metadata.tags %}
{{- super() }}
{% endif %}
{%- endblock markdowncell %}

================================================
FILE: src/LICENSE.md
================================================
# License for this book

All content in this book (ie, any files and content in the `content/` folder)
is licensed under the [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/)
(CC BY-SA 4.0) license.

================================================
FILE: src/_config.yml
================================================
title: Earth and Environmental Data Science
author: Ryan Abernathey
copyright: 2016-2021
logo: eeds-logo.png
exclude_patterns: [_build, Thumbs.db, .DS_Store, "**.ipynb_checkpoints"]
only_build_toc_files: true

execute:
  execute_notebooks: 'off'

# Information about where the book exists on the web
repository:
  url: https://github.com/earth-env-data-science/earth-env-data-science-book
  path_to_book: src
  branch: master

# Launch button settings
launch_buttons:
  notebook_interface: jupyterlab  # The interface interactive links will activate ["classic", "jupyterlab"]
  binderhub_url: "" # The URL of the BinderHub (e.g., https://mybinder.org)
  jupyterhub_url: "https://https://us-central1-b.gcp.pangeo.io/"

# HTML-specific settings
html:
  favicon                   : ""  # A path to a favicon image
  use_edit_page_button      : true  # Whether to add an "edit this page" button to pages. If `true`, repository information in repository: must be filled in
  use_repository_button     : true  # Whether to add a link to your repository button
  use_issues_button         : true  # Whether to add an "open an issue" button
  use_multitoc_numbering    : true   # Continuous numbering across parts/chapters
  extra_navbar              : Powered by <a href="https://jupyterbook.org">Jupyter Book</a>  # Will be displayed underneath the left navbar.
  extra_footer              : ""  # Will be displayed underneath the footer.
  google_analytics_id       : ""  # A GA id that can be used to track book views.
  home_page_in_navbar       : true  # Whether to include your home page in the left Navigation Bar
  baseurl                   : "https://earth-env-data-science.github.io"  # The base URL where your book will be hosted. Used for creating image previews and social links. e.g.: https://mypage.com/mybook/
  comments:
    hypothesis              : false
    utterances              : false

sphinx:
  extra_extensions:
  - sphinxext.opengraph
  config:
    ogp_site_url: "https://earth-env-data-science.github.io/"
    ogp_image: "https://earth-env-data-science.github.io/_static/eeds-logo.png"
    ogp_description_length: 200
    ogp_use_first_image: true

================================================
FILE: src/_toc.yml
================================================
format: jb-book
root: intro
parts:
- caption: About this Book
  chapters:
  - file: LICENSE
  - file: projects

- caption: The Interactive Computing Environment
  chapters:
  - file: lectures/environment/intro_to_jupyterlab
  - file: lectures/environment/intro_to_unix
  - file: lectures/environment/intro_to_git
  - file:  assignments/files_markdown_git
  - file: lectures/environment/python_environments
  - file: lectures/environment/binder
  - file: lectures/data

- caption: The Core Python Language
  chapters:
  - file: lectures/core_python/python_fundamentals
  - file: lectures/core_python/functions_classes
  - file: lectures/core_python/organization_and_packaging
  - file: assignments/python_basics_and_functions

- caption: Scientific Python Fundamentals
  chapters:
  - file: lectures/basic_scipy/numpy_and_matplotlib
  - file: assignments/numpy_matplotlib
  - file: lectures/basic_scipy/more_matplotlib
  - file: assignments/more_matplotlib

- caption: High Level Data Analysis Frameworks
  chapters:
  - file: lectures/pandas/pandas_intro
    sections:
    - file: lectures/pandas/basic_pandas
    - file: assignments/basic_pandas
    - file: lectures/pandas/pandas_groupby
    - file: assignments/pandas_groupby
  - file: lectures/xarray/xarray_intro
    sections:
    - file: lectures/xarray/xarray
    - file: assignments/basic_xarray
    - file: lectures/xarray/xarray-part2
    - file: assignments/more_xarray
    - file: lectures/xarray/xarray_tips_and_tricks

- caption: Geoscience Package
  chapters:
  - file: lectures/mapping_cartopy
  - file: assignments/cartopy

- caption: Climate Model Data
  chapters:
  - file: lectures/models/intro_models
  - file: lectures/models/cmip
  - file: assignments/cmip
  - file: lectures/models/regridding
  - file: lectures/models/xgcm

- caption: Big Data
  chapters:
  - file: lectures/dask/intro
  - file: lectures/dask/dask_arrays


================================================
FILE: src/assignments/basic_pandas.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment: Pandas Fundamentals with Earthquake Data\n",
    "\n",
    "In this assignment, we will review pandas fundamentals, such as how to\n",
    "\n",
    "- Open csv files\n",
    "- Manipulate dataframe indexes\n",
    "- Parse date columns\n",
    "- Examine basic dataframe statistics\n",
    "- Manipulate text columns and extract values\n",
    "- Plot dataframe contents using\n",
    "  - Bar charts\n",
    "  - Histograms\n",
    "  - Scatter plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, import Numpy, Pandas and Matplotlib and set the display options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data for this assignment in .csv format downloaded from the [USGS Earthquakes Database](https://earthquake.usgs.gov/earthquakes/search/) is available at:\n",
    "\n",
    "http://www.ldeo.columbia.edu/~rpa/usgs_earthquakes_2014.csv\n",
    "\n",
    "You don't need to download this file. You can open it directly with Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Use Pandas' read_csv function directly on this URL to open it as a DataFrame\n",
    "(Don't use any special options). Display the first few rows and the DataFrame info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should have seen that the dates were not automatically parsed into datetime types.\n",
    "\n",
    "### 2) Re-read the data in such a way that all date columns are identified as dates and the earthquake ID is used as the index\n",
    "\n",
    "Verify that this worked using the `head` and `info` functions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Use `describe` to get the basic statistics of all the columns\n",
    "\n",
    "Note the highest and lowest magnitude of earthquakes in the databse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Use `nlargest` to get the top 20 earthquakes by magnitude\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.nlargest.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examine the structure of the `place` column. The state / country information seems to be in there. How would you get it out?\n",
    "\n",
    "### 5) Extract the state or country using Pandas [text data functions](https://pandas.pydata.org/pandas-docs/stable/text.html)\n",
    "\n",
    "Add it as a new column to the dataframe called `country`. Note that some of the \"countries\" are actually U.S. states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) Display each unique value from the new column\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.unique.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) Create a filtered dataset that only has earthquakes of magnitude 4 or larger and"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 8) Using the filtered dataset (magnitude > 4), count the number of earthquakes in each country/state. Make a bar chart of this number for the top 5 locations with the most earthquakes\n",
    "\n",
    "Location name on the x axis, Earthquake count on the y axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9) Make a histogram the distribution of the Earthquake magnitudes\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/version/0.23/generated/pandas.DataFrame.hist.html\n",
    "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html\n",
    "\n",
    "Do one subplot for the filtered and one for the unfiltered dataset.\n",
    "Use a Logarithmic scale. What sort of relationship do you see?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11) Visualize the locations of earthquakes by making a scatterplot of their latitude and longitude\n",
    "\n",
    "Use a two-column subplot with both the filtered and unfiltered datasets. Color the points by magnitude. Make it pretty\n",
    "\n",
    "What difference do you note between the filtered and unfiltered datasets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: src/assignments/basic_xarray.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment: Xarray Fundamentals with Atmospheric Radiation Data\n",
    "\n",
    "In this assignment, we will use Xarray to analyze top-of-atmosphere radiation data from [NASA's CERES project](https://ceres.larc.nasa.gov/).\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/b/bb/The-NASA-Earth%27s-Energy-Budget-Poster-Radiant-Energy-System-satellite-infrared-radiation-fluxes.jpg\" width=65%>\n",
    "\n",
    "_Public domain, by NASA, from Wikimedia Commons_\n",
    "\n",
    "\n",
    "A pre-downloaded and subsetted a portion of the CERES dataset is available here: http://ldeo.columbia.edu/~rpa/CERES_EBAF-TOA_Edition4.0_200003-201701.condensed.nc. The size of the data file is 702.53 MB. It may take a few minutes to download.\n",
    "\n",
    "Please review the CERES [FAQs](https://ceres.larc.nasa.gov/resources/faqs) before getting started.\n",
    "\n",
    "Start by importing Numpy, Matplotlib, and Xarray. Set the default figure size to (12, 6)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (12, 6)\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, download the NetCDF file using pooch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pooch\n",
    "fname = pooch.retrieve(\n",
    "    'http://ldeo.columbia.edu/~rpa/CERES_EBAF-TOA_Edition4.0_200003-201701.condensed.nc',\n",
    "    known_hash='a876cc7106e7dcb1344fbec5dcd7510e5cd947e62049a8cbc188ad05ffe00345'\n",
    ")\n",
    "print(fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Opening data and examining metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 1.1) Open the dataset and display its contents \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2) Print out the `long_name` attribute of each variable\n",
    "Print `variable`: `long name` for each variable. Format the output so that the start of the `long name` attributes are aligned. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Basic reductions, arithmetic, and plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2.1) Calculate the time-mean of the entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2) From this, make a 2D plot of the the time-mean Top of Atmosphere (TOA) Longwave, Shortwave, and Incoming Solar Radiation\n",
    "(Use \"All-Sky\" conditions)\n",
    "\n",
    "Note the sign conventions on each variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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   "source": [
    "### 2.3) Add up the three variables above and verify (visually) that they are equivalent to the TOA net flux\n",
    "\n",
    "You have to pay attention to and think carefully about the sign conventions (positive or negative) for each variable in order for the variables to sum to the right TOA net flux. Refer to the NASA figure at the top of the page to understand incoming and outgoing radiation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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   "source": [
    "## 3) Mean and weighted mean\n",
    "\n",
    "### 3.1) Calculate the global (unweighted) mean of TOA net radiation\n",
    "\n",
    "Since the Earth is approximately in radiative balance, the net TOA radiation should be zero. But taking the naive mean from this dataset, you should find a number far from zero. Why?"
   ]
  },
  {
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    "The answer is that each \"pixel\" or \"grid point\" of this dataset does not represent an equal area of Earth's surface. So naively taking the mean, i.e. giving equal weight to each point, gives the wrong answer.\n",
    "\n",
    "On a lat / lon grid, the relative area of each grid point is proportional to $\\cos(\\lambda)$. ($\\lambda$ is latitude)\n",
    "\n",
    "### 3.2) Create a `weight` array proportional to $\\cos(\\lambda)$\n",
    "\n",
    "Think carefully a about radians vs. degrees\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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   "source": [
    "### 3.3) Redo your global mean TOA net radiation calculation with this weight factor\n",
    "\n",
    "Use xarray's [weighted array reductions](http://xarray.pydata.org/en/stable/user-guide/computation.html#weighted-array-reductions) to compute the weighted mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "markdown",
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   "source": [
    "This time around, you should have found something much closer to zero. Ask a climate scientist what the net energy imbalance of Earth due to global warming is estimated to be. Do you think our calculation is precise enough to detect this? \n",
    "\n",
    "### 3.4) Now that you have a `weight` factor, verify that the TOA incoming solar, outgoing longwave, and outgoing shortwave approximately match up with infographic shown in the first cell of this assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) Meridional Heat Transport Calculation\n",
    "\n",
    "We can go beyond a weight factor and actually calculate the area of each pixel of the dataset, using the formula\n",
    "\n",
    "$$ dA = R^2 \\cos(\\lambda) d\\lambda d \\varphi $$\n",
    "\n",
    "where $d\\lambda$ and $d\\varphi$ are the spacing of the points in latitude and longitude (measured in radians). We can approximate Earth's radius as $R = 6,371$ km.\n",
    "\n",
    "### 4.1) calculate the pixel area using this formula and create a 2D (lon, lat) DataArray for it\n",
    "\n",
    "(Xarray's `ones_like` function can help you easily create and broadcast DataArrays.) Verify that the sum of all the pixels equals the Earth's true surface area as evaluated using the formula for the area of a sphere (yes, the Earth is not a sphere ... it's just a homework problem)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "\n",
    "### 4.2) Calculate and plot the total amount of net radiation in each 1-degree latitude band\n",
    "Multiplying the pixel area (m$^2$) from above with the radiative flux (W m$^{-2}$) gives you the total amount of radiation absorbed in each pixel in W.\n",
    "\n",
    "Label with correct units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3) Plot the cumulative sum of the total amount of net radiation as a function of latitude\n",
    "\n",
    "Label with correct units. (Hint: check out xarray's [cumsum](http://xarray.pydata.org/en/stable/generated/xarray.DataArray.cumsum.html) function.)\n",
    "\n",
    "This curve tells you how much energy must be transported meridionally by the ocean and atmosphere in order to account for the radiative imbalance at the top of the atmosphere."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should get a curve that looks something like this: https://journals.ametsoc.org/view/journals/clim/14/16/full-i1520-0442-14-16-3433-f07.gif (Figure from Trenberth & Caron, 2001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Selecting and Merging Data\n",
    "\n",
    "For the next problem, use the following approximate locations of four different cities.\n",
    "\n",
    "| city | lon | lat |\n",
    "| -- | -- | -- |\n",
    "| NYC |74 W | 40 N | \n",
    "| Nome, Alaska | 165 W | 64 N | \n",
    "| Columbo, Sri Lanka | 80 E | 7 N |\n",
    "| Hobart, Tasmania | 147 E | 43 S |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1) Create a `Dataset` for each point from the global dataset\n",
    "Each city should get its own `Dataset` with the same variables as the one you imported. Find the nearest associated incoming solar radiation and net radiation timeseries at each city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2) Merge these four datasets into a new dataset with the new dimension `city`\n",
    "\n",
    "Create a new dimension coordinate to hold the city name.\n",
    "Display the merged dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3) Plot the incoming solar and net radiation at each city\n",
    "\n",
    "Make two separate plots.\n",
    "Try to have your legend created automatically from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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================================================
FILE: src/assignments/cartopy.ipynb
================================================
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    "# Assignment: Making Maps with Cartopy\n",
    "\n",
    "### 1) Plot data from NARR\n",
    "\n",
    "NARR is NCEP's [North American Regional Reanalysis](https://www.esrl.noaa.gov/psd/data/gridded/data.narr.html), a widely used product for studying the weather and climate of the continental US. The data is available from NOAA's [Earth System Research Laboratory](https://www.esrl.noaa.gov/) via [OPeNDAP](https://en.wikipedia.org/wiki/OPeNDAP), meaing that xarray can open the data \"remotely\" without downloading a file.\n",
    "\n",
    "For this problem, you should open this geopential height file:\n",
    "\n",
    "    https://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/NARR/Dailies/pressure/hgt.201810.nc\n",
    "    \n",
    "And this precipitation file:\n",
    "\n",
    "    https://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/NARR/Dailies/monolevel/apcp.2018.nc\n",
    "    \n",
    "Your goal is to make a map that looks like the one below. It shows total precipitation on Oct. 15, 2018 in blue, plus contours of the 500 mb geopotential surface.\n",
    "\n",
    "_Hint: examine the dataset variables and attirbutes carefully in order to determine the projection of the data._\n",
    "\n",
    "![narr_map](cartopy_figures/narr_map.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Antarctic Sea Ice\n",
    "\n",
    "Download this file and then use it to plot the concentration of Antarctic Sea Ice on Aug. 7, 2017. Again, you will need to explore the file contents in order to determine the correct projection.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import pooch\n",
    "url = 'https://polarwatch.noaa.gov/erddap/files/nsidcCDRiceSQsh1day/2017/seaice_conc_daily_sh_f17_20170807_v03r01.nc'\n",
    "fname = pooch.retrieve(url, known_hash='19b74e7e97f1c0786da0c674c4d5e4af0da5b32e2fe8c66a8f1a8a9a1241e73c')\n",
    "ds_ice = xr.open_dataset(fname, drop_variables='melt_onset_day_seaice_conc_cdr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![sea_ice_map](cartopy_figures/sea_ice_map.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Global USGS Earthquakes\n",
    "\n",
    "Reload the file we explored in homework 5 using pandas\n",
    "\n",
    "    http://www.ldeo.columbia.edu/~rpa/usgs_earthquakes_2014.csv\n",
    "    \n",
    "and use the data to recreate this map."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![earthquake_map](cartopy_figures/earthquake_map.png)"
   ]
  }
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================================================
FILE: src/assignments/cmip.ipynb
================================================
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"
    }
   },
   "cell_type": "markdown",
   "id": "e2406ca1-e06e-48fe-9fb1-05ae01429d44",
   "metadata": {},
   "source": [
    "# Assignment: Calculate wet bulb temperature for CMIP6 models\n",
    "\n",
    "Global temperatures are increasing due to the effects of anthropogenic greenhouse gas emissions. This has many potentially disastrous consequences for many people around the world. One of the concerns is that in hot and humid climates, depending on the humidity and increase in temperature could lead to fatal conditions. \n",
    "One way to quantify the combined effect of temperature and humidity for human health is the wet bulb temperature (WBT), which can be approximated as:\n",
    "\n",
    "![Screen Shot 2023-01-04 at 9.00.44 PM.png](https://user-images.githubusercontent.com/14314623/210852697-83d3e1f6-d7fa-416b-a6d0-b830a9a76f08.png)\n",
    "\n",
    "[source](https://journals.ametsoc.org/view/journals/apme/50/11/jamc-d-11-0143.1.xml)\n",
    "\n",
    "[\"The wet-bulb temperature is the lowest temperature that can be reached under current ambient conditions by the evaporation of water only\"](https://en.wikipedia.org/wiki/Wet-bulb_temperature#:~:text=Wet%2Dbulb%20temperature%20and%20health,-See%20also%3A%20Effects&text=A%20sustained%20wet%2Dbulb%20temperature,to%20gaining%20heat%20from%20it.). Simply put, if the wet bulb temperature gets too high, the evaporation of sweat can not cool the human body sufficiently. \n",
    "\n",
    "A sustained wet bulb temperature >35C is likely to be fatal, but lower WBT can also be dangerous depending on other external factors (e.g. solar radiation and insufficient air flow). From [Raymond et al. 2020](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7209987/): \"for example, regions affected by the deadly 2003 European and 2010 Russian heat waves experienced TW values no greater than 28°C\"\n",
    "\n",
    "This exercise aims to practice working with the CMIP6 cloud archive by computing the wet bulb temperature from available variables and summarizing the results in timeseries plots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e09f4c1-ab6c-4faf-8e97-267f55f33608",
   "metadata": {},
   "source": [
    "> Lets start by importing matplotlib, xarray, numpy, and combined_preprocessing from xmip.preprocessing. Set the default figure size to (12, 6).  \n",
    "\n",
    "> Then load the intake-esm collection for CMIP6 data on Google Cloud Storage (you can use xmip, as we did in the lecture, or set this up according to the [pangeo instructions](http://gallery.pangeo.io/repos/pangeo-gallery/cmip6/intake_ESM_example.html)). The result should be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8df78194-8e21-49a3-a4a3-1ff94abb5ab8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "23482f5d-12b5-469f-88d6-e1b54e01051f",
   "metadata": {},
   "source": [
    "> The ingredients we need for wet bulb temperature are surface temperature and relative humidity. \n",
    "\n",
    "> Find the relevant variables using the [spreadheet](https://docs.google.com/spreadsheets/d/1UUtoz6Ofyjlpx5LdqhKcwHFz2SGoTQV2_yekHyMfL9Y/edit#gid=1221485271) from the lecture. You can enter multiple variables in the search query like so: `.search(..., variable_id=['a', 'b'], ...)`.\n",
    "\n",
    "> Select a subset of the variables, the `'historical'` experiment, the native model grid, monthly output, thres models (`['NorESM2-MM', 'MPI-ESM1-2-LR', 'CanESM5']` are recommended but feel free to explore), and a single member (`'r1i1p1f1'`) for starters. *Check out some more models/members later if you are curious!*\n",
    "\n",
    "> Then load the data into a dictionary of xarray datasets like we did in the lecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a01765b1-296f-41bd-b490-2ce6c24854a5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c07d209c-ae0c-4530-a256-bd5e1da565f8",
   "metadata": {},
   "source": [
    "> Now lets calculate the wet bulb temperature according to the formula above. Write a function that calculates wet bulb temperature from temperature and humidity. \n",
    "\n",
    "> 🚨 Watch the units and convert if necessary! You can always check the units by looking at the metadata of a variable (`ds['your_variable'].attrs['units]`).\n",
    "\n",
    "> A good test to see if your function is doint the right thing is to calculate the example from the [paper](https://journals.ametsoc.org/view/journals/apme/50/11/jamc-d-11-0143.1.xml): For relative humidity of 50% and temperature of 20C, we expect ~13.7C for the wet bulb temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10db86d6-d08b-4220-bdc4-47133ad174c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a3c94714-3e38-408e-848c-4423f0c887b9",
   "metadata": {},
   "source": [
    "> To get an idea of the extremes globally lets compute the 90th percentile (`xarray.DataArray.quantile()` is your friend) of wet bulb temperature at every timesteps and then compute the annual average of that and plot is as a timeseries for each model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cbfccce-0eef-4267-8688-0a22193cb49f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "34c85f6d-3189-44af-ab94-a49f5d0455fc",
   "metadata": {},
   "source": [
    "> What we are really interested in is to see how these extreme WBT are going to evolve in the future. Lets use your new CMIP6 skills to get some more data!\n",
    "> Make another query to get two of the future scenario experiments (`ssp585` and `ssp245`- the 'worst case' and 'middle of the road' scenarios) for the same models and load them into separate dataset_dictionaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33e7c796-03c9-4c10-bea9-0204b805aeb0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e92cf583-7107-44dd-a45f-99ae174524c4",
   "metadata": {},
   "source": [
    "> Plot the same quantity as before (90th percentile in space and annual maximum) for each model and all 3 experiments to see how each model predicts wet bulb temperature extremes until the end of the century. Use color/linestyle/lineweight to separate the experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d216def6-b8a3-42d9-a70d-455ac0b3af75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "feef1773-f240-4554-b3b9-bb43f9a1ee4f",
   "metadata": {},
   "source": [
    "> As a final step, lets look where these extremes occur. Plot a map of an annual average from 2099-2100 for each model and scenario. Make sure to restrict the colormap to + 30 to see where extremes occur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "130c11a0-d8b3-4e1f-974a-275a60c53b66",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: src/assignments/files_markdown_git.md
================================================
# Assignment: Files, Git, and Github

## Create a Dummy Resume Repository

Use JupyterLab to launch a terminal and use the terminal to do the following tasks:

1. Create a new directory called `resume` within your home directory
2. Create an empty file within this directory called `Readme.md`

Now use JupyterLab to edit the file:

1. Navigate to the directory in the file browser
1. Open `Readme.md` in the text editor
1. Open `Readme.md` in in Markdown Preview
1. Arrange these files side-by-side so you can see your document rendered
1. Edit the file in the editor. Add the following information:

    1. Top level heading with your name
    1. An image. It can be a photo of you or, if you prefer, a photo of your spirit animal.
    1. Secondary heading entitled "Education"
    1. A list of schools you attended, hyperlinked to the websites of those insitutions

1. Save the file
    
Now go back to the terminal and do the following:

1. Initialize a new git repository in the `resume` directory
1. Add the `Readme.md` file to the repository
1. Create a new commit with a commit message
1. Check the git log to see your commit history
1. Go to GitHub and create a [new public repository](https://github.com/new) entitled `resume`
1. Push your local resume repository to GitHub following the instructions.
1. View your online resume at `http://github.com/<your github username>/resume`

Finally, go back to the editor and add a new subsection called "Research Interests" to your `Readme.md` file. Update your local git repository and push your changes to GitHub. Verify that the remote repository is updated.

To "hand in" this part of the assignment, put a link to it in the `Readme.md` file in the next part.


## Create your Assignments Repository

Now that you know how to create a git repository, you should create your assignments repository.

- Create a new directory called `rces-assignments` in your home directory.
- Create a `Readme.md` markdown file that contains your name and a link to your "resume" repo.
- Initialize a new git repository 
- Add the file and make your first commit
- Create a new **private** repository on GitHub called `rces-assignments`. (Call it exactly like that. Do not vary the spelling, capitalization, or punctuation.)
- Push your `rces-assignments` repository to GitHub
- On GitHub, go to "settings" -> "collaborators" and add `rabernat` and `stb2145`.
- Push new commits to this repository whenever you are ready to hand in your assignments


================================================
FILE: src/assignments/more_matplotlib.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment: More Matplotlib\n",
    "\n",
    "The goal here is to replicate the figures you see as closely as possible.\n",
    "\n",
    "In order to get some data, you will have to run the code in the cells below. **Don't worry about how this code works.** In the end, it will give you some numpy arrays, which you will use in your plots. You are not allowed to use any packages other than numpy and matplotlib to complete your assignment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 1: Line plots\n",
    "\n",
    "In this problem, we will plot some daily weather data from a NOAA station in [Millbrook, NY](https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:US1NYDT0008/detail). A full description of this dataset is available at: <https://www.ncdc.noaa.gov/data-access/land-based-station-data>\n",
    "\n",
    "The cell below uses pandas to download the data and populate a bunch of numpy arrays (`t_daily_min`, `t_daily_max`, etc.) Run the cell and then use the numpy arrays to try to re-create the plot you see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pooch\n",
    "POOCH = pooch.create(\n",
    "    path=pooch.os_cache(\"noaa-data\"),\n",
    "    # Use the figshare DOI\n",
    "    base_url=\"doi:10.5281/zenodo.5553029/\",\n",
    "    registry={\n",
    "        \"HEADERS.txt\": \"md5:2a306ca225fe3ccb72a98953ded2f536\",\n",
    "        \"CRND0103-2016-NY_Millbrook_3_W.txt\": \"md5:eb69811d14d0573ffa69f70dd9c768d9\",\n",
    "        \"CRND0103-2017-NY_Millbrook_3_W.txt\": \"md5:b911da727ba1bdf26a34a775f25d1088\",\n",
    "        \"CRND0103-2018-NY_Millbrook_3_W.txt\": \"md5:5b61bc687261596eba83801d7080dc56\",\n",
    "        \"CRND0103-2019-NY_Millbrook_3_W.txt\": \"md5:9b814430612cd8a770b72020ca4f2b7d\",\n",
    "        \"CRND0103-2020-NY_Millbrook_3_W.txt\": \"md5:cd8de6d5445024ce35fcaafa9b0e7b64\"\n",
    "    },\n",
    ")\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "with open(POOCH.fetch(\"HEADERS.txt\")) as fp:\n",
    "    data = fp.read()\n",
    "lines = data.split('\\n')\n",
    "headers = lines[1].split(' ')\n",
    "\n",
    "dframes = []\n",
    "for year in range(2016, 2019):\n",
    "    fname = f'CRND0103-{year}-NY_Millbrook_3_W.txt'               \n",
    "    df = pd.read_csv(POOCH.fetch(fname), parse_dates=[1],\n",
    "                     names=headers, header=None, sep='\\s+',\n",
    "                     na_values=[-9999.0, -99.0])\n",
    "    dframes.append(df)\n",
    "\n",
    "df = pd.concat(dframes)\n",
    "df = df.set_index('LST_DATE')\n",
    "df\n",
    "\n",
    "#########################################################\n",
    "#### BELOW ARE THE VARIABLES YOU SHOULD USE IN THE PLOTS!\n",
    "#### (numpy arrays)  \n",
    "#### NO PANDAS ALLOWED!\n",
    "#########################################################\n",
    "\n",
    "t_daily_min = df.T_DAILY_MIN.values\n",
    "t_daily_max = df.T_DAILY_MAX.values\n",
    "t_daily_mean = df.T_DAILY_MEAN.values\n",
    "p_daily_calc = df.P_DAILY_CALC.values\n",
    "soil_moisture_5 = df.SOIL_MOISTURE_5_DAILY.values\n",
    "soil_moisture_10 = df.SOIL_MOISTURE_10_DAILY.values\n",
    "soil_moisture_20 = df.SOIL_MOISTURE_20_DAILY.values\n",
    "soil_moisture_50 = df.SOIL_MOISTURE_50_DAILY.values\n",
    "soil_moisture_100 = df.SOIL_MOISTURE_100_DAILY.values\n",
    "date = df.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "units = lines[2].split(' ')\n",
    "for name, unit in zip(headers, units):\n",
    "    print(f'{name}: {unit}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![figure](more_matplotlib_figures/fig1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Problem 2: Contour Plots\n",
    "\n",
    "Now we will visualize some global temperature data from the NCEP-NCAR atmospheric reanalysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "ds_url = 'http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.MONTHLY/.Diagnostic/.surface/.temp/dods'\n",
    "ds = xr.open_dataset(ds_url, decode_times=False)\n",
    "\n",
    "#########################################################\n",
    "#### BELOW ARE THE VARIABLES YOU SHOULD USE IN THE PLOTS!\n",
    "#### (numpy arrays) \n",
    "#### NO XARRAY ALLOWED!\n",
    "#########################################################\n",
    "\n",
    "temp = ds.temp[-1].values - 273.15\n",
    "lon = ds.X.values\n",
    "lat = ds.Y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![fig2](more_matplotlib_figures/fig2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3: Scatter plots\n",
    "Here we will make a map plot of earthquakes from a USGS catalog of historic large earthquakes. Color the earthquakes by log10(depth) and adjust the marker size to be magntiude$^4$/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = pooch.retrieve(\n",
    "    \"https://rabernat.github.io/research_computing/signif.txt.tsv.zip\",\n",
    "    known_hash='22b9f7045bf90fb99e14b95b24c81da3c52a0b4c79acf95d72fbe3a257001dbb',\n",
    "    processor=pooch.Unzip()\n",
    ")[0]\n",
    "\n",
    "earthquakes = np.genfromtxt(fname, delimiter='\\t')\n",
    "depth = earthquakes[:, 8]\n",
    "magnitude = earthquakes[:, 9]\n",
    "latitude = earthquakes[:, 20]\n",
    "longitude = earthquakes[:, 21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![fig3](more_matplotlib_figures/fig3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: src/assignments/more_xarray.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment: More Xarray with El Niño-Southern Oscillation (ENSO) Data\n",
    "\n",
    "Here will will calculate the NINO 3.4 index of El Nino variabillity and use it to analyze datasets.\n",
    "\n",
    "First read [this page from NOAA](https://www.ncdc.noaa.gov/teleconnections/enso/indicators/sst). It tells you the following:\n",
    "\n",
    "- The Niño 3.4 region is defined as the region between +/- 5 deg. lat, 170 W - 120 W lon.\n",
    "- Warm or cold phases of the Oceanic Niño Index are defined by a five consecutive 3-month running mean of sea surface temperature (SST) anomalies in the Niño 3.4 region that is above the threshold of +0.5°C (warm), or below the threshold of -0.5°C (cold). This is known as the Oceanic Niño Index (ONI).\n",
    "\n",
    "(Note that \"anomaly\" means that the seasonal cycle, also called the \"climatology\" has been removed.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start by importing Numpy, Matplotlib, and Xarray. Set the default figure size to (12, 6)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Reproduce the SST curve from the figure below\n",
    "\n",
    "Use the `sst.mnmean.nc` file that we worked with in class, located at <http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/noaa.ersst.v5/sst.mnmean.nc>.\n",
    "\n",
    "You don't have to match the stylistic details, or use different colors above and below zero, just the \"3mth running mean\" curve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![enso](https://www.ncdc.noaa.gov/monitoring-content/teleconnections/eln-f-pg.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data as an Xarray dataset. Drop the `time_bnds` variable as we did in class and trim the data to 1950 onward for this assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now calculate the climatology and the SST anomaly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now reproduce the plot. Keep the rolling 3-month average of the SST anomaly as a DataArray for the next question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Calculate boolean timeseries representing the positive / negative ENSO phases\n",
    "\n",
    "Refer to the definitions of warm/cold phases above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot them somehow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Plot composites of SST anomaly for the positive and negative ENSO regimes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These should be pcolormesh maps. First positive ONI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And negative ONI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Calculate the composite of preciptiation for positive and negative ENSO phases.\n",
    "\n",
    "First load the precip dataset. Code to fix the broken time coordinate is included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "\n",
       ".xr-var-data > table {\n",
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       "\n",
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       ".xr-var-data,\n",
       ".xr-attrs {\n",
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       "\n",
       ".xr-attrs dt, dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\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.Dataset&gt;\n",
       "Dimensions:  (X: 144, Y: 72, time: 362)\n",
       "Coordinates:\n",
       "  * X        (X) float32 1.25 3.75 6.25 8.75 ... 351.25 353.75 356.25 358.75\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 1990-02-01\n",
       "  * Y        (Y) float32 88.75 86.25 83.75 81.25 ... -81.25 -83.75 -86.25 -88.75\n",
       "Data variables:\n",
       "    prcp     (time, Y, X) float32 nan nan nan ... 0.023939257 0.024468819\n",
       "Attributes:\n",
       "    Conventions:  IRIDL</pre><div class='xr-wrap' hidden><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-9d4df79f-2659-40f6-a8d0-e0828b66e509' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9d4df79f-2659-40f6-a8d0-e0828b66e509' 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'>X</span>: 144</li><li><span class='xr-has-index'>Y</span>: 72</li><li><span class='xr-has-index'>time</span>: 362</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1032b644-6305-4bd9-9448-5674726be205' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1032b644-6305-4bd9-9448-5674726be205' 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'>X</span></div><div class='xr-var-dims'>(X)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.25 3.75 6.25 ... 356.25 358.75</div><input id='attrs-d5118480-3464-4910-890c-66cf4f1cbf78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5118480-3464-4910-890c-66cf4f1cbf78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d69af45e-8307-47ce-8de0-d1bbefa66023' class='xr-var-data-in' type='checkbox'><label for='data-d69af45e-8307-47ce-8de0-d1bbefa66023' title='Show/Hide data repr'><svg 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>pointwidth :</span></dt><dd>2.5</dd><dt><span>gridtype :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>degree_east</dd></dl></div><div class='xr-var-data'><pre>array([  1.25,   3.75,   6.25,   8.75,  11.25,  13.75,  16.25,  18.75,  21.25,\n",
       "        23.75,  26.25,  28.75,  31.25,  33.75,  36.25,  38.75,  41.25,  43.75,\n",
       "        46.25,  48.75,  51.25,  53.75,  56.25,  58.75,  61.25,  63.75,  66.25,\n",
       "        68.75,  71.25,  73.75,  76.25,  78.75,  81.25,  83.75,  86.25,  88.75,\n",
       "        91.25,  93.75,  96.25,  98.75, 101.25, 103.75, 106.25, 108.75, 111.25,\n",
       "       113.75, 116.25, 118.75, 121.25, 123.75, 126.25, 128.75, 131.25, 133.75,\n",
       "       136.25, 138.75, 141.25, 143.75, 146.25, 148.75, 151.25, 153.75, 156.25,\n",
       "       158.75, 161.25, 163.75, 166.25, 168.75, 171.25, 173.75, 176.25, 178.75,\n",
       "       181.25, 183.75, 186.25, 188.75, 191.25, 193.75, 196.25, 198.75, 201.25,\n",
       "       203.75, 206.25, 208.75, 211.25, 213.75, 216.25, 218.75, 221.25, 223.75,\n",
       "       226.25, 228.75, 231.25, 233.75, 236.25, 238.75, 241.25, 243.75, 246.25,\n",
       "       248.75, 251.25, 253.75, 256.25, 258.75, 261.25, 263.75, 266.25, 268.75,\n",
       "       271.25, 273.75, 276.25, 278.75, 281.25, 283.75, 286.25, 288.75, 291.25,\n",
       "       293.75, 296.25, 298.75, 301.25, 303.75, 306.25, 308.75, 311.25, 313.75,\n",
       "       316.25, 318.75, 321.25, 323.75, 326.25, 328.75, 331.25, 333.75, 336.25,\n",
       "       338.75, 341.25, 343.75, 346.25, 348.75, 351.25, 353.75, 356.25, 358.75],\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'>1960-01-01 ... 1990-02-01</div><input id='attrs-fca9011a-0032-403c-b857-b300271b3cdb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fca9011a-0032-403c-b857-b300271b3cdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0a98582-c74f-4567-91ea-083335d7fc23' class='xr-var-data-in' type='checkbox'><label for='data-f0a98582-c74f-4567-91ea-083335d7fc23' title='Show/Hide data 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;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;1989-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-01-01T00:00:00.000000000&#x27;, &#x27;1990-02-01T00:00:00.000000000&#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'>Y</span></div><div class='xr-var-dims'>(Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.75 86.25 83.75 ... -86.25 -88.75</div><input id='attrs-a65806af-e587-4873-88bc-ba7e5a101c9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a65806af-e587-4873-88bc-ba7e5a101c9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a952d08-34df-4735-9fe7-1a49be861393' class='xr-var-data-in' type='checkbox'><label for='data-8a952d08-34df-4735-9fe7-1a49be861393' title='Show/Hide data repr'><svg 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>pointwidth :</span></dt><dd>2.5</dd><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>degree_north</dd></dl></div><div class='xr-var-data'><pre>array([ 88.75,  86.25,  83.75,  81.25,  78.75,  76.25,  73.75,  71.25,  68.75,\n",
       "        66.25,  63.75,  61.25,  58.75,  56.25,  53.75,  51.25,  48.75,  46.25,\n",
       "        43.75,  41.25,  38.75,  36.25,  33.75,  31.25,  28.75,  26.25,  23.75,\n",
       "        21.25,  18.75,  16.25,  13.75,  11.25,   8.75,   6.25,   3.75,   1.25,\n",
       "        -1.25,  -3.75,  -6.25,  -8.75, -11.25, -13.75, -16.25, -18.75, -21.25,\n",
       "       -23.75, -26.25, -28.75, -31.25, -33.75, -36.25, -38.75, -41.25, -43.75,\n",
       "       -46.25, -48.75, -51.25, -53.75, -56.25, -58.75, -61.25, -63.75, -66.25,\n",
       "       -68.75, -71.25, -73.75, -76.25, -78.75, -81.25, -83.75, -86.25, -88.75],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-acd72690-ce2f-4014-bae7-e70696a79b46' class='xr-section-summary-in' type='checkbox'  checked><label for='section-acd72690-ce2f-4014-bae7-e70696a79b46' 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>prcp</span></div><div class='xr-var-dims'>(time, Y, X)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan ... 0.023939257 0.024468819</div><input id='attrs-51e69ac2-db30-45dd-ba1c-9d79ada056c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51e69ac2-db30-45dd-ba1c-9d79ada056c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65ca08ca-b440-4fd4-874a-193b266098aa' class='xr-var-data-in' type='checkbox'><label for='data-65ca08ca-b440-4fd4-874a-193b266098aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>pointwidth :</span></dt><dd>0</dd><dt><span>long_name :</span></dt><dd>precipitation rate</dd><dt><span>units :</span></dt><dd>mm/day</dd><dt><span>iridl:hasSemantics :</span></dt><dd>iridl:PrecipitationRate</dd></dl></div><div class='xr-var-data'><pre>array([[[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ]],\n",
       "\n",
       "       [[0.8194625 , 0.88875395, 0.8091981 , ..., 0.9492164 ,\n",
       "         0.82411003, 0.78667456],\n",
       "        [1.0099076 , 0.9892432 , 0.98398215, ..., 0.8937486 ,\n",
       "         1.0330029 , 0.9554615 ],\n",
       "        [1.041718  , 1.124445  , 1.1302058 , ..., 1.0546163 ,\n",
       "         1.0768677 , 1.0590024 ],\n",
       "...\n",
       "        [0.0388062 , 0.02617484, 0.02632195, ..., 0.03951803,\n",
       "         0.04346019, 0.04225701],\n",
       "        [0.02660953, 0.02801275, 0.02830904, ..., 0.02524056,\n",
       "         0.023809  , 0.02494283],\n",
       "        [0.06468108, 0.0645658 , 0.06004713, ..., 0.07233888,\n",
       "         0.06633606, 0.06525925]],\n",
       "\n",
       "       [[0.03070718, 0.02990827, 0.02791063, ..., 0.03007989,\n",
       "         0.03042011, 0.03045618],\n",
       "        [0.07962694, 0.08658402, 0.09354889, ..., 0.06384834,\n",
       "         0.06523342, 0.06917968],\n",
       "        [0.3978056 , 0.3911632 , 0.3591685 , ..., 0.20908825,\n",
       "         0.29971218, 0.35676065],\n",
       "        ...,\n",
       "        [0.01469258, 0.01184803, 0.00796852, ..., 0.03738775,\n",
       "         0.02957931, 0.02097237],\n",
       "        [0.00995823, 0.00965142, 0.00722192, ..., 0.01297611,\n",
       "         0.01213069, 0.01111982],\n",
       "        [0.02359816, 0.02303343, 0.02243581, ..., 0.02560936,\n",
       "         0.02393926, 0.02446882]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e68f98ed-c46a-4eb1-8a2e-245072c37aed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e68f98ed-c46a-4eb1-8a2e-245072c37aed' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>IRIDL</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (X: 144, Y: 72, time: 362)\n",
       "Coordinates:\n",
       "  * X        (X) float32 1.25 3.75 6.25 8.75 ... 351.25 353.75 356.25 358.75\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 1990-02-01\n",
       "  * Y        (Y) float32 88.75 86.25 83.75 81.25 ... -81.25 -83.75 -86.25 -88.75\n",
       "Data variables:\n",
       "    prcp     (time, Y, X) float32 nan nan nan ... 0.023939257 0.024468819\n",
       "Attributes:\n",
       "    Conventions:  IRIDL"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import xarray as xr\n",
    "url = 'http://iridl.ldeo.columbia.edu/SOURCES/.NASA/.GPCP/.V2p1/.multi-satellite/.prcp/dods'\n",
    "dsp = xr.open_dataset(url, decode_times=False)\n",
    "true_time = (pd.date_range(start='1960-01-01', periods=len(dsp['T']), freq='MS'))\n",
    "dsp['T'] = true_time\n",
    "dsp = dsp.rename({'T': 'time'})\n",
    "dsp.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the *difference* between the time-mean of `prcp` during positive and negative ENSO phases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
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================================================
FILE: src/assignments/numpy_matplotlib.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment: Numpy and Matplotlib\n",
    "\n",
    "The goal of this assignment is to gain comfort creating, visualizating, and computing with numpy array. By the end of the assignment, you should feel comfortable:\n",
    "\n",
    "```{admonition} Learning Goals\n",
    ":class: tip\n",
    "\n",
    "- Creating new arrays using `linspace` and `arange`\n",
    "- Computing basic formulas with numpy arrays\n",
    "- Loading data from `.npy` files\n",
    "- Performing reductions (e.g. `mean`, `std` on numpy arrays)\n",
    "- Making 1D line plots\n",
    "- Making scatterplots\n",
    "- Annotating plots with titles and axes\n",
    "```\n",
    "\n",
    "## 1 Creating and Manipulating Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First import numpy and matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1. Create two 2D arrays representing coordinates x, y on the cartesian plan\n",
    "Both should cover the range (-2, 2) and have 100 points in each direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Visualize each 2D array using `pcolormesh`\n",
    "\n",
    "Use the correct coordiantes for the x and y axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 From your cartesian coordinates, create polar coordinates $r$ and $\\varphi$\n",
    "\n",
    "Refer to the [wikipedia page](https://en.wikipedia.org/wiki/Polar_coordinate_system#Converting_between_polar_and_Cartesian_coordinates) for the conversion formula. You will need to use numpy's `arctan2` function. Read its documentation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4. Visualize $r$ and $\\varphi$ on the 2D  $x$ / $y$ plane using `pcolormesh`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Caclulate the quanity $f = \\cos^2(4r) + \\sin^2(4\\varphi)$\n",
    "\n",
    "And plot it on the x$ / $y$ plane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6 Plot the mean of f with respect to the x axis\n",
    "\n",
    "as a function of y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.7 Plot the mean of f with respect to the y axis\n",
    "\n",
    "as a function of x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.8 Plot the mean of $f$ with respect to $\\phi$ as a function of $r$\n",
    "\n",
    "This is hard. You will need to define a discrete range of $r$ values and then figure out how to average $f$ within the bins defined by your $r$ grid. There are many different ways to accomplish this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Part 2: Analyze [ARGO](http://www.argo.ucsd.edu) Data\n",
    "\n",
    "In this problem, we use real data from ocean profiling floats.\n",
    "ARGO floats are autonomous robotic instruments that collect Temperature, Salinity, and Pressure data from the ocean. ARGO floats collect one \"profile\" (a set of messurements at different depths or \"levels\").\n",
    "\n",
    "<img src=\"https://argo.ucsd.edu/wp-content/uploads/sites/361/2020/06/float_cycle_1.png\" width=\"80%\" />\n",
    "\n",
    "Each profile has a single latitude, longitude, and date associated with it, in addition to many different levels.\n",
    "\n",
    "Let's start by using [pooch](https://www.fatiando.org/pooch/latest/) to download the data files we need for this exercise.\n",
    "The following code will give you a list of `.npy` files that you can open in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pooch\n",
    "url = \"https://www.ldeo.columbia.edu/~rpa/float_data_4901412.zip\"\n",
    "files = pooch.retrieve(url, processor=pooch.Unzip(), known_hash=\"2a703c720302c682f1662181d329c9f22f9f10e1539dc2d6082160a469165009\")\n",
    "files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2.1 Load each data file as a numpy array.\n",
    "\n",
    "You can use whatever names you want for your arrays, but I recommend\n",
    "\n",
    "`T`: temperature\n",
    "\n",
    "`S`: salinity\n",
    "\n",
    "`P`: pressure\n",
    "\n",
    "`date`: date\n",
    "\n",
    "`lat`: latitude\n",
    "\n",
    "`lon`: longitude\n",
    "\n",
    "`level`: depth level\n",
    "\n",
    "**Note**: you have to actually look at the file name (the items in `files`) to know which files corresponds to which variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Examine the shapes of T, S and P compared to `lon`, `lat`, `date` and `level`. How do you think they are related?\n",
    "\n",
    "Based on the shapes, which dimensions do you think are shared among the arrays?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2.3 Make a plot for each column of data in T, S and P (three plots). \n",
    "\n",
    "The vertical scale should be the `levels` data. Each plot should have a line for each column of data. It will look messy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Compute the mean and standard deviation of each of T, S and P at each depth in `level`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Now make three similar plot, but show only the mean T, S and P at each depth. Show [error bars](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.errorbar.html) on each plot using the standard deviations. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6 Account For Missing Data\n",
    "\n",
    "The profiles contain many missing values. These are indicated by the special \"Not a Number\" value, or `np.nan`.\n",
    "\n",
    "When you take the mean or standard deviation of data with NaNs in it, the entire result becomes NaN. Instead, if you use the special functions `np.nanmean` and `np.nanstd`, you tell NumPy to ignore the NaNs.\n",
    "\n",
    "Recalculate the means and standard deviations as in the previous sections using these functions and plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2.7 Create a scatter plot of the `lon`, `lat` positions of the ARGO float.\n",
    "\n",
    "Use the [plt.scatter](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html) function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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================================================
FILE: src/assignments/pandas_groupby.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment: Pandas Groupby with Hurricane Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Numpy, Pandas and Matplotlib and set the display options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the following code to load a CSV file of the [NOAA IBTrACS](https://www.ncdc.noaa.gov/ibtracs/index.php?name=ibtracs-data) hurricane dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/csv/ibtracs.ALL.list.v04r00.csv'\n",
    "df = pd.read_csv(url, parse_dates=['ISO_TIME'], usecols=range(12),\n",
    "                 skiprows=[1], na_values=[' ', 'NOT_NAMED'],\n",
    "                 keep_default_na=False, dtype={'NAME': str})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basin Key: (NI - North Indian, SI - South Indian, WP - Western Pacific, SP - Southern Pacific, EP - Eastern Pacific, NA - North Atlantic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many rows does this dataset have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many North Atlantic hurricanes are in this dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Get the unique values of the `BASIN`, `SUBBASIN`, and `NATURE` columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Rename the `WMO_WIND` and `WMO_PRES` columns to `WIND` and `PRES`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Get the 10 largest rows in the dataset by `WIND`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will notice some names are repeated.\n",
    "\n",
    "### 4) Group the data on `SID` and get the 10 largest hurricanes by `WIND`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) Make a bar chart of the wind speed of the 20 strongest-wind hurricanes\n",
    "\n",
    "Use the name on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) Plot the count of all datapoints by Basin\n",
    "\n",
    "as a bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) Plot the count of unique hurricanes by Basin\n",
    "\n",
    "as a bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8) Make a `hexbin` of the location of datapoints in Latitude and Longitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9) Find Hurricane Katrina (from 2005) and plot its track as a scatter plot\n",
    "\n",
    "First find the SID of this hurricane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next get this hurricane's group and plot its position as a scatter plot. Use wind speed to color the points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10) Make time the index on your dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11) Plot the count of all datapoints per year as a timeseries\n",
    "\n",
    "You should use `resample`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12) Plot all tracks from the North Atlantic in 2005\n",
    "\n",
    "You will probably have to iterate through a `GroupBy` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13) Create a filtered dataframe that contains only data since 1970 from the North Atlantic (\"NA\") Basin\n",
    "\n",
    "Use this for the rest of the assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14) Plot the number of datapoints per day from this filtered dataframe\n",
    "\n",
    "Make sure you figure is big enough to actually see the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15) Calculate the climatology of datapoint counts as a function of `dayofyear`\n",
    "\n",
    "Plot the mean and standard deviation on a single figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 16) Use `transform` to calculate the anomaly of daily counts from the climatology\n",
    "\n",
    "Resample the anomaly timeseries at annual resolution and plot a line with dots as markers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which years stand out as having anomalous hurricane activity?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: src/assignments/python_basics_and_functions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment: Basic Python\n",
    "\n",
    "\n",
    "```{admonition} Learning Goals\n",
    ":class: tip\n",
    "\n",
    "This assignment will verify that you have the following skills:\n",
    "- Create lists and dictionaries\n",
    "- Iterate through lists, tuples, and dictionaries\n",
    "- Index sequences (e.g. lists and tuples)\n",
    "- Define functions\n",
    "- Use optional keyword arguments in functions\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part I: Lists and Loops\n",
    "\n",
    "In this problem, we will explore the basic data structures and flow controls of Python by _manually creating data structures_.\n",
    "\n",
    "\n",
    "```{warning}\n",
    "Pluto is [not a planet any more](https://www.loc.gov/everyday-mysteries/item/why-is-pluto-no-longer-a-planet/).\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Create a list with the names of every planet in the solar system (in order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2) Have Python tell you how many planets there are by examining your list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Use slicing to display the first four planets (the rocky planets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Iterate through your planets and print the planet name only if it has an `s` at the end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part II: Dictionaries\n",
    "\n",
    "### 1) Now create a dictionary that maps each planet name to its mass\n",
    "\n",
    "You can use values from this [NASA fact sheet](https://nssdc.gsfc.nasa.gov/planetary/factsheet/).\n",
    "You can use whatever units you want, but be consistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Use your dictionary to look up Earth's mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 3) Loop through the data and create a list of planets whose mast is greater than 100 x 10^24 kg \n",
    "\n",
    "Display your result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Now add pluto to your dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part III: Functions\n",
    "\n",
    "### 1. Write a function to convert temperature from kelvin to celsius\n",
    "\n",
    "and celsius to kelvin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2. Write a function to convert temperature to fahrenheit\n",
    "\n",
    "Include an optional keyword argument to specify whether the input is in  celcius or kelvin.\n",
    "Call your previously defined functions if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Check that the outputs are sensible\n",
    "\n",
    "by trying a few examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Now write a function that converts _from_ farenheit\n",
    "\n",
    "and uses a keyword argument to specify whether you want the output in celcius or kelvin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) Write a function that takes two arguments (feet and inches) and returns height in meters\n",
    "\n",
    "Verify it gives sensible answers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Write a function takes one argument (height in meters) and returns two arguments (feet and inches)\n",
    "\n",
    "(Consult the [tutorial on numbers](https://docs.python.org/3/tutorial/introduction.html#numbers) if you are stuck on how to implement this.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 7) Verify that the \"round trip\" conversion from and back to meters is consistent \n",
    "\n",
    "Check for 3 different values of height in meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: src/intro.md
================================================
# An Introduction to Earth and Environmental Data Science

## History

This book grew out of a course developed at Columbia University called _Research Computing in Earth Science_.
It was written mostly by [Ryan Abernathey](https://rabernat.github.io), with significant contributions from
[Kerry Key](https://emlab.ldeo.columbia.edu/index.php/team/kerry-key/), 
[Tim Crone](https://github.com/tjcrone), and [Julius Busecke](https:www.juliusbusecke.com).
By separating the book from the class, we hope to create an open-source community resource for Python education
in the Earth and Environmental Sciences.

## Motivation and Scope

Computing has become an indispensable tool for nearly all Earth and Environmental Scientists, but it doesn't often appear in our curriculums.
This book focuses on _data analysis_, a subset of computing in which the data already exist, e.g.from observations or from the output of a simulation, but have to be transformed into understanding.
There are many different ways to gain understanding, but most workflows often boil down to:

- read data files
- perform some analysis operations, from very simple (e.g. take the mean) to very complex (e.g. train a deep neural network)
- visualize the output in a plot

This book doesn't attempt to teach deep learning; its goal is to teach the basic foundations of Earth and Environmental Data Science which are often overlooked.
The material is designed to be accessible for Earth Science graduate students in any discipline, with no prerequisites.

This book is intended to introduce new graduate students to modern computing software, programming tools and best practices that are broadly applicable to the analysis and visualization of Earth and Environmental data.
This includes an introduction to Unix, version control, and basic programming in the open-source Python language.
The bulk of the content is devoted to in-depth exploration of the numerical analysis and visualization packages which comprise the modern Scientific Python ecosystem, including Numpy, Scipy, Matplotlib, Pandas, Xarray, using real Earth and Environmental datasets.

## Learning Goals

After completing all of the material, students should have the ability to:

- Use unix commands to work with files and directories
- Navigate the JupyterHub Environment effectively
- Identify common geoscience data formats and the python packages which can load them
- Perform basic exploratory data analysis on Earth and Environmental data, distinguishing between
  - _Tabular data_: rows and columns
  - _Gridded data_: multidimensional numerical arrays
- Use visualization to enhance interpretation of data, including maps and interactive visualizations
- Construct complete, well-structured programs in Python
- Practice reproducible research through version control and binder

## Recommended Course Structure

This course material can be used however you want.
However, it was developed for a full-credit, semester-long course that meets twice a week.
The idea is to complete one lecture and one homework assignment per week.
One of the weekly meetings consists of a lecture in which the instructor presents the week's lecture, with students typing along.
The other should be used as "collaborative time," during which the students work on their assignments, ask questions, and interact with their peers.
A final project is an important capstone experience for a semester-long course.


================================================
FILE: src/lectures/basic_scipy/more_matplotlib.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More Matplotlib\n",
    "\n",
    "Matplotlib is the dominant plotting / visualization package in python. It is important to learn to use it well. In the last lecture, we saw some basic examples in the context of learning numpy. This week, we dive much deeper. The goal is to understand how matplotlib represents figures internally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure and Axes\n",
    "\n",
    "The *figure* is the highest level of organization of matplotlib objects. If we want, we can create a figure explicitly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116913b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116b31c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r6hXZ+T9pvWtqoCNiye/s1dk5xfzW7JyZ+buqurm7/3PNs22yJft6V5KPdPfvVNUPZeea\nCjd39/+sf7wj7bLate5Xui/lEpJ5sUtI8jWW7Guq6u1Jfi3Jqe7+6tBsm+6wvX11kpuTfKKqPp+d\n93LO+jDVoZb+LfjL7v6v7v6XJE9mJ8Jc2pJ9vTvJg0nS3f+Q5Nuyc01mvjGL/g7vt+7ouoTkehy6\nr7unQD+UneB6b2y5F93b7n6uu6/t7hu7+8bsvF9+qrsv+1qsLxNL/hb8RXY+AJiqujY7p5ufGp1y\n8yzZ1y8keVuSVNX3Zye6F0enPJrOJvnp3U8xvyXJc939b4d901pPL7dLSK7Fwn39QJJXJfn47ufS\nvtDdp67Y0Bti4d7yEi3c14eS/ERVPZHkv5P8Snd/8cpN/c1v4b6+N8kfVtUvZef057u8sDlcVX00\nO291XLv7fvhvJPmWJOnuD2bn/fHbk1xI8uUk7170vPYeAGa4IhUADBFdABgiugAwRHQBYIjoAsAQ\n0QWAIaILAENEFwCG/C89W5iskzQcrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116b4b470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0, 0, 1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAFDCAYAAAAzlxvgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1BgFSY1RkNQYBUmNUZDUGAVJjVGQ1PwPylNi4xfKN+EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119fd68d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0, 0, 0.5, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbUAAAFDCAYAAAC9YZDZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0bb208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_axes([0, 0, 0.5, 1])\n",
    "ax2 = fig.add_axes([0.6, 0, 0.3, 0.5], facecolor='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subplots\n",
    "\n",
    "Subplot syntax is one way to specify the creation of multiple axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ErCWL3wQG9mlsjbSX2w50Qq6QjWz3ejuJTsjWuVbRrEJQz20rWsWu+7Dts7lh4PV9HF+j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\nYGaWcf8fiv5/FT1ilzQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c18358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = fig.subplots(nrows=2, ncols=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5o2zvTvLmqtrI5ifNf/tgok2y2+/Lgz62vo7p697p7P4d9zAvfT3nzs65r8m8O3vJ9XXS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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a385438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 6))\n",
    "axes = fig.subplots(nrows=2, ncols=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11a1972b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a3e70f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a4f90f0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a5340f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a56f128>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a310da0>]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a shorthand for doing this all at once.\n",
    "\n",
    "__This is our recommended way to create new figures!__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAADYBJREFUeJzt3HGI33d9x/Hny8ROprWO5QRJou1Y\nuhrKoO7oOoRZ0Y20fyT/FEmguEppwK0OZhE6HCr1rylDELJptolT0Fr9Qw+J5A9X6RAjudJZmpTA\nLTpzROhZu/5TtGZ774/fT++4XHLf3v3uLt77+YDA7/v7fX6/e+fD3TO/fH/3+6WqkCRtf6/a6gEk\nSZvD4EtSEwZfkpow+JLUhMGXpCYMviQ1sWrwk3wuyXNJnrnC7Uny6SRzSZ5O8rbJjylJWq8hz/A/\nDxy4yu13AfvGf44C/7T+sSRJk7Zq8KvqCeBnV1lyCPhCjZwC3pDkTZMaUJI0GTsn8Bi7gQtLjufH\n1/1k+cIkRxn9L4DXvva1f3TLLbdM4MtLUh9PPvnkT6tqai33nUTws8J1K35eQ1UdB44DTE9P1+zs\n7AS+vCT1keS/13rfSfyWzjywd8nxHuDiBB5XkjRBkwj+DPDe8W/r3AG8WFWXnc6RJG2tVU/pJPky\ncCewK8k88FHg1QBV9RngBHA3MAe8BLxvo4aVJK3dqsGvqiOr3F7AX01sIknShvCdtpLUhMGXpCYM\nviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMG\nX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmD\nL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDUxKPhJDiQ5l2QuycMr3P7mJI8neSrJ00nunvyo\nkqT1WDX4SXYAx4C7gP3AkST7ly37O+CxqroNOAz846QHlSStz5Bn+LcDc1V1vqpeBh4FDi1bU8Dr\nx5dvAC5ObkRJ0iQMCf5u4MKS4/nxdUt9DLg3yTxwAvjASg+U5GiS2SSzCwsLaxhXkrRWQ4KfFa6r\nZcdHgM9X1R7gbuCLSS577Ko6XlXTVTU9NTX1yqeVJK3ZkODPA3uXHO/h8lM29wOPAVTV94DXALsm\nMaAkaTKGBP80sC/JTUmuY/Si7MyyNT8G3gWQ5K2Mgu85G0m6hqwa/Kq6BDwInASeZfTbOGeSPJLk\n4HjZQ8ADSX4AfBm4r6qWn/aRJG2hnUMWVdUJRi/GLr3uI0sunwXePtnRJEmT5DttJakJgy9JTRh8\nSWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+\nJDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZf\nkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNDAp+kgNJziWZS/LwFda8J8nZJGeSfGmyY0qS1mvnaguS\n7ACOAX8GzAOnk8xU1dkla/YBfwu8vapeSPLGjRpYkrQ2Q57h3w7MVdX5qnoZeBQ4tGzNA8CxqnoB\noKqem+yYkqT1GhL83cCFJcfz4+uWuhm4Ocl3k5xKcmClB0pyNMlsktmFhYW1TSxJWpMhwc8K19Wy\n453APuBO4AjwL0necNmdqo5X1XRVTU9NTb3SWSVJ6zAk+PPA3iXHe4CLK6z5RlX9sqp+CJxj9A+A\nJOkaMST4p4F9SW5Kch1wGJhZtubrwDsBkuxidIrn/CQHlSStz6rBr6pLwIPASeBZ4LGqOpPkkSQH\nx8tOAs8nOQs8Dnyoqp7fqKElSa9cqpafjt8c09PTNTs7uyVfW5J+UyV5sqqm13Jf32krSU0YfElq\nwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1\nYfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5Ka\nMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSE4OCn+RAknNJ5pI8fJV19ySpJNOTG1GSNAmr\nBj/JDuAYcBewHziSZP8K664H/hr4/qSHlCSt35Bn+LcDc1V1vqpeBh4FDq2w7uPAJ4CfT3A+SdKE\nDAn+buDCkuP58XW/luQ2YG9VffNqD5TkaJLZJLMLCwuveFhJ0toNCX5WuK5+fWPyKuBTwEOrPVBV\nHa+q6aqanpqaGj6lJGndhgR/Hti75HgPcHHJ8fXArcB3kvwIuAOY8YVbSbq2DAn+aWBfkpuSXAcc\nBmZ+dWNVvVhVu6rqxqq6ETgFHKyq2Q2ZWJK0JqsGv6ouAQ8CJ4Fngceq6kySR5Ic3OgBJUmTsXPI\noqo6AZxYdt1HrrD2zvWPJUmaNN9pK0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMv\nSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGX\npCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqYlDw\nkxxIci7JXJKHV7j9g0nOJnk6ybeTvGXyo0qS1mPV4CfZARwD7gL2A0eS7F+27Clguqr+EPga8IlJ\nDypJWp8hz/BvB+aq6nxVvQw8ChxauqCqHq+ql8aHp4A9kx1TkrReQ4K/G7iw5Hh+fN2V3A98a6Ub\nkhxNMptkdmFhYfiUkqR1GxL8rHBdrbgwuReYBj650u1VdbyqpqtqempqaviUkqR12zlgzTywd8nx\nHuDi8kVJ3g18GHhHVf1iMuNJkiZlyDP808C+JDcluQ44DMwsXZDkNuCzwMGqem7yY0qS1mvV4FfV\nJeBB4CTwLPBYVZ1J8kiSg+NlnwReB3w1yX8mmbnCw0mStsiQUzpU1QngxLLrPrLk8rsnPJckacJ8\np60kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1IT\nBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJ\ngy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNDAp+kgNJziWZS/LwCrf/VpKv\njG//fpIbJz2oJGl9Vg1+kh3AMeAuYD9wJMn+ZcvuB16oqt8HPgX8/aQHlSStz5Bn+LcDc1V1vqpe\nBh4FDi1bcwj4t/HlrwHvSpLJjSlJWq+dA9bsBi4sOZ4H/vhKa6rqUpIXgd8Ffrp0UZKjwNHx4S+S\nPLOWobehXSzbq8bci0XuxSL3YtEfrPWOQ4K/0jP1WsMaquo4cBwgyWxVTQ/4+tuee7HIvVjkXixy\nLxYlmV3rfYec0pkH9i453gNcvNKaJDuBG4CfrXUoSdLkDQn+aWBfkpuSXAccBmaWrZkB/mJ8+R7g\n36vqsmf4kqSts+opnfE5+QeBk8AO4HNVdSbJI8BsVc0A/wp8Mckco2f2hwd87ePrmHu7cS8WuReL\n3ItF7sWiNe9FfCIuST34TltJasLgS1ITGx58P5Zh0YC9+GCSs0meTvLtJG/Zijk3w2p7sWTdPUkq\nybb9lbwhe5HkPePvjTNJvrTZM26WAT8jb07yeJKnxj8nd2/FnBstyeeSPHel9ypl5NPjfXo6ydsG\nPXBVbdgfRi/y/hfwe8B1wA+A/cvW/CXwmfHlw8BXNnKmrfozcC/eCfz2+PL7O+/FeN31wBPAKWB6\nq+fewu+LfcBTwO+Mj9+41XNv4V4cB94/vrwf+NFWz71Be/GnwNuAZ65w+93Atxi9B+oO4PtDHnej\nn+H7sQyLVt2Lqnq8ql4aH55i9J6H7WjI9wXAx4FPAD/fzOE22ZC9eAA4VlUvAFTVc5s842YZshcF\nvH58+QYuf0/QtlBVT3D19zIdAr5QI6eANyR502qPu9HBX+ljGXZfaU1VXQJ+9bEM282QvVjqfkb/\ngm9Hq+5FktuAvVX1zc0cbAsM+b64Gbg5yXeTnEpyYNOm21xD9uJjwL1J5oETwAc2Z7RrzivtCTDs\noxXWY2Ify7ANDP57JrkXmAbesaETbZ2r7kWSVzH61NX7NmugLTTk+2Ino9M6dzL6X99/JLm1qv5n\ng2fbbEP24gjw+ar6hyR/wuj9P7dW1f9t/HjXlDV1c6Of4fuxDIuG7AVJ3g18GDhYVb/YpNk222p7\ncT1wK/CdJD9idI5yZpu+cDv0Z+QbVfXLqvohcI7RPwDbzZC9uB94DKCqvge8htEHq3UzqCfLbXTw\n/ViGRavuxfg0xmcZxX67nqeFVfaiql6sql1VdWNV3cjo9YyDVbXmD426hg35Gfk6oxf0SbKL0Sme\n85s65eYYshc/Bt4FkOStjIK/sKlTXhtmgPeOf1vnDuDFqvrJanfa0FM6tXEfy/AbZ+BefBJ4HfDV\n8evWP66qg1s29AYZuBctDNyLk8CfJzkL/C/woap6fuum3hgD9+Ih4J+T/A2jUxj3bccniEm+zOgU\n3q7x6xUfBV4NUFWfYfT6xd3AHPAS8L5Bj7sN90qStALfaStJTRh8SWrC4EtSEwZfkpow+JLUhMGX\npCYMviQ18f+GmWq6NWLIwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a5aa860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116b31be0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S5zH525FuWVSgGYY8jzuYfHx2A5N3M19Ocm1V/XDO2WBYviPAXVX1d0l+h8l3ja+tqv+d\nf7xBtnNOhh5/GTJOFm7PPDvL4/iFnOd5v7PufnnDIflIciPwHuBgVf1kQdl+ZrOMVwDXAl9M8h0m\nv/9YWfCJKUN/zp+pqp9W1beBM0wGvku+W4ETAFX1FeAFTP5SgC4G/be6zcff7kuVdp9nZ3kcv5jz\nPOdfsu8AzgJX8/8nAvzmhjXv5NknpZxY4EkAQ/Jdx+Rkhr2LynWhGTes/yKLPyllyPN4APjI9PZO\nJh8BvbRRvs8Bt0xvv3o6OFnw83gV5z4h5Q959gkpX234M962Wb6AjNs2z87yQjNecvO8iMA3A/8+\nHZD3TPfdyeRVLUxe8XwKWAW+CrxqwU/oZvn+Ffhv4OvTPyuLzDck44a1Cx/wgc9jgL8HHgG+CRxu\nlm8fcP908L8O/MGC830C+C7wUyavum8F3g68fd3zd2ya/5tNf8bbOssDM27rPDvLC8t4yc2zlxuV\nJKk5r2AmSVJzlrUkSc1Z1pIkNWdZS5LUnGUtSVJzlrUkSc1Z1pIkNfd/AOVK25B4VV4AAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a76f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(8, 4), subplot_kw={'facecolor': 'g'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x11a476b38>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x11a85bba8>], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drawing into Axes\n",
    "\n",
    "All plots are drawn into axes. It is easiest to understand how matplotlib works if you use the [object-oriented style](https://matplotlib.org/faq/usage_faq.html#coding-styles)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create some data to plot\n",
    "import numpy as np\n",
    "x = np.linspace(-np.pi, np.pi, 100)\n",
    "y = np.cos(x)\n",
    "z = np.sin(6*x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a866b70>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3ruWs3IOvtxf3jYxn5+FTfL3jiNVx3JIWBjc2L/UgB/JP84crE/SvKuVWxvaP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a866c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This does the same thing as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11aa68f98>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3ruWs3IOvtxf3jYxn5+FTfL3jiNVx3JIWBjc2L/UgB/JP84crE/SvKuVWxvaP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a866a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This starts to matter when we have multiple axes to worry about."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a9a3dd8>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p8ExaBs/vZ6f3slW/tuqcbSkliYzV4KmA0opyVlO7oFj/XalPPY8QEK3h7vCi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\nj442eiqaCvS2h0ikc2RyhXlfY6R2qW8D3xmpnt7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EuYzNBcxIcIExWWDidAyYG4RKxpsd\nUArS2/JyS2mhBUpLn/1xb6GbLS/2nt9pz30+f7XnnvQ8v5489znnd37n9zv/6aUiTXwV52RxuNMX\nfXpqkueTgsCnpw/dXd3A+GHZRFer9VbH8+WqSDONF1o519LmbArs6PPlaHHbXR0dGFpa6H1Rh9hj\nh7qrF3D901M8H3UPXZzrcAOlw7I9P27CFvVthyK8sGYPk4oG85PHxjpJKmO6c1tOFsufLONY/Xm+\n81b5/0zUYeKvOJTJ0dPNtLcrNQ3RbmgX3wEdX/RH6po5e7GVw5Fmxju4Y4XohQxE17WocTCDXme5\nA9KpbbhIS9tl/l3dQEZqMqNzs5wcuzgUHUugqs7GEcDVueWP1DVzuqmF6voLTs51Qhb19/afYMGK\ncopzMlk+r8yeo5te4e7iW1j2jfGUf1LPvDd30HjB7ti9UpyTxcXWdmobLzh7bxmis8qV3TqId/ed\nYG+4EcBJNzREB2aGstKoijRfeeTgohsa4MujQ1xovcyWAxH2hBsYW5Dt6QI2nRXnZNHU0kbkXAs1\njl5ng+jSr+MKs3l33wn21MTOtYPeiR79V0Xk6yKyX0TaRaTsGvtNF5EDIlIpIos7bS8SkR0ickhE\n3hYRT/u/2tuV32yt4tlVuyjJH8DqhXeT7eHye8bcrFml+bzxxBfZW9PI47/6J4dOulumta/lc090\ndEUfjjQTPuOuqAPMHp/PgZPneHtnNQDjCtwUdYg+V6+qayJ8Jvpc3VW77x0ZYnBmGmt3hdlXe9ZJ\nN3SHjnNdGWmi5sx5ZxcyALNL89lb08i6XTUkCYwp6P3d7/uArwFbu9tBRJKBXwIPASXAXBEpiX38\nKvCaqo4CzgALehhPt47WNbNg5U5e2VjBlDuG8PtnJl1Z5MCY3mT6mDxWPD2R+uZLzHr9H6z48AiX\n2py8x95n8rmnimOjol/60z5ON19y1gUOMGNsHslJwvrdtRSHMp3eWBSFMqk4fo5ffHCIwkEZTsYR\nAKQmJ/Hw2Dw27T/JpbZ2Z70TcHUE/OK1ezl7sc3puZ45Lh8RWL+7ltG5/Z1MPd6joq6qFap6vRds\nJwKVqlqlqpeA1cAciT7AehBYE9tvJfBIT+L5f62X2/mwso4X1uxm6rIt/KvqNEtnlfDreRM8n3DB\nmJ64d2SIjYu+xF0jBrP0zx8zddkWfrutytPR8b09n+Mpp38/svqlcKz+PM/efxvfvGuYs2OHsvpx\n78gQ4K7rvUNxTiZNLW18Li2FVc9Mcjpz5uzx+Vd+djVIDiA/O4P01CSO1Z9n0ZRRPHqnuynAh2an\nM6loMOCuzS4qWwFQ3en3MDAJuAVoUNW2Ttvj8t9esm4vH1ScItLUwuV2JT01ibkTh/ODB0deeXfQ\nmN4ud0A6b82fyN8PRnjtrwd5eUMFL2+oIDMtmdzsdH762DjKRgx2HZbzfPaCiDB/chHZGaksmFzk\n/PhzSvPZejBCaaG7bmiAKZ/PZXe4gSUPlzh95AAwYfggCgZmcLH1srNX6SC6BOuCyUXkDkjnyXtG\nODtuhznjC9heVe/sAu66RV1E3geGdvHRElV95waO0dWloF5je3dxLAQWAgwffu3XzwoGZTB5VIih\nA9IZU5DNfaNzyEizwXCm7xERHrh9CA/cPoSjdc1sPnCKY/XnOdF4kYGfodu2N+TzzeSyl56fNtq3\nY88Ym0fF8bPMLM2//s5xNHJIFm88McHpMTskJQlLZ3+Bxgutzt82+uFX73B6vM5ml+ZTeaqJh8Z0\nlXbxd92irqpTe3iMMNC5b6sQqAXqgIEikhK7uu/Y3l0cy4HlAGVlZd0Wf4Dv3W9TvZrgGRHK5OlQ\nz+4qe0M+30wuB1VGWjI/nlly/R0DZlpJrt8hOJfZL4WXHJ5rF+8U7ARGxUbGpgHfAtZrdEWDzcDj\nsf2eAm7kTsEY4x/LZ2N6sZ6+0vaoiISBe4ANIrIptj1fRDYCxK7avw9sAiqAP6jq/tifeBF4XkQq\niT6Te7Mn8RhjPjvLZ2P6PulYArAvKSsr0/Lycr/DMKbXE5GPVLXbd879ZrlszI250VxOyBnljDHG\nmCCyom6MMcYEhBV1Y4wxJiCsqBtjjDEBYUXdGGOMCYg+OfpdRCLAJ37HcYNCRCfmCLpEaSf0rbbe\nqqo5fgfRHcvlXitR2tqX2nlDudwni3pfIiLlvfmVonhJlHZCYrXVXJVI5z1R2hrEdlr3uzHGGBMQ\nVtSNMcaYgLCi7r3lfgfgSKK0ExKrreaqRDrvidLWwLXTnqkbY4wxAWF36sYYY0xAWFH3mIj8TET+\nIyJ7RGSdiAz0O6Z4E5HpInJARCpFZLHf8XhFRIaJyGYRqRCR/SKyyO+YjFtBz2fL5b7Put89JiJf\nAf6mqm0i8iqAqr7oc1hxIyLJwEFgGhAmut72XFX92NfAPCAieUCequ4Skf7AR8AjQWyr6VqQ89ly\nORi5bHfqHlPV92JrUANsBwr9jMcDE4FKVa1S1UvAamCOzzF5QlWPq+qu2M/niK4nXuBvVMalgOez\n5XIAWFF3az7wF7+DiLMCoLrT72ECkhzXIiIjgDuBHf5GYnwUtHy2XA6AFL8DCAIReR8Y2sVHS1T1\nndg+S4A2YJXL2ByQLrYF+pmOiGQBa4HnVPWs3/GY+ErgfLZcDgAr6nGgqlOv9bmIPAXMBKZo8AYx\nhIFhnX4vBGp9isVzIpJK9Etglar+0e94TPwlcD5bLgeADZTzmIhMB5YB96lqxO944k1EUogOrpkC\n1BAdXPNtVd3va2AeEBEBVgL1qvqc3/EY94Kcz5bLwWBF3WMiUgn0A07HNm1X1e/6GFLcicgM4OdA\nMvA7VX3F55A8ISKTgW3AXqA9tvlHqrrRv6iMS0HPZ8vlvp/LVtSNMcaYgLDR78YYY0xAWFE3xhhj\nAsKKujHGGBMQVtSNMcaYgLCibowxxgSEFXVjjDEmIKyoG2OMMQFhRd0YY4wJiP8CgbYmbwac2t0A\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a9a3ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(8, 4), ncols=2)\n",
    "ax0, ax1 = axes\n",
    "ax0.plot(x, y)\n",
    "ax1.plot(x, z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labeling Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qsnIXY5ug2gYMOayoS8YjxCJiIjiG+ceR4Ql++sYAH7hoxZwNp+qRD16yglxB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\no5RSSinf0QRHKaWUUr7zf+TAffK24gHOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ab8f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(8, 4), ncols=2)\n",
    "ax0, ax1 = axes\n",
    "\n",
    "ax0.plot(x, y)\n",
    "ax0.set_xlabel('x')\n",
    "ax0.set_ylabel('y')\n",
    "ax0.set_title('x vs. y')\n",
    "\n",
    "ax1.plot(x, z)\n",
    "ax1.set_xlabel('x')\n",
    "ax1.set_ylabel('z')\n",
    "ax1.set_title('x vs. z')\n",
    "\n",
    "# squeeze everything in\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing Line Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11abb6c50>,\n",
       " <matplotlib.lines.Line2D at 0x11ad442e8>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ECK9dlQ6iQzAcBKKrqqYDI1Q1KWWzlNKMZv0aOCXRfaOOcbeUcqmUcmlpqc0H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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11abb6ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y, x, z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's simple to switch axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11aa86208>,\n",
       " <matplotlib.lines.Line2D at 0x11aea6710>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HLg1x2BL7072jxP90Nq2pYm97kL2W2G9pqsYpvXpBKDjFFPY7gWeAVzDTHQF+W2v9/Uyf\nKZiwD12CF//GWGTmKtT+WrNEuKLBLB+eTVvbbLoR/HUy1puB0XCE493DHLk8xJHLIY50hRgcnwag\n0uNkV1uQPR1B9rQH2dseZE2NGJwJwmIp/wVKJ74Dj/yaeWFYu06EepnRWnN5cMKI/OUhjnSFeK1n\nhJmY+ftqDfhmhX5Pey03rg3IAipByJPy9mP/wW/Bi//LzDt+y+/ButugutV4IMsijGVBKcW6+krW\n1VfywN61gJlqeaJnmCOXQxzrHuZo1xDff+UaAE6HYmtTNbutHv3u9iCb1lTJEI4gFIDS67FrDX97\nL/QcZY6PhdObCDybKQ5hdQu4VsEKzBVK/9gUx7pCHLVto2GzqKjC4+TGtQH2WEK/pz1IS8C39Iuo\nBKFEKP+hmGgERq9ZIah6rHiEPXPjEqZbIFPRYBP/ZusFabUxIPJW26KWWy9N43meKnDI8EEhicU0\nFwfGOdYd4ujlEEe7h3m9Z2R2tWxjtZfdbUF2twXY3R5kV1tgdVgjCEIayl/Yc0FrMzsmHnB2tMcI\nvj0I7dg1Y7af67xxd2WK+KdpEDxVplGYc2wr466UlY4ZmJqJcvLqqBF7q1d/vm989nxnfQW72kyv\nfndbgB2tMl4vrA5E2PNlZtoy9R8x5v1TY5aJ/2jKcboy8TzrOBbJ7Z6e1Mag2tZYVKUsOrIvKkrN\nSznn8pbdu4bhyQivXhnmWHeIY10hjncPc3XYPI05HYotTdXsbguwq8306rc2V+N2SsMplBci7MtF\nPDhBqthPW3v7lrGMlb9grxU1T4OQQ+OQdu9LzlvmOfm9I2GOdQ9z3OrZH+8eZnjSNKpel4PtrTVm\nGKfdCP76+kpZNSuUNCLs5UIsZjPgmsiwzyUv1UcmJZ3rU4Ydh2ueBsGWzssJMUPZeZ5C4lMuj3UP\nW736EK9eGWEyYmx1q70ubrR69bvbAuxqD9IqL2eFEqK8pzuuJhyOxArXpSQeLGLeBiPHc2O9yeUK\n5l2e2dlRuXysc/tY5/LzbrcP1vuIbfIxMO3gyihcGolxPhTlwqUoZ2NuvooHr7+CjjX1dDbXs3lt\nA1vb11AfCFhGZO6yG9ISVgci7ILB6Tabr2Zp7zMn2lDcJnge7/X5AmCEQ2nLOiKTNKJpBPbE62D/\nq48CV63tSHJVYzjQLh/K7cORye4305NGUnQh/9ynFneashJ5SCgQIuxCcYk3ICxxAxJHa4hOZ4ii\nlNiHJye40jfI1f4heodCDIRGCE+O45uZxheeptEXY00FNDhjBHSU6ugMjun+9A1QPs6cSagcGgG7\n3bE9LJ0vfSOSsaxYI5cz8psVyhulzDi9y5u1mA/YaG1xhsanOX5lmONdIZ62XtL29hrRdsVn4rTb\nZuI0VeNyOowJXdJTyGRyQzIbuMTWGNiHq5LK2dLh4ZRrLjJqVPwdSdKTR6YnDcsG2h03vkuXrkg4\njsbzZErvsiAvTwUhD64NhznWbV7MHu8eTpqJ43M72NEaYFebWT27qy1IZ33F0r+cTTe8lRpaMJeG\nI6mxSW14xk0wl3yfRlzpGoQKm+10mvScvKq5n1ulMVxlVowgFAGtNZcGJiyxN7NxXu0ZJhwxK2dr\nfK7ZFbO724xNQkk7XUZnrEZjIhEkJjKRiOI1Jz8lnfb8hGk48nnyiEfzytogVBo3V3/Q2lubz3bs\n9pdUAyHCLgjLxEw0xpneMY53h2anXp66NjrrdNkS8Bmhbw+ypy3IzrYANb7S8+kvOPFFgpEJI/b2\ndGQ8uRFISscbjZT01Jh5qZ6twXB6Mot+UqMQTD7vCyyLvYgIuyCsIIzT5QjHukKzvfsL/QmbhI2N\nlbPGZ7vbgtzQUoPHJePTi0ZrI/STQ2YLhxLpySGYTDkOhxJ502NZLqzMDLLURqGmFToOQMftxka8\nwIiwC8IKJzQxPTt8Y3xxhukfM2PYHqdZObsnLvbtRRqvFxLMTJsX1nOEP0vDMHIlsVq88QZYd7u1\n3QE1LYuukgi7IJQYWmt6hsMcvRyadbt85crw7MrZgN8926vfa+1rK8XpckUxMwU9R+DSc3DpJ3Dp\neTMsBFC3ATa/Bd78+XlnaWVChF0QyoD4eP3RrtCsj/3p66NYw/V01lcYoe+oZU+7DOEsCzPTELoE\nA2dh4Jy1PwuD500P3k79ZvilJ8wY/QIQYReEMmVsambW+OyoFW+2b9QawnE52NlaMxtYfF9HLa1B\n/zLXuITQ2oyth0fMMMzUiC09bNJT1nGoywh46HKyVYa/Fuo3ma1uI9RbW90G49y6CETYBWGVYB/C\nOXJ5yDhdXhlmesZMuWyu8c2K/L51QXa0BvC5y9C/Xmszvh22C3DIlk4R67hAh0cs0R427qo6lv0+\nDst6o2atJdopIl5Rt2Q/opiACcIqQSnF2qCftUE/79hlXtBNz8R4/eoIRy4PcfhyiCNdQ/zgVRNv\n1u1UbG8NsK8jyP51ddy0rpbmQJHn1sditoVUE4k58Knp2eNJM1YdmTSzXOb0pq30fC6lymF6zb4A\neANGoIPt4N1h0r6ACYrjq7H2AVueVb4EFkdJj10QVgm9o6ZXf/hyiMOXhzjeHZpdSLU26Gffulpu\n6ghy07o6bmiqwBULp4isJapxkU0nuLMunymCnFo2MpH/D+D0JBYexcU3rRDXWKIdmJvvqVrxopwN\n6bELQrkRH2pI526ZzjIgZb9mJsxbIpO8JTIJgQli/gkmx0cJT4wSCY+jTk3gPRnGzzQutQB//lkf\n/kqbt0yFiTEc8FsrQuMe/RUJqwF72aRztmu5K8S0LA8K8k0ppb4EvBPo1VrvLMQ1BWHFE4vO6xqZ\neZ/i5ZLLZxYUTcvC6bE5PhqhdLgrqPT7qQzUz4rouHbTNeGgawwuDGsujMBEzENYeamvDdLZ3Mim\ntjXc0NFMY20wIdYuvxh+rSAK1QR+GfhL4CsFup4g5IfWxgwrF0/3tPt5hDXJJMvKW0jUqThpPdyt\nfUVd1oAiC9rnuPy9EthkbfcC41MzHO0K8fLFQQ5eHOJbp4eYeHUG6KatdoCbO+u4Zb3ZNjRUygKq\nFUJBhF1r/WOlVGchriWUCdmGDRYsvnGnwTS2tzOT889myIRyZg+g4a3JElAjl32KD3oJBRuv9Lq4\nY1MDd2xqAMy8+tevjhqhvzTIM2f6+c4RM1e7ocrLresTQr+1qVpizC4TMmi12ogH2846AyHLbIR0\nlq9LOWyQ2vP0VEJlQ57RjDJ4jcf3yxyUu5RwOR3c2BbgxrYAH7tzPVprLvSP89KFQV68MMiL5wf4\n3itXAbNS9ubOWg5sqOfAhnq2t9SI0BeJogm7Uuoh4CGAjo6OYt229IgHr55XcNPNVMihbGQi/56t\nw217uZUS3WchwwZzIgSl9G6XwTVPWBhKKTY0VrGhsYoHbzH/191DE7x4ftAS+wEef70XMEJ/6/o6\nbttYz20b69myRnr0S0XBpjtaQzHfzeXlaVlNd9TaCGa6FWpz5tqOphHkFHGemcy/DrOCWTl3NoF9\ntkHaWQlp0vGe8exsBOnRCgvn6vAkz58b4PlzA7xwYYCuQfM3Xlfp4cCGOu7Y1MCdmxroqBOTs/mQ\n6Y65MjNtW4GWaVVa6vmU/PkCBCiHGaf11iQLaNWaNCJcOVdYs04Ny/3FmCAsBy0BP+/b18b79rUB\n0DU4wQvnB3j+vBH7779iFk611fq5c1MDd25u4PaNDdSJwdmCKUiPXSn1deAeoAG4DnxOa/33mcov\neY89FjX+DYPnEqY8Y73pV6rl0kP2VM+zGCK+WCKYfuFEiS+KEISlIj5G/+zZfp4908/z5wcYDZuO\n0o7WGu7c3MDdWxrZv65OzM1YDV4xWsPo1YRw20V86KKJTB/HU228kNOuVsuwQs0u4NIjFoSiMBON\n8cqVYZ47288zZ/o5fHmISFRT6XFy28YG7t7ayD1bGmmvq1juqi4L5Snsw93w2Oeg/xQMnE/4HAM4\nvQkHtbgpT9ygp7JResyCUIKMTc3w/LkBnj7dy49O9dE9ZJ6wNzRUcvfWRt50QxM3d66e3nx5jrGP\n98O5J2FyMJFX3QJ7fhZ2fQgatoiAC0IZUeV18ebtTbx5e9PssM3Tp/v40ak+vvbiZf7huYtUe13c\ntaWRN96whnu2rpGxeUqtxw5mOmDf6yY6ycVnzX7cTKeicg103ArVrRkC0tYmAtHKTA9BKGkmp6M8\ne7afJ09e54nXe+kdncKhYF9HLW/e3sT9O5tZV1+53NUsKOU5FJMOrc3Y+iVL5LsPmp791HD2z3mq\nbaKfKvyZIpXXmpko8lQgCCuKWEzzas8wj7/eyxOvX+dEzwgAN7TUcP+OZu7f2cyWpqqSn065eoQ9\nE7FoSiDaUO4BabN5gDg9c4XfFzDTE+NTEpPS8fnj8XRV8vRFcawThILTNTjBoyeu8eiJaxy8NITW\nsL6hkrftbObde1rZ1lyz3FVcECLsC0Vrs2gok/CnaxTCIbPCc3o8/wVGTm+ODYIt7bYaiLRp6zri\nticIgPGhf+y16/zHq9f4ybkBojHNlqYq3rNnLe/a1UpHfenMsBFhXy5iMdty/rHEkv6k9Hhi1Wlq\nOtt5+xTOXJhdbZqyj6eTvFNsJlVu6zjJCiB+PsUWoIQMrQShf2yK779ylUeO9nDw0hAAe9qDvH/f\nWt69ey2BipX97k2EvRyJRmyCP2FFphlPSdvOT4+lT8fLzrojTi7CtEslDLjmNAb+NOksplxzfGXS\nNDby0lsoEN1DE3z3+FX+95ErnLw2isfl4P4dzXxofzu3b6xfkT42IuxCfsRiEJ2a69w4xyI3l/N2\ni900kX0W42WeZLGb4SliPgvdnB0hfSUR31JYHFprTvSM8M2DXfzvoz0MT0ZYG/TzUze38+At7ayp\nLnI82CyIsAsrm+hMGp/11EYgJcpQXo1MSmOiowuv65zGIN3em9lqOGvZNHunWxqTZSIcifLYa9f5\nxsEunjnTj9upeMeNLXzk9k72dtQud/VE2AUhiWgkg3f8VH5RlvIJGrJQlCPLE0bqk0bcKM6+T5eX\n4ZzMysrI+b4xvvL8Jb51qJuxqRl2twX4xTds4O03tuBcpmEaEXZBWE60Ni+7c4qBmq5xyaGs/Qln\nenxhTyUOdwbRX2SDYd+7fCU9Q2tsaobvHO7mH35ykfN942xoqOQT92zkvXvX4nYW9+cSYReE1UY0\nkhxQJT48NScv27n58iaBBWiG/QV4pobAW22m7nprwFuVclydkldd9Bfp0Zjm0RPX+Isnz/L61RHW\nBv38yn2b+OD+9qL14EXYBUEoPEmhFQvZcIzD1JgJRpPrMJbLlyz0cxqE6uQtNc9+nIeDq9aap071\n8udPnOVoV4itTdX89jtu4O4tjQv8UnNHhF0QhNIkOgPTowmhnx4z8RPix+nypm3n7Me5TuN1V6QX\nf18QqpuhptUYDta0mq2yEa0c/ODVa/z+D05yeXCCN2xu4L8/sHNJ/WlE2AVBEKKRuWI/ZTUKqY3B\nnDKjxkl27PrcKGnKaQS/uoVodTMnx6v54WXFVV3HvTfv5q0H9uEItJoV4QWkPG17BUEQ8sHpNgHX\nK+oWfo1YFMb7YKTHBPeZ3V+F0R6c/WfYMdLDDjUKCjhkbWB8pKpbTaCfGz8Ie36mAD/U/IiwC4Ig\nZMMR7503Zy83NYoe6eGZQ6/wg58cos01zM+0DFN74bvGarx+swi7IAjCiic6AxMDMNEP432o8X7u\nqh1gx81unj3Wz/T5l4g63Tjf+ntwy0NFq5YIuyAIQpwUoWa83xzPpvvNPp6eHEp7mXrl4F3+Ok6p\nFn5x4uf4f4Lv5Z4iriYWYRcEofSJu6pOj1lmeNZ+asyWN25m20zbDPPCw5ZwZxdqlAP8dSZ+cmUD\nNO1IpCvqbekGk/YHcTicrA1HiH7hBT75z4f5xv91Gze2BYrydcisGEEQiks85kFcgKfswjuWQZzn\nOWcPbD8fDreZ7+6xtjninF6o85nrbqdvdIp3/cWz1FZ6+PdP34FrEatVizorRil1P/BngBP4O631\n7xfiuoIgFBm7FUJ8EZHdZG3WaG1y/jJxsZ5K6SVPj5Hz6lXltIlwZWJf02blV849F9+n+5ynClzF\nDXbdWO3lc+/azie/epivPH+Jj925fsnvuWhhV0o5gb8C3gx0Ay8rpR7RWr+22GsLgmARNzFLEtKJ\nFNfLdGKbS5kUQdaxBVRQJWzlzH89AAAgAElEQVSU4778cSGtaU0EePHYxdY6ThLoFCEuk0Au9+9s\nZv+6Wr7+0uXSEHbgFuCs1vo8gFLqX4D3ACLsQnmitRHanF0gU/ZznCHD8wty6gKZXJkTESsuvn4z\nvJCUZwuUkuRv75+/TJkI8FKhlOLtN7bw+e++xsX+cToblm51KhRG2NcCXbbjbuDWAlxXEHJjQZa8\n84nvVHZhXlCv1iJdQI+4eFatySC2NkGek5fm2O038XRL2FWx3Njdbl6cXhwoDWFP10zPGUBTSj0E\nPATQ0dFRgNsKK5ZYzCac8Z7nRPrhAHvUpXRlsvmfx8W3YEE0UgJg+GrA1ZQ4dnnnibyULciGV3q2\nq57Smu7YDbTbjtuAntRCWusvAl8EMyumAPcV8iHuyjdnnDWb2GYZn812nYXGT1VOa1w1TQxUT5WZ\nnZA1+lAuYe9sZaRHKxSRs72jADTVLH2ovUII+8vAZqXUeuAK8CBQnHWz5UZ8RkLWubep08Pi5ybm\nF+SF+GgrR/rhAFdcbNck56Udn82SZz+WQNVCGfPDE9dZG/Szrbl6ye+1aGHXWs8opT4NPIqZ7vgl\nrfWJRdesFIg7x6Wdg5s6zSvbOZtA5/OSzD6DwB7AwF+3gJdhFWkEukLibwpCATh4cZAnTvby6Xs3\noYrw/1SQeexa6+8D3y/EtYrOzFT6pcLj1pLiyaHMAh2dzv0+7srEFK/4/NqKOgi2g6faNv2r0vKF\nnmd6mMsvwwiCUAJMTkf57HdepTXg45P3bCzKPcvPUmA+oY4vH46np0bSX8fhMqvOKuosw/2ANR/X\nvvghdVFEVXpxdleKCAvCKiQSjfHLXzvM6d5RvvSRm6n0FkdyS1vYe47Cs39sfJFzFepKawvelEjP\n5jda6XoTOUWGIARBWCDTMzF+81vHePJkL//9gZ3cu21N0e5dmsKuNbzwv+Cx3zE96eadItSCIKwY\nBsam+OQ/H+ali4P85lu38uED64p6/9ITdq3hmx+B1x4Gpwdu+xQ07UzEI6yoFwEXBGHZOHRpiF/9\n+hH6x6b485/ey7t3txa9DqUn7GBCVVU1wVgvPPH55HNOT0Lkk/YtiRBV1S1mPrQgCEKBmJyO8j9/\neIq/f+4CrQE/3/zEbexqCy5LXUpP2JWCB79q0tEIjF5LiUNoi0d49Sic+oGZy51KRUNC5H0BW4Ty\nGvNiNB6lPDUvHsV8gRaegiCUF1prnj7dx+/++2tc6B/nwwc6+K233UBVkV6UpqP0hN2O022mCwbb\nM5fRGsKh2cCzjPTY0ldh7Br0n0lEKM911aS7IkX8q9McW41C6nl7GU+VzJgRhBLltZ4R/scPXueZ\nM/2sq6/ga794K7dvaljuapW4sOeCUuCvNVvT9vnLz0xbqztHE2IfT6c7tueFusysnOkxCI9ALJJb\nHeNTJWeFv8rMbXfbFxP5kxchzcnLcE4WGAlCwTnfN8ZfPXWObx/ppsbn5r++czs/d2AdHtfK6KSV\nv7Dni8sDrjozf32xzEyZ5f9xsZ8aTRwnNRJpyoxfTLEImMxvQVQc5ZynQcgnL0ujIkNTwirg5LUR\n/uqpc3zveA9up4OP37GeX7lvM4GKlWWHIcK+lLi8ZqusL8z1ojNzxX52ny4vvg+nLz8xkOYa4wuz\npHV6sjcSmRwU59snOSr6EyZeMnwlFAmtNc+fG+BLz13g8dd7qfQ4+aW7NvCLd26gsXplTsIQYS8l\nnC5wWsM1S0U8iEReDcc8eZOh9La7Cw0eAcaZMZdGYM4+n7K2hkTsdlcd41MzfPvIFb7yk4uc6R2j\ntsLNr79pM79weyfBiuKG18sXEXYhGaWs4SiPibCzlMSfQLIFtZg3+lCaABpTo2YlcrqyCw6QobJY\nAacEzLCbqs2JYJTuvH9uWadHGpJl4rWeEb5xsIt/O9zNaHiGG9cG+KMP7uadu1rwuUtjyFGEXVg+\nivEEYidbSLsFNS62vInB5BB38WtHpxZYWZWlkfCnNCop3vVz0vOV9ZvfxSomNDHNI8d6+MbBLl69\nMoLH6eD+nc185PZO9nUEi+LIWEhW929TWF3Yn0Z8geLcMxab25DYg0inawzmRJBK85nwcMo1Fxkb\n1eHK/PQQb1w8Fda+KkM6bnqXJr0Cn0CmZqI8faqPh4/18Nhr15meibGjtYbfffcO3rOndcUPt2RD\nhF0QlhKHIyFyxSDpBfsiGo54sJaZSWNRPd5n9pEJE9Ql35fsypnSCFQk4gikpmctrtOl05TN42kj\nGtO8eH6Ah4/28INXrzISnqG2ws1P39zOB/e3s3NtkRr8JUaEXRDKiWINb2ltGoC4yMejeEWs/fRY\nohGwp1PLhkNm0eDs58bTrxTPhtObuUHw1xLzBbgy5eOVAQfPX43RNelhylXDT29azxt2b+LW7Rtx\nu0u3d54OEXZBEPJHqcQYPQWazhsnFrMagvGE4EesBiJtetz2NGHSselxxvu7mB47jicyTDsTtANv\nB4hr+Hlr+w5mhbg/aFxg4wsa/bUmz36cdD5onhxW2BATiLALgrDScDgsO46qvD42PBHhR6d7eeL1\nXp661Mvo1AxVXhf3bVvD27Y3cFeHh8rYmImKNruFko/D1nHva4m8bO8tnJ5k0a9pgY7bYN3tsGbH\nsq23EGEXBKFkOd83xpMne3n89eu8fHGIaExTV+nh/p3NvO3GZu7Y1IDXZZ+i2JTfDbQ2TwGpwp+p\nYeg+CCe+Yz7rC0DH7UbkO++A5t1Fm30kwi4IQskQjkR56cIgT5/u46mTvZzvHwdgW3M1n7h7A/dt\na2JPexCno0DDI0olnh6ymQ3aCV2GSz+B80/D64/A6R+YfE8VbLwX3vtF8x5gCRFhFwRhxaK15kL/\nOE+f7uPp0328cH6AcCSGx+XgwIZ6PnJ7J/dtW0N73dIKZUZiMeMUO3DW2s4n0qFLycM4DqcxB1zw\nIrncEWEXBGFFEZqY5vlzAzx7tp8fn+mja9DMktnQUMmDN3dwz9ZGbl1fj99TwFWgWpuXr+ERY8gX\nHjFrBaaGbekR2/lh4+Y6eC7Z6tvlh/qNJlznjgegbiPUbzJ5RYzuJsIuCMKyEo5EOXRpiGfP9vPc\n2X5euTKM1lDldXFgQx0P3bWRuzc30lGfpVc+M2UT3VCyAM8R65R9/Px8i7uUE3w1ZgaNrwaCHWZo\npd4S77qNJnDPCjCoW5SwK6U+CPw34AbgFq31wUJUShCEMsSydIiExzjZdZ1j53t4/dI1zvf04YpN\nUqmmeWODk1/d4WFrnYPWihjOaBiGJuC5iYSpXDzegV2sc7Fu8NpE2ReAqmZo2GJFULPle1P2cTH3\nVK7IqY3pWGyP/VXgfcAXClAXQRCWk/iio4i12jRu4zw9kUgnnZtILheZTMwtnxXhcXRkgtjUBGpm\nAoeO4gZutDYgWYWGrS2OciRWnLr9ibS/FmrXJYuyN5AsxPb0KgtnuShh11q/DpScQY4glAx2sc3V\npCyX/exK0InkNDq/+s368CfEV7srGI35GIhWcX16Dd1jipGomwm8+CuqaG6op72pnvUtjVRXB8zn\nPZWJ67j9CeuBFegxUwrIGLsg5MOsF0s26+Bc9zk6SS4Upye9I6Tb6vHWrE0294oHR5kV2YrMghvf\nnC6mZqIc7x7mxfMDvHhhkEOXhpiYjgKwvqGSA7vrOLChnnduqGdNja9AvwghG/MKu1LqcaA5zanP\naq0fzvVGSqmHgIcAOjo6cq6gIGREaxMuMO8e6yJEONc4tumY48lu21fU5x5VKqe9b8mGHkbDEQ5f\nDnHw4lVeujDIka4Q0zNmCt+25mo+cFMbt6yv45bOOhHyZWJeYddav6kQN9JafxH4IsD+/fvzfN4T\nSoJYzOZXXsBhg7RibOUvdE7wbCzYDIEy/MHcoiulRmbKFuKvRIcUrg2HefniIAcvDvLyxSFOXhsh\npsHpUOxoreHnD6zjlvV13NxZR21leZlplSoyFLNaiIe7S/NyKxHGzubAN+dFmHUuow3sYoJKkDxs\nkCqcniqoXJMlWtEC9s6VFXx4pRCNaU5dG+XQ5SEOXxri5YuDdA+ZeeQVHif7Omr51Tdu5ubOOva0\nB6n0ioSsRBY73fG9wF8AjcD3lFJHtdZvLUjNVhNaWxF80gmuXWSzCG6mmQnxdN5DCCr9+Ku7Airq\nchTQ1B5x8YcNhOyMhCMcuRzi0CUj5EcuDzFujY83VnvZv66Wj96xnls667ihpRqXc/nnaAvzs9hZ\nMd/BmF6uTmambAsc0qxQS5ee0zu2tnyHFJQzEaFm9oVXhW281p8Q5LQvx+zn7C/HKhPCXKJDB0J6\nYjHN+f5xDl82An74UojTvaNoDQ4F25preN++Nm5aV8tN62ppq/XLjLcSZfU+R0VnUlagZVg2nG6F\nWjyd16KIgJlL66uB6uZ5ZiPYZyCkCG68vEvGMoXsjIQjHL0c4sjlEIcvD3G0K8TwpHlyq/a52NtR\nyzt2tXDTulp2twepkmGVsqH8fpNaw1ivMeEZPGcZ8pwzeXYBj4zPfy13RfICiIo6qO1MWQCRbtWa\nlfZUr4jlxUL5E41pTl8f5WhXyIh51xBnesfQ2jx4bV5Txdt2NrOvo5Z964JsaKjCUSgHRGHFUbrC\nPjlkBHvgXMJNbdA6nh5LlHN6oHa9McCvbrZEN2hbrVaTZrWa1buWF2zCCqV3JMyRrhBHu0IcuTzE\nK93Ds2PjAb+bPe1B3nFjK/vWBdndHqTGJ3/Lq4nSEvb+M/DIr0L/KZgYSOQrhzHkqd8E7QcSbmr1\nGyHQLi/mhJJmcjrKK1eGOWYJ+dGuEFdCZqaKy6G4oaWG99/Uxp72IHs7aumsr5Cx8VVOaQm7jpnh\nFLuoV7fArp8yLmttNxcvGrwgLAHRmOZM76hNxIc5fX2UaMws/Vgb9LOnPchH7+hkb0eQHa0BfG7p\nuAjJKK2Lv1Zo//79+uDBRRhBTgzC5efh4nNw6Tm4dtyIvsMFLbuN2OcSkNZbLTM/hGVDa03PcJhj\nXaFZIX/lyvDscvwan4vd7UH2tAfZ3WaGVBqrvctca2E5UUod0lrvn69cafXY41TUwbZ3mA3MC9Gu\nF43Idx804+zhkGkAss1cUc4cIpGnOx+Q8Xchb4bGpznWHeJ4txlWOdYdon9sGgCP08ENLdV88Ka2\nWTHvrK+UF5zCgihNYU/FVwOb32y2VCKTc4POZgpIO9YLfacso/7hudey46lOiL4vYFZHxqcvJqUr\nE/PGZ9OVc8vKvPGyYmJ6hhM9I5aAD3O8O8SlgQnA/Jo3NlZx95Y17GkPsKstyLaW6pSgy4KwcMpD\n2LPh9putpjW/z8WiRtxzaRTCIRi5Yq0WtRYeTY+BjuZ+vyTf6UwNQkXmvNm01VjY0/J0saREojFO\nXRs1vfGuYY51hzh9fRRrWJzWgI9dbUEevLmD3e0BblwboFpmqQhLSPkL+0JxOM2QT0Xdwj4fdx6c\nFfvxucKfLh2xys2mx8yTRDzgQfwa+fhmO9yJxU6eSpIWPtltAOak7Uv/U86n+8wqeOqIr948Hh9S\n6Q7xWs8IU5a7YbDCza62IG/Z3sSutiC72gOsqRaHQ6G4iLAvFUoZRz+XF1hg45AJrW3BEsYS1gRJ\n6XhDYm8QxpPPh4dh7Lpla5DinrggVIrTYZaGI52XTDpb22yNjdO9pA2J1poroclZAT/eNcyrV4YZ\nnTKxMf1uJzvX1vBzB9axqz3InrYg7XWyDF9YfkTYSxGlrGGWCqhsKPz1s0btSWkEIhPznLe5QI73\nzb3mYjzOlXOehiPd3pvRjGx4xsW5oRlO9s/wet80r1yf4toEhPEQdXrZ0FzPe/a2sqvNzFLZtKYK\np7zcFFYgIuzCXJRKCGUxiEUzWAGnaQTmnM/SyEwOpfdyzxCNPgDss7ZZ7KMoA0DICyczOFTm7Nee\nh+2whIYTFoAIu7D8OJzgrTLbEhCORDnRM8yxLjM75dWuQa4ODOFjGh/TrK91saPRw/ZGN1vqXKwP\nOvExnTlUXaYIS1OjiaeS1LILDQgyZ3grdR/fKlL26fKynfPLCu0yQoRdKCsi0Rinr4/Oivix7uSV\nm001Xna1BXngpg7zcrMtQLBiiZ0ytTaBTrKF35uZyjGKVDj5CWai32YFPZlIL6QhcXrzawjybkxW\nxwv2lYAIu1CyxGKaiwPjHO8e5mhXiOPdIU7YZqgE/G52tQV447aN7GoLsLs9SNNyxOBUytgsuzxm\nzcNSMxsLNkXs0+5zPDc5mHhnEs9b0Et2laYRsNLeajM911ud2FKP0+XJdN45iLALJcO14TDHus3y\n++PWop+RcPIMlQ8fWGdEvC3IutVqhmWfkeWvXbr7xGPcFqLhiExY1tqWO+vUqDWtNwecXpvQVxmX\n1lnxj+/T5Hmq5x47y0MSy+OnEMqOkXCEV6yeeHz5/fURYw/hcii2Nlfzzt2t7G4zKzc3r6mSsG3F\nxuFIzM6ivvDXj85YU3gtoZ8aMyaAU6Nz85KOR2HsGgycSeTl+nTh8ieLvS9ovKdqWqC61Sx0jKer\n1qzY9xIi7MKyMz0T4+S1kVlL2mNdIc71JQKhbGio5LYN9exuD7KrLciO1hpxNFwNOF2WT1Nw8deK\nzsD0aLL4zx6ny7OOJweNB9Xo1bmzqZQTqposoW8xop+0X2vOLYPjrAi7UFS01lwamJgV8aNdZuXm\ndNSMizdUednTHuCBPWvZ0xFk19oggQoZQxUWidOVMPFbCLGYmfE02gMjV237qzDSY2JFXPixeXpI\nxRswAr/nZ+GOX13cz5EjIuzCkjI0Ps3R7oQt7bGuEEMTZkGS3+3kxrYAH72jk93txpa2NeBbnePi\nwsrG4YDqJrO17s1cbmosIfajV+HST+DwP0LfsHmXUCRE2IWCEYnGOHl1lCNdQxy5bEK2XbQ5Gm5Z\nU81btjezp8PY0sq4uFDSTE+Y6abj/Sb4z3iflbbyxvuNsPsC8O6/gO3vKVrVFiXsSqk/BN4FTAPn\ngI9qrUOFqJiw8rk2HObI5SGOWHE3j3cPz041bKz2src9yIdubmePNTZe5ZV+hLCCyUWoJ/qt/AHj\nvZQOp9dYfVTUGyvxN38egu1F/VEW+5/2GPAZrfWMUuoPgM8A/3nx1RJWGlMzUU70jHD40hCHL5se\n+dXhMGCCRMSnGu7tMHE3ZUhFKAqzM2firqijtvS49UI0fjyWUnbMBOmZ6M9dqCsbTUzligaTV9lg\npRuhst6kV0BktkUJu9b6h7bDF4APLK46wkqhdyTM4ctDHLo0xOHLJmTbtNUbb6v1s7+zjn2WiN8g\nQSKEXIhFbQJr20+lEVz78VSKWNvFeyac+/1d/kTsAm+12ftroWHzihfqfCnks/HHgH8t4PWEIhGN\naU5dG+XQpUEOXjJi3j1kXvR4XA5uXBvgF27vZF9HkH0dtaxZjtWbwtIRtzyYNVGbSF5lmpSX5jjV\n2TNeJi7SU5ZI57NS1em1BLjKijJmCXLVmsRx6jlPynFcvOPnVuic86VgXmFXSj0ONKc59Vmt9cNW\nmc8CM8BXs1znIeAhgI6OjgVVVigM41MzHO0KcfDiEAcvDXL0cmjWY7ypxstN62qNkK+rZUdrjfTG\nl4voTBYhtblbLqhMimgv1KRs1kM/bg9g89WvaLAJcKVNdFMFuCrlXKXYBCwSpXUekXjSXUCpjwCf\nAN6otc5pDfD+/fv1wYMHF3VfIXf6x6Z4+cIgL10c5OWLg7x+1ZhiKQVbm6q5aV0t+ztr2b+ujrZa\nCRSRFq2NUVdOhl1T6d0f53OITO39ZrAXnpdZ61//XAOueHCTuBDPlvGliHTqcZrruLwlN0RR6iil\nDmmt989XbrGzYu7HvCy9O1dRF5YWrTXdQ5O8eGGQly8YIT/fb14K+dwO9rQH+dQ9G7lpXS17O2oJ\n+EuwZxQ3ucpooWsT05zENxd73jB5hSNMQmX3XK+oTzbFykuQ/cnHLp+Zcy2sahY7xv6XgBd4zOrl\nvaC1/sSiayXkjNYmBueL5wd54fwAL10Y5NqIeaFU43Nxc2cdH7q5nZs767hxbQCPa4n+6VOjLiUN\nBdgMn1KHAzKWSRe9ySa6C/Y3J2MEJVw+8zItbXAMK/rSQqIzLXEIP0FIZbGzYjYVqiJCbmitOdc3\nxguWkL94YZC+UWOOtabayy3r67h1fR03r69jS2MVDh1JCOnwYIobX4bx2XnL2AXZFgpvISinrTfq\nT37899WAqylD9KEMgSfmE1+JSCSsAmTFyEpAaxIBqG2zCKbH0dNjDAwMcPFqL1f6+hkYGERFxqkk\nzAOeCJ/yz9DQMkPAOY2HadT1SeiegCctIV7Q8IHKPK4aj7Oa6YVZ2vHZNKIdLyMvyQSh4Iiw50t8\nyGGOCKeZi5vTOes4gwAroMHa9gNRnER9FTh8VTh91aj4TAJ3Y+aINbm+DJsVW+nVCkIps3qFXWsj\nqElLha3lwhMDifTk4FwR1tHc7qEcc6d2eaqMpWd94njaWcGlUcXpoRiv9M1wYcTBOD4c3ko2tzWz\nY30reze109nSiNPlwymiKwhCFspH2HMV6vjy4fE+iE6lv5a7IrECraIeajvnWfxQaaKv2MXbW5Ux\nvmMspnm1Z5hnzvTz49N9HL48RCSq8bkd3Lq+njtur+f2jQ1sb6nB4RARFwQhP0pb2M88Dk//vvFF\nzlWoq5qgaafl+2AtHa5oMMuH42lPRcGrOjg+zdOne3nqZB/PnOmbta7d0VrDx+/cwF2bG9i3rlYC\nSAiCsGhKU9hnpuGJ34Xn/9IY8my4u+hCPR+xmOZEzwhPnerlqVO9HO0KoTU0VHm4d9sa7t7SyB2b\nGmio8ha9boIglDelJ+xawz89YMJVOT2w9e1Qt96KR2jFIqyoX5ZFGuFIlOfO9vPDE9d58lQvfaNT\nKAW72oL8+hu3cO+2Rna2BmR4RRCEJaX0hF0paNphpvKNXIWf/AVzZpQ4PVDdnCz2qXEJq1vMLJBF\nMjQ+zRMne3nstWv8+HQ/k5EoVV4Xd29t5L6ta7h7a6P0ygVBKCqlJ+wAb//DRDoagbHrKXEIbfEI\nrx6H049ac7pTqKg3Au8LWC9Dq2wRymvS53mqGJjx8vi5cR5+fYQXLg4T09Bc4+P9N63lLdubObCh\nfulWeAqCIMxDaQq7HacbAm1my4TWEB62YhFeSQ5CO3bdmO2P9Fjzy60I5VlWUtYDP2VtEb8XvDW4\n/DWo61UQqoGD9sag2syY8VZnyKtKNCCryFZUEISlo/SFPReUAn/QbGtuyO0z0QhjoyGePn6eZ06c\n52zXNSqZZEON5rY2D3sanTR6p3FPjSY3CFOjMNJtHVt5mWbrpOKuTIi9pyplIVFFhn0e55yr49ct\nCKsd+U9PYSYa49mz/XznyBUePXGNcCTGuvpm3n3PPt6xq4WtTdX529rOTFviP5IQ/6lREwnG3iCk\nlpkJQzhkni7ilq6RSbNQKtdFUnYc7gyin0uDkbpiNUNj4vKLu6AgLDMi7BaXBsb5+ktd/NvhbvpG\npwj43bx/Xxvv29fGvo7g4jzKXR5w1UFFXeEqHI94M2vENZmSTt2Hs5ybNENS6a6xEK8Zl29uIzHH\nxCubS6Jtn83wK76XhkQQkljVwh6Jxnj8tet87aXLPHOmH4eC+7Y18YGb1nLvtjUrO3KQ0w3OgHnx\nu1TEg0vk1HDMkxf3NQ8Pp/dMj04vvJ5Oj83ZcZ5GICcXyBzKiq2DsIJZlcI+OD7NP79wiX964RJ9\no1O0Bnz8xpu28FM3t9MckHiesyhlDcEU4TuJRXMInJFL9KIU3/bpCWMpkS6gxkKGs+Lk8uQxJ21v\nYNIYsMXPp5aVSEVCnqwqYb/QP87fP3uebx3qJhyJcc/WRn7uwDru2boGpywaWl4czoTXTrGIRuYP\nV5d34xKGySHrvUhKkOdcX6LPQWVpJNI1DBXpG5f5ysbPy0v2kmdV/AZPXRvlTx8/zX+cuIbb4eB9\n+9by8TvXs7mpermrJiwnTrflB19TnPvFYrZGIE2806QGYiLD+XieLcJU/P1IUqi/RcRMdbjSPD34\nTaPrrkg0wHPSFYnZXKlpt62cvBNZcspa2M/3jfFnT5zhkWM9VHlcfPreTfz8bZ00VstKUGEZcDgs\nwSuSd1F0pjANR/xdyfSEcUcNXbaOx0xevk8iLr/1PVRagp+SdluNQtp0ZebPybuPWcpS2IfGp/nD\nH57iX1/uwuN08Mm7N/LQXRsIVniWu2qCUDycLnBaC+GWkugMRMaNyM9GAhtP5NnT9gYhNT15xSpr\nNSL5xD4AE/8gtXGIr1/x1yY2n/3YlvZUlU3DUFbCHotpvnGwiz/4j5OMhGf4uQPr+PR9m8SrRRCW\nEqdraWZoaW1mS82K/XiGtNUIpKanxswsrP6z5r3H5FD2pwuHK0X0a3NrFHzBFfdeYmXVZhFcHpjg\nN75xlEOXhrils47PP7CDbc1FGjsVBKHwKGVmBLm8QIHWgEQmEyI/OQSToeTjsO147Br0nTRlpoaz\nX9dbYwS+pgU6DsC6O8x+KacjZ6EshP3fj/Xw299+BRT80Qd38/59axe3oEgQhPIkPlOopjW/z0Vn\nTO8/HMreMAxdgOf/Gp77MzM01LQTOu+EdbdDx+0mTkQRKGlhj8U0n//ua3z5JxfZ2xHkzx/cS3td\n8YNqCIJQ5jhdVvCeHIR5egK6X4ZX/81s147DC39tzq29CX7+4SV/77EoYVdK/X/Ae4AY0Av8gta6\npxAVm49INMZ/+uYxHj7aw8fuWM9n3r4Nt1OmUQmCUCS0holBGDwHA2et7ZzZBs8lW4U7vVC/ERq2\nmrH8JWaxd/hDrfV/BVBK/SrwO8AnFl2reYjFNL/2L0f4/ivX+H/v38qn7tm01LcUBKFciYSN+V54\nxAy3TA2b9JR1nDYdglCX2cdRThP4vn4jrH+D2ddtNOE7a9YWdf7+ooRdaz1iO6xkQY5R+fOFH5/n\n+69c47ffvo2H7tpYjASA8TYAAAiFSURBVFsKgrASic4kRHc+IZ5N28uP5DAPX1kvRwPgqzHpmrXQ\ndosR7/pNZgt2WAvelp9FPxMopX4P+HlgGLg3S7mHgIcAOjo6Fny/o10h/vDRk7xjVwu/9IYNC76O\nIAhFIhZNmMFNj9vM4SYSc9iTztvPTZo58JHJ5HnycVGOjM9/f3dlsihX1Jk4yd4ak+cL2IQ7MDff\nU1Vyq2WV1tk72Uqpx4HmNKc+q7V+2FbuM4BPa/25+W66f/9+ffDgwXzrCsDHv/wyR7pCPP2b91Dt\nWxmtoyCUNHELaLvIphXcVJEdzyLWtrIz4fzrNGv5XGnZGViLjuIWBemEOLVXHT+3wuaYLwal1CGt\n9f75ys37E2ut35TjPb8GfA+YV9gXyrm+MZ442ctvvGmLiLpQvsRiuZmR5bVPtROw9YBjkfzqF1/h\nmU5wfS3Jx5nEefZ8mrTLV3I95JXGYmfFbNZan7EO3w2cXHyVMnP40hAA79rdspS3EYRk7C6QC95n\nM/xKKbMob/oMnvTuCtODdTfbomBlE1x/8vL8eA/aU2n872WdyIpmsc8ov6+U2oqZ7niJJZ4Rc65v\nHLdT0SFz1YWl4OT34bHfKaBve4rdbuq+ak1+0aRyiTYlPV2Bxc+KeX+hKpIL6xsqeN/eNlwyX11Y\nCvxBaN6ZJfJSnmIrPVthmZj35elSsJiXp4IgCKuVXF+eStdXEAShzBBhFwRBKDNE2AVBEMoMEXZB\nEIQyQ4RdEAShzBBhFwRBKDNE2AVBEMoMEXZBEIQyY1kWKCml+jAWBAuhAegvYHUKhdQrP6Re+SH1\nyp+VWrfF1Gud1rpxvkLLIuyLQSl1MJeVV8VG6pUfUq/8kHrlz0qtWzHqJUMxgiAIZYYIuyAIQplR\nisL+xeWuQAakXvkh9coPqVf+rNS6LXm9Sm6MXRAEQchOKfbYBUEQhCysSGFXSn1QKXVCKRVTSmV8\ne6yUul8pdUopdVYp9Vu2/PVKqReVUmeUUv+qlPIUqF51SqnHrOs+ppSqTVPmXqXUUdsWVko9YJ37\nslLqgu3cnmLVyyoXtd37EVv+cn5fe5RSz1u/7+NKqZ+ynSvo95Xp78V23mv9/Get76PTdu4zVv4p\npdRbF1OPBdTr/1ZKvWZ9P08opdbZzqX9nRapXr+glOqz3f8Xbec+Yv3ezyilPlLkev2JrU6nlVIh\n27ml/L6+pJTqVUq9muG8Ukr9uVXv40qpfbZzhf2+tNYrbgNuALYCPwL2ZyjjBM4BGwAPcAzYbp37\nBvCglf4b4JMFqtf/D/yWlf4t4A/mKV8HDAIV1vGXgQ8swfeVU72AsQz5y/Z9AVuAzVa6FbgKBAv9\nfWX7e7GV+RTwN1b6QeBfrfR2q7wXWG9dx1nEet1r+xv6ZLxe2X6nRarXLwB/meazdcB5a19rpWuL\nVa+U8r8CfGmpvy/r2ncB+4BXM5x/O/ADQAEHgBeX6vtakT12rfXrWutT8xS7BTirtT6vtZ4G/gV4\nj1JKAfcB37LK/SPwQIGq9h7rerle9wPAD7TWEwW6fybyrdcsy/19aa1Paysguta6B+gF5l2AsQDS\n/r1kqe+3gDda3897gH/RWk9prS8AZ63rFaVeWuunbH9DLwBtBbr3ouqVhbcCj2mtB7XWQ8BjwP3L\nVK+fBr5eoHtnRWv9Y0xHLhPvAb6iDS8AQaVUC0vwfa1IYc+RtUCX7bjbyqsHQlrrmZT8QtCktb4K\nYO3XzFP+Qeb+Uf2e9Rj2J0opb5Hr5VNKHVRKvRAfHmIFfV9KqVswvbBztuxCfV+Z/l7SlrG+j2HM\n95PLZ5eyXnY+jun1xUn3Oy1mvd5v/X6+pZRqz/OzS1kvrCGr9cCTtuyl+r5yIVPdC/59LSqY9WJQ\nSj0ONKc59Vmt9cO5XCJNns6Sv+h65XoN6zotwI3Ao7bszwDXMOL1ReA/A58vYr06tNY9SqkNwJNK\nqVeAkTTlluv7+ifgI1rrmJW94O8r3S3S5KX+nEvyNzUPOV9bKfVhYD9wty17zu9Ua30u3eeXoF7/\nDnxdaz2llPoE5mnnvhw/u5T1ivMg8C2tddSWt1TfVy4U7e9r2YRda/2mRV6iG2i3HbcBPRgPhqBS\nymX1uuL5i66XUuq6UqpFa33VEqLeLJf6EPAdrXXEdu2rVnJKKfUPwH8qZr2soQ601ueVUj8C9gL/\nxjJ/X0qpGuB7wH+xHlHj117w95WGTH8v6cp0K6VcQADzaJ3LZ5eyXiil3oRpLO/WWk/F8zP8Tgsh\nVPPWS2s9YDv8W+APbJ+9J+WzPypAnXKql40HgV+2Zyzh95ULmepe8O+rlIdiXgY2KzOjw4P5JT6i\nzduIpzDj2wAfAXJ5AsiFR6zr5XLdOWN7lrjFx7UfANK+PV+KeimlauNDGUqpBuAO4LXl/r6s3913\nMGOP30w5V8jvK+3fS5b6fgB40vp+HgEeVGbWzHpgM/DSIuqSV72UUnuBLwDv1lr32vLT/k6LWK8W\n2+G7gdet9KPAW6z61QJvIfnJdUnrZdVtK+ZF5PO2vKX8vnLhEeDnrdkxB4Bhq/NS+O9rqd4QL2YD\n3otpxaaA68CjVn4r8H1bubcDpzEt7mdt+Rsw/3hngW8C3gLVqx54Ajhj7eus/P3A39nKdQJXAEfK\n558EXsEI1D8DVcWqF3C7de9j1v7jK+H7Aj4MRICjtm3PUnxf6f5eMEM777bSPuvnP2t9Hxtsn/2s\n9blTwNsK/Pc+X70et/4P4t/PI/P9TotUr/8BnLDu/xSwzfbZj1nf41ngo8Wsl3X834DfT/ncUn9f\nX8fM6opg9OvjwCeAT1jnFfBXVr1fwTbjr9Dfl6w8FQRBKDNKeShGEARBSIMIuyAIQpkhwi4IglBm\niLALgiCUGSLsgiAIZYYIuyAIQpkhwi4IglBmiLALgiCUGf8HbJYCzQY2l/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ad2e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(y, x, z, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A \"parametric\" graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11aed2ac8>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tBQyDKjQW73DSahhJf4YBlHDSlaFpJjUQSYuE1Sou/f3dWisLqSrJS5hhSHZ+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\nWFj0w5oYLCwsLCz68f8BItdyHuVHOUkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aed2a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(y, z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11b0bd978>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RbPVTvMdd/zHAhGdcq+TpyWmH1kXm7pwAsM/kr0XaEDHjOLaN92uNYO2HoiJm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\nOLCASfxFRy7e9a7NIHrxpLxdJGjltL9yKWsLX+sMBmLl1VLXu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DzQ8kCpNLvvZAGK1Rj0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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af16e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(16, 5), ncols=3)\n",
    "axes[0].plot(x, y, linestyle='dashed')\n",
    "axes[0].plot(x, z, linestyle='--')\n",
    "\n",
    "axes[1].plot(x, y, linestyle='dotted')\n",
    "axes[1].plot(x, z, linestyle=':')\n",
    "\n",
    "axes[2].plot(x, y, linestyle='dashdot', linewidth=5)\n",
    "axes[2].plot(x, z, linestyle='-.', linewidth=0.5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colors\n",
    "\n",
    "As described in the [colors documentation](https://matplotlib.org/2.0.2/api/colors_api.html), there are some special codes for commonly used colors:\n",
    "\n",
    "- b: blue\n",
    "- g: green\n",
    "- r: red\n",
    "- c: cyan\n",
    "- m: magenta\n",
    "- y: yellow\n",
    "- k: black\n",
    "- w: white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a3c1240>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G7t1w6qnm7uGBGcMnnm2FeZyfD0eOqFm3NiEzM5PZs2czbNgwzj333LIHrSKG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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af9a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y, color='k')\n",
    "ax.plot(x, z, color='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other ways to specify colors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a3850f0>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F7DIfHDnEx8ZuxCd2Lj3yc7o4/K0j+7llX5i8v8q3TiZ2/P06TWFBUlyWRG92Mf1qL7Uy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SFUJHWWkcD5nYZKVhSyRGsooMl4kjW0ojWqS3vv20pNXlkoc7PvlhmwINyCt+2SoVprhTZnkG7t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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aeb9400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(16, 5), ncols=3)\n",
    "\n",
    "# grayscale\n",
    "axes[0].plot(x, y, color='0.8')\n",
    "axes[0].plot(x, z, color='0.2')\n",
    "\n",
    "# RGB tuple\n",
    "axes[1].plot(x, y, color=(1, 0, 0.7))\n",
    "axes[1].plot(x, z, color=(0, 0.4, 0.3))\n",
    "\n",
    "# HTML hex code\n",
    "axes[2].plot(x, y, color='#00dcba')\n",
    "axes[2].plot(x, z, color='#b029ee')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a default color cycle built into matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><th>'color'</th><tr><td>'#1f77b4'</td></tr><tr><td>'#ff7f0e'</td></tr><tr><td>'#2ca02c'</td></tr><tr><td>'#d62728'</td></tr><tr><td>'#9467bd'</td></tr><tr><td>'#8c564b'</td></tr><tr><td>'#e377c2'</td></tr><tr><td>'#7f7f7f'</td></tr><tr><td>'#bcbd22'</td></tr><tr><td>'#17becf'</td></tr></table>"
      ],
      "text/plain": [
       "cycler('color', ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.rcParams['axes.prop_cycle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y0NBQ3NzcDpl9dnR07Hb22WQy0dDQcMjMdn19PSUlJezde+Dt28HBgdDQUMLC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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a1c4908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "for factor in np.linspace(0.2, 1, 11):\n",
    "    ax.plot(x, factor*y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Markers\n",
    "\n",
    "There are [lots of different markers](https://matplotlib.org/api/markers_api.html) availabile in matplotlib!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a2de048>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1jS7+4bpW74EX7eRwOPhs9Vu85GoCrFFmWUuW8NhPf0rv3qG/gijCk7f148Ep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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a13ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(12, 5), ncols=2)\n",
    "\n",
    "axes[0].plot(x[:20], y[:20], marker='.')\n",
    "axes[0].plot(x[:20], z[:20], marker='o')\n",
    "\n",
    "axes[1].plot(x[:20], z[:20], marker='^',\n",
    "             markersize=10, markerfacecolor='r',\n",
    "             markeredgecolor='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label, Ticks, and Gridlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I9IO1lhc3VPLzlYW0d3Zx5+IELj01ym1O6egtTw/DFRnRnDkjlJtfyuPGF/N4/bMd3H/h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\ni1SmRURERES6SGVaRERERKSL/hdE/R3tX5ppTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b8b8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "ax.plot(x, y)\n",
    "\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "ax.set_title(r'A complicated math function: $f(x) = \\cos(x)$')\n",
    "\n",
    "ax.set_xticks(np.pi * np.array([-1, 0, 1]))\n",
    "ax.set_xticklabels([r'$-\\pi$', '0', r'$\\pi$'])\n",
    "ax.set_yticks([-1, 0, 1])\n",
    "\n",
    "ax.set_yticks(np.arange(-1, 1.1, 0.2), minor=True)\n",
    "#ax.set_xticks(np.arange(-3, 3.1, 0.2), minor=True)\n",
    "\n",
    "ax.grid(which='minor', linestyle='--')\n",
    "ax.grid(which='major', linewidth=2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Axis Limits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3, 3)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j8YMTePrG0zhYfZwFS1ZTXd/DfM2J7fBsGtdOyjJDMb5sGOPhomhCEnvvlJjp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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba3ecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y, x, z)\n",
    "ax.set_xlim(-5, 5)\n",
    "ax.set_ylim(-3, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text Annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0,'the maximum')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A6SBmrd1HWlaezolUBd7e3mfvu7u7U1BQgDGGiIgI1qxZU+H93dzcSr2Wm5sb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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba487f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y)\n",
    "ax.text(-3, 0.3, 'hello world')\n",
    "ax.annotate('the maximum', xy=(0, 1),\n",
    "             xytext=(0, 0), arrowprops={'facecolor': 'k'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other 1D Plots\n",
    "\n",
    "### Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x11b909d30>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1fFVSxqgB7Xd9HJGfyxH53Xv+SClpy0i8vLUbhoi4gNeA55I14NCNV2yW7aho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\nA/ervxYPmSuFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bb029b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "splot = ax.scatter(y, z, c=x, s=(100*z**2 + 5))\n",
    "fig.colorbar(splot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bacb898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['first', 'second', 'third']\n",
    "values = [10, 5, 30]\n",
    "\n",
    "fig, axes = plt.subplots(figsize=(10, 5), ncols=2)\n",
    "axes[0].bar(labels, values)\n",
    "axes[1].barh(labels, values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2D Plotting Methods\n",
    "\n",
    "### imshow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 100)\n"
     ]
    }
   ],
   "source": [
    "x1d = np.linspace(-2*np.pi, 2*np.pi, 100)\n",
    "y1d = np.linspace(-np.pi, np.pi, 50)\n",
    "xx, yy = np.meshgrid(x1d, y1d)\n",
    "f = np.cos(xx) * np.sin(yy)\n",
    "print(f.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11bd28748>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JlZButvVV//MNV0TyVf/ZRTcOYJPYs/pdZ7FY0+q5+vsBtLV7SIA/g5ThZMo1l8kxAmsA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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b919e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,4), ncols=2)\n",
    "ax[0].imshow(f)\n",
    "ax[1].imshow(f, origin='bottom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pcolormesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x11bee30f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q5OtpdT/rVN+2Wau9rA1b5o7Q1j7WFp/SXZelAzfjhVvXpB11sW7uDW/8Ccvt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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bcfc278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "pc0 = ax[0].pcolormesh(x1d, y1d, f)\n",
    "pc1 = ax[1].pcolormesh(xx, yy, f)\n",
    "fig.colorbar(pc0, ax=ax[0])\n",
    "fig.colorbar(pc1, ax=ax[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x11bfb4240>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bdd6860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_sm, y_sm, f_sm = xx[:10, :10], yy[:10, :10], f[:10, :10]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,5), ncols=2)\n",
    "\n",
    "# last row and column ignored!\n",
    "ax[0].pcolormesh(x_sm, y_sm, f_sm, edgecolors='k')\n",
    "\n",
    "# same!\n",
    "ax[1].pcolormesh(x_sm, y_sm, f_sm[:-1, :-1], edgecolors='k')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11c02c438>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9pRrlNhth6tqZ+IPT7pw8es24tibXo9nblkVNkarxYuAZDag+Oj+/dBYqkAHA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\nVEuSJEltMlRLkiRJbTJUS5IkSW36f9o8tOVLQYiBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba96f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_distorted = y_sm*(1 + 0.1*np.cos(6*x_sm))\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.pcolormesh(x_sm, y_distorted, f_sm[:-1, :-1], edgecolors='w')\n",
    "plt.scatter(x_sm, y_distorted, c='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### contour / contourf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x11bee8358>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u8kCiXacyUH0FjtKOhW9cFzaeDQVH+CBjOx+NuKVD92iLPSlZFFXV8/ptV6Do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+MXAPMSFEsp7wU8jiSp8/faU8jpf3SGN8de53lt0c4zlN1FX1R9PK+UkrESwfpFR3Oc0emU1snP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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bee8160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5), ncols=2)\n",
    "\n",
    "# same thing!\n",
    "ax[0].contour(x1d, y1d, f)\n",
    "ax[1].contour(xx, yy, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x11c42b198>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4u2vx9tQRHepJVJjXX+7zBVCpryKnJZeytjLK9OXUtNfQZmpDb27v7qF1C6Vt\nVTx09BXsFLb42XoSbOdLlGMIQXbnct8KgkCieySD3MJZnLOLT7K3U9Razdu9Z+CqsYjaW4PjOViZ\nx+KsQ9wZ1h9ntSUt1hDvQGA3KRXF+NhfXCiUNzZRXN/IzIR46zWzWeSdDXvw0jnw5MSh1vEYTWZe\nWLyFLSlnGdErlBfvGI3DBfyBq+uaST6eR2ZBFfmlNVTWNNFuMGEwmnCwVeOis8PbXUtcmDc9I3zw\n8zxX8EMmExjbN4KkuGAWrN3P8h9Tqahr5rW7x1tF9oOjB5KSW8zb3+9hZEyo1Sd5dGQYa0+c4XR5\nBX38fC465yMVxehNRsYHRlivNXS08daJHcS7+DE7YpD1ervJwNOpX3OgOpNbAwbxYPgYNPLzLYHl\n+mqO12eQ2VRApb6GqvY6DKLFJ1gtU+GudsZD40KkYzA9dRE4q85ZjdVyJTf69We4Ryxvpa/jw6yt\nlOnreTp6kvW7e17PsRyoyOXl1M2sGXUvyk73ivGB4bxwcDuFTfUEOF7c+p5SWNL5GoVar+VU1LBo\n1xFu6BvN5L7nrML1zW089uE6TuWX8eiNQ5gxsg8KeVcNYTaL5BRVcTyzlNPZZZRXN1Jd30KrvgOV\nUo5aqcDbXUugtzORQR4M6BnUxWrsYKNm9nUJXD8wmmc/38SzizaRWVLFw5MHIwgCSrmcl6eN4rq3\nFjN/yz7enTERsPxNJIUEsjrtNEaz+RdLNB+pKEYABnmfKyGdUV/BhqLTPBA5hETPc+W085ormXts\nCRVtDcyLncL1Pn3OE/tm0czZpnwym/LIaS6mtK2SFlMbPbRd/amvEYqBnybx9uV8F4d7sBTrQRTF\nZEEQNIArUPl7O+2WbzYbuQ0Btv7oTXpqO2pJbzyDQTQAFtES4xhNL11P+jr3QfU/KlY6DEb2p+Sw\ndc8Z0tKLaWrWA6BWKfBy1+Ll7kiArwtKhQy5XI6+3UBDUxu1Da0cP1NMmz4NABuNkoRegQzuF8qQ\nfiE4/MJRz58Zo9nI6cZ0DtYcJq3+OK2mVgDUMjUuKmd0Kh1hmlAUgmWhFRFpMjRR21FLdnMOu6p2\nA6AQFEQ6RJDg0p8+TvHYKWy7bU5/JEajiSMnCth3JIf9KTlU1zYDoFDI8PV0wllnS0iAK/Z2Gsxm\nMwajCX27kYrqRjKyy2ns/DwqFXKiw7wYNjCcMUlRaB1+3Xfvz4goimQ0neVIbQonG05T2W5ZUwUE\n3NVuuKpdcVI5oZH/b/59/RoauYoAO28aDS0crjnJ9opkALxt3El07cN1XonoVBaRKxdk3BM6gmAH\nD146sZIHj3zO5wPux0Fp+eybC4zAAAAgAElEQVQ8HJ3E5uJ0Pj+bzJNxllPMCCc37BRKjlWVMjnk\n4gL4WJHlO7G377kAoU1pGaSXVPL6reOs1kJRFPnnsh1sSTnLQ5MGc/e4fl0EQofByOZ96az78STp\nOeUA2GqUBHq7EOrvhlqlRCGX0dSip6ahhd0p2Xy/y1IkL9TfjeuHxjJuSBTazuAoG5WSJ28eho+r\nI++u2s2D73/HgodvxEatRCYTeO6GEUx57yu+ST7OfSMTuswhrbjsFwXw/tICVDI5/dzP3bMgfQ8N\nHW282HucNX2c3mTgyWNLOVyTzXMxN3KDX78u7bQa29hReYjNZXspaasAwFFhj5eNK6H2/tbv2jZT\nOxXt1WRW5bOlfJ9lzvb+TPBKItGtj9WAoFPZ8lrPW/G1dWFx7i7MoplnY25AJsiwUSh5rucYHkpe\nyfLco9wZaglWtGx0YF9pwS8K4NTiMuzVqi65kr/YnYJKLmfuhCTrtaqGZu57bxWlNY28de9ERvbu\nKu4qa5vYsPs0G/ecoqSyAQBXnR1+nk5EBLpjZ6PCYDTRqjdQUlFPyulCOgwm5DKBXpG+TB3di2F9\nw6wBdh5ODnzy+DTe+GYnX/xwBDuNirvHWebm5mjPrKQ+fLT9IHcWlFkzUvT29earI2mcragixuvi\n5b+PVZYS4eSG409KWS84swd7hZq7w8/5s5e21nH/4c8QgI/6zybOKaBLO2VtVWwq28P+6lRqOizF\nT1xUOvxsPfHUuOJve/FMGd3IESBMEIQgoAS4FbjtZ/cUAiOBxYIgRAEaoOpyOu0WAeyqduHB0Put\nP5tFM9XtNeQ053Cy4TSnGk9zqPYwywqXk+SWxEj3Ybiof38VmWuJqpomlq07wg+702ls1uPmbE9S\n/1B6xfjRK9oXT7fz06/8HLNZpKyygay8yk6Rk82ug1nYaJRcPyqOmyf2wdPtytWc705ajC38UL6N\nHZU/0mxsxlZuS7xTbyIcwgm2C8LbxutXTwxEUaTOUEdOcy7ZzTkcrUvl87wv+DL/K/o792WyzyQ8\nNRdfmP5MtLZ1sGHHSb75PoXK6ibrBmlAfDARIR4E+rigVP56pH1DUxsnM0pISy8m5UQB8xftZOGS\nXQzpF8r0Sf2ICb8mF9FLps3Uxv7qZHZU7KRUX4ZapibKMZIxHqOIcAzHS+N53onBHO7pptF2H84q\nLU9FWuYtiiLVHfWk1qWzt+ooK4q2sLZkB9d7D+NG31HYd24qh3vEoO1jw0NHFvH88W/4V/ydKGRy\nQh3dmOgXy9KcFO6JGIiz2g6FTEYvN2+OVpb84jiOFZdio1RY04e1G4zM37KfaB93JvQ6lx3hg7X7\nWHvgFLPHJ3DP+HOZIsxmke+2p/Hl+sNU1TUT5u/GfTcNZkh8CKF+rhdde81mkYKyWlJOF7Jxz2ne\n++pHPlt9gHumDGTa6F7WnLO3jYjHxdGO5xZt4rVl23ll1jgEQSDcy5XEiEC+3p/GrKQ+qJUKXOxs\n8XPSklZc9stzriol1sUD285KYA0dbazIS+XGwDiiOo/EzaKZZ9OWcbgm22IR9D0XDGc0m1hbsp1V\nxdtoM+mJcAjk3uBp9NJF4mPjcfE5i2byW0pIrc9gZ8Uh5mctZXH+Ou4IuJ5RHgMRBAFBEHggbDRy\nQeDznB/RyJXMjboegFHeEfRz9Wdx5iFuD+mLXJAR5OiEq8aW1KpSbo/sddE5pxWX0cvHyyruKxqa\n2ZCawc0D4nCys2w6DCYTT3+2kYr6Zj58dArxP/G/bmnrYMn6QyzffJQOg4k+0X7cdcMA4qP98HK9\n+HesyWzmTG4Fe4/lsOPgWZ6b/z0BXk7cO20wIxPCrdbe56ePQt9uZMG6/ejsbZgyxOKbfdfQPqw8\ndIJ3Nu5hyQM3IwiCtfT1saLSiwpgsyhyrKqU63+S4eNMfTlbSzJ4KCoRrcoy5xZjO3OPLcFgNrFo\nwP0E2rtb769pr+fbos1sr0hGQCDeKZpZbjcQp41Ap3I4r89rCVEUjYIgPAz8gCXF2SJRFE8LgvAy\nkCKK4npgLvCZIAiPY3GPmCWK4s/dJC6Ja+JsUybIcNe44a5xY6DrAERRJL3xDNsrd7KpbDM/lG9l\nvOdYrveegFp+fhWYPwOtbR0sW3uY5etTMJnNDE0I47rhsfSNC0AuvzSXD5lMwMdTh4+njmEDw5l7\n7yjOZJez5odUVm9OZfWmY4wbFsODdw7901rs9CY9W8q38kP5VlpNbfRxiifJdQix2hgUskv72AqC\ngLPKGWdnZ/o59+VWv5vJa8nnQE0yu6v2klxziMGuA7nBZxJu6t+el/Nawmgys+L7FL5ac5imZj29\nYnx5/J6R9O8V+LtcGLQONgzpF8qQfpYjyOz8Kjb9eIofdqezKzmTkYMjuO/2RLw9dFd6KlcFURQ5\nUJPMt0UraTA0EmQXyOygu0hw6Y9Kdk0Fj1xzCIKAm9qJMZ6DGeM5mNK2SpYXbmRV8VZ2VB7kyYi7\nidFaPjfxzsE8E3MDr536jn9nbOKJaIs4ejAqkY1Fp7tYgePdvfnwxEFaDB0XLft6rKiUHt6e1iwN\nW46fpay+iZemjbZa6TYcTGfx1hSmJcbxwPUDrc+WVTXyyidbOHamiN6Rvsy7byz9YwN+k5+oTCYQ\n5ONCkI8LN43pzdn8Cj76dh/zl+5i7Y7j/OOhCUQGWcTN2L4RFFbW8dH3yfQO9WFqoiVY8K5hfbn7\nk1WsP5rOTQMs13r7epGcV4goihcch1kUOV1T0SX4bVV+Gm0mg9WqCrA8/wD7q87yRNT1XcRvZlM+\nC7KWUdBaSoJzHDf5jSXMoavF8KJzFmQE2/sRbO/HFJ9RHG84y7eFm1mQvYxdVUd4KHQ63jbuCILA\nfWGjaTF28E3Bfvo6hzDUIxpBELgzrD+PJK/ix7IsRnlHIAgCsS6enKqpuGi/ze3tZFZWd3F/WLrv\nGKIoMjPxnOvLwnX7Scsp5bW7xncRv8nH83jt0x+orm9h3OAoZk8dhO9vXKfkMhmxoV7EhnoxZ9og\nfjycxeJ1h5j3wQZ2HYngybtGorW3QSYTeOnOMTS26vnnsh0EejgRH+aLrVrF/aMG8OqanaQWlBIf\n6IOX1gFPR3uOFZVyR/8LB3lm19fQ1NFOvNs5K//CM3txUKqZFWY5MTCJZv7v+Lfkt1Qxv8+sLuJ3\nf/UxPshahsFsYKznEG7yG9vFbeXPQGdO300/u/bCT/6fDgy+kn1eEwL45wiCQIw2mhhtNNXt1awu\nXsv3ZRs5UJPMSzEv4Ki8tnczPyf5aC6vf7iF2vpWRg6O5P4ZiXi5X7kPp0wmEBPuRUy4F/dOH8I3\n61P4bksa+1NyeGLOaIYNDL9ifV0Ncppz+SjnE6raq4l36s2NPpPxt/X79Qd/I4IgEGwfRLB9ENd7\nT2BT2RZ2VPzIkdqj3B4wnaFuib/eyDVEbmEVr76/mcy8Sgb1CWbmtIFX3EIbGujGo3cNZ/atg60b\nuT2Hspl100DunJpwWcEmV5uytnIW5S0mszmLELtgHgl9iDCH0F9/8C+MiHhRkeZt487ciLuY7DOS\ndzO+YN7J+cwInMQUn1EIgsBk377kNVeyLH8f/Vws4ijE0ZWJ/jEszUlhTuRgtCob4t19MIkix6vL\nGOR1vkhr7TCQUV7FvYPOHe2vOnyKQFcnBoVZgn8q65t5a8Uueof68PStw63jTT6ex7wPNiCKMG/O\nWCYkxfziZ9ZgMNHeYcTWRnXBAhoRgR6899QUDqTl8dYX27nvH98w776xjB5oseDdMy6BtJxS3l6x\ni5hATyL93OkX7EuMrweL9xxlSv9Y5DIZvXy9WX8yg5KGRnx1538n5DXW0mo0EONiEdcm0czXOSn0\nc/W3Wn8zG0tZmPkDQ92jucn/3FH5rsojfJC1FK3Sgeei5pDgEnfR+ZpEE2bRkg9XLijOO1UTBIFe\nukjitOFsr0hmcd5a5qa9xbNR9xKns/gmPxIxlrS6PF45tZporS9uGkdGekXgZePIkqzDjPK23Bfr\n4sHe0jz0RiOaC5QJPlFSjgj06nQRaWprZ8XBk4zpEY5PZ9W13SdyWLLtKDclxTG+v+U1F0WRrzem\nsPCbPQT7uvLG3yYRG+Z9XvvWOZvMtOkNaNQKFBeoGieXyRg1IILh/cNY+v0RPl19gONnS3jniRuI\nCPRAqZDz5uwJ3PraUl5aspVv592BjVrJpPgo/rVxL6sOnSQ+0CJoe/t6c6z44lm7/nvy0cfdMt7s\nxiq2lmTwcFSS1fr7Tf4B9lVl8FT0JPq7hlrft89zV7OxbA8RDoE8Hj4TL5uLG3HMohmjaLzo7/9q\nXJMC+Ke4ql25L2Q2Q90SSa1P+9OJ39WbU5m/aCchAW68/vSNf/jRsYerI4/dPYIJI3rwxodbmPfO\nem6aEM8js4ZftBLStcSeqr18mb8UnVLHc1FPE+Hwx4p3rVLLdP9bGOMxmv/kLWJR3mLyWvKZ4T/9\nki3N3cH2fRm8vnALtjYqXn1i0h++2bG1UTF7+hAmj+nJgi938dnyfRSU1PDMg2NRXWZk/dUgqymb\nf2XOR0DgrsCZJLkN+csF3f4eKvSlzD1+Bw4KLf62oYQ7xBDt2BuXn5yYhNr7826vp1mYvYwl+esQ\nRTPT/MYC8HD4WA5WZ7IgcwtD3CORCzJmhPRjfeEp9lbkMNEvlrjOCmHptZUXFMC5NbWYRJFYb4sY\nrGxoJjW/lEfHDbaK2YXr99NhMPLSnWOsmRhSzxTzzHvrCfBx5o3HJuH9M+NDm76D3fszOX6yiPSM\nUopK6jB1FsBQKeV4emgJCnRjUEIIA/uFoHW0CBJBEBjcO5gvgmfw3Pz1vLBwI636DiYPj0MmE3j1\nrvHc8soS3l25m8/+fpPFIpoYz9PLN5OaX0rfYF96dB6JZ1RUX1AAZ9XVABDlZHmdU2uKKW6p5+8x\nlgwaoijyZvp6HJU2PB87xfo67Kw4xPtZS4nVhvFM1GyrW8p/aTW2cLz+EBlNJyjTF1OlL7MGEMsF\nBZ4aH7w0fsRo44nV9rGeisgEGWM8B9NbF8XL6R/xSvrH/F/0/cTpIlDKFLwSdwu3H/iABZlb+Efc\nzShkMm4O6s389N1UtDXhYeNApLMbJlEkt7GWaGd3fs7ZymoAq7vAtlNZtLR3cEeixXpqNJl569td\nhPu6MXfaUOtzH6/cz5frDjEyIZz/u28cGnVX16Wq6ib2H8rm4OEcCopqqahq7PI+Bwa4Eh3hTXxP\nfwYlhFpdxeQyGTMnJ5AQF8gz763jsTdW88kLtxLg7YytRsWLd4zh3vdW8vXOY8wen4CtWsXYnuFs\nO5mFwWRCKZcT6+XB5vRMGvV6HDXnxxGcqa3EQakisNMvemtJBgJwW2de52aDnkU5OxnsFsFUvwTr\ne/9h9jdsr0hmsvcI7gycbM3l/F/K24pJb0wjs+kUJW0FNBsb6anrWjzmr8y1/43VSaRjBJGOEb9+\n4zWC2SyycMkuvv3+KEP6hfDi3yZgo7l6R6uhgW589NptLFyym5Ubj2Ewmph776hr2lK3tmQ9a0rW\nEeMYzYOh92GvsL9qfbuonXky4u+sKv6OjWWbqdRX8nj4o9d0xojt+zJ4ef5GekT48PLc63Fxsrtq\nfbu5OPDS4xMJCXDj02X7qKxu4p9PTcbxGna5Od2Qzr+zPsBJqeOpyLm4qq/NkqDXIg4KR4a5TaDB\nUEtuSwYnGg4jsJieuv6M8piEn62lGIGtQsMTEXchEwS+KvgeLxt3Brv2RiGTc2/oSJ5NW872spOM\n9e5JD2dvdCob9lfkMtEvFme1DQ5KFUVNDRccQ3Gd5bpfp1A8kFUAQFJkIAA5pdVsPHiG20fGW/O0\nZuZX8sS7a/B0c+T9Z6aiczgnBCurGlm26jBbd5yipbUDraMNURFeDEoIxdZGhUolp7auhbLyBtLP\nlLB731nkMoHRI2KYdftgvDws43DW2jL/mWk8/d463vpiB4HeLvSM8MHJ3oY7RvflvdV7OJ1fTkyg\nJ8Ojg1Er5Gw/lU3fYF98nSxtFNdfeM6FTZYgJn8Hy3x+LM1EIcgY6mWxAO6tyuBkfSHzYqegU1nm\nllJ7ivezltJDG8686PtQ/yQLRHV7BRvLVnCi/ghG0YCzyhUfm0DitP1QyyzCrNXUTJm+mLNNJ0mp\n24daZkM/5yGM85yKg9IyXjeNM6/2eJR5J9/nlfSPeStuLkH2vgTYu3Fb4BAW5+7izqAkQhw8GeUT\nwfz03ewqy+KW4Hj8HSxtFDU1XFAAF9U14KBW49RZ+njHqWx8nBzp4WfZIG0/lklZbSNP3jzJuune\nsv8MX647xOThPXj67tFdjD05eZV8/tU+9h/MBsDHW0dkhCfDkyLROmrQ6420tLaTnVvJ1p2nWbsx\nFWcnOyaMieOWKf1wcLCMIzLIg/efu4n7/rGcx95YxacvTsfdxYE+4b4M6xnCkk63G529DYkRgaw5\ncpqTheXEB/ng99/3ua6RaK/zBXBBZ1aM/34/76vIJcbJC1eN5Tvw24IDNBn13B92Lpf0mpLtbK9I\n5ma/cdweMNHaliiKZDafZkfFes42nQTAQ+1NlGNPHJVO+NoGXvCz9lfkTyOA/2ws/HIX3244yrTr\nevPIrOGX7Od7JVAq5Tx293DUKgVfrz2MSqngkVnDrkkRvKtyN2tK1jHEdRB3B81CLvy2kqhXEpkg\n42a/aXhqPPg8bzGf533BnODZ16SFcPfBTF6Zv5G4SB/emTf1PGvH1UAQBO6cOgBPdy2vL9jC3NdW\ns+Aft6DuhrH8GsfqUlmY/TFeGk+ejPw7WuWfyz+uu7FTODDJZzrw3yC4CpKrf2R/9TbS6g8x0GUE\nN/rcgVquQRAEHgmbQYW+ln9nLsFN7US4QyDDPWIItndnUc5ORnn1QC7IGOgexL6KXKt7hb+DjoLG\n+guOobi+EQBfJ0uA7/6zBbg62BLhZbGOfrwhGVu1krvGWlwkmlvbeeLdNdjbqruIX7NZZO3GVD5d\nvBujwcywxAgmX9eL2Gifi66NoihyNqucbT+ms35TGtt3pTNtch9m35mEUilHrVLw6iMTmTVvKc+/\n/z1f/vMOXLR23Dg4ls82HmTJ9qO8OXsCtmoVvYN8OJRtyfnvZKPBTqW6qAAuam7AUaVGq7aIpp1l\nWfRzC8BBafn5m/z9eGi0XOdtsY7WdzTyftZSAu28u4jfdpOerRVr+LFyE3JBziDXkfRzHoKfTfAv\nBsHlNJ/hSO1eDlTvJKV2H2M9pzDUfTxyQY5W6cArsY/wt7Q3eDdzMe/2fAq1XMVtgUNYnr+PFQXJ\nPBt7I+GO7vjYavmxLJNbguPxs9d1zu3i77OvzhKoZjCZOJJTzMT4KARBQBRFlmw7SqCHE0k9LJuu\nnKJq3vh8K70ifHhy1kir+G1pbeffH25n687T2NupmXnbIEYkRRHg53zxIDiTmSPH8lm3KZWvVx5k\n49YTPHr/SIYNsfgv+3s68e+np/Lgqyt45t/r+fSl6SjkMh6aNIibX/2KJdtSePTGRAaE+SOXCezP\nLCA+yAffzmp/xfUNRHudL/oLm+qtm4FmQztpNcXcE27xX2826llesJ9Et0giOsuNJ1en8WX+Ooa4\nxjPd/zprO02GBr4p/JRTjcdwVOi43ns6fZ2GoFM5X3C+f3WuvW/2/wE27jzZKX7j+ds9I7tF/P4X\nQRC4f0YiN02IZ8WGo3zy9d5uG8vFSKs/zpf5S+mhjeWuwJndIn5/SpJbItN8p5Bcc4jvSzd261gu\nRPLRXF58bwNRYV689dyUbhG/P2VMYhQv/X0iZ7LKeevjbVxmYO4VJ685n4XZHxNg68+zUU9J4vcy\nsQTBeTLJZzr/iF3ACPeJHKz5kbfPPkthaw4AKpmS56PnoFM68u7ZxbSbOpAJMu4JGUFeSxU7yy0p\nxYZ4BFPR1kROk+XYO8DRicKmugv2W1zfgM5Gg71ajclsJjmrgEHhliC2yvpmdh3PYWqSxQIHsGD5\nHqrrWnjt0evxcLEIkMamNv72zHLmf7SdHtG+fPXZbOY9OZEeMb6/aBgQBIHIcC8euW8kyz6fw9gR\nMXyz+giPPbOcyuomAOxt1bz+t0k0tbYz74MNGE1m7G3UTE2KY8exLIqrLIIvIcSPrPJqaptbEQQB\nX52j1br9c4qaGvCz77SYttSR01TNcC9Lqq+cpnJSanOZ5j8AhcxS0vf9rK9pM7Xz94hZVvFb31HL\n+1n/YHvFeuKdBjIv+l9M9Z2Jv23IL85ZJsgIc4jhtoD7eSbqTYLtI1hX+jWf5b6DvjMNpU7lyGNh\nd1DUWs6X+es6r9kyzrsXm0rTaOiwzHGYVxgHKvNoNxnRqTXYK1UUX8TSX1TXYLWYphdX0tphICHU\nEgNy5GwRGUWVzBjVB5lMoKW1nWfnr8fORs2rj0y0+vLm5lcx59El7NiVzu03JfDNovu4e8YQAv1d\nfnHOcrmMAf2Cef3FqXw6/07cXOx56fX1vPzm97R3WHxnIwI9eHb2GNJzy/lm81EAQrxdGdErlLX7\nT9FuMOJoo6GHnyf7M/MBrO4t/93E/RST2Uxxc4PVyn+oKh+jaGaIh0Xgryw4SKOhjXtCRwBQ17nJ\nCXcI5NGwGVYDTXpDGm9kPEVG00kme9/OCzHzGeUxSRK/v8D/nAA2mA2YRTO1HbUYzIar3v/JjBLe\n+WQ7feMCeHjWsKve/4UQBIFH7xrO5DE9WbrmMDv2Z3T3kKzktVjEiZ+tHw+HPnDN+N1O9LqOab5T\nGOw68NdvvoqUlNfz4nsbCPZ3453np2Jrc21kLBiaEMbs6YP5YU86y9cd6e7hWGk2NrMg+0O0Skce\nD38UO8XVcxP5K6CR2zLZ53YeCp2HwWzgg6xXyGpKB0CrdOCRsNsp11ezqngrACM8Y/GzdWFFgSWP\n8ODOL/m95Rbh7O+go6i5AfMFNlFF9Q34dFrS0ksqqW/VMzg8EIC1+09hMovWdFRHThWwducJpl/X\nh9hQS9xFVXUTjz61nPSMMp55fDxvvTzN6sZwKbi5OvDU38bz0rOTyM2v4t5HviQzx5LVIMzfjafu\nGkXqmWLW7jgOwG3DeyOTCXy9MxWA/iGdYi63GLCIowsJI7BYSf06hdGRqkLLa+YeBMCKgmTUMgWT\nfS0W783lezlad5pZgTdYc70Wt+bzr8z/o6q9nDnBTzIj4EG0yksXRB4aH+4LeZpb/GZztvEE/858\nidoOy6alt1MU13sPY2PZbo7VWd77mwMG0m42sL7EUv12sEcwepORk3WlFtFvr6Wo+XwBbBZFShoa\nrILxcI7FUt4v2JLlYcm2o7g42jIhwVIt7bPVBygqr+PVhyfi6mRxFzh4JJcHHl9Ka1sH771+K3Pu\nGmp1Y7gUwkI8+PC9O5g9M5GdezJ4+oVVtLRaCuGMTAhnaN9QPl21n4LSWgCmJsZR36JnZ6rF1WJw\neCCniyuoa2lDa6PBQa2+oKW/rLUJg9lsFcB7K3KxlSvp7eKLSTSzqvAgCS5hRGstr8HnuasxmI08\nHn6ndZOTUruPT3PfwkGhZW7Eq4zwmIhSymbzq1wbauMKoDfpyWzKoqC1kNzmXEraSglzCOV674lX\nLcdrfWMr895ej4erAy//feJ51Wi6E0EQePyeEWTnV/L2x9voHeOHs657xYDRbOSj7E9xUNjz9/DH\nrqkiA4IgcL33hO4eRhdEUeS1BZuRCQL/fHIS9nbXVkrAmVMHkJNfxUdL9xAZ6kl87B9flvPX+CLv\nS+oNDTwf9QwOf7IA2muJho5SVhQ8hoPSHX/beEIdElHLz/nohzlEMzfiVRZmv8onOW/ySNgLBNiF\nEKcLZ5hbf74r3sZIjwF4alyZ5NuXhZk/UNJai4+tM8EOLhyozOOu8AH42DliMJupbms5bwwl9Y2E\ndxZGSM6yiMGBYf6Iosi6A6dJiPTHz02HwWjizUXb8fN04t5plippNbXNPPrUcuobWnnrlWnE9+wa\nZNfW1sHxYwUcTs4mJ6uC1uZ22to6cHV3IDDIjegeviSNiEajOXfaMjwxkqAAN576v5U8OW8ln86/\nEw93R65LjGbzvnQ+XXWA8UOicdPZM75/JOv2n+KhSYOI8fXATq3iSE4xY+PC8XVy5EBewXknJ6Io\nUtz8/+ydd5hU1f3GP3d6n92ZbbO9N5YuSBGUqogdaxR77DGJ3ZiYokZjN2oSe8MuioiIIr33Xrb3\nvjszO73f3x93WVjAX1QWS5L3eeZ5YGfmnHvuuXPve97z/b5fF5PTpQpgW7sbMSs15JkS8UaCLGzZ\nznTbUOJUOjwRH2/WzWdYXDGn26RCEV3Bdp6rehC1TM2vC/5Emq7/mANRN3WeDbT499AeKCcc6y3G\nJNNj05Zi0w4ix3AiCtnB+8y4hClYVIm8Vvs0z1U+wB1Ff0Wn0HN59tlsd+7nheoP+MfI+ykw2hgR\nn8Pchg1clj2BEVaJ9G/tauSEhEwyjXHUuY5U+rs8XoKRaF/IwKaaJgpSrFgMOura7KzdW8dNZ41D\nrVTQ1O7kw8XbOXvSEIaXSORwX0Urf3hoHtmZVh750yyslv55JC6Xn2WL91Cxr4X62i6cDi86vRqD\nQU3xoDRGjcmjbGhmXwKcQi5j9kVjSU408ciTC7njvg946uGL0WiU3HnlFC6563X+9urXPH/fBYwu\nyiQ9wcy8tbuZMbqY8UVZPL94HRuqGjhtaBHpcSaae45c6LR6pR2E1N4S4OvaaxiVmIVKrmBjVxWd\nQRe3lUjPot09lazq2sIlmTNJ1UohEzudm5hT/w/yDSX8MvdO1Ic9R70RO5WuFTT7d+EJd5Ci/f9L\nMv834afD0I4BwWiQBS0LWd+9kagYZXrKNB4d+jAJqgTmNc//wY7jtQ/W4XT7eeDOs36SyUAKhZz7\nfjWDQCjMS++u/rEPhxWdK2kPtnNF9mzifmaehT8GlqwpZ+e+Zm654hRSBtBGb6AgCAK/u+U0UpPj\neOrlJcRiP24oxHbHDpqGAGsAACAASURBVDY7tnJu2tnkGnJ+1GP5uUMuKJELClp8u/i67QleqrqA\nRS1/xR0+WIjJpIzj5vzfY1SaebnmcXrCEsG5PPssBATmNvaqwMllAKztLAdgmCWdvU6pGluCVorT\n7Qr4jjiGLo+PJKNEaHY3tpGdEI/FoKOyuYtWu4tTR0lJ0l+t3U9Tu5Pfzj4FjUopOSU8vYhuh4cn\nHrqwH/kNBMK8++YaLj7rGf5w5/ssXrgTpVJOepaVsqEZyASBlUv38diDn3HxWU/z/FNf4uitqgiQ\nnWnl8YcuIByO8IeH5hEMRaTiEBedhMsb4NPlUhLSmWNKCYQjbKloQiGXUZyayP4WqdpgksGAPxzB\nG+q/Y+kKBQlGIyTrpDHvd7ZTGi8Vh1jbWU4wFuaMNMkXd0n7evzRAJdnn40gCERiEd6oexaAWwv+\n2I/8eiPdrO54kVerf8GXrY9Q7lqCWmYgQZ1DgjoHmaBgp/MzFrb8hVerL2VT9zsEowfHXGwawg35\n9+AIdfN+40uIoohKpuSSzJm0BbrYYt8DwMy0EbT4HZS7WrCodaTpzJT39I5Zp6fzKIucLo807wfm\neX9LJ4PSpeS31btrATjjRInAvbNwMzJB4NrzpF06h9PL/Q/NwxKn47EHLuhHfpub7Dz64HwuOesZ\nnntiEZvWV6PTqygbmkFqWjzRmMgnH2zkrlvf5rJZz/L5vK1EI7G+70+fPIg//e5s9pa38sLrUlXR\nhHgDvzx/HFv3NbKjohmZTGDayEK2Vjbh9gUoSUtCpZCzq1HaHUg06un0HGXMvechUavHEw5S67Ez\n3CoR+tWd5ahkCsYnStf2ew1fEK8ycV665J3d7K/nzbrnyNTlcV3eXf3Ib3ewnnmNv+OVqotZ0fE8\nnYEqlDIdesV/RlGxgcDPXgGOxCK8UvsaAjLOSTsLmzal7z2r2oo74iYmxo57IlNHt5v5i3dy+qQy\nCrKPDHL/qSAz1cKsGcP5YMEWzjttOAU5P86xBqIBPm35jCJjIUPMg//9F/7LEQyG+cdbKyjMSWLG\npEE/9uF8I7QaFb+85CT+9NQClq0rZ8r44n//peOASCzCnIZ3SNWmclrK9B/lGP6TYFAmMivzCURR\npD1QTrlrKbucC6hxr2VMwhUMs5yHTJBjUsZxXe6dPF5+H/Oa3uKKnFuxquOYljKOL9tWc3Hm6WTo\nraTrLKzrquSCrLEUmZP4uH4H3QEvFo1EgO0BHybDQeUxFI3iDgaJ10nCQnV7N4U2ycVj1S6JGI0f\nlA3AJ0t2kGWLZ+xQadGz8KtdbNhcw603TKG0+KAv7Ia1VTz5yALsXR7GTSjk7PNPoGxoJqrDCseI\nosienY189skWFnyyhWWL9/DrO2cwYZK0DZ+ZbuW+O2byu798wvMvLeW2m6czKM/G8OJ03l+0lQun\nD2dIjg2tWsm6ffWcPDSPgpQEFmzbhyiKWHsrmzl8/Um/vXcRYNHoiIkiVa5OLsiRkt1WdOwlXqVn\nSHwWUTHGwtaVlJhyyTNISuvnre/T4KvmqpzfYFUfvMdXuJbzddsTRGJBCownM9wyiyRNAbLD8i6i\nYphm3y622j9kbeerbLfP4/S035Omk7yEc/SFnJF6IfNb3mVt9xLGJ0xljHUIVlUcn7euYLR1MBOS\nipEhsLxjL8XmNApMSVS4OvrG5Az6icRi/fq1+/zS+3ot3R4fdo+PwhRpntftqycnxUKKxYjD5ePz\nFXs4bXwJCfEGRFHkwcc+x9nj5/nHf0Gc+WDC42efbOHl55cgyASmzxzCmeeOJDf/yF1hvy/Ets21\nfPDOOp5+dCFz39vA3X88m6IS6ZqZOK6QWWeNZO78LUwYW8CIoVmccXIZ//pgDfOW7GRYUToTBufw\n2pebWLevnukji8hJtFDTLlnZWXRaqjq7j+i3u3eerRotlS5pQVlslo5vfVcFw+Nz0MhVlLtq2dVT\nwTU5s1DJlMTEGO81vIRaruHa3NtR9Sr14ViADV1vsc3+EUqZjlHWX1BomoRVfegOwMVHHMe3hTdc\nx/rWK7/3939K+NkrwI6wg65gNzfmX4dNm0JUjNIeaGdN1zrWda9njPXEHySL/62PNyAicsWsE497\nX8eKKy8Yi8mg5e+vLfvREpa+av+anrCLC9Nn/SRdKX5qeLe3tPGtV/04jiLfBZPGFpKdbuW1D9b2\n+Wz+0FjXvZ7OYBcXZZz/k4kr/0+AIAikaIs5OfkmZue8QppuKKs6X+CLlgeJxEIA2LQZTEs+m63O\ndex37QTgrNRJRMUYyzo2ADAmoYAt9hrCsQjFcb1euD3tWHsJcPdhCrDjEGIUDEdo7O4hL1lSslbv\nrqE4I4lEs4HKhk52V7VyzpShCIJAW3sPz724lOFDMjn3DEktFUWRj9/fwP13vU9cvJ6n/nk5f/7b\nhYwYlXsE+T0w5rKhmdz7p3P51xu/JCnFzF/um8tTj3zed32PH1PABeeMZP7C7ZRXSmr2ZWeMor3b\nzeL15aiUCkYWpLN+n2Tdlp9ixRMI0dbjwaLrHbPX36/fPmKk1dHsc+KLhikwJyGKIpu6qxmTUIhc\nkLHVsYe2QBczbZInbqV7D0s7FjA+YSrD4g5UEQuzvP05vmh5kAR1DrNzX2VG2n2kaIuPIL8gKf6Z\n+hGck/EwF2U9h0quY27DHWzufq/vmTEp6QyKjUP4pOlNOgKtyAU5M2wT2O7cT6OvjTiVnkFxGWzs\nkmJiC8yJ1Li7CceiJGh0iIAjeNiYvb2kX6ejuq2771wFwxG2VjYxpkQicXMXbycYjvCLmScAsGJN\nBZu31XHjNadQmC+JYIFAmN/f+R7PPbGIwcMyefXdG/n1nacflfwCaHUqxk0s4ql/XsGf/3YhoVCE\n2296k5VL9/V95rorJ5KeGs/fnl6EzxdEp1Ex46QSlm6soMftZ3CODbNew+reRVl+spWqPgKsw37Y\nHAPYA9Lf4tU69jsltbjInESb30mdt5MxCVLS41fta9HIVExLkcJ61nR9TYOvmnPTLseklOKHA1E3\nnzTezRb7+xSbp3J57muMTbzyMPL7PxzAT/tJ+i2QqE7EHwuwvGMFqzvXsK57A6s611DlqWJ68lQK\njQXH/Rjau1ws+HoXMycP/kluTR8Oo17D1ReOZdueRrbvbfrB+/eEPSxsXcSIuGHk/68C17+Fzx/i\nvU83M/HEAoYNGriKeMcLcrmMqy4cS12TnaVry3/w/mNijM9bvyBTl8FQ8zdXv/ofjg1mlY2z0h9k\nQtINVLlX8VnzHwjHpIf51OSzSFLb+LDxVUKxEDZtIqWmPJa0r0cURcYkFOKPhtjhqKfIfJAAWzSS\nGnqAFByAo5c4WHQ6ajsdxESRvCQrTo+fXbVtTBgsqb3zluxApZRz+gRpm/yJ575CROTu385AJpNs\ntJ578kv++cxixk0s4pkXrqRs6LePVc/KSeTvL17JRZeNY+H8bTz58IK+UJ8rLz0Js0nHsy8sQRRF\nxg7NISvVwie9yXBjS7Jo6HDS3NVDQYpE3itbu7D0KsAHyN8BdPeeA6taS2WPpAwWmhKp8bTjCHkZ\nZZESCBe0rMCiMjPWOoyYGGNe8xwsqkTOSZsNQEyM8kXzQ+xwzGN4/CxmZT5JvCqdb4sUbTEXZz1P\nvnECazpfZnXni4DkEnFp1o3IBDkLWt8DYHrKOBSCgs9bpTCBEyy57HM1440EKTQlEo5FqffY+yn9\nh8Leq4Jb9Voq2qQku4IUK9urmgmGo4wtzSIai/Hpsl2MG5ZDTpqVYDDMP19eRl5OImefPgyQdszu\nv+t9tmyo4ZbbT+OhJy4mIfHb5QAIgsC4CYU8+/LV5BWm8MDv5/LhO+sB0GiU3Hvb6XR0unjx9ZUA\nnDNlKKFwlIWr9yKXyRhXms2aPXXEYiJ5KVZanW68gRDxOi2BSATfYaEu9oAPo0qNSi6nvKcdk1JD\nqs7M+q5KQFosBqJBVndtZXzCCLRyNa6wkwUt71FoLGNkvESIvREHcxtupyNQwemp9zPNdic6xcCX\nqtcrsxlje/07vX6q+NkTYIBrsq+gwddEuaeSVn8rZqWZaclTGR4/7Afp/625kvo7+7yfvvp7AGdM\nGUycSct78zf/4H1/3voFgWiAWenn/eB9DxQisQiusAt7yIE9ZCcYDR63vhYs2YXHF+Syc38+FXwm\njS0iNzOB1z5Y94OrwFscW2kNtHGG7fT/7S4MEEQxQkw80lVHEARGWM5nasodNHq3Mb/p98TEKAqZ\nkgsyrqYr1M7X7ZI91pTkMbQEOtnvrmGkJReFIGddVyUWtY4EtZ5qVxdxai0yQfhGYmTRaaluP6gM\nbqlsIiaKjC3Nwh8Is2jNPqacWITZoGXbzgY2bqnlqktP6nN7eOeNNcyfu5lZF5/IHx6c1S+p7QCi\nkSjObg/NdV2EgkeOWaGQc+1Nk5l99QS+WriTZx5diCiKGPRqfnnFBHbtbWbpyv3IZAKnn1TKzooW\nWjp7GFMqqXDr9tWTnyxt61e1d/UpwAe2//vGfEgIxIHQgXxTIpvtNQCMtObS7G9nu3M/p6WchEIm\nZ6tjLU3+OmbaLkIlUyGKIsvbn6Xas5qJSTcyMflG5MKRKnc45iEQ6SQQ6SQSOzL+Wi3XMyP19wyJ\nO5ut9g/Z0v0BIMV9T046gx3OjdR6KzArjUxIHMGy9g34In5GWnOJijF2OOooMEl+zZWuzr6FzuFK\nv93rRymTYVSrqWrrIk6nIcGoZ+3eehRyGSML0tm6t5FOh4eZE6VQsA8+2Uxbh4tbrpuMXC4jFIrw\np3s+ZPuWOu6470zOnnXCUe8Dfm+Q5rouXA7vUe9R8RY9j/39MiZOLuHF575m6VeSdV9ZaRpnzRjG\n/C920NLqpCAzkcEFNuYt2SEtfAZl4fD4qWrpIi9Jctyo6bD3LXTsvsMXOj6saum9KlcX+aYEBEFg\nXVcFSRozuYYk1nZtIxANMiVZKnU9r3kOETHCBelXIwgCwaiHuQ234wy1cGb6gxSYJh4xHoBoLEAw\nemQYxn8r/iP2BvON+eQb84nEIsgEWb+Qh+Md/+vyBFi4bDenTyojJdF03PoZaKjVSs6aNpS3Pl5P\nl8PTZyFzvBEVo6zqWs0J8SNI16X9IH0ONKo9Nex3l2MP2ukO2WnyN1FmLuO0lOnHxXFkwZJdlBbY\nKC04vmW0BxIymcAV54/hj08uYMP2WsaNzPvB+l7asZxEdQKjLCf8YH3+p8MbqefLuhPQKtJJNZxG\nlukXqOUHk2kGxZ2GIMhY3PooW+0fcoL1YgqNZQyNG83Kzi+Zlnw24xNG8EL1B6zq3Mp1eXkMjstg\nay+ZyzRYaPA4kAkCZpXmSAXYJzkUxOu0bCxvRBAgKyGOhev2oVTIKc1MZsXmKrz+EGedIuUUvD93\nI5Z4PefMlISQXdsbeOOl5UyePojrf9W/KqbXE2Dlwp18PW8re7fV9/1doZSTX5rKCRMKOXv2eAym\ng8nNs6+ZSDgS5b0311I6OJ1TZw5lxrTBfLJgG6/NWc2kCcVMG1vMPz9YzcrNVVx02ggSzXq2VTZz\n/oQhJBr1/YiRw+cnnYPPEHtveIBFo6XW3U2y1ohBqWano54UTRw2bTwf9SYWTk0eiyiKfNU+jzRt\nFiPipcSwPT0L2eVcwEjLRQy3zOp3ToORLho9n9DhW44zuBOQlGwBBRbNCSTrTiHNeA5KmfRsEASB\nU5Jvxh91sLrzJRI1eWTqRzIpaSarur5icdunXJd3J9OSx7GsYyPbnPsZGV+GXJCxw1HPVXlS+eYa\nVxdTUiQy7Dh8nv1+4nRSMZXaTge5SVLRim1VzQzujaNesbkKjVrB+OG5eH1B3v1oAyf1xuQCvP3a\nKjZvqOG2e2cybUb/HaDuDhcfv7aK7RuqqStv61PvZXIZw8fmMe3cExg7pQRVr7e6Sq3gnj+eg6Pb\nw1N/+5zi0lRS0y3MvngMC77cwUefbuHWG6Zw1imDeeilr9hf287QXClmeHddG8MLpWdcbaedeMPB\neT4UzmCAuN4FQb3XzrikHERRZKu9lglJxQiCwOqubSSrrZSa8nCGutnqWMvkpDNI0kjPhFUdL+AM\nNXFexqOk6/sLf75wE7WuN2n3LiMQbSNFN43/QcJ/hAIMUjGFDfZNR5Dd4x3/u3TNfkLhKGdPH3pc\n+zkemHpSMaIIK9ZX/mB9lrsrcEc8jLH+fNTyA4jEIrxZN4c36+bgj/gZZC7l0qxLeHzo31DLVHzV\n9vWA91nX1E1NQxfTJ5YMeNvHGxNHFxBn0vLFsj0/WJ/OkJN9rv2Ms479SVbw+7lCLU8kL+5a9MoM\nqpwvsqxxOvu6H+unCpeap5NnOIn1XW/iCEmhVeOsU/BHvezu2YJWrqbMXMA2pxRTOcicQaW7jUgs\nSqrORKtfsogyqtR4wv13VDwh6f9GtZpWp5sEgx6VQkFlUxe5KRZUSgWrt9UQZ9QypCiV9g4XG7bU\nMnP6ENRqJT5vkEcf+JQUWxy/uWtmP/K7atEurpz6KH//4ye4XX4uuv4Ubvz9mdz28Pmcc/l4ZHIZ\nc55bwtXTH+Pj11cRCUcBiRBedd0kSsvSefn5JXjcAeRyGZecP5rGZgdbtteTmmQmIyWeTbsbpGIa\nmUlUNEnhDKkWEy0ONxqFAoVMhjcYOmLMSpkMtVxBq89Fmk5SsavcbRSaJOKzq6eCTJ0NqzqOCvdu\n2gPNnJI4A5kgwxtxsLL9n2ToRjAu8eq+dkUxRr3rPVY0nUGF4xlEMUJ+3PWUWe+nzHo/OebZBKMd\n7LU/wsqms2jzHixuIwgyptnuxKLK4OvWJ4nEQqjlGsZZp7DXtY2uYDtFxhw0cjW7nBVo5Eqy9AlU\nudvQyJVY1Xpa/S6MKilhyxM+bMzBEMbeqnfNDhdpFjPRWIyq5i5KMqVkvk276xlenIFGpeTrZXvx\n+kJcdqGkjDY12vnwnfVMObWMGWcO72vX7w3y+tNfcs2pjzP/7XWY4/RcdP0kbn/4Am743Rmcc/l4\nGqo7eOT2d7l2xhPs2lTb912lUs49fzoHuVzG3x6YTzQaI8FqZOK4Qr5auodgMMyEkXkIAqzeWk2a\n1Yxeo6KyuYuUOCnsoq3Hg1Gj7hvjoXCHgxiVaiKxGJ1+D6k6M61+Jz1hH4PMGYRiYXb1VHCCZRCC\nILDRvgoRkXEJUwBo9G5nT88XjLCc34/8hmNudnTey/Km02lwfUCcuoyCuJvIMPZfCP034z9CAQYo\nNZXS7G8GwBV2s6ZrLenaNPQKPXqFjmRN8nFRg5etqyAz1ULhj+SmcCzIzUwgO93CsnXlzJox/N9/\nYQCwyb4ZlUzFYHPZD9LfQKLGW4sn4uGekjvRyvvb3MWr4nGEjl7B6liwbG05ggCnjCkc8LaPN5RK\nOZPGFfHFst0Eg+EfpETyBvsmRETGWH8+4SI/ByhlJgrjfwWAJ1RLTc8r1LrewBncyYikp1ArpC39\nU5Jv4a3aa1jS+iSzMh+n0FhGnNLKBvtKhsePZURcCS/XzqU90EWhyUYoFqHe24VNa+JL/35ioohB\nqToqMQLQq1W09bixxUvEorK5kxOLJS/gTbvrGV2WhVwmY+FXOxFFkZmnSmrwu2+tob2th6f+dQVa\nnVQgIOAL8Y+H5rP44y0UDUnn+nvPpHhoxlG3y6v2tvDak4t46W8L2ba2ivuevhSNToVMJvCr20/j\npqtf5o2XVnDzbacyYVwhep2Kr5fvZdSIbEaVZfLFqr1EIlGK0pNYu6eOYDhCapyJXY1tCIKAQa3C\nEzpszOEQBqUaQRBo9bsoi7cRiIap93YxOaWMcCzMXlc105PHA7Cq6yv0CiPDe9XfDV1vEhXDTEq5\ntS/RLRx1saXjVuyBzVg1JzLI+nsMqiMtAostt+MM7GRX95/Z2vFbbPrTGJr4MDJBiVKm5eTkW/ik\n8S52OuczwnI+4xOm8nX7fFZ3LeactMsYZMpnR48U/59vTGGnQ/JttulMtPoOEmB3OIiJg/dSTzCE\nQa0iHI3S0eMhNd5EY4eTQDhCUXoi7d0u6lsdnD1ZUnYXL9tLTlYCxYUpiKLIP57+EqVSzi9vntLX\npr3Tzf3Xv071vhZOmTmU2bdOIzXzSCuwq28/jW1rK/nXQ59x9xUvceF1JzP7lqnIFXKSks386vbT\neOTPn/Ll5zs4/azhnHnaUJau3M+KNRVMnzyIktwUNu1p4Jfnjyc/1UplcydalZI4nYY2pxuDSrru\njlzohEjVm+gIuIkhkqI1UeluBaDIlMpeVzWhWJgR8aWIoshG+wryDSUkqJMJxwIsaXsSszKVExMu\nP6TNWrZ03Iov3EiO+XJyTLPRKH6Yegg/J/zHSCQqmZIcfTYAG+2b+KjpYwKxAF+0fcnunj08uv8J\n/ln9Ip6w5/9v6DvA7Q2wfW8TJ43+/0tK/pQxaWwRO/Y20e040p/w/8O7sy7i3VkXfafvxMQYWxzb\nGGoejFr+0yri8G0gItLob0Yr1+KN+OgMdrLfVc7nLQvZ5tjOOOuYAe9z2boKBhenkWD5YUJUBhoT\nRuUTCEbYvKvhO33v+1xfAOu7N5ClyyRVm/rvP/w/fC8YVDkMSXyQYYmP0RPax5qWi/CEpFAGgzKB\nCUnX0+zfSaV7JTJBxmjLBPa7duAM2RkeLyWnbXXsI88oPZBrPO2k6MyEY1HsQS9GlRp3qL8C7A6E\nEAC9Skm7002K2YjD46ezx0tBeiINrQ7sPT5GlGYQjcb4/KtdjBqRgy0lDofdy7wPNnHK1EEMGiwl\nkYZCEf5085ssmbeVS26YxONzbqBkWOY33sfzS1N56OWr+dWfz2XrmkruueolenrvmflFKZxxzkjm\nf7yZ6sp21CoFp5xUxIo15QQCYUaVZeEPhtld3UphegLRmEh1SzepFhNtTjfRWAy9SoUneNiYQ0GM\nKjUxUaTV14NNa6LW00EMkQKjjXJ3HaFYmCFxhfgiHva6tjEqfgJKmQpHsJHdzs8ZHHdGX8JbOOZm\nY9t1OALbGZzwAKNTXj4q+T2AOM0Qxqe+T2H8r2j1LmJbx+19in+mfgRZ+lFs7HqbQNRNnMrCIPNw\ntjrWEhNjDI0rpMXfQVfQQYHRRlvAiTvsx6Y10eLrQa9Q9o3xUHiCQfRqJe09HmKiiC3OSHmvYl6Y\nnsim3dJ9ZFRZFq1tTnbtbWbapFIpZnZ1JZvWVTP7molYE6QFUkt9F7df+i+a6jr587+u4O7HLz4q\n+QUpefeECUU8O/dXTDt3JO+/sJxn/vhJn/o9eXoZxaWpvP3aKkKhCMOGZJKWGseCRVKS4/DidPZW\ntxEIhilIS6SyqQtRFLHFSfNs1EgE2H34PIeDGJUqWn3SDohNZ6LaI7lB5BqS2ObYi0JQUGYuoNZb\nQWewjdEWyfFju+NjesItTEm5DaVMUs7t/s2sbbmEcLSH0SkvUWK543/k9xvwH0OAgb4LdZx1DInq\nREZZTuDm/BuIiFEiYoR6bz31vu/2IP7/sH5rLdFojAmjfr5OBpPGFUphEBsqjntfVZ5qesI9P9vY\nzCJjIRnaNB7Y+1fmNc9necdK1nVvwBv1cXXOFWTpB9Zqpr43/GHS2KIBbfeHxPBBGeh1KlZvqjru\nfXUH7dR4aznRMuq49/U/QKphBuNsc4iJYTa330IoKpV5LTWfSrwqg03d7yKKIqOtJyMistmxmjRt\nEonqeHY6K8jSJyIXZFR72rHppNjXVp8L49EU4FAQg1paNLc63djijFQ197oEpCWwbb8UcjG8OJ0t\n2+vp7HJzxqmSSvj+nLWEQhFmXy0lBsViMZ64+wN2rK/mtr+ez+W/no5CeaQV2NFw+oWjue+ZS6kt\nb+N3V79CwC8d55XXn4LRpOXF56QwqKmTSvH7w6zdUMXIUqmgxqbdDRSmSzuFFU2dpJgNRGIxHF4/\nerXqiK1xTziIQanCHvQSikWx6cx9ymCB0cYOZzkyBAaZ89nVs4WoGO2L/V3T+QpKmYbRCZcBUpLb\nprbrcYX2MyL5aTKM534r0UYmKMiPu55S6+9o9y3tR4LHJ/6SYMzD5u53pXMfN4aesINabwWDzdI9\na6ezgkKjFK5R6W4jVWemze9CJghHVfq9wRAGlZqOHkmossUZqWjqRCGXkWuzsmlPA/EmHfkZCXy9\nXAqlmXJyCdFojBeeXUxWTgLnXCD9/u2dbu66/CV87gCPvHYto0/+dp7kWr2a3z40i0tvnsLij7fw\nxtNSnLUgCFx+7cl0tLtY9Nl2ZDKBM04dyo7dTTQ12xlRkkEkGmN3VSsFaQm4/UHaHR5S4gy0Ot3o\n1UcPgfCEghhUalr90u/HpjVR427Hpo1Hp1Czw1lBiSkXjVzNRvsKVDI1w+JOJBzzs80+lyz9aDJ6\nQx+84Qa2dNyKWpHI+LT3sWr/dy/8//AfRYAP/KB1Ch3pujQe3Pswj5c/RY2nllNTpnF38Z0MMg9c\nGcA1m6uJN+t+VslJhyMnQwqDWL7u+BPgzfYtKAUFQ+J+voUvrsm5iiuyZ1NqKiZFk8zJiRM4J+1s\nko9D8tvy9ZW94Q/H38rveEGplDNmeA5rNlcfd8/pXT1S1a0fyv3lfwCTupiRyc8QiLSyvfNORFFE\nJsgZZb2ErmA19d5NJKpTyNUXsdG+EkEQKDHlsc9djUqmIENnpdrdjk3bS4D9LgzKIxVgbzCEXq2k\nxxcgEI6QEmeksllSBvNTJQJsMevItMWzcm0FWq2SMaPzcNi9fPbxFqaeNpiMLEn5e+Ppr1i5aBfX\n3DGDKWeP+M5jHjd1EL//+2XUlrfx4iOfS+fBpOX8S8awdVMtNVXtDBucSWKCkcXL92LSayjOTWbT\n7nrSE8zo1EoqmztJMkm7Ou09HgxqFe4jYkNDGA5TBqvcbWjlKtJ08ezuqSTPkIlBoWO7cz0WVSKZ\nujza/Pup9qxmpOUidIp4APZ0PUBPcC8jkp4kWXfKdx5ztukXlFruod23lCrnCwAkanIpMU9nu+MT\nPOEuBplHoBSUYpLE2QAAIABJREFUbHduIFufiklhYFdPBflGyZe30t1Kis6ENxLCFQ5gUKqOVIBD\n0jx3uCQCnGQ2UNncRU6KBYVcxuY9DYwaJCn1Xy/fy9CydFKSzaxbXUFLk4Mrrj0ZhUKOKIo8dtf7\neFx+Hnn9Woq/g9XdAVx68xRmXDia919czqIPNwFwwom5lA3N4J031hAOR5lyspSbsWpdJUOL0pAJ\nAtv2N1GQLoUEVTR3khJnpL3HjUF9QAE+OM/RWAxvJIxeqaKtb57NVHvayTUk4YsEqPc2U2rKIxwL\nsdWxnmFxY1DLNexxLsIf7WG09dLetgJsaf8VIHBC8vNoFT9fXvJD4cchwLEuxMBixEgNohgZ0KZ7\nwj38o+oF7EE7VZ5qzkydyRXZlzE8bhhWtWVA+9qxt4kRZZkDXphAFEXKN1ez/P01fPr8It76y4d8\n+foyWmvaB5xECILAmBG57NrfQvAolj8Dif3ucgqM+UfEzw4ExGgLou9DRO+bva85iOE9A36+ArEA\nXcEuhscPY0LiSeQaclDJlMeF3G3ZVU9BdhKJ1m/nX/ldsW3pLt76y4fMfWoBX89ZydYlu45q+3Ss\nOGFIFnanj8bWgY+RPhSVnipMChM2zcDe+EVRRIy2IQZXI3rfHNC2fy6IxZx4/F8SibYd8V68Zjgl\n1nvo8q+lwS3ZYxWaJqOVx7Gn5wsAhsePoT3QTHewg3xDJo6QC2fITYbOSrPfToJGIoPdAS86pRJ/\npP916A2F0atUdPeWyrUa9TR29mDQqLCadFTUdVCalyI5BuxoYPiQTNQqBV8t3EEoFOGiyySv1Kba\nTj56ZSXTzzuBWVdPOGIsbqeX9Yt28PkbK3nnyYWsX7SDgC90xOdGTSzivKtO4osPNrJnax0Ap581\nDLlcxpIvdyOTCYw/MZ9tOxoIh6MMKUyjvK6DmCiSnhhHU1cPCUY9IHkc61RK/If5w/rCIfRKFV3B\n3lK5GgONvm4ydFZEoNrTSLEph6gYocqzj0Gm4QiCwP6exSgENcPizwXAHthKi/dz8uKuJVk/+Shz\n68Hl/Qin5xWcnldw+xYQE4+0dcw2X0aqfiY1Pa/iD7dI58F6CVExTKV7BRq5ljxDCRXuXcgEGYXG\nLKo9DSSojejkKpp83ST1znNXwItOqcJ32Dz7eufZ7un1QDboaOrqITMpjg67m26nlyFFaXR0uqhr\n6OaksZI4sOTL3VisBsZNlJTnpfO3sX19Nb+8eyY5RUfeD9xOL8s/3sS7Ty7ki7dWs2nJboL+/vMs\nCAI33382Q8fk8crjC3H3+BEEgYsvG0d3l5v1qytITjKRnWll285GDDo1WakWKuo6yEqSFh5NnU4S\njHpc/iCIIkqZrN88+6PSvw0KFV0BLxq5Ar1CRbPfTqYugQZfCzFE8g2Z1HjKCcb8fcVN9vQsIklT\nSKpOsoOrcD6HJ1zNsMRH0Sv7E35RFAlH6nH7PsPrX3rE+fhvxY+TBBdtQ3TeLP1blgL660B3AYJw\n7HGhZqWZAmM+F2dewLKOFVhU8egUumNu93B0dLvptHsoKxq4WEOf289Xry9nwQtfUf8NBSqsqfFM\nvWwil/zuPPSmgRnX0JJ03pu/mf017Qwt+fYG6d8FgWiABl8jZ6WeMWBtijEf+N5GDC6C8K6jf0ie\njqiehqCbjaA49rFp5dqjJrsd2H0YqETLSDTG3spWzphy/Ao5LH17FYteW9bvb3GJJk69ahIzr5uG\nLXdgVO1BhdJvZE95C5mpA7sIPRTVnhryDLkDFo8viiKEViB6nofwjgFp8+eKcLSN1u6rADBozyQx\n7s8o5AfLzmcaL6TN+yUVjmdJM5yJQqajyDSZXc7PCERd5BkkpazGW06WTiIkDb4WbNp4tjpqiVdL\n9zJHyIdWoSQQ7S+MBCMRNErFwYIYei2t3S5sVhPhSJSGVjsnn5BPt91DU4uDM2dIrjxffr6DsqEZ\nZGZLitxbf1+MSqPkqttO7W+D5vIz78UlfPyvJfjcgX59qzRKxs8czg0PXoDpkFj8S2+awsovdvLc\nnz/l2Y9uwWTWMXJ0Lsu/3sM1N05m5LAs5n2+jX0VrRRlJREMRWhsc2CzGGnudhF/wBfWI425I9I/\nDyMQjaBVKHEEJdIfr9LR4rOTZUikPdBFMBYiW5dGo6+WUCxInqGEmBil0r2KbMNoVHIdohhjX/ej\nqOVJ5Jqv7td+ONKE3f0Mbt8niGJ/b1q5zIJRdyEW06+Qy+L7/l5k+Q1tviXsdzzF8KTHiFelk6DO\no9K9kuGWWeQbSljQ+j6esItsfRrbnPuIiBFStRZafA5O7I0Btwd9aOUKApH+8xwIS/Ns9/oQBDBq\n1LR1uzhpUDaV9b2xwFmJbNkuWdWNHJaF1xNgw9pKZp49ArlchrvHz0uPLqR4aAYzLuwfAtDe0M3z\n977HlmV7iR3m/WtJNnPhracy47KTUPX6Q8vlMq67eyY3n/t35r62kit/cyonjMkjMcnIVwt3MmFS\nCUPKMvh62V6i0Rh5GQnsqW4lzqBFo1LQaneTkS6dP6c3gEapJBCJoENq/8D4tQolDo8Pi1qPM+wl\nEA2Tqo2n3ictNLL0NrY4liEgkGcopjNQTVewmlOSbwHAF26mrmcO6YbzSNSN7zeuULiSDue9+INr\nAdCpj+4R/N+IH0cBVpYiWD5EMD0M8lRE918QO6dICt4AKGmTk07BorJwbtrZJKoTB+CAj8TucunC\nLDvK6vL7wtvj45+3vY5Gr+a2l27gxZ1P8EHrSywMvMOLO5/g1uevpfjEAt5/9FOuKrqVRa8tIxY7\n9iIDJQXSg2x/1ZHqzkChxluLiEi+YWD8YMXQJsTuMxE9jwEgGO5ASPgCIWmD9EpcKV1finzwzUHs\nPgPR9+4xX18qmYpxCVKcXZOviXcb3merYxuesAd/1D9gLiN1jd0EghFKC4/fNtY1j1zKAu8c5na9\nymv7n+Evn97NoPFFfPjEZ1xR8CtevmcO0Uj0mPvJTrei16nYU9E6AEd9dHgiHloDbeQZcgekPTG0\nFbF7FqLjOoh2IRjvRoh/EyFxzYC0/3ODWllIeuI8LMbf4PUvpr5tIj2et/veFwSBwvhbCcecNLg/\nBKDEPI2oGKbCtRybJgOtXEeNp5wsveSNWu9twaaNwxsJEoyGMCjUOII+tHJJAT70t+oPR9AqlX0E\nOE6vpcXuItVqoq7ZTjQmkpeRwI7dknAwtCyDhrouGuu7OWWqpJBV72th5aJdnHv5eOKsB4lszZ4m\nfjnuT8x57HOGTSjm0Xm38db2h/mk9hn++sGtnPqLcayav4WbJj/EzjUHQ8W0ejXX33smdRVtLP1s\nGwCTpg2io93Fvj1NDBuSgSDAlm115KRL4Re1Td3YLCZau12YdZLgIxEjBYFwfzXUH4mgkSuw9yrA\n8SotLX4nqVoLtV7J8SjbkEaVR4qFzTeU0Orfgy9qp8AokZwW7+f0hHZTFP9rFLKDgok/uJHGjtNw\n++Zi1J5JeuJn5Np2k2vbTWrCu2jVY3F6XqKx4wxC4YPx+1qFjVzzlbR6v8ARkMZcYJxIq38P7nAn\n+UaJ4FZ595GtTyMqxmj0tZOqi6fF78ByxELn4JhFRAIRaZ6d3gAmrQa3L0ggHMFmMVHTLBVwyE1P\nYMv2euLMOnKyElm7qoJwKMqkadI8z31tJS6Hj1v+eA4y2cH78Yp5m7lp8oPs2VDF+TdN48kFdzKv\n7hne2PIQf3nnZlJzk/jXfR9wy9S/0tlyUOTILbZx8ulD+PSttTi63MjlMiZMKmHLphp83iBDBqXj\n84eoru0gLyOB1k4XvkCYVKuJlkMWOg6f/4h59vcSYI1CgSPoJ16lpdXvBCBFG0e9twWNXE2i2kKN\np5w0bRYauZZ9PYuRoaDQ2Out3POqNBfxNx08n6JId8/j1LdPJRjaQ4L5D2QkfYEt4Q3+Bwk/Ugyw\nDEE1FEE3C8HyLkL8GyDPQHTdh+h+4JjDIuS9li/H0wd0T0ULKpWC/KyBsz9LTLfyevnfeW7DI8y4\nZgo5ZZnEJ8ehVCnJKcvkzBtP5U9z7+S5DQ+TkpPEE9f8g3tnPITfc2R98e+ChHgDSQlG9lYePwJc\n6ZZuonnHSIBFMUTM9VdEu5TcIVjmILPORTBch6DIQ5DFSy95CoJuFrL4FxESF4NyGKLrj4iOqxCP\nso37XXAghKPSU8XKztWEYiGer/4XyztWsrRjOS3+lmNqH2BflUQWS/JS/s0nvz/iEs2otWpMFiPp\nhamMPfME/vTxXbxd9w9Ou3oy7z/6KXdN+wv2tmMLXZDJBEoLbOyuOPbz8k2o8Ui+nQNBgEX/fET7\nbIg5EEwPISR+haC/BkE9BkF+fBbUP33I0KpHYzXfRWbKUtSq4XQ478TpORgSEq8ZhlVzIrU9rxGN\nBUlU52NVZVPuWoZMkJGtK6DGW06c0ohJYaDe14pNK6ljrX4H8WqdpAwqFERFkYh4cHEfCIf7KcBx\nOg2t3RIBrm6UkuHyMxLZuacJjVpJQV4Sa1ZKNlzjJkgWgm8+8xUGk4bzrjoY+lC+tY67z3sKuVLO\n04vu5g+vXc/gsQUk2OLQ6FQMP7mEmx6+mKcW3oVGq+KeWU+z9KONfd8fN7WUnKIU5r66SqoCNqEQ\nlUrB8sV7MRm15OcmsWtvM9mpFgQBapu7sVlNeAMhiIFcJuDw+dEqFfjDh6mhkTBahRJ70IdKJicY\nCxGMScpgnbcZGQKZOhtVnn0ka9IwKs1UulYgF1RkG8YQEyNUOJ7FpColzXBmX7su71yaOy9EJosj\nM2kxyZan0KpHIpdbkMst6DUnY7O+RHrix8RiLho7zsQXWN33/Vzz1ajliZQ7ngGgwCQ5ElS5V5Kp\ny0UlU1Pt2UdO70KnzttEqlYiwPGq3qp3QYkA+w9RgIO9i+0D8xyv09Jq742LtZqobeomyWLAoFOz\na08TwwZnIJMJLPt6D8kpZkoGpeH3Bvn8vQ2Mm1pKXsnB3dl3n1zII9e/QmahjeeX3MdVvz+HklG5\nqLUqktItjJpSxqOf/JY/v30zXa1O7jr7Cdrqu/q+f9ktUwkFI7z/4nIAxk8sIhyKsmlDNUPLpJ3F\nnbubyM+U7g81TV2kWs20druI0/USYM+R83xgASDNs5d4tY5Wv3S/tWnjqfe1kqWzISJS76siR1+I\nKIpUupeTbRiFVmEmEOmgyf0x6cZz0Sqk54UoinT1PIDd/SRG7Rlkpawi3ngjGtVQZAOw0348IAjC\naYIglAuCUCUIwj3f8JkLBUHYKwjCHkEQ3jnWPn/0JDhBEBDUYxEsb4PuGkmtc96EGPtutlw/NPZU\ntFKUm4zyW2YPf1t8m23nolH5PL36QW59/lq2L9nFPac9hLfn2M5XSV4K+6uPHwGu8lSTqk1Ffwzh\nKKIYRnT+Gnyvg+5SBOtnCKp/7/cqyFMR4l9DMP0FwtsR7ZceEwmO9T6YR8aPJEFtZYz1RO4uvgO9\nQs/nLV/wu133Yz9GT+B9VW0Y9GrSbQNfy/3fISHNym0v3cBdb9xC+cYqbhxxFxVbqo+pzbLCVGoa\nuvD5j4ynHAhUe2sQEPqsEL8PRFFE9DyP2HMHqEYgJMxD0F2AIBx//+KfE1SKbNIS5qDTTKXTeS9u\n37y+9/LjricY7aLR8zGCIJBrHEerfw+BqJtcQxFtgSb8US+Zehv1vhZStNL13eJ3Eq/S4gj50Cj6\nbw9DrwKsOKgAywUZ3kAIm9VMVWMnSoWcjJQ4du5uZFBJKgqFnLUryykqSSUxycS+7Q1sXFHOBdee\n3FfNrXxbHfde8AwGk5bHP72douHZ3zjm/CGZPPv1vZSNzeep377F3k3S70EQBGZdNYGG6g42r6pA\nr1dz4rh8ViyVtsQHFaexr7wVpUJOaqKZmqYubJbe4ggON3E6LU6vX9oaP4wA+yNhtAoFjpBPIkYB\nSRm09RLgVG0yckFGjaec/EPDH/SjUcm0dPiW4Y+0kB93PUKvGOQLrKTdcSsa9Sgykj5DpfxmQUKr\nHkVG0kIU8hRauq8kGJbUb4VMR475cuyBzbiC+3vDIHKpdK9ELijI1hdQ5d6HTZuISqak1tuMTRuP\nPxrigCDrCHrRKBT9Yr2DB8IBlEqcPj9xes1BAmwxUtPURU6alW67h7YOF4NKUulx+ti6sZZTpkpF\nIr76ZAueHj+zDlnkrJi3mTf/9hmTZo3msU9vJyUr4ajjFQSB0VPLePijX+Nx+bnj7Cdob5BU5/Sc\nRKadM4LP39tAZ1sPg4ZkYI7TsXZlOUmJJlKSzezY3URehtR2dWMXNouJlu4eLAcqwB1lng+MX5pn\nP/Eq3UEFWGOm3ttCpi6VFn89oViQXEMxncEqPJEu8gwnAVDT8zoiMfIOCXFxuJ/D6fkXZv2VJFue\nQyE/+ph/KhAEQQ48D8wASoFLBEEoPewzBcC9wHhRFAcBvznWfn90AnwAgiBDZrobwfRnCK5E7PnN\ncc8a/76IRmNU1HZQWnD81Ll/B5lMxpk3nsp97/2W8o1VPHTJ00Sj33+7ujg/heY2J17fkckP34Ro\nLMa21hYWVlbwRVUF21pbiH3DnDX4Gsk9BnICIHqegeASBOP9yEz3I8i+PZkWBAFBd7G02xCzIzp/\njSh+v4SvAzsLBoWeYDTIc1X/5PHyp9ho38TkpJO5Mns2GtmxrbIrazsoyk3+1vGskXCE6h11tBy2\niDmWEJlps0/m2Q0Po1QreeCCJ45pp6GkIIVYTKSytuNbf+e7XF913npsWtuxJVj6XpOuMc05CPGv\nIMjM37+t/3AIghKb9QU0qtG022/r2ya3aEYRpx5KvettRFEkW38iIjGafNvJ1ksJSw2+GjJ1Npp8\nbaRoJALcHnASp9bhDPnRyKXUlOAhccCBcBi1Qk6PL4BWpcTukhb8tngjdc12Mm3xhCNRauo6GVya\nhqvHx/69LYw5Serzq7mb0erVnHWplAwXjcZ4+rdzMMbpeGz+HSR/gzfsodDqNfz+letITI3nbze8\nis8jxQpPnDEES6KRhe9vkP4/uQSH3UvF/lZKimz4/CGaWhxkp1qpb3WQYpEcL9ocbuL0Gpw+PxrF\nUUIgohE0ciXOoJ84lZbOgEQGkzQmWvwdZOiSafM3EYz5ydUX0xGoxBe1k2+UyF+j+xM08pQ+14dY\nzEe74zZUinxSrW/0i+39JigVGaQlvoNM0NDhuB2xd/GfYTgPmaCiyfMJAPnGCbT69+CP9JCnL6Yl\n0EA4FiRNm0SLv4MkjTTmnrAPnVyJMxRAI1cQOIQAHyCGaoUcpzeAWaehzSG5QSTHG2lodZCdZqWi\nSvLILS60sXlDNdFojImTpRjzhe9toGhIBiXDJUvKjiY7z9w2h9LRefzmqcu+ld1d0fBsHv34t/jc\nAV74w4d9f7/4hklEwlGWfbYNuVzGiePy2bS+BlEUGVyaxp79zdgSTOg0Sknptxhx+YKo5FKfPb7A\nEaT/gAKulitwhnyYVVraA070cjUIMdwRL5k6G3Ve6feVrS+g0bsVgCzDKKIxP43uj7DpT0OnlDyu\nfYFVdLsexqA9h8S4Bwe8RoEj1MhH9bd9p9e3wGigShTFGlEUQ8B7wNmHfeaXwPOiKDoARFH89g+T\nb8BPhgAfgKC7BMH4OwiugMD8H/twjoq2ThehUISc9B9/VTXx/LH86rlr2LRoO2/c//73biczTUpO\n+raZ+ivqapn61ms8s2EdqxvqWFVfx983rmPKm6+yoq6232cD0QDOsJMUzfdfMIjB1eB9EbQXIegv\n+97tCKqhCKYHIbwN0fP3793O4vYlPLD3r8QQsQftXJxxAVdkz2Zq8hROSTr5mBIvRVGkscVBVtq3\nSxjzuf3cf86jfPLMQhb8S/KsdLQ7CQVC/WLgvg9yyjK5Z86ttNd38dJdc75/O73KSH2z/Vt9/rtc\nXwBtgTZSj8H9QYw0ILqfBPVUBPMjCILqe7f13wKZoMVmfREEGd2uJwFpoZlhPA9vuA5XaC9JmgLk\ngpI2/35sGukB3RZoIkltwRcNoJCBDAFHyItRKZVAPkAYQrGDC/pQNIpKocATCGHSqrG7e10CzDpa\nu3pIT46jrqEbUYS8nCT27ZZiZAcPzSAWi7Fh+X5GTShE01sFbvnHm6jb18zVfziXhO+wy2KyGLjj\nuSvobHbw5sPS80mpUjBxxhA2r6rA6w5QNlQaZ/neFrJ6iXVDox1boon2LhcWY28crNuPQa3GEwih\nUsgJx2LEetd30ViMmCiilsvxRIJSfHSoNxZYqacz6CBJbaU1IMU8p2ozafFJicAZ+mFEYl66A+tJ\n0U9H6A0JdLifIxJtISn+UWTfQTxQyG0kmP9AILQFt+8jacxyMwnacbT7liGKIuk6yXqwxb+bNK1E\nPtsDLSSqLXQFncSrpJhrZ8iLQanGEwmikisIHjLHkd7FukquwBsMYtCocbh9KGQyiIn4g2FsCSbq\nGyVVNiczgf17W9BoleQVJFNf2U5DdQdTzjpY1fT5u99FFOGu569C9R0qUeYMSueCW6azbtGOPrU/\nJd1CfmkqG5btB6BsaAZul5+mBju52Yl02714PEFsiWZaO3uw9iaph3uJvScYRKWQE4r0v64BVDI5\nnnAQk1KNI+jFojbQHpDGmaSx0B5oRi3TEq+00urfh1mZil5hod23nKjo6ytvLIU+PIxCnkGy5Yk+\n5f8ngARBEDYf8rrusPfTgMZD/t/U+7dDUQgUCoKwRhCE9YIgnHasB3XMLhCCIGQAbwIpQAx4URTF\nZ46pUd1lEFiA6H4E1Kd8LyUmEovQFmjDpDRhUpqO6XAOR0PvQzwz/fhltH8XzLxuGnvXV/D+o58y\ndfbJZBYfft38e2TYJDWgscVB8beIO/3LymXMOfcC0kz9z22zy8UVn37EydkHKwy1B6QVe8r39MoV\no12IPXeCogDB9Lvv1cahELQzEUNrwfsiomoMgvr/2HvvMEmu8vr/cyt2mhw3B21eZSQkJBArJJBE\nkIQRIFsiYwwGg7HBP/w1GEw0wcaAeWSDjckZWRhEEAJloWxJK612V5u1u5Nneno6Vrq/P25V9XTv\nzE737CiZPc+zT+/0dPXUrXvr1rnnnvd9z537oDqc3n4qJ7edSNadZH9hP0tTC5c9I5srki9WWNZg\nxoTrvvRLOvrauOqDr+K7n7yWz7/t33Adj9zYFOdf+XwuuOrIdE/N4MRzN/Cq976cH//zz3j+H53F\n6Rc2n5mir7sVyzLYHwayzIVmxpcXeIxURjmzY34FVqSUyNw/gDARrR9+Jj00njQs1Lxt6D20p9/I\nRP7fcNy/wjLX0Js6H/gIw8VbWduxmW77BAZLj3FuTytpPcNQ+TBLUyr/7rgzSbuVYsIpkDFCAqxF\nZXunEQXPx9Q18uUyadtifCrMjJBJMjCS48wTV7Av9GyuWtHNjdc/hKYL1m1czOOPHmJidIrnblGF\nEJyKyzc//T+sOXkZL7i0+TzAm848gYuvPpfrv3kbr3n3RXT2tXHeJSdz3Tfv4K7fbeOCy06nq7uF\n7dsO8ZKXqXtl/xNj9He3MlWsYIdpMyfyRdIJi3y5ghmSfj8khG6otJq6TsFz6LLTZEMCbGhQCRy6\n7Q6GK4cRCHrtfu4f2xoSoy4GCr8mkA79KZX2zPX2MzF1DS3JV5K0z2q6zS2pVzNZ+Dajk58gnbwY\nXWulL/Uihos3k3O205dYjy5MDpceYV2ryvQzVD5Mt93Bo5O76LDClG9OgbRpU3ArJLV0TADVOXph\nmzXyZYdMwmJiqkR7S5Kh8SkA+rtbufuOXXR2pGlpSbBj22HWrl+Ermvc+quHVfq5l5wIKH/3PTc+\nwps/9MqGFP56vPJtL+JnX7uZr33sv/nsT/9aWSS2bOB719xEdjzPptD7u+2Rg6wOs4zsPTBKf3cr\nAyPVhc5U0cHQNabKDqau1VggnED9P0AigYxp83ipQIeVZrSixKheu5P7xg/Rn1iCEILB8mMsTaks\nJyOlWzG1djoTzwGgWLmFivsgve2fRRMLn24UoMNaxhUr/rnJoz4/KqU82gQ9k0xdv91nAGuBLcBS\n4DYhxIlSymyTJxNjIWZ6D/hrKeVG4GzgnfXejWYhhIZo/QgEE8ipz8/rOybdHH/3yIe5b+KBhj7f\nTOnVA4cVAW5UoTu0a4CpiYUrwTwT/vTTV5NI23zl/fPLU7qkvx0hGleAAynpSaePeL83nY5VjAiD\nZbVTMd9iETL/eQhyiLZ/QSzQTS1aPwj6SmTuo/MKuuyyu+hL9LG+ZR0v6j1/Qc4pwoEwAnnZ4rm3\nKAG23/M4r3z3S1m0uo/c2BR2yuayd17MS96whYdveZTc2NQxn9MbP/Zalq1fzD+99RoKueLcB9RB\n0wTLFnXwxOGFH1+jlVF86c97gUXl1+Dchsj8JUL/gykZ2uS8PbuVpr3l7QhhMT71JQBsvZM2azMj\nJRU4tSi5kaHyTiQBfYklDJYP0hPmZB8uj9NhZZhw8tOUwZAATwuCc/0Ay9DJVyJipMagLkSsDO47\nMIpl6ixe1M62rQc5YU0fyaTF3TdtR9MEZ4Y5Yq//+q0MPzHOmz/4yll3SHzfZ3xwYlYL0RXvegm+\n6/M//3kzABtOWUbvonZuv+ER9fOmxex47DCplE1Pdwv7nxijL8znnZ0qk7RNxqdKZGxLKcBhmyMV\n1AtqlcGMaTPu5MkYCbKuskL02B0Mlw/TZfViaKHKnlRdOFT4HZbWQUdCqaETU9cA0N3+wRnbI6VE\nyiD8d6TNSAiNnvaP4gcj5Aoq9qg3tQXQGCr+FkOz6E2sY7D0GN12Hxo6w5UBeuwOCn6JRNi+CSev\nFjqeg6UbNQTYCdOSWYah+jlc6HS2pBgcVW3uDxXg5cs6cV2fXY8PsmGTCna7/dePcOIZq+j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m41MmRdpfi3m51knUN0WOrv55zttFpqIVeq/B6QpBLVuUxKBznxZ4BAdHz1GU1+ny4cMwEWarb4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ahYU3s6z0UQQEpke+3k5+AVaULyLHdIwZ4Tfgq/bH4XbwfdkGkj3jiPeIpKK1gaNNJfQPkGpM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\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6a5/F85+zjQ1re6vu5qQcXvqrL+DmL9/Ok09BTkPf8/mX//s1tlywiTP7zCIAsP/QAqvG\nCz2ldusH4+4Yc948ftR7eV8AISxE9i3g3YZsLrP6Ufc+jSwi9TPI6Zcjq59G+g8hpadSQKVeDv6D\nS1Z/izHtTWMLm7y1dMDc8YAQgle+86U8eNt2bvm3pYt4/JRi0ft2+z37ye17ef3qX+dPfuGvOO2i\nU/mjr7y7QxXc/cgEd9/wEFe/WqWMfPDuXURhxJkXbOTJXVP4fsimU1ewd0I9vFeuKDK7UGV8OMdC\ntU4xm6LWFQxnteXCNYQgiCKytkMt8CjYGUp+nSEntqSVGHHGmPEOMeaq4LdDjccYds9HYDJd/wEA\nufTPIGWdSq1FjoQQiMKfQuZXof5F5MFziWbfRDT/O8ipy5HlD4F9EaLwRx3np9q4niCcoJBRNpDZ\nhsopPJQ6j6mGUuJG3VOYah4ga+ao6SpwI06RBb9G0clQC3yyloMfKfXb6gqCC2RE2rapeT6FTIpS\nrcGYJr7NICCXdZncP88pOuXZE48f5MxnKVX+nh8o7/OVr7yIqYk57rmpFXMhhOA9//ROLv6ZC/jM\n+/6Fa7Nv4H0v/7/84Sv+H2/Z+k6u++tv8bJfv4Zf//AbO9p8w7/eSeAFXPGz/z975x0eR3V+/89s\n71Vl1WXJlixZtty7KbaxKaZDKKF3QiAJEOBLC4QQQkgjNJNAKIFQQu9gbGzcG65yk2RZfVfSrlbb\n+/z+mJVsgcGWLRPgx3kePbZhdGeuZjR77rnnPe/4vnPIZALDqwvYXSsJRUXFGbQ5veQ6LHT2FaLr\n6QlGMOk0hKJxdGol8WQShUzWp3L3Focpen23iRhmlXSfbSoz3bEerErp86kr6iRDPYSkGKc71oJV\nPZoUcbxpcmrSnU04uoJobG+htSAoECx/BtVkRN/diJ3HkvLeTMp9PmLnbIh+jmC8E8Fwbb85+0L/\nBQQMWqnzX2d4BSq5Hb2yiI7ILuSCEquqAHesE7s6i+70Ql8r0xJLJbAo9YQSMfQKFbFUEtU+BLhX\n+e3t/JYUpXbIvmCEDKuBTk+AvHTxW0tbNyWlWbQ0uwkFo1SOK6ajzUtbkxu1VsW0k8aw4OWV+NL2\nE5Ci7+5//3bMmSZ+Peteflp8Lc/e9TI/QsIBCbAgCJ8KgrB1P1+n7nPMHUACePHrxhFF8R+iKI4X\nRXF8ZmbmQV3c4798BoArH7zwAEd+FYlkilaXl4Jc60Ed//cTTmJBfR2Tn57Psc89zbHPPc2kp+bz\nSX0dfz9hXr9jszRZuCIdh7SaEgw/AzGI6P/jgL/3QBADj6a3b249pO93RTpwaA4+BuxgULunA2fa\nY9nbhnQgOPe201Dr1Dx39+D/0n741CKcDR1cdv95A74ukPKN83MG38/q0GQjItIRPbiFYj/oLgJ5\nPqL/AalBxSBA0F+CYLobMeVGDL2E2HM7Kc+liD03gfbgW227Ih1kajIHbYdhMHDyNXMYOmYIT/zq\nGYK+ATQf+Y5jMN7b+76zVSoV4+ZUc+vz1/PQwt9g+FJdxct/+witXs3pV0mFt+uW7kKpUjBqYgk7\naiSVqqIqj8ZmN5l2A+FYnGRKxJFhwuMPYzXoCKS9wL3Ph3If65pKJieWSiuC8ShWlR5vLIg9bafp\njHaTpc7DFWnDrMxFKzfTHtqGUm7GqhmDKyTZHrTqKSgVpXT7H+v3+yEIMmSmWxFs/wXdJSBGILYW\n1Ech2F5CsL2AIOs/527/kyjkBRi0UoFzV3glKpkNo7IMZ2QHAjKyNMNwRVrJ1uTRGZUU0gyNFU8s\niFUlKYR6pbqvzqS3QKp37rFEEoNGRSASw2rU4vGHyLZLpN/l8VOYb6OpxcPQMinFqHank2Ej8rBn\nm1i5UCJ/0+aNwZpp4t1/7eN9BiyZZu55/df86bN7mHf1cbjbuulq9TDq6Eqe+OIhfvnk1Wj2yTgX\nRZH3nllC2egiyscWA7By4TYqxxZjsRmo3eXEZNaS5TDT1OymMN+Gyy0JPXargZ5gBKtRSzAaw6BW\nEUsm+3JvpTnL+93/QDyKRaWnOxYgQ20lKaaIppKYFBackTayNZLX3BXegV0zAZDRFZYW/mbDhQiC\nlu7AP/rNWZA7EKzPIlj/ITWoiqXtT5q5CPb/Iugv6nd8MuXDG/gXOs2xKBUFpMQ4XaFlZGmPRhBk\ntIe3kakeWjy4XgAAIABJREFUiiDI6Yi2k6XOoSt9n2WC5Ou1qfQEEjEM6fusku+Nce39e+/9Dkbj\nWA1aPP4wjgwTLref/FwbggCNTW4qqvIRRdi1o53x0yXRZv1Syfpx7i+OJxKK8tY/+u+YZhdl8tia\nB/jFE1dRNr6U6Pewa6YgCDZBEBYIglCb/nO/xE4QhEJBED4RBGG7IAjbBEEo/qZxDxioK4ri7ANc\n2MXAPGCWOIj6+qr31rP8rbVc/sBPyS46OMK8L5wdPSQSKQpzD05tzDeZ+4huTySCiIhFs/+t7RxN\nDtFUlO64F5vq4Ah2LwRlJaL+cgj+E1Ezt5/R/nAgJuoh9B/QnoMwgOYXvQgnw/TEewadAC9ZuYvK\nshwcWWYEQRgwCTZnmDjrxpP592//y9qPNjDh+DGDcl1dbR6ev+cVRs6oYPzcgRULgvRh0NzezaiK\nvEG5nn3h0EgfZs6Ikzxt7oC+VxBUYLwF0XsDhF+TinwGAYJ6GiirIdkOqQ4QAyBY+gXFHwjtkXby\ntYPukjosyBVybnj8Sn4x9Q6e/82rXPvXS/7XlzQoGOz3dk5JNrf9+4b9/r/mWiefv72es38+B2Pa\ncrZu6S5GTRyCRqti+7Y2TGYtuXlW9jS5KSyw40wTI0eGiW5/iGF5GQSjMXQqZZ8aqpLtSxTkxJJJ\nDEo1nmgQm8pAUkyhSXdO7Ix6yNHms6VnLQkxjkNbSXtEIoBZumPY4fkToXgrOmUedtMtOD1X4w+9\nhknf//dDUFUjqKoP9OMgHF1PJLaGTPNvEQQFKTFOZ3gpWbqjEQQBV3gnGeoSFDI1rkgroywT+whw\npsqKNxbEqjLg8vswKFR9BFj5pSiwWDKJXqMmGI2RazTQHQjjSG+Ju9x+CvJtfLGxEYNRQ06uhZ3b\n25DJZEyZWcmCt9YTjcRRa5Qcf+F0Xv7rh9Rvbaa0qqDfXKqPHkH10Qfu4rjiw0001zq56ZGLAWhv\n9tCw08lVt54ESKRsWHkO0WgCZ4ePE+eMwpm2vKjVErm1GXUEozEyTXpiiSQqxT4EWNargoqoZQoC\niRg2lYHGYCdZalvffc7W5OKKtGJV5aOS6XBGdlBpmYtVPYqO0GLKrD9HLrNi0p9HT+DfZJhvRSHP\n2XuPBRmoj0lHR34zvP75pFLd2E23AJLKnxCDZOuOISnGcUV2McpyMl1RJ0kxQY6mgHXdrZiVRgIJ\niWha1QYC8SgGpRpXMtCP9PfOv9fvHozEsBl1ePwhqoZlEk8kCcfiZGeZ2dPcxenzpM+/HTWtjL5o\nGjmFNtYt3cnJP51C0fBcpp88lref+owzrp2NYR+Lod6sZ97VxzHv6uMAqRDye4bbgIWiKP5BEITb\n0v/en9L3PHC/KIoLBEEwAKn9HNOHw02BOD59EaeIojho8kkkFOWxG56mqDKfM3910iGN0ZQOuD9Y\nBXhfmDWaryW/ADlaiaC0h9sP6doEww0gL0XsuX1QrBCimEDs+Q0IWgTjLw5pDGdE2r7K1hxcHvLB\n4orzpjN1XCndPSF2N3UektJ6zq2nMmRkIX+8+NEBBbh/HZLJJA9e+HeioRi/fPLqQ7omtzdIOBKn\nIGfgz9eBkK2ROli5Ih0HOPJroJ4LynGI/r9I0U2DBEFmQFAOkxphaOYiqCchCAfXlCaRStAZ7SJn\nkJ+vXrz+4QZiaQ/pQNfhFZOGceKVs3nrkQ+o29hwJC7vO4XBfm+/8vBHqDRKTr9mFgDOFg8tDZ2M\nTxfD7djaSnllLqIITc1uigsz+oiRI8NIdyCM1SgpwAbNPmRQsY8CLFcQTSYxKFQE41Gsaoloh5MJ\njAodHREPDk2+tHMSaSdHW4E31kI42UOW7hgAOsKSAmrQzkOtHI3b9xCpQ2gcI4oiHv/fkAlmTHqp\nALQrvJJ4yodDPxdRTOGK7CRbO5xAwkcwGZAU4Ij0maRV6IilElj3VQZT/QlwrxoaSyTRq5UEozGs\nBi3haBxzurGTy+2nMN9OpztAKBSlsiqfbVtaEEWRKbMqiYbjbFgh2SBOv3omRpue+Xe8ekg7l9Fw\njH/e/RpF5Tkcc/oEAFam/aZTZlcSDERoqHNRWZVHS5v0ji7It9Hh8WM16QhFpd9NqyGdBpFWgJX7\nKsB9pD+BQSmlfEgKcJDMNAHuiHrI1uTREW0DBLI15bgiUvGlQz8XX2wHgfie9LmuBJJ4fF9f3PhN\nSCRddAeexKA9FY1qlHT+0GJkggq7djKdkXqSYowcbSXtEcmil6PNxxVxk6W20R2T7B9WpU4iwAo1\nsVQC1T4xaL1kWJEmwoGo5P31BiQFGKC9y0dxoZ3GJjcmk5a8Ahvb07sq42eUs2n1bmLptuLn/eoE\nQv4Ibz/12SHN+WDQ0uTmpuueH9DXIOBU4Ln0358DTvvyAYIgVAIKURQXAIiiGDjQ++1w9yIfBYzA\nAkEQNgqCMP8wx0MURf55y79x7unkhsev7OczGwia07+EB+s3HQh6P8TbI4dGLgRBjWB5EFJdiP7f\nH/b1iP4HIb4GwXQnguzQ5ttLgAdbAe7t9ubq8nH/ox/x4BMf82G6IrX3/x3ohazWqrnj5V8RCUb5\nw4V/P+xYtJd+/yYbP6vh549eTuHwQ1Nwe1/y+UeAAOsUOkwKE85Dfr4EBPN9QAyx+2dHtDvcwaIj\n2klSTPYtHgcLvc/Ohq1NrN8s5ZoKgkA0NrAouMsfOB+T3chfrniCcGAAbc6/nxi093bDtlY+e30N\nJ108A0tamVz2sZQ4MPGY4QSDURr3dFIxIo/OLj/hSJziQjvOrvTWuFnyhkrKYBS9Zq8dYF9ypJbJ\niaWSGJUaAokYVlU6AzcqkaOOqBuHRtpdaI+0kKOpBMAZ3o5BWYxeWUxHaDEgPR8Z5ttJJNvoCTw7\n4DkHwm8TiizEZvolsrQtoj34MQqZkQztVLzxNqKpAA5NOa6IVCTl0OTSGfVgVhoJ9SqDKj2BuJQT\n29sdrbcQrDe5KJZMYlCrCESiWNPFttFkCqNOjdPtozBfet83tXioGl1AtydIa0s3oyaWoDdqWJG2\nQRgtei75v1PZuqqOxW+sHfCcX3t8Aa5mN9f+/hwUab/qyk+3UTI8B0e+jZotLYgiVI0upLFZ6oBX\nmG/D2eXDkWHE45d4iNWoJRBNE+CvUYBjKUnpD8Sj2FR6emIhrGmvd1e0m2xNHuFkCF/CS7Z2OF2R\nehKpGA69pG46A1L6g1JRhFl/IT3BZwlH1w94zh7f3xDFOHazpP6KokhHaDF2zSQUMh3OsOSpdmgr\ncYZbEBDS5NxDlsaGJyp5cbUKNSlEjH0WiK8qwIgiCpmMwD4KcHaaADvTBLi5xUMymaJiRB47trUh\niiKTjh1ONBJn7RJpEVAyIp/Jc0fx1pML8Xy34h0zeusJ0l9XDfD7s0VRbAdI/5m1n2PKAK8gCG8I\ngrAhHff4jVFYh5sCMVQUxQJRFEenv645nPES8QR/vepJ3nn8Y8781TxGHVV5yGM1NLsxG7WYB9gh\n6mBgUVrQybW0hFsPeQxBOQr0V0L4DVL+hw4pu1UURVL+v0LoOdBdjKA945Cvpz3cjoBAlmZ/z9Wh\no7dATKdR4ekOcsHpk/B4g1x/9yusWL+bDrf/oBTYoop8rnvkcjYu2srfr/3nIZPgBf9ewvP3vMqs\nn85gzsXHHNIYAI2th77DcDDI0TpoDbcd+MCvgaAYimB+CBJbEbuvRkwdmtAnioPjF2sJSwpJrmZg\nlo4DIZmUdrgmVBezaMVOPl6yjQef+JgnX1zKC2+uZvWGg1N0jVYDNz51LfUb93DzzHvxOA9/p+G7\nisF6b3e1e7nnoscx242cfb1UIJRMpvjw1TVUjC4kt9CeViShYkQe9Q3SjkZxUQatHV6sJi3htGpv\nM2rxhaOSNzTR64fdVwGW9ymDvlgEW5oAd0X9ZGsyaA93kqXOQS7IaQ3vIVtbjgwFLaFNAGTrjsUd\nXkMkIfnqdZrp6DQz6eq5n3B03UHPORavpaP7VtTKMVgMVwAQT/lxBT8lWzcLuaCiJSS1W3ZoK2gP\nS+1pszX5uKKSMuhOEyObykAgHsGgVKdTLOR749AEAYVMRiyRwKBR449EsRnTrXD9IRwZJto7fJQU\nS0Vhu+pdjBqdLn5bsxuFUs6kY4az8tOaviYYc86fyrDRRcy/81Xa9hx8fUHtpkZe/ttHzDhlLNXT\nJVXf1dpNzReNTJ0tfUZ/sbYBhUJGxYg8autdKBQyCgtstHf5yLZLNheQFOBgJNqn9Pcjg70EOK30\n++JRbCoDKUSiqRRGhQ5npAuHRhIt2sPN5GgqSJGkLbwVrcKBVT2WlsDbff5uu/l2FPIcnJ6fkUge\nfOF6IPwBPcEXMOsvQKUYAkBPbCuhREvfjkJLaBNGRRZGZSat4UbsqizkgoKOiIdstR13zJ++l9Iu\nWa/Sr5R91fccT6XQa1R999ntC5KTznlu6/BSXJhBLJ6kubWbyqp8PO4ArS3djJ5Uii3TyPsv723o\nc+mdpxGLJrj3wif6coEHE/mFdv782EUD+gK6eusJ0l//+PK4B1O3cAAogBnAzcAEoAS45Ju+4TtT\njRLsCXLHvAf48OmF/PSOM7n6Txcd+Ju+Adtq26kYemS2WwVBYIh+CPWB+sMbx3ADaM+V/MCei/t1\n2DoQxFQPou8OCD4B2rO/kls4UOwONpCvzUMlOzTF/UDIc1jQaJTkOSzMmzWSG6+cxTsLNnHjb187\n6DGOv/RYzr/9DD54aiE3Hv0bWnYdPEEM+kL85cr5/PHiR6k+ppJf/ePQrA+92F7nxGTQHHTKyEBR\nrCuiKdRMInXoTS0EzWwE8x8gthqx+xLJJ36QEMUYKd/vpOfyGxoEHCzqA7tRCgoKdIPrAe7dOsy0\nG9DrVNTsamNEWS6jK/OJxZM89fLyviLMA2HKyeO5581baNrWwvWTb/9W2nB/X9HjDnD7Tx7G3x3i\n3hd/1qf+Lv1oM21Nbs68TGo6sXpFLWq1gpGjC9m6rRW5XEZZaTa7m90MycugPe0FzrGZ6AlFsOi1\nRBPSM69V7rXXaBRKIskEVpWWaLqyHsAZ8VKoy8EZ6SIppijUlbI7sBOlTEu+vpp6/wpEUaTAeDYg\nUt/zz74xHda/o1QU0tp1LqHI8gPOORbfTVvXRQiCmhz7k305683+10iIQYpNkh1il28JFlU+NlUR\njaE6DAoTVqWd5pCTfF027WFpcZWtNdMdC2NV64gmE2gV/d+9GqWCSCKBRafBG4yQlW657vL4Kcq1\nsbu1i7xcKzarns1bWygoslNQaGfpZ5IyecJPJhLwRVj0nkTI5XIZtzx2CUqVgpvn/YmGbQcWcDZ8\nvoPbz34Ya6aR6/6wN+/7vZdWIQgw54zxiKLI0s+2M25iCVqtik1bmhk+LAdRhBZnN0W5NpzdEhk0\n6jREE0msei2ReAKdcu+cNelmEJFkAqtahzcaIlsrFRm7wl7ydQ5aQi4KdCUICDSG6ijQj0UpaKj1\nS/aWYtP5hBJNuNJqv1xmJMf+FMlkJy2dp5FIHpj4+0Jv0O6+Go1qNHbz3ojs2u7HUMrM5BpOIpGK\n0hhcxxDDZERRpD64gyGGMtrCnSTEBIX6XJxhLzaVgVBcen9aVToiiQS6fe5z7zMeiSew6rR4gxFy\nbEai8SSxZIosm4H6FjeVwyXhoGZ7K+MnSSkna1bUIVfIOf3i6WxYWceOTdL7qrAsh//7xxXUbWnm\ntxfPJxY5/Pf3twFRFGeLoli1n6+3AZcgCDkA6T/3R5ZagA2iKO5Oxzy+BYz9pnP+zwlwOBDmlT++\nzSVlN7DpsxpuevpnXHLfuYdFToKhKA3NXVSW5Rz44EPEUEMpzaEWIslDX2EJggKZ+bcI5j9CfAui\n+zQpn/Ab2jWKYlI6pnMOhF8H/ZUIpt8NuOlF/zFFdgcaGGIYcshjfBPeW7iFp19ZQbc3xLW3/4eb\n7nuNe/7yHqFIjMI8G6EBVKVect+53Pr89TRta+Hq0Tfz+l/fIxKKfu3xqVSK1R98wVWjbuLjZxZx\nzi2ncv/7t6PWqr/2ew4GW3a0UlWee1jP6TdhiGEIsVTssFRgAEF7BoLlYUjUIXadTMr3AGLK/7XH\ni2JS6vzmOR9Cz4Ny5GGdvxd1gd0U6YtRyA7OMzwQrNvcyCvvrqdyWA4XnzWFOUdVMG18KZf9ZCq5\n2RY+X1N34EHSmHLyeP685F4S8STXjb+Vh6/9B67GQ0jj+AFj1UebuGHOAzgbu7jn+WsZVi0pj6lU\nipfnL6ZoaDZTZlWQTKZYvmQnYyeUoFYr2bytlfKh2ahUChpauygtyKCtlwDbTXQHw1j1mj5VWKXY\n+6xo5RIZtKolFTSSTGBW6mgPd1OkzyWFSHPYSalhOE2h3USTEUoN0+mJt+KJNaFXFpJvPIMm36uE\n49LvlFxuIz/zDZTyfNq6LqQn8O/9LvZEUcQXfJWmjjkkRR859mdQKqSFXEqMs6fnBeyaiZjVIwgm\n3LSENlJmPBZBEGgI7qJYN5RgMown1iOR9bDU5jxLbaYnFsaq0hJOxNEq+v9uaJUKIvEEFr0WbyhM\njk3aEm/z+BhamEl7p49gOMaoEfls3CIpzUfNqmDzxia6PUFGjCtmWFUeL89fRDRNgvKHOvjN89ci\nkwvcNO8hVnywcb8WtFQqxXvPLuGu8x7B7rDw0Fs3YU433nB3+Hj3PyuZPqeKzBwLO7a10eHycfSs\nSkLhGDtrnVSPLKCh1U0yJTKsMJO2Lh8mnbqvwNGi1xJJSA0xeqFOJyKEE3GsKh3dsTA5aQLcHvFS\noHXQFGpHI9OSrcmlIViLUqahxDiVOt/nJMU42frZaBV5NPQ80zeuRjWanIxnSCY7aHLNJhhZvN/n\nOpUK4vb9FZfnerTqyeRlvIxcJi3sPJH1dIaXUWq+HKXMQFNwPQkxQqlxGq5oG8GEn1JDBY1B6dkq\n0uXSHvaSq7XiiabVb7UufZ/3If3pBYB0nzVfuc+lBZnsbu6iMN+G2aRlc00LOXlWioZksCwda3fi\nuZMwmrW8/OTeeU2eO4qb/n4Rm5bt5JcnPMj2tbv3O+fvEd4Beiv3Lgb210xhLWAVBKE3NWEmsG0/\nx/Vh8D+NDgIhf5h3Hv+YPTXNLHl1BT63n/Fzq7n43nMYPnHg+bVfxo56F6III4YdPAE+7/VXAHjp\nzIOrmi81lCAi0hDcQ4Vp+CFdZy8E7WmgqETsuRXRdzf4H0TUnIKgrAaZFWQWSDQgxtdJfeuTLelc\nyjsRlIduE+lFR7STYDJIif7IEODGFjcatRKjQcPxx4zg2Cll6LSqPvVuIBAEgdkXHMXomVX87eon\nmX/Tczx718tMOHEM006diDlTennEwjHWfbyR5W+tweP0UlCey1+X/Y7KyQNPyPgyevxhGls9HH/M\ngSunDxW996Ih2ECRvvAAR38zBM1cqQ134C8QehYx/DqiarLUHUlZJUU+pboREw0QfgNSbSDLRLA8\njKA54bDnkkglaAzuYVb2zMMea3/4YmszoyrymPMly9T6LU0oFDLGjRzYz69sXCmPrf0DL/z2NT76\n1yI+fHoRM86azLAxQyiq/G6lWHxbiASleKX1n9WwbtE2ispzuG3+r6iYUNJ3zMpPt9FY5+LWP52D\nTCbji7W76er0c80NxxGLJ9i5q53T5o3B2eUjFIlTWpBBu2evAuwNhrHqdUTicZQyWb8YNK1CSTgR\nx5YmwBI5stIe9lKkmwJAY7CNUn0Fn/IOe0J1lBim8JnrYer9y7CrixhquZrWwNvUeh9nVObvAFDI\ns8nLfJ129+V0eG/F438Mi+FSlIoi5DIr4egagpFPiMTWo1VNIdv2CErFXhtPo+9lIkkXVRn3AFDr\n+xwQKTcdgzfmpjPqZFrGbJpDUsF0oS6HBT11WJQ6YqkUIhIxqk/696MAKwnH49hsNpIpEQQwaFS0\ndvUwo7wYgLrmTkaPKmTxsp20O3s4emYlLz6zjGVLdnDy6eO44tcncuvF/+SNZ5dx3jXHAjCsuoi/\nfXQb9170OPdd+iTFFXmccOF08kol+1vdpiY+emEZziY342eN4LYnL0e/j5Xwub99QjKR4uJfSbaX\nzxdtR6GQMWV6GTXbW0mmRMaMKqCuSVo4DivM5M21NeTazXiCkr/ellaA9SpV37gqmdQCI5KIY1Xr\n6I6GyNFIFjNnWFL6P3GtoCcukc11nmUkxQRlppns9C2iKfgFQwyTGGK6iG2eB3CFFpOdtivoNUeT\nn/UOTve1tHWdj1Y9A4PmONSqkSRTbmLxXXgDT5NMuTFo55FtexiZIM1ZFEV2eh5GLc+gKK3y1wWW\no5LpydNVs9otqc+l+uF84lqHDBkFaaV/uCmX7phEgG1qHeFkok/phr0KcDgRx2bQ0eLpIScdc9fu\n9lGSb2ddTRPJZIqqyjy2plX7mXOqeObJxbS3dZOTa+W0i6bx70c+pX57G6XpNskzz5qEzqDhsf97\nhRvnPcRRp45j1LQyKsbv/Z39HuEPwKuCIFwONAFnAwiCMB64RhTFK0RRTAqCcDOwUJCUqfXAP792\nRP5HBLhlVzuP/PwptAYN1ceO4Pzbz6Ri0uET317UpLfGKwZAgAeKUoP0ENUF6g+bAANSdJn9DYhv\nQgy9BOE3EMMvfekgKXZKMP4a1McPmvq4OyCtDkv0xYMy3r4QRZHr0l7baRNKGZJvR51uOSyKIqmU\n2BcBMxBk5Nq4753b2PjZVpa+torlb61h6ZeaGWh0aiacOIbpp09i+ukTUWlUXzPawND7fI0sH1w/\n677IUmeil+upD+zmmKyBd6n7MgS5HcF8P6LufKmzVXwtYnQ/bVBVUxB0t4F6FoIwOHaYxlATcTHB\nUEPpoIz3ZZSXZPHCm2s4bkYFjS0eana1sauhgx5/mLlHVVJSOPDmLhm5Nn45/yrOv+MMXnnwLZa+\nvorFLx94m/yHCleLmyfv+i92h5lL7zyNM66Z3VcMBRCLxnnu4U/ILbQz43ipYn7Bh1vQG9RMmV7G\nrjoXsXiSkSPyqWuWiFFpQQZb12zDYtCCAOF4AqtegysW6lPGeqFVKPBGw1hVaQIcDZGrtbA70EGO\nNhOVTEljsI2pGccjILA7sINyYxU52hHs9C1kgv18tAoHhcZz2eN7gRLzZRhU0jtcIbeTn/kmochC\n3L4/0dVzb79zq5UjyTDfi8VwWb+dtkjCxa7uR8jUTidTOx2AXf7PyFCXYlMXsSZNjMoMVWzpkRTa\nAp0DZ3gdDq2V7rQyaFNJyqBG8dU5R+KJvuI3byhCXoaZNrePYYWSyFXX2Mm4kVKs2cYtzZxwXBUF\nhXY+X7Sdk08fx6iJJUydPYJX/7mYuWeMw5YlkauMHAt/eudmFv53NR/+eylP3P5Kv3OPmlrGxbef\nyoxTxvV7P9fVtPLpW19w5qUzyC20I4oiny/axriJJRiMGjZuaUYuExhRkcfy15ajVinId1hod/sY\nmpdBd7rA1KKX/N92/d6oLkEQ0gsdac6BRBSVXIlBoaE93M1Iq+Q/bgy1U2asYnnXpzQG6ynSj0Mt\nM7DL9xlDDJMoNP2EJv+rbHM/QIZmEnKZNn0fh1OQ/QHdvkfwh9+ls6d/YxOd+ihsppvRqsf3++9d\n4eV0R79ghP1O5DIt8VSEev9SSg3TkAsK6gLbMSrMZKodNAbbyNNlIRfktIe9HJs9ou8+SxaItAKc\nLmHppwDrtGxtdvYR4Da3j6EFUhRas9PLyMo8lq+qo9sbZPbckTz7j8V8+tEWLrzsKE65YCqvP7OU\npx/6kPufvqyPG0w+vprqGeW8+Kf3+eSllXz+9nomzRmcXb1vE6IouoFZ+/nv64Ar9vn3AmDUwY77\nPyHA+WU5/GfhfDLybEdkC7lmVzuFuTZM6biYIwGDwkCOxkHdYfqA94UgCKAajaAajSjeA8lOSHWD\n2A2yXFAMlTIMBxk7/LvQyDTkD7I/E/b2so/HkwwvlTzZvVnAgiAglx/6/RcEgTEzRzJm5kh+/ujl\n7N7cSDRd8CHIBEpGFfULch8sbNnRhlwuY/gR8piDNLdhxqHs9O8a3HGVI6QEEpAi0hI7QDCkdxoy\nDrqr20DQ65UvPUIE+KhJw4jGkzz4+CcoFDLGVBVw9kljGTOiAI368Eh8VkEG1z96Bdc/egU+j5/m\nHW1UTTt43/oPBY7CDF5c9Ads2ft/Pp7968c07+7kd09dilwuIxiMsmzxDmbNrUKlVvDFxkYARo3I\n583PNgNQkmen3eMj1y6pvwAWnZbGYA8aZf/dIbVcIW2NpxVgTzRIjtbK8s6dyBAo0DnYE2pFK9eR\npy2iLiBtD48wH8+nzj/TFt5Cnm4UpZYraPa/Ro37d0xwPIksvcgTBAG9djY6zSySKReJpItk0o1a\nVdEvQ7YXoihS434AkSSV9tsRBIGeWDvt4W1MzbwcgJ3+rRgUJnK0BbzdtgK9XEuW2o4z3E2xIavf\n1ngkkUAj7/9xrFYqCMXj2PQSgfMEQ+RmmNnj9JBpM2AyaKht6uTM40ZjNmnZuKWJE+eM5KhZFbz0\n3HK6Ov1kZBq5/ObjWXPyDv750Afc+tC5e8fXqjjxohmceNEMmna1E+hJE/IsM46iry4ak8kU8x94\nD5NVx7lpNXnb1hY6XD4uueoYADZubqZ8mAOdVkVtUyelBRkICLS5fRw1qoTuYPocaaVf8yXbh0au\nIJSIU6JOd/iLhnBoLbSHvQzRS8Vv9YFm5jomIyCw07+FEkM5Q40z2OVfTDwVQSnTMMJ+J6udl1Lr\nfZzhtpv6xpcJWuzmW7CbbyEW3008sQeFPAu5PAeF3P6VOcdTfrZ5HkSryKPAeCYAu3yLiaVCjLCc\nIPl/AzsoNQxHEAQag20MMxbRFfWTEJPkaK20pgsATUqNtNCRK/YS4PT8Q7E4Vr0GTzCMQaPCqFXT\n5vYxZYq0e1XX3El1Or95/cZGZh9TSfXYYhZ8sJmfXjIDg0nLpTcez2O/fZv3X1rFvPOn9M1Bq9dw\nxW9PgSgXAAAgAElEQVTO5PK7z6Cj2UM0EuPeF677ylz/f8T/xAOsM2rJzLcfEfKbSCTZuK2Z6srB\nb1DwZQw3lrPdt4PYAFvWHgwEQYugKJSC2dXHICjLjgj5TYkpNno3UmmuQH4YPuIDQbnPB9qRuO8y\nmYyho4cwYmo5I6aWUzm57IiQX4AV6+qpKss9bHJ1IIw0V+GKdhy2D/jrIMgd0rOlGo+gKD0i5Bdg\nc89WstVZA24ac7AQBIE5Myp47Hfn8vA9P+GSs6YwZWwJGrWSVEo8pNzT/cFkMzJiavmgjPV9g1qr\n+lry++lbX/Dmc8s5+fzJjJsmWYzefWM9kUicE04ZgyiKfLp4OyMr87CYdWzf7SQ/24Jep6bR1U1B\npoVOf7plrlFPMBZHp+q/W6NXqggm4mRqJB9qRyRAoT6DaCpBe9jLUEMhtf5GkmKKMmMVuwM7CSWC\nlJmOQSu3sKZLananltsYYb8Dd2QNW7ruRhT75+QLgoBC7kCjqkavnblf8gvQ4HsWV+hThlmuQ6+U\nSMqm7rcQkDHcNIt4KkaN7wsqTaORCTJqeuooNw0hKaZoCXnI19npSqdB2NV6gvFYv+IoAJ1KRSgW\nJ8MkFfx1+oIUZ1tp7vSSSKaoLHGwaWcrgiAwYWwxq9buJpFIMvfEagRB4MVnlwKQW5TB+T+bxeL3\nNvHSE/27hPWisCyHygmlVE4o3S/5TaVSPHz3G9Ss38PlN5+A3qhBFEWe++cSTGYtU48qx+0JsG1n\nGxPGDSGeSFJT305liYNWdw+xRJKiLCsdvt77rCMYkxqf9JuzUiXZATTSQqcrEqBQZ6cx2IlJaSBX\nk8m2njr0CgPF+jI2elcjiiKV5rnEU2G29UgRaHbtBAqMZ7O75xla/e/sd84qZQl67UzUqqr9kt9k\nKsp61/WE4s2MyrgPmaAkJSZZ536JDHUJudqRtEea8cbdlBtH4on14Iq6GWooZE9A2uUo0NvpjASw\nqXUkxRRJUUSn3Ptsa5UKBCQCnGHUk0im6AlFKMyy0NTRTXGeDZVSzvbdTsqHOciwG1i4RFrcnXTq\nGNrbvCxfsgOAE8+ZyPgZZcx/4D02r/mq51cQBLIL7RQewdqo7xv+50Vwg40NNS0EQzGmjD3yPpcx\n1tFEU1G2+3Yc8XMdKdQG6vDGe5hom/C/vpTvBZraPNQ3dXH05MGz7HwdxlvHIiCwxjPw3M7vCsLJ\nMNt82xljHXi3vUNFbzQaSDF8R6pQ8UfA4vc38vDdb1A9uZSrbpM6aUYicV5/aRXjJ5VQXpHL9p3t\nNDa7OX52FclUik07W6kuzyMYiUnbvLkZtHvTjTEsRgLpVrn7wqhSE4hJ3bSMSjXtIR9DjdIOTF3A\nSZV5GKFkhIZgC6Mtk0mRZIN3JUqZlvG2c2gKrac1tAWAfOOplFmupzXwLjXu+we0QBJFkdrux9nh\n+TMO3XGUmC8BwBtrZbP3HYabZmNUZlHj20A4GWKcdRpd0W5awi5GW8rZE+wkLiYpM+bgCktzztYa\nCcRjGFX9F+xGtYpANIbDLBViOb1+huVlkEimaHB6GD+ikD1tHjq7Axw7Yzg9vjBfbGoiJ8/KyaeP\n44N3NtC0R4r++smVRzPr1DE8//cFvDR/YE0SUqkUD9/1JgveWM9Pr5vFcadL3R/Xrd7NhnV7uODS\nGej1aj5fUUsqJXLsjOHU1LUTiSYYP6KQXS0SGSzLz8TZ48es06BLz82g6T9ng1KFPx7FoUm3ew77\nGWbMoSXkIZSIUm0ZzhZfLYlUkvHWaTgjLbSFm8jRjiBXW8Xqrn8TTUoLi0rbrdg1k9ncdRct/rcG\nNmcxzqbO2/BE1lGd+Xvs2okAbO9ZgDfeyuSMixEEgbWepciQU2UeR02PVGxbZR5GrV/KcB9mdNAW\n6iFXZ8Yfl3YnTcq9cxYEAYNaerYdFuk+t3v9lOZmUN/WhUqpoKLEwYYdLcjlMubMHMHqtbtxewLM\nOLaCvAIbLz67DFEUkclk3Pbn88gttHPfDS/slwT/iP74wRHgT5dtR6dVMWl08RE/V4WpArVMzYbu\nDUf8XEcKaz3rUAoKRlsO2jbz/zWWrJI6K30bBNiisjDMOJS1noPPKf2uYUvPVpJikrHWwWlh/XXw\nBSJcc/t/AL7iKR/EDu0/Io1kMsXTD33Igze/QvmoAu746/l9nuD/PLsMrzfETy+RfLEffroVtVrB\nsUcNZ2dDBz2BCBOritjdLjVMGJprx5kmwDkWI4FoFIP6q8QomIiTTKXI0ZpoD/VQapCa9tT62xlh\nGgpATU8thboScjWFrOiS1M6R1pPRK+ys7Hym71kotVxFiflSmvyvsM71M6IHkRGbTEXY6v4ttd7H\nyTOcxuisP/btyi1xPY5MUDA18zIA1nmWYVJYKDNWsdkrNSmotgxnl08qhhtmkoiRRq7AotISiEe/\nQoANahXBaAyTVo1OpaTd66csX/L+1rZ2MX6EpDyvq2liwrgh6LSqPnXwp5dOR6NW8q802ZXLZfzq\n/rOYefJonn/4Ex6/721Cwa9Pz+mFp9PP73/5Hz55Yx3n/2wmF/xc6rCdTKZ46vGF5ORamJcmxIuW\nbKe40M6QogzW1jQhEwTGVhZQ29qFTBAoybXT3u3HYTYSTyYJxxMYvqT0m1RqArEYDl3aBxv2McyU\ng4hInd9FtaWcSDJKbWAPY6yTkSFnXfcyBEHg6OzrCCd7WNUldR6TyzSMy34Yu2Yim7vupK77SVIH\nkbcfiO9hZdsFOEMLqLDdQq7hRGnOYpw17hfI0pRRYphKIhVntWcJVeZxmJQWanrq0Mo1lBjyqfM7\nyVAbsaoMOMM+HFoTgZj08/7ynHvv874LnaG5drp8IboDYSZWFbGjwYXXH0ovIkU+XbwNuVzG+RdP\no77WxcfvS3nXeqOG3z55CdYMI/932dO8/cKKH99/34AfFAGOxhIsWVXL0ZOG9RVaHUmoZEpGmqv4\nwruRlPiNLae/k0iJKdZ61jPSMhKtfPAbhvwQsXjVLiqH5fR16TnSmGidQGu4jbYjZIM40ljv2YBR\nYWCYYegRPY/JoOF3N58CQKfbT1Orh2g0TjKZ6qcA//hhcHhIJpIsfn8jN5z9KK/963NOOncSD/zr\ncowWacu6vtbFqy+u5LgTR1FVXUg0GmfRku0cNbUMvU7Nqs0NCAJMHFlEfZtEgEvSBFirUmLSqvFH\nY18hCb3kMBCPkaMz0x72oVOoydfZqPM7sast5Ggy2eKtRRAEpmbMoiXcQHNoN0qZhgn282kNb2an\nTyLFgiBQbr2RyrQdYmnLGezx/YdY8quZ0SkxQbP/dZa0nESz/7+UmC/v2xIH2O1fyZ7gaibbL8Kg\nzCCYCLDNt5Gx1qnIBBmbvDsxK40U6nLY5W9HLVNQqMvoUwYFQcAfj2FQfmnOajX+aBRBEHBYjDi9\nfgqzrKgUcna1dFJWlIVJr2H9tibUKgUzjx7OZ0t34POHsVj1nHPhVJZ/vpOt6XxYuVzGjQ+czakX\nTuW9l1Zz1Ul/YfH7G/si0vZF0B/hvf+s5Op5f2H14h1cfvMJfeQXYOHHW9hd18Hl185EqZSze08n\nm2tamDurCoD1NU2UD5Gur7alk8IsC1qVEmePnxyLkWBMOqdxPwpwIB4lU2NAKZPTFvRSZpS27Hf5\n2xhpKUNAYJN3J3qFkUpTNeu7V5ASU2RphjHSchKbut+iKyKpnwqZnvGOx8nRn8gu7yMsbT0dZ3Dh\nV6wvIBU11nmfZHnr2YQSLYzN+htDzHv7Eaxzv4Iv7mRKxiUIgsDmnnUEE36mZkjpNlt6dlFhKkEu\nyKkLOPt2KPYqwBIBNiq/qvT7IzFyrGkC3CMpwAD1bV1Mri5GFGHNlkaKCuxUDs/lwwVbEUWR2ceP\nYmR1AU8+8iket6R8O/Jt/PXla5l4dDnz73+X2y55inVLd/747tsP/idFcEcKqzY0EAhFOW5Gxbd2\nzkn2CazrXk+NbxsjzVXf2nkHA3WBerxxLxOt4w988I+gzeVlZ72Ln1141Ld2zvG2sbzY9BJrPOs4\nLe+Ub+28g4FQIsQX3g1Ms09BdgT86/tCFEUybJI/dE+Lm8eeX0Jxvp2iPBtGg4YpY0twZJoOKXHk\nR0jZr3dd9Qz129vp7vKTPySTWx46h2Pn7bW2JBMp/vzAexhNWq65QWpLu2xVHYFglBOPkyrPV27a\nQ8UQB1aTju1NLgwaFXl2M84ePw6LEUEQCO7PAqHclwCb2OSR4qCGGh3U+qTt5tHW4SxyrSaWijPO\nOo23W19kRdciziksYaRlHjt9i1jseoQ83UiMyiwEQaDYdB52zQQ2d93FNvfv2eF+CJt2Imp5BgqZ\nnkCsHm90M0kxjEVdzejMB7Fp974vE6koSzoew6YqpNp2OgBrPUtJignG26YjiiKbvDuptpQhE2Ts\n8rVRanSgkMlpC/WQp7MgiiKBWBTjlwiwPm0TEEWRHIuRdq8fhVxGSY6d2tZOZDJJYV23tQlRFDnj\n5LG899Fm3v94M+edNYkzzpnEu2+s5y9/eJ/Hnr4crU6FXC7jmttP5ugTq3nknjd58OZXUGuUVE8q\n6UuIaG/2sHVdA8lEiupJJfz8N6eRPySz77o6O3z849GFDK/M5aiZ0mftG+9+gUql4KS5owhH4myt\na+fcEyRleFdLJ5XFEhl0ev2MG5KHP5JWQ9UqEuwlZgaVmgZfNzJBIFdnpjXUQ7bGjEmppdbXzlmF\nkxlqKGSTdyfnFp7IeNsMtu75gm2+jVSZxzI18zJq/Z+z2PUoZxb+CUGQIROUjM58kBz9XHZ2/40v\nOn6BUmbCoq5GpywgkfITTjjxRNYDKTK1MxiZcQ8aRXbfdbnCO1nd9TzlplkUGyQ7xIquhdhUGZQb\nR+KKdNESdjHHMZVEKsluv4tziqfij0cIJmLkaE0EYrH0HPdzn2NRbHodCrkMp9fPnCpph3FHUwfn\nzRyD2aBh1eY9zJlawQnHVfHnRz5hZ62T4WU5/Oq2eVx98T949C8fc/f9UqGe3qDhrkcu4J0XVvLa\n059z11XPkp1npbjMwZipR1aM+D7hB/Vp8P7CLVjNOsYOMPfzcDDaMhq9XM9C18B8Vd8FLO9aiVJQ\nMtpa/a2e991PN/PH+fuJ4PqO45PPpe3FowchS/hgYVVZGWYcyoquVd+7XYZlXSuIpWLMyJx+xM8l\nCEKfwlGYZ8PjDXLn9ScwflQRsXiSNz/eyHV3vYy7++ubzPyIr0ckFMPrDjBq4hDufvRCnnzvl/3I\nryiKPD1/EbU72vn5jXMxmbSkUiKvv7MeR5aJ0aMKcXuD1NS1M7m6GIAtDU6GF2Yjkwm0dftxmKUF\njD8a3a8HGMAXi5CrM+ONhQkmYgwz5tAcchNMRJloG0k0FWO9Zxs6hZ4x1sms615GIOFDJsiZk3Mr\nKTHBB633kUhF9xl7KNNyX2J67msUmn5CLOnGHV5Nq/9t4ik/+YbTGJf9KFNyXuhHfpNinA/bfocv\n7uSY7OuRCwqiyQiLOt6jRF9OgW4IDcEWuuM+qs3DSYopdvraKDPmIIoizYFu8vUWAvEYIlKr3H5z\nVqtJiSLBWJxcq4m2bikzubwgkx1NHYiiyISqQpxuPw2tbkqHZDF6ZAGvv/MF0WgcjUbJrXefSmuz\nh9/f8ybJxN73R8XoQh557efc/9RlzD1rPG1NHtYs3sGaxTvo8QQ545IZ/OnFq3ngmSv6kd9QMMpv\nbvsv0WicX995CoIg0OX288miGmYfU4HZpGVtTSOJZIoJIwrxBSO0un2U5WXiD0fxhaN9Nhf4Khk0\nKtX0RKXmUnk6M03B7nQiTg7bfNKip9pSzk5/A754gFGW8ViUdj52vkFKTKGRm5iWeSWt4c0scT3W\n904QBAGHfhYz8t5kdOYfceiOI5xoo9X/Dp7IFyRTIUrNV3B0/gdMcDzRj/yGEl4+aLsPvcLOsdnX\nA9AWbqI2UMNk+7HIBBnLu6RuexNto2gIdhAXkwwzOmgJSk1PcnQmfGkF+Cv3WaPGF4kikwk4zEba\nuv3YTXocNiObG9qRy2RMGlnMio0NxBNJZh5VgVqt4LW31wNQUGTngktnsPSz7bz75vq+cWUyGadd\nNI1nFvyaGx84i6GVubhau9mz08mPkPCDIcB1ezpZsX43Z54wBsW3qPKoZErmOGazwbuRPcHGb+28\nhwt/3M/yrhVMzZjyrdsfQuEY7yzYzNpN35+fly8Q4eV31zF1XAl5Dsu3eu5ZWcfiirrY7N3yrZ73\ncBBLxXiv/QPKjWVHrMHKl9FLgrMzTGTYjGyrc6JSKVCrFGyoaaa9o4fGVve3ci0/NOQVZ/DI69dz\n25/PY8qsSmSyve/YVErk8b9+wn//s4qTzxjXpwp+9OlWara3cdF5U5HJBD5avo2UKDJn6nA8vhA7\nmjuYNLwQURRp6vJSlGElnkzii0Sx6fq/k6xq6d/dkTAlxvT2sK+LkZZCREQ2dzdSbSnHrrLwiUvK\na56VfQrxVIxPnFIBlEWVy5zcW+mI7OKN5lv6iqV6YVIPp9L+f0zPe42ZhZ8yp3g10/NeZUTGHWTr\njulnpUmKCT5svZ/dgZUck309BXrJ476w41164h5OzpUaJizpXItckDHRPpIdPa34ExHG2UrojATo\niUcoNWXgiaSjwTS6ftdjTf8MukNhSrJsdAfDuAMhxgzNwxuMUN/m5tgJZSgVct5cKEXLXXrBNDq7\n/Lz+zhcAjB5XzHW/msuqZbX84d63+pFguULO2GnDuPaOU/jnBzfy4ue38+Lnt/PE27/gspuOZ8TY\n4n5zjkTi3PN//6W+1smdvz2DwmLpPjzz4nKSyRQXniNFb725cDN2i56xlQWsr20BYOywPOo70paX\nLDue0N6GGPvCptHijUVIplIMNWWy29dFShQZbythl6+d7liAaRljSIoplndtQC4oOCnnJzSF6lnn\nWQZI0XdjrGexyfs2Szvm99v6lwkKcg0nMjLzXo7Kf5s5xas4tuBjpuW9Qrnthr5Ej14E4l281Xwr\nwYSHk/J+g1puQBRF3mt7BY1My/SMOYiiyALncipMJeRoM9no2QPAKEsR9X7JW77vfbZ/+T5rtXhD\nEukvyrDQ2CW1yp5YXsiaHU0kkinmTB2O1x9mxcbdGPRqzjplHAs+28bGLZK95ZyfTmXilKH8/aEP\nef3l1f3GV6oUHHfaOO78+wU88fYv+MV9Z/AjJPxgCPALb65Gq1FyxglHtthmf5iTPRudXMebrfvr\nzvfdxIfOT4iLceY6jvvWz33a3NFkZRh58sXPvze+pBfeXE0wFOWq82d86+cebx2HXWXj7bZ3vzcq\n8GcdS+iJ93B63qnfagpDjz/MivX1IIrc9dA7/O3pRdQ3dnLtBUfxxpNXM7bq29sd+v8BkUicP973\nNm+9tpazzpvM9TdJDXq8PSHm/2sxIyvzOOG4kRJpWFJD1dAcinPtrNwuLX6nVBbh7Angj0QZ6rDT\nnSZGVn1/ktBLGtyREENNEvGq83Uy2lqEWqZgRddO5IKc47KnsKF7O65IFw5NHlPsM/m88yMaglKe\n9lDjDI7LuQVXeAevNv6Cnlj7gOccTHh4t+Uu6gPLOCrrZ1RbT5WuLdrJIte7jLVOpcRQTjwVZ3HH\nWsZZR2BSGljlrkVAYFLGUGp96WQEUxZdvcRI23/Otn0I8FCHFNNV73QzvkzKg123qxmbWcesSWW8\n//lWgqEoo0cWMmVCCS++ugq3RyL4p5w5nsuvOZbFC7dx6y9f7POKDgRNe7q4/vJ/sXH9Hm6+/WQm\nTZO26OsbOvjgky2cdtIYcnMsNDm7WbmpgdNnjkKpkLNuVzMalYKqYgd1TokAD3PY+zrCWXVfvc8p\nUcQbizDMlEkoGac15GVyxjBERNa66xmiz6dA5+BT10pEUWS8bTpFulLebXuJSDKMIAjMyLqa0dbT\n2dD9Oh+03UcsGRrwnF3hnbzceB098Xbm5d2DQys1vFrfvYIa3xfMcZyOXmFgS88u2iKdHO+QdrpW\ndO0iX2cjV2ulrqcTuSAwxGDHE0mTfs2XSL9eiyckXd/QbDv1LjfJVIoplUX4QlFqGp1Mrh6C3aLn\nvSU1AFx03lQc2Wb+8ugC4vEkcoWM3zxwFkfNrGD+3xfw9PxF/RY7P2L/+EEQ4KY2D4tW7OT0uaOP\naPOLr4NOoeOEnLls9G6iIbDnWz//QNEZ7eQT5ydMtU8hT3vkupl9HdQqBVecM40d9S4WrxrcRg9H\nAp1uP699sIE5MyoZWpx54G8YZChkCs7IO43dwQZWudd86+cfKKLJKO+3f0CFcfigdEkcCBqa3Tzz\n6koqh+WQYTfwxP3nccs1cxg/qgi5XEYq9f1YcH0fsGp5LVf+dD4LP97KJVcdw1U/n9W32Jn/ryUE\nglFu/PkcZDKBmnonDa1u5h0t1Ums3LYHq0HL8IIsdrVLZLA8J3OvMqj7qjII4Pl/7J13eBTl2sZ/\nsy2b3U3d9N47IRAIJUCkI6j0ItgQu4hYAAsqYgFROdZjRQGVJk060nsPnTTSe+99d+f7Y8LGSFGO\nR4HvcF8XF+zstHdm2Lnf57mf+2mox0trj1Im52JVMWq5imh7Pw4US0U+/V26IwC/FhwEYKj7eOxU\nen7K/IKmFtlDiE1fhnnOpdZQytKMpzhZtgqDqekPxyuKIslVu/kx/RFy6k7T2/lZOtiPMH+3OncR\nIHCP23gAdhcdo6K5miFuUifHIyUXCbZ2w1alJamyCIAgG8ffRAbbjvlSl7TS2joCXSTSn1JYgpve\nGne9NUeTpO5yowd2oK6hmY37JHL05CO9aWo28u6Hm8zP+7gHYpk2824Sz+fyxIPfsGfnhTZ2gVdD\nU5OB9atPMPmR7ygvr+Xd+ffS/07JMai+oYk3567H1kbD/eOk6O+qbadQyGUM7yvJ6o4lZRPl74ZS\nISeloASNSomrrTWlLYRP/7uJzqUoeFlDHcE2Umvm5MoiQmzcsVZacqTkIoIgMMQ1jos1WZyvuohM\nkDHC40GqDBVszpea1AiCQC+np4h1fITU6v38lPEY2bWn/nC8AI3GWg4Vf8/PWc8hQ8For4/Mut/K\n5jJW5XyPjyaQ3k5DANhSsB8rhYbuDh1oMDZzvDSN7g7BCIJASlUxXjp7VHIFpQ116JQqLH7X8MRe\nI3XFq29uJsjNkUaDkcySCrqEeCEIcOhCJgq5jEGxoRw8lUZpRS1qtZLnnupHZnYpy1ZL7wSVSsEr\nbw5n8D0dWLb4IE9PWsCFczl/aszXg5z0YqY/8PV1/blZccsTYFEU+WjBTtQWSsbefeOKufo790Ur\n194SUeDlWT8jCDJGe468YecwMC4MHw89Xy/Zj+FP/BDfSHz/8yFMJhOTxnW/YefQ3aEbPhpvVmSv\npNH4x/ZFNxK7inZT2VzFcI+h//ixO4R78s179/Hi4/2ZNVXypTUYTWYiIJPd9gT+TyGKImWlNaxb\ndZypTyzktWnLUVko+eDz+5nwUA8z+T10NJXN284ydkRn/FomjD9uOIZOY0H/rsGYTCKHLmTSNcwb\nmUwgKV9KEwf+JgJ8JQmEAJQ21qGQyfCz0nOxStou1jGYnLoysupKcbCwI9o+nO2Fh2g2GVDLNYz3\neoLixgI25C0z789T24GxPp/hpA5gb9EX/JD+MMdKl1DckNomKyWKJiqb8ogvW8ni9IlsznsbG6Ur\n432+JNLubvN6O4s2cK7yBEPcxmCn0mMSTazN3YGv1oP2NsHUGBo4W5FFVwepACmlqggHCy32FlpK\nzQRY23bMLdegrK4evU6DrUZtjqJ2C/PhSGIWDU0Gwv1dCfN3YeW2U5hMIt6eep55vA/HT2awdGVr\nOnzA4PZ8+u3DWNtY8vbM1Twy4UvWrTpOVkZJmzEbjSYuJhew7IeDPDDqcz75YDMBgc58sfAROnVp\n7eb46Zc7yMop5dUXh2Bro6GuoYkNe87Rp0sQelstuSWVXMwrpVuYDwAXC0sJcNEjkwmU19YjFwSs\nf+cC4XCpAUZ9HQHW0rOTXFmMXJDRWe/PkZIURFGkj1MXrBU61uRsB8BHG0isQz92F2/iVIU0ZkEQ\n6KQfxyiv+QjIWJ39IssynuZ8xWaqm4vbjNkkGsmrO8/B4u9YlPYAR0t/wk/XjbE+n+Gg9mt5FkSW\nZX1Ls6mZCd5PIhNkVDRVcbj0NL2duqCSKTlRlkajqZlYR6lhzsXqEgJaJDtlDXWXyVyk+9xC+mvr\nCXaV1k3KK8ZWZ0m4twsHz2cAMKxPJKIISzdLOt+unf2Jiw1i8dJDpKQWApLTx9QZg3nt7ZFUVtTx\n7GMLeeX5pWzbfIaa6obLjv2/jlveBWLnwSSOnspgysTe6O20f7zB3wRLuSWDXQfxc84qTpaf+keN\n/68HCVWJHCs/wQj3YX9bZ64/A7lcxuMTevLye2vZsOMswwb8s4V4fxaZOaVs3HGW4YOicHP+Z7W/\nv4VMkDHB+17eSZjLhvxNjPQYfsPO5Vooayrjl7wNhFuHEWz1zxUL/h7NzUY83aTn+5+sCfj/itzs\nMob2f5/6llbj3r4OPDGlP/eM7NSmy2PyxQLefG8dgf5OPHivNGFMySpm97EUJg3vilZjQUJWIeU1\n9XQL85a2yS/Gw94andrCnBr/fWRQLpNha2FJab1EFgOtHTleIkVAYx1DeD9hPfuLEvH27cGdLj05\nVnaOnUWHGejSg0CrcHo6DGBP8RY8LH2J0UsuLnYqD4Z7ziOr7gSHixdxsPg7DhZ/h0qmwUKmQyFT\nU91chEGUiIOrZRgxrjMIsu6NXGh9dSZWnWF93lKibLtwh6PkGXus7Bw59YU8FyQ1TDhRmoZRNNFF\nL0kHkiqLCGyJcJa2pMb1akuyqDDv95I+trS2DkEQCHBxILlAIv29owJYue8MRxIyiWvvz+gBHXjz\ni80cOZtBt/a+3D2oPfGnMlmweB+B/s7EREs6fF9/J75a/Bj7dyeydPEBPv1wCwBWVmqsrFskF/Uh\ngYkAACAASURBVOW15vsc1dGbaTPvpmNn3zZSpi3bz7Hx17PcN7YrnTr4ALBp73lq65sYM0CSIe46\nLbVA791eIs0pBaXcESqRybK6Ouw0lsh+J4+y/43URae0wF1jQ3KVFC3vog9kR8E50moK8bdyYYhb\nL5ZmbSKzNg9vrRsj3B8gpy6DJZlf4mThipulJHdy00QwwfcrLlRu5Uz5erYXfAiASqZFp9DTZKqn\nwViNQWxAQIaXtiPdHCbibNna9VEURbYUrOJC1UmGuz+Ak1qyZvsldycmUWRgi/zhQHEiarmSjva+\n1BuayaopY7BHmHlMv5c/QOtkr6yujmAnBxQyGUn5xdwZFUy3MG8WbD5KRU09ni529OsWzKptp5gw\npBN21hqefbIfF5J+4OU3V/P5BxNwdrKWot99QunUxY8VSw6xffNZ5r0ldcPTWanp1fuvuWR5+Doy\nb/Fj17XN+z88/peO+XfhlibAOfnlzPvyV0IDXG6I9vf3GOjSnyNlR/k67VteD5uJq6XLjT6lNqg1\n1PJN2gIcLRwZ5DLgRp8OPTr7ExXuweeLdhMa4EKwn/Mfb/QPoqa2kdc+XI9GY8GDo7re6NMhyCqQ\n7vqubMjbRHubdgRY3Vx2Nk2mJj5O/gyTaOQB7wk39Fx+S8pu469DrVYy6K4oHJ2siY7xxdff6TJt\nd0JyPtNm/oy1zpI5b4xErVYiiiIfLtyBtU7NmEEdAdh6PAmFTEZsmA+iKBKfnksHH6l1fXGt5NKh\n12qoNbSVJThrdBTWSQ0zovQebMg+T3ZtOZ5aO4Kt3dicd5LxPrF0tAsj1NqPxRnriLGPxE5lzVD3\nCRQ25PFT1hfUG+uIcxoESFFCb20nvLWdqDWUklFzjOLGizQZ62gy1eGt7YTewgcXy1AcLC4v5jxT\ncYwfMj/HVe3JeK8nEAQBg8nI4ox1uKgd6OEgjXlN9lHsVVoi7byoNzSTVFHIg4FdACiorcZaZYH6\n962QlUq0KhVF1ZJmN8TNkZVHztJsMNIpyAN7Kw1rDpwjrr0/fbsE8eWK/Xy6ZA8dQj1Qq5S8OGUQ\nOXnlvPLmal55YTB94iTiI5fLiOsbRq8+oWRllpJwLofE87k01EvevDprNWERHoS188DFte2kXxRF\nlq8+xpff7SYq0pOJE2IBKK2sZcGaQ7QLdCM8QHK5WLP/LGHezng42pJXXkVZTR0hblJUt6C6Bicr\n3WXX01kjLStouc/hdq6cKpXS+D2cQlBckLMm+xgvht3NYNderMvdxcKMtbwe9iQKmZKHfacyP/k1\nPkqexZP+L+GrkybhSpkl7e2GEWk7lMKGRAobkilrzKTOWI5KpkEl0+JqGYqXNhq1vK3HuxTN/5E9\nxZuJsY+jl+NAANJrcvglbyd3OHXGQ+NMo7GZbfln6e4QjIVcyYHCNIyiSJS9R8t9rsHfxv6yMTvq\npMBdSU0d7dwUBLk6cDJD8n2Pi/Tnm01H2H4yhVE9I3l4eFd2HEnm82V7mfnYIPT2OubOGsmzM5by\n7EtL+de7Y3FtKdLWaC146NE7ePCROC6cy+HsySyKi6vx9Lq87fP/Km7Z0EhDYzOvvr8OuUzG7Bfu\nvimiPEqZkmcDJyMXFHyU8gm1husX3v+dWJu7jormSp70fwwLucUfb/A3QxAEZk29C2udmunvrCa/\n6HIT+huF5mYjr8xbS1ZeGW+9cDd2Njcuu/Bb3O89Ab2FPZ9c/DcF9TePnY0oiixIW0hmXRZP+D+G\ny002+buNvwa9oxVPTR3A6PFd8QtwbkN+TSaRFWuO8cyLS9BqLfh43r04Okim/hv3nudUUi6T7+2F\njc6S+qZm1h+6QGyED3ZWGlILSymqqqV7kBQNzquswlKpwNby8loOD50N2dXSb0QPZymSuL9Aangw\n1KMTydX5XKjMQRAEJgeMp9HYxJepyxFFEaVMxWP+04i06czq3EX8lPkljca2KWGtQk+47SDucJ7M\nALfp3OUxizjnp4iwHXwZ+TWKRtblLmFB+nxc1B48GfAyFnLpnJdlbyKnvoBJviNRyOSkVhdwsCSZ\n0d7dUMoUxJdm0yya6OrkA0B2dSWeOpvLxisIAh621uRUSGPu6ONGQ7OBC7lFKBVyRvZsx75zaeQU\nV6BSKnhpUn/Sckr5YrnkhqDTWvDB22MICXLhzffWs2jJwTZyM0EQ8PZxYNBdUUydMYSXZg3jpVnD\nmPz8IPoMiLiM/DY2NvPhZ7/yxYLdxMUGM+/NUSgUckRR5N2vf6W2vomXH+mPIAgcTcwivaCMcXdI\nmdAT6ZKFWUdfqeYkp7wKD9vLmwnZWqixUqrM97mLow+5dZVk15bjYGHFQNf2rMs9TkVTHdZKHWO9\n7iS+/AI7ig5L26v0TAl8AxulHZ+kzGZv8ZY2UgdBEHCxDKW93VB6u0xhiPsb9HedRpzzUwRZ976M\n/FY1V/B9+sfsKd5MnOMg7vV6DJkgwyga+TTlJ3QKLQ/7SjrwXYXnqWyuY7hnZwD2FaSilMnp7Cg5\nneTUVOJpdfl9drGWSH9epWRz1y3Im9OZ+dQ0NBLq5YS/q55Ve88giiI+bnomDO7Ehj3nOZkgTQwC\n/Jz48J0xVNc08MiUReZugL8dc3g7T8Y9EMszLwxi2OjOl53D/ypuPGv8D1BX38S0d1aTllXMa88O\nxtXp8ofqRsHBwoEpgU9T3FjCF6lf0Wy6vMvOjcIIj2E8FzgFf53fjT4VMxzsdXwwcySNzQZefHsV\nVdX1N/qUMJlE3v18C/Hnsnnl6UF0ivS+0adkhkah4fmgZzGJJuYmfkBRQ/GNPiUA1uVt4HDZEUZ6\nDL9p5T+38d9HUkoBL85cweff7CKmky9ffXQ/rs7S7/HFrGLmL95FZJAbd/WSit9W7TtLeU099/eT\nmiQcTJZsnC4R4KyySjztbK/oHOJpZUN2TSWiKOKr0+OusWFv4UUABrlFoZYrWZUt6T89NC6M8xrM\n4dLT7C+RLMGUMhUP+T7LQJcRHCvby5yEaZyrjL8uJxpRFEmsOsO/kl5jR9F6Yh368WzgG1grJbJ4\ntPQsP2dvpZ9zN2L0UvOPH9P3o5YrGekpZZEOFaWjEGREO0gp+qyaCrysriyv8rSzIadCIkad/DwQ\nBDiQnAHAqJ6RyAUZK/ZIbXC7tfdl9IAOLN8Sz5Ez0jq2NhrmzxlL/95hfPfjfh59ZhHx12k/KYoi\nu/Ylcv9jC1i/+TQTxnTljZfuMXdbXb3jNAdOpfH0uF74eUga1mW7T2Gns2RAtBSBPZCcga1GTaCL\nAyZRJLeyEg+7q5B+K1uyqyUpSLeWScLhImk89/n2oMHYzKpsifDe5RZHpE0QX6WuIK1GIoQOFs48\nFzSbMOv2rMpZxBepc8isTb2uMRtFI4dLdzMn4UXOV8Uz1G0Cw90fMDf0WZ2zndTabB73H4O1UiKw\nq7OP4m5pT2e9JPnYX5hKJwcvNAoVxfW1NBgNeOouv8+OOi2WSgVZ5RLpjw30xmAycSxVmszd1y+a\npJxi9p6VJnsPD++Km6MNs77YREl5S3YgyJWvP34ALw89s99bz5vvrSMnt+y6xvy/iFuOAFdW1/Ps\nrBWcScjhtWeH0K3jzUPmLiHIKpAHfe7nbOU5Pkj6F7WGm8N831JuSTvbm69bna+nA3NnDCOvsJLp\nc9ZQeQNJsNFo4uPvdrJtXwKPT+jJgF5hN+xcrgY3Szemh7xAk6mJ9xLfp7TxxnnbmkQTS7OWszp3\nLd30XbnLdfANO5c/giiKLPz5EImpN0/k/FZEeUUtO/Yk8Pwry3ns2cUkJufzwjMDeOe14djatGg4\nK2p58YM1aC1VvD35LmQygYYmA4t/PUanIE86Bkpp4YMpmfg52eNqK0WMs8sr8LoCMQLw1NlQZ2im\nrFGyuurh7M/hogyaTUZ0CjV3u0ezOe8UWbWSTna4R18Cdd58kvIjCVUSeZALcga7jubZwFmo5BZ8\nk/Y+cxJeZHfRZkobi69Khiuby9hfso2PU97ki9Q51BqredDnGcZ4TkIhk4hgfn0xHyUvxl/ryeP+\nYwAoaqhka/5p7nHvhK1KujZHijOJtHdDq1BhukZkEMDD1oacCon02+s0RHq5svuCNBZHWx19Oway\n9uB56hokucjT9/bEz0PP7K+2UFgqEWeVUsGrLw5h9itDqatv4rmXlzP5xZ/4ZeNJKiqvnqUsLKpi\n6cojTJq8kFlz1mFlpeajueN47KFe5mLSpIxCPvlpD10jfRgzUJIh5hRXsPdsGiN7RqJSKjAYTexL\nzKBniC9ymYzimloaDUY8bK9+n7NrJDLob+WAg4WWI8UZLZ9d6O4QxM+Zh2g0NiMX5LwQPBErhZa5\nCd9Q05J11Si0TPJ7gRHuD5JTl8785Jl8lTqPY2X7qDVc2QbOJJoobMhlS/4qZp+fwtKsr3BRezA9\nZC59nO8yT8oOlMTzU+YGYh06EusgjfloyUVOlWcwxlvqellQV0VyVTE9WzIVl8ZzpfssCAKetjZk\nlUukP8rHFUuVkoMp0kRlcJcQ3PXWfLPpCKIoorZQMmfq3VTVNDBt/loaGqUgm7urHZ++P56JE2LZ\nfzCF+x77ltff/YUDh1Ooqb25C6dvFG4pDXBhSRUvvr2K3IIK3pk+jB6d/f94oxuEOMeeqAQl36Z/\nz1sX5vBC8LM4Wvx9FlpG0ciCtIUIAkzynfi3t579byMq3JNZzw3hzY828sj0H5gzY/g/bjlWVdPA\nrPkbOHo6g3H3dOK+4TH/6PGvB14aT6aFPM+8xA94O2EOT/s/8Y9rghuNjXyd9i3Hy+Pp59SHCd73\n/qOev9eL6tpGNu48y5JfjjFnxjCi/8GOkbc6ioqref6V5RSXVJOVI0WW9PZanng4jnsGR6HVtEqq\n6huamTZ/LRU19Xz52jic9BK5XbP/LCVVdcyZJNlHNTYbOJ6Ww6guUqTUJIpkV1TSK+DKjVMuRUmz\nqyvRqzX0cPFjeXo8p0pz6OzozUT/3qzPPcHXKdt5O2occkHOzLDHeenMv3jr/Be8GzkVH62kNfbV\nBTE9eC7x5Qc5ULKdNbmLWZO7GJ3CGje1FxZyNUpBSZWhkrKmYsqapEyLg4ULozwm0k3fB4Ws9fVZ\n2ljB2xe+RBAEZoROQtVCipdlHsQkmrjXR9LKljTUcLYsjydDpaKp4voamoxGPK8SAfawtaa+2UBp\nbR0OOi13hPrx8ZYDFFXW4GSjY0Lfjmw9nsSPO+J5bEhX1Cols58ewuOzl/H0Oz/z71fH4KSXWkzH\n9Qima2c/1m48yaZfzzL/823M/3wbjg5W+Hjp0bTcw6qqetIzS8zkOCzEjelTBzGob0SbVuIpmUU8\nM2cltlaWzHxsoPn//lcbD6OUyxnVS7JLO5OVT2VdA3eESWTwkqTjShII6T7bsDc3HVEUEQSB7s6+\n7C1IxWAyoZDJuM+3J08dW8CmvJMM94zBVmXFjJBJvHL2I9698DUzwx5Ho7BEJsiIcxpEF30ce4o3\nc6BkOxeqTiJDhr2FE3qVI1q5jiaxiQZjHbn1WdQbpWBViFUkIz0eIsImus279FR5AvOTFhFi7cuz\ngfcB0sT68+StuKhtGeEpvTP2F0qTlB7OEkfJqb46AQbwtLM1E2CVQkFnPw8OJksEWCmXM+nOLsz+\ncRsHzmfQI8KXYB9nZk8ewvT5a5n1xWbefuYuFHIZCrmMhybEcved7Vm9Pp61G0+yZ38SMpmAj5cD\nenstHdvfPBnNG41bgiUZjCZWbDjBA88tpKC4ig9eHXlTk99L6ObQlWnBz1PVXMkb595iR+FOjKLx\nbzlWaWMZidWJmESRqubqv+UYfzfiugbx2VvjaDaYeOzln1ix4QQGw99zvX4LURQ5cjKdiS8sIv58\nFjOeHMDkB++4qckcgK/Whxkh05Ah552E91iRvYqmP+Fn+t/A+coLzL7wDifKTzLeaxz3eY+/6Sdd\n1jo1/377Xpz0Oqa+uYIPvtp2U0hubgUYDEYaGpvx8rDn0Qd78eW/7mfFoie5d1SXNuQ3M6+MR2Yt\nITGtkNlPDSHEVypsra5r4PutR+kY6E50kBT9PZqaTUOzgW6B0kSksKqGRoMRz6tFgFvIQ2aV1Cmr\nu5MvckFgZ34KAA4WVtzrHcuvBWc4VZ4BgK3KmjcjJmMhVzHjzHziyy+Y96eQKYjR9+K54Nm8FDKP\n0R4PE2rdniZTIyWNhWTVp2MUDfhqg7jLdRwvh7zPzND59HQc0Ib8plRnMv3Mh5Q2VfJK6GM4qyUZ\nQFpNISuzDtPPpR3uGqn4aWP2eUyIDPKQCtLSW8ZyLQkEQGYLObpEIrefk6QfET4u9I8OYuHWY2QU\nSBOTAC9HPn5pFGWVdTz+1jIS0lozHhYWSsaOiGHhFw/zzacP8vjEODpEelFVVU9mVgmZWSU0NjYT\n28WfyY/14advH+WL+fcxZEBkG/J77FwmT7/zM2qVgn/PHIODnSQDOJqYxcYjCUzo2xEnW2nZr2dT\nUMhlxLbIXLLLWsjgVSLAXla2NBgNFNRJkdoB7qFUNNWzr0Aac7S9H2E2HnxzcQdljdI6wda+TA16\ngMTqNF4+8xGFDSXm/anllgx0GcGs8M94PuhtBrgMx9PShwZjHVn16ZQ3lWAUjXSw7cq9Xo/zetjH\nPBnwMpG2nc2/aaIosrPwMO8mfIOHpQszw57AQi61cd6Ud5KEqlweDeiLhVya+OzMT8ZJrTN7GWdU\nS/fZ4wpabwAveykCbDBJGu3uQd5kllSQUSxtN6RrKG56az775QDNRumd2LOjP1Pv683uYylMfW8V\npRWtmWa9vY5HH+zFmp+e5qO547hvTFecHa2oqWmkrPz6G6H8f8VNHQE2Gk0cik/jmyX7Sc0qISbK\nhxce7fePt6L9KwixDub18Jl8n76IxZk/sa1wJ2M9RxFl2/5PE6xaQx27inZT1lROtF0Hwm3CzLPj\nS8hvKKCHQywpNSlUNFdgq7p5dNHXg7BAVxbMu5+5/97CJ9/v4ueN8Tw0uisD48L/lkLH+HNZLFh2\nkNMJOXi62fH5W/cSHuT6Xz/O3wUfrTezI95gadYyNuZv4kjpEQa5DqSXQ4+/pdAxpy6H5dkrOVN5\nFgeVA88FTaG9beR//Th/Fxz1Vnw99z6+XXaAVZvi2XUomYljujG4dwQaS9WNPr2bFm6utvz7w/uu\n+r3BaGLDnnN8/ONuVEo586ePoGukDwBGk4lXvttMRU0DHz0VZ97m+z0n0Os0dA2QCPDpXKkrW4Tb\nld1g/GzsUcsVnCrJZ6h/GFZKNXe4BrI28wzPRfRGJZPzoF8cv+af4c0zK/khdjI6hRpntZ53201l\nXuIC3jz/b+507clDPsNQ/+b/h6ulJ66WnvTgz3fGbDQ2sSZ3Oyuzf8VWZcW77Z7FTyd1aaszNPLy\nySVo5CqeDZFkQQaTiUUpR+mo9yTYRhrj6WJpzOH2Tlc8RrCTlAVLLCgm2tOdAGc94R7OLDt0mnu7\nS++QF0fFcSwxi1e+28zCaWNRKRVEBLjy+aujefWTDUx6fQkPDevCg/d0wUIlvfIFQSDI35kg/+tz\n3qmubeDzZftYu/MM3m72/GvaCNxaanAqaup5beEWfJzteOROyeGirrGJX45foH9EILoWz9+EwmLU\nCgVe9ld+j4frW6+Nq9aKO1wDcbG05ruUw/R2C0IQBF4JH86kw1/w+pkVfNzpIeSCjJ6O0egUGt5P\n/I6nTrzNJL8RDHSJRS5IjjAyQYa31h9v7fUFzyqbq/ni4jIOlZ4m3DqA6SEPo1NIcpaC+go+TNhA\neztvBrtLcoiShhp256fwQECM+R19ujifABs9lr9z+riECFdnGg1GUopKCHVxYkC7QD7cuI+Fe44z\na1R/lHI5z4+K48Wv1vPhz3t4aVwfAMYO6ohOY8G877dz/yuLmfFwP3pFB5iPq1Iq6BDpRYfIttmu\nZ25OV7J/HDdlyKaotJof1xxh7NPf8tLctdTWN/HO9KF8OHPkLUV+L8FF7cxLIdN4NvAZREx8lPIp\nM868wrq8DZQ1XVuobhJN7CvZT0ZdJl4aT5ZlryCzNvMy8ry7eA89HXtgq7Qjv+HW1jjq7bTMe2UE\n7786AhsrNXM+38q9kxewYNkBMnL+ut61vLKW1VtO8sQrS5jyxgpyCsp57pG+LJr/4C1Ffi9Bq9Dw\niN/DTA9+AVuVLT9mLuH509NZmbOazNqsv9xuut5Yz97ifbxzYS6vnnuDizUXGec5hrmRb99S5PcS\nNJYqpkzszYL3H8DXU89HC3Yy7NEvmf/NdlLSi26Z9tw3AwwGI9sPJzHhpUXMXbCNYB8nfnj3ATP5\nBfho9T4OnM9g+rjehHlL5OZYag5HU7N5pHdnLJQSKTuZk49aoSDU+crSJ6VMTntHV04U5ZqXjfXt\nSGljLRuyzgGgUVjweruRFDRUMC3+BxqNkj7S1dKR99q/wFC3PmzO38djx99gZfZWag3XnwGoNzby\na8FBnop/i6VZm4jRR/Jh1HQz+RVFkTnn15JZW8Jb7cfhqJZS/VtyL5BTV8Ejwd3M+zpRlIePlS0O\nlld2mXGx1mGnseR8geSFKwgC42OjSCsq4/BFqYDQ0VbHG/cPIDG7iA9+3mN+fkP9XFgwezwDuofw\n3ZrDTHhpEau2nzLrha8HFdV1LF53hLHTvmfdrrOMHxzNwrfuM5PfZoOR1xZuoaK2gXcnDcaypUBu\nfXwC1Q2NjI9tLYy9UFBEiLMjCtmV6UeE3hmVTE58sXSfVTI5DwbGcKQ4k7Nlkj1YkLUrL4bdw9HS\niyy4uNO8bQe7UP7V4SUibaXCuOdOzuVASfx/VIxe1lTJwvS1PHZ8FsfKzvOQzzDeajcFW5V0Pyua\naply/HtERF6PGIW8JVr848VjGEQTo30lQmwSRU4U5RHtdPWuqx08pO/ic6QJkZONjlFdIlh7/AK5\nLRHzPlEB3N8vmhV7TrN8d2tXuyG9wvlu9gRsrSyZ8a91PPLGUo6cybjd9fJP4IZHgBubDOTkl5OZ\nW8b5pDyOnMowk5yOEZ5MfvAOesQE3BQ2Z38FgiDQ0S6KSJsIjpQdZW/xflblrGF1zlo8LN15Nmjy\nFTXCMkHGzsJdzAh5Eb2FniZTEwdLD6NVaHGwaOkw01SOl6UnjhYO+Ov8SKhKwEZpTZAusE2q7laC\nIAh06+hH1w6+HDieyooNJ1i48hDf/3wIPy8H2od5EBbgQkiAC65ONqgtrjyzNhiMlJTVkJxexIWU\nfM4l53E2IRejScTHw54pE3sztH+kuaL5Vka4TRjhNmEkV6ewMX8zG/I2sT5vIw4qPZG27fDR+uBl\n6Ym7xg2V7MrRToPJQHlzOQUNhaRUXySpOpnUmlSaRQMuamfGeIwkzrEXOuXlHp63GgJ8HPnsrXFc\nSMln1eaTrN9+ltVbTqG31dI5ypsO4Z54e+jxdrfHSvvPt1i/GSGKIoWl1SRlFHLodAY7jyZTVdOA\nr7ueuVPvIa5TQJvJ+cq9p/lpRzz39o5iVM/WydK/tx3CwUrD6K6ty07m5BHh5oxSfnUP505O7nx5\n9gh1zU1olCp6uQQQYefKx+d3M9gzDLVcSQd7X95oN4rXz6xg5unlzIm6F4VMjkqm5GG/EXR3iGJF\n9hZ+yFzPiuytRNmGEKNvR4iVLy5qRxSytscXRZHCxlISqtI4U5HEwZKTNJia8Nd68nzQg4TbtGrv\nTaKJz5K3sjX/NI8H9jM7AoiiyIKkQ/ha6enjGmReFl+cRy83n6uOVxAEwl2cOJdXaF52Z/uglujg\nCboFSrKCuPb+PDigE4t+PY5CLmPaGEnCZWetYdZTg7mzRxhfrNjP+9/v4Ivl++kVHUDnCC+igj1w\n1ltd1iHRYDSRV1xJ/IVsjp3LZH98Go3NBjqFezH53l5maQtAU7OBGd9u5MD5DF4Z35cQTyfzPn7c\nf5IwdyeivKXAgtFk4kJBEXdHXL1FuoVcQYTemRNFeeZlY3078u+EfXybfIiPu0odTO9xj+ZUWQYL\nUndhb6FjlJfksuGs1vN62JMcLj3Dwow1zEv8DiuFlp6O0UTYBBBi5Ye9yuayIFKTqZm8+iLOV17k\naNlZzlWmYBJN9HDsyGjPQXhpWoMjtYZGpp5YRH59OR93moinVvLWLayv5vuUIwz2CMPfukUKU1lG\nZVMD0c4eVx2zm40VjjotJ7PzmNBJagr1SO8YVh45x9c7j/LmKCkzMWV4DzIKy/jg5914ONgQGyHp\n5f09HVj87gNs3HuOBasP8ex7q3C009GvazCdI7wJ9nVCf5NYed5MuCHsKCOnlDFPfUNdfROV1fVc\nCriolHLah3kwpG8E3Tv64e3x/8+wWSFTEOvQnViH7hQ1FHOk7ChJ1cnYKq8c2W4yNeGv8yOvIR+9\nhZ4Q62D2Fe8nqy7bTICTqpI4Wn6cZrGZY2UnKGkswSSa8NP6orjxc5y/BEEQ6NE5gB6dAygpr2HX\nwWT2H7vI1j0XWLOldRas01qgt9WiUEgvL1EUKa+so6Kqzvx8yeUyAnwcGT8shn49QvDzcrjpdb7/\nCYKsAgmyCqSyuZJTFWeILz/JgZJD7CzabV7HUm6JTqFFI9dgFE0YRQONpkYqmioxIenQBAR8tN70\nde5DJ7toAnT+/y+vV1igK2GBrkx+8A4Onkjl6KkMDh5PY8vuVr2oTmuB1lLVRu/6v4TcwgpGTP2W\nypp6auulCKKlhZKe0f707xZC9yipwv8STCaRLzcc4tvNR4gN9+G5ka3Sh6Op2RxLy2HGPXGoW6K/\njQYDF/KLeKhrx2ueR7STO0ZR5HRJAd1cvZAJAi9F9ue+PYtZlHKUx0OkYrNBblFUNtfxYcIGXjuz\nnNciRqJRSPcuxNqP18OfIrUmm22FBzlaepYjZWcAUAhyHCzsUMmUKAQ5NYY6ypuqaBYNAGjkano4\nRtPPuRshVm27oxlMRt46t5rNeScZ4RnDRL87zN/tL0zjfEUBb0ffZe5+ll1TSUl9LR2utm9dZwAA\nIABJREFUERkE6Ojpxqd7DlFWV4+9xhKVQsHEuGg+3LiPPQlpxLV0V5syrAdGo4kfd8RjNJmYPra3\n+Z50ifQhpp035y7ms2rbKQ6cTGXTvvMAKBVynPVW5iBCbX0jRaXVGFsiiI52OobEhTOyXxT+ng5t\nzq2hycD0bzaw/1w6M8b2bjPJWXroFOnF5Xz0wN3m63ShoIiaxiY6eblfe8xObixOPEmT0YhKLken\ntGCcXzQLkg6RXl2Kr5VeKjgMH0plcx3zLqyjpLGaxwP6IQiCFEBxaE+Mvh2nKxLZVnCI7YWH2JS/\nFwALmQo7lTUauZpm0UCjsYmSxnJMSGN2t3TmbrfeDHSJxdWybWCqtLGal04tIakqj/c6TKCjfWvR\n5sfnd2MwGXk+oo952fEiyZ7tWhFgQRDo6OHKyZxW0u9so2N0l3asOHyGR/vE4GFvg1wm492HBzPp\nwxU898U6ZozrzciWa66QyxjaO5JBsWHsPpbC9sNJ/PzrSXPrZGudGmutmtgON59z1h9BEAR7YDng\nA2QAY0RRLL/CevOAIUjqhm3As+I1Uno3hB0pFXIigt3QqFXY2Wrw8dDj5W6Pl5u9WaP0vwAntSN3\nuw3hboZccz0HCweKGorABmyVtlgrrSloaI0I+Gp9cFTpsVZYMTngCZZkLedut7tQy/9/Ra0c7HSM\nHtKR0UM6YjSayMwtIzmtkOKyGkrKaiitqMXYYvQuCALhQW446nU42Onw83Ig0Nfpf+r5slHaEOfY\nkzjHnphEE0WNxWTXZZNXn0+NoYZqQw11hjrkMjkKQYFKpsROZYejhSNOFo74aL2xlF/euvP/K+xs\nNAzp044hfdphNJrIK6wkK6+MzNwyCooqqWtooq7+5vH1/iehVMqJDHLDSqvGx92eYB9nArwcUKsu\nz5yUVtXy7pId7DqdyrDuEbx8bx9zBs9oMvHp1oM4WmkZ3aWVLJ3LL6TZZDKngq+Gji0k4kRRLt1c\nJV1jjKM3fVwD+TJxP6N8otCrpUjXWO/uGExGPk3aQlpNEe+0H0eAVWuDFn+dJ/66sTzuN4bMujzS\nanLIqS+gpLGCJlMzBpMBL40bdiprnNV6Qq398NS4mlPdv0VWbQlzzq/hRFk6TwT2Z6JfaxFtnaGJ\n2ae24Km1Y6hXO/M28S1Sjo6O1x5zdz8vPtlziMPpWQwOl9rz3hfbgdXHzjPnl93E+HtiqVIiCALP\njeyFXC5j0a/HSc0v5Y37B+DpKAVXBEGgXaAb7QLdMJlEkjOLuJBaQF5xJQUlVTS3FBxbqBS4dbfB\nzcmGdoFu+LjZX3HieyGzkNcXbSEtv4xXxvdtQ36Lq2r4/NdDxAZ50ze8VXN7ME2SbXT18bzmmDs6\nufPt+eOcKy003/OHAruwLO0Er8dvZGGv+5ALMtRyJfM6TGDuhV/4LnUXWbUlPB96Fw4WkvOIXJDR\n0S6MjnZhNJsMpNfmkFSVTnFjOeXNldQbGlHKlKhkSpzVejw0zvjrPHG3vLI2+nhpKq+fWUF1cwOz\nI8fQy6m1rXBiRSGrM07zQGAMXjo78/ITRXnYWVjiZ315F7jfooOnG1sTL1JcU2vuDjepd2dWHj3L\nZ78eZO64OwHQqlV889woXl6wmXeW7CA5p4Qpw3ugVUtZPQuVgoGxoQyMDaW2vonkjCKSMgrJLiin\nuq4RR7tbMnv3ErBDFMW5giC81PJ5xm9XEAShOxALXHoQ9wNxwO6r7fSGsAF3F1tef/bapO82JCgF\nJfYqe7LrpL73GrklImIbYuJi6cLzwVPNn+1V9uTW5+Fo4XDTV+b/p5DLZfh5OeDn5fDHK98GMkGG\ni9oZF/XN1W76ZoVcLsPTzQ5PNztiO7Utmpkz4yob/T+Gk70Vs566tsdzbUMTP2w/wQ/bT9DcbOSF\nUXGM79PBTJ5EUWTuut2czMjjrdEDzNFfgD0p6cgFgY6e1yaDthaWhNs78WtWCpPbt2ppp7Xrx9Dt\nX/PC0TV8HTsOlVza9wTfngRau/L66eWMP/AJd7l3ZKJfb3PKGiRi6KN1N1ukXQ9KGqtZkrGfZRkH\nsZApeKPdKIa4t0axTaLIjGO/kF1TzsJe92Ehbx3z6tQLOFhqCba7tt1jOzcX7DWWbDiXZCbASoWc\n14b1YdI3K3ln7U7eGj3AHPmcMqwH/q563lu+i1GzFzOqZyQPDeiEo20r8ZHJBEJ8ndtIGf4sMgvL\nWbz9OL8cOI+9tYbPnxlBt7BWay2D0cS0JZsxGE28PLR3m/u//lwiEa7OOOiunY6PcfZAKZOxNvW8\nmQA7qnW80n4ALx9fz/xzu5jWri8ACpmcV8OH42Fpz9cXd3CoOJnxvj241zsWnbI1CKSUKQiy8iHI\nyue6x5xQmcuC1J3sLUrAU6Pn404PEWjVKomoaKrnhaNrsFFZ8lRoT/PyJqORndmpdHf1+sPsWYy3\nJJHYnZLO6A6SX7+zjY6HekXz9c6jhLs7c39P6dmy0qj5+OmhfLJmPz9sP8H2+GQeHdKVkT3aoVS0\nSni0lio6hHrQIbSt/OL+674CNxxDgTta/r0IidT+/pdYBNSAChAAJVDINfC/Ew67RSEIAiFWwewv\nOUBxYzGOFo6kVF+kn3MfMmszyarLpoNtFDqlDoPJgEKm4HH/R270ad/GbdzG/wCajUZOp+ax8UgC\n204kU9fYTL+OgUweGouXk12bdRftjWfpwdM82Cua4Z3DzcsNJhMbzifRzc8LO80fZxxGBbbjzSM7\nSCgrIrTFPcHf2oHZHYfw0vF1TDm8kk+6jUbVouWN0QewrMdzLE7fw8+Zh9icd4pYx2D6ubSjp1Mo\nWsX1yVrqDU2cKEtjU95JdhWexyiauMu9I08FDTRHHkEifB+f383W3ERmRPYztz4GuFBWxN7cdKZF\n97pqMdglKGQyxnRox9cHj5FTUWluIBET4Mnjfbvw5fYj6HUapt7Zw0yC7+oaRkyIF1+sP8jy3af4\nee9pBkQH0b9jEDGhXlheIXJ/LVTXNXDgfAZbjyex92waCrmc0XHteeqe7lhZtl6/ZoORV5Zv5Xha\nDu+OHYiPY+szcCQzh5TiUt69e8AfHs/BUstw/3BWpJzl2Q6x6NWS68JInyjOlOXxTdJB/Kz0jPSR\niusEQeAh/zvo4xLBR4mb+D51Nz+k72WQaxQDXdsTaeeF8jrrYSqaatlVeJ5f889woiwNK4WaxwL6\ncp9vT9Ty1hqK6uYGHt73Exk1ZXwTey+2qtZneGdOKqUNdYwK+OMGVGEuTnjZ2bDubIKZAAM8PaAb\nqUVlzNuwBxdbK/q3CwRALpPx3MheDIgO4uM1+5i3fBdfbzzMnZ1DGNIlhGBPpzaypP8WclILmT58\n/n99v38AZ1EU8wFEUcwXBOEy2xRRFA8JgrALyEciwJ+Jopjw+/V+i9sE+BaAq6ULgboAVmavoaK5\nAqVMia/Wl0ZTA/46P3NB0q1a8HYbt3EbNzeamg2cScuntqGJvNJKckoqScwq4nRaHg1NBjQWSvpH\nBzGqZyThPi5tthVFkYV7T/Dhxn0MaBfIC4N7tvl+7ZkL5FZU8Ur/OP4MhvmFMefYbpYnn2VW177m\n5SN82tNgbGbWyc28cGQ1H8YMN0eCbVUapgTfyXjvWJZkHuDXvNPsLUpALsgIsnIlwtYTL60DLmpb\n7C10qGQK5IKMOkMjVc31FDZUklFbREpVAWcrsmgWjVgrLRnr3Z3hnjF4a9tmoppMRt48uZmf008y\nyieKhwO7tvn+q7NH0CqU3Bf859qGj4uO5JuDx1h6/AzT+rVev6f6daO8tp4Fu49TWdfAayP6mkmP\nU4s7xKRBMSzZeZINhy+w6WgiaqWCUG9nQjwd8XPV42CjRW+tRdUSOaxvaqasqo7iylpScotJyi4m\nMasIg8mEvZWGR+7swpi49uit20Zx65uaee6HDexPyuD5wT25J7ptF82fjp3C1lLNkJYo9h/hsYgY\nVqScZVFCPM936GFePjNqIBk1Zbx8fD0FdVU8FdrTHF310jowP/oBEipzWZV9hM15p1ibcwxLuYpI\nW28CrJzx0TnhaGGFtVKDWq7EYDLSZDJQ0lhNQUMF6TVFnKvIJq2mCBERT42eJwMHMNqra5uIMkBV\nUwOP7l9KUkUhn3UbTXfntk1cfk45i7NGR0/3Kzd3+S0EQWBcdCTztu/jaGaOOSIsl8l47947mfTV\nSl5auplmg5HBHVqLCMN9XPhq6iiOJGaxZv9ZVu47w9JdJ7GytCAqwI0gd0c8HG1xsbdCY6FEb635\nU9f/vwwHQRCO/+bz16Iofv3bFQRB2A60/fGQ8OqfOYAgCAFAKHAp3L1NEIReoijuvdo2txnTLYJR\nHiO4WJNKZXMVUbaRLR6vVn+43W3cxm3cxl9FTkklD72/zPxZqZDj62LPsO4RRAd60D3cx2x99VtU\n1jUwc8VWdl1Io3+7AOaMG9TGcaDRYODTPYdo7+5C3+A/589qp7ZkkE8QK1LO8ES7GFy0rb+D4/07\n0Wg0MOfMNtJ3fMu8mGGE2ba+Ux3U1kwJvpPJQQM5XZ7JweJkzlVmszE3njrjte3BLOUqfHVOjPXu\nTheHAKLsfMyND36LgroqXjy6lqMlmTwREsvU8N5t0t/Z1ZVsSE/k4bBO2Fj8uToNVxsr+gX7s/LU\nOZ6J62aWj8hkAjOH9cHGUs3XO4+SV1HNGyP64m7f6gPv4WjL9LG9eW5kL+JTcth7Np3zmQWsOXCO\nhibDNY9rZWlBsKcj9/ePplekHxE+LleMKibmFfH6z9tIzCtm1sh+5u5+l5BXWcX2pFQmdYtuI325\nFgJs9fT3CmBxQjxPRMSgUUpRV6VMztex45h5YgMfX9jDxeoSZnW4E5vfRF5DbdyZaTOC50KGcKIs\njcMlyZwuz+LnrHSaTNces7XSkggbT/q6RNDTKZQgK9cryhcOFKbx8vH1FDdU81HXkfR2C2rz/ZmS\nfHZmpzK5fbc/jPJfwoROUSw6cpIPd+xn2cSx5uOqlQo+mziUKYvWMX3pZo6n5zLj7jizhaAgCHQN\n9aZrqDeVtQ3sP5dO/MUcTl7M4+D5DHNBI0DvqL/WMdTD35l5a56/rm3eF14oEUWx07XWEUWx39W+\nEwShUBAE15borytQdIXVhgOHRVGsadlmM9AVuE2Ab3UoZApCrP/czPk2buM2buO/CWc7Kz6dPByN\nhRIXeyucbHXXTK82G4ysPHqWL7cfobK+gZfuuYMJsVGXEYmfjp2moKqGeUMHXZfDyAsderAlM5k5\nx3fzcdzdbb6bGNQVXys9r57YwOgdC3g4qBuTgru1SU3LBBkd7H3p0FLBL4oi5U21FDZUUtZUg8Fk\nxCAa0cgtsFJa4mBhhZPa+po1FfWGZr5NPsg3SQcxiSLvdx7KUO/LfbIXnD+GTBB4OPyafOAy3BfT\nga2JF1l8NJ7HYlvbtAuCwJRBsTjb6Phg4z6GfriYJ/t1ZXxsVBupg1Ihp0uoN11CJb2u0WSiqKKG\nsqo6SqvrMLQUEFso5Nhba3Cw1uJgo73mfamsa+DrnUf5cX88NhpLPnrgbvqEXz6R+XzvEQSkSPb1\n4Il2XdiWdZHlKWeZGBZtXm4hVzCv81ACrB2Zf24nBwrTmBIWxzi/6DZkU6uwoJdTqLlYzSiayK8v\np6yxlmpDPQ3GJpQyBUpBjt7CCme1DdZKy2uOOb+uks8T9rEi/SS+VnqW936YSPu22nWTKPL64e3o\nLbU8GhFzlT1dDrVSwTNxXZm5YTs7ktPo95tJoZ3Wku8eH8WnWw/y3e7jHE7JYvLA7gyKDGozqbTR\nqhnSJZQhXaQxNxuNFJRVU1ReQ31TMzZaNf+4gOGvYx3wIDC35e9frrBOFvCoIAhzkCQQccBH19rp\nbQJ8G7dxG7dxG9eEVq0iNtznD9dLLSxl48lE1sUnUFBRTSdfd6bdHUe4x+XFVrkVVXy5/wg9/L3p\n8geuAL+Ht7UdT0TE8MnpQwzxCWGAd2Cb7+9wDWRD/8d55/SvfJ10gB8uHuVOzzCGeUUS7eB1WURO\nEATsLXTYW1xfhbwoiiRUFLAq8zTrs85R0VTPnR5hTGvXFw/t5daWh/KzWJJ8mqF+Ybhqry+DF+Pt\nwcCQAD7dc5hOXh6XFQyO7daeXqF+zPllF//avJ/v9hxnaHQYd7YPJsLT+TJSJ5fJcLW3xtXe+rrO\nw2gycTozn19OXGDTyUTqmw2M7tKOqXf2wEZzeUR70/kkVp46x6PdO5n1y38W0U7uxDh78Mmpg/R0\n8yHAtm3x4uMhsfRy8efd078y+9QWvko6wHDvSIZ6RZp9eNuMWZDhodHjobk+i9Umo4EDRelsyDrH\nlhzJHvGhwC48H9Eb9RWyAN+dP86p4nw+7DkYa9X1acyHtw9nwaETzNu+lxhvd6zVvynkk8t5fnBP\nugR48cGGvUxfsokvtx+W7nNUMG52l99LpVyOp6Ot2Q3kFsVcYIUgCJOQiO5oAEEQOgFPiKL4CLAS\n6AOcRSqI2yKK4vpr7fQ2Ab6N27iN27iN60ZdYxNpRWVcLCzlZEYex9NyyCypQCYIdAnwZNbIfsQG\neV8xmpZaUsakn1ZjEmHmwN7/0fGfiuzK7tx0pu7dwIJ+I822aJdgZ6Hhg5hhPBbcne+SD/NrbiKr\nMk5jp9LQzcmHKL0HEXau+Or02Fto/lQEurq5gZTKYpKrijhRks2honSKGmpQyeT0cwvm/oAYoh2u\nTOZPFOYyafsqvK1seaXzHf/RmGcN6UfS98t4Ytlaljw0lgDHtkTO1daKTx68hxPpOSw5cJplh06z\neF88jlZaYoO9ifB0IczdCR9HO6wt/1h+IYoiFXUNpBaWkpBbxOmsfA6lZFFZ14ClUsHgDiGMj40i\n2PXKThaHM7KZ/stWoj3deCau2xXX+SO8FzuIMZuXMn7LMlYMHo+PddviylBbFxb3up/d+Sn8lHaC\nrxMP8mXiATy1dnR38iVK706QtRMB1o5XbUX8W5hEkaKGatKrSzlTlsep0hwOF2dQa2hCp7BgvH8n\nJgZ1xU1zZTL/S+oF3j2+mwFegYzwD7/iOteCQiZj9pB+TPxxFQ8sXsm344df5poRG+RN16kT2HI6\nmSUHTvGvzfv51+b9BLk60MnXgyhvV/xd9Hg72P1pycnNDFEUS4G+V1h+HHik5d9G4LqaPP+lKyMI\nwltI9hQmJE3GQ6Io5l17q9u4jdu4jdu4UfhPfrfzK6p5/NvVNDYbKK+rp7S6joq6BvP31pYWdPRx\nZ3xsFAMjg3CwurrN1ZncAh5dugaFTMYPD4zCV2931XWvBbVCyTd9hzN+y3Lu27qcmTF9eCi042VE\nNsjGibmd7+E1wyD2FVxkW24Sx0uy2JTT2uhEq1DhpNZho7LESqlGLsiQCwKNJgN1hmYqm+oprK+m\nxtBo3sZOpaG7sy/dnXzp7x7SRmLxe+zKTmXKnvU4a3T8NHAs9ur/rBDJXmPJt+OHM+775YxfuJzZ\nQ/oxKCzosvWifT2I9vWgorae3Qlp7EtMZ/eFNNYebx2zldoCJxstVmoLrNQWZklLs9FITUMTVfUN\nFFRUU9/cqpl1stZyR6gfPUN86BHsg0595eimKIr8eOwU7+/Yh4+9LV+MHYqF4j+jG7429vw0cCzj\nWkjwF72H0d6xbct6QRDo7RZEb7cgCuqr2JWXwt6Ci2zMPs/y9HjzejZKNc6WVlgp1WgUKlQyOUZR\nxCAaqWpqoKKpnqKGahqMrWP20dkz2DOcAW4hdHPyMRdW/h4Gk+n/2rv36KjLO4/j7+9cM5OZZCZX\nCElICBAgIAgYL4gK3lA8eGlpRdfVWo/b1rK6R3e31rV7uuf0bI962vWsnm1dddfjettq1a1aXa22\nqFUQUEDuyC0hmAu5XyczefaPiSxCSDKThN9vyPf1F8nAzOcXJp955vn95nl4YMOfeOzzT6jML+SX\nFyxPeuOgysmF/Or6q1n9m9+x6j9f4IkbrqM46+szuE6Hg+VnzmD5mTOoOtLMW5t3sXZPFS+v38qz\nf/6s/+cC4XQ/2QE/IX8aXreL+aWJL/d3uhrpW4MHjTH3A4jIXwM/Ab434lRKKaXGSsK9Hevro7Wr\nB6/LSWluFgunFJKfGWRKXhaluWFKc7NO2E73eI0dnTyy5mOe37CZglAGTw7wop6ofH+QV666ib9Z\n8zo/XfsHPjp8kHvmL2Z6+MTT3+kuD8sKZ7GsML46QW1XG9uav6SqvYkDHY0c6e6gOdJFS6SLPgzR\nvj68Thc+p5uyjBwW5U8h3xekLJhDeWYeBf4Tt9M9Xl1nO/+07l1e27eDaaFsnrp0JXn+kW1EUBQO\n8czN3+KeV37PnS+9znV79nHXkkXkB0+831C6j2sWVnDNwgqMMRxubmPboTqqjjRzuKmNutZ22rt7\naOzooq//g1Iup4NAmocJoQDnl5dQEM6gOCdERWH+oG9svrLvSBM/e+uPvP/Ffi6cWso/r7iMzGHM\nNg9mejiHpy//Fre+8xLXvPY0N8+cz93zFxMc4PKCCb4MVpUtYFXZAmKmj4PtTexqqWNv2xFqu9uo\n7WqlozdCS6SLSF8Ml8OBUxxkeNIoDoTJSQswOZDF5PQwFeGJhL1Dv1lZX1vNAxvWsK62mptmnMn9\nlUvxDLKl93AsLivhqZu+ye3PvcKKx57mtnMXcuu5C/EPsIRdUXaI25ZUctuSSnpjMb6obWRfXSP7\n6hupb+3gSHsnzR1dNHd20XrMG9fxbkQDYGNM6zFfpgMn3XJOKaWU9ZLp7cKsTJ5bvSqZx2LL4Vpe\n3bydVzZvoyvSy8r5c7jzovPIGsaav8MR9Hh57OJr+dWWtTy66SPePribK0rKWTl1NosKSk46EMn3\nBcn3jf5KOn3GsKHuEM/u/IzX9+8E4h/au31O5dc2whiJkuwwz93ybR5ds5Zff7iO323ZwZUV5Xx7\nwRzmTZo44AcURYSCcMaA14mOVCQWY+3+Kp75ZBN/3L0Xr8vFT65Yyg0Lzhi17dMrsvN559rv8tDG\n93lq+0Ze3PM515ZVsKp8LjPDuQM+jlMclAazKQ0mds3vcHT2Rni3ei/P79rEBzUHyEnz8+D5V7By\n2pyh//EwzZ00kRe/ewMP/eF9/nXNxzy/cTPXza3g6jNmUZYz8M5ybqeTGQW5zCg4+QYrfztqCVPb\niH8bReRnwF8CLUByF3MppZQ6ZcaqtyPRKHsbmthRW8/6qkN8tO8g1c2teJxOLikv444LzjnhutXR\n4BDhB2ecw6rpc3li63qe2rGRN/bvJMPjZXFBCWflFzInZwLTQzkDzhqORHe0l51NDWxtrGXdl9V8\nULOfhu5Ogm4Pq6bP5TuzFpxw3epocDud3LXkPL4xbxZPrfuUlz7dyqtbtpPl93Hh1FLOLJpIxcR8\npuVmJ335wcl0RCLsqmvg85pa1h88xPtfHKAjEiHs9/GDxWdzw8K5Q+72loygx8tPz7mElVNn8+S2\nDbywezNP7/iUSekZnF8wmQX5hczOzmdaKBu3Y2QzsMdr6u5iV3MDm+oPs662ig9qDtAdi5LrS+e+\nsy7ixvJ5R5dqG01F4Uwe/uZVbKyq4d8+WMvjf17Prz/8hKk5WZxbWsz8ogLK83KYnB0e9nJrKm7I\n34rBFic2xrxqjLkPuE9E7gV+CPzjSe7nduB2gOLi4oH+ilJKqVEwGr19bGdPLCrmnR17iPb10d0b\npbO3l/aeCE1dXTR1dHG4tY2allYONbcSM/EJ5Yw0L5WTC/mrRZUsmzXta59mHyvhNB/3LFjM6nnn\n8mHNAd7Yv5MPaw4cnYkFyPcHKApkMiE9SJ4vnZDXR8ibhs/lJs3pwut04RDBIUK0r4/evhjdsSgd\nvRHaeiM0dndypKuTw51tVLU1c7ij7egUenaan8UFJVxYWMrlxdPGZEB0vKJwiH+4fAl3Xngea/bs\n593de3lv915e3hy/3leACRlBisKZ5AXSyQ2mE/L5CHo9pHs9eJxOPC4Xzv4Z1JgxRKJReqJR2nsi\ntPVEaOrsor69g9q2dqqamqlv7zz6+HnBdK6smM7S6VM4r3TyKfnQ1eycCfziguXcX7mUN/bvZM2h\nfbx5YDcv7N4CgEscFAQyKApkku8PkOPzE/b6CXg8BNwevE4XLnHgcjjoM4aYMfTEonTHonT2Rmju\n6aa5p4vazvb+/+cWjnT//zGXZoRZOW0OV5aUU5lfOCY7rh1vflEB/77qWurbO3h9607e/2I/L372\nOU9/Er/e1+10UhjKYFJmBvkZAbL8PkI+HwGvB5/bTZrbhdPhIH8Yl7GMF0M+UwdbnPg4zwKvc5IB\ncP+uH48BLFy4UC+VUEqpMTIavX1sZ4cml5o7fnPiikJel5Msv58JGQHmFEzgyopyyvNymJabzZSc\nrFMyMBiI1+liaVEZS4vK4te+drSxrbGOXc0NfNHSyKH2FrYeqeW9znY6or0J3bff5SY7zU++P8A5\nE4opCmYyM5zHrOw8CgOZOEbplH+igmlels8uZ/nscowxVDW1sKO2nl31RzjQ2ER1cyubar6kvq2D\n7ujgm0EcL93jITeQTm7Az+KyUkqyQpTlZjNnYj55wcHXCh5L4TQfN86Yx40z5hHr62NfaxPbGuvY\n2VRPVXsLB9uaWftlFfXdHURisYTuO+jxku8LMCE9wKXFU5mamU1ZKJs52fnk+KwbROYG0rnl7Pnc\ncvZ8IrEYe+qOsKu+gV11DVQ3t1LT0srOugaau7rpHeCYL5sxso0wTicjXQVimjFmd/+XK4AdI4+k\nlFJqrCTT20XhTF647UYcDsHvduPzuI/OLNmdiFAQyKAgkMElxSe++Pf2xWjp6aYr2kt3LD7zaYhf\ny+t0OPA4nHidTtLd8dnDtGEspWU1EaE4K0RxVojLZk474faeaJS27h46Ir1EolEisRh9/TP3DhG8\nLhcep5NAmpeg14N7hB/oOhWcDgdTQ9n9awXP/Nptxhi6or2090bo6I0Q6YvRG4t8KICIAAAF4klE\nQVQRMwaHCCJydPY/3e0mw5OWEpcTeJxOZk3MY9bEvBNuM8bQEemlIxKhK9JLdzRKX58h3evhEQuy\n2tFIz1X8XETKiS+ncwBdAUIppewu4d72ulwDvsieDtwOp6Uzelbwulx4Ay5OXCvj9CQi+N2eU3JJ\nil2ICAGvh4B3/Bxzoka6CsQ3RiuIUkqpsae9rZRSuhOcSlJhZubRU2ZqaBODQWbmnHxZGqXU17kd\nTmbn5RE+BR+eswu/28PsvLxxNVMZTktjdl4ebqf9LzkYLbnpASryTtwePBVU76zh7iUDftQr5Yix\nYBAjIvXET70NJAdoOIVxRluq54fUP4ZUzw+pfwync/7Jxphx9W5mkM626/+zXXOBZkuWXbPZNRec\nPFvSHSYib/bfbyIajDHLknm8sWTJAHgwIrLeGLPQ6hzJSvX8kPrHkOr5IfWPQfOPD3b9Odk1F2i2\nZNk1m11zgb2z2cH4OeeglFJKKaUUOgBWSimllFLjjB0HwI9ZHWCEUj0/pP4xpHp+SP1j0Pzjg11/\nTnbNBZotWXbNZtdcYO9slrPdNcBKKaWUUkqNJTvOACullFJKKTVmdACslFJKKaXGFdsOgEVktYjs\nFJGtIvKA1XmSISL3iIgRkZTacVJEHhSRHSKyWUReFpGQ1ZmGS0SW9T9v9ojIj6zOkwgRKRKR90Rk\ne//z/k6rMyVDRJwi8qmIvGZ1lmSISEhEXuz/HdguIudancnO7N7Vduxhu3WsXXszFTrRrn2nPTY0\nWw6ARWQJcDVwhjGmAnjI4kgJE5Ei4FLgoNVZkvA2MNsYcwawC7jX4jzDIiJO4FHgCmAWsEpEZlmb\nKiFR4G5jzEzgHOCOFMv/lTuB7VaHGIGHgTeNMTOAuaT2sYwpu3e1jXvYNh1r895MhU60a99pjw3B\nlgNg4PvAz40xPQDGmDqL8yTjl8DfASn3KUNjzP8aY6L9X34MFFqZJwGVwB5jzF5jTAR4nviLc0ow\nxhw2xmzs/3Mb8cKaZG2qxIhIIbAceNzqLMkQkQzgAuAJAGNMxBjTbG0qW7N7V9uyh23WsbbtTbt3\nol37TntseOw6AJ4OLBaRtSLyJxE5y+pAiRCRFcAhY8wmq7OMgluB31sdYpgmAVXHfF2NjcoyESJS\nApwJrLU2ScL+hfiAo8/qIEmaAtQD/9F/WvNxEUm3OpSN2barU6iHre7YlOhNm3aiXftOe2wYXFY9\nsIi8A0wY4Kb7iOcKEz/lcRbw3yIyxdhozbYh8v8YuOzUJkrMYPmNMa/2/537iJ+CeuZUZhsBGeB7\ntnnODJeIBICXgLuMMa1W5xkuEbkKqDPGbBCRi6zOkyQXMB9YbYxZKyIPAz8C7rc2lnXs3NV27uEU\n6ljb96YdO9Hmfac9NgyWDYCNMZec7DYR+T7w2/4SXScifUAO8Xc0tnCy/CIyBygFNokIxE9tbRSR\nSmPMl6cw4qAG+/kDiMjNwFXAxXZ64zGEaqDomK8LgRqLsiRFRNzEi/4ZY8xvrc6ToEXAChG5EkgD\nMkTkv4wxf2FxrkRUA9XGmK9mmV4k/sIxbtm5q+3cwynUsbbuTRt3op37TntsGOx6CcQrwFIAEZkO\neIAGSxMNkzFmizEmzxhTYowpIf5EnG+nwe9QRGQZ8PfACmNMp9V5EvAJME1ESkXEA1wP/I/FmYZN\n4q/UTwDbjTG/sDpPoowx9xpjCvuf99cD79rkxWDY+n9Pq0SkvP9bFwPbLIxkd7bsarv3sM061ra9\naedOtHPfaY8Nj2UzwEN4EnhSRD4HIsDNKTQLeTp4BPACb/fPnnxsjPmetZGGZoyJisgPgbcAJ/Ck\nMWarxbESsQi4CdgiIp/1f+/Hxpg3LMw0Hq0GnukfDOwFvmNxHjvTrk6ObTrW5r2pnZg87bEh6FbI\nSimllFJqXLHrJRBKKaWUUkqNCR0AK6WUUkqpcUUHwEoppZRSalzRAbBSSimllBpXdACslFJKKaXG\nFR0AK6WUUkqpcUUHwEoppZRSalz5PxpPJ7O26FERAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c2837f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5), ncols=2)\n",
    "\n",
    "c0 = ax[0].contour(xx, yy, f, 5)\n",
    "c1 = ax[1].contour(xx, yy, f, 20)\n",
    "\n",
    "plt.clabel(c0, fmt='%2.1f')\n",
    "plt.colorbar(c1, ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x11c7da1d0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dnCVhggNk/b4+WlQjR382jH1/L4DyL8v5AF5oON+XALzfdyOlE6rPllKBaqsK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\nRVEURVGUwaDyqyiKoiiKogyG/x9xGn7JfrIXhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c523048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5), ncols=2)\n",
    "\n",
    "clevels = np.arange(-1, 1, 0.2) + 0.1\n",
    "\n",
    "cf0 = ax[0].contourf(xx, yy, f, clevels, cmap='RdBu_r', extend='both')\n",
    "cf1 = ax[1].contourf(xx, yy, f, clevels, cmap='inferno', extend='both')\n",
    "\n",
    "fig.colorbar(cf0, ax=ax[0])\n",
    "fig.colorbar(cf1, ax=ax[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### quiver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.quiver.Quiver at 0x11bd07b70>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k06g5PcGp4bBayE5yIUsSDf4wwWjPA7EMFkbIZwpimxMlNS8hiI/DoFwd4Wsi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jI6duyI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\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MKRSitzEQFHIODgIxYcQfhAnblRD7s09QT/B2q9ckO8AHKa6rZU+ycyaFaX1kTzclxdXV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SvowoULJJXyK36uCFano5KIC5S8eBIlLXibii4cI5ZHUeH/ApQZzyhtzUJKnPMmFRzf1+jfg64w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\nDQ0NDQ2Ncx7NKNbQ0NDQ0NDQ0Djn+f/SUjck4c4b5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd07a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u = -np.cos(xx) * np.cos(yy)\n",
    "v = -np.sin(xx) * np.sin(yy)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "ax.contour(xx, yy, f, clevels, cmap='RdBu_r', extend='both', zorder=0)\n",
    "ax.quiver(xx[::4, ::4], yy[::4, ::4],\n",
    "           u[::4, ::4], v[::4, ::4], zorder=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### streamplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.streamplot.StreamplotSet at 0x11cd34a58>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cRoElg3xTGkbNoTcydzAIKwqNXhfVbgf17R2s2dLEyqp66p0uAOIMeopzMxmX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Mye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NGcM7G0X6YVu6Ir5GedaxtrXI0YjVW0hFroCHXilXcmMAICDoODlBhhnpgYwSDqkERzbPXW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\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m17BMosUyz6HMdx3MLzkpfJ0Zz4qEI4SYOPKf0HCNrutGOjr8MGc28zZs5J6t29l61yLs\njG4n6L62ViwaGsiG5HQWDPFX6TY1c4gvB85f5rMTcTQJCjzMzVgcrDpz3FdkFpQB4OOkfqKXXlSK\noCPi3qFD+k3SSxsbWL5zG1dra1k9eRoT3Hqux6hubWZF/FF2X8nCx9SSlSMm4WOmuu7lvwH/Z0gx\ndGmE5nr7McbRhdfPHOejhFj25Vzm/choAqz6n6431dPl/enR/PvYIX5IP8cjQ0K7Xc/KUJ+FIwI5\nW1FCm0KhtnNOf5Bb2RUl7S0RgK7B8ouEBByNjJg+SPXFq7alhUf37MFELufzqVM10sHuvZzF80cP\nYSzTYfOcBQRa907e0alUcrL4CkcKcjlWlEd5cxNikYhgS1v+MzScKEc3PIzNBoyozXQKJMrWCwPp\nn8nMWJs/BoUWRQfna0o4V1VESlUhKeUlfJGahJexBUsHBTPLxVdlpXN3sJDr8U3ULHbkZbIi4SgT\nd67ji/BpRLuoH4hsDA1YPXMqSzZu5andB/h27oxuo8wikYiXp0XwzG/72JCSztORYRq/txtwsTSl\noaWNqsZmzA3+iEr9ei6dr+OTqGlsQSkIjPryWxYM9uPxHgqtbuCFbQdpaG1j4wML1a43xFU9URIE\ngTePnkBPqs23s2fib927/3hcSSGPHdlDh7KT98ZOYaqLag/j9s5ONmanc6Agm7MNeRhqy4i08SDa\nfhCjrdz+8fSgRCzG1dAMV0MzZrn5cb2xgW2XM9icdYEnj+/DUFvGLA8fFnj737GLlkxLizUzp3Hv\n1h1sybyIs5mx2nF3YYA/axISWZ2QyLq5fSPFcm0pI/2dqBcPvC3dwZwclAhMGNT3Ys2Ozk4e3LmL\nrIoKvpoxneGODj1vBLR0dPDG6RNszrzAEBtbPhs3BVsDzaKtLYoO4q4Vcqwwj+NF+Vxv6rJlG2xh\nw9PBYUQ6uuFnZjkg46dYJMZQW46hthyH34eICBsvHiCMTkFJdm0ZyZWFJFcWcvJ6LjsKzuOub4lE\nocM93iFMcR6ETKLF2eslLNi2mUnunnw5ceqfjiHXkvIv7+Escg/i4+RYvj+fwsWKMr4cPw0LDaLl\ntoYG/DBnNgs2beaebdvZvGgBJt10WX1s7Aj2XMji3YOnWLd0zm3fT1Z5BZ8kxNFuDN+kpAAQaGt9\nR0hxZ6cSLwcLLE3UBzO2pV0ks7qCb4fN7tfx6ltbeefkKUobG/lxxmxGOPSc0d13JZtX449Q397K\ns8GjeDgg9L+yuPOv+D9Fim/AQk+P1ROncTg/l9dOHePRg3t4IGgodwf03VXgBsJcnPGxsuTrlGTu\n8gtQ6R87ytOJ77JSiC0qJNJFs6r03iKnsgptiQSHHrrudYeTV65wsayM98aPV0l0lYLA0wcOUNrQ\nwMb58zHXVR+h6FQq+TA+hrUpyYTY2LJm8nQs9DRP8XUqlezMvsRniXHI9CVca2pgjL0L4xzdGOvg\ngqmO5hGS3uKvhPivkGtJCbVwJtTCGegqHNx9JZN1WSm8nHCIlSknme8ewNJBwTgZaFZMIBKJmO3u\nS5itI+/Hn+HBvbtZER7B8kD1cocQezteGx/Bq4eO8fGZOJ4b0713pqOZMT6OVvyQkMKy0CBMNSyK\nvAEXq65ozpWymj+R4sa2NkobGm8+L2tspK5VM1LSrugkpaCEmUH9t7b67fxF9mZls2JcBMMcNCdV\ngiDw/fkU3ks4hauxKWsnzMDVuPsovVIQ2J2fyUfnYihqrCPUyoE1I+Yx1s69X1rzOw0bfQMeCx7O\nI0GhxJcUsjn7Ahuzulo2B1hY8WxwGOFOA28bpyPV4suZU5my8WeeP3aIXQsWq4w6yqVS7h8Swgdn\nYjh/vbTXGtsbKBc301Ff8adlEm0RwZ62/WrzvD/nMh6mZnia9d4i7AZWxp2mRdnBBxMnEOmm2XUg\nt7qKxw7uJbuqkkdChvH08DCNghGVzc2sSU4ks6GM+NIi9KRSRts580xIGGPtXbDU7bv2uC+QiMT4\nmNjgY2LDMo9QBEEgv6GKuOsF/HQplWdi9vFeyknmuwWw48IllAjsy83mX2VDCOxmMqUvlfHayEj8\nza158fRhpm1bz9fRMxhs1XPQxcvCnLUzp7N863Ye2LGL9fPm3JbpNZbr8GTESN7cf5wjWblEe/95\nMlTZ1Mz5+NZuAgAAIABJREFU62VdOj267gJ6ORHXFEvGh7BkfIjaddoVCg5n5jBukBtyDbzbVUEp\nCDx36BAnr1xh3eyeCXFlSxOvxh9l/9Vs/M2s2DBxAYNM73xHyYHC3+93QZfWrlOp/CcO/SdEu7pz\n+K5lDLd1YMWJ43wYF4MgqG47rSn+PWIUje1trE1JVrnOCHtHDLS1OZSf0+/jqcLV6lpczUx73TBD\nEAS+TEjAztCQmT6qycnn8fGcvnqV1yIiCLJVr+Oqa23l/j07WJuSzCK/AH6dPV9jQiwIAofycpi8\n4WeeO3IQYx05zwePJnXJY6yJms5sD987Soj7Ah0tKfM9Atk39R62TlzCWDtXfspKYeyOtdxzbAsx\n169qfK5Z6Rrw3thool3deeP0Cb5OSepxm0WDA1g02J99l7KJKyhUud7SYUG0d3ay+dwFTT/aTbj+\nXmF9pfzPDgl3BQeiL/sjKq4lFnN/6BCN9nmhpJTm9g6Gu/ZPW15SV8+7J04z3NGBxUGaR2paOjp4\n6th+3o4/yXhnd3bMWtwtIRYEgRPF+UzZ/RNPnd6HgbaMdePnsmnSQqIdvP6rCfGtEItEhNk78XnU\nVJKWPMTrYZGYynS5Z9dO7tu9k5L6+gE/pqlczjsR48muqmR1cqLade8aHIiRjozVierXUwdLXT3K\n/lIT0C5WEtdQhFLSt/G+vKmR5JISJnn0PUqcWFLMj2nn8LexZoaP+iLmG9h2KYPpm3+hsrmJddPn\n8NzI0T0S4vq2Nj6Oj2Xsuu/5OT0NTwMLfp00j9Qlj7F23Ezme/r/7YS4O4hEItwMzbnbK4QjM+7j\n53Hz8TW1YnVyItcbu6LZIkS8G3tK7dg5y9OHbTPvQkssZv6uTWy6dF6j44c6OPDx5El0KDv5IiGh\n23Xmh/jjaWnO+4dP09qh+NNro1yceHL0H9kwAfCz+eekAjG5BdS1tDE1oH+uKGuTkzmal8eL4eHd\nFiPeirTS6zxzYj9HC3N5ISScndPu/kcJsSAI1LS2UN3SrPE2/0ik+FJlBV5ffYqFrh5W+vpY6XXd\nrH+/t9L//bG+Pgbad9Z43lCmw8qoaLTFEtYkJ1Hb2sobYyN7TSRvhbe5BdO9vFl3PpV7Bgdj2U31\nr7ZEQqSLG0fyc3knQnU0tj/IFpUy2K33lluxhYWklZby1rhxKlsIp5ZdI/l6MTO9vVkUEKB2f80d\nHbxw7DCZFRW8HTGOu/w1JymxRQV8GBdDelkpbiamrJ48jYluHn2OYP3dzSZEIhFDLO0ZYmlPWXMD\nGy6n8evlNL5ITmCdJI13x47HUoPJgUyixZcTp/LskQO8H3eG9s5OHh86XO1neSVqLLN+3cDLR49w\ncNmybmU67hZmjHJ1YkNKOveNHKK2ZfRfYWmkj462FsU1dX9abiCT8a/QIXxyOg7oIujWBppddBPy\nixCJYKhz39Ll0PUbv3jwMAgCKyeN19hpoai+lgcP7eZSVTnPDRvFI0HdtzE/V36NlWdPklRWjKOB\nMZ+Nmco0F++/zdHhTsFYR85yv2CW+Azm5/Q0Po6PJXr9Op4eEcbywUEDOkZFurgyy8uHr1KSmOjm\ngY9F9+TBQCZjWVAQP6Sc40pNNS4mvbe6stTTJ7VsYLsvHszJQQAme3r2afum9naeP3oQB0MjXhip\nWUeyr84mcrLgKgFW1nwWPQUrffX/qZaODn5OT+Prs0nUtbUxxcOTp0eMxLUP3+HfDZFIRLidC3a6\nRpzK+vHmcgGBpGvFfJoUx9OhqiVfvuaW7J1zN48f3cd/Th0mvbyU10dF9qiFnuTlSVJxMWuTkol2\nd79N360lFvPSxLH8GJ9CY1vbbVryR0eGcr2+gd/SLwLg38fsRqeNBIVR/8aTPeezMNGVM6IfAYbY\nwkI+jo1lmpcXy4LUu8akll5n2a6tWOnrs2f60r+VDAuCQHFDHecryiiqryOt4jqF9XUUNdTS0N7O\nAwGaBWXgHyLFtgYGPBg8jPKmRkqbGrlSW0NCSRH1bX9OsboZmyJpFzHew51od3f8re9MJzOJWMzb\nkeMw0tHh67PJ1Le18WH0xF4RhL/i6dCR7MvJ5ovkBN4aO67bdSa6erAr+xJJJcWM1ECj81cIgoCr\nqQlaWrdfrJSCQElTPeMc3Xu9310FGYx2c2SOiiixIAi8k3CKq001fD1thtrfRCkIPH14P0ev5PHV\npGlEayDOB8irrmbFqWPEFRVio2/AyqhoZnv79uvC/ElsLD+npnFvSDDLgoJUFqHdKVjpGvD04NE8\n6j+SH8+f46PEWCZsWsfbY8YxxV11280bkEokfBI9GW2JFp8kxtGmUPDvEaNUfv8yLS1eHBPO8m3b\nWXculQeHde8Ru3RYEA9s2smhzBym+WseVRCLRdh6m5DVVnXba3eHDObT03EIwAMj1HvT3orE/CJ8\nbCwx1u37b7Mx7TxxBUW8HR2FvZFm0qGC2lrejT1JUX0tP0yeTYTj7alspSDwSWIcKeXF5DfX8Nbw\n8SzwDOjXOPHfCC2xmHuDgpng5s6Kk8d598wpdmdf4p3I8fhbDZyDw2vhY4kpKuC5owfZOX+xysKo\nxYMD+S4/iY256bw0tPeFyVZ6+lS1tNDe2Tlgv9XJq1f6JZ1YGXeaovo6Ns1eoFFjjp/TU1kVF8NU\nDy8+ip6ktoisvbOT3zIu8mVSAuVNTYxxcubfI0epbezz3wpHQyPeCI8ks7KCnOoqsqoqaO7o4PPk\nBPJqqvl8wlSVk1FjHTnrJs/mo+RYvj9/FnGniBVjIns8B54ZFcah3BxeOXKUHUsW33bNGe7iwHCX\n7rXfIpGINyZEsS8zm6aODgz60HQFurTm/claN7a1czw7jzlBfn02Erje0MBT+/bhZmrKO+PHq73O\np5ZeY9mubZjKdflp+hyN9e19gSAIXGts4EJFKefLy7hQWcaF8lJq21oBsDcwREeqhYOhEUOt7XAw\nNGKItR0va7j/f4QUm8l1eW7E7TrH5o6Om0S5vKmR0oZGTudd5ZukZL5KTMJaX5/x7m6M93BnmL39\ngLhG3IBIJOL5sNEY6+iwMuYMDW1tfDNtRp+P4WRkzEJff3ZlX+LhIcOw1b/9JAl3ckZHS4uDeTl9\nIsUiLciTXUeh3XHba1WtzbQrO7HT752euKq1me1XLnK/7zCVBYCHruZytrSEd0aP7zGS/2F8TJd2\ne3SExoR4y4WLrDx1CjMjXV4ZPZbF/oEDUoxYXF9PfVsbn8bF803yWeb4+hDm6ISHuRkCXX+2TkFA\nKQgolV33ErEIU7kcCz29AZuQaUskPBg0lCgnV549dpBHD+3lQF4Ob42JwkSFBv0GJGIxH4ybgExL\nQtK1EtqVnWqjH6OdnYlydWVNYiKzfX26layMdnfG2dSEn5NSe0WKASz09bje0HDbcgOZ7KYRk6ZR\n4taODs6XXGdxaN+1/eWNjWy5mEGYsyMLAjWzC6prbeW+Pdupbmlh08wF+FreTvya2tt59sgBDuXn\nMs/bl28nzUKvF4WT/xNhZ2jIt9NmcCA3hzdPnWDW5g28PHoMSwMH9yuTdgPGOnLeHhvFg/t381VK\nEk8M674Q01xPj2E2Duy9ksV/hoztdUT+hjVZZXPTgFysmzraiW3J4xF/zQpH/4rYogJ+uZDOfYND\nGGbXc0Zk26UMXj91nPGubj0S4qLaOlYcO0ZyWQneFpZ8PmmKRsf4b4VUImFpwJ8jlBfKS7l/7w72\n5V6mumULH46bqPJ3lYjFPB86Gic9I148fgSFUsnKqGi1Y7mBTMarERE8tmcv61PTuCekd5aVWmIx\nj4aF8sHJGLLKK7E2/HvaPt+K+MJC3OzNmOTft0yGIAisSUpE0dnJmmnT1E7cUkuvsXTnNsx0ddk4\nez42+gP/eZWCQGJxEQdycthXkEVVa5fHu0QkwsvMgomuHvhbWONvYYWXmbnG7ijd4b+q0E5XKsXZ\n2ARn4z8KkR4IGUptSwvH869wOCeHLRczWJ+WjqFMRrSHO0sCAvG3HTgx+wMhQzHUlrEtK5MXTxxh\nVdSEPpOhh0KGsvd6Br/kpPJ80JjbXteVSpnj501x++2Rtv6ipLErpW2n17uLwP6r2XQKAjNcu48S\nd3R28kHiadyNTVkwSD3p2HrpIl+lJHGXXwDLA9WnXgDaFAreOnGSjefPE+boyCeTJ2vcEU8dBEGg\nuK6e4tp6RHRpvZo7Olifls76tHS125rryqlsbsFcVxdfK0t8LC3xs7TE18oKe0PDfhFld1Mzts1Z\nxNpzyXyaHEdCSRErI6IZ5+KmdjuxSMTbY8fRqlBo9Od/cewYJq37iY9iYlg5YUK3+7t7+GAO5F3m\nUmUF3uaap71sDAxIv959anqEkwNVzZpruTIqy+m0EhHs3veW5GvPJZNRV8aB/0fdWYdHdW1t/HdG\nk4m7u5GQhBCS4O5uRVqgfqvU3ZX2trdAe1sodaOFlhYp7g5Bg0RIiBJ317HvjxSKDMk5k/S23/s8\nPAnJ2Wd2Zs45+91rvetdt94h6rPR6vU8tGUj+bW1/DB9lklCXFBXx32b15NeWcGrg4dzZ6/e/0h/\nzetR29xCXUsrPg7SC20vQxAEJoSEMsjXjw+OHOL9Awc4VVDI4vHju2WjOiYohFujothRlM6tzdG4\nWJqWEk0O6MHu/CxOlxUS5yaN5HnYWtPPz4PKtkY86TopTqoopM2gI8ZNujStTa/np7SzBDs48nT/\nzh1ftmZm8Nyu7Qz08eW/4yZ1SIj35+TwxOYtGI3w/tixjAsxX2b2T0aUqzuJdz3AL2nJvHlwL+NW\nfc9bQ0cyNezmuuw5kdEUNdTz8fFE/O3tefAmzlCXMS4khGEBASw9fJhxoSF42EgjevP7xLD04FES\nL+UzLDhA0tjuwK68LLLbqon1Ma9j6dbMi/yYcpa3h40isIPOn6eL2yPEzhoNP/0FhLissYFfU1JZ\nk3KevNpabNVqxvUIJsLFlSgXd8KdnK9Y9HUX/lGk+Gawt7RkRs8IZvSMoFmr5WBuHjszM8mprGbm\n16sYGRrEUyMGEmyG9ZgpzI2KprS5gQ+PHyXW3ZPbIjvWzN4MXjZ29HbzZG12Mk/1GmwyuhLkZs+q\npBOUNNVJMjDvDEWN7QUynhJJ8cacVELsnQh3ME2MVl84R3ZtNV+Ond6hlOF4YQEv7tnJQG9fXh/S\nceMPaE/VLNy4kTPFJTyQEM+TAweaHY0yGI1klFVwMr+QU/lFnMwvpLS+AYPCiPGqK97dul23PiMi\nAhsLNQICMkFALmv/KhMEdAYDpQ0NpJaVk1JWyqHcPPR/pLVs1WoiXF0ZExTEAD8/s6zvFDIZD8f1\nZbh/AE/u2sa9W9bzUO8EHu87oMM0nyAIWN7EB/t6BDg4cGdsLF+ePMm8mBiTKfDxEaG8dnQP2zIz\nJJPi6uYWmrXaG+ajVigkpatTy8tpaGsjws28NG9FUxOrks8xNSycIKfOdZNGo5HX9u3maMElPhg9\nzmRE7WRRIQ9s+Z02vZ6vJ89gqIl2sN0NncFAeUMjRbV1FNc1UFxXR1FdPVZyJRZKBW621rjatF+7\nrrbWOFhaXLm/9AYDR3IusfZMCjsvZKKQyzn93MNd1jvbqtW8OXwk/rb2LNq/n+qWFlZMmYKNuus1\nH/f2jmPkui/4JvUkz/a5MXgAMMo3GLVcwcacC5JJsZ1GSVJbJlXaP2U8SgX09fZAJkhPUR8vy2+3\ngHSRvnlbm5XM5qI0vho5s9PF/HhhAY9v20xvdw8+mzTtppsQg9HIimPHWXLoMGEuziyfOgU/Mzo2\n/n+CIAjMiYiin5cPT+7cwuM7t7A7N4u3hrbLIE3h8b4DyK2p4T9HDuFn58CEkJtHUQVB4PWRIxj3\n7Xe8tXcvy6dMkTQ/S6WSPl6eHM7JkzSuu3CsIJ++Xt6S1lCD0YhA+8bt/cMHCHVyZk7kzQNf6RXl\nVwjxqhmzce8mQqwzGDiQl8vP58+zJycbvdFIXy9vHuvXn3EhId1Ogq/H/wtSfDUslUrGhAQzJiSY\npjYt3x1P4oujJ5j02Q/M7NWTR4b0x92265W0j8T353RJMW8c2EOki6vZPsazgqJ48MB6DhbnMszr\nRp1inz+6Yp2qKGCib9dtqC6jsKGdFHtLkE8UNtRxvLSAp3oPNkli69ta+fDkEfp6eDPS7+b2QY1t\nbTy0dSPetnYsGz+5UwnK8YICFm7cRItWy7LJkxhnZvHK8dwCDmblsOrMeer+sABztbYizteLOB8v\nTpQUsik9nVAnJ54cNJBRQUGSIymtOh3pFRWklJWRUlpGalkZa8+m8vaO/ST4enNb72hGhwVL1i5G\nOLvy+6x5LDl2mN9T0sgur+LjSR1HhqTg4X59OV9aik5vunWok6WGPh6e7MjO4ol+4j2LL0dQSuob\nCHDsWt/61IoyHCwscDezLe03Z07RqtPxYJy4tqNfJZ1idcp5Horry4zwGxvg/JqWzIt7duJlY8uX\nk6YR5Nh9HTAvo6qpmd3pWeRUVXG6sJiSunpK6xuubLwuw85CjUamvMbq7jJUcjnO1hqKauuRCcKV\nxc0IqJWKbi0AvLtPHxwtLXluxw7mrVnD19On4yzBVtEUAu0cGe8Xxg8XTvNAVD9sTUiyrJVqRvoE\nsTnnAq8kjJBUW2Cvas821ba1XPmZUa4jVZ+BoDDdnawjnCjPp6eDGzZKaRsCncHA8vOJRDu5M8K7\n42wQQISLK3Mjo3mq/0A0N9kA17e28szWbezMzGJKeA8WjR6Npgv2W2JhNBqpaGgir7KavKoaEMAA\nOGkscbLS4GilwclK85fPxc/Onp9nzGXF6eN8dPwoBh08O3CQya6VgiDw/qixFNXX8dSOrXja2BDT\ngUe+j50djw7sx+/ZFzhTWizK2u1qDAzwZcmBw1Q2NnVLxlMsCuvruFRXyx0x4mUfeoOBhJ8+xUal\nJsTWiby6Gr6bestNSXVdayv3bdpAvKcX74wY3S2E2Gg0su58Kj+lnON0aTFOGg33xPZhdmQUgQ5d\nW1uk4G8hxVlllTy8cgOe9rZ4O9jh5WDb/s/etsPOaddDo1Ly4KAE5sRGseLQMX48eZbfk9O4IyGW\n+wbEdamQSiYILB09nsm/rOShbRvZNGf+TT2HO8JI7xAc1JasyTpnkhSH27thKVdysiK/W0lxi6KG\nOD8bkwvMzbAxJw2AKQGm01Drc5IJdLLnhfihHZJJK5WK90aOIdDB8aa79ss4nHeJT44exc5CzU+z\nZxHsJI14GI1GjmRf4tP9xziZV0iEpytje4QQ5+NFnK8X3nZ/ShxG1QcxOyqS/r6+ZhMFtUJBtLv7\nNf6TlU1N/HYuhVVJ53h8wxacrTTM6hXJ3JgoPE10R7oZVHI5zw8YgruFNW/t28eTW7eydMKEbqn6\nt1Gr+XH2rA6PGRMYzKJD+7lUW4OvnbhI0+UNaHF9/Q2k2M3ZCo2N+EdMank5Ec7mNQ2obWnhh7Nn\nmBASKqq6/kBeLu8e2s/44FCeNJHGXnXuHJ8nnSDB05tPxk8y696/GSobm9iVnsm21Iscy81HbzQy\nINAXpUxGgp83nra2eNja4GFrg6etDe62Nlcs7tr0esrrGymtb6CsvoHSP/4V1tRTVFuP4Q8yfYVS\nG2FnaiaDQ/y71HXtakyLiMDe0pKFGzcya/Vqvps5U3Lr7OvxQFRftuSl89GZQ7ySMNLkMZMDerAt\nL4PT5QUkuImvwbjcdr2mTbyU52Zo1etIqihiXkjncrDrsTEnjUv1Nbw8Yoaoa9xapeKNYabfC2jf\nUC3at489Wdm8PHwYd8Z2r6zHaDRS3dRMXmUNeZXV5FbWkFdVQ15lDZeqamhsbbtybLCnMxllFTec\nw1KpaCfIGg39/X0IcXVicHBAlwppr4dCJmNhXD/6enrz4PqNLCj6lZ/nzsHdhORBrVCwYuJUZvzy\nE//auJ7Nty7AtQMXj3nRMXxyJpGvzp7i4zGTbnqcKcQFeKHOknGqopAxVtKs+9yCNKgdzXvuJxbk\nA9DPW1wjGGjnOzVtLVS0NJFTV42ggS2X0hng42ty/Xlz/x6K6uv4aNzEbiHERbV1vLx1Fwdz8hgb\nHsLySXGMCAj8W4qY/xZSrFTIKaqp43hOwTU3FoCthRrPPwhyrK8nkZ7u9Anw6rDlq6PGkhfHDGNB\nfG8+2n+Ez4+cIK2kjAVxMQwLNb8xhqOlhuXjpjD7t9U8sXMrX02aLplMqeRypvpH8NPFM9S0NmOv\nvnZxVcrkxDh5caqiQPL8jEYjFnIFmKhSLW2toU1okvSQzGouZGpwKH62N+7KjEYj31w8jpultagd\n88hOdLEAKaVlPPT773ja2rB69mycJEScjEYj+y/m8On+Y5wtKMHd1pqXJwznltjImy7+7jY2Jh+U\nXYWTRsN9/eK5t28cB7Nz+en0OT47eoLPjp5gWFAATw4dQJireEnCnbGxtOn1vHfwICq5nPfHju2W\nwqbOMPoPUrwzO4t7endsDH8ZbrZWuAZaUG64MYJZZKilXN9oYtSN0BkMpFdWsCDavO5P359LokHb\ndtMuklejtqWFV/btYnRQMB+MHnfDPX0wN5dXd+9mTHAwH06Y0C3R+vKGRnZeyGRb2kVO5BVgMBrx\nc7Tn3gFxjAsPJdzdRdS9qpLL8bJvfz7eiImcLSzmhQ07yKps945ubdPx6KqNaFRKRvQIYlxkKIND\n/Lrc8nVYQAA/3HILz27fzgu7drBs0uQubRyinT2QIfBV6kmmBvYk2vnGzNwQL3983IwcrEyTSIrb\nCVhtW7PZ87uM5KoSWvU6ElzFEw5oj8R9cu4IPRxcGOUj3RHoejRptfxr/TpSSsv4fMY0hgV0n241\np7yK304kc6m6hp3pWVd+LpcJeNnb4ufoQB9fT/yc7PFzcsDPyR5HjYbalhYqG5uoamyisqn5z+8b\nm6hsaGJrSgafH6pFLgjE+noxPCyQ4aGBBDh3TxQw3tObr2dMZ8GaX7n9199YNWc2TiYaSjlpNHw1\nZTqv7tvNc3t28PXk6Te996xVKuaGR/P1uVM8Xz8ELwmFmhFOLjRo20iuLGWMnzRSXNhYS4PePKeX\nxIJ8HCwsCJPgjCIIAg5qC8r/8PM1AltzM3i133AUsmuL7LZczGDthVQeSejXYaRdDIxGI6vPnOe9\nPQfb5WxjhnNbbK+/1d7ybyHFvo72rFu4AKPRSG1zK4XVtRTW1FFQXUtRdR0F1XXkVlZT3dDM4o0H\n8XKwZUqfCKb0CcfH6eYRCR8HOz6YNp67+sby9rZ9PLB6A48PH8j9g+LN3kH3cnPnpUFD2ZR9gV8z\nzjM7TLq++JbgKL5NP8X+ohymBtwYDe7j7M3ytMM0aFuxlpCSU8qNuDmXYq25sVtYVWsjjmrxaegG\nbQu7y89yd7Bpz8zEsjzyGqp5NFKcp2ZnyK+t5e61a7FRq/l6xgzRhNhoNHLwYi4f7jlCanEZnva2\nvD5pJDN6R4ha6PUGA7ll1aQWlJJaWEZhZS2XamtRKRSoFHLUCjlqhQLlH1/VCjn2Vpa42VkTF+BN\nqLvzTTdoMkFgaFAAQ4MCKKyt4+cz59mVnsXsL1Zxe99YHhneX/TO9774eNr0epYeOYJKLmfRaPF+\nu+bCz86eMCdndmRniibFThoriprqKG++kRRLQU5NNa16HRHO0vXELTodx0sLGOEfSPhN/G6vxuLE\nwxTU1/HphClYKpWUNzaSW1NDnKcnuTU1PLp5MyFOTrw/dmyXCXFdcwuf7DlKVmUVh/IuEeDkwP0D\nExgXEUKYq3O3F0L18vJg/f3z+erISZYdTOTWPr0YHhzAtuQMdqZmsuV8OtHubszt24vJvcM7DDZ0\nht6envx77Bhu+3UNT2zbyldTpQcNrsZw70B2F2QxY8sPfDh4EpOuy1hZKdUE2jpxoiJH0nnlggxb\npQU13UCKs5su0ctTRayztAKmbZcyyKqt4uOhU7p8H2v1eh7ZuIlzJaUsmzy5WwhxU5uW7ecyWHsy\nmdO5RShkMoZHBPL8uKFXyK+XvS0qxc3vB2sL1U02a+0wGIwkF5WyNyObPelZvL/jAO/vOECAkwPD\nwwKZ0ycKP6euEeRod3e+mD6NO3/7jbt+W8vKWbeYzBgHOzoxKjCItw7uY0d2JmM7cEa6I7o3X587\nxffnk3hhgGnNuylolCqC7BxJqSw1628xF7WGRoYGBEi+zuyuIsWeVjasHD8bzXUOO2WNDby8dydR\nrm4sjO/XpXleqq7h5a27OJqXzwB/XxaNH4W3Gd13uxt/q6ZYEATsNRbYayzo6XXjrqi5TcuelCzW\nn0xhxe5EPt2VSFyAF1PjejImKgQrC9M2IT093Ph6/gxe+n0nS/ce5kJpOe9MGWO2vmlBVAwbLqXy\n/umDTAzsIdmKKdLRnd1T/kWQnWlpQLyrFwNrPMisLybG0d+sOV6PqrYGPC3FpzRPV+WhNxqIdzId\nWf856wy2SgvGeXfup9sZKpuauPO339Aa9KycPUt0ZW9Tm5Y3N+wmv6aWhtZWFk0dzZRe4R1bFFXW\ncCqrkLTCMlILSkkvKqe5rb0TkYVSQYiHM14OdhiMRlq1Olq0OuqbW2nV6WnV6dDq9GjUKrL/6Npm\nr7EgLtCbvkE+JAT5EOTqaJLYeNnZ8uTQgdzXL473dhzgi8MnOJyVx/szxhHsIk4isrBfP9r0epYd\nO4ZKLuf1EZ0XLHYVk8JCOVqeS21rM3bqziN/tko1SpmMypaupaazGssIDbIizEV6Y4F9BdkcrMjh\nm1G3dHpsankZPyafZX5UrysNI5YdP8b3Z88Q5+lJWX0DckHgs6lTRfnH3gwGg5F1SSks2XmImqYW\n5iRE89y4oYS4OP3ln6FKLufBwX25La7dytBCqWBAkB+vTBrBwYxcPtt9jBfXbOfHo2d4btJQ+vib\n7/bRx9OLV4YO59W9u/ko8ShP9B9g9rnCHFzYXZCFzmBg4f7fOVdRwrN9hl6Tvo13DuDzjP3UaZux\nVYo3Q43eAAAgAElEQVSPTPdx9kDTDfLWjIYi2mjCxVJaxulgSSYxLh5M8Ova89NoNPLyzl3sy8nh\nrVGjGBPStahzTlkV3x8+zeYz6TS2tuHv7MBT4wczJTb8mtbt3QGZTCDa251ob3ceGzGAwpo69mVk\nsyc9m3WnU1h9+CzjokJ5bPRA3OzMrwtK8Pbm0ylTuH/9Bu5dt55vb5lpUpN9e3Rvfkltd7AY4ut/\n08Jlbxs7xgWG8lPqOR6N7y9p/Y90cuNo8c07inY3alqb2VWZznMx0v28a1raNffBdo6smjjnhi6H\nRqOR53btoFmrY0kn1oAdwWg0sirpHP/ecwC5TMai8aOY1SvyH+OU8o8utLNUKZnYuwcTe/eguKae\njafT+P1kKq+s2cHmpAv0DfThzmF9UJrYvVoqlSyeMZ5wdxcW7z5ETmU1y+ZMNmsnIggCLycMZ9qm\nlXx2/jhPxt7osdwZbkaIAcLtXcltSyG7MaRbSXFPO/FV2gdK01EKcno53JgWrG5tYntBOnODene5\n8rNNp+O+9esprq/n+1tuIUSkhjizrJInVm0iu7yKB4b15YFhfU1+7tAeDT6QksP3e0+BDE7mFGKp\nUhLu5cLMvlGEe7sS4e2Kv4sjCrk4WUJxTT0nsvM5lpnP8ewCdiVnAuBkrWFiTBgjwoOID77xvbNW\nq3lr8miGhwby0u87mfn5jzw9ajDzEmJE7eSfGDCAVp2O4wWF/HT2HPNizJMXiEUfbw+WZOwlqbKI\nYZ6dS2AEQcBRraGqtWukOKehiqyGCnxNSHc6w4bsVJwtrRjs5d/hcUajkdf278bBwoInr+qGVdXc\njACcLCoCIwzz98elC4Ux5wpKWLR5L+cKSoj19eTlO4YT/je0e7W7rj5DKZczIjyIYWGBbD57gaVb\nD3H7il8YGxXCk+MH4+1oXpRmXnQ0Z0tL+PhYItFu7owMNE+ypjXorxQIAnyecpxAO0fmhv55zcc7\nBbCCfSRVXWKom3iCaWFRTCN/XqNKGfSwc0WGQdIcs+pLCLSRVnSdU1/BhuJTPBc1pssyqMWHDvNr\nSgqP9u/Hbb3Mc0UCaGxp45PtR9idnEl1awujI0OYmRBJrJ/n/4yceNnbMi8hhnkJMVQ2NPHNwZP8\ncOQM289ncNfgOO4eEmd2IGtoQAAfTpzAI5s289jGTXw6beoN2liFTMYbQ0cyd+3PLDt5jKdN9E64\njHt69WFzVjpr0pK5M1p8AVukkxvrslIpb268qd1gdyKtpj0qHeEgTXqhNxio+COwsWHqAqxNBAS2\nZV3k4KUcXh0ywuyiY6PRyKLt+0gtKSPe24u3J4zG42/wce4IXbpDBUGYJQhCiiAIBkEQxPfRMwMe\n9jbcNyKBjc/cwcqH5+BspeG/Ww5zy+KVnMwyrccVBIF/DYzns1unUVhTx8sbd5osBhCD3q6eTAoI\n4/PkE5Q23disoCtwVNlgq9CQ01DSLeczGA3UtDXhJFI+YTQaWXPpBHqjAQv5jQ+hdbnJtBn0zA0y\nv6nCZfz7yEGMMlg6YQJxXuIiVL+fSWPO8p+oaWzhy7tm8sioASYJcXOblp8PnmXaou94/MvfKamu\nZ2h4ABuevYOjix7iu4VzeG7aMKbERRDs7iyaEEP79TclNoJFs8ey8/l72P7c3bx1y2j6h/iSUVDB\n3ct/5d5Pf+VMTtGVMU2tbXy86TAl1fWMCAvi9wcX0C/Al0Xb9vGvH9dRasJJ4HoIgsBzgwfjZGnJ\nov37yKmqFj1nc9DLyRO5IHCqIl/0GEcLTZcjxZcaqnG2sJIkH4J2R5TdBVlM8g/rtCBxXXoaJ4uL\neHbA4GsKQBva2v4sTBNgX14u50qlpzwrG5p4ef1O5ny2iqKaOt6bOY6V987+WwhxR5DJBCb3DmfT\n03fy8Kh+7L+Qw+Ql3/Hr8fNmddESBIG3Rowg0tWVJ7dtJbfGvGtUazBwuUxQJgjcGtqLsb7XOtFE\nO3ijkikkSyg0cguadH+6T1goW5CpzqNSmHZjMQWD0UBOQylB1tJI8eaCZARgglekpHHXY2fWRU6X\nFDE7MpJH+5vXOARgf2o20z74nh8PJTEkPJBtz9zNO7PH0sff62+L1jlZa3h6/BA2P3EHQ3sEsnxP\nIuMXf8PaUynoDdI2LpcxLjSURaNHUVRfz/LEYyaP6evlzbSwcL44fZKcDq7bWHdPpkSEsrc8Q9Ic\nIp3ayen/SkKRWl0GQA97ac+c/YW5ALwzcLRJQtyq1/HGkT0MDQpgfrR5PMBoNPLezgP8cPwMkR5u\nfDF72j+OEEMXSTGQDMwADnTDXERBEARi/Dz597zxLLt3Kq1aHXctW8Mrq3dQ3WBaMzY0JIDVd88h\nv6aW+1atF0VGTOG5PkPRGfQsPn2oK3/CDRAEgQBrd3Iau4cUN+qaGORmQ4C1uPTikrRtADioTO9k\ne9i7cndYAmESb7TrkVRSzLdnT9PT3ZWxIeIKD97bsp/n12yjp5cbvy2cR/+gGwtsKuoa+WTzYca+\n9iXv/LoHWysL3r9zAhtfvos7R8YT6ObY7YVq3o52zIiP5L2541l2zzSemTqUi8WVLPj4Zx78fB3n\n8op45pvNfLnjOJ9uOQq0d39bcetUXp84glN5hUz99AeqmzrXOcpkMhaNGY1aruDZ7dvNXiTEQKNQ\nEe7gxqly8YWfTmoNVa03/h0uViq8bMRdg5caqvG1kh4l3nHpIm16PZMDb27cD+2RkKXHDtPLzZ1b\nwq8lJ6UNfz4P7NRqPpkwSXIXsDN5Rby6bicbklK5e2Aftj52J1Niwv8xKUFT0KiUPDSqP5ufvpOp\nsRG89usunvlpCy1aneRzWSiULJ84GVdrKz46cfSKC4YUuGtssFKo8LazItRdw7sDxt3Q4VEtVxLt\n4M2pylxJ57aUq2nWt3R+YAcobK6ixaCVRIqNRiOb8s/T1yUAV4mSi6tR19rCK/t2U6dt4Y2R5smo\nyusaeOr7TSz8egPWahXfPzyHl2eMwMnmf2cX1hm8He1YcutEfrx/Dh72Nrz82w5uWfYTWaXmNbia\nHRVFqLMzyxOPkV5uOiD2wsAhqORyliQe7vBcvdzcOVCcQ259lejXD3d0IcZfQ36rNFKc4G+Dh710\necKF6lJcLKxwsZQmP/kp7SzOFhpmhZr2Jf4lLZmSxgbuju5jlibeaDSyZM9hvkk8zfz4GF4YMxTZ\n/6B43Bx0aVZGozHNaDSmd9dkpGJIRCDrnr2de0bEs+lkGlPe+5b9Kdkmjw12ceLjWyZT29LC/avW\n03Cd64UY+Nrac0d4LL9knCetqqyr078G/lZu5DSWdKnf+WU06Zuo1KZjo+r841136RTfZx8BQGc0\nHTUZ4ObPS71HdWlObXo9L+zZgbu1Nc8OEF+sF+Hpyr1D4vn67ltwvc5/2mAw8u2ukzz/3Ra+3Hmc\nPsHefPvYbFY+MZexvcMkRYK7AguVgtuHxrL1pbt5fOIgzuUVM3/JzxxKzQVg88kL1DW1L8iCIDA3\nrhdr75/Hg0P64qARRxrdrK15dcRwThcV8e3ppL/qTwEgztmbs5XFaA3iomgxntb4ON1IpHSqKlpV\n4haDvIYqfK3NkU6k4W1tR6xLx4VPcpmMldNm8Z9R17pNGAwG0irKARgVGMTOO+5igkSf7MTMS9z7\n5VpySqpY88BtPDNuCNYWXW9q8b+Cu50Nr00fyZPjB7HtbAZ3f7aGinpxriFXw9vOjn/F92FdZiob\nMy9IHv9AVF+S5z3Og9F9yWuq5GKd6Wfsa9FT+az/HZLOrZFbdJkUZ9W3By2CJMgnUmqKyWusYqJ3\n16LE/z50gIqmJt4dNdYs55DDF3K559Nf2ZeazSPjBvDLE/OI8Tev29n/Ar39PFn1wFw+mDMBG6WK\nWz9axd6UrM4HmsArw4djo1bz/Pbt6EwEFFytrHl8QH/2lGR2GNEd69Mu19mal05uQwWZ9Z1zADu1\nJTXaBnIayiXNObupkAYTjj6dIbW6lHCJ0omSxnp252cxOyzKZCF4m17Pp0nH6OPuyQAv8a4vV+Pj\n/Uf5/PAJ5vSJ4uVxw7o1WGAwGMkqNE8BYAr/TKouAZYqJY9PGsSap+cR7uXKiz9tY8X2RJPkMsLD\nlY9mTiKjrILHf9uM9iaNDDrCIzH9GeDlw+qs090x/SsIsHanUddCeWut6DEKmZ6BLi04qa51n2jS\ntaeyreQdRwASy7N48/yGK/+v1TZT2lwnYdbiseLUcdIrK3hr2ChsJHgnT44J58mxg24guBV1jTz0\n6Vo+3HAQZxsNv790F0vvmUzvwL8vBahRK7lnZDwPje7H1Y2ytHo96xNTrjk20NmRO/qJ16YBTAsP\nZ2RgIIsPHyK7Sny0Qipinb1p1mtJqxa38TMIWi42SLcUvIxWvY7S5nr8JJLimtZmSltqmRzQQ9Rn\n7mdnT8h1WrgtFy8CEOzgyOdTpuJswsKpI+xNzeLBb9bj7WjLdw/MpoeHeOu9fxIEQeCe4fEsXTCJ\njOIKbv14FRnF0heaW3pE0tPZlfcTD9Ci05o1jzFeEcgQ2FqQYvIYP2snSUV2AJYKC5q6SIpbDMUM\ndrMnwEr8Z7y54DxKmZzRnh1nMjpCYkE+q1POc2/vPkSZaEPeGTaeSuPhr9bj42TPb08t4L5RN6/H\n+CdBEAQm9ArjowWTCXZz4onvNrLuuOlroiM4aix5beQIzpWU8s0p0+v2LWGRGIzwXbLpgIPWoGd/\nWRqudgKf5Wxnyt7/Mu/gZ6KCWL5WTuQ1mhfploI2vQ49bTftRnsz7Cm6SIiTA3PDTEeJf0tPoaih\nnkf79DdrbV1+4BjLDhxjRkxPXp8wstvW5/zSalasP8y0F75i3hsrqb2JUkAqOiXFgiDsEgQh2cS/\nqVJeSBCE+wRBOCkIwsnycmm7JjEIdndm2b+mMaxnIMu3H+XdtXsxGG68YIcE+/PGxJEczMrlja17\nJEdm7dWWhLk48kvumS4XF12NQGs3Yhy8KWsRL6EQgBZDBYJwbZSuSd8+L42i4wX+66wDN6Q5z9eI\n15KKRWZVJctOHGNySJgo/+LOcCQtl9n/XklSViEvzx3Ju3dMwNfln9PWVGcw3iDXWLz+AA1NXVuU\nBUHg7dGjsFAoeHbbdrNS1GIQ6+xFlLODaI27tdKSel2L2VmO0pZqRvrbE+Yg7TM8Wp5Nnq6I0f7m\nFXa16nS8f/ggYU5ObF1wu+TxW8+m8/gPmwhxd+bb+2bh0s3V+s0tbdQ1tFBV00hZZT2FpTXkFlaS\nmVfOhexSyqvrO33PpUptRkeF8N2Ds9HpDSz8dgPnLhVLGi8TBF4aMIzChnq+Pmde4MDFwpqZAREU\nt3Vf1b6D0goHZdc+n9LWfLTGIjQKcY0njEYjxc2VjPbscaWBiFS06LS8sHsHfnb2PNZXurPHjweT\nePGnbcQFevP+/An4u/zvOoN1FxysLPnygZkkBPvw6i87+HrvCcnnmBgWyqigIJYePkxO9Y3aYTu1\nBTNCIliflUqVifqI5JoC3kvZQjNNGP4ozvTWmHYeuh4+Vo5c+h+Q4tKWGuqVeUS5SCuYXXfpHNbW\ngsn+BFq9nmWnE+nl6s4QH3/Jc9qaksE3iaeYGh3O25NGddmOsKGplXX7z3HPu6uZ/uI3fLXpGD6u\nDrx291gs1N3TPbHTPIzRaOxa3vzP83wOfA4QFxf3l6zmSrmct+aOxdFaw3f7TlHT2Myi28bdsCue\n1TuK/OpaTuYX8nvKBaZGStvFzw3szfcXT7I+9zx3h3XeLEAMfDQOtHGSKm0U0KNL52r8o2FCZ5Hi\nt2Nmsrs4lc8zN1OvFdAajFS0ds1v1hS+PncaK5WKV4dIb6l6NfQGA19ub9foBns48fkjMwn2EG9Q\n/r/CHSP6MGtgNGdzijiekc/qA2doatNy7ye/8fnCmdh2oZuTq7U1Lw8bxo/JZ9l8MZ3JoV27VkzB\nQ2NLq6KctIYcptJ55y5rhQU6o55WgxYLuXQbs6q2WtIbcnC2GCtp3LHyXDQKFVGO5qWBv046TUFd\nHStn3ryd6c3w2/FkXlu7k1h/L5bfObVb5BKtbTrOXCjgSFI2R87kYKFSkHnp5tFaJ2tLQCC6hxe9\nengR4OOEva2GjOxSzlwoJCkln8qaRn7+6G7cnMU3Hejp7caqR27l+dVbefCb9axeeGuH/vDXY4CX\nL6P8g1h++hize0TirJFORkPt7fg25yh12kZsu0hmAdwtmrBXncJg1CMT5AiAtcKKq3r/dYqK1gpc\n1OKfN5eayjhTf5qne3TcSbIjfH/uDHm1Nfw4fdZNLcNMwWg0smJHIst3JDIiMoj3509A3U0dDf8O\naNQqlt09jRdXb2Pp5kNUNzTzxMTBon22BUHgzdEjGfvNdyw5dJj/Tpp4A6G9IyKWH9POsurCOR6O\nudaDN8bBl8nevdhYcPbKz+Kd/UW9tp+VE5sKztKi15osZO8uFDa3E28vjXhniKrWJs5UFrAwwrSk\ncfelLCqaG3lzsPQIb0F1LS9t2EG/QB/emdI155XahmbW7TvHF5sSadXq8fdwZOHMQYzvF46bY/cW\n6/3/vUtuAplM4KkpQ3C01rB000GaWrUsvnMi6useKI8NG8BtP/zCop37GBzoj6NIbSdAmL0rvZ28\nWJ2VxF2hCd2SDrBTOiAX5FS1dT2Kflk+0Vmk2NXClik+0fxS9AMP+04mwTEeDwnexmJwqrSQny6e\n4dV+wyWnpq+GwWDkrVW7SM4rYebAKJ6ZMQwL1T/38tWolfTv4Uf/Hn48NmUQ6xOTefvnPSxYsppl\nD0zD29n893laRASfJZ1g+YljTAoJ63a5SHvhpxvZjeL0wNZ/RM4adC1mkeKK1nbJjrNaPHGDdlIc\n5+SLUiY9FVzd3Mzy48cYHRjEAB9pOrmdyRd59bedDAz146MFk7E00zYKoLSyjiNJORw9k8OJ5Dxa\nWnWolArievoQHebJpGFRKOQylAoZCoX8j+/lyGQCZRX1pGaWcO5CIfuOXbzmvILwZ6NLSwvp83O3\nt+GNW0Yz95NVPPztBn56eK4k4v9Cv6GM/eVblp44wqKhoyW/frR9EEaMnKvJZpCL6bSuFKhk7XPX\nGtpQyy2xUzYTZpWKRiZe4lHRVk6AlfisRFJ1u21jbwfzsmPVLc0sSTrE/Jhe9Jd4ja48mMTyHYlM\njY/g9Vmju7W+ok2ro76hlfqGFuobW9q/NrRe+d7V2QY3Z1vCgt2wseq+ds5KhZz3bpuAg5WGb/ef\noqqhiTdmjxH9t7lZW/PM0IG8eWAviQX5N7ynoY7ODPT0ZWXqGe6PTrjGzUYQBF6NnkJuQyXna9ql\nYrGOfqJe19eqnaTmN1YRYmtelzoxMIcUHyzJwggM8zDtd/1N+kn8Xe0Y7istG2c0Gnll4852O9vx\nwzt1BuoIx5LzeOur7dQ1tjBhQARTh0TSM8D9L5NJdolVCIIwHfgYcAE2C4Jwxmg0dhruaWxsJS+/\nEg93O1R/0e71rhFx2FtZ8MvRc7yxZjeLbh17zZsol8l4c/wopn/9I//Zc5B3J42RdP65gb157sQm\nTlUUEOcireWnKcgEGY4qJyrbui4Yt5C1EGfvi0beOdHPbSwEIMDa58rN25347Oxx7NUWzO1hvqem\n0WjkP2v3sf5oCveN68tDk8xvEACg1xtIu1hMTm45GdllaHUGtFodOp0BrU6PVqtHq9MTEuCK2kJJ\nbJQvPXt4ou4CCZ/WLxJfF3se/2Ij85es5r/3TSXa37wWmTJB4P4+8Ty9cxv78nIYbqZ8oCMEWrmx\no+QMRqOx04ePjaL9OmvQtlxDbFUyGWoRBu+Vbe06eie1+LRfaXM9OQ2V3OLfeSTbFL45fxofF1ue\nGSTNczyjuIJnf9zK1NhwXp85yux2yZeKqvj210R2nchAq9Pj7mzLhCE9Gdg7kNiePlhIJNpllfW8\n98UOTpy7hE5nuEKIHe00ZqcV/ZwdWDp/Evd/tY5nVm3lkzumiI72BDk4cldULOeqiylprMfdSlo0\np4etLyqZgnM1Wd1CipVXSHErahHPxethMBqoaqsi3kF8ZjCpOgtXtT2eluY9V39ITaJFr2N+pDQL\nrK1n0nnv9/3cNTyOJyYM6lLXwsuoqGxg75F09hy8ADKB5PSimx5rb2dJTW27vtPXy5HwEHd6BLsT\nHuJBSIBLl9Z8mUzghWnDcLS25EBqDsu3H+XRCQM7H/gHZkb05MNjR/gi6aTJjcadkX341451HCzM\nZbjPtc9VtVzJR/G3MnbXErRGPZH24hxqAqztGeNjR622AhBHioe42+NrJe19KmyqxEKmxEkl/l7b\nX5yJo1pjMttW1FjH8dJ8Ho8ZJJmArjmdzNGcfF6fOBJPO2nBjstoadOybM0hft6ZhL+HI/95bCrh\n/n/dpuIyusRIjUbjOmCd1HEFRdXcfv9XCAK4Otvi6WGPp4c9Xh72xET50CPUA3k37Gyn942kpKae\n5TsS6R3gyaz+1xKzMFdn7kqI5YvEk8yIjiDeV7wN0wTfcN46s5NVWUndQooBHFUuVLZ2PVJsNJZg\nMBxELXul02Or20rpYeONv6b7K5Gza6rYmZfJwt79b2gXKQXLNh1h1b4zzBvemwcnmufP2dDYyomk\nXI6eyCLxZDa1dc04OVqjNxhQKOVXInEqpRyFQo5SISe/uJrEU9l8/8tRVCoFUT08iY32Izbal7Bg\nd8nRl9ggb75/ci4LV6zjlZXbeXveWKICzCPGk0N7sPjoYVacPP6XkGJ/a1cadC2Ut9bhatExWXWx\nUDLBywGd8VrHAm/rcmy0nReOVrTWYSFXoZGLj0Qer8gFIMHFX/SYy9AZDPx84TzhTi4ESzChb9Pp\neGH1Nmwt1Tw9cYhZhLi1Vcv3647z44YTqJRy7p7ej6EJIfh7idMn3gyuTjYsfn4m5VX1PPPeejJy\n2oskq2oambPwK26f0ZeJIyIlE5J+wb68NHU4b6zbzeItB3l2kvg2t/MjezF03THWZJ7nkV7SNrIq\nmYIIW3/O1pjnOHA9FLL254/W2NrJkaZR3VaF3qjHWaR8wmA0kFSdSX/nCLM+1xadlu+SkxjuE0io\no3jJRm55Na//uote/h48On5AlwhxTW0T+46ks+dQOmdT8jEaIcjfhcH9Qxg9JBwbawtsrS2wtrbA\nxkqNzR9fm1u1pGeWknqxmLSLJZw8m8f2fakA9I8NICLUg9lT4tBYmrcmCILAA6P7UVHXxBe7jxMX\n5M2AMHFRWwuFktt79WZp4hEyKisIdbr2vR3hE8hvU24j1tX0euhsYUOcsz9Hy7MoaKzCU9N5xs/V\n0oacpiwqtOJ1xeVt6fhYSbNVK2yuwlMjvmOm3mDgQEk2wz2CTWp9N+WkYQSmBkRImkdxbT3v7ThA\nX38fZvcxb0N7IbeU1z7fSk5RFbNHxbBw1uBu0wx3hr8l/+zr48RLT0+kqLiGgqJqikpqOJyYSU1t\nEz6eDuj1BmZMiWX8mGisrbqm1bt/dD/O5hXz7rp9RHi70dPn2p3Gw4P6sSUtg9e27Wb9PfNNWpKY\ngkahYn5wDGdr8mjVa1F3g1bISeVMcu0Z0cfLBQPxtnbYKa5NAWoNzShllghC56Stqi0DpSwFR1X3\nF6p9ef4kSrmc2yPMi+YBfLPzBF9uP870AZE8PWOo5AUmLb2Y3QfTWLspCb3egK2NBX37BNA/PoiE\n2ABsrDtO7zU0tnIutYBTZ/M4ff4SX6w8CMDgviH0ivBmxqTeKCRUcvu7OvDd43N4aPk6nvpqE788\nPx97kX7SV0Mpl/Ov2DjePLCXk0WFxHma36rXFAKt2++TnIbSTkmxlUJOYUsyOuMks16rsrUOZ5Wt\npM82o7YIO5UFEfbSGikA7MnLprSxgbcGSyuXWL4zkfTicj65cwqO1tKlQIlJOSz5ag+FpTWMHtSD\nR24fhpND9xbnuTjasOLNuSxavp3dR9O5fUZfks7n88EXuzhwPJP50xLoEyUtFT+7XzQZJRWkFJZy\nID2HIWEBosb52Tow0MOP1RfP8nB0f8lFNr3sg/g+dwf12iZslF3z0lUK7etIm8E8UtyoKyPO3gdP\ntbhNVE5DCXXaJrOlE79lpFDZ0sR9veJFj2nR6njyh00o5TI+mD/R7Da8dfXNfLnyEEdPZVNaXoev\ntyN3zh3AiEE98PPu/O+3UciJ6+VHXK8/iWp5ZT0p6cVs25vMV6sO8+vm08yf0Zfp42NQm0l2np4y\nhBNZ+by8eju/PbWAmqZmEi9eYla/6A4DFvOjYlhx8jhfnD7Jf0aPu+Z3cpmMPm4dP0tfjZ7C+N1L\nyW4oJ8Gl84CEndIWGTKq2mrE/WFmorylBi9LR9HHp9eW4G2jYYi76Wt0Q04avZw98JfQZdRoNPLa\npl0YjAbeniK9sM5oNPLLziQ+/PkAjjaWfPz0TPpGitvwdBf+FlJsaaFkzIieN/y8obGFYydzWL8x\niWWf7+XrHw4xfnQUM6bE4u0l/sO+GjKZwLu3jWf20h956vtN/PzEPOyuKnTSqJS8OmY496/ZwPrz\nqcyOEb+z6evmw8pL+zldlUd/l671oAdwV7tRoLSlzdCCSta5FksuGNHrz6JgwjU/1xqbUQriiFZV\nWwlOqu7X51Q0N/LrxWRmhvTExYxCG4DDabkcSMlmfJ8wXp4rTehfXlHP598dYMeeFAL9nZk9LY4B\n8UFE9PCUFOG1tlIzID6IAfHtD46a2iaSkvPZuTeFZV/tZfOOczx6/0j69BJ/4zrZWvHGvDEsWLya\n11bu4MP7p5j1/s/uGcXHx4+y4uRxvpwyXfL4jhBg5Ya7hT3N+s79vNV/RHhbDOa5a3hrdLhZSEuL\nXWhKZaiXA3IRG7/r8VPqWdysrBnhJ0EfmlvE1/tOMiM+kmER0ohOWWU9H32zl33HLuLr6cB/X50l\nmZhKgYVayZuPT+S2KXH0CHSDuXDiXB7f/ZrIk2+u4eE7hjFrYqyka+6ZiUOY8clK3tm8j35BPmck\nb6cAACAASURBVKKj5HNDevHIgd85WJTDUC9pGY0Y+0AuOPmQ15RHpJ35lmYAKpkKAQGtmddoi76U\nVt1RHNT3iDq+WtuAp6UTvR2krwt6g4Evz58kytmNfh7is5Dvrt9LRnEFn94zDQ976cVHRqORnftS\n+eTrfdTXNzNnejyjh4YT6OfS5fXBxcmGYQNsGDYglNSMYr788SDLvt3Hz7+f5I7Z/Zk4MgqlUhqJ\nt1AqeG/eeOZ8+BMzlvxARX0jRqCHlyu9O/BgdrC0ZFZEJKuSz/FU/4G4W0t7rzwt7XG1sCWp+hJz\nAzqX08gFGfYqW6pa/1pSbKW+QC9H8T0AztbmUKTNp7/bjc+izJoKUqpKeTV+pKQ5HMjKpbq5mcdG\nDMRHopsQwFfrEtl/OpOhMUG8cNco7MwIGHUV/yifYmsrC0YODefjD27js//ezuABofy+5QwL/vUl\nn3y2m9Iy8zx0HawtWXz7REprG/hk25Ebfj88JJAJUaEsPnqYVp34bk5xTgGoZAoOl1/s/GAR8LaU\n4yA7Qouua618L0eKxaCyrRRHVffrdLbmZmCtVHFvlPhIx9Wob27ljVU7qWls4fX54itXW1u1/LD6\nKPPv+5K9By4wb3Y/lv1nHg/cOZTont5dLjixt9MwfGAYi16azjuvTKdNq+PJl3/h1X9vkHR99vBx\n5fGpg9ifnM2q/eKzA1dDo1Ryb1wchW21FDd0c+txtTXrhjzPMLfOmw6oZV2LwpW2pqCQiffn1hn0\n5DaUEGQjXXpSUF/L/ks5zOkRKbr4o6m1jRd/3oaHvQ3PTRYvHwC4mFvG4i92cfhUNvfNHch3H9z+\nlxLiyxAEgfCg9s2uIAgk9PLnPy/OYEBcEP/9Zi9v/3crra3ii8zUSgXPTxzGpcoafjgivnnMGN8Q\nHNSWrL54tvODr0OQjSdlbWfIb5LW0tkUHBQGYjXZWAjm3SeNfzyTNQpxUbM4x1B+GvACbhbSLdCO\nFOWRV1fDfb3EF3EfTs/lQFoO946IZ3APcZH8q1FQVM1Tr67h7aVb8HCz44ult/PAHUMJ8nft9oBJ\nRKgHS96YzX/fnouHmx2LV+zkvmd+IC1Dmv0fwJpj59FjpLyu8YqPiFyEZOSe3nHojUbWXkiV/JqC\nIBDr6EtSpXjLQCeV/V8aKW7Vt9Ksb8ZOKV6/m1Kbj6uFHc4WN47ZV5SFUiYwyV+au9HyQ8eobGlm\nfnwvSeMAth5O5Yt1RwnxceHdhZP+FkIM/zBSfDXCQtx58emJ/PzdAyy4tT8nT+Xyr4e/IfG4eRqz\naD8Pnpg4iNWJZzl6Me+G38+M6klFUxNbM8T3NtcoVMQ6+nG4rHtIsaW8PRrerO9aYwatoVlUpNlg\nNFDdVobTX0CKf7h4iiBXe4LszYvwL91wgPLaRt6YN0a0ldClgkre+2gbX/5wkIQ+AXy/4h7uu2MI\nGk33dxcTBIGBCcF8u+xu7p0/iMQT2Sx48Cu+XXUEg0hv2NuG9WZIZABL1x/kQr55HRLHBIaQVlXO\ntlzx121343Jlf6uZzRGadI1YKcTr5y41laE16gm0lq6D/yXtPIIgMCdcfOHnL8fPUVnfxKI5Y7Gy\nEK+DzC+q5sk3f+Vidimfv3Mbd8zs95cVFouBxlLFomemcs+cAWw/kMrCV3+mtk681/rgUH+G9wjk\n073HKK8TZ92oliuYGRTJzkuZlDdL65JnpbDCQelIQXPXvdPlMhWCAAZje+bDRtZMb00NFjJxnU2b\ndFXIUGAh6177J1NYlXWGQGc7xgeI66yo0xtYtHEvjnYaFo6RXoS8/3A6by3ZTGpGMY/fP5Ll791G\ncICr5PNIRe9IH5a9cyv/eWUmTvZWPPLCKo6ekLa+O12WMV3Fg8VIIH3s7JgYEcrK9CSz/NX7u/jS\n08mC8hZxRNdRZXeloPivQO0ftRt2SvHR2bTaQiLsTEtFthUn08/HHVeN+OdyUkERSQXF3JkQi0Ki\ndOd0Wj5vf7GDPuE+vHDP6G7fiEn5jP+xpPgynBytuXvBYN5+bTouLrY8/+qvfPHNfnR6aab0AHMG\n9MLd3oaPth2+4U0a5OdHkKMj3yVJa6E70DWE7IZyipu7vgu8HIVo7mKk2F5hjYdF52m3em01OqMW\nR3X3kuKcuioyaisY7xtm1vgjaXn8diSZ20f0Ee3QkHqhiIVP/8TpM3m89/pM3nppGp4ef31DD7VK\nwYI5/Vm54h4GJARx9HgWryzagE7XebdEQRB4Y/5Y7K0seO6bLTSZ0Xo8yN6RMAdntub8faT4cqS4\n1YxIsdFopFFfj0Yu/uGb1dAeUQqylh4pPlCWzcyIcLxsxEVUqhqa+HjnUUbHhBAXKL4Qt6yynife\nXIPRaGTpq7MI/R+QDDGQyQTumj2Afz8/HbVKwRNv/kpTs/jr7tkJQ9HqDSzZcUj0mDnB0fR282Bf\ncabk+XprvClsNr9b4mXIaNetGozaP/5vBGMNYpfeRn01GoWDqDqNrqBFp2VfYTYJHj6iMxkbTqeS\nV1HDw6P6S86E7dibwuvvb8TKQsXK5fcwY2JstxS5i4UgCPTrE8jLT07E39eZF99Zz95D6aLHPzSm\nP4vmjEF51ZzFdusb5O1PUWM95yvEWU9ejUBbJy40ZJDfLK7BUYStE5G24olilI0TrhIcaGq07ZzB\nXiQprm1rIr+pkp52N/KEBm0rKTXFxDpLMxD45thpbC3UzIy5URrbEfKKq3juo9/xdrPn349N7rZu\ni1UVDaxdlcgLj/7Ij18fFD3uH0+KL8Pby5HlS+czaXwvfvw5kTff2UB9vbTIlFqp4OEx/UnOL2XH\n+et9PQUWxMRwrqSUM8Xi0ziDXEIAuiVafDlS3NTFSLGl8TT2Qnanx9VqS3FTe3Z7pHh7fjtBG+Mj\nLtJxNRqaW3lz9U4C3Bx5aII4p4nEE1k88cLPWFupWbZ4Hv3iu941TypcXWx5/bkpjBvRk0NHL7Jo\n8Wb0IjZuDtaWvHPHeJQKGT8flJ5iBhgfGMqJ4gLKmrq/8YoYqGRqEuw9sb7O8zVMU0e0dceSklZD\nC3qjXlKkOKu+CKUgx1cjjWjm19dwpqKYCGfx41YlnqVFq+PuIXGix9TUNfHkm2uoa2jhg5dniipO\n+l9jUHwQ86YnkJlXzlsfbRF1rQL4Odlzx8BYtpzLIKNEnH1kiIMzVbp6thdckDxPL0tviluK0BnE\ny9pMQf7Hxs1gpvtEk65atHSiKzhUnEuTTss4kQGFNp2O5bsSifJxZ3iENM32xu1neWfpFmIifVj0\n8jScnaS5HXQn7G01fPj2HCJCPXjjg41s3Z0seuzUuJ78uHDulYziiSxxmYVRfkHIBYHtOdLXbj9N\ne4FvXqM4Umwl11LYfAy9sfNgCYBgPIuFIJ6s/xkpFmdrmVbXbsUabiJSfLoyH73RSIKL+DqZ/Opa\ndlzIZE5sFFYq8dm06romnvhgHXKZjCVPTcO2i77WjQ2t7Nh0hucfWcltk5ayYukObGwscHUTLyv5\nf0OKAdRqJU8/No4Xnp5IXn4lL7+5lrY2aQ/LybHh2FmqeWrlZmqu65U9vWcE1ioV350WHy0OsHZh\nqFswRc3Sd5vXw1Levstr1okjxQIGAjU90FwXUTAYW5ELnUsGtPoC7ITTuKi792G//VIGUY7ueFtL\nazcJ8OvR87g5WPP6raNEySYOJ2by4htr8fVx5JMP5uHl8fe2MZ0+OZb77xrKnv0XWLp8p6i0TXyo\nD8Fezny27Ri1jdIlCBMCwjAC23O7R8YjFQqZAr3uBDKuvQf0xmZ0ho6JepO+/fdSSHFOQzF+Vm4o\nJDbt2F/Yrk0d4iVOc9ncpuWno2cYFh5IkKs4YtvY1MpTb/9GUVkd778wnR5B0t0x/lfoHxvIo3cN\n5+CJTFasPCB63H1D43F3suGLw+Lb7Q52DyCxLI9WvbTntbelN3qjntJWceTjZpDRvlDrjdKzMQD+\nVvGE2UrryNmka+K9C++QXHtO9JhtlzKwUarpZ6L4yRR+OXaektp6HhszQFLKecPWM3zwyQ4SYgP4\n96szsJQgC/qrYG2l5oM3biE2ypd3P9rK2s3i24RHeLvxn9vGA3Ak40Z5pCk4WFjS18PHLOmZndIa\nW4UVl5rEXZcaRbvspkX/1wQuarU1f8xLXKS4sKkMB5UFEbY3Zr+Ol+ehEGT0dhKfGfvu+GlkgsCC\nOPFOU0ajkS/XHqW8qp7/PDkVL1fzM7vZGSV88dFO5oxfzAdv/k5xQTVz7xzElz8/yIuLZjJmkniv\n7/9XpPgyxo6K5PZbB3D2fD7vLt6MwSBeLyKXyQjzbI8UTVnyHReK/vQFtlapmBUZyYHcXKqaxGnt\nBEHAXg3HqsTvbG8+NxUahRNag7jXlgMq3V5UwrU3msHQgkwEKb6sXb4coe4OlDbVk1RRxFhf6VHi\nljYd3+w9iZ2NJTGBnVuMXbhYzKLFmxg6KIyP/j0Xx262tzIXt83qy7zZ/di49Swrvt4vihjfMzqB\n5jYtqw5IL7oLcXAiyN6RrdnSH+4ptWmk1YlPV94MckFlFuFo09fR29YLe6V4UmyUZdPPRfo1e6Ao\nBy8rWwJtxY1ddyqFmqYW7h7cR9TxOr2BDz7fRWZuOW8/PZmYnt3jX/5X4pYJscwYF8Oq30+ybV+K\nqDHWFmoGBvuxPfUitc3iNnGD3ANp1mtJqiyUND9vS296WAdS1dZFUixcK5+QihjH6cQ63iJpTElL\nCRcbMtCKjHLrDAZ2F2Qy0jtYlC62qU3L53uPkxDoQ79g8cWbp87msWHrGYYOCOXtl6aZbYn2V8DS\nQsW7r8xgYEIwH362u71ZiEiMiAxmZkIkh9PzqKgXp18f6x9CVk0VmTXiPYShfd33tXIXTYot5e2k\nuEnfcaFnReMOcqrex0lWjKA9Qk7V+zRpO8/6WsnKGOQgw0oubg281HIRP1st1sobI7PHy/OIcvBE\noxC3UWrV6citrWF6rwjcbcU/x/efyuTX3Wd5bN4wooLN65NQUVbH4tfX89DcFSSfymX8lN589NXd\nfLt2IXc+MBzfABfJ5/x/SYoBRg6P4P57hrF3/wU+/3qfpLELBrXvZqoampn735/4/sDpK8R6RmQE\n9bSyPVu8/i3U1pvC5goadM2dH9wJgtWN2AhdM6w3GNuQ/R915xkYR3V24WdmtmvVe7Ek9ybb2Lhg\nDLYxPbSEEAgtQIAEAoSEBNL4EkgIoSShh4QWOpgECB2MMbZxAeOCbbkXWbZ6l3a1fWa+H6uVZFna\nubNaEji/bHlmdqy9c+fcc897Xsl4G8IfaUORbNjk5G2brW2sJt1m59QErBPvbNxBe7ef7y0wJiHe\n7iC33/UWKSl2fnrdyV9KMd1wcPVlx/PNM6bz8qvreP6VTw2PH1uUw4KKUby4YpNpb7EkSXxr/CQC\ncojusLlzXzr0Km/UvWvqnMGgyDYiCZBiXe9CUz8lXdBHpuoqzcEWMmzmuiSFNZU19dXMLx4ppKip\nmsazqzYybUQhM8rFMqDfXVbJ2g37uenqkzj26P++hSdR/Pj7izj5+Ik8/spqmlvF0hnOm1FBSFV5\na4sYaZmTV4ZFklnVYPyC7488Rx7eyAY6QgdMnTcQFsnCGLsLtzT8OVoUDYGoDa/QKeZ939RcR3vQ\nz2mCgsKSbXsoy83gupOPEVaJPd4Ad97/LqGIyq9v+sb/tPBzKNhtFv7wy7M5fVEFd973LgdrxO2E\nVyycSUhVeWGVmLhwSnnU/ris2ty4BCh1RUmxiOjh6iHF/kj856um6zFquh4jQ2oGdRM1XY/R5vvI\n8Pp+tYWQ1oUs6EOv9zdR6DiSMPoiIXZ2NpiyTnx68BDLDxzglIljhc+JqBqP/ms15UVZfPME891u\nfd1BnnnkI75/zoN8/O4Wzr1kLnc8fAnX3Xw6E6eUDKtQ72tLigG+e95svnnWDP7z9iY+WSOuktn6\nvYAjmsY9b6/grjeXAzAxJ5dcVworDx4Qvt7Ynir4vZ6hW1+KwiK7iWiJRc/FoOoBZMl4leeLtOJU\nhtdJayA+adqL2yUxJt2cj1LXdZ5bsZEJxXkcPTo+CdF1nXsffJ/Gpk5+d8vZpKUmJ7ol4AsJJ0cY\nQZIkbrz2JE4+YRLvL61k3QbjSffKk2fR6Qvw6hrzuw6Tc/LY2FLLllZzsUa59hyag8NvLZ6oUhzb\nFbEIRgi2h9rR0MixiXf5AtjYVIc3HGKBoHXis/2H0CW4/HixPF+fP8QTL69iZGkOZ580/LbERji0\nt5HfXv4Yrz728bCvZVFkrrpwHq3tPv7x0mqhcyYV5jGpMI9/bdwqRApSrXam5xTziUlSbJMdOJVU\nOkKJpbPEIEsWJK0aiI43pwwjHSVYEfN4JoKGQD2KpJBjE1Or1jTvoyjdwbxCMULy6sZK2oMBZo4U\n3+a+7+9LaevwcetPz/ivdQhLBBaLwg8vm4/dZuG+R8VsaADluZmcPGUsi9duxh8y3hUodKdyXPkI\nVjWbJ8Xj3AVUpGXTGTZ+XzstMaU4/rFFqZcCIEnRQA1ZspPnPtfw+kHVi91EsXJ9oJlC55Hjcq+n\nAbcryIxc8SLmZfuqcFmtHGOiI/B7q7dzoK6Na78zz3RxaN2hNu67/Q1efGIlcxeM54nXb+AHN51K\nalpyeMDXmhRLksQN15zItKkjuPf+9+nsElMBBnb6cVgtTC7J673m/LJyVh+qJqyKTZhjU6ODYY/H\n3NbgYLDIaYS14WXOuixF2BVjf45fbcVpSW4R0Ja2OqZlF5sm2qt3VrO/sY3vLTAmIW++9wXLV+3i\nqu/Np2JS4p3cWhs7Wfafjdz3i1e4YsGd/OHap/nmpF/zg5Pv4bfff4K/3fY6rz+5kt1bDiVElmVZ\n4hc/PT06sT+ylHA4/niaNrKImWNKeHbZBkJhc97Lo7KjC7NNzeYWZrn2HFqCbWj68BYDimRD1RIn\nxVZZrGNZSzC6zZkr2G43hm2d9RxdlM+cArFt5re27KQzHOSEiWKK70tvfk5bh4/rL1uY9Dih/ggF\nI7xw3/tce8rdfL5sO6vfS6w4cyCK8zM4/4wZvLdiGzv2iW0Jf2dGBbsaW6isE6unWFQ0hjS7ja6Q\nObU2w5pHZ7jZ+MA4kKSYIhp9rhQiWNRKZMl8HJcoGgL15NpzschiauyWtjqyHC5Sbca7fI1dXjZU\n13L6FPGEn6UrdrB05Q4u/+6xTBj71fW6x5CVmcIPLpvPxi0Heem1dTz14mpu+NVL7KuKv0C6+Lij\nSE93snS72G7viNQMvmipQzMZzVbgTKU2UEmHQPvmFNlNhXsEsh7f1pGbciYOS2lv5nJR6mXYFON3\ndED14BDc8e0Ke/FGfBQ6jiw43u9tIqhFGJsmVnyv6zof79vPvPJS7IINfYKhCI+/tpbJowpYcLS5\n5ja11a3cfNU/+eLz/dz56KX86q7vUFCc3Dqi/wkprq1q5tHfvsr7L61l1xfVBExEAg2Eosj88IqF\neLuD/PM5sZigWAGXBNjtFt77xRWcM7MvRmRh2Ug8oRAbGsQIRpY9lRx7WpJIceqwleJsbT3pkrEv\nOaIFcCXRT9wR8nPA28a0LPP+oOdWbCAvLYVTjzLeOqyYVMK3zpzBd7892/TnhIIRlv57HVefdDeX\nzP0D9970Ims+2MrIiUVUzBrJOZcfR+nYfFqbulj62noe++ObPPzb17jh7PtZ9/EO05mWVovCD69Y\nQF1DB2+8a7yld/mJMynOTWPNbvFgeIgWjYxKy2JTizlSnGfPIaJHaB9msHyBvYBU5fDdiQJpFyOt\n8V9gvaRYEiXFUVU7R7DdbgybWmtoC3WTaTdWEzRNZ+WeKo4fWy4UD9TS5uWlNz7nxHnjmTzOfEzc\nUAgFwxzc08AXq3fz1rOruPm8B7lg2q95/r73USPRRUxLffIaAlx27hwy0108+PRyoXF+RsV4HBYL\n728T26WbkJnLhvZ97O4yp/qm23LpCA9PKZZ6Itl0fXgpFmbQEKinwCE2HnRdZ0tbHVME5873K3ej\n6/CNCjGrhccb4MOV25k8vpCLzzPuwvZVgTvFjs1u4e/PrOTpl9eweVsNh+riR5ZOLyvGGwyxfIeY\n+ntUTiFdoSAHPOaiUDNt0XdnW8jY3uG0ONHVtUh6/AWkJCmUZdzYExUoU5wu1kExoHmwK2KWsvpA\ndIE5mH1iv7cJu2yl0ClW9LaruYW6Lg8njBZPPnnto800tnr40QXHmRIQDh1o4edX/ZNwWOWex67g\n6GOG30V4MPxPDEWaqrHklc8I+KJkWJIkTj5/FqdfdCwTppebvt6okbmcc8Z03nhnE2eePo0xo+JH\nLk0ZUcBt3z6JkKZyx9sfs6+5jdx+BvFjR5RikWVWVh/gmGKxYpk52eOJJKCUDYRFTiMiqBRL6GTY\nZ2E9oqgujCQZb4252UG2dX4Cdzk4trZFCdlUk6S4uctLq8/PhfOnC5GQ0eW5/OTak0zf377ttfzl\nZy9StbOemQsncuoFczhq7hhGTiwaNJtT13U8HT7WfljJy39bxu+ufJJJR5dzxc3foGK2+CQw++iR\nzJhWyrMvreH0kytIieN/PnZiGb95+X2WfLGLhZNNtsXNKWJlXRW6rgtPNjHFtTnYQrY98QVShrQP\nG4ePW5GuimaV4uZgCxISWTZz97qjo5EJGWLqx9baBtq6/SwYJ2a1eOLl1URUjR9cJN5iNR662rt5\n/fHlbPt8H1s/i/9ib6nv5K2nV7Lo27NIGaaNKMVl5+oL5nHPYx+yZuN+5hn4otOcDk6bOo63d+zi\n5ycfbzjmxqVF5+XdXY3MzBH3LGZYc9nn2WRqXA9ETCnWEyy0Mwtd18mxOxmTIrYzcai7g/aQX1hQ\neK9yNxMLchkpWHD61odbWLNhP/+45+Jhd/X8b+KDj7cTHJAwVVQQn7DJssSC8SP5cNtewqp6xM7w\nQEzLiS5cNrfUCRfhAmRaowpluwAptvTMbxHdWKzKTTmTXS0/Q0IWUokhap/ItInZF5oCLYBOoXNw\npbjcnYMimMe9bF800WfhqHKx+wxF+LSymlmTS5k5Sbw4tHpfE7/44TPous49j19O+egvL/f9f/J0\njBiTz6vb7+LJlbdy62Pf59wfLGTd0m389Oz7uOfHz9JssBIcDFdcOg+328FDf//IUOWQZYnz5kzh\nW0dPxmWz8u6Ww6vvU212ji4sYvUhsWgXgAybwhddXyTUHac/okqxGCmWJUhVP8VGXwW4rquAJkSK\nw5oHm8VcwVI8bGmrQwIqMs2pZWv3HGRnfTPHjhd/UZpBJKzy4oNLuPGc++ho9XLbE1fyh39ezXlX\nL2RMRcmQYfWSJJGWmcKp58/hsSU3c/0fzqXhUBurP9hq6vMlSeKHVyygs8vPy/9eF/dYRZaZN76c\n1TurUU1aNqbnFtES6KamW7xzUq49m2JHJl3h4SmOEpaEKvstUoQ8ewEWgQ6MADa5jWOzC7DK4n5I\nfyTMAU8bEzPEJtIVu6uQJYnjxpQbHlvb0MHHa3fx7dOnU2zwsjZCZ5uXp+96i8vn3s7LDy3Bne7i\np3++kLsXX8fjH/+KF9b/nkc/vIVzvj8fpzu6sNJ1nSfvfJPrTr2H5W+IR1gNhTMXVXDM9HJeemeD\n0PEVRfnUe7zUdhrvbuU70kizOtjdZS6+MttWQK69mIBB5X589JBikqcUN3R/SJNv8Cg7n+qlzr8Z\nt0VsnG4xISjUtney+VA9p08R7HgXUXn17Y3MmFLKpHGJVfkPet2wStW2Gjat3MlnS7ay8o0NfLh4\nLZtX7cTTId4pMR5+/dPTGV1+uKJpRIoBFk4YhScQZFO18c7Z2PQcXBYrm1vM1WO4LalYJCvtIWO+\nokg2JCxEBJKlJElBllLIdJ0gfC9jnTZK7GIRqH61jlKnlxz7kb/H/d4mRrvF+xbUeDo5flQZeW4x\n68b6HQdZu+0A3zlFPCItHI7wz4c/IiXVzr2PX/GlEmL4HynFALIsU1SeQ1F5DvNOm8pFN57KK48s\n5bXHP2brp/u49vZvc+zp4lWJaalOrrhkHss/2cWWyhqmTTFWeJ02K9+YOp7Wbh+apiP365k+r7SM\nRzd9ij8cxmk1ntjyHJkE1CDeiI9Ua+LRYOmKi7ASQtcj/Xxw4tCJqdXx71nTQ2h6AIuUvLal+z0t\njEzNJnWQmJd4WLO7mqwUJ+MLzcenGCESVnnq7nd4/cnlLDx7Otfedi5pCUS3WW0Wzrj4WE769iwi\nJrOxASaMK2TR/Am88vp6vnnmdLKzhp5Ejp80knc27qTyYCPTBDv6AUzNKeCY4mL2djYzwi1G0LJs\nGUAVPtXcC2EgJMmaEOFwUkuOtA1ZEkuf6Aw3EjLIPh6IPZ3N6MB4QVK8fHcV00cUkuEyHsdvfrQV\nTZK48JxZpu5pILZ9vp9X/7GMT5dUcvwZR3HhjadQPuFIApOVl8Y1t53LVb85h88/3k5TTRsTZpTz\n99+9xt3XP8NnSyu57o7zcKeLKe8DoSgyFeOLefyV1TS2dJGfE3/RPKunuObzg7WUZMR/KUuSxLi0\nfPaYtU9YnYQim/Gr7TgTXMRLkkSu/WicggWdItjX8QQ2JYs815G7bZ6erqSpVjG/4+a2OhyKhbHp\nxnOgJElccsxRwn7ij1fvoqnVw8+uPVnoeCOEQxGWvLSGl+57j/QsN/u3Hd5xcMy0Uqp31XPcmdM5\n9cJjmXLsWOFUhIHISHPxwB8v4IZfvUzVwRYsFhl3inHS0NwxpVgVheU7q5g9Kj4XUGSZiqx806RY\nkiQybZl0hMXSMSyySzxuVXJgU8Tfh57QJrIdE8WODXdhla04lcPnN284QGOgk5FusXlS03Xe2rOT\n8yaLd7BbsXEfKQ4bc6eUC5/z1uJ1rF2+kzseupjSUcnnCAPxlcljcbkdXP6LMzn9ork8fscb3HHN\nP/n5fRez6FzxTlLfOGUK/3hmBR8sqxQixQAVIwr43ZtLOdTeSVl2H5EYk5mFLxJhX0cbK2x/WAAA\nIABJREFUFbnGK6fcnq3n5mDbsEixLFlB70TTAyhSAlFpPWqdkVIca6pgUZJHird3HWJyljmVWNd1\n1u4+yDFjSw9blCQLT939Dq8/tZJrbjuXcy477oh/V1WN1W9tpHZ/Iwd21OFw2XGk2HCm2Bk3vZwp\nc8eR2o9E2x1W7I7Eqravuux49u5vYtnKnXznm0OP63njy5EliVU7qkyR4lGpWaxvrWaRZzQnIBaP\nY5cdKJIFn0FUkBEkyZrQ1rSuh5AQbxzgi3T1xhuJYkdHVJmcKGCfaOv24QuFOEWwwO6Tz/cyaUwB\nOZmJxxpuX1/F/136d7Ly03jw3Z8zpsJ4G9RiVZh7Sl/KxZ9f/TGLH17KC/e/T2tDBzfecyHFCWR0\nApw8bzyPv7Kaj9bu4qKz4pP9sbnZpDvsfH6wlm9NnWR47XFpebxxaLMpK4SjxysZMKjcN4I1sh1F\nPwYA3fso6dZxWJTEC3XDmheXdfAtYE9P291UwS54zcEOFhSOxCrQkKYoI43fnCGmIuq6zstvrKes\nJItjZpizYw2EGlFZ9u91vPDnd2g81Mqk2aP5xveOI68kG5vDisNpw+aw0t7cxfLX1rPs1XV8/Orn\nTJ8/gQt+fCrTjhMvCuyPtFQnD991IWdc9BCRiIaqaoatqFPsNmaPKmHFzv3c8g1ji+DsomI+qttt\n2qIzMqUYuyy2o2eVXUL2CbPQdbWnYZfYgq8r0knqIJ3vGoNtTMxIYUyqmIWkpquT7nCY8dliRc+q\nprFy0z6OnTpSOAqwq9PHC4+v5Oi5o5l1nPmY10TwlTMX5Y/I5uYHLmHq3DH85aYXWPGm+Jag3W5l\n/rHjWLF6t3D1/uSeRh7bBlRQj82Kenl2tYnFVeU5osc3BYfXojmWL6zr5jubAWhaCElyIxlEssUS\nLqxyckixpmvU+zspcpnrYre7voVWr4+545JvnVj+5iZef2ol51x+3BGEOBKOqh0/nPc77rzqMT5f\nWsnezdWs/6iSpS+v5ZUHP+CZP/6Hy2b8iufveYvuruFPZsWFmRTkp/Pme/EL7tJTHEwrL2TljipT\n10+1OciwOanpFrdCSJJEipJK9zAJh4wlIVKs6UGhRjMxdKtduEyqhR1hL8cVllCSYqyeV9Y3UdXZ\nwYxyY2JaXdtKdW0bx89KvOBjx4Yqbr30UbLy0rh78Q1ChHgwKBaFi35yKn95/UY6Wrv53RWP4e1M\nbMyWFGQycXQBH642ziCWJYmZpcWsP1hjeCzAhPQCxqTm0hAQt/g4lOicMlxSjCRDT5tdi+zAZSlE\nEfRsDoaI7sUyRMV/V49SnCaoFO/qasRmSf7ruLW9m44uH+efPXNYokN9dQv3Xv80f73xWdKyUvjD\nS9fz5zd/xonfOYYpc8cyfno5ZROKKCzPZdKs0fzoTxfwwuY/cfPDl6HrOr/57kN88OKahD8/1e3g\ne9+JFghu3SFW0L5gwkgsdkWokUeW3cWermY6TCajpFoCdIV3CB2bZSvGKbj7K0kWRINR1B6uoAju\ngnjCnaQOMoe2BjupD7SQbRdb4O9sjnKjCTlii+/KffW0dflYMEM8w/2lx1fi8wa4+qenCp8zXHzl\nSDGA3WHjtqeuYtKsUbzz3Gr2bBHrZQ5w0oJJeLuDfLZejFCMzcvBqihHkOLy9ExsssJuUVIcU4oD\nwyXF0YGt6YmFzMuSSo4cwmlAimO+ZUuSSHFrsJuQplLsMuerjLXknDtW3HQvgqodddz/y1eomDWS\nq3511mH/tu7DrXx/1q389YansTtt/PrJH3DvWzfz5Lo7eKHyHl7d/wDvNDzKLX+/kqPmT+T5e97i\nshm/Zv0ysY5f8TBn1igO1rRRWx/fhzZ/0ijcDhsd3ebGQUlKBodMkGKAFEsa3cNUii1yCpYB5DZf\nCZOvxCfKZkmxL+IhRbDKOoZdngaagp3IAgrQvuZovNJYgbbOn3webbJz/KzEGnU01rTxu8sfIzMn\nlbsWX092gfm26AMxYXo5P/7T+TQcbOWeG59HVROL2jt53gR2VTVxsM54PptZWsKBtg6avcbko9CV\nxg7vQRr8JkixnBylONoDNHm5xBHNi3UIUtynFBvPh5quU+froMiZ+Pff4fWz42DjEWJQTpabxX+/\nmtNPqEj42p72bn578SPsWF/Fb568mgc++CUzF002VFTtThuLzpvDrU/9gKnzxnH/Tc/z7N1vJVx3\nc/F3jsFhtwp3uRuZm8Xuxhb2txh7fotTor/7Wp/4uARwKCkEVLHueZlSLU5drDFXmcVDjkWscF/V\nou8IUaXYE+kk1XLkWGsLRd8BmYKNkXa1tiABY7PFFpYrNuzFosgcO1WsgLnuUBtvLl7HKWdPZ+RY\ncZ/zcPGVJMUADpedX//tcg7ta+SRW/8lnBM746gyMtJdLF2+Xeh4m0VhfH4O2+oO97lZZJlRGZns\naRdr/5hqSWFUSgnBYSZQSLK4Uixhw2qbi6z09wD1/J4MPJqS1k2WxYVNoPOdCOp8USJWZJIUN3R5\nWDhpFAUZybNx6LrOG8+sIj3bza8evhSLte93sfTlNTxyy4tk5Kbx+5du4OFltzL/nJlHbMdJksTo\nKaX89plreeij33DU8RO49fwH+OAFsdi/oXDMzOgWptGibWxRDuv211DVZG6RVZKSYUophuSQ4hyl\nmxzZXAEVxLovipFiTVfxq15cFnNjpTngIdchds6+ljayXE4yXcYvmJXr9jJ+VL6h73YoPPTLxeSV\nZHHXKzeQUzi8Ir3+qJgzmmtuP5fPl23nw3/FL+wcCifOHUeK08bG7caCREVhHoXpbnY3GQsIOY4o\niWwNivvC++wT5gjLQEgo6CSnOY+mh9H04JBKsYTGmJRJ2BXjcRQTFMzOnf3x6Y5qLv7TixxsOvLZ\nt1gUrFYxz/5AhEMR/vD9x2g42MrND1/OcWeKNbPpj5RUJ79//jpOuXAu65dt491nP0noXpwOG8fO\nHs3Hq3cREVjsjepJ5ogtdOOhuGeHs85EkTKAQ3YLk2IJG7oeNHV9EcQsGcKkONxJ2iD2ibZQdNGZ\nbRObK3e2NFOakUGKTcz+Vt3YzsyJpbgFu8++8PhyLFaF7/1okdDxycJXlhQDZOam8v1fnsWuTdUs\n/49YNbRFkTnh+PHs3d+EPyBGUCcX5bG9vumIFezYrBxhpViSJEJaF03B4RUsmVGKZSUbl2RF6n6q\n74d6TAmJ/9VqBNC0VhQ5Oe2Ra3tJsTm1Y93+GrQkB+jv3FTNB6+s44JrFpGV20dY9m45yIM3PU9B\naTb3vfcLZp88RWiCHzutjFsevZIZJ0zi/p88x8o31id8byVFmRQXZfDZ+vhRW+W50W3XA83mCO6I\nlAxqujtNBdG7FDdhLTG7Th9k9ARUOElXUQQzioOaj8mpJWQPMqHHQ1PAQ54gKd7b3MqYXGPlo8Pj\nY3dVI/NnJ2ad2PVFNRtW7GTB2TPITSIhjuGMS+ax6NyZPHbbawnZKHKzUsnJdrPmC+MdtxEZ6dR7\nvNQIJFDk2qPfQ0tQfBFmlZ04lcyEOiYehn72ieEionlJs43HJg9uj+gK1+EJi2WN1yU4d/bH/vo2\nFFmiNC+5Y+lvv17M1rV7uOn+S6kYRi6sxapw418uJjM3jcdve5X66sS6aC46bjzBUJide4zfswVp\nblw2K/ubjYWFomEoxWE9SEQzto5Jkg2N4ce2HgE9SLmzAqdArKWu6xQ4dPIHsUi0BbtIsTiwK2Ik\nN6SqTM0XU3ADwTArtuynQjDH3e8LsnHtPs65cA7ZuckTzETwlSbFACeeN4uxU0ew+JGlhAWr/qdN\nGUFNfTtVgg/exMI8RmSmU995+EQ9PjsHh9UqHI3lVBz4IonZHmJQJAdu6zgkLdEVZexe4ysDWs+K\n1czWdTzU9UwmRYKh3xB9QGtaOxmRNfxt4/748NX12J1WFp4zvfdnnvZu7rjsUdKy3PzqiR+gCOQh\n94fNYeV3z/6IE88/hrHTxPzP7/9nAx+/f2R82zEzR7Nxy0GCwaEn0qLMNCyyzEGBrb/+GJmaxfSc\nQloC4kpcjs2ClWG2KJcS25pOlTvIVkS3CQM0B7ZikcUJv67rtAS85DqMfXK6rrOvpY3RAtmv2/c2\ngCIxZUJihVqLH/oQd7qLMy6dl9D5RpAkiXOvXoi/O8i7LyTm5Rw5IocDtcaEIi/VjSJJ1AmQ4gyb\nC0WSaQ6Ik2JJknBIfiLq8NqRW60zUOTkNCvS9ABqeCvWIRbVIS2ATdDj2Tt3DkMp3l/fyojcDOEC\nJhHs2FDF3s0HueDHp3LCucNLV4Fo4tT191yIrCg8dMuLCdkojqoYgT8UYfN2Yw+7JEmMzs1ir4BS\nnGlz4lSs1JpVipVoEXZAM1aLZcn2peRkSwQIh1ZjkYx5SkQP0xFuRBkkDaQt5CFL0DoB8Gn9IXLc\nYoJGTY+4U5Yv5rHfsaWGthYv02aWC99PsvCVJ8WyLHPRT06lqbadjSt3GZ8AjC7PA0nioMCEDlCc\nmc62xqYjSLHLamVvRyuekBhBdVmc+NThKW4SMlpkJ0iJPjxq73XiQU8yKfZFAox0Z5JiFb9ee7cf\nXyhMSRJJccAfYsVbX3DcaVNxuWNWFJ2//+plWus7uPXpa8jITWy72+608fNHrqCwXKyw4IM3N/HO\nq58f8fOZ08spHZHNnv1DR1NZFJmS7HSqm82R4hSblU3t1XSFxcehVbYRGaYKJ6EACSj+umpo9Ykh\ndo8WA798f3RHgvjVsJB9orXbRygSYXS2MXGqb+okFFYpKzLfYrRqRy1rl2zlm1ct6B2jXwZGTy5h\n+vHj+c+TKwgFzcfllRdnUdvYcUTzhIGwyDL5qW7quoyJrizJZNlSaDFhn4CeNuLDHaORbUi6mB3O\nCDFyIw+R8hPS/MKk2BcJMi2rmFzBAqfBsL+hjZGFiRcNDoYlL67h0N5Gzv9x8oqccosyufSWMwgH\nwmxbJ+av7Y+0VCeZ6S5qBDs5jsrNElKKJUniuMIyZBMLbgCH7MRtSSMoYKGQJBv6cHc7BkGsS6NY\nb4Loe986yA5xmlWhIl1MyQ2qEbzhEJlOsTF+sDH6fZXGIcXhcISVH27jgzc38Z+XP0WSwesNDLv3\ng1l85UkxwIz50fy9DSvEqjwL89NRZImaWjFCEcsj7fAfTiQy7D0/D4oRjKZAC7s9Yq0lh4LUQxB0\nPVHvW49aZ0A0YkqxRHJIcXvYR7dq7oGvaY8qS8VJJMVrl1Ti8wY4+bw+ZaN6Rx0bV+zg4lvOZMLM\n4cUSmUH5mHyq9h5pyxk9Mpc9B5o4cCj+C7o8N5MDLWb9wVHC2B0R32mwSDYiw/a6SQltTUctF4Kk\nuGdCH1jQFw9twW7m5JaS7zQmxY0eL35dJSfNOFKxvrkLm1UhK918/OLy/2zEkWLj7MuT101yKHzz\nqoXkFKRT+ele0+eWF2Wh6TqHGozn0cK0VCGlGGCkOxfd5AJKkYdPiqNjNPq5CjLKMF5/mh7rxjr4\nAi2kBrAJNqRpCXrZ2lGD25bYXByOqBxqamdkQXJUcIDOVg8fLv6U2SdNTvrC7YzvzWfvlkN8/Gpi\nfvfiwgzDQuUYxuXnMCIng2DYWGRqDHio9ppbNLktFhzUCtUAKTgMxSoziKit1LdeQ2vHrdgllfau\ne2houx4tThZyzCZ3ZBdcqA004DGoLQmqEd7eu5Nnt36BLunsbGvmg6o9hvd6sDH6fY3IH3o3ZMv6\nA/zxl6/w19v/w2ef7EYH/vTrf/e2tP9v4WtBim12C9OOHcv65WJVp1arQkqKnRVrxJTlWFFNu+9w\n64NZUtwZ9hDWw3RHhhPfFdv+Sqzzkq7rKFIeRhHUybZP+CNhnIq5/N6a1uhWVTLtEzu/qKZgRBZT\n5vSR3/UfVdLe2MmJF8xN2ueIoHx0Ht4uP20th080GT2NFdo64qsLowuySXWa+35SLNHjfRFxAhFV\n4SLDWIgBiRYx6WqPymwMtWfMWkz44P1aiE0dVTgMWr0C+HpenOkOYxJQ19RJQW5aQjFXy9/YwKyF\nk0jNSKzBhhnMOH48Vbvq2bR6t+lzR5ZElUcRC0VxepqQUgyApFLnN0c+FMk+fFKsJ6/Nc0zxk4do\nkhTSAtgUMTLpV0NYJBmbnJj1oa61E5fdRrng1rQI7rvpedSIylHHT0jaNWOwOazMPHEyn76/RbiA\nvj9KCjOpFVSKLRaF9Qdr8QlEtLoUK37V3PiQe96zqm58/RRZxy2J3XeGdTSOSHyyqevdeP1vEQxv\nQgZC4S14fW/HbaIU6plDB1uwhdQwdjn+LtyqQ9Vc/+Hb/HHtcpDh7X27uH6JcaLIoaYOstJcuOO8\nz6YcXR71D/ebUheeWnFYofx/A18LUgwwc+FE6qtbqDvQLHR8lyfAwZp2fH7jiTTD2UN+B5Di9Bgp\nDhiT4jp/A1oPKXi15l2hexwMsS52eoIFIRI6kt6K0ataQiHFOjGhrnmDwaeGcFnEt7UB/OEQ08uK\nyE9PnpF+3bLtjJ9aclgHpfUfVVI+sZjc4uQpKSIYOSZahFC1p88m0djcxX1//xCrVWbN5/t4e8kW\ndu5pGPR8m83C+upaU+2e+5RicQJh7ZkIh2OhSJFdZAxoqpEq20g1mGRBBcEx2GefECfFYS36HFkF\nCIc3GL2+SDV1fVMnRXnmF3OhYITmug5KxxWYPjcRWKwKpWMLOLDTvGd8RGEmiizR2GKsAI/ISCc/\nVUw1t8oKEZPzWzLsE+jNaIEP+v098aI7jfhNktKsWaRbxZoa+FXzgkJ/tHb58PiDZKcmZ5G16u1N\nfLYkWgtx6sXHJuWaAzH3tKm0NXaye1O16XOLCzJoavXErcmIwdXTjdYXMj7WaUmAFPfMXZqIiCX1\n7VQYH2o3tENYLaW4nWcBEjFre7r7MhR5aHtgr1LcT1h4cPfT3LDxNuoCtWzprOTKz3/J1s7BBcV5\nJaXkOA8fZ98aN8mwYL2t00dxbvz50mazcOkPTzjMhXfRlQvinvNl4GtDiscfVcroycVCpDgQCPcq\nOA8/sczweJfNik1RjlCKY6S4M2RMit9vWN775/fql1HnNx9PFUVsVZSYUtyH+INUx48/sj1p2zn+\nSAinYNVqDK1eP5sO1uFI4krQ095NenafN8/nCVC5dg8zT0o8pzNRxHq0H9jXNxbqmzp596NKwhGN\n7bvrueeRD7j2Fy+gaUdOltaemDiR+KEYYkqxOftE9JzIMOIEJSKQQLa2jio+BvUIZa7JpjzFfaRY\nXClOsRkTlPrmLgoNJvnB0HioFV3XKSoXI0zJwMjxhVTtMJ+K47BZcditNLUZK8AhTaWyXqx9s1VS\ner8XUSjDtPhokVgSRCgqOKitENmAFhKz5B1xPT2MVc4akri0hqoJCRRfQXTudAyDFMfmBzNFdiuW\nVPLBm5uO+Pm7z63izquf6CUmge4vIS0BmD5/AuNmlFMbp65iKJQURhXxuibjojhXz7PsF7BPOBQr\n/kiCpFhAKY6+k5NrBchKu4k+FmkhM/VHcY/X9QijU8qwy33j7aCvjrpAExoaYT1CR7gLeQj+4LBY\n+emsvuJgCbh2+hzD+2zs8JAuYMM5+cyjSM+Mku4Zc0ZRmmBXzuHga0OKnW4H+ypr6Go3tias21TV\nSzLeWbKVVZ/G34aQJImSzLQjFLkUq5XpuYWGWwO+iJ+Pm/oqvDU0nj3wL8P7HAw6ClbbbLSEvxqx\nlWiyzet+NWxaKY7dg9ncy6EQCUfo9gQOI5j11c1MmDWKSbMTa7AwHKRluJhydDmq2nc/UyeWkJN9\neEHNOadNG3Qb3tKjdkdMKMUuxcqY1BxTnk2bLJNjcwtO7MmFU07rbc5gBJ0IzYFNSAJV1jHEyJfI\n1nR3TCm2xx/Huq6Tnu4kPU2syKQ/Yov6wrL/Hikun1hEa2Mnno5EbV3Gz6csScIxgFbZYpoUy5Id\n9MR3tSLev/X8KYQaeBuIqt96+NOErqejEdDaScYr1KeGcZqcO/sjNj9YDFof98fSdzbz1iuHe3rf\neOJjHrr58FSItsbhZUMPhdRMN7s3VdNUY77ZldNpxeWyEwgIqL8xUiygFLsstgSU4uhiWxPadZBJ\nqBg5DuzWcTis0fqZFMcpWBSjiDSVxsAu+pPzi8rOOeyIMSllTEobO+QVzp9QQWaPxWxB6UhGZhjb\ndiRJQhKYRyxWhdHjo8V+806cbHj8l4GvDSmOTcweAx8mwIoB/rm7Hniftvb45/lDETr9hysRmg6b\nmuoJqfEH/Pau3YQGqGy7POYrayFqf/CFPidxpdjkQ5ckQprnSCPXbi7VIfYSFXlYRFC9J6rINvQr\nYAsHwlSu2YPyX/YlxbB/dwNtrX1KmyxLnHXK1N6/Z6a7uOri4wc919oTGxeOiBMISYIDviZCArmZ\nMehE8EVaeyf4/yZ0rQtdF0si6Bsn4mM8pEWfo2QqxZIk0dTmISDwoh0IT7sPm91KfmlykwLiwe6w\nkZaVQlAwt70/dMSmCEWSTZBi8/YJlbDYFvUg0CI1qP5/9/494vkLek+8m979dEI+42QWTOXaU5mQ\nlridJta1cGADoniwWhUi4cO/gx092elSvwX6l0WKlZ6W1gPvQQSqquPzh7AIxGo6e+wT3QLPar7T\nzYR0c8qkGaVYQk5a85j+sFqigo/DOkXgHo6cQ6dnTGJiap9odG7JqXGFKquiMDE7ugs6v6Tc8DOD\n4QjBUITmDi//Xr7ZcN6cOiN6zfJR/32VGL5GpLizx9e254v4HZZ0XWfNgKgXb3eQuob4Bnd/ONz7\nAMUguvU6LWMSN4+/lpmZU3HIdv487bc8POOPcc8ZCn0PzXCJotH5yV2xNvo9NPrNtWGNvUOTxMtZ\n0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1aXFJoDnwmoaW5ctjmmO0sNhQJnVPVt9JsjuBUl+XgTaCoQD2dfdhwWq8JrT67oVWll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wzfgB+5iYY6pBMmhvaszqyBA2n0tnbfQ4fIZIfwRwNjPhs5ULWPvNj9z9/S6+WL3olkWkW+np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\n/wFLcM7hi7YQiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd07a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "ax.streamplot(xx, yy, u, v, density=2, color=(u**2 + v**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}


================================================
FILE: src/lectures/basic_scipy/numpy_and_matplotlib.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy and Matplotlib\n",
    "\n",
    "This lecture will introduce NumPy and Matplotlib.\n",
    "These are two of the most fundamental parts of the scientific python \"ecosystem\".\n",
    "Most everything else is built on top of them.\n",
    "\n",
    "\n",
    "<img src=\"https://numpy.org/images/logos/numpy.svg\" width=\"100px\" />\n",
    "\n",
    "**Numpy**: _The fundamental package for scientific computing with Python_\n",
    "\n",
    "- Website: <https://numpy.org/>\n",
    "- GitHub: <https://github.com/numpy/numpy>\n",
    "\n",
    "<img src=\"https://matplotlib.org/_static/logo2_compressed.svg\" width=\"300px\" />\n",
    "\n",
    "**Matlotlib**: _Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python._\n",
    "\n",
    "- Website: <https://matplotlib.org/>\n",
    "- GitHub: <https://github.com/matplotlib/matplotlib>\n",
    "\n",
    "## Importing and Examining a New Package\n",
    "\n",
    "This will be our first experience with _importing_ a package which is not part of the Python [standard library](https://docs.python.org/3/library/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What did we just do? We _imported_ a package. This brings new variables (mostly functions) into our interpreter. We access them as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['In',\n",
       " 'Out',\n",
       " '_',\n",
       " '__',\n",
       " '___',\n",
       " '__builtin__',\n",
       " '__builtins__',\n",
       " '__doc__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " '_dh',\n",
       " '_i',\n",
       " '_i1',\n",
       " '_i2',\n",
       " '_ih',\n",
       " '_ii',\n",
       " '_iii',\n",
       " '_oh',\n",
       " 'exit',\n",
       " 'get_ipython',\n",
       " 'np',\n",
       " 'quit']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find out what is in our namespace\n",
    "dir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ALLOW_THREADS',\n",
       " 'AxisError',\n",
       " 'BUFSIZE',\n",
       " 'Bytes0',\n",
       " 'CLIP',\n",
       " 'ComplexWarning',\n",
       " 'DataSource',\n",
       " 'Datetime64',\n",
       " 'ERR_CALL',\n",
       " 'ERR_DEFAULT',\n",
       " 'ERR_IGNORE',\n",
       " 'ERR_LOG',\n",
       " 'ERR_PRINT',\n",
       " 'ERR_RAISE',\n",
       " 'ERR_WARN',\n",
       " 'FLOATING_POINT_SUPPORT',\n",
       " 'FPE_DIVIDEBYZERO',\n",
       " 'FPE_INVALID',\n",
       " 'FPE_OVERFLOW',\n",
       " 'FPE_UNDERFLOW',\n",
       " 'False_',\n",
       " 'Inf',\n",
       " 'Infinity',\n",
       " 'MAXDIMS',\n",
       " 'MAY_SHARE_BOUNDS',\n",
       " 'MAY_SHARE_EXACT',\n",
       " 'MachAr',\n",
       " 'ModuleDeprecationWarning',\n",
       " 'NAN',\n",
       " 'NINF',\n",
       " 'NZERO',\n",
       " 'NaN',\n",
       " 'PINF',\n",
       " 'PZERO',\n",
       " 'RAISE',\n",
       " 'RankWarning',\n",
       " 'SHIFT_DIVIDEBYZERO',\n",
       " 'SHIFT_INVALID',\n",
       " 'SHIFT_OVERFLOW',\n",
       " 'SHIFT_UNDERFLOW',\n",
       " 'ScalarType',\n",
       " 'Str0',\n",
       " 'Tester',\n",
       " 'TooHardError',\n",
       " 'True_',\n",
       " 'UFUNC_BUFSIZE_DEFAULT',\n",
       " 'UFUNC_PYVALS_NAME',\n",
       " 'Uint64',\n",
       " 'VisibleDeprecationWarning',\n",
       " 'WRAP',\n",
       " '_NoValue',\n",
       " '_UFUNC_API',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__deprecated_attrs__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_functions__',\n",
       " '__file__',\n",
       " '__getattr__',\n",
       " '__git_version__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_add_newdoc_ufunc',\n",
       " '_distributor_init',\n",
       " '_financial_names',\n",
       " '_globals',\n",
       " '_mat',\n",
       " '_pytesttester',\n",
       " '_version',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'add',\n",
       " 'add_docstring',\n",
       " 'add_newdoc',\n",
       " 'add_newdoc_ufunc',\n",
       " 'alen',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'alltrue',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfarray',\n",
       " 'asfortranarray',\n",
       " 'asmatrix',\n",
       " 'asscalar',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool8',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'byte_bounds',\n",
       " 'bytes0',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'cfloat',\n",
       " 'char',\n",
       " 'character',\n",
       " 'chararray',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'clongfloat',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'compare_chararrays',\n",
       " 'compat',\n",
       " 'complex128',\n",
       " 'complex256',\n",
       " 'complex64',\n",
       " 'complex_',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'core',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumproduct',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'deprecate',\n",
       " 'deprecate_with_doc',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'disp',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'fabs',\n",
       " 'fastCopyAndTranspose',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'find_common_type',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float128',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'format_parser',\n",
       " 'frexp',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_array_wrap',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'geterrobj',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'infty',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int0',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issctype',\n",
       " 'issubclass_',\n",
       " 'issubdtype',\n",
       " 'issubsctype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kernel_version',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loads',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'longcomplex',\n",
       " 'longdouble',\n",
       " 'longfloat',\n",
       " 'longlong',\n",
       " 'lookfor',\n",
       " 'ma',\n",
       " 'mafromtxt',\n",
       " 'mask_indices',\n",
       " 'mat',\n",
       " 'math',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'matrixlib',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'maximum_sctype',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'msort',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'nbytes',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndfromtxt',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'numarray',\n",
       " 'number',\n",
       " 'obj2sctype',\n",
       " 'object0',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'oldnumeric',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'os',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'recfromcsv',\n",
       " 'recfromtxt',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'round_',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'safe_eval',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctype2char',\n",
       " 'sctypeDict',\n",
       " 'sctypes',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_numeric_ops',\n",
       " 'set_printoptions',\n",
       " 'set_string_function',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'seterrobj',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'singlecomplex',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sometrue',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'source',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str0',\n",
       " 'str_',\n",
       " 'string_',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'sys',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'tracemalloc_domain',\n",
       " 'transpose',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint0',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulonglong',\n",
       " 'unicode_',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'use_hugepage',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vectorize',\n",
       " 'version',\n",
       " 'void',\n",
       " 'void0',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'warnings',\n",
       " 'where',\n",
       " 'who',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find out what's in numpy\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.21.1'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find out what version we have\n",
    "np.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no way we could explicitly teach each of these functions. The numpy documentation is crucial!\n",
    "\n",
    "https://numpy.org/doc/stable/reference/\n",
    "\n",
    "## NDArrays\n",
    "\n",
    "The core class is the numpy ndarray (n-dimensional array).\n",
    "\n",
    "The main difference between a numpy array an a more general data container such as `list` are the following:\n",
    "- Numpy arrays can have N dimensions (while lists, tuples, etc. only have 1)\n",
    "- Numpy arrays hold values of the same datatype (e.g. `int`, `float`), while `lists` can contain anything.\n",
    "- Numpy optimizes numerical operations on arrays. Numpy is _fast!_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"http://docs.scipy.org/doc/numpy/_images/threefundamental.png\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url='http://docs.scipy.org/doc/numpy/_images/threefundamental.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an array from a list\n",
    "a = np.array([9,0,2,1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find out the datatype\n",
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find out the shape\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what is the shape\n",
    "type(a.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dtype('float64'), (2, 4))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# another array with a different datatype and shape\n",
    "b = np.array([[5,3,1,9],[9,2,3,0]], dtype=np.float64)\n",
    "\n",
    "# check dtype and shape\n",
    "b.dtype, b.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "The fastest varying dimension is the last dimension! The outer level of the hierarchy is the first dimension. (This is called \"c-style\" indexing)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Array Creation\n",
    "\n",
    "There are lots of ways to create arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create some uniform arrays\n",
    "c = np.zeros((9,9))\n",
    "d = np.ones((3,6,3), dtype=np.complex128)\n",
    "e = np.full((3,3), np.pi)\n",
    "e = np.ones_like(c)\n",
    "f = np.zeros_like(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`arange` works very similar to `range`, but it populates the array \"eagerly\" (i.e. immediately), rather than generating the values upon iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`arange` is left inclusive, right exclusive, just like `range`, but also works with floating-point numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.  , 2.25, 2.5 , 2.75, 3.  , 3.25, 3.5 , 3.75])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(2,4,0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A frequent need is to generate an array of N numbers, evenly spaced between two values. That is what `linspace` is for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.        , 2.10526316, 2.21052632, 2.31578947, 2.42105263,\n",
       "       2.52631579, 2.63157895, 2.73684211, 2.84210526, 2.94736842,\n",
       "       3.05263158, 3.15789474, 3.26315789, 3.36842105, 3.47368421,\n",
       "       3.57894737, 3.68421053, 3.78947368, 3.89473684, 4.        ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(2,4,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10.        ,  12.91549665,  16.68100537,  21.5443469 ,\n",
       "        27.82559402,  35.93813664,  46.41588834,  59.94842503,\n",
       "        77.42636827, 100.        ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# log spaced\n",
    "np.logspace(1,2,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy also has some utilities for helping us generate multi-dimensional arrays.\n",
    "`meshgrid` creates 2D arrays out of a combination of 1D arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50, 100), (50, 100))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(-2*np.pi, 2*np.pi, 100)\n",
    "y = np.linspace(-np.pi, np.pi, 50)\n",
    "xx, yy = np.meshgrid(x, y)\n",
    "xx.shape, yy.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing\n",
    "\n",
    "Basic indexing is similar to lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-6.283185307179586, 6.283185307179586, 5.775453161144872)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get some individual elements of xx\n",
    "xx[0,0], xx[-1,-1], xx[3,-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100,), (50,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get some whole rows and columns\n",
    "xx[0].shape, xx[:,-1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 10)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get some ranges\n",
    "xx[3:10,30:40].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many advanced ways to index arrays. You can [read about them](https://numpy.org/doc/stable/reference/arrays.indexing.html) in the manual. Here is one example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2500,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use a boolean array as an index\n",
    "idx = xx<0\n",
    "yy[idx].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the array got flattened\n",
    "xx.ravel().shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Arrays with Matplotlib\n",
    "\n",
    "It can be hard to work with big arrays without actually seeing anything with our eyes!\n",
    "We will now bring in Matplotib to start visualizating these arrays.\n",
    "For now we will just skim the surface of Matplotlib.\n",
    "Much more depth will be provided in the next chapter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For plotting a 1D array as a line, we use the `plot` command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efecaf6f3a0>]"
      ]
     },
     "execution_count": 23,
     "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": [
    "plt.plot(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many ways to visualize 2D data.\n",
    "He we use `pcolormesh`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7efecae663a0>"
      ]
     },
     "execution_count": 24,
     "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": [
    "plt.pcolormesh(xx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7efecadce9a0>"
      ]
     },
     "execution_count": 25,
     "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": [
    "plt.pcolormesh(yy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Array Operations ##\n",
    "\n",
    "There are a huge number of operations available on arrays. All the familiar arithemtic operators are applied on an element-by-element basis.\n",
    "\n",
    "### Basic Math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = np.sin(xx) * np.cos(0.5*yy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7efecada8df0>"
      ]
     },
     "execution_count": 27,
     "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": [
    "plt.pcolormesh(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating array dimensions ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Swapping the dimension order is accomplished by calling `transpose`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7efecad14130>"
      ]
     },
     "execution_count": 28,
     "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": [
    "f_transposed = f.transpose()\n",
    "plt.pcolormesh(f_transposed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also manually change the shape of an array...as long as the new shape has the same number of elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 5000 into shape (8,9)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_442/3844134479.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.8/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m    296\u001b[0m            [5, 6]])\n\u001b[1;32m    297\u001b[0m     \"\"\"\n\u001b[0;32m--> 298\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'reshape'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    299\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.8/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0;31m# A TypeError occurs if the object does have such a method in its\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 5000 into shape (8,9)"
     ]
    }
   ],
   "source": [
    "g = np.reshape(f, (8,9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, be careful with reshapeing data!\n",
    "You can accidentally lose the structure of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7efecac11c70>"
      ]
     },
     "execution_count": 30,
     "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": [
    "g = np.reshape(f, (200,25))\n",
    "plt.pcolormesh(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also \"tile\" an array to repeat it many times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7efecab78250>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mXb0wevxoyh6Aps+leQBTucklBW7TwJMqZb0renyu+pyWtmqzTlk05TMWzWrjt8Xo72Ssq2q5Fn0u0bjGRU+TkIK613qRqQ48aphCDgwWll0wrA2bkC3HQz8IPJ0cmwBfNb90bALcyNmxCWzy+bKY8Y7PDTbr/aymLybHJkA4LY7NnFdybGbdJcdmLlNybELhc4NNKHXdsAnZM33R6fGj6daxCTg+VZ+Wzzb42/i0bGrZt9iseuzrugkBNS6jZxNKWNGzl0LwbAKE5NjUa63qxlfSAxCRPwH8auBnicgXgN+dUvrDo3NTSj8iIt8P/E3gDPy2e88AAkNvW+4918p1rh2AxlQ/Md3yiZCheGHcw34M4Jim2imoRKTOZHk1ZW3fTDe1UvfL9pOk4V70bf56i/H3HUA4LQ0K7QhM6KJO4ggBDjoHvBwTIZVpYlJmCI3mbIuNCJiGoZ9NdZiWOi1Rx08+mloH0PR5x81gDOB5yr/5WJ7V4+p2qz5fTjk8dAhLrdxuWwkNp9hoQF+5FjPTp+qxVS5NOUekizWICKlMm0ylbHabjVD0mVcW65hL+fHFEJA3CKaQHJs5PTk2IevzRRe6sHweS/ksn7GGhRqfN1WnjU/Lpi1ivQ9KA2LYzPedHJuQ9enYBEjJswlwmBybAGkWxyZ0DaZ9zl2nZWdT2Xn+ls2c3jo2s87WYwBbfFo2IRtGq03lZBWp8jP+TCjIsglZj5bNrIv2vKXqLDg2IfNp2YTM52r2z1eyA0gp/eaN77+x+/v3AL/nzYq1yy677LLL25antRK4iIRUN36aS/dnrQIdDPpouq2WlloFz+XUQkDFKlhS4GVsFkH+TrgtA2/PzGKyfnO5OkNgVM4Lg8Arq+AUkTsNAZ1LGs3OWdUfrq6idvphEuKpWAUHnTe83uP9wlB79WRquCIsK0/qxXRXrSvV54fhjoPooGUJARGqznTbvJikDqhVTypkXR+mtT5H/qsNuzQ3OzmLFTr3+lj0eFraIJvqcwpmp7LyWxFSGWBPB81Xmhs+eivUQOqsqhAdm5A9KssmZH1aNqGEgAybgONTB4YtnzddCMhuj3yJTU1X+9WckmMTQO4WzyaUXQcbm5BDGZZNgHgSx2ZOt9mE7MlYNsF7Ui+KB/CJcOvYBByfqk/Lp05KsHxaNsFv1neJTf3KsqmpZTOni2cTMoOWTSi7PDY2AdIUHJv5+unebD5EnlYHYFcHlodS3cLpXKd/KRQfhrtauT4ZXgHwXM6EfhqoTDWOrXJMc3XNPyiu+U1YqmsYOpffydXYdVtQUyvXyVSuOw2SRli0FS+Va2pQqKsocyCUcEZbqJPMVLyuTFakhV7qroohVj1qY/UiHKseP1H0+GG444AfAzil9dS6U5q5Lc/jy0uuXFqR5xCZej1e0GfbldLMsOg6UjmbylVSzgt0sz1YBFkmdyiEUKfdxbL7a35W9w0BlaTqMzk2IU9PtGxCNkwsm4DjUzcitHweS0Dd8qk6dXxusKn31TdcYUmOTciGiWMTMp+WTchhIcMmQJiDYzOnsskmaF1vbELWo2Uzp3eOTcDxeUrracl6zPJp2cy/HY+lVDF9g2UTdMpnYxMyl45N8HzWDrLxGXTXgUNwbEKeBfQ2Q0D7ZnC77LLLLu+pPC0PoIgYy3U2oaDqARjroFpaogNtZ27wVuqRialYJktoswW+HJ4DJgQ0nY2LrYOXWihTPusx9pbrMrAKTkuzrk5lldjZhC70IrH1x7rRVjgF4k2xCop1EM7G0ipu7nixTXKzViBb56pHHZT8MNxVPdZUjm2WRbnJRc7cJo/MKc115sWLbqGO3VCuf50f4C2agSfVXG3jSalbfSr6PJ7WE85DgPp2KRNeO001b73W8A1jV0SM5Tp3Ycpn4ezYhOKlGjYBx+exzACxfKo+LZ/qZVg+N9ks92PZhBKSNGxC8Uwtm5D53GATIN4ExyYUL3WDzXwfybEJWY+WTU0tm4DjcyneleWzegCGT8sm+MH0i2wCDDypzGjnSZ0WzyZ4PnUwfU6Ozfzbab0YL96fzYfIk+oAbOXS0IGtXAqFxlKfy7HC8KHOtJCFQ0fcIcXq6+isi+dy4lnwKzRnibVSt8p1ReujENC5xfvqdLrT0iqVwnE+DzqA5haKuoVzXO8tEnGuqZZl9f5FaaEsnV44h6XuoaL3/0xOq8r1Ipy4QWeclOIl2xmEct4dH5f4tVZIrWR2D/eRd13v1YaALPhWpxR9qh61cp0udAD1PQTlynOozyNouoTrs6lsGfsQkCTHJuQO1bIJWZ+WTcDxedDtUg2fmoflU6/l+NxgU++r1yMxeTYh69WyCV0HMGYTymyYTo/uRT01lVW4JUhybGpq2YTSARg2AcdnDbEbPjWsafm0bEJZCLbWXrtXGwLqWYmsps5yXjybMO4AUnJsah6WzXqN0WyqR5In1QFUsQ2XNDgOtQKVwSJX4fKxFxJ1ynKVicRCqaR6vv2triw0Fmu/w2W6ZB1UD6Ck0UxftFBU6yqXI53PLTaoMdWDqXCzTslbmpVxZQzgkpW1GgMwnpTXhR7TNPJcf1uqyJEExXpdSqN2mw5Vf7XBCq1DrStnB2aMmxnaQx5TrQxNj6YDWFrDlZZmUQMQzMt7Jq1cc5uOZ1a61o6iW/p/UWzDJb7hOsji2ISsY8sm4PisDZbhc2XohNNqbMryeZHNkvYTBsI5eTYh82nZhMynZRPy34bNnEbHZk5lk01Np4En1dfN53J2bOa08XmsN9z4VMvf8mnZ1Otvsam6s2xCNiQcm1DGpEynCp5PXVENjs2cb/RsQubzvmw+QPYxgF122WWX91SelgdgrIJ+DOCyddVbBcKhvj9VLYTEUtxFjcM+T8eVR2Fd7f5lMcPiYiweax3UqYXGKiifq3V1OlfLoLrUUGdYUDZbY0n15TRtgc7944LSeVJB0uq+b2SpU+rUWn0uiedFj6GYvcHsOX1Ufcqp/rbPN0h0ltZFsZ6UdbOrNWTGVM4+TJE9qWJp6XnTRH0f4dSssRr20OezJBeygCvudefJ2DEAe9/rMOXZsZnPC45NwPH5PB1X+dowJWQ+t9jUe+0tV2LybALE6NiEbLlaNvN9i2ezpJbNqq57WKxiPKk6ZdnU9Zuqg7NjM6eNz9AtOz4SzYrh8ypf3d/KeqlDsZ5UH+aKybMJmcuVJ7V4NiHzOXlPQc7RsVmv9RZCPypPqwMw0seufcNVHihL3RvcVq5D59gEiSwoPBaGc/1er9G/S+Dqi6Jd7No0znUanVY403CdDBzlN0lDPNB2KqtQLA0ynSIa03gMoBdp5W/rAZY65bB2qHI0emmV6xl+KuUkgrrYp1LhbmQxldSHKXwIqJVpJbbseo+2k6t6jK2xVz2eTqtpoAmqvmUu97DE2ujVcIWt1K9ZuWzs2jdcjc18LDo283fB5JMLYPnsObcvObJ8brHZ7rFrnGP0bELW16lruGLybELm07Cp+Tk2y7W22NR7sGxCntZp2Wx68Q2/5XOqymh83qw6kcWxma+fHsRmTS2bkBt7yyY4Pmt257Nns+Rh2QQfenobsoeAdtlll13eU3mSHoCwDl1Y62oy1uehfl+sLAIH8ZYrCQ7lN5NxvXvLdTIrZ4fWVV9O6+Zqj70kM6VRrYJYwz11QGhZSP2r94JUC7f+NqXVStdLM1WuifWo+k3e8jH1CvL5Iz1GIku50UOyA5/+uVhPYGS5jmQ1wyHa/emNLuwiJXC6rXlBW/5aB37btFu3T3tvpW6F1DQ192YtdstmPpYcm/mY0Wu5nuVTGbV8Tp23scWnnx1WjlVr1bMJmUvLJuD51Bkry+Kfh6Z9KPQ1+Oy9/YMsjs18LDo287Gmx1hXqzc+e09qItJ7qSOvfySuzum9LmmtC7vA0+i25lNvevFsljxW7xAYtDGPKU+rAxhAHSSaz77CBUkctGIYKEYNl85kaR3GuebdKtf6Wq1s6V4PQJKBIq4riIKSTAcgdp7l0jVcyXQog0rtXVQtRDu0DqXFFvLSRkdi1cGhxvulxv4n0W0LpDZwOpMlN1i+4e9XXfcyeuf6SrcJ38BA1s2q8Vl3ALkQof2GEgsf7M8+rPtXQhZWQnefNkxZWSU5Nm0KreGyfPahyWyYrK+1uomtzqt2gHHFIzE5NiF3AI5NGD+DyDrcYxnFHOv06ENpsaaWTcg6sGzadJLAUqCyfDZjr/G5xSYwfuf6iFVrnNl6a9gELvNp2ASG77a4N58PlD0EtMsuu+zynsrT8gCKyMAtG7lpdlvdyVmu3SAwoVkLztVuFodeYz0IvL7uyPga99TGyurCOCmmZiFE3WgrsrJ6U1q9OnIog1O2wgS62M6GHXQwbRKpln/Vp0CollbRt7HgerGDl61MaXW2DKwrH15be1I6q8LqseUhK0tq9be5Vr6eXuO60vrQZP9Z5RKbOTWDwPiZVhNxZbleHgS+zqb+vcmmppbN/MGzqed1v93k8w3Z1NSymVPDp052MHz23q8VW+dbmdKquBfZ1L97Pcbk2cwfTB7iz+8/d9fK17sfmw+RJ9kBWLEPaOrcwvy5nNeFK1weV9f6vYFccs9GD7S6zQMXNDVgViWNowr3BmMAktYNDOsyhfKvF618YXD9Pm5r5epUuzeRgT6TfU9wHFQ0+5P6rF6fkb7hn4irEMMkT4xNuMznBpvQacmGkUYzZO4pfefmjLMLbNrUyhafT4FNwE8Xr18mVrebBnGzR5Q9BLTLLrvs8p7K3gHssssuu7yn8uRDQNHEvfqXaOTP5bzixi0pEjpXOz7msLmVkWeW57Cuj+smUIMwQN1zPYx+Z1Zz6Ysj5PXDgarHmKS+cET1GQd2QCz/wLvaS/fCGiv6XEbba6W3EL8Eiu66aaBB2rRFq9O6Ko71sQdI7O5pITg2IfNp2cxFaud8xdmEy3w+hE3Nz7Bp0/uIZRPyC3G22LTp6/D5lWcTLJ9iuex1KrI2yd+Az/vIk+wAUpJV5er/htaA5c/64FMFo/6WWCtaa/wMZNpwDa47gsMeugq8rCtI3QwqSBtgqxUprBp7RNqLua/J4JStsbnFVbjyOame2vYZmveSrB6LvpMMK1X+Lry2PrH67BssEaM/fcGvkPqGy+qxHhuU0T1HWR0bSeoaq/6zyiU2c9r4bI1Z43PVQRs92mtt6pIrxsKAM8sm5MkJjk09b2WQbCjtDdnU1LKZ08andq6Wz6qzQQFsnW9leg029e9ej0Ecm/l8w6fVYz32OGw+RPYQ0C677LLLeyqbHoCIfC/wLcBPpZR+STn2+4B/DTgCfxf4LSmlf1a++27gO8h+z29PKf3pe5dmNDnBuNTaW5/Knv4xCSe0l0/lu/WiiyWlaj2cah5zzVutjNG1WtnkXj1wEiF1FoC1rur2utO0nioRpO0FFIyloFuIO8vDfKb7bLLtraBsTXr3ekmh6uBkLNJqsRad2pXAdR8w40npc+nDIL3U2x6V3f7dW1fT5PUCIKFtsKU/naZqhdXvgpCmLj8uWceDYxts5r/FsQlwQjbZtOkpSX2RieVzdK3VTWx5L2qxh7Di0YYa24Zl5qbDlWcQOjY1HT3TTo/Wg2xWeXBsQtbBqfOWLJ/9SuAlsfKkTmnaZLPe9qjs/WfrDdl6a9gEHJ/6WUJwbAKZz84buDefD5T7hID+CPAHgT9qjv1Z4LtTSmcR+b3AdwPfJSLfBHwb8IuBnwv8ORH5hSkNyL8ieSald3lPaaoVQx/oMU2mQdepZHEF2YnIqbqGDSh9OUxtuGghoHtNvR80xGkyDXZthMzDLmlK690cZZoMFAOXu7qUbzAGgKwa6nxMK0nZTEsioXtskcSpuNra8Y6ei20ER6GLkaxCaabBrnoUaXqp+lzW9WEK6440TC1kEUw6ariulVNTc2+nytHs2MzHxLNpM6F1uJZPvSPLZzNS7sfnyFhIbc60YzOnk2MTcHzWhmvYCYtj013/HtIbKbahbseCYxNwfLZOtvFpnwtk3fYd9CjsOxJX50yD7diErBvDJnRITYZfy2bOxLOpP74nmw+Rze4wpfQXgH/SHfszKaWyZSB/Efj68vlbgT+ZUrpLKf0Y8KPAr3jE8u6yyy677PJI8hiDwL8V+L7y+evIHYLKF8qxlYjIZ4DPAEyf+tTq+3XoYt2jn5iq5Xpb+qNArD1lswoStyU/fWeotVyvDwKPCt/SavGYHruGgKrFNcFcVH1oVkH/PgDmuVkIZavYNE81v2Qt2N7SuhC2aAsUiyUVm3Wl+rxNN0Yv+m7VpsdQrbDIbcnwtvz2mKaVJ6XXOqdQPbmqx2v6BDOwaO6t6rFZV3Io+kwJ3fjN7rkuqu9qjYVqcVXPQkAjAg/xqGJnlVumTqhOgmez3K9lE3B89pxfHgQeFKz3SKWzJil8WjYhs3fwFqt9H0DldwqOTc0vdd6VrRvXQmp5AXJjE7JnatlsejFsmnwDUi1/y+fRMAr5+Vg28/XlQWzW1LIJME2eTYCwfh+AzLNns+Rh2YTM59uapARv2AGIyO8CzsAf00OD04bOakrpc8DnAJ59w6fLGmxtLKQ2HOfYGqsKRcyvertNh9pw6btVA5FTNy3wlFqDdRv1RdE3pqLl9JymCoZW7mtTxBImnKDLyN0URNOYlxdoa8OUMA1/fe3ebCqawiEVshbD3qhctoxdw+E70lZB2gu087HsXvuNyo4pcVcbO+14D63RW3UEwVW0i2LDFMGk1R0uecymYTcbaWnjU68QpDZSNk3l3au1kZqkXm/VofaSPA8piWNTU8tmTmfHJuD4PJXaYfnURs/me66MNj632NR7dWxCmYLoDQ1OwbEJhU/LJhQjxbBZUstmVdc9Gi4bSvMdad94zx2bWQtQXglZGlvLpz6DkzP6GpvgjZShWGPPsgk5TGnYhFK/u03ekohnE7LeO0bTHByb9VoPCKvdVx7cAYjIt5MHh785pdqHfgH4tDnt64F/+PDi7bLLLrvs8rbkQR2AiPwa4LuA/35K6aX56geBPy4iv588CPwLgL/82hdIrKyCc5xWFuZtPHAruZc/mAGhQ+d0nJJUT6FaZvHgPkN2QetilOiHR9x4rbW+tXcunXkKEOd8MMxq9U9wOJQTzJ4qk26cor2+sQp0QG6aSFMXugj3s1ytJ6X3c06BuzjQRdBjOQ0pEevCpZxfTFKtsI/jTc1D9XdX0nNs1mrqLGdrFrqJLF0oJgUhFv2pHplb+IGzWk9xvUgphOZJqVU7h9XsrGxdect102o1ITXLJlzwUuXg2ATPp05isHyuvIh4qOGREZ8X2SypZRMyn45NyHxaNiHz2c9ymSfHZk6DY7Nea4NNTS2bAHdxXtXN2zA7NrMuGp+x6rHx2edxFw+OTb3+Fpt6P324N87Bs6n6MWwCnk8bUjNs5nyDZxPKJJBV8R5N7jMN9E8Avxr4WSLyBeB3k2f9PAP+bBn9/osppX8jpfQjIvL9wN8kh4Z+2+vMAGp7nkldCOKgWLnXN3yc8mvXQomxLeHMTXfJIxMvTYOlaW2wjHutbn2LC153D3u3MM0tBFQf7GFCFIoCMSLtIrWShdpRpBuFKNQOxV2rr1wCq33hbUdKa6y0Euj936UDH8dnQNvTnwgn3d+/LsBZd6Qv4zPjamu+c7lWqJX72qQVG++39+h0StZnbbBuDi0D3WhPxXYAhxZS0+ehHUs0ISDbAY0bLE1LA5zEsQmdUVHCOB+nk2MTcHwey1iB5VPzsHzqtRyf9whdjPRIEMcmkPm0bOpFLJsAh4NjE3KHsgqPjMKU9p0atiM1bGpq2QT4OD5zbILnU2dJWT5fFqYtn5ZNyB3pFpuarlgJnk0oHaplEzyftgMwbGoels16jd44esSOYLMDSCn95sHhP3zl/N8D/J43KdQuu+yyyy5vX57oVhCsBoFt6EKtpZfxpr3uLZRFIjFw220FcWJyFitki6K6hkvO97jM1WKNK0/AlG8w0KUucJyEeNDQRenZDxFK2Zt7PbW3COny8ClUqyAVCzYeTAhoVtfTuNobbnaMPmRwjJOzWFUXzyR7UtXKCi2splscn9JcB+W+FJ/X39bnseRU8z+akFp1sy+FK3rrygws1vs/TKRzcaut97T0HoA0q+rQ0nToQ2lr62p7UF1TcWzqfVs2obwm0rAJOD51tpDlU70xy+exMGr53GSz3I9lEyAegmcTIM6eTch8WjYBDrNjE0oIyLAJJUx5jxBQjOLYBO/tqy6eycmxCZ5PHei1fOpvLZ+WTcgeyCab5ZqWTU0tmwDpHD2b4Pk0C+osmzkNjk1N78vmQ+RpdQA2BFQartNSZgMsc+sAltKIh2ODoshRTtyUTsFuHqcNvzZcL+NNBeNVge0Yp1Ws9erUsJFbOImraACyTG3prP70HM1iJX3oAQZQxIOvwKMxgMvTQLsxgNg60lemsXouWT/6Io5IaJ1r0lWWYVUxX8ab+jz6ynWO7VV9V99rakNAqrs5tfvWDvU0kW46PYrAXOMJOZ3CKsQRD1N1r6vbPgmxi48PV7Cacjd9imNT79+yCTg+j6WTvZHzamNDy2ffibyKh6pTx+d9poEOGi5rpMiijb1Z9FU62bxYybAJcJgcmwDxII5Nve4Wm4AfA4itI33V6eC5PHNsAo5P1aflszX8jU/LZr5t2WQTSgjIsFnv27AJOD7r4rA5OTYhh3ssmznf4NgEiBP3q+sPlLB9yi677LLLLj8T5Wl5AEVSlOouq3t9jBOvlty7v5pyz/7l5Xl9c1Bd3BRm9zo+yG6hDiZ9afmg/lbzqyGgOBkXW2pZLpbTWFdqScYZklquZ7WyAkGnYmiXO5lVWjW/ZhXoQFucQ7O0qnXAambH2Cow2xUYN/u23PfLkn4Qnq08qSUJN/0xs5S+eQDN0npVQkDH4gmclrU+R/EAP8OipCYEpPcdb0K98VB/G/JLzt1trwc5443xpAahtD4EdUmUhxiDYzPf/8GxCfktYZZNPaaiIQvLp/7W8qnXsPrcYlNTy2bWiTg2gcynZRMcn3W++2FybOZjwbGp6RabUNaldCGg2+Xg2ATvSalXafnstyHJHoAPAb1abhybkPW5xaZ+NQoBOTbLjVs2Ac+nLiazHsCN8aQGobT7svkQeVodgAkXaAjovCgU86rhmuV53V+luoDp0EJAdSOpUGcVfKlUrpfLTW2wNN/jMlUY17FrWfMhFvhWAfQhSmlwqptNW/wlJlZYt3s1McUKxY2pXOVpWbdwOHvFuLSpGwM4LZNrsAC+HJ6tXmV4SlOtdPrdYhbXWH1+fH7m8jsuLVyxLDqWYpSn7Yutd63+1Hus02m14V7Cyk2XSUjlfqo+zR4ttfO8mVYdaZxl3JEOQxY+lLVEcWzm9ODYhLz/j2UTSgjIsAlenzqWYvlsOm18puQbLsdnDal5NvW+t9iErE/LZs4jODY1P8smdKEnW8wu3JLMGEANqRlj78tBQ5Nm3KTciOVT9Wn51Lpu+bRsAizLYCFYYj1+ETybet+OTXNfkNnM9zg5NrN+gmMTfEfqjL23GAJ6Uh2AjKBQ62Bplqs+0MnOpy7W1SGc3XHI0xe1gmnl+vLyjI9rBStWgfEAqnV1baDNxVdzGud1B0AKtYGTMiAos1D3BquNuekAFKwbYVlZrq3hoofDSmqWtzbEd8tc7/flWa2rWOOrWpHuQht00042miX6Vo86hqLPp+pzmWpHrvpMyRT16lhK08GyFH3atRnlt7KElSdlN9WKJk5d9ahLMgae1KWl95ZNyB2qZVPvf4vN/judvmj5/LKOIxg+T50HkOI4dj2cTtt5ALYDqCcGzyaARM9m1llwbAIsznI1nc0Gm1AGgQ2ber+WTchjU5bN/F3jU1f2Wj5Vj5ZPyybkjtyyWYt7dSyllP0g22zajAG7IaFlE4oeDZuajoy9x5J9DGCXXXbZ5T2VJ+UB2JkBqYaA1MqaeXlW91pXAMZqQZ3LjJo5LqsxALtC85XxIl6evQdwPM/VGrkWFxxZBTbOutx01hWRtjd4s65kNQZgZlMMrIIaF5wgdlaBLZf1X3s9npfA7TlndFM2UZvDTdPVpFbWvB4XINRFc2qtvYoHvnQqYbWzH1NxC8F6j8rd98iTkjrbQtTSTNS3K6lFKjHVPVdqfmIWJpmwj7W06rHeA7gYAiqJhluiODb1/i2bWWfi2AQ/BtCvbn9lvAjL57E8M8fnBpua9mMAy414NoH8XoXGJng+q0c1iWMTskdl2YTM5ypmLaxiK1aPmt6e5002oRsXKLas5VMtf8unZRPKQrANNjW1bOY0OTbzbQXHpk3z99Ly60OSh4G3P7G2/H+mhoAsHAr6qaygPU5TffAvTTxQVw/q4M4sC6HbZz8mqWC8Wpo7qA3Wq3OLXSuEq5jeYI6wX2VZrjVLe/AmgJhCPhYWhWJw+8FUtALAcsOg4WI9b34ARTJjADqmcVwmbrUjDebVhGYTLciu9xzWi7jbKmIT9y75aaqN1ek8Nfd61KHqcxJxDQeAzO1+3ZhG18BJlNV7dUYDdnGW2jG3UEh7bjakNnSxu4ZrWYJjE3LD9LJ7sOc4OTYBx2fbnbLxWce6DJ8aZnJ8brCpqWUTSqdpg9tACmmTzZyKYxP6hqtlu8UmZC4smwC354NjE8pgsWET2OSz1+PL88GxqdffYhO80SXakR7WYTjLnpR8LZ8ufNxNclhuxLEJ+dn14d49BLTLLrvssssbyxPzAEoaWc1eOS4T09nvsRERMxWvbBQlcewBVE+hzdxQy//u1KyCOmuls1zta+Kqa2vdQu31D5huvs3yqVbY2buHTkSMK90sKrWukrEKVguYQiufHbCsHoDxqI5TtqBeGX1WKyxqeOi80iOw0mMenC/eQMlPQ0znJVR9qk6sdTncZ0XvMVK3UGnFkJVlLxGGg8Cat7F+Fw2lGWt1tL/L1U3NdeDermAtlqvls+5vk4JjM6cDD8DoVfVp+VSL1fKZegtTOh7oLFctXhLHpuppi00oOjNsQubTsgndAiYzGNwPpqcojk2A4xQdm6onyyawyeexn6V1Pjg2oehzg03wnpS+jiBGz2Y9rwulDQeBZe2ZLQfPZs2vD/f+jA0BFUlRSItvuI6nuX9dJhGpD/mmvH4tkC6EgHxlPcWpNvz12KADGMYFnRtXPuvDTPY3CkWq96Puo7qHfb79VLM0mTEAnSFgYLzmForZMGwp877DFDie/GNPqa1qPRQ93oR5XMG6TclOy1Tjqnede70sod53DXtfiFuvxgAmDJ2m0nTx7DyW4vO0cfymRxtrbXnY7y8Vz+WtDdcijk3A8akdwHGZHJtwKQTU+NQZP5bPUQewxabel2MTyu8am3o/W2xqfpZNKGMAhk0o19xis3xp2QQcnzrmclomxyZc6AAMn8q05XOlx0U22dT76utm5rOxCcpZ+Wk1YFq+No7fs5fHqfRzu9Z92XyI7CGgXXbZZZf3VJ6WB+AWMBUX7VwGpiRx1P02ynmnGDiUwaLbkk4hVksrGl9pMaP++lu1BsYWq7pxowGidshbAzAckJuEqPu+2RkWqTvPzF7RrjlaC07Tg7EKrrmFCVjU0iuW5mlan5aEU3k14LQYPRYLy+qz34/+vIRqsa70eZ6qlbwKV9j7tp6UtfA7/SA0T0r1aMMVLhTSXHLNt1pcdoCtHwSWvpBd3jWEERybAMfT5NgEOITo2ISsz9g9MMun/tbq01msAFE22YTOk7rAJkBcBrN/bNjTzF6xbEIJHxk2c773YBNgkU02AU5hcmwCjs/+9ZxLDDXMY/m0bOb7Dpts1vvpvE+rn3r+Ipts5nxl6PX2oTQ3CFyvNeDzgfIkOwCJUv1VXbJ+BrODYDk2RU4Kg86ykVQrZM02mdfOmWmRtdKVa8RzqNdrDc0IjvJHMCtJdWELrCqhzKAz1qzre232Sq1kozCTgWK4gKnGq4WkEOoK0nPWpepFdTFPqketZNf1qPd7XqbVYrNauc5SO1JGITXbAej9zu32aySiFDhNRo/GbR/pcTgbpruGbbi2FoI5NgFicmxC0Y9hE+AUomMTcHpdvbLTTIu0fEbDKGhDc51NKA2XZZOsG8smZL1aNvt8XWjJsFmv0c9eubSAybAJZD4Nm+DruupinqJjE67rMaW2i4Dl07Gp5dhgU1PLZr0FwyYUPXYhpZEeh4sfg2cTLoxTPWIoaA8B7bLLLru8p/LEPIA2YFgtcPRQm/dse3TprCtgaLkm8xlwsw+ihkkWAZ21cs9BYDqr0lquYqwC1XSzDgb3f8ly7QcqbTijutnjsIVaiUnNVEIdDKwzhKa27kL1Kaz1CGtLi8R65lTVZ1iFbC7ed+8O0wZV67zqxejUhmT6PI2F5Kynay735iygxqbeo2OzlKX3hiSsJyWMLFfLZz9zKy5tUoTj8x6DwFywXB2bAPM9nlFJR9t2rEJ4LgR0OaQmURybkJ1/yyb4ul6LssVn+drNnDJs5lTud992ELhe37MJWa+OzT7fS2z21zBe/3szC6hNEQPRB1XdqNRW55YFK1FShaJOgRwox87CsrN7+gUgeUZAud5q9goOBoA0mYP6tjpTueoiy7gBRVVAy27UyVgXcNUpjBquKLVcLjqkG6XpdLazqVzS9HlVl/qs0mAhjW0ka8PVQmr1OVd9plrRlfEk1HeR1MYqtuu6xUobDRbgQjs2proaSwlpGGu1bELmw7IJpfE3bELpUO/JZs6/NVxWr6uxKTd7xdyrY7McXNxPsz4Nm/X+ttgs6aiT6cMTmzvW1rrcsQkQgmMTsh4tm7DNpzX2auEMm1D0OAj3WjYhd0SWzVymjs1y3dVCuvsae4ZHt5mfZbP+9nHGAZ5UB9DHB8EoM1IHi/Tmk22kOjgu5m1j2F1cV5K5dvTfuaxcR6BlaZZufTareeyD8lgZAX3BK2jHLt+3pNZg6QkSpYLUfium4zEFG1rC6wKm7rmJ1Wv03w2toQBJB5qNHuvmXLZyra4/KOOg7FaPo7GHytTGGMAlNvNvxbEJhc8tNm3+Vp9Gj9JfP96HzZyJZRMtzmOxWb4bWalbbILy6RtgQvJsan59Ybf47MarMHoUW8832MxfJcem5tt7D66I19po6wlYLod1Pflj1zh6TdnHAHbZZZdd3lPZ9ABE5HuBbwF+KqX0S8qxrwa+D/hG4MeB35RS+qflu+8GvoPseP72lNKfft1CZVffd3NJxLiA5ru+N9zyAPS0UU9tZ5RcO9/20p2r6Bb9dV7HtfLYfC9deGgpDI5V6wpWoSxrCcs1PV46Nij3JZ1d06fN/6Ieh1ZVb9oP8jV5rws51pn77oqF1c/wyB+LZShpHZeQC597GXFjv+tnlFw6vw/PCF95Nks6OmbZBFwoy54v1/R47dh9+bT63GJT/+71OPRIrWk/yLfL3xdwrLOL3z2C3CcE9EeAPwj8UXPss8CfTyl9j4h8tvz9XSLyTcC3Ab8Y+LnAnxORX5hSWu/aNJBr7pOY/78zGbjy155xsp9e1297hBjfSJ9PQo8q99RnGv31FdbntdCTDD69E5G07tAunJr6T6+jz7fKpv/0zuRBbJYj76CuP1Q2Q0Appb8A/JPu8LcCny+fPw/8BnP8T6aU7lJKPwb8KPArHqeou+yyyy67PKY8dAzg56SUvghQ0q8px78O+PvmvC+UYysRkc+IyA+JyA8tX/74gcXYZZdddtnlofLYs4DuGZWDlNLngM8BPPuGTyfoPKdhbHYwq+CBYwAMXNDNMQAz913Ta9M73yzO2gX0bTYbYwD9TBSb3b31eOnYNT/4nmMAK72M4v2PPgYwyGJrDCAMdH81v3UI5iFjAENW7zMGYOa+O919pdks6TCevcWm5nufev0mfF4ZA3iduv44YwCDn2+NAYRrmd9fHuoB/KSIfC1ASX+qHP8C8Glz3tcD//Dhxdtll1122eVtyUM9gB8Evh34npL+gDn+x0Xk95MHgX8B8JdfN/O8+KmzUkNarywUHnUdACm1RSPdOoDE2gKQiJkLLu2Ynvcoc61lNSNhNGvGLcYZzcUezWYIV/R4oTyvsw4gv9eh+26wgEsiK30PF30NLa5BGQdlt3q8tg7A6nEkwwVjVZ+sOHysdQB1//7BOgA9XRzTJu10/3jrAKR+N1yt2rHn9a3H0vrl8W4dQEmsHq+U6b7rANp7Hdhms5wv/XmJR1oH0PR4bR1AvyD0MeQ+00D/BPCrgZ8lIl8Afje54f9+EfkO4CeA3wiQUvoREfl+4G+St0n6bfedAZQvVtKQYDKVCpDJrPo1q1YfuhI4L5LyvqfdG7yuRC5PsU6nxNRns2LZblLWLxB6myuB9QerRTKah8Ki+jR6FNP4vclKYDo9DlcC20L1OjB6rG/5M3p0aV8xbT5WRo29ftbVm0aPdZEhrWF32fYd7pQcm0DWYafHh6wEboukml779yrIIpts6nf9Bnq+wzW/22KzpPddCbzJZvmtZROyzqTrmN9kJbDjc7QSuC+U1YXRo2UTfF1/7JXAYvTo2Cy/fZwA0D06gJTSb77w1TdfOP/3AL/nTQq1yy677LLL25cntRWEtQqqNTA1iyuULXb1WAjpUTeDQ6RuttXcamN5DawCKRtZaa8fzqwtrqX99tE2gxv8duUaWk9K9TknJHR6nNqWxY+5GRwS2pYI9ffUB12dt9isKzGpjI71oQtrrRld1PJubAbXTiyJSCtYLXxabbGQpuTYhKxHy2ZWweNsBldNQsOndB4XNrSjFu7Zs5m/69gsv33UzeAGv3V8Wk/KsAkgITo2wdf1WpQtPsvXo83g1MVI0rFZDlo2IXumQx77Y1ve/iU2YbgZnP19DRWFZBrLN5Mn1QG0WNegcs2RaS77gU8tnd7kfQDlFXT1fQASGrjVv18r2oWA+sp19p/1/PX7AFiFNJN4GIDx+wBM2LQNXwirmivUWGqtXFMkqB41DfGN3gegz6O+D6DskBWJ9UZq6O1S3LZrkMK56S+YTnb9PoCxHocdqepUd8dMpqEyu7X2YZn8RRd+nJJjE7I+LZtQXqzz0PcBGD5jabAcnxtsatrzaHVbz19YNVxWr6OQ5PB9AKomejZNxmCeT3JsQlfXC49v8j4Ay6dnM5d0k838A8cmdHXd8HttXODa/knunQOpXna1k/CQzwfKvhfQLrvssst7Kk/MA8hJMoNAal3Nh4V5zl3woaTzFOsrIac3eSVksQrOxgKoIY6yvW+Stv+7HeysFqlaV6dmFTiX+4GvhJTRS70xW/vqecb1tFvZ1u9Ds67mQ3m5ttHnPNJjyfD6KyGX1Sshm6ImluQtZ2dmXfOkTlmX0AaGh+G1B7wSUvXk9DnSI0YMm5D1btkEmOfFsQn5lZDTm74SUibHZi67ODZrEbuwkCyeTch6tGzW8yybqgDDZr5/HJuQ+YydVRrFs5nz9WxCPseyCVmfN31dH+nR8Dl+JWT+rePTsAlkPjfY1NSyCaWu917Bss0mZD5Hr4TcYhOKHnkceZIdANKgsGGfm1LRns9Z2zfzmUN5AjdTTgPrmGtMwrlo+1jcwjlOtWFTVzK/0q9r+J276aeHSFoDEM6motUKl1qMu581YCRJWsUAZZJV5crl0u/1wPochBqm0JCarVw3h1zo50aPh6LHm7Cs9AhUPZ61kVom7paM0er8RNvXvcZe/ffQhXaMPvuONFe45M5zMVfNVqgHo4lNy6yNRLt8nYGnepwwrdQ6ZFFnoIR1SPLmsDg2AQ5hcWyC15M2WJbPuRTa8pm6ECZL6tgshevCDmG5oEfDpp63xSZ0Henk9enOD/dgE8CMpVjDxLIJWY+WTRjwhufzVOr6kE9lIG6zCd6Ic3W971yXNDD2TLaVo+TYhMynZRMyn47NmsnjdAFPqwMoImYMINTKda4wPJ+ztj84nHg+lQpXoYjjDqBo+zjl9HaZ23nHct4cagWL+tKUQVxQRg3XoAOYjqkd04arxgwHD1CkxQC1cs3rAZ8UWj41PjiqhNIaBxtT1cr1waHocTZ6rJXsPK5gqkdNw1QtMhUbz9Y4dpvrbTrSWlBf0cA3XFWPJ9Ng2dhrHxQVqSoL9d2qYs6Tqp86qNuP0VwQMWMAlk3IjZVlE+D5dHZs5nTQARg+b/sG65jZBByfW2xC5wGYxsqyCaXh2mATcozasQmOz2qYnNlkM2ebVvH+m8PZsQlFj4ZNuNABGD6PYXL5AquxwGUKHZvg+BzUdWv0WTbzsbQaF/DbsDb2HJv1PMMm5IHhe7L5ENnHAHbZZZdd3lN5Wh6A6fXU0qpWwbRU6+rDQzbZX8wnnk/52LNiwc6yDl3kEFC2Bl4th3zewCpYUqgzMM7dtEiM1+VWB6o1oDHsE0ynUna1Co7JuNrJ52EkhUQ4N0srH7Tfl1jvhL7VkWjKQm+Q2WmyGpOeW0hCrasX84kXc9ap6vMgkbmufGmiFtZdKeBtOFR9txkYRZ9LYNEZGBpCCCaaYsIV1WK1Myy6UNp0TISTWq5Nj+sQUFuklexCvS60k4LU8ZXRYjMnhk3IXFg2IXumlk3I+rRswqUQUONzHnhUi4Y2DJ/9Ikg3ldN4AJZNyHxaNkHDlNfZzOfJJpuQ+YxdWRiOASTHJmR9WjZzenRsApt83oZc1y2flk2AZYqOTcBuWeS9/X421Ym1t39Kjk2bB5hQWujYzF9i2YQ8vjJabPZY8sQ6gKb1qYdiXnhWQkAKxUeHWz4oULwo6SwLUzdL/pQmTqWC1UpIau6gVsJl4lRCRKdy/Vhf+mkytFB0DVc4p9ZwFSimY6wNlyy24fJPMgWp78aVfnUtMNVBo1YJR5CpiLBaJ2E70qbHOz6ccsP1QUmfhTMH8RVsIdSGSuOqB1MJbUcKOZ59LAOZWo7ka0NJBx3p2XakqkfTAag+Y1qFLOxgejoEpycwlWuCqJVaa8KgQ8kn+BZhmqJjE+DZfHZsAnwwnRybgONTubR81kF3w6dOabR8brGpqWUTSsNl2MzHkmMz32pThOosTrLJJmQDZotNwK2TsB2pZRPgw+no2AQcn0vpmS2fh66DsB2pjgUez9Mmm9B1pBqaPCXHJpQOwLBp05xtG0zfYhMyn5ZNeNxQ0B4C2mWXXXZ5T+WJeQAlMXvT6HS6m+ns3EGAj6YjH87ZQngR8rFDODN1PtKCcBezO3gwbrhOHdMZLXfzmdtzGYAL3uJzMvAARgND1bo6JqZT/iyaxtQWdtSFIIJMfrAPwsoqsNZItM5OHcNqZe/1OE+xDaaX0MSH05FPqsVa9Pg8nIyuijeUQrVUX8pNue91KE0HiG/DoYZJVuEKI86Tqnpsg2nNWk2EarGWfJc0HgRWi7WGIYRU5iVOZvZFWPx5bk8cl6dm3UIXlk3wobSPirX64Xzn2AQcn0vJ2PJZQxaGz7vyzByfG2xq2s9Um47JsQkwnaJjU39r2QSQKTg29TvLJmQ+HZtats6TsnrU9Pl8dmwCfPJw69hUXUHmM5aCWj4tm5D5tGxCDjFvsampZTOnnk2AcIyeTRgPAgdxbAIkCY5NyHw6NuFnbgioztww8dWDHQMorrQ2+h/Od3zV/BIwHYAs3BSfV929hcBdORaM9trMi6yGl2G53GAZOEYrgf30RR+mmO4i4ZRPEI0PnmN1sesS70nasnizEllXPmq8OpzN1LVrcUFpHZmG1J5NrXK9MHr8aModgKbPpXUAU7nJJQVu06AjLWW9K3p8rvqclrZqs05ZNOUzQK82fluM/k6mctWGq+hzicY1LnqahBTUvdaLTDXurGEKOTBYV3ChXTVsQm44Dv0YwHRybAJ81fzSsQlwI2fHJrDJ58vSijs+N9is97OavpgcmwDhtDg2c17JsZl1lxybuUzJsQmFzw02odR1wyZkw+RFp8ePplvHJuD4VH1aPlvsv/Fp2dSyb7FZ9djXdRMCalxGzyaUsKJnL4Xg2QQIybGp11rVjT0EtMsuu+yyy5vKk/IA7L7pUw1dqHV1rh6ADqp9YrrlEyFbBS+Me9gPAh/TVL0ClYjUmSyvptzd3kw31arr921JkoZ70bcFTG2Qt/cAwmlpVoF6AiZ0USdxhAAHXQRUjomQyjxhKTOERot2xEYEjGXYz6Y6TEudl64D6B9NzQNo+rzjZjAI/Dzl33wsz+pxdbtVny+nHB46hKVad25fIQ2n2GjAcB1A0U/VY7OuNOUckS7WICKkMm8+lbLZfZZC0WdeWayD7uXHF0NA3iOcQnJs5vTk2ISszxdd6MLyeSzls3zGGhZqfN5UnTY+LZu2iPU+KBakYTPfd3JsQtanYxMgJc8mwGFybAKkWRyb0FnM9jl3XoudTWUXelk2c3rr2Mw6Ww8Cb/Fp2YTsGa82lZNVpMrP+DOhIMsmZD1aNrMu2vOWqrPg2ITMp2UTMp+r2T8/U0NAKhJS3fhpLndvodBY4EfTba1oCsVzObUQUIFiSYGXsQGRvxNuS9z1mVlM1m8uV2cIjMp5YQxgBcUpIncaAjqXNJqds6o/XF1FfeZhEuKpQHHQaWPrPd7HULQZFjVcEZZVR/piuquVS/X5YbjjIBqzLiEgQtWZbpsXk9R4au1IQ9b1YVrrc+S/2rBLc7OTa7Cgc6+PRY+npcVYVZ9TMDuVld+KkMr4SjpovtLc8NFLQQZSZ1WF6NiE3KFaNiHr07IJJQRk2AQcnzouYPm86UJAdnfMS2xqutqu4JQcmwByt3g2oew62NiEHMqwbALEkzg2c7rNJuSOzLIJviN9UTqAT4Rbxybg+FR9Wj51TMryadkEv1nfJTb1K8umppbNnC6eTcgMWjah7PLY2ARIU3Bs5uune7P5ENlDQLvssssu76k8LQ/ALg8vvXJ1C6dznf+rVsGH4a5aV58MrwB4LmdCvw5ApjqQqXJMc3XNPyiu+U1YqmsYOpffydXBy7agplpXJ2Nd3ekoWYRFzfhiXU3NKlBXUeZAKOGMtlAnmbnYXZmsSAu91G11Q6x6VGv1RThWPX6i6PHDcMcBPwh8Suu51ac0c1uex5eXbF2pJTeHyNTr8YI+27bEZoZF50nJ2VhXJeW8QDfbg0WQZXKHQgh13nUs23/nZ3XfEFBJqj6TYxPy/HTLJmTP1LIJOD51I0LL57GMqFo+VaeOzw029b56yzUsybEJ2TN1bELm07IJOSxk2AQIc3Bs5lQ22QSt641NyHq0bOb0zrEJOD5Pab0uRY9ZPi2b+bfjwfQqxjmwbILO+W9sQubSsQmez+ohNT6DbjtzCI5NyLOA3r8QkGm4ZhMKqh2AgaNWNNE465kbfCN1ZGIqYC6hzRb4cngOmBDQdDYutsautVCmfNZj7BuuZQDFaWmV61RWiZ1N6EIvEptDphtthVMg3hQoChzhbCpacXPHi22Sm7UCuXFWPWpM+sNwV/VYUzm2WRblJhc5c5s8Mqc015kXL7qFOnZDuf51foAHetCRNlfbdKTqVp+KPo+n9XzDEKC+XMSE105TzVuvNXzBzBUR03DNXZjyWTg7NqEYKYZNwPF5LDNALJ+qT8undjKWz002y/1YNqGEJA2bUAwTyyZkPjfYBIg3wbEJxUjZYDPfR3JsQtajZVNTyybg+FxK52r5rB2A4dOyCX4s5SKbAIOONDPadaSnxbMJnk8dS5mTYzP/dlovxov3Z/Mh8qQ6AFu51HK0lUuh0FjqczlWGD7UgTZZOHTEHVKswS4ddHsuJ54Fv0Jzllgr9fAl6b2MPIBzi/fV6XSnpVUqheN8HnQAzSoQtQrmuF5aHnGWiZZl9fotaZ6MTi+cw1KX0Ov9P5PTqnK9CCdu0AHHUrxkO4NQzrvj4xK/1gqplcxu4Tsyruq9Wg/Agm91StGn6lEr1+lCB1C3oS5XnkN9HkHTJVwfTLdl7D0ASY5NyB2qZROyPi2bgOPzoNulGj41D8unXsvxucGm3levR2LybELWq2UTug5gzCaUwdBOj+5FPTWVlbUdJDk2NbVsQukADJuA47OG2A2f6tVaPi2bUNYBrLXX7tV6AD0rkdXUWc6LZxPGHUBKjk3Nw7JZrzEaTH8k2ccAdtlll13eU3kjD0BE/lfA/5Lcx/914LcAL4DvA74R+HHgN6WU/unrZWwsV2nWwaFaUGW2gLO48rEXEnXNSpWJxEKx0vR8+1tdWWhCFv0Wx+mSe1hDQCWNZvqitQqqe53Lkc7nFhvUmOrBWFyzTslbmpt5ZQzgkpu9GgMwoTSvCz2maeS5/rbYSEcSlPDFUqza23So+qsWa2geVV05O/Bj3czQ3sqJqVpDTY/GA1ia5ZqWFlIBIJiX90xqXc1tOp5Z6Vo9hW7vl4tiLVfDJmQuLZuQdWzZBByf1WI1fK483XBajU1ZPi+yWdJ+xlg4J88mZD4tm5D5tGxC/tuwmdPo2MypbLKp6TQIpfV187mcHZs5bXwe6w03PjX0Y/m0bOr1t9hU3Vk2IXuSjk0oY1LGqwLPp66oBsdmzjd6NiHzeV82HyAP7gBE5OuA3w58U0rplYh8P/BtwDcBfz6l9D0i8lngs8B33S/TBkU/BnC5cvVQCIf6/lQFJLEUd1HjsM/TcdWhWFe7f1nMsLgY4C0cdWqhgaJ8rpXrdK5gVJca6gAbZbM1llRfTtPmZ98/LihdRxokre77RpY6pU4bq+eSeF70GEqrF8yWo0fVp5zqb/t8g0RX0S6K7Uitm10rgxlTOfswRe5IS0XT86aJ+jqqqVXGGvbQ57MkF7KAK+5115HZMQB73+sw5dmxmc8Ljk3A8fk8HVf52jAlZD632NR77RsuYvJsAsTo2ITccFk2832LZ7Okls2qrns0WGI60jpl2dT1m6qDs2Mzp43P0C07PhLNiuHzKl/d3sQaKUOxHWkf5orJswmZy1VHung2IfM5+Y5CztGxWa/1FkI/Km8aApqBD0RkJlv+/xD4VuDz5fvPA7/hDa+xyy677LLLW5AHewAppX8gIv974CeAV8CfSSn9GRH5OSmlL5ZzvigiXzP6vYh8BvgMwPSpT62+7wcvveVaenSWuje4ta4OXb8WJLKg1oO1Bs71e71G/y4BGY0QWaugt65Ss1irhRSN5Xoy1kH5TdIQD7SdyqpVsDQrQ6eIxjQeBO5FWvnbgrClTjmsHpUcjV6adfUMP5VyEkFd7FOxuG5kMVaaD1P4EFAr00ps2fUerZdT9Ribta96PJ1W00ATVH3LXO5hidXqreEKa9W9pnVlBy+95drYzMeiYzN/F0w+uQCWz55z+5Y7y+cWm+0eO+s8Rs8mZH2dOss1Js8mZD4Nm5qfY7Nca4tNvQfLJuRpnZbNphdv+Vs+p6qMxufNyotYHJv5+ulBbNbUsgnZ2rdsguOzZnc+ezZLHpZN8KGntyFvEgL6FNna/3nAPwP+fRH51+/7+5TS54DPATz7hk+7WxTWoQtbuSbT+Bzq96WSETiIb7hIcCi/mYzr3Tdck1k5O6xcnTg3Vx/YksyURoUi1nBPjQcuC6l/9V6Q2sDV36a0Wul6aabKNbEdar/JWz6mnUI+f6THSGQpN3pINu7tn4vtCEYN10hWMxyi3Z/e6MLOUQen25oXtOWvNe7fpt26fdr7RmorpKapuTfbYFs287Hk2MzHjF7L9Syfyqjlc+o6my0+/eywcqw2Vp5NyFxaNgHPp85YWRb/PDTtQ6GvwWdv7B1kcWzmY9GxmY81Pca6Wr3x2XekE5HeSBkZfSNxdU7vdUlrXdj1PUa3NZ9604tns+SxeofAoI15THmTENC/DPxYSum/SimdgD8F/HeBnxSRrwUo6U+9eTF32WWXXXZ5bHmTWUA/Afx3ROQFOQT0zcAPAR8D3w58T0l/4N45DqyaINF89hZXkMRBLSNjFYwsV53J0jyGc827WVfra7WypXv1wJKMVRDXFpJaCsl4AGIn2i+d5ZqMRzGw6ryLqoVoh9ahtNhCXmp1Sqw6ONQBX6mDv5PovjVSLVydyZItVm/596uue+nezLj+XO/R64xl8XoBSGsPIBcitN9QBkMH+7MPjb8rIQsrobtPG6asrJIcmzaFZrlaPvvQZPZM19da3cSW91I9oLjikZgcm5A9AMcmjJ9BZB3usYxijnV69KG0WFPLJmQdWDZtOklgKVBZPpu33/jcYhOwb2b0Ze8/W+/c1lvDJnCZT8MmMHy3xb35fKC8yRjAXxKR/wD4YeAM/KfkkM5HwPeLyHeQO4nf+Lp5y8AtG7lpdlfFyTVc3RgAocHiXO0GnF5jPQawvu6o7o0flKlkXRgnxdQAibrRVmTV6KW0enXkUAanbIUJdLGdDTtoLHUSqQ1/1adAqBWt6NtU4F5s7LqVKa3OlkHl8uG1dUeqsyqsHlsesqpIq7/NtfL19BrXldaHJvvPKpfYzKkZA8DPtJqIq4br8hjAdTb17002NbVs5g+eTT2v++0mn2/IpqaWzZwaPnWsy/DZGz9WbJ1vZUqr4l5kU//u9RiTZzN/MHmIP7//3F0rX+9+bD5E3mgdQErpdwO/uzt8R/YGdtlll112ecLypLaCGIntoafOLcyfy3lduMLlcXWx9xvIJfds1KNXt3nggqZmMaxKGkcW1xsMAktaW5isyxTKv17U+gqD6/cDd1auzrV+ExnoM9kXxceBpWV/Up/V6zPSW/4TcRVimOSJsQmX+dxgEzot2TDSaIbMPaX3bpx3foFNm1rZ4vMpsAn49UL1y8TqdtMgbvaIsm8Fscsuu+zynsreAeyyyy67vKfy5ENA0Qx89G9Ryp/LecWNW1IkdK52fMxhcysjzywvYlgf110AB2GAuud6GP3OrObSNwfJ648HqR5jkvrGKdVnHNgBsfwD72ov3RvLrOhzGe2vmN7CABZQdNetAwjS5q1bndZVcayPPUBid08LwbEJmU/LZi5SO+crziZc5vMhbGp+hk2b3kcsm5DfiLbFpk1fh8+vPJtg+RTLZa9TkbVJ/gZ83keeZAeQkqwqV/83tAYsf9YHnyoY9bfEWtFa42cg04ZrcN0RHPbQVeBlXUHqZlBB2gyLWpHCqrFHpL579aoMTtmanLG4Clc+J9VT2z9J816S1WPRd5JhpcrfhdfWJ1affYMlYvSnL/gVUt9wWT3WY4Myuucoq2MjSV1j1X9WucRmThufrTFrfK46aKNHe61NXXLFWBhwZtmEPDvNsannrQySDaW9IZuaWjZz2vjUztXyWXU2KICt861Mr8Gm/t3rMYhjM59v+LR6rMceh82HyNPqAEZjU8ai0oelL3mOSTihDzmV79ZzbpeUKjynmsdc81bIRtdqZZN7PYAk0l6gbS2k4EHJG5Z1NxykbQURDCi6hbgDz3ym+2yy7StBbky8dbWkUHVwMg1SbbCKTu1K4LoPmOlI9bn0VnAv9bZHZbd/95VrmrxeACS0Dbb0p9NUK2H9Lghp6vLjUuM4OLbBZv5bHJsAJ2STTZuektQXmVg+R9da3cRW56UNdggrHq2n2TYsMzcdrjyD0LGp6eiZdnq0BkRrlINjE7IOTl1nafnsVwIviVVHekrTJpv1tkdl7z/bztDWW8Mm4PjUzxKCYxPIfHadwb35fKDsYwC77LLLLu+pPC0PoEieSeld3lOaqmWkPfoxTcai16lkcWVlnIicqmvYLAp9O1i1XGkhoHutvRpY4mkyFnu1Qk1vX9KU1tv5yjQZq2DgcleX8g3GAJCVpZ6PqZVUNtOSSOgs1kjiVFxt9bxGz8VawaPQxUhWoTRjsVc9ijS9VH0ua4NoCmtPKkwtZBFMOrJcr5VTU3Nvp8rR7NjMx8SzaTOheVyWT70jy2fzUu/H58hbTG3OtGMzp5NjE/z0yGq5Dr0wcWy6699Dei/VWurtWHBsAo7P5mU1Pu1zgazb3kMbhX1H4uqcsdgdm5B1Y9iEDqnJ8GvZzJl4NvXH92TzIfIkOwAYhS7WD/TEVBuu22ReuF0U1aBI3Jb89J2htuG6PgYwKJx5IBV488BqCKhWuAnmoupDg6J/HwDz3AApOwWmear5JduA9RXtQtiiLVAsFSm2yqX6vE03Ri/6btWmx1ArYeS2ZHhbfntM06oj1WudU6gdedXjNX2CG0ys91b12CqXHIo+U6obv9k910X1XStjqBWudiwCGhF4SIcau0bZMnVCdRI8m+V+LZuA47Pn/PIYwKBgvUEiXWNC4dOyCZm9g2+w7PsAKr9TcGxqfqnrXG3duBZSywuQG5uQDRPLZtOLYdPkG5Da8Fs+j4ZRyM/HspmvLw9is6aWTYBp8mwChPX7AGSePZslD8smZD7f1hg17CGgXXbZZZf3Vp6WB1CtRamW4zk2a7VaBTG/6u02Harlqi/XDkRO3bTAU2oW622cy29vjKWV03OaqmWg1t21KWIJE07QfUTcFERjzZ+Kq1gsqYSx/Otr92Zjaal1INXKaIOYG9aVLWNnOXpPqllIam2qnrJ77TcqO6bEXbV21fM6NKt35QkEZ2ldFBumCCat7nDJYzaWvdlIS63PeoUg1Uq1aSov365W6iT1eiuPqpfkeUhJHJuaWjZzOjs2AcfnqRiJlk+1em2+58po43OLTb1XxyaUKYje0+QUHJtQ+LRsQvFSDZsltWxWdd3DcrWhNO9J9db73LGZtQDllZDF2rZ86jM4Oa+/sQneSx2K9fYtm5DDlIZNKPW72+QtiXg2Ieu9YzTNwbFZr/WAsNp95Wl1ACqJFRTnOK0amNt44FbyQz6YeOCh8+VOSWpHUStmPLjPkF3QOhc5eufIhett46sPpzzLFCDO+WCYtdGf4HAoJ5gl9ZOum9eHbqDQeOw0kaYudBHu13DZjlTv55wCd3Ggi6DHchpSItZ56zm/mKRWwo/jTc1D9XdX0nNsjVXqGk7bKriJLF0oJgUhFv2pHplb+IGzVp64nqMeQutItVGbw2p2Vq5cvuHabLRMSM2yCReMFDk4NsHzqWNYls9VJxIPNTwy4vMimyW1bELm07EJmU/LJmQ++1ku8+TYzGlwbNZrbbCpqWUT4C7Oq7p5G2bHZtZF4zNWPTY++zzu4sGxqdffYlPvpw/3xjl4NlU/hk3A82lDaobNnG/wbEIZA1wV79FkDwHtsssuu7yn8qQ8gLbnmdSFIM4qWLnXN3yc8mvXQhlkWcKZm87iOjLx0lismlaL1bjX6ta3gaHr7mHvFqa5hYBqz36YELUKihWDSLtItbJC9RTSjVoRoXoU7lq9dSWw2hfeelI0a1WtIL3/u3Tg4/gMaHv6E+Gk+/vXBThrT+plfGZcbc13LtcK1bq7NmnFDvjae3Q6JeuzWqw3h5ZB9Av/nAdwaCE1fR7qWUQTArIeyNhi1bRY4Ekcm9B5lSWM83E6OTYBx+exDBZbPjUPy6dey/F5j9DFSI8EcWwCmU/Lpl7EsglwODg2IXsUq/DIKExp36lhPSnDpqaWTYCP4zPHJng+dZaU5fNlYdryadmE7EltsanpipXg2YTiUVk2wfNpPYCDD/emOTg26zV67/gRPYEn1QGo5M0FFYp16EIry8t40173FsoikRi47VYCn5hcgwUZqOoaLjnf4zLXBiuuOgJTvkGcU13gOAnxoKGL8mAPEUrZm3s9tZdI6OrAKVQoUmnA4sGEgGZ1PY2rveFmx+hDBsc4uQZLdfFMckdaK1loYTXd4fKU5hqT/VJ8Xn9bn8eSU83/aEJq1c2+FK7oK5eJK9f7P0ykc3Grbee59B2AtEp1aGk69KG0deXaHlPRVBybet+WTSiviTRsAo5PnS1k+dTO2PJ5LIxaPjfZLPdj2QSIh+DZBIizZxMyn5ZNgMPs2IQSAjJsQglT3iMEFKM4NsEbe6qLZ3JybILnU+P8lk/9reXTsgm5A9pks1zTsqmpZRMgnaNnEzyfZkGdZTOnwbGp6X3ZfIjsIaBddtlll/dUnpYHYENAxXI9LWU2wDI3D2ApVnw4NqugyFFO3BSvwG4ep5a/Wq4v4021DF4Va+MYp9Vg29W5wSO3cBJnaQHIMrW9E/Sn52gWK2mvH2BgFcSDt+BGg8CX1wF0g8CxeVKvjLX6XLJ+9E1MkdC8q6TL7MPKMnsZb+rz6K2rc2yv6rv6YmsbAlLdzandt3pUp4l00+lRBOYaT8jpFFYhjniYqntd3fZJiN0A6XALA1Pupk9xbOr9WzYBx+exeFk3cl5tbGj57L2IV/FQder4vM86gIHlar1UWdTaN4u+ipeVFysZNgEOk2MTIB7EsanX3WIT8IPAsXlSrzodPJdnjk3A8an6tHw2y7/xadnMty2bbEIJARk2630bNgHHZ10cNifHJuRwj2Uz5xscmwBx4n51/YHytDqAIilKdZfVvT7GiVdLfrivpvxgv7w8ry+OqIubwuxexwfZLdRY4peWD+pvNb8aAoqTcbGlluViOU3l0oYkzpC04TprJQsEnYqhD3Myq7Rqfg0KjbPGObSKVuFgNbNjDIVZrWrc7Nty3y9L+kF4tupIlyTc9MfMSsrWAbSK9qqEgI6lIzgta32O4gF+hkVJTQhI7zvehHrjof42tHcw19tex7jjjelIB6G0PgR1SZSHGINjM9//wbEJ+SUxlk09pqIhC8un/tbyqdew+txiU1PLZtaJODaBzKdlExyfdbrjYXJs5mPBsanpFptQpiV3IaDb5eDYBN+RqlFh+exXoecOwIeAXi03jk3I+txiU78ahYAcm+XGLZuA51MXk9kO4MZ0pINQ2n3ZfIg8rQ7AWIvqAZwXhWJeNVyzPK/L66sFkA7NA6gbSYU6qPSlUrleLje1wdJ8j8tUYVzHrmXNh1jgWwXQhyilwalWFm3uv5hYYd3tz8QUKxQ3pnKVp2WtguHgpbFoUjcGcFom12ABfDk8W73J6pSm1UveFzO32urz4/Mzl99xadbqsuhYilGeti+23rX6U++xTqfVhnsJKytNJiGV+6n6NEv0a+d5M6060jjLuCMdWqzek1miODZzenBsQt7+wbIJxQMwbILXp46lWD6bThufKfmGy/FZPSrPpt73FpuQ9WnZzHkEx6bmZ9mEzvOwxeys7WTGAKpHZYy9Lwf1TM24SbkRy6fq0/Kpdd3yadkEWJbBOoDEevwieDb1vh2b5r4gs5nvcXJsZv0Exyb4jtQZe2/RA9jHAHbZZZdd3lN5Iw9ARP4F4A8Bv4Tc9/1W4G8D3wd8I/DjwG9KKf3Te+U3sgrUPVxa6EJ79MkuqCnu9SGc3XHI0xfVwlLr6svLMz6uFlZxC00IqLrX12ZauPhqTuO89gBIoXa1UmaEyCzUvcGqNW88ALUsboRlFbpoliu9dWAltdCLWuJ3y1zv9+VZ3etY46tqSd2FNutCvaxo9mixetQxFH0+VZ/LVD051WdKpqhXx1KaDpal6NMuziu/lSWsQml2U61o4tRVj7ombxBKu7T3imUTskdl2dT732Kz/06nL1o+v6zjCIbPUxcCSnEcux5Op+1CQNYDqCcGzyaARM9m1llwbAIsLnRhvI0NNqHMAjJs6v1aNiGPTVk283eNT13Za/lUPVo+LZuQPTnLZi3u1bGUUvaDbLNpMwbshoSWTSh6NGxqOvL2H0veNAT0B4D/V0rpfyoiN8AL4N8B/nxK6XtE5LPAZ4HvulduZmAo1RCQVrKZl2d1r3UFYKwV6FwGVOe4rMYA7ArNV6YTeXn2HcDxPFcYr8UFR1DYOOty01UuIm1v8Fa5ZDUGYAbTBlDUuOAEsYPClsv6r70ez0vg9pwzuimbqM3hpulq0ko2r8cFCHXNhFbWV/HAl04lrHb2YypuHUDfobr7HnWkUgfbRBuaRH25hjZIYl8AX2/fzEs3YR9b0eqxvgO4GAIqiYZbojg29f4tm1ln4tgEPwbQr25/ZToRy+exPDPH5wabmvZjAMuNeDaB/F6FxiZ4PmuHOoljE3KHatmEzOcqZi2sYitWj5renudNNqEbFyi9jeVTG37Lp2UTyjqADTY1tWzmNDk2820Fx6ZN8/fS8utDkoeBsTexbvifQghIRD4J/CrgDwOklI4ppX8GfCvw+XLa54Hf8GZF3GWXXXbZ5W3Im3gAPx/4r4D/m4j8N4G/AvwO4OeklL4IkFL6ooh8zejHIvIZ4DMA06c+lQ8a60AtnVNZQXucptrzvzQDQrp6UEf3Z1kI3T77MUm1DF4tzR1Ui/XVuQ1eqhWyGtQZLBLxqyzLtWZpPb8ZQUohHwuLWgVrneT8vAWw3DCwXFkvnBpYBckMAuug9nGZuFVPKphXE5pNtCC73nNYv8GqrSI2A58lP03VWj2dp+ZejzwqfU4iznIEkLndrxvU7ixcibJ6sdpoxkacpXpmLRTSnpsNqQ1d7M5yXZbg2IRsmb7sHuw5To5NwPHZtidufNbJDoZPDTM5PjfY1NSyCcVrsqObQAppk82cimMTesu1ZbvFJmQuLJsAt+eDYxPKbCHDJrDJZ6/Hl+eDY1Ovv8UmeK9b1JM6rMNwlj0p+Vo+Xfi4m+W23IhjE/Kz68O9TyUENAO/DPi3Ukp/SUT+ADnccy9JKX0O+BzAs2/4dBkiL19GVrNXjsvEdPZLrCNipuKVjaIkjjuA2lG0mRva8N+dGhR11krXcNnXxFXX1rqF+tAPmKfcZvnUSnj27qETEeNKtwqllSsZKFbz10Mrn41X1w7AdKjHKVegV0aftRJGDQ+dV3oEVnrMYzOlMyj5aYjpvISqT9WJbVyGy+z1HiN1BX0rhqwadokwHAPQvE3jt2gozTRWo+X910JAGjGx41TacFk+6/YGKTg2czroAIxeVZ+WT22wLJ+pb2Ck44Gu4dLiJXFsqp622ISiM8MmZD4tm9DNXzdjAf1YSori2AQ4TtGxqXqybAKbfB77WVrng2MTij432ATfkerrCGL0bNbzulDacAxA1h3zcvBs1vz6cO9TCAEBXwC+kFL6S+Xv/4DcIfykiHwtQEl/6s2KuMsuu+yyy9uQB3sAKaX/UkT+voj8opTS3wa+Gfib5b9vB76npD/w2nlHIS3ecj2e5v59yUSk9vI35fVrgXQhBOSttVOcquVfjw08gOHAkHPjymftzZP9jVoFqd6Puo/qHvb59nON02QGgXWGgLFGrrmFktoXS1n4E6bA8eQfe0ptVeuh6PEmzGMLq9uU7LRMdWDtrnOvlyXU+67jnhcGLleDwBOGTmM1dQOaeTDd52kHcpse7WBby8N+f6l4Lm+1XBdxbAKOT/UAjsvk2IRLIaDGp874sXyOPIAtNvW+HJtQftfY1PvZYlPzs2xCGQQ2bEK55hab5UvLJuD41EH30zI5NuGCB2D4VKYtnys9LrLJpt5XXzczn41NUM7KT6sH2/K1A7k9e3mign5u17ovmw+RN50F9G8Bf6zMAPp7wG8hP/bvF5HvAH4C+I33zs0tYCou2rnEJSVx1OXW5bxTDBxKrPC2pFOItaJF4ystZtRff6swjBssdeNG8cF2yMMAw3jsJETd983OsEjdeWb2ilaeaCuwpgcDxTW3MAGLVvTS0Jym9WlJOJVXA06L0WOpYFaf/X705yXUBmulz/NUG8lVuMLet+1IbQPf6QehdaSqRxuucKGQ5pJrvrXC2fhqPwYgfSG7vGsIIzg2AY6nybEJcAjRsQlZn7F7YJZP/a3Vp2uwAKJssgldR3qBTYC4DGb/2LCnmb1i2YQSPjJs5nzvwSbAIptsApzC5NgEHJ/96zmXGGqYx/Jp2cz3HTbZrPfTGR9WP/X8RTbZzPnK0OjpQ2luDKBea8DnA+WNOoCU0l8Ffvngq29+k3x32WWXXXZ5+/LEtoLIiUSp/qruWXKmWQN1n6ApclJrQGfZSKoWWc02mdfOmXnx1eoq14jnUK/XLM2RdVD+CGYrAV3YAisrTGbQKcvW9b02e6VaWaMwk7EKhguY6oClkNQK0S0EzlmXqhfVxTypHtXKuq5Hvd/zMq0Wm1Xr6izVk2IUUrMegN7v3G6/RiJKgdNk9Gjc9pEeh7NhumtYy3VrIZhjEyAmxyYU/Rg2AU4hOjYBp9fVKzvNvHjLZzSMglqa19mEYrlaNsm6sWxC1qtls8/XhZYMm/Ua/eyVSwuYDJtA5tOwCb6uqy7mKTo24boeU2rbyFg+HZtajg02NbVs1lswbELRYxdSGulxuPgxeDbhwkSFRwwFPbEOoMWLawOMHgr1AdkHKl3lAoYNVzKfATf7IGqYZBHQWSv3HAPgSsMlBgrVdINjcP+XGq4+Tm3DGdXNHocttJFI2koRaiy4zhCa2rRb1aew1iOsKxqJ9cypqs+wCtlcvO/eHabF1Ou0usXo1IZk+jxNBXGV55rLvTkLqLGp9+jYLGXpO0MJ6zGpUcNl+exnbsWljYk5Pu8xBsCFhsuxCTDf4xmVdLRqexXCcyGgyyE1ieLYhGz7WTbB1/ValC0+y9du5pRhM6dyv/u2YwD1+p5NyHp1bPb5XmKzv4Yx+p7qLKBddtlll11+GsuT8gDaHGEQ7amrG5Xa9gxlwUqUVK2COgd+0Dvaabh2dk+/ACTPCCjXW81ewVkDAGkyB/Vtdca6qqvs44ZVUBXQsht5GdYFXHkFI8s1Si2Xiw7pTpk6n/lsrCtp+ryqS31WabCQxlrJ1XJtIbX6nKs+U7X01CJJQn0ZVbVWY7uuW6y0YbECLrRjB9VWg+khDQfbLJuQ+bBsQrH+DZtQPKp7spnzb5ar1etqcoKbvWLu1bFZDi7up1mfhs16f1tslnTkZfThic0ty2td7tgECMGxCVmPlk3Y5tN6+7Vwhk0oehyEey2bkD0Ry2YuU8dmue5qId19vX3Do9vN1bJZf/s4A8FPqgPo44NglBmpswX05pNtpDo4LuZtY9hdXFeSuXb037msXEegZWmhjvpsVguZBuWxMgL6QlioHbt835Jag6UnSJQKUvutmI7HFGwYClkXMHXPTaxeo/9u6A4HSDrTyOixbs5lK9fq+oMyDspu9Tgae6hMbYwBXGIz/1Ycm1D43GLT5m/1afQo/fXjfdjMmVg20eI8Fpvlu1GYYotNUD59A0xInk3Nry/sFp/deBVGj2Lr+Qab+avk2NR8+/CRK+K1NtqGgiyXw7qe/LFrHL2mPK0OoEi29PxdJhFjAZjvemVsdQB62uhB2QHFa+fbh9RZCm7RX9fpXCuPzffShYegDI7Zd8P3noxtCOWaHi8dG5T7ks6u6dPmf1GPw0rVt+yDfE3e60KOdea+u1LB+gG+/LE0DJLWZqlc+NzLiBv7XT+geOn83joXvvJslnR0zLIJOE/Gni/X9Hjt2H35tPrcYlP/7vU4NEhsyz7It8vfF3Css4vfPYLsYwC77LLLLu+pPCkP4Jr7JOb/70wGrvy1Tj7ZT6/rtz1CjG+kzyehR5V76jON/voK6/Na6EkGn96JSFp7NBdOTf2n19HnW2XTf3pn8iA2y5F3UNcfKrsHsMsuu+zynsreAeyyyy67vKfypEJAznMaDs4NZhU8cBCYUbhpaxDYLH7S9Nr0zjcbaOtGdG02G4PA/UwUm9299Xjp2DU/+J6DwCu9jAZ8H30QeJDF1iBwGOj+an7rEMxDBoGHrN5nENgsfnK6+0qzWdLhgOYWm5rvfer1m/B5ZRD4der64wwCD36+NQgcrmV+f3lSHYBKnvveNVIhrVcWCo86DZSU2pzhbhpoYg2ARDMrpG4eZ857lKl2spqRMJo14+Zij6bijWYzhCt6vFCe15kGmt/r0H03mL8vkZW+h3P+hxVuUMZB2a0er00DtXocyXC9QNUnKw4faxpo3b9/MA1UTxfHtEk73T/eNFCp3w1Xq3bseX3rsbR+d7CbBloSq8crZbrvNND2Xge22SznS39e4pGmgTY9XpsG2q8HegzZQ0C77LLLLu+pPC0PwFpUk7GqAJnMql+zavWhK4HzIinve9q9wetK5NKN1/n0GIPOrFi2m5T1C4Te5kpg/cFqkYzmoV286tPoUYz1+yYrgen0OFwJbAvV68Dosb7lz+jRpb1lZvOxMrL29bOu3jR6rIsMaZa9y7b3uKbk2ASyDjs9PmQlcFsk1fTav1dBFtlkU7/rN9DzHpf53RabJb3vSuBNNstvLZuQdSadZ/YmK4Edn6OVwH2hrC6MHi2b4Ov6Y68EFqNHx2b57eMEgJ5YB2ChqDBMrcKFssOiHgshPepmcIjUzbaaW20q3gAKKRtZ6UMPZ9YVbmm/fbTN4Aa/XbmGtiNVfc4JCZ0ep7Zj5WNuBoeEtiK2/p76oGvfHVvlEpPK6FgfurCV1eiilndjM7h2YklEWsFq4dNqhW2akmMTsh4tm1kFj7MZXG0RDJ/SdbjY0I42cGfPZv6uY7P89lE3gxv81vFpO1LDJoCE6NgEX9drUbb4LF+PNoPTHiZJx2Y5aNmEbJgMeeyPbRl7l9iE4WZw9vc1VBSSaSzfTPYQ0C677LLLeypPygNogx0D62qOTHPZD3xq6fQm7wMor6Cr7wOQ0CyX6t+ve1oXAuqtq7P/rOev3wfAakwribcGgPH7AMy4WRu/Flamm1AH06p1NUWC6lHTEN/ofQD6POr7AMoOWZFYb6SG3i4N3HUWaTg3/QXjZa3fBzDW49CTUp3q9sjJWKpmu+4+LJO/6MKPU3JsQtanZRPKm9Ue+j4Aw2csFqvjc4NNTXserW7r+Qsry9XqdRSSHL4PQNVEz6bJGMzzSY5N6Op64fFN3gdg+fRs5pJuspl/4NiErq4bfq8NDF/bP8m9cyDVy662kh/y+UB5Yh1ATpKJAWrlmg8L85yfwKGk8xTrKyGnN3klZIHibACoIY6yu2OStv+7jXXXBkkr16lB4VzuB74SUkbvdMXs7KjnGdfT7mRYvw+tcs2H8m5Vo895pMeS4fVXQi6rV0I2RU0syTecrpZd60hPWZfQxgWG4bUHvBJS9eT0OdIjRgybkPVu2QSY58WxCfmVkNObvhJSJsdmLrs4NmsRu7CQLJ5NyHq0bNbzLJuqAMNmvn8cm5D5jF2jFMWzmfP1bEI+x7IJWZ83fV0f6dHwOX4lZP6t49OwCWQ+N9jU1LIJpa73ncKyzSZkPkevhNxiE4oeeRx5kh0A0qCwVv9NqWjP56ztm/nMoTyB130p/Byn2rCpJZHf6NQ1/M7a8KODktYAhLOpaLXCpRbj7geNjCRJqxigTLKqXLlc+r0eWJ+DUK1U9ahs5bo55EI/N3psL95eXvul8KvzE21b3xp79d9DZ9kbffYdaa5wyZ3nYq6arVAP2hdvi74U3rZvWizV44RppdYWax2ADGuP9OawODYBDmF57ZfCz6XQls/UebAsqWOzFK6zOsNyQY+GTT1vi03oOtLJ69OdH+7BJoAZS7GGiWUTsh4tmzDgjesvhXfnKwNxm03wRpyr633nuqSBsWeyrRyl1UvhZRbHJmQ+HZs1k8fpAvYxgF122WWX91Te2AMQkQn4IeAfpJS+RUS+Gvg+4BuBHwd+U0rpn75WnmYMIFTr6lytgedz7m4/OJx4PhWLq1oFcewBlO72OOX0dpnbecdy3hyqhRX1pSmDuKCMLNeBBzAdUzumlmuNGQ56cJEWA1Trak6rQqTQ8qnxwZEVJs06tDFVta4+OBQ9zkaP1co6jy0s1aOmYaouuYqNZ2scuy32MZ5ULai3tMBbrlWPJ2Ox2thrHxQVqSoL9d2qYs6Tqp86q6cfo7kgYsYALJuQrVXLJsDz6ezYzOnAAzB83vYW6zGzCTg+t9iELgRkrFXLJhTLdYNNyDFqxyY4PqtnemaTzZxtWsX7bw5nxyYUPRo24YIHYPg8hsnlC6zGApcpdGyC43NQ163Xb9nMx9JqXMDvw93Yc2zW8wybkGcG3ZPNh8hjhIB+B/C3gE+Wvz8L/PmU0veIyGfL3991r5zMTWtFq1BMS61cHx5yi/1iPvF8yseelQZslnXoIoeAMgyvlkM+bwDFkkIdgDt30yIxXpdbHagwaAz7BNOplF2hOCbjaiefh5EUEuHcKlo+aL8vsd4JfakX0ZSFvj7aabIak55bSEIr14v5xIs561T1eZDIXCc+N9EKdlcKeBsOVd9tAK7ocwksOgCnIYRgoikmXFEbLDvA1oXSpmMinLThanpch4DaHP1k12l0oZ0UpI6vjNYaODFsQubCsgnZMLFsQtanZRMuhYAan/OgQ100tGH47NfAuKmcpgOwbELm07IJGqa8zmY+TzbZhMxn7MrCcAwgOTYh69OymdOjYxPY5PM25Lpu+bRsAixTdGwCdscKb+z1g+kn1sbeKTk2bR5gQmmhYzN/iWUT8vjKaK3BY8kbhYBE5OuB/yHwh8zhbwU+Xz5/HvgNb3KNXXbZZZdd3o68qQfwfwL+beAT5tjPSSl9ESCl9EUR+ZrRD0XkM8BnAKZPfaocbN3u1FsF88KzEgJSq+Cjwy0fFKvgRUlnWZi6ZVKnNHEqFla1wkjNHVQrbJk4lRDRqVw/1pd+mgytVdBZruGcmuVarILpGKvlKou1XH1XnoLUd+NKv7oW0GhKmpoVNrIyVERYLZSznlTT4x0fTtly/aCkz8KZg3gLayFUS1UH1g7GCrOeFOQBzWOZyaLlSN4cKunAkzpbT0r1aDwA1WdMq5CFnU2VDsHpCYx1NUFUq05rwsCjyCd4k3CaomMT4Nl8dmwCfDCdHJuA41O5tHzWWVeGT53SaPncYlNTyyYUy9WwmY8lx2a+1aYI1VmcZJNNyB7sFpuAWyhnPSnLJsCH09GxCTg+l2LLWj4PnYdgPSmdDHI8T5tsQudJaWjylBybUDwAw6ZNc7ZtNtUWm5D5tGzC44aCHtwBiMi3AD+VUvorIvKrX/f3KaXPAZ8DePYNny6a0rybe6vT6W6ms3MHAT6ajnw4Z0BehHzsEM5MnY+0INzF7A4ejBuuU8d0RsvdfOb2XOKvwVd4X/jylZ2+OIgL1sp1TEyn/Fk0janN663zgAWZfKwXwgoKC2O0fV0NYbay93qcp9jGUkpo4sPpyCe1wSp6fB5ORlelM0yhNlQv5abc9zqUpuMDt+FQwySrcIUR15FWPbZYamusEqE2WCXfJY3HALTBqmEIIZV5iZOZfREWf57bEsHlqVm30IVlE3wo7aPSWH043zk2AcfnUjK2fNaQheHzrjwzx+cGm5r2M9WmY3JsAkyn6NjU31o2AWQKjk39zrIJmU/Hppat60itHjV9Pp8dmwCfPNw6NlVXkPmMpaCWT8smZD4tm5BDzFtsamrZzKlnEyAco2cTxmMAQRybAEmCYxMyn45NeNQQ0Jt4AL8S+PUi8uuA58AnReT/AfykiHxtsf6/FvipxyjoLrvssssujysP7gBSSt8NfDdA8QC+M6X0r4vI7wO+Hfiekv7AvfPUmRtmgO1gB4GLK61W/4fzHV81vwSMByALN8XnVXdvIXBXjgXTfbaZF1kNL8Ny2WI11sFoJbCfv+7DFNNdJJzyCaIDROdYXey6x8ckbV8UsxJZVz7qgGU4m7nL1waGpHkyGlJ7NjXr6oXR40dT9gA0fS7NA5jKTS4pcJsGnlQp613R43PV57S0VZt1zropn7FoVhu/LUZ/J2NdVcu16HOJxjUuepqEFNS91otMdeBRwxRyYD1n+0IIyLIJ2XI89IPA08mxCfBV80vHJsCNnB2bwCafL4sZ7/jcYLPez2r+enJsAoTT4tjMeSXHZtZdcmzmMiXHJhQ+N9iEUtcNm5A90xedHj+abh2bgONT9Wn5bIO/jU/LppZ9i82qx76umxBQ4zJ6NqGEFT17KQTPJkBIjk291qpuPBEP4JJ8D/D9IvIdwE8Av/HevzS7/U01dKGV61w7AI2pfmK65RMhQ/HCuIf9GMAxTbVTUIlIncnyasravpluaqXul+0nScO96NsCphbj7zuAcFoaFNoRmNBFncQRAhx0EVA5JkIq08SkzBAaLdoRGxEwDUM/m+owLXVaoo6ffDS1DqDp846bwRjA85R/87E8q8fV7VZ9vpxyeOgQllq53bYSGk6x0YC+ci1mpk/VY6tcmnKOSBdrEBFSmTaZStnsNhuh6DOvLNYxl/LjiyEgbxBMITk2c3pybELW54sudGH5PJbyWT5jDQs1Pm+qThuflk1bxHoflAbEsJnvOzk2IevTsQmQkmcT4DA5NgHSLI5N6BpM+5y7TsvOprILvSybOb11bGadrccAtvi0bEI2jFabyskqUuVn/JlQkGUTsh4tm1kX7XlL1VlwbELm07IJmc/V7J+n1gGklP5j4D8un/8x8M2Pke8uu+yyyy5vT57WVhBFJKS68dNcuj9rFehg0EfTbbW01Cp4LqcWAipWwZICL2OzCPJ3wm0ZeHtmFpP1m8vVGQKjcl4YBF5ZBaeI3GkI6FzSaHbOqv5wdRW10w+TEE/FKjjovOH1Hu9jq6DNsKjhirCsPKkX0121rlSfH4Y7DqKDliUERKg6023zYpI6oFY9qZB1fZjW+hz5rzbs0tzs5CxW6NzrY9HjaWmDbKrPKZidyspvRUhlgD0dNF9pbvjorVADqbOqQnRsQvaoLJuQ9WnZhBICMmwCjk8dGLZ83nQhILs98iU2NV3tV3NKjk0AuVs8m1B2HWxsQg5lWDYB4kkcmzndZhOyJ2PZBO9JvSgewCfCrWMTcHyqPi2fOinB8mnZBL9Z3yU29SvLpqaWzZwunk3IDFo2oezy2NgESFNwbObrp3uz+RB5Wh2AXR1YHkp1C6dznf6lUHwY7mrl+mR4BcBzORP6aaAy1Ti2yjHN1TX/oLjmN2GprmHoXH4nV2PXbUFNrVwnU7nuNEgaYdFWvFSuqUGhrqLMgVDCGW2hTjJT8boyWZEWeqm7KoZY9aiN1YtwrHr8RNHjh+GOA34M4JTWU+tOaea2PI8vL7lyaUWeQ2Tq9XhBn21XSq0ANoRW9Hg2lauknBfoZnuwCLJM7lAIoU67i2X31/ys7hsCKknVZ3JsQp6eaNmEbJhYNgHHp25EaPk8loC65VN16vjcYFPvq2+4wpIcm5ANE8cmZD4tm5DDQoZNgDAHx2ZOZZNN0Lre2ISsR8tmTu8cm4Dj85TW05L1mOXTspl/Ox5LqWL6Bssm6JTPxiZkLh2b4PmsHWTjM+iuA4fg2IQ8C+hthoD2vYB22WWXXd5TeVoeQBExlutsQkHVAzDWQbW0RAfaztzgrdQjE1OxTJbQZgt8OTwHTAhoOhsXWwcvtVCmfNZj7C3XZWAVnJZmXZ3KKrGzCV3oRWLrj3WnxXAKxJtiFRTrIJyNpVXc3PFim+RmrUC2zlWPOij5YbireqypHNssi3KTi5y5TR6ZU5rrzIsX3UIdu6No/zo/wFs0A0+qudrGk1K3+lT0eTytJ5yHAPXtUia8dppq3nqt4RvGrogYy3XuwpTPwtmxCcVLNWwCjs9jmQFi+VR9Wj7Vy7B8brJZ7seyCSUkadiE4plaNiHzucEmQLwJjk0oXuoGm/k+kmMTsh4tm5paNgHH51K8K8tn9QAMn5ZN8IPpF9kEGHhSmdHOkzotnk3wfOpg+pwcm/m303oxXrw/mw+RJ9UB2MqloQNbuRQKjaU+l2OF4UOdaSELh464Q4rV19FZF8/lxLPgV2jOEmulbpXritZHIaBzi/fV6XSnpVUqheN8HnQAzS0UdQvnuN5bJOJcUy3L6v2L0kJZOr1wDkvdQ0Xv/5mcVpXrRThxg844KcVLtjMI5bw7Pi7xa62QWsnsHu4j77reqw0BWfCtTin6VD1q5Tpd6ADqewjKledQn0fQdAnXZ1PZMvYhIEmOTcgdqmUTsj4tm4Dj86D7ZRs+NQ/Lp17L8bnBpt5Xr0di8mxC1qtlE7oOYMwmlNkwnR7di3pqKqtwS5Dk2NTUsgmlAzBsAo7PGmI3fGpY0/Jp2YSyEGytvXavNgTUsxJZTZ3lvHg2YdwBpOTY1Dwsm/Uao9lUjyRPqgOoYhsuaXAcagUqg0WuwuVjLyTqlOUqE4mFUkn1fPtbXVloLNZ+h8t0yTqoHkBJo5m+aKGo1lUuRzqfW2xQY6oHU+FmnZK3NCvjyhjAJStrNQZgPCmvCz2maeS5/rZUkSMJivW6lEbtNh2q/mqDFVqHWlfODswYNzO0hzymWhmaHk0HsLSGKy3NogYgmJf3TFq55jYdz6x0rR1Ft/T/otiGS3zDdZDFsQlZx5ZNwPFZGyzD58rQCafV2JTl8yKbJe0nDIRz8mxC5tOyCZlPyybkvw2bOY2OzZzKJpuaTgNPqq+bz+Xs2Mxp4/NYb7jxqZa/5dOyqdffYlN1Z9mEbEg4NqGMSZlOFTyfuqIaHJs53+jZhMznfdl8gOxjALvssssu76k8LQ/AWAX9GMBl66q3CoRDfX+qWgiJpbiLGod9no4rj8K62v3bwobFxVg81jqoUwuNVVA+V+vqdK6WQXWpoc6woGy2xpLq28naAp37xwWl86SCpNV938hSp9SptfpcEs+LHkMxe4PZc/qo+pRT/W2fb5DoLK2LYj0p62ZXa8iMqZx9mCJ7UsXS0vOmifo+wqlZYzXsoc9nSS5kAVfc686TsWMA9r7XYcqzYzOfFxybgOPzeTqu8rVhSsh8brGp99pbrsTk2QSI0bEJ2XK1bOb7Fs9mSS2bVV33sFjFeFJ1yrKp6zdVB2fHZk4bn6FbdnwkmhXD51W+ur+V9VKHYj2pPswVk2cTMpcrT2rxbELmc/KegpyjY7Ne6y2EflSeVgdgpI9d+4arPFCWuje4rVyHzrEJEllQeCwM5/q9XqN/l8DVF0W72LVpnOs0Oq1wpuE6GTjKb5KGeKDtVFahWBpkOkU0pvEYQC/Syt/WAyx1ymHtUOVo9NIq1zP8VMpJBHWxT6XC3chiKqkPU/gQUCvTSmzZ9R5tJ1f1GFtjr3o8nVbTQBNUfctc7mGJtdGr4QpbqV+zctnYtW+4Gpv5WHRs5u+CyScXwPLZc25fcmT53GKz3WPXOMfo2YSsr1PXcMXk2YTMp2FT83Nslmttsan3YNmEPK3Tstn04ht+y+dUldH4vFl1IotjM18/PYjNmlo2ITf2lk1wfNbszmfPZsnDsgk+9PQ2ZA8B7bLLLru8p/IkPQBhHbqw1tVkrM9D/b5YWQQO4i1XEhzKbybjeveW62RWzg6tq76c1s3VHntJZkqjWgWxhnvqgNCykPpX7wWpFm79bUqrla6XZqpcE+tR9Zu85WPqFeTzR3qMRJZyo4dkBz79c7GewMhyHclqhkO0+9MbXdhFSuB0W/OCtvy1Dvy2abdun/beSt0KqWlq7s1a7JbNfCw5NvMxo9dyPcunMmr5nDpvY4tPPzusHKvWqmcTMpeWTcDzqTNWlsU/D037UOhr8Nl7+wdZHJv5WHRs5mNNj7GuVm989p7URKT3Ukde/0hcndN7XdJaF3aBp9Ftzafe9OLZLHms3iEwaGMeU55WBzCAOkg0n32FC5I4aMUwUIwaLp3J0jqMc827Va71tVrZ0r0egCQDRVxXEAUlmQ5A7DzLpWu4kulQBpXau6haiHZoHUqLLeSljY7EqoNDjfdLjf1PotsWSG3gdCZLbrB8w9+vuu6lezPj+nO9R68zlsXrBSCtO4BciNB+Q4mFD/ZnH9b9KyELK6G7TxumrKySHJs2hdZwWT770GQ2TNbXWt3EVudVO8C44pGYHJuQOwDHJoyfQWQd7rGMYo51evShtFhTyyZkHVg2bTpJYClQWT6bsdf43GITsG9m9GXvP1vjzNZbwyZwmU/DJjB8t8W9+Xyg7CGgXXbZZZf3VJ6WB1BEBm7ZyE2z2+pOznLtBoEJzVpwrnazOPQa60Hg9XVHxte4pzZWVhfGSTE1CyHqRluRldWb0urVkUMZnLIVJtDFdjbsoINpk0i1/Ks+BUK1tIq+jQXXix28bGVKq7NlYF358Nrak9JZFVaPLQ9ZWVKrv8218vX0GteV1ocm+88ql9jMqRkExs+0mogry/XyIPB1NvXvTTY1tWzmD55NPa/77Safb8imppbNnBo+dbKD4bP3fq3YOt/KlFbFvcim/t3rMSbPZv5g8hB/fv+5u1a+3v3YfIg8yQ7Ain1AU+cW5s/lvC5c4fK4utbvDeSSezZ6oNVtHrigqQGzKmkcVbg3GAOQtG5gWJcplH+9aOULg+v3cVsrV6favYkM9Jnse4LjoKLZn9Rn9fqM9A3/RFyFGCZ5YmzCZT432IROSzaMNJohc0/pOzdnnF1g06ZWtvh8CmwCfrp4/TKxut00iJs9ouwhoF122WWX91T2DmCXXXbZ5T2VJx8Ciibu1b9EI38u5xU3bkmR0Lna8TGHza2MPLM8h3V9XDeBGoQB6p7rYfQ7s5pLXxwhrx8OVD3GJPWFI6rPOLADYvkH3tVeuhfWWNHnMtpeK72F+CVQdNdNAw3Spi1andZVcayPPUBid08LwbEJmU/LZi5SO+crziZc5vMhbGp+hk2b3kcsm5BfiLPFpk1fh8+vPJtg+RTLZa9TkbVJ/gZ83keeZAeQkqwqV/83tAYsf9YHnyoY9bfEWtFa42cg04ZrcN0RHPbQVeBlXUHqZlBB2gBbrUhh1dgj0l7MfU0Gp2yNzS2uwpXPSfXUts/QvJdk9Vj0nWRYqfJ34bX1idVn32CJGP3pC36F1DdcVo/12KCM7jnK6thIUtdY9Z9VLrGZ08Zna8wan6sO2ujRXmtTl1wxFgacWTYhT05wbOp5K4NkQ2lvyKamls2cNj61c7V8Vp0NCmDrfCvTa7Cpf/d6DOLYzOcbPq0e67HHYfMhsoeAdtlll13eU3mwByAinwb+KPBfJ3tcn0sp/QER+Wrg+4BvBH4c+E0ppX96r0xHkxOMS6299ans6R+TcEJ7+VS+Wy+6WFKq1sOp5jHXvNXKGF2rlU3u1QMnEVJnAVjrqm6vO03rqRJB2l5AwVgKuoW4szzMZ7rPJtveCsrWpHevlxSqDk7GIq0Wa9GpXQlc9wEznpQ+lz4M0ku97VHZ7d+9dTVNXi8AEtoGW/rTaapWWP0uCGnq8uOSdTw4tsFm/lscmwAnZJNNm56S1BeZWD5H11rdxJb3ohZ7CCsebaixbVhmbjpceQahY1PT0TPt9Gg9yGaVB8cmZB2cOm/J8tmvBF4SK0/qlKZNNuttj8ref7bekK23hk3A8amfJQTHJpD57LyBe/P5QHmTENAZ+N+klH5YRD4B/BUR+bPA/wL48yml7xGRzwKfBb7rdTLOMym9y3tKU60Y+kCPaTINuk4liyvITkRO1TVsQOnLYWrDRQsB3Wvq/aAhTpNpsGsjZB52SVNa7+Yo02SgGLjc1aV8gzEAZNVQ52NaScpmWhIJXYMVSZyKq60d7+i52EZwFLoYySqUZhrsqkeRppeqz2VdH6aw7kjD1EIWwaSjhutaOTU193aqHM2OzXxMPJs2E1qHa/nUO7J8NiPlfnyOjIXU5kw7NnM6OTbBT4+sDdewExbHprv+PaQ3UmxD3Y4Fxybg+GydbOPTPhfIuu076FHYdySuzpkG27EJWTeGTeiQmgy/ls2ciWdTf3xPNh8iDw4BpZS+mFL64fL5S8DfAr4O+Fbg8+W0zwO/4Q3LuMsuu+yyy1uQRxkEFpFvBH4p8JeAn5NS+iLkTkJEvubCbz4DfAZg+tSnVt+vQxfrHv3EVC3X21T2TyHWnrJZBYnbkp++M9RartcHgUeFb2m1eEyPXUNA1eKaYC6qPjSroH8fAPPcLISyVWyap5pfshZsb2ldCFu0BYrFkorNulJ93qYboxd9t2rTY6hWWOS2ZHhbfntM08qT0mudU6ieXNXjNX2CGVg091b12KwrORR9poRu/Gb3XBfVd7XGQrW4qmchoBGBh3hUsbPKLVMnVCfBs1nu17IJOD57zi8PAg8K1nuk0lmTFD4tm5DZO3iL1b4PoPI7Bcem5pc678rWjWshtbwAubEJ2TO1bDa9GDZNvgGplr/l82gYhfx8LJv5+vIgNmtq2QSYJs8mQFi/D0Dm2bNZ8rBsQubzbU1SgkfoAETkI+A/BH5nSumfy2hEeyAppc8BnwN49g2fLmuwtbGQ2nCcY2usKhQxv+rtNh1qw6XvVg1ETt20wFNqDdZt1BdF35iKltNzmioYWrmvTRFLmHCCLiN3UxBNY15eoK0NU8I0/PW1e7OpaAqHVMhaDHujctkydg2H70hbBWkv0M7HsnvtNyo7psRdbey04z20Rm/VEQRX0S6KDVMEk1Z3uOQxm4bdbKSljU+9QpDaSNk0lXev1kZqknq9VYfaS/I8pCSOTU0tmzmdHZuA4/NU2gjLpzZ6Nt9zZbTxucWm3qtjE8oURG9ocAqOTSh8WjahGCmGzZJaNqu67tEU2FCa70j7xnvu2MxagPJKyNLYWj71GZyc0dfYBG+kDMUae5ZNyGFKwyaU+t1t8pZEPJuQ9d4xmubg2KzX2mLzDeSNZgGJyIHc+P+xlNKfKod/UkS+tnz/tcBPvVkRd9lll112eRvyJrOABPjDwN9KKf1+89UPAt8OfE9Jf+C1M0+srIJznFYW5m08cCu5lz+YAaFD58udklRPoVpm8eA+Q3ZB62KU6PtGN15rrW/tnUtnngLEOR8Ms1r9ExwO5QSzp8qkG6dor2+sAh2QmybS1IUuwv0sV+tJ6f2cU+AuDnQR9FhOQ0rEunAp5xeTVCvs43hT81D93ZX0HJu1mjrL2ZqFbiJLF4pJQYhFf6pH5hZ+4KzWU1wvUgqheVJq1c5hNTsrW1fect20Wk1IzbIJF7xUOTg2wfOpkxgsnysvIh5qeGTE50U2S2rZhMynYxMyn5ZNyHz2s1zmybGZ0+DYrNfaYFNTyybAXZxXdfM2zI7NrIvGZ6x6bHz2edzFg2NTr7/Fpt5PH+6Nc/Bsqn4Mm4Dn04bUDJs53+DZhDIJZFW8R5M3CQH9SuB/Dvx1Efmr5di/Q274v19EvgP4CeA33jfDtueZ1IUgDoqVe33Dxym/di2UGNsSztx0Fe7IxEvTYGlaGyzjXqtb3+KC193D3i1McwsB1Qd7mBCFokCMSLtIrWShdhTpRiEKtUNx1+orl8BqX3jbkdIaK60Eev936cDH8RnQ9vQnwkn3968LcNYd6cv4zLjamu9crhVq5b42acXG++09Op2S9VkbrJtDy0A32lOxHcChhdT0eWjHEk0IyHZA4wZL09IAJ3FsQmdUlDDOx+nk2AQcn8cyVmD51Dwsn3otx+c9QhcjPRLEsQlkPi2behHLJsDh4NiE3KGswiOjMKV9p4btSA2bmlo2AT6Ozxyb4PnUWVKWz5eFacunZRNyR7rFpqYrVoJnE0qHatkEz6ftAA4+3Jvm4Nis1+iNo0fsCB7cAaSU/r9c7pO++aH57rLLLrvs8pWRJ7oVBKtBYBu6UGvpZbxpr3sLZZFIDNx2W0GcmJzFCtmiqK7hkvM9LnO1WOPKEzDlGwx0qQscJyEeNHRRevZDhFL25l5P7S1Cujx8CtUqSMWCjQcTAprV9TSu9oabHaMPGRzj5CxW1cUzyZ5UtbJCC6vpFsenNNdBuS/F5/W39XksOdX8jyakVt3sS+GK3royA4v1/g8T6Vzcaus9Lb0HIM2qOrQ0HfpQ2tq62h5U11Qcm3rflk0or4k0bAKOT50tZPlUb8zyeSyMWj432Sz3Y9kEiIfg2QSIs2cTMp+WTYDD7NiEEgIybEIJU94jBBSjODbBe/uqi2dycmyC51MHei2f+lvLp2UTsgeyyWa5pmVTU8smQDpHzyZ4Ps2COstmToNjU9P7svkQeVodgA0BlYbrtJTZAMvcOoClNOLh2KAocpQTN6VTsJvHacOvDdfLeFPBeFVgO8ZpFWu9OjVs5BZO4ioagCxTWzqrPz1Hs1hJH3qAARTx4CvwaAzg8jTQbgwgto70lWmsnkvWj76IIxJa55p0lWVYVcyX8aY+j75ynWN7Vd/V95raEJDqbk7tvrVDPU2km06PIjDXeEJOp7AKccTDVN3r6rZPQuzi48MVrKbcTZ/i2NT7t2wCjs9j6WRv5Lza2NDy2Xcir+Kh6tTxeZ9poIOGyxopsmhjbxZ9lU42L1YybAIcJscmQDyIY1Ovu8Um4McAYutIX3U6eC7PHJuA41P1aflsDX/j07KZb1s22YQSAjJs1vs2bAKOzzojck6OTcjhHstmzjc4NgHixP3q+gPljWYB7bLLLrvs8tNXnpYHUCRFqe6yutfHOPFqyb37qyn37F9entc3B9XFTWF2r+OD7BbqYNKXlg/qbzW/GgKKk3GxpZblYjmNdaWWZJwhqeV6VisrEHQqhna5k1mlVfNrVoEOtMU5NEurWgesZnaMrQKzXYFxs2/Lfb8s6Qfh2cqTWpJw0x8zS+mbB9AsrVclBHQsnsBpWetzFA/wMyxKakJAet/xJtQbD/W3Ib/k3N32epAz3hhPahBK60NQl0R5iDE4NvP9HxybkN8SZtnUYyoasrB86m8tn3oNq88tNjW1bGadiGMTyHxaNsHxWee7HybHZj4WHJuabrEJZV1KFwK6XQ6OTfCelHqVls9+G5LsAfgQ0KvlxrEJWZ9bbOpXoxCQY7PcuGUT8HzqYjLrAdwYT2oQSrsvmw+Rp9UBmHCBhoDOi0IxrxquWZ7X/VWqC5gOLQRUN5IKdVbBl0rlernc1AZL8z0uU4VxHbuWNR9igW8VQB+ilAanutm0xV9iYoV1u1cTU6xQ3JjKVZ6WdQuHs1eMS5u6MYDTMrkGC+DL4dnqVYanNNVKp98tZnGN1efH52cuv+PSwhXLomMpRnnavth61+pPvcc6nVYb7iWs3HSZhFTup+rT7NFSO8+badWRxlnGHekwZOFDWUsUx2ZOD45NyPv/WDahhIAMm+D1qWMpls+m08ZnSr7hcnzWkJpnU+97i03I+rRs5jyCY1Pzs2xCF3qyxezCLcmMAdSQmjH2vhw0NGnGTcqNWD5Vn5ZPreuWT8smwLIMFoIl1uMXwbOp9+3YNPcFmc18j5NjM+snODbBd6TO2HuLIaAn1QHICAq1DpZmueoDnex86mJdHcLZHYc8fVErmFauLy/P+LhWsGIVGA+gWlfXBtpcfDWncV53AKRQGzgpA4IyC3VvsNqYmw5AwboRlpXl2houejispGZ5a0N8t8z1fl+e1bqKNb6qFekutEE37WSjWaJv9ahjKPp8qj6XqXbkqs+UTFGvjqU0HSxL0addm1F+K0tYeVJ2U61o4tRVj7okY+BJXVp6b9mE3KFaNvX+t9jsv9Ppi5bPL+s4guHz1HkAKY5j18PptJ0HYDuAemLwbAJI9GxmnQXHJsDiLFfT2WywCWUQ2LCp92vZhDw2ZdnM3zU+dWWv5VP1aPm0bELuyC2btbhXx1JK2Q+yzabNGLAbElo2oejRsKnpyNh7LNnHAHbZZZdd3lN5Uh6AnRmQaghIrayZl2d1r3UFYKwW1LnMqJnjshoDsCs0Xxkv4uXZewDH81ytkWtxwZFVYOOsy01nXRFpe4M360pWYwBmNsXAKqhxwQliZxXYcln/tdfjeQncnnNGN2UTtTncNF1NamXN63EBQl00p9baq3jgS6cSVjv7MRW3EKz3qNx9jzwpqbMtRC3NRH27klqkElPdc6XmJ2Zhkgn7WEurHus9gIshoJJouCWKY1Pv37KZdSaOTfBjAP3q9lfGi7B8Hsszc3xusKlpPwaw3IhnE8jvVWhsguezelSTODYhe1SWTch8rmLWwiq2YvWo6e153mQTunGBYstaPtXyt3xaNqEsBNtgU1PLZk6TYzPfVnBs2jR/Ly2/PiR5GHj7E2vL/2dqCMjCoaCfygra4zTVB//SxAN19aAO7syyELp99mOSCsarpbmD2mC9OrfYtUK4iukN5gj7VZblWrO0B28CiCnkY2FRKAa3H0xFKwAsNwwaLtbz5gdQJDMGoGMax2XiVjvSYF5NaDbRgux6z2H9ApO2itjEvUt+mmpjdTpPzb0edaj6nERcwwEgc7tfN6bRNXASZfVendGAXZyldswtFNKemw2pDV3sruFaluDYhNwwvewe7DlOjk3A8dl2p2x81rEuw6eGmRyfG2xqatmE0mna4DaQQtpkM6fi2IS+4WrZbrEJmQvLJsDt+eDYhDJYbNgENvns9fjyfHBs6vW32ARvdIl2pId1GM6yJyVfy6cLH3eTHJYbcWxCfnZ9uHcPAe2yyy677PLG8sQ8gJJGVrNXjsvEdPZ7bETETMUrG0VJHHsA1VNoMzfU8r87NaugzlrpLFf7mrjq2lq3UHv9A6abb7N8qhV29u6hExHjSjeLSq2rZKyC1QKm0MpnByyrB2A8quOULahXRp/VCosaHjqv9Ais9JgH54s3UPLTENN5CVWfqhNrXQ73WdF7jNQtVFoxZGXZS4ThILDmbazfRUNpxlod7e9yLQSkERM7UUEtV8tn3d8mBcdmTgcegNGr6tPyqRar5TP1FqZ0PNBZrlq8JI5N1dMWm1B0ZtiEzKdlE7oFTGYwuB9MT1EcmwDHKTo2VU+WTWCTz2M/S+t8cGxC0ecGm+A9KX0dQYyezXpeF0obDgLL2jNbDp7Nml8f7v0ZGwIqkqKQFt9wHU9z/7pMIlIf8k15/VogXQgB+cp6ilNt+OuxQQcwjAs6N6581oeZ7G8UilTvR91HdQ/7fPupZmkyYwA6Q8DAeM0tFLNh2FLmfYcpcDz5x55SW9V6KHq8CfO4gnWbkp2WqcZV7zr3ellCve8a9r4Qt16NAUwYOk2l6eLZeSzF52nj+E2PNtba8rDfXyqey1sbrkUcm4DjUzuA4zI5NuFSCKjxqTN+LJ+jDmCLTb0vxyaU3zU29X622NT8LJtQxgAMm1CuucVm+dKyCTg+dczltEyOTbjQARg+lWnL50qPi2yyqffV183MZ2MTlLPy02rAtHxtHL9nL49T6ed2rfuy+RDZQ0C77LLLLu+pPC0PwC1gKi7auQxMSeKo+22U804xcCiDRbclnUKsllY0vtJiRv31t2oNjC1WdeNGA0TtkLcGYDggNwlR932zMyxSd56ZvaJdc7QWnKYHYxVccwsTsKilVyzN07Q+LQmn8mrAaTF6LBaW1We/H/15CdViXenzPFUreRWusPdtPSlr4Xf6QWielOrRhitcKKS55JpvtbjsAFs/CCx9Ibu8awgjODYBjqfJsQlwCNGxCVmfsXtglk/9rdWns1gBomyyCZ0ndYFNgLgMZv/YsKeZvWLZhBI+MmzmfO/BJsAim2wCnMLk2AQcn/3rOZcYapjH8mnZzPcdNtms99N5n1Y/9fxFNtnM+crQ6+1DaW4QuF5rwOcD5Ul2ABKl+qu6ZP0MZgfBcmyKnBQGnWUjqVbImm0yr50z0yJrpSvXiOdQr9camhEc5Y9gVpLqwhZYVUKZQWesWdf32uyVWslGYSYDxXABU41XC0kh1BWk56xL1YvqYp5Uj1rJrutR7/e8TKvFZrVynaV2pIxCarYD0Pud2+3XSEQpcJqMHo3bPtLjcDZMdw3bcG0tBHNsAsTk2ISiH8MmwClExybg9Lp6ZaeZFmn5jIZR0IbmOptQGi7LJlk3lk3IerVs9vm60JJhs16jn71yaQGTYRPIfBo2wdd11cU8RccmXNdjSm0XAcunY1PLscGmppbNeguGTSh67EJKIz0OFz8GzyZcGKd6xFDQHgLaZZdddnlP5Yl5AG3AsFrg6KE279n26NJZV8DQck3mM+BmH0QNkywCOmvlnoPAdFaltVzFWAWq6WYdDO7/kuXaD1TacEZ1s8dhC7USk5qphDoYWGcITW3dhepTWOsR1pYWifXMqarPsArZXLzv3h2mDarWedWL0akNyfR5GgvJWU/XXO7NWUCNTb1Hx2YpS+8NSVhPShhZrpbPfuZWXNqkCMfnPQaBuWC5OjYB5ns8o5KOtu1YhfBcCOhySE2iODYhO/+WTfB1vRZli8/ytZs5ZdjMqdzvvu0gcL2+ZxOyXh2bfb6X2OyvYbz+92YWUJsiBqIPqrpRqa3OLQtWoqQKRZ0COVCOnYVlZ/f0C0DyjIByvdXsFRwMAGkyB/VtdaZy1UWWcQOKqoCW3aiTsS7gqlMYNVxRarlcdEg3StPpbGdTuaTp86ou9VmlwUIa20jWhquF1OpzrvpMtaIr40mo7yKpjVVs13WLlTYaLMCFdmxMdTWWEtIw1mrZhMyHZRNK42/YhNKh3pPNnH9ruKxeV2NTbvaKuVfHZjm4uJ9mfRo26/1tsVnSUSfThyc2d6ytdbljEyAExyZkPVo2YZtPa+zVwhk2oehxEO61bELuiCybuUwdm+W6q4V09zX2DI9uMz/LZv3t44wDPKkOoI8PglFmpA4W6c0n20h1cFzM28awu7iuJHPt6L9zWbmOQMvSLN36bFbz2AflsTIC+oJX0I5dvm9JrcHSEyRKBan9VkzHYwo2tITXBUzdcxOr1+i/G1pDAZIONBs91s25bOVaXX9QxkHZrR5HYw+VqY0xgEts5t+KYxMKn1ts2vytPo0epb9+vA+bORPLJlqcx2KzfDeyUrfYBOXTN8CE5NnU/PrCbvHZjVdh9Ci2nm+wmb9Kjk3Nt/ceXBGvtdHWE7BcDut68seucfSaso8B7LLLLru8p/LWPAAR+TXAHwAm4A+llL7n3r+NrLq5JGJcQPNd3xtueQB62qintjNKrp1ve+nOVXSL/jqv41p5bL6XLjy0FAbHqnUFq1CWtYTlmh4vHRuU+5LOrunT5n9Rj0OrqjftB/mavNeFHOvMfXfFwupneOSPxTKUtI5LyIXPvYy4sd/1M0ound+HZ4SvPJslHR2zbAIulGXPl2t6vHbsvnxafW6xqX/3ehx6pNa0H+Tb5e8LONbZxe8eQd5KByAiE/B/Bv4V4AvAfyIiP5hS+ptXf3fFfRLz/3cmA1f+2jNO9tPr+m2PEOMb6fNJ6FHlnvpMo7++wvq8FnqSwad3IpLWHdqFU1P/6XX0+VbZ9J/emTyIzXLkHdT1h8rbCgH9CuBHU0p/L6V0BP4k8K1v6Vq77LLLLrs8QN5WCOjrgL9v/v4C8N+2J4jIZ4DPlD/vfux3fOffeEtleUz5WcA/eteFuIfs5Xxc2cv5ePLToYzw06ecv+hNfvy2OoDN6FxK6XPA5wBE5IdSSr/8LZXl0WQv5+PKXs7HlZ8O5fzpUEb46VXON/n92woBfQH4tPn764F/+Jautcsuu+yyywPkbXUA/wnwC0Tk54nIDfBtwA++pWvtsssuu+zyAHkrIaCU0llE/k3gT5OngX5vSulHrvzkc2+jHG9B9nI+ruzlfFz56VDOnw5lhPeknJL6tei77LLLLru8F7KvBN5ll112eU9l7wB22WWXXd5TeecdgIj8GhH52yLyoyLy2XddHhUR+bSI/L9F5G+JyI+IyO8ox/9dEfkHIvJXy3+/7h2X88dF5K+XsvxQOfbVIvJnReTvlPRT77iMv8jo66+KyD8Xkd/5FHQpIt8rIj8lIn/DHLuoPxH57sLq3xaR/8E7LufvE5H/XET+MxH5j0TkXyjHv1FEXhm9/nvvuJwXn/MT0+f3mTL+uIj81XL8nejzShv0eHymlN7Zf+QB4r8L/HzgBvhrwDe9yzKZsn0t8MvK508A/z/gm4B/F/jOd10+U84fB35Wd+x/B3y2fP4s8HvfdTm7Z/5fAv+Np6BL4FcBvwz4G1v6K8//rwHPgJ9X2J3eYTn/VWAun3+vKec32vOegD6Hz/mp6bP7/v8A/G/fpT6vtEGPxue79gCe7JYRKaUvppR+uHz+EvC3yCucfzrItwKfL58/D/yGd1eUlXwz8HdTSv/Fuy4IQErpLwD/pDt8SX/fCvzJlNJdSunHgB8lM/xOyplS+jMp1dep/EXyept3Khf0eUmelD5VJO9E95uAP/GVKMsludIGPRqf77oDGG0Z8eQaWRH5RuCXAn+pHPo3i9v9ve86vEJeYf1nROSvlO01AH5OSumLkCECvuadlW4t34avWE9JlyqX9PeUef2twP/T/P3zROQ/FZH/j4j8S++qUEZGz/mp6vNfAn4ypfR3zLF3qs+uDXo0Pt91B3DPDV3fnYjIR8B/CPzOlNI/B/4vwL8I/LeAL5JdxXcpvzKl9MuAXwv8NhH5Ve+4PBelLAr89cC/Xw49NV1uyZPkVUR+F/n15H+sHPoi8A0ppV8K/K+BPy4in3xX5ePyc36S+gR+M95Ieaf6HLRBF08dHLuqz3fdATzpLSNE5EBW/B9LKf0pgJTST6aUlpRSBP6vfIVc1kuSUvqHJf0p4D8q5flJEflagJL+1LsroZNfC/xwSukn4enp0sgl/T05XkXk24FvAf5nqQSCSwjgH5fPf4UcC/6F76qMV57zU9TnDPyPge/TY+9Sn6M2iEfk8113AE92y4gSB/zDwN9KKf1+c/xrzWn/I+Cd7WIqIh+KyCf0M3lQ8G+Qdfjt5bRvB37g3ZRwJc6yekq67OSS/n4Q+DYReSYiPw/4BcBffgflA9CXLn0X8OtTSi/N8Z8t+Z0ciMjPJ5fz772bUl59zk9Kn0X+ZeA/Tyl9QQ+8K31eaoN4TD6/0iPbg5HuX0ce3f67wO961+Ux5frvkd2n/wz4q+W/Xwf834G/Xo7/IPC177CMP5886v/XgB9R/QH/NeDPA3+npF/9BPT5AvjHwFeZY+9cl+QO6YvAiWxBfcc1/QG/q7D6t4Ff+47L+aPkmK/y+e+Vc/8nhYe/Bvww8K+943JefM5PSZ/l+B8B/o3u3Heizytt0KPxuW8Fscsuu+zynsq7DgHtsssuu+zyjmTvAHbZZZdd3lPZO4Bddtlll/dU9g5gl1122eU9lb0D2GWXXXZ5T2XvAHbZZZdd3lPZO4Bddtlll/dU/v+cc8gMMxjJXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_tiled = np.tile(f,(3, 2))\n",
    "plt.pcolormesh(f_tiled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another common need is to add an extra dimension to an array.\n",
    "This can be accomplished via indexing with `None`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 100)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[None, :].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 100, 1, 1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[None, :, None, None].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Broadcasting\n",
    "\n",
    "\n",
    "Not all the arrays we want to work with will have the same size.\n",
    "One approach would be to manually \"expand\" our arrays to all be the same size, e.g. using `tile`.\n",
    "_Broadcasting_ is a more efficient way to multiply arrays of different sizes\n",
    "Numpy has specific rules for how broadcasting works.\n",
    "These can be confusing but are worth learning if you plan to work with Numpy data a lot.\n",
    "\n",
    "The core concept of broadcasting is telling Numpy which dimensions are supposed to line up with each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"http://scipy-lectures.github.io/_images/numpy_broadcasting.png\" width=\"720\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url='http://scipy-lectures.github.io/_images/numpy_broadcasting.png',\n",
    "     width=720)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dimensions are automatically aligned _starting with the last dimension_.\n",
    "If the last two dimensions have the same length, then the two arrays can be broadcast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 100) (100,)\n",
      "(50, 100)\n"
     ]
    }
   ],
   "source": [
    "print(f.shape, x.shape)\n",
    "g = f * x\n",
    "print(g.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7efecabed310>"
      ]
     },
     "execution_count": 37,
     "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": [
    "plt.pcolormesh(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, if the last two dimensions are _not_ the same, Numpy cannot just automatically figure it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 100) (50,)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (50,100) (50,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_442/1156475932.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# multiply f by y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (50,100) (50,) "
     ]
    }
   ],
   "source": [
    "# multiply f by y\n",
    "print(f.shape, y.shape)\n",
    "h = f * y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can help numpy by adding an extra dimension to `y` at the end.\n",
    "Then the length-50 dimensions will line up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 100) (50, 1)\n",
      "(50, 100)\n"
     ]
    }
   ],
   "source": [
    "print(f.shape, y[:, None].shape)\n",
    "h = f * y[:, None]\n",
    "print(h.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7efecaabaee0>"
      ]
     },
     "execution_count": 40,
     "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": [
    "plt.pcolormesh(h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reduction Operations\n",
    "\n",
    "In scientific data analysis, we usually start with a lot of data and want to reduce it down in order to make plots of summary tables.\n",
    "Operations that reduce the size of numpy arrays are called \"reductions\".\n",
    "There are many different reduction operations. Here we will look at some of the most common ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3083.038387807155"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum\n",
    "g.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.616607677561431"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean\n",
    "g.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6402280119141424"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# standard deviation\n",
    "g.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A key property of numpy reductions is the ability to operate on just one axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply on just one axis\n",
    "g_ymean = g.mean(axis=0)\n",
    "g_xmean = g.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efecaa9b790>]"
      ]
     },
     "execution_count": 45,
     "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": [
    "plt.plot(x, g_ymean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efecaa00610>]"
      ]
     },
     "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": [
    "plt.plot(g_xmean, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Files\n",
    "\n",
    "It can be useful to save numpy data into files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('g.npy', g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{warning}\n",
    "Numpy `.npy` files are a convenient way to store temporary data, but they are not considered a robust archival format.\n",
    "Later we will learn about NetCDF, the recommended way to store earth and environmental data.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_loaded = np.load('g.npy')\n",
    "\n",
    "np.testing.assert_equal(g, g_loaded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: src/lectures/core_python/functions_classes.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Functions and Classes\n",
    "\n",
    "For longer and more complex tasks, it is important to organize your code into reuseable elements.\n",
    "For example, if you find yourself cutting and pasting the same or similar lines of code over and over,\n",
    "you probably need to define a _function_ to encapsulate that code and make it reusable.\n",
    "An important principle in programming in **DRY**: \"don't repeat yourself\".\n",
    "Repetition is tedious and opens you up to errors. Strive for elegance and simplicity in your programs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions\n",
    "\n",
    "Functions are a central part of advanced python programming. Functions take some inputs (\"arguments\") and do something in response.\n",
    "Usually functions return something, but not always."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a function\n",
    "def say_hello():\n",
    "    \"\"\"Return the word hello.\"\"\"\n",
    "    return 'Hello'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# functions are also objects\n",
    "type(say_hello)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0msay_hello\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m Return the word hello.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Teaching/rces_book/content/lectures/core_python/<ipython-input-1-5cff6abf0dfe>\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this doesnt call\n",
    "say_hello?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this does\n",
    "say_hello()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assign the result to something\n",
    "res = say_hello()\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# take some arguments\n",
    "def say_hello_to(name):\n",
    "    \"\"\"Return a greeting to `name`\"\"\"\n",
    "    return 'Hello ' + name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello World'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# intended usage\n",
    "say_hello_to('World')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "must be str, not int",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-56dd0f5596c8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msay_hello_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-6-a444b0f12be2>\u001b[0m in \u001b[0;36msay_hello_to\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msay_hello_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;34m\"\"\"Return a greeting to `name`\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m'Hello '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: must be str, not int"
     ]
    }
   ],
   "source": [
    "say_hello_to(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# redefine the function\n",
    "def say_hello_to(name):\n",
    "    \"\"\"Return a greeting to `name`\"\"\"\n",
    "    return 'Hello ' + str(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello 10'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "say_hello_to(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take an optional keyword argument\n",
    "def say_hello_or_hola(name, spanish=False):\n",
    "    \"\"\"Say hello in multiple languages.\"\"\"\n",
    "    if spanish:\n",
    "        greeting = 'Hola '\n",
    "    else:\n",
    "        greeting = 'Hello '\n",
    "    return greeting + name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello Ryan\n",
      "Hola Juan\n"
     ]
    }
   ],
   "source": [
    "print(say_hello_or_hola('Ryan'))\n",
    "print(say_hello_or_hola('Juan', spanish=True))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# flexible number of arguments\n",
    "def say_hello_to_everyone(*args):\n",
    "    return ['hello ' + str(a) for a in args]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hello Ryan', 'hello Juan', 'hello Xiaomeng']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "say_hello_to_everyone('Ryan', 'Juan', 'Xiaomeng')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pure vs. Impure Functions\n",
    "\n",
    "Functions that don't modify their arguments or produce any other side-effects are called [_pure_](https://en.wikipedia.org/wiki/Pure_function). \n",
    "\n",
    "Functions that modify their arguments or cause other actions to occur are called _impure_.\n",
    "\n",
    "Below is an impure function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_last_from_list(input_list):\n",
    "    input_list.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ryan', 'Juan']\n",
      "['Ryan']\n"
     ]
    }
   ],
   "source": [
    "names = ['Ryan', 'Juan', 'Xiaomeng']\n",
    "remove_last_from_list(names)\n",
    "print(names)\n",
    "remove_last_from_list(names)\n",
    "print(names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do something similar with a pure function.\n",
    "\n",
    "In general, pure functions are safer and more reliable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_last_from_list_pure(input_list):\n",
    "    new_list = input_list.copy()\n",
    "    new_list.pop()\n",
    "    return new_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ryan', 'Juan', 'Xiaomeng']\n",
      "['Ryan', 'Juan']\n"
     ]
    }
   ],
   "source": [
    "names = ['Ryan', 'Juan', 'Xiaomeng']\n",
    "new_names = remove_last_from_list_pure(names)\n",
    "print(names)\n",
    "print(new_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could spend the rest of the day talking about functions, but we have to move on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Namespaces\n",
    "\n",
    "In python, a [namespace](https://docs.python.org/3/tutorial/classes.html#python-scopes-and-namespaces) is a mapping between variable names and python object. You can think of it like a dictionary.\n",
    "\n",
    "The namespace can change depending on where you are in your program. Functions can \"see\" the variables in the parent namespace, but they can also redefine them in a private scope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ryan\n",
      "Kerry\n",
      "Ryan\n"
     ]
    }
   ],
   "source": [
    "name = 'Ryan'\n",
    "\n",
    "def print_name():\n",
    "    print(name)\n",
    "\n",
    "def print_name_v2():\n",
    "    name = 'Kerry'\n",
    "    print(name)\n",
    "    \n",
    "print_name()\n",
    "print_name_v2()\n",
    "print(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A more complex function: Fibonacci Sequence\n",
    "\n",
    "The Fibonacci sequence is the 1,1,2,3,5,8..., the sum of each number with the preceding one. Write a function to compute the Fibonacci sequence of length n. (Hint, use some list methods.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fib(n):\n",
    "    l = [1,1]\n",
    "    for i in range(n-2):\n",
    "        l.append(l[-1] + l[-2])\n",
    "    return l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fib(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classes\n",
    "\n",
    "We have worked with many different types of python objects so far: strings, lists, dictionaries, etc. These objects have different attributes and respond in different ways to the built-in functions (`len`, etc.)\n",
    "\n",
    "_How can we make our own, custom objects?_ Answer: by defining classes.\n",
    "\n",
    "### A class to represent a hurricane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Hurricane:\n",
    "    \n",
    "    def __init__(self, name):\n",
    "        self.name = name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Hurricane at 0x108680940>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h = Hurricane('florence')\n",
    "h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our class only has a single attribute so far:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'florence'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add more, along with some input validation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Hurricane:\n",
    "    \n",
    "    def __init__(self, name, category, lon):\n",
    "        self.name = name.upper()\n",
    "        self.category = int(category)\n",
    "        \n",
    "        if lon > 180 or lon < -180:\n",
    "            raise ValueError(f'Invalid lon {lon}')\n",
    "        self.lon = lon\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Hurricane at 0x1086966a0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h = Hurricane('florence', 4, -46)\n",
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'FLORENCE'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Invalid lon 300",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-28-88629bebad1a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHurricane\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ryan'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-25-24766bacf597>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, category, lon)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlon\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m180\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlon\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m180\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Invalid lon {lon}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlon\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Invalid lon 300"
     ]
    }
   ],
   "source": [
    "h = Hurricane('ryan', 5, 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's add a custom method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Hurricane:\n",
    "    \n",
    "    def __init__(self, name, category, lon):\n",
    "        self.name = name.upper()\n",
    "        self.category = int(category)\n",
    "        \n",
    "        if lon > 180 or lon < -180:\n",
    "            raise ValueError(f'Invalid lon {lon}')\n",
    "        self.lon = lon\n",
    "    \n",
    "    def is_dangerous(self):\n",
    "        return self.category > 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = Hurricane('florence', 4, -46)\n",
    "f.is_dangerous()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Magic / dunder methods\n",
    "\n",
    "We can implement special methods that begin with double-underscores (i.e. \"dunder\" methods), which allow us to customize the behavior of our classes. ([Read more here](https://www.python-course.eu/python3_magic_methods.php)). We have already learned one: `__init__`. Let's implement the `__repr__` method to make our class display something pretty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Hurricane:\n",
    "    \n",
    "    def __init__(self, name, category, lon):\n",
    "        self.name = name.upper()\n",
    "        self.category = int(category)\n",
    "        \n",
    "        if lon > 180 or lon < -180:\n",
    "            raise ValueError(f'Invalid lon {lon}')\n",
    "        self.lon = lon\n",
    "        \n",
    "    def __repr__(self):\n",
    "        return f\"<Hurricane {self.name} (cat {self.category})>\"\n",
    "    \n",
    "    def is_dangerous(self):\n",
    "        return self.category > 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Hurricane FLORENCE (cat 4)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = Hurricane('florence', 4, -46)\n",
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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================================================
FILE: src/lectures/core_python/organization_and_packaging.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Organization and Packaging of Python Projects\n",
    "\n",
    "A complex research project often relies and many different programs and software packages to accomplish the research goals. \n",
    "An important part of scientific computing is deciding how to organize and structure the code you use for research.\n",
    "A well-structured project can make you a more efficient and effective researcher.\n",
    "It is also a key component of [scientific reproducibility](http://lorenabarba.com/blog/barbagroup-reproducibility-syllabus/).\n",
    "\n",
    "\n",
    "Just putting all of your code into git repositories won't magically turn a mess of scripts into a beautiful, well-organized project.\n",
    "More deliberate effort is required.\n",
    "\n",
    "## Types of Projects\n",
    "\n",
    "Not all projects are created equal.\n",
    "Based on my experience, I categorize three different types of \"research code\" scenarios commonly encountered in geosciences.\n",
    "\n",
    "1. **Exploratory analyses**: When exploring a new idea, a single notebook or script is often all we need.\n",
    "1. **A Single Paper**: The \"paper\" is a standard unit of scientific output. The code related to a single paper usually belongs together. \n",
    "1. **Reusable software elements**: In the course of our research computing, we often identify specialized routines that we want to package for reuse in other projects, or by other scientists. This is where \"scripts\" become \"software.\"\n",
    "\n",
    "This lecture outlines some suggested practices for each category. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Analysis\n",
    "\n",
    "When starting something new, we are often motivated to just start coding and get some results quick.\n",
    "This is fine!\n",
    "Jupyter notebooks are an ideal format for open-ended exploratory analysis, since they are totally self-contained: they encapsulate text, code, and figures.\n",
    "If we find someting cool or useful, it is important to preserve these exploratory notebooks.\n",
    "\n",
    "A dedicated github repository can be overkill for a single file.\n",
    "Instead, I recommend github's \"[gist](https://help.github.com/articles/about-gists/)\" mechanism for saving and sharing such \"one-off\" notebooks and code snippets.\n",
    "Gists are like mini repos you can easily share and embed.\n",
    "(You can create one right now by going to <https://gist.github.com/>.)\n",
    "\n",
    "You can upload any file (including an `.ipynb` notebook file) by dragging and dropping it into the gist website.\n",
    "You have the choice of making you gist public or secret. (There is no private option, but a secret gist can only be seen by others if you give them the URL.)\n",
    "\n",
    "GitHub's rendering of Gists is a bit buggy. For a more consistent rendering experience, you can share your gist via <http://nbviewer.ipython.org/>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Single Paper\n",
    "\n",
    "### Scientific Reproducibility\n",
    "\n",
    "Reproducibility is a cornerstone of the scientific process.\n",
    "However, today one often reads that science is in the midst of a [reproducibility crisis](http://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970).\n",
    "This crisis may be due to increasing complexity and cost of scientific analysis, together with mounting pressure to publish as much and as quickly as possible.\n",
    "\n",
    "Today almost all earth science relies on some form of computation, from simple statistical analysis and curve fitting to advanced numerical simulation.\n",
    "In principle, computational science should be highly reproducible.\n",
    "However, it also brings unique challenges.\n",
    "A great overview of the challenges and best practices is given in\n",
    "_Barba, Lorena A. (2017): Barba-group Reproducibility Syllabus. figshare. <https://doi.org/10.6084/m9.figshare.4879928.v1>._\n",
    "Many of the suggestions in this lecture are adopted or paraphrased from _Barba (2017)_.\n",
    "\n",
    "Keep in mind that the audience for a reproducibile project is not just other scientists...it's _you_, a year from now, or whenever you need to repeat and / or build on earlier work. Most scientists build on their Ph.D. work for a decade following graduation. Extra time spent on reproducibility now will make you more productive in the long run.\n",
    "\n",
    "We begin with an important observation.\n",
    "\n",
    "> An article about computational science … is not the scholarship itself, it’s merely scholarship advertisement. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures.\n",
    "\n",
    "_Donoho, D. et al. (2009), Reproducible research in computational harmonic analysis, Comp. Sci. Eng. 11(1):8–18, doi: [10.1109/MCSE.2009.15](http://dx.doi.org/10.1109/MCSE.2009.15)_\n",
    "\n",
    "[Sandve et al. (2013)](http://dx.doi.org/10.1371/journal.pcbi.1003285) give some specific recommmendations for computational reproducibility.\n",
    "\n",
    "1. For every result, keep track of how it was produced\n",
    "1. Avoid manual data-manipulation steps\n",
    "1. Archive the exact versions of all external programs used\n",
    "1. Version-control all custom scripts\n",
    "1. Record all intermediate results, when possible in standard formats\n",
    "1. For analyses that include randomness, note underlying random seeds\n",
    "1. Always store raw data behind plots\n",
    "1. Generate hierarchical analysis output, allowing layers of increasing detail to be inspected\n",
    "1. Connect textual statements to underlying results\n",
    "1. Provide public access to scripts, runs, and results\n",
    "\n",
    "These recommendations suggest a certain structure for a project.\n",
    "\n",
    "### Project Layout\n",
    "\n",
    "A reproducible single-paper project directory structure might look something like this\n",
    "\n",
    "    README.md\n",
    "    LICENSE\n",
    "    environment.yml\n",
    "    data/intermediate_results.csv\n",
    "    notebooks/process_raw_data.ipynb\n",
    "    notebooks/figure1.ipynb\n",
    "    notebooks/figure2.ipynb\n",
    "    notebooks/helper.py\n",
    "    manuscript/manuscript.tex\n",
    "\n",
    "A great example of such a paper is [Cesar Rocha](https://crocha700.github.io/)'s Upper Ocean Seasonality project: <https://github.com/crocha700/UpperOceanSeasonality>.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reuseable Software Elements\n",
    "\n",
    "Scientific software can perhaps be grouped into two categories: single-use \"scripts\" that are used in a very specific context to do a very specific thing (e.g.~to generate a specific figure for a paper), and reuseable components which encapsulate a more generic workflow. Once you find yourself repeating the same chunks of code in many different scripts or projects, it's time to start composing reusable software elements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modules\n",
    "\n",
    "The basic element of reusability in python is the [module](https://docs.python.org/3/tutorial/modules.html).\n",
    "A module is a `.py` file which contains python objects which can be _imported_ by other scripts or notebooks.\n",
    "Let's illustrate how modules work with a simple example.\n",
    "\n",
    "A common task in geoscience is to calculate the [great-circle distance](https://en.wikipedia.org/wiki/Great-circle_distance) between two points on the globe.\n",
    "There are several [pacakges](https://pypi.python.org/pypi/geopy) that could do this for you, but let's write our own as an example of a module.\n",
    "\n",
    "The formula for great circle distance is\n",
    "\n",
    "![great circle distance formula](https://wikimedia.org/api/rest_v1/media/math/render/svg/75af53b063ee43aed3de186b1a98af5c150185b8)\n",
    "\n",
    "(Note that this formula requires 64-bit precision for adequate accuracy.)\n",
    "\n",
    "Let's write a module to do this calculation. Open a file called `gcdistance.py` in a text editor. (The file should be in the same directory as the notebook you are working in now.) Populate it with the following code:\n",
    "\n",
    "```python\n",
    "\"\"\"\n",
    "A python module for computing great circle distance\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "\n",
    "# approximate radius of Earth\n",
    "R = 6.371e6\n",
    "\n",
    "def great_circle_distance(point1, point2):\n",
    "    \"\"\"Calculate great-circle distance between two points.\n",
    "    \n",
    "    PARAMETERS\n",
    "    ----------\n",
    "    point1 : tuple\n",
    "        A (lat, lon) pair of coordinates in degrees\n",
    "    point2 : tuple\n",
    "        A (lat, lon) pair of coordinates in degrees\n",
    "        \n",
    "    RETURNS\n",
    "    -------\n",
    "    distance : float\n",
    "    \"\"\"\n",
    "    \n",
    "    # unpack coordinates\n",
    "    lat1, lon1 = point1\n",
    "    lat2, lon2 = point2\n",
    "    \n",
    "    # unpack and convert everything to radians\n",
    "    phi1, lambda1, phi2, lambda2 = [np.deg2rad(v) for v in \n",
    "                                    (point1 + point2)]\n",
    "    \n",
    "    # apply formula\n",
    "    # https://en.wikipedia.org/wiki/Great-circle_distance\n",
    "    return R*np.arccos(\n",
    "        np.sin(phi1)*np.sin(phi2) + \n",
    "        np.cos(phi1)*np.cos(phi2)*np.cos(lambda2 - lambda1))\n",
    "```\n",
    "\n",
    "The module begins with a [docstring](https://www.python.org/dev/peps/pep-0257/) explaining what it does. Then it contains some data (just a constant `R`) and a single function.\n",
    "\n",
    "Now let's import our module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on module gcdistance:\n",
      "\n",
      "NAME\n",
      "    gcdistance - A python module for computing great circle distance\n",
      "\n",
      "FUNCTIONS\n",
      "    great_circle_distance(point1, point2)\n",
      "        Calculate great-circle distance between two points.\n",
      "        \n",
      "        PARAMETERS\n",
      "        ----------\n",
      "        point1 : tuple\n",
      "            A (lat, lon) pair of coordinates in degrees\n",
      "        point2 : tuple\n",
      "            A (lat, lon) pair of coordinates in degrees\n",
      "            \n",
      "        RETURNS\n",
      "        -------\n",
      "        distance : float\n",
      "\n",
      "DATA\n",
      "    R = 6371000.0\n",
      "\n",
      "FILE\n",
      "    /Users/rpa/Teaching/research_computing/content/lectures/python/gcdistance.py\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import gcdistance\n",
    "help(gcdistance) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's try using it to make a calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1460007.1890493981"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcdistance.great_circle_distance((60, 0), (50, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could just import the function we need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6371000.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gcdistance import R, great_circle_distance\n",
    "R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we change the module, we need to either restart our kernel or else reload the module. (Note that functions imported via `from module import func` cannot be reloaded.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'gcdistance' from '/Users/rpa/Teaching/research_computing/content/lectures/python/gcdistance.py'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from importlib import reload\n",
    "reload(gcdistance) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modules are a simple way to share code between different scripts or notebooks in the same project. _Module files must reside in the same directory as any script which imports them!_ This is a big limitation; it means you can't share modules between different projects.\n",
    "\n",
    "Once you have a piece of code that is general-purpose enough to share between projects, you need to create a package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aside: Python Style\n",
    "\n",
    "There are few absolute rules for python code style, but there is a detailed [recommended style guide](https://www.python.org/dev/peps/pep-0008/). Some especially relevant points are:\n",
    "\n",
    "* Line length should not exceed 79 characters\n",
    "* Module names should be `lowercase`\n",
    "* Function and variable names should be `lower_case_with_underscores`\n",
    "* Class names should be `CamelCase`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Packages\n",
    "\n",
    "[Packages](https://docs.python.org/3/tutorial/modules.html#packages) are python's way of encapsulating reusable code elements for sharing with others. Packaging is a huge and complicated topic. We will just scratch the surface.\n",
    "\n",
    "We have already interacted with many packages already. Browse some of their github repositories to explore the structure of a large python package:\n",
    "\n",
    "* NumPy: <https://github.com/numpy/numpy>\n",
    "* Pandas: <https://github.com/pandas-dev/pandas>\n",
    "* Xarray: <https://github.com/pydata/xarray>\n",
    "\n",
    "An example of a smaller, more understandable package is our group's xrft package:\n",
    "\n",
    "* xrft: https://github.com/xgcm/xrft\n",
    "\n",
    "These packages all have a common basic structure. Imagine we wanted to turn our great-circle distance module into a package. It would look like this.\n",
    "\n",
    "    README.md\n",
    "    LICENSE\n",
    "    environment.yml\n",
    "    requirements.txt\n",
    "    setup.py\n",
    "    gcdistance/__init__.py\n",
    "    gcdistance/gcdistance.py\n",
    "    gcdistance/tests/__init__.py\n",
    "    gcdistancs/tests/test_gcdistance.py\n",
    "    \n",
    "The actual package is contained in the `gcdistance` subdirectory. The other files are auxilliary files which help others understand and install your package. Here is an overview of what they do\n",
    "\n",
    "| File Name | Purpose |\n",
    "|-----------|---------|\n",
    "| `README.txt` | Explain what the package is for |\n",
    "| `LICENSE` | Defines the legal terms under which other can use the package. [Open source](https://opensource.org/licenses/category) is encouraged! |\n",
    "| `environment.yml` | A conda environment which describes the package's dependencies ([more info](https://conda.io/docs/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file)) |\n",
    "| `requirements.txt` | A file which describes the package's dependences for pip. ([more info](https://pip.pypa.io/en/stable/user_guide/#requirements-files))|\n",
    "| `setup.py` | A special python script which installs your package. ([more info](https://pythonhosted.org/an_example_pypi_project/setuptools.html)) |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The actual package\n",
    "\n",
    "The directory `gcdistance` is the actual package. Any directory that contains an `__init__.py` file is recognized by python as a package. This file can be blank, bu it needs to be present. From the root directory, we can import a module from the package as follows\n",
    "\n",
    "```python\n",
    "from gcdistance import gcdistance\n",
    "```\n",
    "\n",
    "Yes, this is a bit redundant. That's because the `gcdistance.py` module has the same name as the `gcdistance` package directory.\n",
    "\n",
    "However, this import will only work from the parent directory. It is not globally accessible from your python environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`setup.py` is the magic file that makes your package installable and accessible anywhere. Here is an extremely basic `setup.py`\n",
    "\n",
    "```python\n",
    "from setuptools import setup\n",
    "\n",
    "setup(\n",
    "    name = \"gcdistance\",\n",
    "    version = \"0.1.0\",\n",
    "    author = \"Ryan Abernathey\",\n",
    "    packages=['gcdistance'],\n",
    "    install_requires=['numpy'],\n",
    ")\n",
    "```\n",
    "\n",
    "There is a dizzying range of [options](https://setuptools.readthedocs.io/en/latest/setuptools.html) for `setup.py`. More fields are required if you want to [upload your package](https://pythonhosted.org/an_example_pypi_project/setuptools.html) to pypi (so it is installable via `pip`).\n",
    "\n",
    "To run the setup script, we call the following from the command line\n",
    "\n",
    "    python setup.py install\n",
    "    \n",
    "The package files are copied to our python library directory.\n",
    "If we plan to keep developing the package, we can install it in \"developer mode\" as\n",
    "\n",
    "    python setup.py develop\n",
    "\n",
    "In this case, the files are symlinked rather than copied."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing\n",
    "\n",
    "A software package requires [tests](https://en.wikipedia.org/wiki/Software_testing) to ensure that it works properly. Matt Rocklin has a great [blog post](http://matthewrocklin.com/blog/work/2016/02/08/tests) on why we should write tests for our code.\n",
    "\n",
    "Tests don't have to be complicated. They are simply a check to verify that your code does what it is supposed to do.\n",
    "\n",
    "To add tests to our project, we create create the file `gcdistance/tests/test_gcdistance.py`. (We also need an `__init__.py` file in the `tests` directory.) The example below shows an example of a test function for our package.\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "import pytest\n",
    "\n",
    "from gcdistance.gcdistance import great_circle_distance\n",
    "\n",
    "def test_great_circle_distance():\n",
    "    # some known results\n",
    "    # distance between two same points should be zero\n",
    "    assert great_circle_distance((20., 30.), (20., 30.)) == 0\n",
    "\n",
    "    # check distance between new york and london\n",
    "    new_york = 40.7128, -74.0060\n",
    "    london = 51.5074, 0.1278\n",
    "    dist_nyc_london = great_circle_distance(new_york, london)\n",
    "    \n",
    "    # very strict, doesn't actually work\n",
    "    # assert dist_nyc_london == 5.587e6\n",
    "    \n",
    "    # an approximate version of the above\n",
    "    np.testing.assert_allclose(dist_nyc_london, 5.587e6, rtol=1e-5)\n",
    "\n",
    "    # now check that we can't pass the wrong number of arguments\n",
    "    with pytest.raises(TypeError):\n",
    "        great_circle_distance(1, 2, 3, 4)\n",
    "```\n",
    "\n",
    "We will use [pytest](https://docs.pytest.org/en/latest/getting-started.html) to run our tests. If you don't have pytest installed in your active python environment, take a minute to run `pip install pytest` from the command line. Now run\n",
    "\n",
    "```bash\n",
    "py.test -v\n",
    "```\n",
    "\n",
    "from the root directory of your project. You should see a notification that the tests passed. Try playing around with the tests to cause something to fail.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuous Integration with Travis CI\n",
    "\n",
    "You can configure automatic testing of your package by integrating github with [Travis-CI](https://travis-ci.org/). Travis-CI is a free \"continuous integration\" service: it automatically downloads your package and runs your tests in the cloud every time you commit to your repository. The travis [getting started guide](https://docs.travis-ci.com/user/getting-started) gives a great overview of how to use the service.\n",
    "\n",
    "For us to use travis with our project, the steps are simple:\n",
    "\n",
    "1. Push the repo to github (repo must be public)\n",
    "1. Log in to <https://travis-ci.org> and click the switch to enable your repo\n",
    "1. Add a `.travis.yml` file to your project with the following contents:\n",
    "\n",
    "        language: python\n",
    "        python:\n",
    "        - 3.6\n",
    "        script:\n",
    "        - pytest\n",
    "1. Add the file, commit, and push to github\n",
    "1. Go to <https://travis-ci.org> and watch the magic happen!\n"
   ]
  }
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================================================
FILE: src/lectures/core_python/python_fundamentals.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Fundamentals\n",
    "\n",
    "This material is mostly adapted from the [official python tutorial](https://docs.python.org/3/tutorial/), Copyright 2001-2019, Python Software Foundation. It is used here under the terms of the [Python License](https://docs.python.org/3/license.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Invoking Python ##\n",
    "\n",
    "There are three main ways to use Python.\n",
    "\n",
    "1. By running a Python file, e.g. `python myscript.py`\n",
    "1. Through an interactive console (Python interpreter or iPython shell)\n",
    "1. In an interactive notebook (e.g. Jupyter)\n",
    "\n",
    "In this course, we will mostly be interacting with Python via Jupyter notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Versions ##\n",
    "\n",
    "There are two versions of the Python language out there: Python 2 and Python 3. In 2019, the vast majority of the scientific community now uses Python 3. As new Python learners, you should definitely learn Python 3. But it is important to be aware that Python 2 exists, since you might encounter it in the wild. \n",
    "\n",
    "Some of the main changes introduced in Python 3 are:\n",
    "\n",
    "* ``print`` is a function\n",
    "* Integer division returns a float\n",
    "* Iterators behave differently\n",
    "* Unicode is used for encoding code\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Variables: Numbers and String ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# comments are anything that comes after the \"#\" symbol\n",
    "a = 1       # assign 1 to variable a\n",
    "b = \"hello\" # assign \"hello\" to variable b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following identifiers are used as reserved words, or keywords of the language, and cannot be used as ordinary identifiers. They must be spelled exactly as written here:\n",
    "\n",
    "    False      class      finally    is         return\n",
    "    None       continue   for        lambda     try\n",
    "    True       def        from       nonlocal   while\n",
    "    and        del        global     not        with\n",
    "    as         elif       if         or         yield\n",
    "    assert     else       import     pass\n",
    "    break      except     in         raise\n",
    "    \n",
    "Additionally, the following a built in functions which are always available in your namespace once you open a Python interpreter\n",
    "\n",
    "    abs() dict() help() min() setattr() all() dir() hex() next() slice() any()\n",
    "    divmod() id() object() sorted() ascii() enumerate() input() oct() staticmethod()\n",
    "    bin() eval() int() open() str() bool() exec() isinstance() ord() sum() bytearray()\n",
    "    filter() issubclass() pow() super() bytes() float() iter() print() tuple()\n",
    "    callable() format() len() property() type() chr() frozenset() list() range()\n",
    "    vars() classmethod() getattr() locals() repr() zip() compile() globals() map()\n",
    "    reversed() __import__() complex() hasattr() max() round() delattr() hash()\n",
    "    memoryview() set()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "hello\n",
      "1 hello\n"
     ]
    }
   ],
   "source": [
    "# how to we see our variables?\n",
    "print(a)\n",
    "print(b)\n",
    "print(a,b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All variables are objects. Every object has a type (class). To find out what type your variables are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(a))\n",
    "print(type(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as a shortcut, iPython notebooks will automatically print whatever is on the last line\n",
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# we can check for the type of an object\n",
    "print(type(a) is int)\n",
    "print(type(a) is str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Different objects attributes and methods, which can be accessed via the syntax ``variable.method``\n",
    "\n",
    "IPython will autocomplete if you press ``<tab>`` to show you the methods available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function str.capitalize()>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this returns the method itself\n",
    "b.capitalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this calls the method\n",
    "b.capitalize()\n",
    "# there are lots of other methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "helloWorld\n",
      "3\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_376/1516236190.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
     ]
    }
   ],
   "source": [
    "# binary operations act differently on different types of objects\n",
    "c = 'World'\n",
    "print(b + c)\n",
    "print(a + 2)\n",
    "print(a + b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Math ##\n",
    "\n",
    "Basic arithmetic and boolean logic is part of the core Python library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# addition / subtraction\n",
    "1+1-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiplication\n",
    "5 * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# division\n",
    "1/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# that was automatically converted to a float\n",
    "type(1/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exponentiation\n",
    "2**4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rounding\n",
    "round(9/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.2+0.4j)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# built in complex number support\n",
    "(1+2j) / (3-4j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logic\n",
    "True and True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True and False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True or True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(not True) or (not False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditionals ##\n",
    "\n",
    "The first step to programming. Plus an intro to Python syntax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive Number\n"
     ]
    }
   ],
   "source": [
    "x = 100\n",
    "if x > 0:\n",
    "    print('Positive Number')\n",
    "elif x < 0:\n",
    "    print('Negative Number')\n",
    "else:\n",
    "    print ('Zero!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive Number\n",
      "Huge number!\n"
     ]
    }
   ],
   "source": [
    "# indentation is MANDATORY\n",
    "# blocks are closed by indentation level\n",
    "if x > 0:\n",
    "    print('Positive Number')\n",
    "    if x >= 100:\n",
    "        print('Huge number!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More Flow Control ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "# make a loop \n",
    "count = 0\n",
    "while count < 10:\n",
    "    # bad way\n",
    "    # count = count + 1\n",
    "    # better way\n",
    "    count += 1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# use range\n",
    "for i in range(5):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Important point__: in Python, we always count from 0!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what is range?\n",
    "type(range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m/\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "range(stop) -> range object\n",
       "range(start, stop[, step]) -> range object\n",
       "\n",
       "Return an object that produces a sequence of integers from start (inclusive)\n",
       "to stop (exclusive) by step.  range(i, j) produces i, i+1, i+2, ..., j-1.\n",
       "start defaults to 0, and stop is omitted!  range(4) produces 0, 1, 2, 3.\n",
       "These are exactly the valid indices for a list of 4 elements.\n",
       "When step is given, it specifies the increment (or decrement).\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "range?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dog 3\n",
      "cat 3\n",
      "fish 4\n"
     ]
    }
   ],
   "source": [
    "# iterate over a list we make up\n",
    "for pet in ['dog', 'cat', 'fish']:\n",
    "    print(pet, len(pet))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the thing in brackets? __A list!__ Lists are one of the core Python data structures.\n",
    "\n",
    "## Lists ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = ['dog', 'cat', 'fish']\n",
    "type(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cat', 'dog', 'fish']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list have lots of methods\n",
    "l.sort()\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can convert a range to a list\n",
    "r = list(range(5))\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p: 4\n",
      "r: [0, 1, 2, 3]\n",
      "p: 3\n",
      "r: [0, 1, 2]\n",
      "p: 2\n",
      "r: [0, 1]\n",
      "p: 1\n",
      "r: [0]\n",
      "p: 0\n",
      "r: []\n"
     ]
    }
   ],
   "source": [
    "while r:\n",
    "    p = r.pop()\n",
    "    print('p:', p)\n",
    "    print('r:', r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many different ways to interact with lists. Exploring them is part of the fun of Python.\n",
    "\n",
    "__list.append(x)__ Add an item to the end of the list. Equivalent to a[len(a):] = [x].\n",
    "\n",
    "__list.extend(L)__ \n",
    "Extend the list by appending all the items in the given list. Equivalent to a[len(a):] = L.\n",
    "\n",
    "__list.insert(i, x)__ Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x) inserts at the front of the list, and a.insert(len(a), x) is equivalent to a.append(x).\n",
    "\n",
    "__list.remove(x)__ Remove the first item from the list whose value is x. It is an error if there is no such item.\n",
    "\n",
    "__list.pop([i])__ Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in the list. (The square brackets around the i in the method signature denote that the parameter is optional, not that you should type square brackets at that position. You will see this notation frequently in the Python Library Reference.)\n",
    "\n",
    "__list.clear()__ Remove all items from the list. Equivalent to del a[:].\n",
    "\n",
    "__list.index(x)__ Return the index in the list of the first item whose value is x. It is an error if there is no such item.\n",
    "\n",
    "__list.count(x)__ Return the number of times x appears in the list.\n",
    "\n",
    "__list.sort()__ Sort the items of the list in place.\n",
    "\n",
    "__list.reverse()__ Reverse the elements of the list in place.\n",
    "\n",
    "__list.copy()__ Return a shallow copy of the list. Equivalent to a[:].\n",
    "\n",
    "\n",
    "Don't assume you know how list operations work!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 10, 11, 12, 13, 14]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"add\" two lists\n",
    "x = list(range(5))\n",
    "y = list(range(10,15))\n",
    "z = x + y\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first 0\n",
      "last 14\n",
      "first 3 [0, 1, 2]\n",
      "last 3 [12, 13, 14]\n",
      "middle, skipping every other item [10, 12, 14]\n"
     ]
    }
   ],
   "source": [
    "# access items from a list\n",
    "print('first', z[0])\n",
    "print('last', z[-1])\n",
    "print('first 3', z[:3])\n",
    "print('last 3', z[-3:])\n",
    "print('middle, skipping every other item', z[5:10:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__MEMORIZE THIS SYNTAX!__ It is central to so much of Python and often proves confusing for users coming from other languages.\n",
    "\n",
    "In terms of set notation, Python indexing is _left inclusive_, _right exclusive_. If you remember this, you will never go wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_376/2572331846.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# that means we get an error from the following\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mN\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "# that means we get an error from the following\n",
    "N = len(z)\n",
    "z[N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ryan\n"
     ]
    }
   ],
   "source": [
    "# this index notation also applies to strings\n",
    "name = 'Ryan Abernathey'\n",
    "print(name[:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can also test for the presence of items in a list\n",
    "5 in z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists are not meant for math! They don't have a datatype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 'fish', 10, 11, 12, 13, 14]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[4] = 'fish'\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python is full of tricks for iterating and working with lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a cool Python trick: list comprehension\n",
    "squares = [n**2 for n in range(5)]\n",
    "squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first: 0 second: 10\n",
      "first: 1 second: 11\n",
      "first: 2 second: 12\n",
      "first: 3 second: 13\n",
      "first: 4 second: 14\n"
     ]
    }
   ],
   "source": [
    "# iterate over two lists together uzing zip\n",
    "for item1, item2 in zip(x,y):\n",
    "    print('first:', item1, 'second:', item2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Data Structures ##\n",
    "\n",
    "We are almost there. We have the building blocks we need to do basic programming. But Python has some other data structures we need to learn about.\n",
    "\n",
    "## Tuples ##\n",
    "\n",
    "Tuples are similar to lists, but they are _immutable_—they can't be extended or modified. What is the point of this? Generally speaking: to pack together inhomogeneous data. Tuples can then be unpacked and distributed by other parts of your code.\n",
    "\n",
    "Tuples may seem confusing at first, but with time you will come to appreciate them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tuples are created with parentheses, or just commas\n",
    "a = ('Ryan', 33, True)\n",
    "b = 'Takaya', 25, False\n",
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33\n"
     ]
    }
   ],
   "source": [
    "# can be indexed like arrays\n",
    "print(a[1]) # not the first element!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# and they can be unpacked\n",
    "name, age, status = a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dictionaries ##\n",
    "\n",
    "This is an extremely useful data structure. It maps __keys__ to __values__.\n",
    "\n",
    "Dictionaries are unordered!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Takaya', 'age': 25}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# different ways to create dictionaries\n",
    "d = {'name': 'Ryan', 'age': 33}\n",
    "e = dict(name='Takaya', age=25)\n",
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ryan'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access a value\n",
    "d['name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Square brackets ``[...]`` are Python for \"get item\" in many different contexts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# test for the presence of a key\n",
    "print('age' in d)\n",
    "print('height' in e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'height'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_376/1885707668.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# try to access a non-existant key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'height'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'height'"
     ]
    }
   ],
   "source": [
    "# try to access a non-existant key\n",
    "d['height']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Ryan', 'age': 33, 'height': (5, 11)}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add a new key\n",
    "d['height'] = (5,11) # a tuple\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Ryan', 'age': 33, 'height': (5, 11), 99: 'ninety nine'}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keys don't have to be strings\n",
    "d[99] = 'ninety nine'\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name Ryan\n",
      "age 33\n",
      "height (5, 11)\n",
      "99 ninety nine\n"
     ]
    }
   ],
   "source": [
    "# iterate over keys\n",
    "for k in d:\n",
    "    print(k, d[k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name Ryan\n",
      "age 33\n",
      "height (5, 11)\n",
      "99 ninety nine\n"
     ]
    }
   ],
   "source": [
    "# better way\n",
    "### Python 2\n",
    "### for key, val in d.iteritems()\n",
    "for key, val in d.items():\n",
    "    print(key, val)"
   ]
  }
 ],
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   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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================================================
FILE: src/lectures/dask/dask_arrays.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing with Dask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Dask Arrays\n",
    "\n",
    "A dask array looks and feels a lot like a numpy array.\n",
    "However, a dask array doesn't directly hold any data.\n",
    "Instead, it symbolically represents the computations needed to generate the data.\n",
    "Nothing is actually computed until the actual numerical values are needed.\n",
    "This mode of operation is called \"lazy\"; it allows one to build up complex, large calculations symbolically before turning them over the scheduler for execution.\n",
    "\n",
    "If we want to create a numpy array of all ones, we do it like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       ...,\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "shape = (1000, 4000)\n",
    "ones_np = np.ones(shape)\n",
    "ones_np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This array contains exactly 32 MB of data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones_np.nbytes / 1e6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the same array using dask's array interface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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> 30.52 MiB </td>\n",
       "                        <td> 30.52 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1000, 4000) </td>\n",
       "                        <td> (1000, 4000) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 1 Tasks </td>\n",
       "                        <td> 1 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",
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       "        </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<ones_like, shape=(1000, 4000), dtype=float64, chunksize=(1000, 4000), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dask.array as da\n",
    "ones = da.ones(shape)\n",
    "ones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dask array representation reveals the concept of \"chunks\". \"Chunks\" describes how the array is split into sub-arrays. We did not specify any chunks, so Dask just used one single chunk for the array. This is not much different from a numpy array at this point.\n",
    "\n",
    "### Specifying Chunks\n",
    "\n",
    "However, we could have split up the array into many chunks.\n",
    "\n",
    "![Dask Arrays](http://dask.pydata.org/en/latest/_images/dask-array-black-text.svg)\n",
    "_source: [Dask Array Documentation](http://dask.pydata.org/en/latest/array-overview.html)_\n",
    "\n",
    "There are [several ways to specify chunks](http://dask.pydata.org/en/latest/array-creation.html#chunks).\n",
    "In this lecture, we will use a block shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 30.52 MiB </td>\n",
       "                        <td> 7.63 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1000, 4000) </td>\n",
       "                        <td> (1000, 1000) </td>\n",
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       "                        <th> Count </th>\n",
       "                        <td> 4 Tasks </td>\n",
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      "text/plain": [
       "dask.array<ones_like, shape=(1000, 4000), dtype=float64, chunksize=(1000, 1000), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chunk_shape = (1000, 1000)\n",
    "ones = da.ones(shape, chunks=chunk_shape)\n",
    "ones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we just see a symbolic represetnation of the array, including its shape, dtype, and chunksize.\n",
    "No data has been generated yet.\n",
    "When we call `.compute()` on a dask array, the computation is trigger and the dask array becomes a numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       ...,\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to understand what happened when we called `.compute()`, we can visualize the dask _graph_, the symbolic operations that make up the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our array has four chunks. To generate it, dask calls `np.ones` four times and then concatenates this together into one array.\n",
    "\n",
    "Rather than immediately loading a dask array (which puts all the data into RAM), it is more common to want to reduce the data somehow. For example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_of_ones = ones.sum()\n",
    "sum_of_ones.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see dask's strategy for finding the sum. This simple example illustrates the beauty of dask: it automatically designs an algorithm appropriate for custom operations with big data. \n",
    "\n",
    "If we make our operation more complex, the graph gets more complex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fancy_calculation = (ones * ones[::-1, ::-1]).mean()\n",
    "fancy_calculation.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Bigger Calculation\n",
    "\n",
    "The examples above were toy examples; the data (32 MB) is nowhere nearly big enough to warrant the use of dask.\n",
    "\n",
    "We can make it a lot bigger!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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> 5.96 GiB </td>\n",
       "                        <td> 7.63 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (200000, 4000) </td>\n",
       "                        <td> (1000, 1000) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 800 Tasks </td>\n",
       "                        <td> 800 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=\"79\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"29\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"6\" x2=\"29\" y2=\"6\" />\n",
       "  <line x1=\"0\" y1=\"12\" x2=\"29\" y2=\"12\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <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>"
      ],
      "text/plain": [
       "dask.array<ones_like, shape=(200000, 4000), dtype=float64, chunksize=(1000, 1000), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigshape = (200000, 4000)\n",
    "big_ones = da.ones(bigshape, chunks=chunk_shape)\n",
    "big_ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6400.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_ones.nbytes / 1e6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is 3.2 GB, rather MB! This is probably close to or greater than the amount of available RAM than you have in your computer. Nevertheless, dask has no problem working on it.\n",
    "\n",
    "_Do not try to `.visualize()` this array!_\n",
    "\n",
    "When doing a big calculation, dask also has some tools to help us understand what is happening under the hood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  2.4s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.diagnostics import ProgressBar\n",
    "\n",
    "big_calc = (big_ones * big_ones[::-1, ::-1]).mean()\n",
    "\n",
    "with ProgressBar():\n",
    "    result = big_calc.compute()\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reduction \n",
    "\n",
    "All the usual numpy methods work on dask arrays.\n",
    "You can also apply numpy function directly to a dask array, and it will stay lazy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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> 31.25 kiB </td>\n",
       "                        <td> 7.81 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (4000,) </td>\n",
       "                        <td> (1000,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 3472 Tasks </td>\n",
       "                        <td> 4 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=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
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       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4000</text>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<mean_agg-aggregate, shape=(4000,), dtype=float64, chunksize=(1000,), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_ones_reduce = (np.cos(big_ones)**2).mean(axis=0)\n",
    "big_ones_reduce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting also triggers computation, since we need the actual values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (12,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f18689fbb80>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(big_ones_reduce)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distributed Clusters\n",
    "\n",
    "Once we are ready to make a bigger calculation with dask, we can use a Dask [Distributed](http://distributed.dask.org/en/stable/) cluster.\n",
    "\n",
    "```{warning}\n",
    "A common mistake is to move to distributed mode too soon.\n",
    "For smaller data, distributed will actually be _much slower_ than the default multi-threaded scheduler or **not using Dask at all**.\n",
    "You should only use distributed when your data is much larger than what your computer can handle in memory.\n",
    "```\n",
    "\n",
    "### Local Cluster\n",
    "\n",
    "A local cluster uses all the CPU cores of the machine it is running on.\n",
    "For our cloud-based Jupyterlab environments, that is just 2 cores--not very much.\n",
    "However, it's good to know about."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-0d54068d-5381-11ec-8481-f2f93f425864</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"/user/rabernat/proxy/8787/status\" target=\"_blank\">/user/rabernat/proxy/8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
       "    </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">5a3ea957</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"/user/rabernat/proxy/8787/status\" target=\"_blank\">/user/rabernat/proxy/8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 2\n",
       "                </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 2\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 8.00 GiB\n",
       "                </td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
       "</tr>\n",
       "\n",
       "            \n",
       "        </table>\n",
       "\n",
       "        <details>\n",
       "            <summary style=\"margin-bottom: 20px;\">\n",
       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
       "            </summary>\n",
       "\n",
       "            <div style=\"\">\n",
       "    <div>\n",
       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
       "        <div style=\"margin-left: 48px;\">\n",
       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-1286f3af-8fb6-42d9-b7d0-61d90390e02f</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:36005\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 2\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"/user/rabernat/proxy/8787/status\" target=\"_blank\">/user/rabernat/proxy/8787/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 2\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> Just now\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total memory:</strong> 8.00 GiB\n",
       "                    </td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
       "        </summary>\n",
       "\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://10.8.2.9:33951\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/user/rabernat/proxy/38391/status\" target=\"_blank\">/user/rabernat/proxy/38391/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46605\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /home/jovyan/earth-env-data-science-book/src/lectures/dask/dask-worker-space/worker-cl1bqnsp\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://10.8.2.9:38345\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/user/rabernat/proxy/33795/status\" target=\"_blank\">/user/rabernat/proxy/33795/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:44415\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /home/jovyan/earth-env-data-science-book/src/lectures/dask/dask-worker-space/worker-iv640uvq\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "\n",
       "        </details>\n",
       "    </div>\n",
       "</div>\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:36005' processes=2 threads=2, memory=8.00 GiB>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.distributed import Client, LocalCluster\n",
    "cluster = LocalCluster()\n",
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the \"Dashboard\" link will open a new page where you can monitor a computation's progress."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_calc.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is another bigger calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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> 800 B </td>\n",
       "                        <td> 800 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100,) </td>\n",
       "                        <td> (100,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 668 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> int64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >100</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<sum-aggregate, shape=(100,), dtype=int64, chunksize=(100,), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_values = da.random.normal(size=(2e8,), chunks=(1e6,))\n",
    "hist, bins = da.histogram(random_values, bins=100, range=[-5, 5]) \n",
    "hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# actually trigger the computation\n",
    "hist_c = hist.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot results\n",
    "x = 0.5 * (bins[1:] + bins[:-1])\n",
    "width = np.diff(bins)\n",
    "plt.bar(x, hist_c, width);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dask Gateway - A Cluster in the Cloud\n",
    "\n",
    "Pangeo Cloud makes it possible to launch a much larger cluster using many virtual machines in the cloud.\n",
    "This is beyond the scope of our class, but you can read more about it in the [Pangeo Cloud docs](https://pangeo.io/cloud.html#dask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Dask + XArray\n",
    "\n",
    "Xarray can automatically wrap its data in dask arrays.\n",
    "This capability turns xarray into an extremely powerful tool for Big Data earth science\n",
    "\n",
    "To see this in action, we will open a dataset from the [Pangeo Cloud Data Catalog](https://catalog.pangeo.io/) - the Copernicus [Sea Surface Height](browse/master/ocean/sea_surface_height/) dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 8901, latitude: 720, longitude: 1440, nv: 2)\n",
       "Coordinates:\n",
       "    crs        int32 ...\n",
       "    lat_bnds   (time, latitude, nv) float32 dask.array&lt;chunksize=(5, 720, 2), meta=np.ndarray&gt;\n",
       "  * latitude   (latitude) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
       "    lon_bnds   (longitude, nv) float32 dask.array&lt;chunksize=(1440, 2), meta=np.ndarray&gt;\n",
       "  * longitude  (longitude) float32 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
       "  * nv         (nv) int32 0 1\n",
       "  * time       (time) datetime64[ns] 1993-01-01 1993-01-02 ... 2017-05-15\n",
       "Data variables:\n",
       "    adt        (time, latitude, longitude) float64 dask.array&lt;chunksize=(5, 720, 1440), meta=np.ndarray&gt;\n",
       "    err        (time, latitude, longitude) float64 dask.array&lt;chunksize=(5, 720, 1440), meta=np.ndarray&gt;\n",
       "    sla        (time, latitude, longitude) float64 dask.array&lt;chunksize=(5, 720, 1440), meta=np.ndarray&gt;\n",
       "    ugos       (time, latitude, longitude) float64 dask.array&lt;chunksize=(5, 720, 1440), meta=np.ndarray&gt;\n",
       "    ugosa      (time, latitude, longitude) float64 dask.array&lt;chunksize=(5, 720, 1440), meta=np.ndarray&gt;\n",
       "    vgos       (time, latitude, longitude) float64 dask.array&lt;chunksize=(5, 720, 1440), meta=np.ndarray&gt;\n",
       "    vgosa      (time, latitude, longitude) float64 dask.array&lt;chunksize=(5, 720, 1440), meta=np.ndarray&gt;\n",
       "Attributes: (12/43)\n",
       "    Conventions:                     CF-1.6\n",
       "    Metadata_Conventions:            Unidata Dataset Discovery v1.0\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Sea Surface Height measured by Altimetry...\n",
       "    contact:                         servicedesk.cmems@mercator-ocean.eu\n",
       "    creator_email:                   servicedesk.cmems@mercator-ocean.eu\n",
       "    ...                              ...\n",
       "    summary:                         SSALTO/DUACS Delayed-Time Level-4 sea su...\n",
       "    time_coverage_duration:          P1D\n",
       "    time_coverage_end:               1993-01-01T12:00:00Z\n",
       "    time_coverage_resolution:        P1D\n",
       "    time_coverage_start:             1992-12-31T12:00:00Z\n",
       "    title:                           DT merged all satellites Global Ocean Gr...</pre><div class='xr-wrap' hidden><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-ceec6d1f-22bc-4509-928d-6629b22c10c9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ceec6d1f-22bc-4509-928d-6629b22c10c9' 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>: 8901</li><li><span class='xr-has-index'>latitude</span>: 720</li><li><span class='xr-has-index'>longitude</span>: 1440</li><li><span class='xr-has-index'>nv</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4bbf03f5-3030-4285-82a5-380b38fdde23' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4bbf03f5-3030-4285-82a5-380b38fdde23' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7e0185c3-933d-4a71-8c1c-852a676b90f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e0185c3-933d-4a71-8c1c-852a676b90f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d20a62cf-d622-4d8a-8af5-11c3d4dfb91e' class='xr-var-data-in' type='checkbox'><label for='data-d20a62cf-d622-4d8a-8af5-11c3d4dfb91e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>This is a container variable that describes the grid_mapping used by the data in this file. This variable does not contain any data; only information about the geographic coordinate system.</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>inverse_flattening :</span></dt><dd>298.257</dd><dt><span>semi_major_axis :</span></dt><dd>6378136.3</dd></dl></div><div class='xr-var-data'><pre>array(-2147483647, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>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=(5, 720, 2), meta=np.ndarray&gt;</div><input id='attrs-f1406d99-8ac6-4a26-ada1-ff5d8a34d44f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1406d99-8ac6-4a26-ada1-ff5d8a34d44f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d77767de-4a83-4fa4-881d-90a16121ea9a' class='xr-var-data-in' type='checkbox'><label for='data-d77767de-4a83-4fa4-881d-90a16121ea9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>latitude values at the north and south bounds of each pixel.</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 48.89 MiB </td>\n",
       "                        <td> 28.12 kiB </td>\n",
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       "                        <td> (5, 720, 2) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-cff9cca3-2545-4e64-a2bc-9a824890802e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cff9cca3-2545-4e64-a2bc-9a824890802e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f49f90c-be9a-49ca-9c97-34e7c46698db' class='xr-var-data-in' type='checkbox'><label for='data-1f49f90c-be9a-49ca-9c97-34e7c46698db' title='Show/Hide data repr'><svg 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>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>valid_max :</span></dt><dd>89.875</dd><dt><span>valid_min :</span></dt><dd>-89.875</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>lon_bnds</span></div><div class='xr-var-dims'>(longitude, nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440, 2), meta=np.ndarray&gt;</div><input id='attrs-3a90d229-fedd-47c2-86d0-b27d2e43f0bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a90d229-fedd-47c2-86d0-b27d2e43f0bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-288eb434-038b-4de4-ac3b-b21a87df8068' class='xr-var-data-in' type='checkbox'><label for='data-288eb434-038b-4de4-ac3b-b21a87df8068' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>longitude values at the west and east bounds of each pixel.</dd><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> 11.25 kiB </td>\n",
       "                        <td> 11.25 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1440, 2) </td>\n",
       "                        <td> (1440, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2 Tasks </td>\n",
       "                        <td> 1 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=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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-8c69ce12-441f-40e0-b2fd-b15a3875a18d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c69ce12-441f-40e0-b2fd-b15a3875a18d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fca4683-2662-4f00-b0fe-9cfd86173d9c' class='xr-var-data-in' type='checkbox'><label for='data-2fca4683-2662-4f00-b0fe-9cfd86173d9c' title='Show/Hide data repr'><svg 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>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>valid_max :</span></dt><dd>359.875</dd><dt><span>valid_min :</span></dt><dd>0.125</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'>nv</span></div><div class='xr-var-dims'>(nv)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-e1c497bd-95ae-4ffb-83a6-880bb79b6962' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1c497bd-95ae-4ffb-83a6-880bb79b6962' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac66f1e3-4232-4024-be89-1912cbb10281' class='xr-var-data-in' type='checkbox'><label for='data-ac66f1e3-4232-4024-be89-1912cbb10281' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Vertex</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([0, 1], dtype=int32)</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'>1993-01-01 ... 2017-05-15</div><input id='attrs-7402c360-5b64-401c-9830-69ce288e6904' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7402c360-5b64-401c-9830-69ce288e6904' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-188d66d5-4a60-49fa-ade5-d1b1dedaf163' class='xr-var-data-in' type='checkbox'><label for='data-188d66d5-4a60-49fa-ade5-d1b1dedaf163' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1993-01-01T00:00:00.000000000&#x27;, &#x27;1993-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1993-01-03T00:00:00.000000000&#x27;, ..., &#x27;2017-05-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-14T00:00:00.000000000&#x27;, &#x27;2017-05-15T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-912365ac-b46d-4939-94a4-86e047421f0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-912365ac-b46d-4939-94a4-86e047421f0a' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>adt</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=(5, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-47020810-5e99-4ef6-820e-6981adcf36ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47020810-5e99-4ef6-820e-6981adcf36ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bf7f2ff-dc7f-40c5-ac10-c00f3860e69a' class='xr-var-data-in' type='checkbox'><label for='data-1bf7f2ff-dc7f-40c5-ac10-c00f3860e69a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>The absolute dynamic topography is the sea surface height above geoid; the adt is obtained as follows: adt=sla+mdt where mdt is the mean dynamic topography; see the product user manual for details</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Absolute dynamic topography</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</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> 68.76 GiB </td>\n",
       "                        <td> 39.55 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8901, 720, 1440) </td>\n",
       "                        <td> (5, 720, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 1782 Tasks </td>\n",
       "                        <td> 1781 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </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>err</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=(5, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-3cb60594-78ea-4f0f-9ef1-f654b7407d78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cb60594-78ea-4f0f-9ef1-f654b7407d78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7318073e-8e37-4d56-9527-52e39f75c7ec' class='xr-var-data-in' type='checkbox'><label for='data-7318073e-8e37-4d56-9527-52e39f75c7ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>The formal mapping error represents a purely theoretical mapping error. It mainly traduces errors induced by the constellation sampling capability and consistency with the spatial/temporal scales considered, as described in Le Traon et al (1998) or Ducet et al (2000)</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Formal mapping error</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 68.76 GiB </td>\n",
       "                        <td> 39.55 MiB </td>\n",
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       "                    <tr>\n",
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       "                        <td> (5, 720, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 1782 Tasks </td>\n",
       "                        <td> 1781 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </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>sla</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=(5, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-091d826d-a896-4d20-aca9-33a9e2f5c03f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-091d826d-a896-4d20-aca9-33a9e2f5c03f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dc59d84-49d7-45dd-a232-b6c205488409' class='xr-var-data-in' type='checkbox'><label for='data-4dc59d84-49d7-45dd-a232-b6c205488409' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>The sea level anomaly is the sea surface height above mean sea surface; it is referenced to the [1993, 2012] period; see the product user manual for details</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Sea level anomaly</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2088012c-7d59-47ef-bce8-ba50e7b4e863' class='xr-section-summary-in' type='checkbox'  ><label for='section-2088012c-7d59-47ef-bce8-ba50e7b4e863' class='xr-section-summary' >Attributes: <span>(43)</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>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>Sea Surface Height measured by Altimetry and derived variables</dd><dt><span>contact :</span></dt><dd>servicedesk.cmems@mercator-ocean.eu</dd><dt><span>creator_email :</span></dt><dd>servicedesk.cmems@mercator-ocean.eu</dd><dt><span>creator_name :</span></dt><dd>CMEMS - Sea Level Thematic Assembly Center</dd><dt><span>creator_url :</span></dt><dd>http://marine.copernicus.eu</dd><dt><span>date_created :</span></dt><dd>2014-02-26T16:09:13Z</dd><dt><span>date_issued :</span></dt><dd>2014-01-06T00:00:00Z</dd><dt><span>date_modified :</span></dt><dd>2015-11-10T19:42:51Z</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.875</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.875</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.875</dd><dt><span>geospatial_lon_min :</span></dt><dd>0.125</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>down</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>point</dd><dt><span>geospatial_vertical_units :</span></dt><dd>m</dd><dt><span>history :</span></dt><dd>2014-02-26T16:09:13Z: created by DUACS DT - 2015-11-10T19:42:51Z: Change of some attributes - 2017-01-06 12:12:12Z: New format for CMEMSv3</dd><dt><span>institution :</span></dt><dd>CLS, CNES</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Topography &gt; Sea Surface Height</dd><dt><span>keywords_vocabulary :</span></dt><dd>NetCDF COARDS Climate and Forecast Standard Names</dd><dt><span>license :</span></dt><dd>http://marine.copernicus.eu/web/27-service-commitments-and-licence.php</dd><dt><span>platform :</span></dt><dd>ERS-1, Topex/Poseidon</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_version :</span></dt><dd>5.0</dd><dt><span>project :</span></dt><dd>COPERNICUS MARINE ENVIRONMENT MONITORING SERVICE (CMEMS)</dd><dt><span>references :</span></dt><dd>http://marine.copernicus.eu</dd><dt><span>source :</span></dt><dd>Altimetry measurements</dd><dt><span>ssalto_duacs_comment :</span></dt><dd>The reference mission used for the altimeter inter-calibration processing is Topex/Poseidon between 1993-01-01 and 2002-04-23, Jason-1 between 2002-04-24 and 2008-10-18, OSTM/Jason-2 since 2008-10-19.</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table v37</dd><dt><span>summary :</span></dt><dd>SSALTO/DUACS Delayed-Time Level-4 sea surface height and derived variables measured by multi-satellite altimetry observations over Global Ocean.</dd><dt><span>time_coverage_duration :</span></dt><dd>P1D</dd><dt><span>time_coverage_end :</span></dt><dd>1993-01-01T12:00:00Z</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>time_coverage_start :</span></dt><dd>1992-12-31T12:00:00Z</dd><dt><span>title :</span></dt><dd>DT merged all satellites Global Ocean Gridded SSALTO/DUACS Sea Surface Height L4 product and derived variables</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 8901, latitude: 720, longitude: 1440, nv: 2)\n",
       "Coordinates:\n",
       "    crs        int32 ...\n",
       "    lat_bnds   (time, latitude, nv) float32 dask.array<chunksize=(5, 720, 2), meta=np.ndarray>\n",
       "  * latitude   (latitude) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
       "    lon_bnds   (longitude, nv) float32 dask.array<chunksize=(1440, 2), meta=np.ndarray>\n",
       "  * longitude  (longitude) float32 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
       "  * nv         (nv) int32 0 1\n",
       "  * time       (time) datetime64[ns] 1993-01-01 1993-01-02 ... 2017-05-15\n",
       "Data variables:\n",
       "    adt        (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>\n",
       "    err        (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>\n",
       "    sla        (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>\n",
       "    ugos       (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>\n",
       "    ugosa      (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>\n",
       "    vgos       (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>\n",
       "    vgosa      (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>\n",
       "Attributes: (12/43)\n",
       "    Conventions:                     CF-1.6\n",
       "    Metadata_Conventions:            Unidata Dataset Discovery v1.0\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Sea Surface Height measured by Altimetry...\n",
       "    contact:                         servicedesk.cmems@mercator-ocean.eu\n",
       "    creator_email:                   servicedesk.cmems@mercator-ocean.eu\n",
       "    ...                              ...\n",
       "    summary:                         SSALTO/DUACS Delayed-Time Level-4 sea su...\n",
       "    time_coverage_duration:          P1D\n",
       "    time_coverage_end:               1993-01-01T12:00:00Z\n",
       "    time_coverage_resolution:        P1D\n",
       "    time_coverage_start:             1992-12-31T12:00:00Z\n",
       "    title:                           DT merged all satellites Global Ocean Gr..."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from intake import open_catalog\n",
    "cat = open_catalog(\"https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean.yaml\")\n",
    "ds  = cat[\"sea_surface_height\"].to_dask()\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the values are not displayed, since that would trigger computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f17ae3e4f10>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.sla[0].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the computationally intensive step\n",
    "sla_timeseries = ds.sla.mean(dim=('latitude', 'longitude')).load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sla_timeseries.plot(label='full data')\n",
    "sla_timeseries.rolling(time=365, center=True).mean().plot(label='rolling annual mean')\n",
    "plt.ylabel('Sea Level Anomaly [m]')\n",
    "plt.title('Global Mean Sea Level')\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  }
 ],
 "metadata": {
  "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: src/lectures/dask/intro.md
================================================
# Dask for Parallel Computing in Python

In past lectures, we learned how to use numpy, pandas, and xarray to analyze various types of geoscience data.
In this lecture, we address an incresingly common problem: what happens if the data we wish to analyze is "big data"

##  What is "Big Data"?

There is a lot of hype around the buzzword "big data" today.
Some people may associate "big data" with specific sortware platforms (e.g. "Hadoop", "spark"), while, for others, "big data" means specific machine learning techniques.
But I think [wikipedia's definition](https://en.wikipedia.org/wiki/Big_data) is the most useful

> Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them.

By this definition, a great many datasets we regularly confront in Earth science are big data.

A good threshold for when data becomes difficult to deal with is when the volume of data exceeds your computer's RAM. Most modern laptops have between 2 and 16 GB of RAM. High-end workstations and servers can have 1 TB (1000 GB) or RAM or more. If the dataset you are trying to analyze can't fit in you computer's memory, some special care is required to carry out the analysis. Data that can't fit in RAM but can fit on your hard drive is sometimes called "medium data."

The next threshold of difficulty is when the data can't fit on your hard drive. Most modern laptops have between 100 GB and 4 TB of storage space on the hard drive. If you can't fit your dataset on your internal hard drive, you can buy an external hard drive. However, at that point you are better off using a high-end server, HPC system, or cloud-based storage for your dataset. Once you have many TB of data to analyze, you are definitely in the realm of "big data"

## What is Dask?

Dask is a tool that helps us easily extend our familiar python data analysis tools to medium and big data, i.e. dataset that can't fit in our computer's RAM. In many cases, dask also allows us to speed up our analysis by using mutiple CPU cores. Dask can help us work more efficiently on our laptop, and it can also help us scale up our analysis on HPC and cloud platforms. Most importantly, dask is almost invisible to the user, meaning that you can focus on your science, rather than the details of parallel computing.

Dask was created by the brilliant [Matt Rocklin](http://matthewrocklin.com/). You can learn more about it on

* [The Dask Documentation](http://dask.readthedocs.io/en/latest/)
* [The Dask Github Site](https://github.com/dask/dask)

Dask provides _collections for big data_ and _a scheduler for parallel computing_. It is probably easiest to illustrate what these mean through examples, so we will jump right in.

================================================
FILE: src/lectures/data.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "596b7615-cbce-4e6b-8f81-c3a241b01056",
   "metadata": {},
   "source": [
    "# All About Data\n",
    "\n",
    "This lecture attempts to give a broad overview of Earth and Environment Science data formats, exchange protocols, and best practices."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924c3c94-67c6-4e73-a4c1-db608f35ddc2",
   "metadata": {},
   "source": [
    "## What is (are) data?\n",
    "\n",
    "Ultimately, as we know, all digital data are just 1s and 0s. Each 1 or 0 value is called a _bit_.\n",
    "Bits are usually grouped together in groups of 8 bits, called a _byte_.\n",
    "A byte can represent $2^8$, or 256, distinct values.\n",
    "We can choose to _encode_ and _organize_ the bytes in many different ways to represent different types of information."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e3be857-0442-4c86-a0c3-376274f2d757",
   "metadata": {},
   "source": [
    "### Data Encoding\n",
    "\n",
    "Encoding is the way that we map raw bytes to meaningful values.\n",
    "\n",
    "#### Numerical Data\n",
    "\n",
    "The most straightforward way to interpret a byte is as an integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0e3b0271-f7ec-4616-828e-e3d11a412025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = 0b10101010  # 1 byte base-2 literal\n",
    "value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4ba51b4-d9f4-460d-b42c-9f81f3345ce1",
   "metadata": {},
   "source": [
    "However, many different numerial data types can be encoded as bytes.\n",
    "The more bytes we use for each value, the more range or precision we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4a513751-4307-448d-b8a6-72fe3c816295",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int16 2 bytes\n",
      "float64 8 bytes\n",
      "complex256 32 bytes\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(np.dtype('i2'), np.dtype('i2').itemsize, \"bytes\")\n",
    "print(np.dtype('f8'), np.dtype('f8').itemsize, \"bytes\")\n",
    "print(np.dtype('complex256'), np.dtype('complex256').itemsize, \"bytes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77d43114-984b-4132-ad25-25664d4b76b6",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Text Data\n",
    "\n",
    "We can also encode text as bytes.\n",
    "\n",
    "The simplest encoding is [ASCII](https://en.wikipedia.org/wiki/ASCII) (American Standard Code for Information Interchange).\n",
    "ASCII uses one byte per character and therefore the ASCII alphabet only contains 256 different characters.\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/c/cf/USASCII_code_chart.png\" width=\"500px\" />\n",
    "\n",
    "As computers became more powerful, and the computing community grew beyond the US and Western Europe, a more inclusive character encoding standard called [Unicode](https://en.wikipedia.org/wiki/Unicode) took hold.\n",
    "The most common encoding is [UTF-8](https://en.wikipedia.org/wiki/UTF-8).\n",
    "UTF-8 uses four bytes per character.\n",
    "This web page the the Jupyter Notebook it was generated from all use UTF-8 encoding.\n",
    "\n",
    "Fun fact: emojis are UTF-8 characters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01120a01-fbe4-4661-9eb0-211f73d9d46a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\xf0\\x9f\\x98\\x80'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"😀\".encode()"
   ]
  },
  {
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    "Single values will only take us so far.\n",
    "To represent scientific data, we need to think about data _organization_."
   ]
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    "### Tabular Data\n",
    "\n",
    "A very common type of data is \"tabular data\".\n",
    "We discussed it already in our Pandas lecture.\n",
    "Tabular data consists of _rows_ and _columns_.\n",
    "The columns usually have a name and a specific data type.\n",
    "Each row is a distinct sample.\n",
    "\n",
    "Here is an example of tabular data\n",
    "\n",
    "| Name | Mass | Diameter |\n",
    "| -- | -- | -- |\n",
    "| Mercury | 0.330 $\\times 10^{24}$ kg | 4879 km |  \n",
    "| Venus | 4.87 $\\times 10^{24}$ kg | 12,104 km |  \n",
    "| Earth | 5.97 $\\times 10^{24}$ kg | 12,756 km | \n",
    "\n",
    "(Via https://nssdc.gsfc.nasa.gov/planetary/factsheet/)\n",
    "\n",
    "The simplest and most common way to encode tabular data is in a text file as [CSV (comma-separated values)](https://en.wikipedia.org/wiki/Comma-separated_values).\n",
    "CSV is readable by humans and computer programs.\n",
    "\n",
    "For larger datasets, [Apache Parquet](https://parquet.apache.org/) is a good alternative.\n",
    "Parquet files are not human readable, but they can be parsed by computers much more quickly and efficiently.\n",
    "They also use compression to achieve a smaller file size compared to CSV.\n",
    "\n",
    "Multiple related tabular datasets can be stored and queried in a [relational database](https://en.wikipedia.org/wiki/Relational_database).\n",
    "Databases are very useful but beyond the scope of this class."
   ]
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    "### Array Data\n",
    "\n",
    "When we have numerical data that are organized into an N-dimensional rectangular grid of values, we are dealing with array data.\n",
    "\n",
    "<img src=\"http://xarray.pydata.org/en/stable/_images/dataset-diagram.png\" width=\"600px\" />\n",
    "\n",
    "(via http://xarray.pydata.org/en/stable/user-guide/data-structures.html)\n",
    "\n",
    "In python, we work with Array data in Numpy and Xarray.\n",
    "\n",
    "Array data can be stored in the following standard formats:\n",
    "\n",
    "- [Hierarchical Data Format (HDF5)](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) - Container for many arrays\n",
    "- [Network Common Data Form (NetCDF)](https://www.unidata.ucar.edu/software/netcdf/) - Container for many arrays which conform to the [NetCDF data model](https://www.unidata.ucar.edu/software/netcdf/docs/netcdf_data_model.html)\n",
    "- [Zarr](https://zarr.readthedocs.io/en/stable/) - New cloud-optimized format for array storage\n",
    "- [TileDB Embedded](https://docs.tiledb.com/main/) - New cloud-optimized format for array storage\n"
   ]
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    "### \"GIS\" Data\n",
    "\n",
    "A common term in Earth and Environmental Data Analysis is \"GIS\": Geographical Information Systems\n",
    "The term GIS was [coined by the company ESRI](https://www.esri.com/en-us/what-is-gis/overview) and is most commonly associated with that company's software products like ArcGIS.\n",
    "Are we doing \"GIS\" in this class? Hard to say for sure.\n",
    "However, it is important to understand some of the common terminology from GIS as it relates to data."
   ]
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    "#### Raster Data\n",
    "\n",
    "From [ESRI's definition](https://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/what-is-raster-data.htm):\n",
    "\n",
    "> In its simplest form, a raster consists of a matrix of cells (or pixels) organized into rows and columns (or a grid) where each cell contains a value representing information, such as temperature. Rasters are digital aerial photographs, imagery from satellites, digital pictures, or even scanned maps.\n",
    "\n",
    "![raster data](https://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/GUID-6754AF39-CDE9-4F9D-8C3A-D59D93059BDD-web.png)\n",
    "\n",
    "_(Image credit Environmental Systems Research Institute, Inc.)_\n",
    "\n",
    "Raster data is thus similar to array data, but specialized to 2D images of the earth.\n",
    "Raster data nearly always includes a [Coordinate Reference System](https://en.wikipedia.org/wiki/Spatial_reference_system) that allows one to translate the pixels of the image to specific geographic locations on Earth.\n",
    "\n",
    "The most common format for Raster data is the [GeoTIFF](https://en.wikipedia.org/wiki/GeoTIFF) and its recent evolution, the [Cloud-optimized GeoTIFF (CoG)](https://www.cogeo.org/).\n",
    "In Python, raster data is commonly read with [rasterio](https://rasterio.readthedocs.io/en/latest/). Rasterio integrates with Xarray via the [rioxarray package](https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html), allowing you to read geotiffs directly into Xarray."
   ]
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    "#### Vector Data\n",
    "\n",
    "From the [Data Carpentry vector data lesson](https://datacarpentry.org/organization-geospatial/02-intro-vector-data/):\n",
    "\n",
    "> Vector data structures represent specific features on the Earth’s surface, and assign attributes to those features. Vectors are composed of discrete geometric locations (x, y values) known as vertices that define the shape of the spatial object. The organization of the vertices determines the type of vector that we are working with: point, line or polygon.\n",
    "\n",
    "<img src=\"https://datacarpentry.org/organization-geospatial/fig/dc-spatial-vector/pnt_line_poly.png\" width=\"500px\" />\n",
    "\n",
    "_(Image credit: National Ecological Observatory Network (NEON))_\n",
    "\n",
    "There are many different file formats for vector data. One of the most common is the [ESRI shapefile](https://en.wikipedia.org/wiki/Shapefile).\n",
    "However, others [have argued that shapefile is not a good format](http://switchfromshapefile.org/) and have promoted the use of other formats including:\n",
    "- [GeoJSON](https://geojson.org/)\n",
    "- [GeoPackage](https://www.geopackage.org/)\n",
    "- [FlatGeoBuf](https://flatgeobuf.org/)\n",
    "\n",
    "Vector data can be read in python using [Fiona](https://fiona.readthedocs.io/en/latest/#) and [GeoPandas](https://geopandas.org/en/stable/docs/user_guide/io.html)."
   ]
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    "### Graph Data\n",
    "\n",
    "Some phenomena are best represented as a discrete [graph](https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)), consisting of nodes and edges (vertices).\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/5/57/6n-graf.png\" />\n",
    "\n",
    "Graph data can be stored in a range of different formats.\n",
    "In python, we can analyze graph data using [NetworkX](https://networkx.org/).\n",
    "\n",
    "We do not work with graph data in this class, but it's important to know it exists."
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    "### Unstructured Data\n",
    "\n",
    "Data that does not fit one of the above paradigms is often called [\"unstructured\" data](https://en.wikipedia.org/wiki/Unstructured_data).\n",
    "Unstructured data is often characterized by nested or hierarchical structures.\n",
    "\n",
    "A common container for unstructured data is [JavaScript Object Notation (JSON)](https://www.json.org/json-en.html).\n",
    "JSON can be read into a Python dictionary using the [json](https://docs.python.org/3/library/json.html) package from the Python standard library."
   ]
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    "### Metadata\n",
    "\n",
    "Metadata are \"data about the data\", for example:\n",
    "- Who created the dataset?\n",
    "- What is the license for the data?\n",
    "- What is the appropriate scholarly reference for the data?\n",
    "- What variables or fields are contained in the data?\n",
    "- What geographical or temporal extent does the data cover?\n",
    "\n",
    "Metadata should attempt to follow established conventions and encoding formats to permit search engines or other tools to automatically \"crawl\" data repositories in order to index and catalog the data contained therein. \n",
    "\n",
    "In Earth and Environmental science, we are fortunate to have widespread robust practices around metdata.\n",
    "For self-contained file formats like NetCDF, metadata can be embedded directly into the data files. \n",
    "Common metadata conventions for NetCDF-style data include:\n",
    "\n",
    "- [Climate and Forecast (CF) Conventions](https://cfconventions.org/) - Commonly used with NetCDF data\n",
    "- [Attribute Convention for Data Discovery (ACDD)](https://wiki.esipfed.org/Attribute_Convention_for_Data_Discovery_1-3)\n",
    "\n",
    "Other types of data may have other metadata conventions. [Schema.org](https://schema.org/) provides a very general framework for metadata."
   ]
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    "## How do programs access data?\n",
    "\n",
    "### Reproducibility and Manual Download Steps\n",
    "\n",
    "To perform a reproducible scientific workflow, you cannot assume that the input data for your analysis are already present on the computer where the code is running.\n",
    "Such code may work for you, but it won't work when you share it with others, unless they happen to have the exact same files in the same locations.\n",
    "\n",
    "Instead, your code should aim to _explicitly acquire the data from the original data provider_ via a persistent identifier such as a DOI or stable URL.\n",
    "Manual data download steps, in which the user must click on buttons on a website, save the files manually, and then move them to a specific location, are a significant barrier to reproducibility and open science.\n",
    "\n",
    "Below we will explore some strategies for writing data analysis code that avoids manual download steps.\n"
   ]
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    "### Loading Data Directly Over the Network\n",
    "\n",
    "A great way of loading data is to read it directly over the network into your program.\n",
    "When you write code in this way, you avoid any reliance on the local filesystem and local path names.\n",
    "This is particularly effective for smallish datasets (< 100 MB) that are quick to transfer.\n",
    "\n",
    "#### HTTP streams\n",
    "\n",
    "HTTP - HyperText Transfer Protocol - is the standard way that web pages and other data are transferred over the internet.\n",
    "HTTP connections are typically established via URLs - Universal Resource Locators. The URL for this book is\n",
    "\n",
    "```\n",
    "https://earth-env-data-science.github.io\n",
    "```\n",
    "\n",
    "When you point your web browser to this URL, it downloads the data (in this case, unicode text files structured as HTML) to your computer for viewing.\n",
    "A key point is that **URLs are the same from every computer**.\n",
    "\n",
    "Data analysis applications can also get data over HTTP. For example, Pandas can directly open files from `http[s]://` URLs.\n",
    "If you can find such a link to your dataset, you can pass it directly to Pandas.\n",
    "However, sometimes it takes some sleuthing to find the right link.\n",
    "\n",
    "As an example, let's consider the [NASA Meteorite Landings Database](https://data.nasa.gov/Space-Science/Meteorite-Landings/gh4g-9sfh).\n",
    "\n",
    "When we go to this site we see something like this:\n",
    "\n",
    "![meteorite page](figures/meteorite_page.png)\n",
    "\n",
    "Clicking the \"export\" button gives us an option to export CSV, which we know pandas can read.\n",
    "If we just click the \"CSV\" button, we download the file to our computer (not what we want).\n",
    "Instead, _right click or CTRL-click the download link_ and select \"Copy Link Address\".\n",
    "\n",
    "<img src=\"figures/meteorite_export.png\" width=\"500px\" />\n",
    "\n",
    "Pasting the results into our code editor gives us the desired URL.\n",
    "\n",
    "```\n",
    "https://data.nasa.gov/api/views/gh4g-9sfh/rows.csv?accessType=DOWNLOAD\"\n",
    "```\n",
    "\n",
    "We can feed this directly to Pandas."
   ]
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       "      <th>id</th>\n",
       "      <th>nametype</th>\n",
       "      <th>recclass</th>\n",
       "      <th>mass (g)</th>\n",
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       "      <th>reclat</th>\n",
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       "      <td>Aachen</td>\n",
       "      <td>1</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L5</td>\n",
       "      <td>21.0</td>\n",
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       "      <td>50.77500</td>\n",
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       "      <td>720.0</td>\n",
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       "      <td>107000.0</td>\n",
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       "      <td>54.21667</td>\n",
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       "      <td>780.0</td>\n",
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       "       name   id nametype     recclass  mass (g)  fall  \\\n",
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       "2      Abee    6    Valid          EH4  107000.0  Fell   \n",
       "3  Acapulco   10    Valid  Acapulcoite    1914.0  Fell   \n",
       "4   Achiras  370    Valid           L6     780.0  Fell   \n",
       "\n",
       "                     year    reclat    reclong           GeoLocation  \n",
       "0  01/01/1880 12:00:00 AM  50.77500    6.08333     (50.775, 6.08333)  \n",
       "1  01/01/1951 12:00:00 AM  56.18333   10.23333  (56.18333, 10.23333)  \n",
       "2  01/01/1952 12:00:00 AM  54.21667 -113.00000    (54.21667, -113.0)  \n",
       "3  01/01/1976 12:00:00 AM  16.88333  -99.90000     (16.88333, -99.9)  \n",
       "4  01/01/1902 12:00:00 AM -33.16667  -64.95000   (-33.16667, -64.95)  "
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    "import pandas as pd\n",
    "url = \"https://data.nasa.gov/api/views/gh4g-9sfh/rows.csv?accessType=DOWNLOAD\"\n",
    "df = pd.read_csv(url)\n",
    "df.head()"
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    "This code will work from any computer on the internet! 🎉"
   ]
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    "#### OPeNDAP\n",
    "\n",
    "[OPeNDAP](https://www.opendap.org/) - \"Open-source Project for a Network Data Access Protocol\" - is a highly established protocol for tranferring NetCDF-style data over HTTP.\n",
    "NetCDF and Xarray can open OPeNDAP links directly.\n",
    "\n",
    "As an example, we will use a dataset from the [IRI Data Library](https://iridl.ldeo.columbia.edu/):\n",
    "\n",
    "<http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.UNIFIED_PRCP/.GAUGE_BASED/.GLOBAL/.v1p0/.Monthly/.RETRO/.rain/>\n",
    "\n",
    "Clicking on the \"Data Files\" tab shows a link to \"OPeNDAP\", which point to the URL <http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.UNIFIED_PRCP/.GAUGE_BASED/.GLOBAL/.v1p0/.Monthly/.RETRO/.rain/dods>. We can open this directly in Xarray.\n"
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       "  --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.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;\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-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:  (X: 720, T: 324, Y: 360)\n",
       "Coordinates:\n",
       "  * X        (X) float32 0.25 0.75 1.25 1.75 2.25 ... 358.2 358.8 359.2 359.8\n",
       "  * T        (T) float32 228.5 229.5 230.5 231.5 ... 548.5 549.5 550.5 551.5\n",
       "  * Y        (Y) float32 -89.75 -89.25 -88.75 -88.25 ... 88.25 88.75 89.25 89.75\n",
       "Data variables:\n",
       "    rain     (T, Y, X) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL</pre><div class='xr-wrap' hidden><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-5fdeea4e-f6b4-4460-8d69-d24da6acda76' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5fdeea4e-f6b4-4460-8d69-d24da6acda76' 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'>X</span>: 720</li><li><span class='xr-has-index'>T</span>: 324</li><li><span class='xr-has-index'>Y</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-747acdd0-3f5a-4bd6-b7f0-b220b3360ef2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-747acdd0-3f5a-4bd6-b7f0-b220b3360ef2' 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'>X</span></div><div class='xr-var-dims'>(X)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.25 0.75 1.25 ... 359.2 359.8</div><input id='attrs-4c529a24-20e2-47ae-b2c7-6afdcf4d3381' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c529a24-20e2-47ae-b2c7-6afdcf4d3381' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c6f059a-7e83-4e1d-9c1a-361b61b0ee29' class='xr-var-data-in' type='checkbox'><label for='data-8c6f059a-7e83-4e1d-9c1a-361b61b0ee29' title='Show/Hide data repr'><svg 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>pointwidth :</span></dt><dd>0.5</dd><dt><span>gridtype :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>degree_east</dd></dl></div><div class='xr-var-data'><pre>array([2.5000e-01, 7.5000e-01, 1.2500e+00, ..., 3.5875e+02, 3.5925e+02,\n",
       "       3.5975e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>T</span></div><div class='xr-var-dims'>(T)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>228.5 229.5 230.5 ... 550.5 551.5</div><input id='attrs-8b14dd35-6cb0-4160-b9b1-f15ac81cd864' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b14dd35-6cb0-4160-b9b1-f15ac81cd864' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6297818c-4cca-4f7f-97ee-611a249f9ed1' class='xr-var-data-in' type='checkbox'><label for='data-6297818c-4cca-4f7f-97ee-611a249f9ed1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>pointwidth :</span></dt><dd>1.0</dd><dt><span>calendar :</span></dt><dd>360</dd><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>months since 1960-01-01</dd></dl></div><div class='xr-var-data'><pre>array([228.5, 229.5, 230.5, ..., 549.5, 550.5, 551.5], dtype=float32)</pre></div></li><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'>float32</div><div class='xr-var-preview xr-preview'>-89.75 -89.25 ... 89.25 89.75</div><input id='attrs-663522fc-985d-4483-b1d3-87e8a91081b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-663522fc-985d-4483-b1d3-87e8a91081b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b8d58ad-4422-4424-8230-fbf244db082d' class='xr-var-data-in' type='checkbox'><label for='data-6b8d58ad-4422-4424-8230-fbf244db082d' title='Show/Hide data repr'><svg 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>pointwidth :</span></dt><dd>0.5</dd><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>degree_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d3b2b4cd-2917-4310-aeb5-a157e4f9f8cb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3b2b4cd-2917-4310-aeb5-a157e4f9f8cb' 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>rain</span></div><div class='xr-var-dims'>(T, Y, X)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b7cefb8-eb36-4544-ae3e-e9f85ad731bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b7cefb8-eb36-4544-ae3e-e9f85ad731bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-292604fe-9393-4b72-9351-4b1b3d2ffa95' class='xr-var-data-in' type='checkbox'><label for='data-292604fe-9393-4b72-9351-4b1b3d2ffa95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>pointwidth :</span></dt><dd>0</dd><dt><span>standard_name :</span></dt><dd>lwe_precipitation_rate</dd><dt><span>file_missing_value :</span></dt><dd>-999.0</dd><dt><span>history :</span></dt><dd>Boxes with less than 0.0% dropped</dd><dt><span>units :</span></dt><dd>mm/day</dd><dt><span>long_name :</span></dt><dd>Monthly Precipitation</dd></dl></div><div class='xr-var-data'><pre>[83980800 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7192f24c-5ed5-4d4e-93f8-0d7a5d98318f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7192f24c-5ed5-4d4e-93f8-0d7a5d98318f' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>IRIDL</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (X: 720, T: 324, Y: 360)\n",
       "Coordinates:\n",
       "  * X        (X) float32 0.25 0.75 1.25 1.75 2.25 ... 358.2 358.8 359.2 359.8\n",
       "  * T        (T) float32 228.5 229.5 230.5 231.5 ... 548.5 549.5 550.5 551.5\n",
       "  * Y        (Y) float32 -89.75 -89.25 -88.75 -88.25 ... 88.25 88.75 89.25 89.75\n",
       "Data variables:\n",
       "    rain     (T, Y, X) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "url = \"http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.UNIFIED_PRCP/.GAUGE_BASED/.GLOBAL/.v1p0/.Monthly/.RETRO/.rain/dods\"\n",
    "# decode_times=False is required because the IRI Data Library uses non-standard encoding of times\n",
    "ds = xr.open_dataset(url, decode_times=False)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9bd48c8-712d-4ba4-bfb9-4a5e39e8ca33",
   "metadata": {},
   "source": [
    "#### Filesystem-Spec (fsspec)\n",
    "\n",
    "> Filesystem Spec ([fsspec](https://filesystem-spec.readthedocs.io/en/latest/)) is a project to provide a unified pythonic interface to local, remote and embedded file systems and bytes storage.\n",
    "\n",
    "Fsspec allows us to access many different types of remote files in a simple, uniform way.\n",
    "Fsspec has many [built in implementations](https://filesystem-spec.readthedocs.io/en/latest/api.html#built-in-implementations) including local files, HTTP, FTP, and Zipfile.\n",
    "Additional packages implement the fsspec interface to provide access to cloud storage.\n",
    "These include\n",
    "\n",
    "- [s3fs](https://s3fs.readthedocs.io/en/latest/) for Amazon S3 and other compatible stores\n",
    "- [gcsfs](https://gcsfs.readthedocs.io/en/latest/) for Google Cloud Storage\n",
    "- [adlfs](https://github.com/fsspec/adlfs) for Azure DataLake storage\n",
    "\n",
    "and [several others](https://filesystem-spec.readthedocs.io/en/latest/api.html#other-known-implementations)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b719bfce-8ff1-42a6-90fa-f72980bf10cd",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Cloud Object Storage\n",
    "\n",
    "Cloud object storage is the easiest and most performant way to share data over the internet.\n",
    "Data stored in cloud object storage is always available and can be read extremely quickly, particularly from the same cloud region.\n",
    "\n",
    "More TODO here..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "088b27e2-16f4-4525-aa42-334a158ff6e9",
   "metadata": {},
   "source": [
    "### Local Files\n",
    "\n",
    "Some programs require data to be on your local hard drive to read.\n",
    "\n",
    "To download files to your local computer, we strongly recommend [Pooch](https://www.fatiando.org/pooch/latest/).\n",
    "\n",
    "Here is an example of using Pooch to download data from the [CRESIS Radar Database](https://data.cresis.ku.edu/).\n",
    "As with the examples above, the first step is to nagivate to web page and try to figure out the actual URL of the files you want to download."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "23ad3b5f-52a5-482e-a57e-68cdeb52ac3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/jovyan/.cache/2017_Antarctica_P3/CSARP_mvdr/Data_img_02_20171124_03_020.mat'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pooch\n",
    "POOCH = pooch.create(\n",
    "    path=pooch.os_cache(\"2017_Antarctica_P3/CSARP_mvdr\"),\n",
    "    base_url=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_P3/CSARP_mvdr/20171124_03/\",\n",
    "    registry={\n",
    "        \"Data_img_02_20171124_03_020.mat\": None,\n",
    "    }\n",
    ")\n",
    "\n",
    "local_fname = POOCH.fetch(\"Data_img_02_20171124_03_020.mat\")\n",
    "local_fname"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d24593bc-e1f4-48da-9637-ec6a11a38db7",
   "metadata": {},
   "source": [
    "The file is now saved locally and can be read, e.g. with h5py."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5438739d-54a3-4ec9-b8c4-143d4c7caec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#refs#',\n",
       " 'Bottom',\n",
       " 'Data',\n",
       " 'Elevation',\n",
       " 'GPS_time',\n",
       " 'Heading',\n",
       " 'Latitude',\n",
       " 'Longitude',\n",
       " 'Pitch',\n",
       " 'Roll',\n",
       " 'Surface',\n",
       " 'Time',\n",
       " 'param_combine',\n",
       " 'param_csarp',\n",
       " 'param_records']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import h5py\n",
    "\n",
    "f = h5py.File(local_fname)\n",
    "list(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3dbee63-fe1e-4f34-b2b7-14c7c515dc11",
   "metadata": {},
   "source": [
    "Here is another example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "605f9aa2-e707-49ba-8d4a-0fc6813626e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/jovyan/.cache/greenland_ice_sheet/vel_2010-07-01_2011-06-31.nc'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "POOCH = pooch.create(\n",
    "    path=pooch.os_cache(\"greenland_ice_sheet\"),\n",
    "    base_url=\"https://zenodo.org/record/4977910/files/\",\n",
    "    registry={\n",
    "        \"vel_2010-07-01_2011-06-31.nc\": \"md5:80ad1a3c381af185069bc032a6459745\",\n",
    "    }\n",
    ")\n",
    "\n",
    "fname = POOCH.fetch(\"vel_2010-07-01_2011-06-31.nc\")\n",
    "fname"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5256d094-3561-454d-8fdb-0c84d842aafc",
   "metadata": {},
   "source": [
    "This downloads a netCDF file we can read with Xarray:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb4344c0-d9ef-437f-ac18-f15a34dbbb12",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:       (x: 10018, y: 17946)\n",
       "Coordinates:\n",
       "  * x             (x) float64 -6.38e+05 -6.378e+05 ... 8.644e+05 8.646e+05\n",
       "  * y             (y) float64 -6.576e+05 -6.578e+05 ... -3.349e+06 -3.349e+06\n",
       "Data variables:\n",
       "    coord_system  |S1 b&#x27;&#x27;\n",
       "    VX            (y, x) float32 ...\n",
       "    VY            (y, x) float32 ...\n",
       "Attributes: (12/27)\n",
       "    Conventions:               CF-1.6\n",
       "    Metadata_Conventions:      CF-1.6, Unidata Dataset Discovery v1.0, GDS v2.0\n",
       "    standard_name_vocabulary:  CF Standard Name Table (v22, 12 February 2013)\n",
       "    id:                        vel_final.t.nc\n",
       "    title:                     MEaSURES Antarctica Ice Velocity Map 450m spacing\n",
       "    product_version:            \n",
       "    ...                        ...\n",
       "    time_coverage_start:       1995-01-01\n",
       "    time_coverage_end:         2016-12-31\n",
       "    project:                   NASA/MEaSUREs_UC-Irvine\n",
       "    creator_name:              J. Mouginot\n",
       "    comment:                    \n",
       "    license:                   No restrictions on access or use</pre><div class='xr-wrap' hidden><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-1159c5ec-c009-4575-851c-8c1b1800d2f8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1159c5ec-c009-4575-851c-8c1b1800d2f8' 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'>x</span>: 10018</li><li><span class='xr-has-index'>y</span>: 17946</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-75ddde74-b038-453c-ad78-669a6ec92871' class='xr-section-summary-in' type='checkbox'  checked><label for='section-75ddde74-b038-453c-ad78-669a6ec92871' 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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-6.38e+05 -6.378e+05 ... 8.646e+05</div><input id='attrs-5eb99ea4-b274-4f0b-8f16-e9bf9e5342d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5eb99ea4-b274-4f0b-8f16-e9bf9e5342d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9790caa0-3a15-478c-9126-30d93260aaeb' class='xr-var-data-in' type='checkbox'><label for='data-9790caa0-3a15-478c-9126-30d93260aaeb' title='Show/Hide data repr'><svg 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>Cartesian x-coordinate</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-638000., -637850., -637700., ...,  864250.,  864400.,  864550.])</pre></div></li><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'>-6.576e+05 ... -3.349e+06</div><input id='attrs-2198e59c-bab1-4b76-98c3-8327b179bf35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2198e59c-bab1-4b76-98c3-8327b179bf35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08e3b6c0-62c8-45bb-9417-6285a40ccc07' class='xr-var-data-in' type='checkbox'><label for='data-08e3b6c0-62c8-45bb-9417-6285a40ccc07' title='Show/Hide data repr'><svg 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>Cartesian y-coordinate</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([ -657600.,  -657750.,  -657900., ..., -3349050., -3349200., -3349350.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d67cc7bf-8caa-4340-acb9-82036ed7c4b7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d67cc7bf-8caa-4340-acb9-82036ed7c4b7' 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>coord_system</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7dc19cf4-f815-4ec3-acf5-fd5ce3d79b1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7dc19cf4-f815-4ec3-acf5-fd5ce3d79b1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf79b69f-2a63-4cb1-ad31-d19927a12ef8' class='xr-var-data-in' type='checkbox'><label for='data-bf79b69f-2a63-4cb1-ad31-d19927a12ef8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>ellipsoid :</span></dt><dd>WGS84</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>grid_mapping_name :</span></dt><dd>polar_stereographic</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>45.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>90.0</dd><dt><span>standard_parallel :</span></dt><dd>70.0</dd><dt><span>straight_vertical_longitude_from_pole :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array(b&#x27;&#x27;, dtype=&#x27;|S1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VX</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cd79798f-6918-4e40-bbb4-8397d9efa9f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd79798f-6918-4e40-bbb4-8397d9efa9f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9631b42e-f14a-4d89-955e-95f106616bdf' class='xr-var-data-in' type='checkbox'><label for='data-9631b42e-f14a-4d89-955e-95f106616bdf' title='Show/Hide data repr'><svg 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>Ice velocity in x direction</dd><dt><span>standard_name :</span></dt><dd>land_ice_x_velocity</dd><dt><span>units :</span></dt><dd>meter/year</dd><dt><span>coordinates :</span></dt><dd>lon lat</dd><dt><span>grid_mapping :</span></dt><dd>coord_system</dd></dl></div><div class='xr-var-data'><pre>[179783028 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VY</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-541cad93-c964-41c9-a21a-b893a1de4f50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-541cad93-c964-41c9-a21a-b893a1de4f50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9056733e-efea-472b-aa26-169e3fbc04d3' class='xr-var-data-in' type='checkbox'><label for='data-9056733e-efea-472b-aa26-169e3fbc04d3' title='Show/Hide data repr'><svg 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>Ice velocity in y direction</dd><dt><span>standard_name :</span></dt><dd>land_ice_y_velocity</dd><dt><span>units :</span></dt><dd>meter/year</dd><dt><span>coordinates :</span></dt><dd>lon lat</dd><dt><span>grid_mapping :</span></dt><dd>coord_system</dd></dl></div><div class='xr-var-data'><pre>[179783028 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e2620a59-ca15-45c4-a61c-c8c433638497' class='xr-section-summary-in' type='checkbox'  ><label for='section-e2620a59-ca15-45c4-a61c-c8c433638497' class='xr-section-summary' >Attributes: <span>(27)</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>Metadata_Conventions :</span></dt><dd>CF-1.6, Unidata Dataset Discovery v1.0, GDS v2.0</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v22, 12 February 2013)</dd><dt><span>id :</span></dt><dd>vel_final.t.nc</dd><dt><span>title :</span></dt><dd>MEaSURES Antarctica Ice Velocity Map 450m spacing</dd><dt><span>product_version :</span></dt><dd> </dd><dt><span>summary :</span></dt><dd> </dd><dt><span>keywords :</span></dt><dd> </dd><dt><span>keywords_vocabulary :</span></dt><dd> </dd><dt><span>platform :</span></dt><dd> </dd><dt><span>sensor :</span></dt><dd> </dd><dt><span>date_created :</span></dt><dd>2017-06-14T23:00:25.00003874301591Z</dd><dt><span>institution :</span></dt><dd>Department of Earth System Science, University of California, Irvine</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lat_min :</span></dt><dd>60</dd><dt><span>geospatial_lat_max :</span></dt><dd>90</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180</dd><dt><span>geospatial_lon_max :</span></dt><dd>180</dd><dt><span>spatial_resolution :</span></dt><dd>150 m</dd><dt><span>time_coverage_start :</span></dt><dd>1995-01-01</dd><dt><span>time_coverage_end :</span></dt><dd>2016-12-31</dd><dt><span>project :</span></dt><dd>NASA/MEaSUREs_UC-Irvine</dd><dt><span>creator_name :</span></dt><dd>J. Mouginot</dd><dt><span>comment :</span></dt><dd> </dd><dt><span>license :</span></dt><dd>No restrictions on access or use</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (x: 10018, y: 17946)\n",
       "Coordinates:\n",
       "  * x             (x) float64 -6.38e+05 -6.378e+05 ... 8.644e+05 8.646e+05\n",
       "  * y             (y) float64 -6.576e+05 -6.578e+05 ... -3.349e+06 -3.349e+06\n",
       "Data variables:\n",
       "    coord_system  |S1 ...\n",
       "    VX            (y, x) float32 ...\n",
       "    VY            (y, x) float32 ...\n",
       "Attributes: (12/27)\n",
       "    Conventions:               CF-1.6\n",
       "    Metadata_Conventions:      CF-1.6, Unidata Dataset Discovery v1.0, GDS v2.0\n",
       "    standard_name_vocabulary:  CF Standard Name Table (v22, 12 February 2013)\n",
       "    id:                        vel_final.t.nc\n",
       "    title:                     MEaSURES Antarctica Ice Velocity Map 450m spacing\n",
       "    product_version:            \n",
       "    ...                        ...\n",
       "    time_coverage_start:       1995-01-01\n",
       "    time_coverage_end:         2016-12-31\n",
       "    project:                   NASA/MEaSUREs_UC-Irvine\n",
       "    creator_name:              J. Mouginot\n",
       "    comment:                    \n",
       "    license:                   No restrictions on access or use"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset(fname)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb8ae19-607e-4fb3-95a0-eeaef51a19f9",
   "metadata": {},
   "source": [
    "## Best practices for data sharing: FAIR\n",
    "\n",
    "The acronym FAIR - Findable, Accessible, Interoperable, Reusable - has recently caught on as the gold standard for data sharing.\n",
    "The [FAIR principles](https://www.force11.org/group/fairgroup/fairprinciples) are [growing rapidly](https://eos.org/editors-vox/enabling-findable-accessible-interoperable-and-reusable-data) in the geoscience community.\n",
    "\n",
    "Here we repeat and discuss the FAIR principles (via https://www.force11.org/group/fairgroup/fairprinciples)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb224da2-1536-4ca4-b4c9-0bf09c37fd94",
   "metadata": {},
   "source": [
    "### To be Findable:\n",
    "\n",
    "F1. (meta)data are assigned a globally unique and eternally persistent identifier.\n",
    "\n",
    "F2. data are described with rich metadata.\n",
    "\n",
    "F3. (meta)data are registered or indexed in a searchable resource.\n",
    "\n",
    "F4. metadata specify the data identifier."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cebb9d6-e103-4f6f-8392-a0bf3c70ce92",
   "metadata": {},
   "source": [
    "### To be Accessible:\n",
    "\n",
    "A1  (meta)data are retrievable by their identifier using a standardized communications protocol.\n",
    "\n",
    "A1.1 the protocol is open, free, and universally implementable.\n",
    "\n",
    "A1.2 the protocol allows for an authentication and authorization procedure, where necessary.\n",
    "\n",
    "A2 metadata are accessible, even when the data are no longer available."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04d91ba2-9a17-49dc-8448-db4912c4314e",
   "metadata": {},
   "source": [
    "### To be Interoperable:\n",
    "    \n",
    "I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.\n",
    "\n",
    "I2. (meta)data use vocabularies that follow FAIR principles.\n",
    "\n",
    "I3. (meta)data include qualified references to other (meta)data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5584d1f-98e9-4025-b73e-dcb5a46326ed",
   "metadata": {},
   "source": [
    "### To be Re-useable:\n",
    "    \n",
    "R1. meta(data) have a plurality of accurate and relevant attributes.\n",
    "\n",
    "R1.1. (meta)data are released with a clear and accessible data usage license.\n",
    "\n",
    "R1.2. (meta)data are associated with their provenance.\n",
    "\n",
    "R1.3. (meta)data meet domain-relevant community standards."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98f1d940-43aa-4955-9047-d8789f0066e7",
   "metadata": {},
   "source": [
    "### Persistent Identifiers (PI / PID)\n",
    "\n",
    "Persistent identifier are quasi-permanent unique identifiers that can be used to look up data, articles, books, etc.\n",
    "They are a key aspect of making data _Findable_.\n",
    "Digitial persistent identifiers are usually HTTP URLs that are guaranteed to continue working for a long time (in contrast to general URLs, which could change or disappear at any time.)\n",
    "\n",
    "#### Digital Object Identifiers\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/1/11/DOI_logo.svg/1200px-DOI_logo.svg.png\" width=\"20%\" />\n",
    "\n",
    "In science, the [DOI system](https://www.doi.org/) has become a univerally accepted way to identify and find digital scholarly objects.\n",
    "A DOI consists of a string of characters. Prepending the URL `https://doi.org/` to a DOI will generate a web link that can be used to look up the object.\n",
    "\n",
    "Publishers (of journal articles, books, data repositories, etc.) will typical generate (or \"mint\") a DOI for new content when it is published.\n",
    "The publisher commits to making the DOI work \"forever\". \n",
    "That's why not just anyone can create a DOI; it's a commitment.\n",
    "\n",
    "Here are some examples of DOIs. Click the links to see where they go.\n",
    "\n",
    "| Product Type | DOI | DOI Resolver |\n",
    "| -- | -- | -- |\n",
    "| Article | `10.1109/MCSE.2021.3059437` | https://doi.org/10.1109/MCSE.2021.3059437 |\n",
    "| Dataset | `10.6084/m9.figshare.3507758.v1` | https://doi.org/10.6084/m9.figshare.3507758.v1 |\n",
    "| Software | `10.5281/zenodo.4821276` | https://doi.org/10.5281/zenodo.4821276 |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b42aebd6-05c6-44ed-8927-9c720a85b163",
   "metadata": {
    "tags": []
   },
   "source": [
    "### How can YOU can create FAIR data and code?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff53940-b13f-4304-b097-fd83bc6abe44",
   "metadata": {},
   "source": [
    "### What not to do\n",
    "\n",
    "- Put the data / code on your personal website\n",
    "- Put the data / code in Google Drive / Dropbox / etc.\n",
    "- Put the data / code in GitHub\n",
    "\n",
    "The problem with these solutions is that they are _not persistent_ and therefore not _findable_.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08f03e4b-5ed9-418d-b65e-61a2f330afee",
   "metadata": {},
   "source": [
    "### Rough guide to FAIRly sharing data\n",
    "\n",
    "This guide applies to _small_ data (< 10 GB). \n",
    "Sharing medium or large datasets is more difficult and complicated--clear solutions and best practices do not yet exist.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d837dfc-6a0d-4d36-a6df-0df5cf18f307",
   "metadata": {},
   "source": [
    "#### Step 1: Quality control the data and metadata\n",
    "\n",
    "Before sharing a dataset, you should apply quality control to ensure there are not bad or incorrect values.\n",
    "Choose a standard data format appropriate to the structure of your data, as reviewed above.\n",
    "You also need to generate metadata for your data.\n",
    "If your format supports embedded metadata (e.g. NetCDF, Zarr), you should provide it following one or more of the metadata conventions reviewed above (e.g. CF, ACDD, etc.)\n",
    "If not (e.g. csv files), you should provide a separate metadata file (e.g. `README.txt`) to accompany your data.\n",
    "Once your data and metadata quality control is complete, export the data file[s] to a local folder.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa16fcc-054b-4352-99f2-ddf9615162ab",
   "metadata": {},
   "source": [
    "#### Step 2: Upload the data to Zenodo\n",
    "\n",
    "![Zenodo](https://about.zenodo.org/static/img/logos/zenodo-gradient-200.png)\n",
    "\n",
    "Our recommended data repository is [Zenodo](https://zenodo.org/).\n",
    "\n",
    "> Built and developed by researchers, to ensure that everyone can join in Open Science.\n",
    ">\n",
    "> The OpenAIRE project, in the vanguard of the open access and open data movements in Europe was commissioned by the EC to support their nascent Open Data policy by providing a catch-all repository for EC funded research. CERN, an OpenAIRE partner and pioneer in open source, open access and open data, provided this capability and Zenodo was launched in May 2013.\n",
    ">\n",
    "> In support of its research programme CERN has developed tools for Big Data management and extended Digital Library capabilities for Open Data. Through Zenodo these Big Science tools could be effectively shared with the long­-tail of research.\n",
    "\n",
    "Via https://about.zenodo.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43c3333e-01da-4313-90c5-4570d0b8013a",
   "metadata": {},
   "source": [
    "First create a Zenodo account: https://zenodo.org/signup/\n",
    "Make sure to link your Zenodo account to your GitHub and [ORCID](https://orcid.org/) accounts.\n",
    "\n",
    "Then go to https://zenodo.org/deposit to deposit a new record.\n",
    "Follow the instructions provided, and make sure to include as much metadata as you can.\n",
    "Make sure to choose an open-access license such as Creative Commons.\n",
    "This will make your data more Interoperable and Re-useable.\n",
    "\n",
    "When all your data are uploaded and you have double-checked the metadata, you can click \"Publish\".\n",
    "This will create a permanent archive for your data and generate a new DOI! 🎉"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc61eaf8-97c0-47b1-8625-6a192b0c9e65",
   "metadata": {},
   "source": [
    "#### Step 3: Verify you can access the data\n",
    "\n",
    "Use the [Pooch DOI downloader](https://www.fatiando.org/pooch/latest/api/generated/pooch.DOIDownloader.html) to download and open your data files from a Jupyter Notebook.\n",
    "\n",
    "Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01c987e9-fe05-468a-af70-9118a3b963aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/jovyan/.cache/pooch/fc13d29563dcf659960ec752e43618fa-wind_po_hrly.csv'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doi = \"doi:10.5281/zenodo.5739406\"\n",
    "fname = \"wind_po_hrly.csv\"\n",
    "file_path = pooch.retrieve(\n",
    "    url=f\"{doi}/{fname}\",\n",
    "    known_hash=\"md5:cf059b73d6831282c5580776ac07309a\",\n",
    ")\n",
    "file_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd91ae16-a017-4321-9464-e0100065e35d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date.time</th>\n",
       "      <th>ny_1_onshore</th>\n",
       "      <th>ny_2_onshore</th>\n",
       "      <th>newe_onshore</th>\n",
       "      <th>mw_onshore</th>\n",
       "      <th>newe_offshore</th>\n",
       "      <th>ny_offshore</th>\n",
       "      <th>rfce_offshore</th>\n",
       "      <th>srvc_offshore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2011 0:00</td>\n",
       "      <td>0.725240</td>\n",
       "      <td>0.450004</td>\n",
       "      <td>0.693996</td>\n",
       "      <td>0.646463</td>\n",
       "      <td>0.759451</td>\n",
       "      <td>0.528019</td>\n",
       "      <td>0.353946</td>\n",
       "      <td>0.189323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2011 1:00</td>\n",
       "      <td>0.702001</td>\n",
       "      <td>0.446462</td>\n",
       "      <td>0.677913</td>\n",
       "      <td>0.688890</td>\n",
       "      <td>0.767231</td>\n",
       "      <td>0.530822</td>\n",
       "      <td>0.357297</td>\n",
       "      <td>0.212965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2011 2:00</td>\n",
       "      <td>0.670164</td>\n",
       "      <td>0.432219</td>\n",
       "      <td>0.645567</td>\n",
       "      <td>0.717634</td>\n",
       "      <td>0.764237</td>\n",
       "      <td>0.538845</td>\n",
       "      <td>0.374450</td>\n",
       "      <td>0.232722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/1/2011 3:00</td>\n",
       "      <td>0.627149</td>\n",
       "      <td>0.436257</td>\n",
       "      <td>0.608017</td>\n",
       "      <td>0.745687</td>\n",
       "      <td>0.760258</td>\n",
       "      <td>0.541974</td>\n",
       "      <td>0.387670</td>\n",
       "      <td>0.246672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2011 4:00</td>\n",
       "      <td>0.580882</td>\n",
       "      <td>0.456108</td>\n",
       "      <td>0.584728</td>\n",
       "      <td>0.767698</td>\n",
       "      <td>0.759814</td>\n",
       "      <td>0.552363</td>\n",
       "      <td>0.393835</td>\n",
       "      <td>0.261140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date.time  ny_1_onshore  ny_2_onshore  newe_onshore  mw_onshore  \\\n",
       "0  1/1/2011 0:00      0.725240      0.450004      0.693996    0.646463   \n",
       "1  1/1/2011 1:00      0.702001      0.446462      0.677913    0.688890   \n",
       "2  1/1/2011 2:00      0.670164      0.432219      0.645567    0.717634   \n",
       "3  1/1/2011 3:00      0.627149      0.436257      0.608017    0.745687   \n",
       "4  1/1/2011 4:00      0.580882      0.456108      0.584728    0.767698   \n",
       "\n",
       "   newe_offshore  ny_offshore  rfce_offshore  srvc_offshore  \n",
       "0       0.759451     0.528019       0.353946       0.189323  \n",
       "1       0.767231     0.530822       0.357297       0.212965  \n",
       "2       0.764237     0.538845       0.374450       0.232722  \n",
       "3       0.760258     0.541974       0.387670       0.246672  \n",
       "4       0.759814     0.552363       0.393835       0.261140  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(file_path).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca65ef75-0ad0-48c6-8fb9-8aac36f0f85e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Sharing Code\n",
    "\n",
    "Putting code on GitHub is very convenient for collaboration.\n",
    "But GitHub does not meet the FAIR requirements.\n",
    "Therefore, to make your code truly FAIR, you need to deposit it in a permamenent repository.\n",
    "Again we recommend Zenodo.\n",
    "Fortunately, Zenodo and GitHub integrate very well with each other.\n",
    "\n",
    "The steps for archiving a GitHub repo in Zenodo are [very well documented](https://docs.github.com/en/repositories/archiving-a-github-repository/referencing-and-citing-content).\n",
    "\n",
    "```{note}\n",
    "Prior to archiving your repo, you need to [create a CITATION.cff file](https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-citation-files).\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea73fa2a-b67f-4f0e-b6d2-61dc0ff5cbb9",
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   "outputs": [],
   "source": []
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
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 "nbformat": 4,
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}


================================================
FILE: src/lectures/environment/binder.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binder for Reproducible Research\n",
    "\n",
    "This lecture is concerned with the topic of reproducibility.\n",
    "Nearly everyone agrees that reproducibility is an important principle for science:\n",
    "if results are not reproducible, they are not valid.\n",
    "\n",
    "But how do we achieve reproducibility in practice?\n",
    "In computational / data science, a particular analysis, calculation, or notebook may depend on hundreds of different software packages, each with many different versions.\n",
    "Reproducibility of our results depends on having the correct version.\n",
    "\n",
    "In the [lesson on python environments]({filename}/Lectures/python_environments.md), we learned how to use [conda](https://conda.io/docs/) to manage the packages in our environment.\n",
    "In this lesson, we will learn how to package our own code / notebooks together with an environment in such a way that it can be executed by anyone / anywhere, using cloud computing.\n",
    "\n",
    "![Binder Logo](https://mybinder.org/static/logo.svg)\n",
    "\n",
    "\n",
    "From the [Binder Documentation](https://mybinder.readthedocs.io/en/latest/)\n",
    "\n",
    "> Binder allows you to create custom computing environments that can be shared and used by many remote users. It is powered by [BinderHub](https://github.com/jupyterhub/binderhub), which is an open-source tool that deploys the Binder service in the cloud. One-such deployment lives ... at mybinder.org and is free to use.\n",
    "\n",
    "BinderHub uses a combination of open source technologies, including JupyterHub and [Docker](https://docs.docker.com/) (a containerization service), to achieve this magic.\n",
    "\n",
    "In addition to the <http://mybinder.org> deployment, the Pangeo project operates a BinderHub service at <http://binder.pangeo.io>. This BinderHub is customized to allow users to also launch Dask clusters in the cloud.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An Example Binder\n",
    "\n",
    "All binders start with a github repository. As an example, let's consider the the official dask examples repo: <https://github.com/dask/dask-examples>\n",
    "\n",
    "### Contents\n",
    "\n",
    "The repository should contain the following two elements:\n",
    "\n",
    "- Python code and / or notebooks (these can live in sub-directories)\n",
    "- An `environment.yml` or `requirements.txt` file to specify the package dependencies (can be at the top level or in a subdirectory called `binder/`)\n",
    "\n",
    "The dask-examples repo contains about 10 different example notebooks.\n",
    "\n",
    "To specify the environment, the dask-example repo has the following file at `binder/environment.yml`\n",
    "\n",
    "```\n",
    "name: dask-examples\n",
    "channels:\n",
    "  - conda-forge\n",
    "dependencies:\n",
    "  - python=3.8\n",
    "  - bokeh=2.1.1\n",
    "  - dask=2.20.0\n",
    "  - dask-image=0.2.0\n",
    "  - dask-ml=1.6.0\n",
    "  - dask-labextension=2.0.2\n",
    "  - jupyterlab=2.1\n",
    "  - nodejs=14\n",
    "  - numba\n",
    "  - numpy=1.21\n",
    "  - pip\n",
    "  - pandas=1.3\n",
    "  - pyarrow\n",
    "  - python-graphviz\n",
    "  - seaborn\n",
    "  - scikit-learn=0.23\n",
    "  - matplotlib\n",
    "  - nbserverproxy\n",
    "  - nomkl\n",
    "  - h5py\n",
    "  - xarray\n",
    "  - pooch\n",
    "  - bottleneck\n",
    "  - requests\n",
    "  - py-xgboost\n",
    "  - dask-xgboost\n",
    "  - pip:\n",
    "      - mimesis\n",
    "      - pystan==2.19.1.1\n",
    "      - prophet\n",
    "```\n",
    "\n",
    "This is a rather complex set of dependencies.\n",
    "In addition, there are other files in the `binder/` directory that help further customize the environment.\n",
    "These customizations are described in the [Binder Documentation](https://mybinder.readthedocs.io/en/latest/using.html#executing-post-build-commands).\n",
    "\n",
    "### Binder Link\n",
    "\n",
    "Once the repository is ready, it's time to generate a link to the BinderHub.\n",
    "These links have the following structure:\n",
    "\n",
    "    https://mybinder.org/v2/<provider-name>/<org-name>/<repo-name>/<branch|commit|tag>?filepath=<path/to/notebook.ipynb>\n",
    "    \n",
    "For the dask-examples repo, the link used is:\n",
    "\n",
    "    https://mybinder.org/v2/gh/dask/dask-examples/master?urlpath=lab\n",
    "    \n",
    "In this case, `provider` is `gh` (i.e. github).\n",
    "\n",
    "The <https://mybinder.org/> website has a nifty tool to automatically generate badges that can be placed on a website or markdown file to make it easy to launch the binder.\n",
    "For dask-examples, the markdown code looks like this:\n",
    "\n",
    "`[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/dask/dask-examples/master?urlpath=lab)`\n",
    "\n",
    "And renders like this\n",
    "\n",
    "[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/dask/dask-examples/master?urlpath=lab)\n",
    "\n",
    "Take some time to launch the binder and play around with it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## nbgitpuller tricks\n",
    "\n",
    "The original binder model kept both the code itself and the environment specification in the same repo.\n",
    "The way binder works means that _every time the repo is updated, the environment must be completely rebuilt from scratch._\n",
    "As we have seen, building a complex python environment can take upwards of 20 minutes. \n",
    "So even if you only change a comment in your code, the entire environment is rebuilt when you try to relaunch the binder.\n",
    "Users usually update their code much more frequently than they update the environment.\n",
    "Furthermore, many binders may want to share the _same_ base environment.\n",
    "\n",
    "To address these issues, we can leverage a clever tool called [nbgitpuller](https://jupyterhub.github.io/nbgitpuller/).\n",
    "Nbgitpuller is a JupyterHub extension that allows you to automatically launch content from a github repo into a specific\n",
    "JupyterHub or Binder via a simple URL.\n",
    "That's how this book works.\n",
    "The \"Launch JupyterHub\" button for this page points to the following link:\n",
    "\n",
    "```\n",
    "https://https//us-central1-b.gcp.pangeo.io//hub/user-redirect/git-pull?repo=https://github.com/earth-env-data-science/earth-env-data-science-book&urlpath=lab/tree/earth-env-data-science-book/src/lectures/environment/binder.ipynb&branch=master\n",
    "```\n",
    "\n",
    "Ok, maybe not a \"simple\" URL. 😬 In fact, constructing these links can be so tricky that nbgitpuller has a [special webpage](https://jupyterhub.github.io/nbgitpuller/link.html) to help you generate them.\n",
    "\n",
    "An nbgitpuller link can also be generated for a binder. Here is a link that will open this notebook in mybinder.org.\n",
    "\n",
    "```\n",
    "https://mybinder.org/v2/gh/pangeo-data/pangeo-docker-images/2021.09.30?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Fearth-env-data-science%252Fearth-env-data-science-book%26urlpath%3Dtree%252Fearth-env-data-science-book%252Fearth-env-data-science-book%252Fsrc%252Flectures%252Fenvironment%252Fbinder.ipynb%26branch%3Dmaster\n",
    "```\n",
    "\n",
    "```\n",
    "https://mybinder.org/v2/gh/pangeo-data/pangeo-docker-images/2021.09.30?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Fearth-env-data-science%252Fearth-env-data-science-book%26urlpath%3Dlab%252Ftree%252Fearth-env-data-science-book%252Fsrc%252Flectures%252Fenvironment%252Fbinder.ipynb%26branch%3Dmaster\n",
    "```\n",
    "\n",
    "Yes, very complicated and messy. But it works! As with a regular binder, you can hide it behind a button link."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pangeo Docker Images\n",
    "\n",
    "For students of this class, when creating your first binder, we recommend _not defining your own environment from scratch_ but rather using the default environment that comes with our JupyterHub.\n",
    "This environment is curated and automatically built into a Docker containter at https://github.com/pangeo-data/pangeo-docker-images.\n",
    "The list of official \"tags\" is at https://github.com/pangeo-data/pangeo-docker-images/tags -- you can see that a new image is released every few weeks.\n",
    "The built Docker images live at https://hub.docker.com/r/pangeo/pangeo-notebook/tags.\n",
    "\n",
    "Our class has been using the tag `2021.09.30`.\n",
    "\n",
    "To create a binder that uses this environment\n",
    "- Go to https://jupyterhub.github.io/nbgitpuller/link.html\n",
    "- Click the \"binder\" tab\n",
    "- In \"Git Environment Repository URL\" specify `https://github.com/pangeo-data/pangeo-docker-images`\n",
    "- In \"Branch\" specify `2021.09.30`\n",
    "- In \"Git Content Repository URL\" put the _GitHub link to YOUR repo_.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Creating your Own Binder\n",
    "\n",
    "When creating your own binder, you have two options:\n",
    "- Use a pre-existing environment\n",
    "- Define your own custom environment.\n",
    "\n",
    "### Using a pre-existing environment\n",
    "\n",
    "In this case, all you need is a GitHub repo with one or more notebooks in it.\n",
    "Then use the nbgitpuller approach described above.\n",
    "\n",
    "### Defining a custom environment\n",
    "\n",
    "To create your own binder with a custom environment, you will need two ingredients:\n",
    "\n",
    "- Some code to share\n",
    "- An appropriate environment\n",
    "\n",
    "First create new github repo with the following file / directory structure:\n",
    "\n",
    "    binder/environment.yml\n",
    "    some-notebook.ipynb\n",
    "    Readme.md\n",
    "\n",
    "Fill out your environment.yml with your desired packages.\n",
    "(See the [lesson on python environments]({filename}/Lectures/python_environments.md) to review how to specify environments.)\n",
    "\n",
    "Then add some basic content to the notebook. For example, try importing the packages you might want to use:\n",
    "\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "    import xarray as xr\n",
    "    import cartopy.crs as ccrs\n",
    "    from matplotlib import pyplot as plt\n",
    "    \n",
    "Once things are working, push the repo to github. Then use <https://mybinder.org> to generate a binder badge and add it to your `Readme.md` file.\n",
    "\n",
    "You should now be able to run your binder on mybinder.org!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Updating your Binder\n",
    "\n",
    "An important thing to remember is that **you cannot save changes from within a running binder**. The running notebooks will automatically shut down after 10 minutes of inactivity, and you will **lose any modifications you made to the notebooks**. (See [mybinder FAQ](https://mybinder.readthedocs.io/en/latest/faq.html) for more details.) This is very different from our research computing jupyterhub, where all changes are saved. Binder is meant for demonstrating and sharing finished projects, not development of new ones.\n",
    "\n",
    "To update your binder, you need to go back to your personal copy of the repo, make changes, commit, and push back to github."
   ]
  },
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================================================
FILE: src/lectures/environment/intro_to_git.md
================================================
# Intro to Git for Version Control

## Overview

[_Version control_](https://en.wikipedia.org/wiki/Version_control) is a powerful way to organize, back up, and share with collaborators your research computing code.
A Verson control system keeps track of a set of files and saves snapshots (i.e. _versions_, _commits_) of the files at any point in time.
Using version control allows you to confidently make changes to your code (any any other files), with the ability to roll back to any previous state. This help avoid filling our directories up with files that look like this:

    my_code.py
    my_code_version2.py
    my_code_version2B-RPA-edit.py
    my_code_FINAL_VERSION.py
    my_code_THIS_IS_ACTUALLY_THE FINAL VERSION.py

Version control also allows you to share code with collaborators, make simultaneous edits, and merge your changes in a systematic, controlled way.

Version control has been used for a long time in software development.
More recently, it has become an essential part of modern data and computational science.
Our strong recommendation is that _all of your research code be stored in a version control system_.

The tool we will be using for version control is called [Git](https://git-scm.com).
Git is incredibly powerful--it also has a somewhat steep learning curve.
Fortunately, in this class, we will only be using a small subset of what git can do, avoiding the more complex aspects.

For a full-length tutorial, we recommend the article [Version Control with Git](http://swcarpentry.github.io/git-novice/) on Software Carpentry website.
Here we simply enumerate the most common git commands.


## Summary of useful Git commands

Set up your username and email

    git config --global user.name "Ryan Abernathey"
    git config --global user.email "rpa@ldeo.columbia.edu"


Create a new repository:

    cd my_project
    git init      

Stage files for addition to the repository:

    git add <filenames>  

Commit staged files:


    git commit -m "your brief commit message goes here"

Get information about your repository:

    git status    # tells you what files are staged, which ones have been modified, are new,... )
    git log       # view the commit log
    git diff      # view file content differences


Revert a file to an earlier version:

    git checkout <commit tag> <filenames>

## Using Git / GitHub from remote JupyterHub

The recommended way to move code in and out of a remote hub is via git / GitHub.
You should clone your project repo from the terminal and use git pull / git push to update and push changes.
In order to push data to GitHub from the hub, you will need to set up GitHub authentication.
[gh-scoped-creds](https://github.com/yuvipanda/gh-scoped-creds/) should be already setup
on your 2i2c managed JupyterHub, and we shall use that to authenticate to GitHub for
push / pull access.

Open a terminal in JupyterHub, run `gh-scoped-creds` and follow the prompts.

Alternatively, in a notebook, run the following code and follow the prompts:

```
import gh_scoped_creds
%ghscopedcreds
```

You should now be able to push to GitHub from the hub! These credentials will expire after
8 hours (or whenever your JupyterHub server stops), and you'll have to repeat these steps
to fetch a fresh set of credentials. Once you authenticate, you'll be provided with a link
to a [GitHub App](https://docs.github.com/en/developers/apps/getting-started-with-apps/about-apps)
that you have to [install](https://docs.github.com/en/developers/apps/managing-github-apps/installing-github-apps)
on the repositories you want to be able to push to from this particular JupyterHub. You only
need to do this once per JupyterHub, and can revoke access any time. You can always provide
access to your own personal repositories, but might need approval from admins of GitHub
organizations if you want to push to repos in that organization.


## Collaborating with Git and Github

* Create a new repository on GitHub
* Follow the instructions and run `git remote add origin <repo url>` on your local repo
  ````{warning}
  In order to authenticate with your SSH key from the previous section, you need use SSH-style GitHub urls.
  When setting up a new repo, underneath where it says "Quick setup — if you’ve done this kind of thing before",
  make sure you click the SSH box. The repo URL should look something like `git@github.com:rabernat/planets.git`
  (NOT `https://github.com/rabernat/planets.git`).

  If you already added a remote with `http://`, you can remove it by typing
  ```
  git remote rm origin
  ```
  (assuming the name of your remote is indeed `origin`.)
  ````
* make your changes and stage them with `git add`,
* commit your changes with `git commit -m`, and
* upload the changes to GitHub with` git push origin main`
* update your local repo with `git pull origin main`

All of the above commands are available from our course's cloud-based JupyterHub.
This is an excellent way to move code in and out of your cloud-based environment
(while simultaneously backing it up.)


================================================
FILE: src/lectures/environment/intro_to_jupyterlab.md
================================================
# Intro to JupyterLab

[JupyterLab](https://jupyterlab.readthedocs.io) will be our primary method for
interacting with the computer. JupyterLab contains a complete environment for
interactive scientific computing which runs in your web browser. Jupyter is an
open source python project that was started by scientists like yourselves who
wanted a more effective way to interact with their computers.

JupyterLab has excellent documentation. Rather than repeat that documentation
here, we point you to their docs. The following pages are particularly relevant:

- [The JupyterLab Interface](https://jupyterlab.readthedocs.io/en/stable/user/interface.html)
- [Working with Files](https://jupyterlab.readthedocs.io/en/stable/user/files.html)
- [The Text Editor](https://jupyterlab.readthedocs.io/en/stable/user/file_editor.html)
- [Notebooks](https://jupyterlab.readthedocs.io/en/stable/user/notebook.html)
- [Terminals](https://jupyterlab.readthedocs.io/en/stable/user/terminal.html)
- [Managing Kernels and Terminals](https://jupyterlab.readthedocs.io/en/stable/user/running.html)

You will gain experience and familiarity with JupyterLab over the course of the
semester as we use it in our weekly lectures and assignments.

## Markdown

Throughout the course, we will write rich text documents using Markdown.
Here are some useful references on Markdown syntax.

- [Official Markdown Documentation](https://daringfireball.net/projects/markdown/)
- [Markdown Guide / Basic Syntax](https://www.markdownguide.org/basic-syntax) -
  A more user-friendly syntax guide.

## Our Course JupyterHub

[JupyterHub](https://jupyter.org/hub) is multi-user Jupyter environment designed for companies, classrooms and research labs.
The Columbia version of this course uses a cloud-based JupyterHub environment
managed by [2i2c](https://2i2c.org/infrastructure/):

[![Launch JupyterHub](https://img.shields.io/badge/jupyterhub-us--central1--b.gcp.pangeo.io-orange?style=for-the-badge&logo=jupyter)]([https://pangeo.2i2c.cloud/](https://us-central1-b.gcp.pangeo.io/))


================================================
FILE: src/lectures/environment/intro_to_unix.md
================================================
# Intro to Unix

An introduction to basic Unix commands and the file system.

*The notes below are modified from the excellent [Unix Shell tutorial ](http://swcarpentry.github.io/shell-novice/) that is freely available on the Software Carpentry website. We highly recommend checking out the full version for further reading. The material is being used here under the terms of the [Creative Commons Attribution license](https://creativecommons.org/licenses/by/4.0/).*

---

## Navigating Files and Directories

Several commands are frequently used to create, inspect, rename, and delete files and directories.

To get started, open a terminal using the JupyterLab launcher. You will see
something like this.

~~~
jovyan@jupyter-rabernat:~$
~~~

The dollar sign is a **prompt**, which shows us that the shell is waiting for input.
`jovyan` is our **username** and `jupyter-rabernat` is the **hostname**.
Because we are using JupyterHub on the cloud, we all have the same username
("jovyan" = resident of the planet Jupyter). However, we each have a different
hostname (the name of the computer we are using), which corresponds to a
"virtual machine" running in the cloud. From now on, we will just use a `$`
to indicate the prompt.

To find out your username in general, you can use the command

~~~
$ whoami
jovyan
~~~

and to find out your hostname

~~~
$ hostname
jupyter-rabernat
~~~

Next,
let's find out where we are by running a command called `pwd`
(which stands for "print working directory").
At any moment,
our **current working directory**
is our current default directory,
i.e.,
the directory that the computer assumes we want to run commands in
unless we explicitly specify something else.
Here,
the computer's response is `/home/jovyan`,
which is the **home directory** of the user named `jovyan`.

~~~
$ pwd
~~~


~~~
/home/jovyan
~~~


To understand what a "home directory" is,
let's have a look at how the file system as a whole is organized.  For the
sake of this example, we'll be
illustrating the filesystem on our scientist Nelle's computer.  After this
illustration, you'll be learning commands to explore your own filesystem,
which will be constructed in a similar way, but not be exactly identical.  

On a Unix computer, the filesystem looks like something this:

![The File System](https://fsl.fmrib.ox.ac.uk/fslcourse/unix_intro/tree.gif)

At the top is the **root directory**
that holds everything else.
We refer to it using a slash character `/` on its own;
this is the leading slash in `/home/jovyan`.

Inside that directory are several other directories:
`bin` (which is where some built-in programs are stored),
`lib` (for the software "libraries" used by different programs),
`home` (where users' personal directories are located),
`etc` (system-wide configuration files),
and so on.  

Now let's learn the command that will let us see the contents of our
own filesystem.  We can see what's in our home directory by running `ls`,
which stands for "listing":

~~~
$ ls
~~~


~~~
config.yaml  examples  lost+found  work  worker-template.yaml
~~~

`ls` prints the names of the files and directories in the current directory in
alphabetical order,
arranged neatly into columns.
We can make its output more comprehensible by using the **flag** `-F`,
which tells `ls` to add a trailing `/` to the names of directories:

~~~
$ ls -F
~~~


~~~
config.yaml  examples/  lost+found/  work/  worker-template.yaml
~~~


`ls` has lots of other options. To find out what they are, we can type:

~~~
$ man ls
~~~

`man` is the Unix "manual" command:
it prints a description of a command and its options,
and (if you're lucky) provides a few examples of how to use it. To navigate through the `man` pages,
you may use the up and down arrow keys to move line-by-line,
or try the "b" and spacebar keys to skip up and down by full page.
Quit the `man` pages by typing "q".

Here,
we can see that our home directory contains mostly **sub-directories**.
Any names in your output that don't have trailing slashes,
are plain old **files**.

We can also use `ls` to see the contents of a different directory.  Let's take a
look at our `examples` directory by running `ls -F examples`,
i.e.,
the command `ls` with the **arguments** `-F` and `examples`.
The second argument --- the one *without* a leading dash --- tells `ls` that
we want a listing of something other than our current working directory:

~~~
$ ls -F examples
~~~

~~~
content/                                                LICENSE           publishconf.py
develop_server.sh*                                      Makefile          README.md
DONT_SAVE_ANYTHING_HERE.md                              pelicanconf.py    themes/
fabfile.py                                              pelican-plugins/
github_deploy_key_rabernat_research_computing_2018.enc  plugins/
~~~

The output is the list of all the files in the `examples` directory.
The examples directory on our JupyterHub is automatically populated with files
from the [Git Source Repository for the class](https://github.com/rabernat/research_computing_2018)

The command to change locations is `cd` followed by a
directory name to change our working directory.
`cd` stands for "change directory",
which is a bit misleading:
the command doesn't change the directory,
it changes the shell's idea of what directory we are in.

Let's say we want to move to the `content` directory we saw above.  We can
use the following series of commands to get there:

~~~
$ cd examples
$ cd content
~~~


These commands will move us from our home directory onto into
the `examples` directory, then into the `content` directory. `cd` doesn't print anything,
but if we run `pwd` after it, we can see that we are now
in `/home/jovyan/examples/content`.
If we run `ls` without arguments now,
it lists the contents of `/home/jovyan/examples/content`,
because that's where we now are.

We now know how to go down the directory tree, but
how do we go up?  
There is a shortcut in the shell to move up one directory level
that looks like this:

~~~
$ cd ..
~~~


`..` is a special directory name meaning
"the directory containing this one",
or more succinctly,
the **parent** of the current directory.
Sure enough,
if we run `pwd` after running `cd ..`, we're back in `/home/jovyan/examples`:

~~~
$ pwd
~~~

~~~
/home/jovyan/examples
~~~

e special directory `..` doesn't usually show up when we run `ls`.  If we want
to display it, we can give `ls` the `-a` flag:

~~~
$ ls -F -a
~~~


`-a` stands for "show all";
it forces `ls` to show us file and directory names that begin with `.`,
such as `..` (which, if we're in `/home/jovyan`, refers to the `/home` directory)
As you can see,
it also displays another special directory that's just called `.`,
which means "the current working directory".
It may seem redundant to have a name for it,
but we'll see some uses for it soon.

Note that in most command line tools, multiple parameters can be combined
with a single `-` and no spaces between the parameters: `ls -F -a` is
equivalent to `ls -Fa`.

These then, are the basic commands for navigating the filesystem on your computer:
`pwd`, `ls` and `cd`.  Let's explore some variations on those commands.  What happens
if you type `cd` on its own, without giving
a directory?  

~~~
$ cd
~~~


How can you check what happened?  `pwd` gives us the answer!  

~~~   
$ pwd
~~~


~~~
/home/jovyan
~~~

It turns out that `cd` without an argument will return you to your home directory,
which is great if you've gotten lost in your own filesystem.  

Let's try returning to the `content` directory from before.  Last time, we used
three commands, but we can actually string together the list of directories
to move to `data` in one step:

~~~
$ cd examples/content
~~~


Check that we've moved to the right place by running `pwd` and `ls -F`  

If we want to move up one level from the data directory, we could use `cd ..`.  But
there is another way to move to any directory, regardless of your
current location.  

So far, when specifying directory names, or even a directory path (as above),
we have been using **relative paths**.  When you use a relative path with a command
like `ls` or `cd`, it tries to find that location  from where we are,
rather than from the root of the file system.  

However, it is possible to specify the **absolute path** to a directory by
including its entire path from the root directory, which is indicated by a
leading slash.  The leading `/` tells the computer to follow the path from
the root of the file system, so it always refers to exactly one directory,
no matter where we are when we run the command.

This allows us to move to our `examples` directory from anywhere on
the filesystem.  To find the absolute path
we're looking for, we can use `pwd` and then extract the piece we need
to move to `examples`.  

~~~
$ pwd
~~~


~~~
/home/jovyan/examples/content
~~~


~~~
$ cd /home/jovyan/examples
~~~


Run `pwd` and `ls -F` to ensure that we're in the directory we expect.  

#### Two More Shortcuts

The shell interprets the character `~` (tilde) at the start of a path to
mean "the current user's home directory". For example, if Nelle's home
directory is `/home/jovyan`, then `~/examples` is equivalent to
`/home/jovyan/examples`. This only works if it is the first character in the
path.

Another shortcut is the `-` (dash) character.  `cd` will translate `-` into
*the previous directory I was in*, which is faster than having to remember,
then type, the full path.  This is a *very* efficient way of moving back
and forth between directories. The difference between `cd ..` and `cd -` is
that the former brings you *up*, while the latter brings you *back*. You can
think of it as the *Last Channel* button on a TV remote.

#### Tab Completion

Typing the full path to directories and files can be slow and annoying.
Fortunately, we have "tab completion" to help us. Try typing `cd ex` and then
press the `<tab>`. The system will try to "auto complete" your command.
Pressing tab twice brings up a list of all the files, and so on.
This is called **tab completion**,
and we will see it in many other tools as we go on.

#### Key Points:
- "The file system is responsible for managing information on the disk."
- "Information is stored in files, which are stored in directories (folders)."
- "Directories can also store other directories, which forms a directory tree."
- "`cd path` changes the current working directory."
- "`ls path` prints a listing of a specific file or directory; `ls` on its own lists the current working directory."
- `pwd` prints the user's current working directory.
- `whoami` shows the user's current identity.
- `/` on its own is the root directory of the whole file system.
- A relative path specifies a location starting from the current location.
- An absolute path specifies a location from the root of the file system.
- Directory names in a path are separated with '/' on Unix, but '\\\\' on Windows.
- '..' means 'the directory above the current one'; '.' on its own means 'the current directory'.
- Most files' names are `something.extension`. The extension isn't required, and doesn't guarantee anything, but is normally used to indicate the type of data in the file.
- Most commands take options (flags) which begin with a '-'.

## Working with Files and Directories

We now know how to explore files and directories,
but how do we create them in the first place?
Let's go back to our home directory
and use `ls -F` to see what it contains:

~~~
$ cd
$ pwd
~~~


~~~
/home/jovyan
~~~


~~~
$ ls -F
~~~


~~~
config.yaml  examples/  lost+found/  work/  worker-template.yaml
~~~


Let's create a new directory called `thesis` using the command `mkdir thesis`
(which has no output):

~~~
$ mkdir thesis
~~~


As you might guess from its name,
`mkdir` means "make directory".
Since `thesis` is a relative path
(i.e., doesn't have a leading slash),
the new directory is created in the current working directory:

~~~
$ ls -F
~~~


~~~
config.yaml  examples/  lost+found/  thesis/  work/  worker-template.yaml
~~~


## Good names for files and directories

 Complicated names of files and directories can make your life painful
 when working on the command line. Here we provide a few useful
 tips for the names of your files.

 1. Don't use whitespaces.

    Whitespaces can make a name more meaningful
   but since whitespace is used to break arguments on the command line
   is better to avoid them on name of files and directories.
    You can use `-` or `_` instead of whitespace.

 2. Don't begin the name with `-` (dash).

    Commands treat names starting with `-` as options.

 3. Stick with letters, numbers, `.` (period), `-` (dash) and `_` (underscore).

    Many other characters have special meanings on the command line.
    We will learn about some of these during this lesson.
    There are special characters that can cause your command to not work as
    expected and can even result in data loss.

 If you need to refer to names of files or directories that have whitespace
 or another non-alphanumeric character, you should surround the name in quotes (`""`).


Since we've just created the `thesis` directory, there's nothing in it yet:

~~~
$ ls -F thesis
~~~


Let's change our working directory to `thesis` using `cd`.
We then create a blank new file called `draft.txt` using the `touch command`:

~~~
$ cd thesis
$ touch draft.txt
~~~

Now we can edit the file in JupyterLab's text editor.
Let's type in a few lines of text.
Once we're happy with our text, we save the file, and
return to the shell.

`ls` now shows that we have created a file called `draft.txt`:

~~~
$ ls
draft.txt
~~~

Let's tidy up by running `rm draft.txt`:

~~~
$ rm draft.txt
~~~


This command removes files (`rm` is short for "remove").
If we run `ls` again,
its output is empty once more,
which tells us that our file is gone:

~~~
$ ls
~~~


#### Deleting Is Forever

The Unix shell doesn't have a trash bin that we can recover deleted
files from (though most graphical interfaces to Unix do).  Instead,
when we delete files, they are unhooked from the file system so that
their storage space on disk can be recycled. Tools for finding and
recovering deleted files do exist, but there's no guarantee they'll
work in any particular situation, since the computer may recycle the
file's disk space right away.


Let's re-create that file
and then move up one directory to `/home/jovyan` using `cd ..`:

~~~
$ touch draft.txt
$ cd ..
~~~


If we try to remove the entire `thesis` directory using `rm thesis`,
we get an error message:

~~~
$ rm thesis
~~~


~~~
rm: cannot remove `thesis`: Is a directory
~~~

This happens because `rm` by default only works on files, not directories.

To really get rid of `thesis` we must also delete the file `draft.txt`.
We can do this with the [recursive](https://en.wikipedia.org/wiki/Recursion) option for `rm`:

~~~
$ rm -r thesis
~~~


#### With Great Power Comes Great Responsibility

 Removing the files in a directory recursively can be very dangerous
 operation. If we're concerned about what we might be deleting we can
 add the "interactive" flag `-i` to `rm` which will ask us for confirmation
 before each step

~~~
 $ rm -r -i thesis
 rm: descend into directory ‘thesis’? y
 rm: remove regular file ‘thesis/draft.txt’? y
 rm: remove directory ‘thesis’? y
~~~


 This removes everything in the directory, then the directory itself, asking
 at each step for you to confirm the deletion.


Let's create that directory and file one more time.

~~~
$ mkdir thesis
$ touch thesis/draft.txt
$ ls thesis
~~~


~~~
draft.txt
~~~


`draft.txt` isn't a particularly informative name,
so let's change the file's name using `mv`,
which is short for "move":

~~~
$ mv thesis/draft.txt thesis/quotes.txt
~~~


The first parameter tells `mv` what we're "moving",
while the second is where it's to go.
In this case,
we're moving `thesis/draft.txt` to `thesis/quotes.txt`,
which has the same effect as renaming the file.
Sure enough,
`ls` shows us that `thesis` now contains one file called `quotes.txt`:

~~~
$ ls thesis
~~~


~~~
quotes.txt
~~~

One has to be careful when specifying the target file name, since `mv` will
silently overwrite any existing file with the same name, which could
lead to data loss. An additional flag, `mv -i` (or `mv --interactive`),
can be used to make `mv` ask you for confirmation before overwriting.

Just for the sake of consistency,
`mv` also works on directories

Let's move `quotes.txt` into the current working directory.
We use `mv` once again,
but this time we'll just use the name of a directory as the second parameter
to tell `mv` that we want to keep the filename,
but put the file somewhere new.
(This is why the command is called "move".)
In this case,
the directory name we use is the special directory name `.` that we mentioned earlier.

~~~
$ mv thesis/quotes.txt .
~~~


The effect is to move the file from the directory it was in to the current working directory.
`ls` now shows us that `thesis` is empty:

~~~
$ ls thesis
~~~


Further,
`ls` with a filename or directory name as a parameter only lists that file or directory.
We can use this to see that `quotes.txt` is still in our current directory:

~~~
$ ls quotes.txt
~~~


~~~
quotes.txt
~~~


The `cp` command works very much like `mv`,
except it copies a file instead of moving it.
We can check that it did the right thing using `ls`
with two paths as parameters --- like most Unix commands,
`ls` can be given multiple paths at once:

~~~
$ cp quotes.txt thesis/quotations.txt
$ ls quotes.txt thesis/quotations.txt
~~~


~~~
quotes.txt   thesis/quotations.txt
~~~


To prove that we made a copy,
let's delete the `quotes.txt` file in the current directory
and then run that same `ls` again.

~~~
$ rm quotes.txt
$ ls quotes.txt thesis/quotations.txt
~~~


~~~
ls: cannot access quotes.txt: No such file or directory
thesis/quotations.txt
~~~

This time it tells us that it can't find `quotes.txt` in the current directory,
but it does find the copy in `thesis` that we didn't delete.

### Key Points
- `cp old new` copies a file.
- `mkdir path` creates a new directory.
- `mv old new` moves (renames) a file or directory.
- `rm path` removes (deletes) a file.
- Use of the Control key may be described in many ways, including `Ctrl-X`, `Control-X`, and `^X`.
- The shell does not have a trash bin: once something is deleted, it's really gone.
- Depending on the type of work you do, you may need a more powerful text editor than Nano.

## Learning More

The goal of this lesson was to familiarize you with the basics of working
with files and directories.
There is a **lot more** to the unix shell and filexsystem than what we have  covered here!
To ge deeper with self study, we recommend the excellent
[Software Carpentry Unix Shell Lesson](https://swcarpentry.github.io/shell-novice/),
on which the above material was based.


================================================
FILE: src/lectures/environment/python_environments.md
================================================
# Managing Python Environments

_Note: many elements in this guide 2343 adapted from
[Daniel Rothenberg](http://danielrothenberg.com/)'s excellent
[getting started guide](http://danielrothenberg.com/gcpy/getting_started.html)._

## Python

Python and nearly all of the software packages in the scientific python
ecosystem are [open-source](https://opensource.org/).
They are maintained and developed by a community of scientists and programmers,
some of whose work is supported by universities, non-profits, and for-profit
corporations.
This work mostly happens in the open, via [github](http://github.com/) and other
online collaboration platforms.

Because there is no single authority which controls all of these packages, it
can be intimidating for new users to figure out how to set up a functional
scientific python environment.
Coordinating the compatibility between these different packages and their
multiple versions used to be a nightmare!
Fortunately, the problem is solved completely by using a Python _distribution_
and / or a Python _package manager_ (described in detail below).

One important rule about scientific computing in Python is this:

**Do not use your system Python installation!**

The version of Python that ships with operating systems such as Red Hat Linux and
macOS is usually outdated and configured to support system functions. Although it
is entirely possible to install and use the necessary packages using the system
Python, it's much more practical on both
your local machine and any other computers you work with to use a specialized Python
installation.

```{note}
Everything in this course uses Python 3, the latest version of Python.
Python 2 is similar, but has some important differences that can make
Python 2 code incompatible with Python 3.
Python 2 was officially deprecated (declared obsolete) on
[Jan 1, 2020](https://www.python.org/doc/sunset-python-2/)
```

## Recommended Installation Method: Use Anaconda Python

The easiest way to set up a full-stack scientific Python deployment is to use a
Python distribution. This is an installation of Python with a set of curated
packages which are guaranteed to work together.
For this class, we recommend the
**[Anaconda Python Distribution](https://www.anaconda.com/download/)**.
Follow the link above to obtain a one-click installer for Linux, Mac, or
Windows.
(Make sure you select the Python 3 installer, not Python 2.7.)
In addition to the packages themselves, Anaconda includes a graphical
utility to help manage any packages you may want to install which are not
already included in the default inclusion list.

## Accessing the Command Line

These notes assume you have access to a Unix command line to type in shell commands.
For Linux and MacOS users, you can access the command line by opening the _terminal_ program.
It's more difficult for Windows users.
For Windows users, you should first install Anaconda (described above) or Miniconda (described below), which gives you access to the "Anaconda Prompt" desktop app.
(Instructions for this are given on the [Andaconda Website](https://docs.anaconda.com/anaconda/user-guide/getting-started/#write-a-python-program-using-anaconda-prompt-or-terminal).)
From the Anaconda Prompt, you should be able to run `conda` and other shell commands.

## Lightweight Alternative: Miniconda

If you are installing python on a remote machine via ssh, or you simply don't
want to download a huge file like the Anaconda Python Distribution, there is a
lightweight alternative installation method.

1. **Obtain a minimal Python installer**
  We recommend the [Miniconda](https://conda.io/miniconda.html) installer;
  this provides a Python install for your operating system, plus the **conda**
  package manager. This way, you can only install the packages you *want*.

2. **Run the installer**
  You'll probably need to do this from the command line, e.g.

        $ sh Miniconda3-latest-MacOSX-x86_64.sh

  Follow the instructions; you can choose where to place the installation (
  preferably somewhere you have write access without super-user/root access,
  like your home directory). At the end of this process, add this path to your
  \*rc configuration

        $ echo ". $HOME/miniconda3/etc/profile.d/conda.sh" > ~/.bashrc

  If you do this, your ``$PYTHONPATH`` will be implicitly configured correctly
  and you will never have to touch it.

3. **Create an environment and install packages.**
  This is described in the next section.

## Managing Environments with Conda

Python coupled with a package manager provides a way to make isolated,
reproducible *environments* where you have fine-tuned control over all packages
and configuration. **You should always work within an environment**, rather
than the "default" environment.

It is strongly recommended to read official
[Getting Started with Conda](https://conda.io/docs/user-guide/getting-started.html#)
guide.

To create a conda environment, you execute the following command:

    $ conda create --name my_environment python=3.9 numpy

This will create a special environment in ``$MINICONDA_HOME/envs/my_environment``
with only Python and numpy to begin with. Here, we've also told conda to install
Python version 3.9; you can specify exact versions or minima, and conda will
take care of figuring out all the compatibilities between versions for you. To use
this environment, simply "activate" it by executing:

    $ conda activate my_environment

Regardless of your shell, you should now see the string ``(my_environment)``
prepended to your prompt. Now, if you execute any Python-related tool from the
command line, it will first search in ``$MINICONDA_HOME/envs/my_environment/bin``
to find them.

You can deactivate your environment by typing:

    $ conda deactivate

To see all the environments on your system:

    $ conda info --envs

If you want to permanently remove an environment and delete all the data
associated with it:

    $ conda env remove --name my_environment --all

For extensive documentation on using environments, please see
[the conda documentation](https://conda.io/docs/using/envs.html). The most
important feature to review here is the ability to *share and export* your
environment; this is the basis for reproducibility in the scientific Python stack.
At any time from the shell, you can execute

    $ conda list

to get a complete summary of all the packages installed in your environment, the
channel they were installed from, and their full version info. Using this info,
you can create an **environment file** in
[YAML syntax](http://docs.ansible.com/ansible/latest/YAMLSyntax.html)
which documents the exact
contents of your environment. With that file, a new environment with the exact
configuration can be installed by executing

    $ conda env create -f my_environment.yml

Below we will see an example of an environment file.

## Installing More Packages

Once you have a basic Python environment, you can easily add or remove packages
using conda.
Conda was created to help manage the complex dependencies and
pre-compiled binary libraries that are necessary in scientific python.

If you set up your python environment using the Anaconda Python Distribution or
with miniconda, you should already have the **conda** command available on
the command line.
With it, you can easily install packages from an official, curated set of packages which are
built and tested for a number of different system configurations on Linux,
Windows, and macOS

    $ conda install <package-name>

Additionally, there is a
community-maintained collection of packages/recipes called
[conda forge](https://conda-forge.github.io/>)
which is accessible through conda as a special "channel"

    $ conda install -c conda-forge <package-name>

While conda allows you to install almost any science-related package, there may
be other general-use python packages you wish to you that are not available in
via conda. For these, you can use an alternative installation method.

Outside of the scientific python community,
the most common way to install packages is to search for them on the official
[PyPI](https://pypi.python.org/pypi) index. Once you've found the package you
want to install (you may have also just found it on github or elsewhere), you
use the **pip** command from a the command line:

    $ pip install <package-name>

This will fetch the source code, build it, and install it to wherever your
``$PYTHONPATH`` is set. This works in the vast majority of cases, particularly
when the code you're installing doesn't have any compiled dependencies.

If you can't find a package on either PyPI or conda-forge, you can always install it
directly from the source code. If the package is on github, ``pip`` already has
an alias to do this for you:

    $ pip install git+https://github.com/<user>/<package-name>.git

## Channels and Conda Forge

Where do conda packages come from?
The packages are hosted on conda ["channels"](https://conda.io/projects/conda/en/latest/user-guide/concepts/channels.html). From the conda docs:

> Conda channels are the locations where packages are stored. They serve as the base for hosting and managing packages. Conda packages are downloaded from remote channels, which are URLs to directories containing conda packages. The conda command searches a set of channels. By default, packages are automatically downloaded and updated from the default channel https://repo.anaconda.com/pkgs/ which may require a paid license, as described in the repository [terms of service](https://www.anaconda.com/terms-of-service) a commercial license. The conda-forge channel is free for all to use. You can modify what remote channels are automatically searched. You might want to do this to maintain a private or internal channel. For details, see how to modify your channel lists.
>
> Conda-forge is a community channel made up of thousands of contributors. Conda-forge itself is analogous to PyPI but with a unified, automated build infrastructure and more peer review of recipes.

Don't be worried by the "commercial license part".
The Anaconda channel terms of service clearly excludes all educational activities
and all research activities at non-profit institutions from their definition of commercial usage.
Even companies with fewer than 200 employees are excluded.
The aim of the commercial paid license for Anaconda is to require large corporations
which use the repository heavily to contribute financially to its maintenance and development.
Without such contributions, Anaconda might not be able to sustain itself.

Despite this, we still recommend **always using the conda-forge channel for your python environments**.
The reason are as follows:
- Conda Forge is always free from commercial license restrictions
- Conda Forge generally has the largest volume of packages and the most up-to-date versions
- You can contribute your own packages to conda forge! This is not covered by this book,
  but you can read about it in the [Conda Forge docs](https://conda-forge.org/docs/maintainer/adding_pkgs.html).

A simple way to use the conda forge channel is to pass the `-c` option when you run conda:

    $ conda install -c conda-forge  {package_name}

As shown below, you can also add conda-forge to your `environment.yml` file.

## Speeding things up with Mamba

In order to put together an actual python environment from your package specifications,
conda has to solve a difficult puzzle.
Each package specified has certain dependencies on other packages.
For example, Xarray depends on Numpy, Pandas, and several others.
Moreoever, each version of Xarray requires certain minimum versions of other
packages (e.g. Xarray 0.19 requires Numpy >= 1.17 and Pandas >= 1.0).
Other packages in your environment may have different or incompatible versions.
Finding a combination of packages that are mutually compatible can be framed
mathematically as a [boolean satisfiability problem](https://en.wikipedia.org/wiki/Boolean_satisfiability_problem).

The default "solver" of this problem for conda can be [slow](https://www.anaconda.com/blog/understanding-and-improving-condas-performance)
It is not unheard of to spend 30 minutes or more solving large environments! 😱
Fortunately, a much faster alternative called [mamba](https://github.com/mamba-org/mamba)
has recently come out.
To install it, just run:

    conda install -n base -c conda-forge mamba

Now you can install environments and packages as before, but using the `mamba` command
instead of `conda`. Everything will be faster.


## Pangeo Python Environment

The environment on our cloud JupyterHub is a highly curated combination of packages
maintained by the [Pangeo Project](http://pangeo.io/).
Then environment lives at <https://github.com/pangeo-data/pangeo-docker-images/>.
In addition to just specifying a combination of packages, this repo automatically
builds [Docker containers](https://docs.docker.com/get-started/).

The latest Pangeo notebook environment lives at <https://github.com/pangeo-data/pangeo-docker-images/blob/master/pangeo-notebook/environment.yml>.
Below are the contents of that file as of 2021-11-04.
Copy and paste the following ``environment.yml`` file somewhere
on your local hard drive:

    name: pangeo
    channels:
     - conda-forge
    dependencies:
     - python=3.9*
     - pangeo-notebook=2021.09.30
     - pip=20
     - adlfs
     - awscli
     - boto3
     - bottleneck
     - cartopy >= 0.20.0
     - cfgrib
     - cmip6_preprocessing
     - ciso
     - dask-ml
     - datashader
     - descartes
     - earthdata
     - eofs
     - erddapy
     - esmpy
     - fastjmd95
     - fsspec
     - fsspec-reference-maker
     - gcsfs
     - gh
     - geocube
     - geopandas
     - geopy
     - geoviews-core
     - gsw
     - h5netcdf
     - h5py
     - holoviews
     - hvplot
     - intake
     - intake-esm
     - intake-geopandas
     - intake-stac
     - intake-xarray
     - ipyleaflet
     - ipytree
     - ipywidgets
     - jupyter-panel-proxy
     - jupyter-resource-usage
     - lz4
     - matplotlib-base
     - metpy
     - nb_conda_kernels
     - nbstripout
     - nc-time-axis
     - netcdf4
     - nomkl
     - numcodecs
     - numpy
     - pandas
     - panel
     - parcels
     - param!=1.11.0
     - prefect
     - pyarrow
     - pycamhd
     - pydap
     - pygeos
     - pystac
     - python-blosc
     - python-gist
     - python-graphviz
     - rasterio
     - rechunker
     - rio-cogeo
     - rioxarray
     - rise
     - s3fs>0.5
     - sat-search
     - sat-stac
     - satpy
     - scikit-image
     - scikit-learn
     - scipy
     - sparse
     - stackstac
     - tiledb-py
     - timezonefinder
     - xarray
     - xarrayutils
     - xarray_leaflet
     - xarray-spatial
     - xcape
     - xcube
     - xesmf
     - xgcm
     - xhistogram
     - xmitgcm
     - xpublish
     - xrft
     - xskillscore
     - zarr


Create this environment using mamba

    $ mamba env create -f environment.yml

Activate this environment

    $ conda activate pangeo

This environment should be sufficient for all of your work in this class.


================================================
FILE: src/lectures/introduction.md
================================================
Title: Course Introduction
Date: 9/4/2018
Category: Lectures
Author: Ryan Abernathey

Here is the Keynote presentation given on the first day of class:

<script async class="speakerdeck-embed" data-id="2fdb253ed5e94e409c46fd6e166cf0e7" data-ratio="1.33333333333333" src="//speakerdeck.com/assets/embed.js"></script>


================================================
FILE: src/lectures/mapping_cartopy.ipynb
================================================
{
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   "cell_type": "markdown",
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    "# Maps in Scientific Python\n",
    "\n",
    "Making maps is a fundamental part of geoscience research.\n",
    "Maps differ from regular figures in the following principle ways:\n",
    "\n",
    "- Maps require a *projection* of geographic coordinates on the 3D Earth to the 2D space of your figure.\n",
    "- Maps often include extra decorations besides just our data (e.g. continents, country borders, etc.)\n",
    "\n",
    "Mapping is a notoriously hard and complicated problem, mostly due to the complexities of projection.\n",
    "\n",
    "In this lecture, we will learn about [Cartopy](https://scitools.org.uk/cartopy/docs/latest/), one of the most common packages for making maps within python. Another popular and powerful library is [Basemap](https://matplotlib.org/basemap/); however, Basemap is [going away](https://matplotlib.org/basemap/users/intro.html#cartopy-new-management-and-eol-announcement) and being replaced with Cartopy in the near future. For this reason, new python learners are recommended to learn Cartopy.\n",
    "\n",
    "### Credit: Phil Elson\n",
    "\n",
    "Lots of the material in this lesson was adopted from [Phil Elson](https://pelson.github.io/)'s excellent [Cartopy Tutorial](https://github.com/SciTools/cartopy-tutorial). Phil is the creator of Cartopy and published his tutorial under an [open license](http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/), meaning that we can copy, adapt, and redistribute it as long as we give proper attribution. **THANKS PHIL!** 👏👏👏"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background: Projections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most of our media for visualization *are* flat\n",
    "\n",
    "Our two most common media are flat:\n",
    "\n",
    " * Paper\n",
    " * Screen\n",
    "\n",
    "![](https://raw.githubusercontent.com/SciTools/cartopy-tutorial/master/static/flat_medium.jpg)\n",
    "\n",
    "### [Map] Projections: Taking us from spherical to flat\n",
    "\n",
    "A map projection (or more commonly refered to as just \"projection\") is:\n",
    "\n",
    "> a systematic transformation of the latitudes and longitudes of locations from the surface of a sphere or an ellipsoid into locations on a plane. [[Wikipedia: Map projection](https://en.wikipedia.org/wiki/Map_projection)].\n",
    "\n",
    "### The major problem with map projections\n",
    "\n",
    "![orange peel](https://raw.githubusercontent.com/SciTools/cartopy-tutorial/master/static/orange_peel.jpg)\n",
    "\n",
    " * The surface of a sphere is topologically different to a 2D surface, therefore we *have* to cut the sphere *somewhere*\n",
    " * A sphere's surface cannot be represented on a plane without distortion.\n",
    " \n",
    "There are many different ways to make a projection, and we will not attempt to explain all of the choices and tradeoffs here. Instead, you can read Phil's [original tutorial](https://github.com/SciTools/cartopy-tutorial/blob/master/tutorial/projections_crs_and_terms.ipynb) for a great overview of this topic.\n",
    "Instead, we will dive into the more practical sides of Caropy usage."
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introducing Cartopy\n",
    "\n",
    "https://scitools.org.uk/cartopy/docs/latest/\n",
    "\n",
    "Cartopy makes use of the powerful [PROJ.4](https://proj4.org/), numpy and shapely libraries and includes a programatic interface built on top of Matplotlib for the creation of publication quality maps.\n",
    "\n",
    "Key features of cartopy are its object oriented projection definitions, and its ability to transform points, lines, vectors, polygons and images between those projections.\n",
    "\n",
    "### Cartopy Projections and other reference systems\n",
    "\n",
    "In Cartopy, each projection is a class.\n",
    "Most classes of projection can be configured in projection-specific ways, although Cartopy takes an opinionated stance on sensible defaults.\n",
    "\n",
    "Let's create a Plate Carree projection instance.\n",
    "\n",
    "To do so, we need cartopy's crs module. This is typically imported as ``ccrs`` (Cartopy Coordinate Reference Systems)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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   "source": [
    "import cartopy.crs as ccrs\n",
    "import cartopy"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cartopy's projection list tells us that the Plate Carree projection is available with the ``ccrs.PlateCarree`` class:\n",
    "\n",
    "https://scitools.org.uk/cartopy/docs/latest/crs/projections.html\n",
    "\n",
    "**Note:** we need to *instantiate* the class in order to do anything projection-y with it!"
   ]
  },
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       "\" style=\"fill:none;stroke:#b0b0b0;stroke-width:0.8;\"/>\n",
       "   </g>\n",
       "   <g id=\"patch_3\">\n",
       "    <path d=\"M 7.2 146.7 \n",
       "L 7.2 7.2 \n",
       "L 286.2 7.2 \n",
       "L 286.2 146.7 \n",
       "L 7.2 146.7 \n",
       "\" style=\"fill:none;stroke:#000000;stroke-linejoin:miter;stroke-width:0.8;\"/>\n",
       "   </g>\n",
       "  </g>\n",
       " </g>\n",
       " <defs>\n",
       "  <clipPath id=\"pb602600efb\">\n",
       "   <path d=\"M 7.2 146.7 \n",
       "L 7.2 7.2 \n",
       "L 286.2 7.2 \n",
       "L 286.2 146.7 \n",
       "L 7.2 146.7 \n",
       "\"/>\n",
       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n",
       "<pre>&lt;cartopy.crs.PlateCarree object at 0x7fc4bc764dc0&gt;</pre>"
      ],
      "text/plain": [
       "<Projected CRS: +proj=eqc +ellps=WGS84 +a=6378137.0 +lon_0=0.0 +to ...>\n",
       "Name: unknown\n",
       "Axis Info [cartesian]:\n",
       "- E[east]: Easting (unknown)\n",
       "- N[north]: Northing (unknown)\n",
       "- h[up]: Ellipsoidal height (metre)\n",
       "Area of Use:\n",
       "- undefined\n",
       "Coordinate Operation:\n",
       "- name: unknown\n",
       "- method: Equidistant Cylindrical\n",
       "Datum: unknown\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccrs.PlateCarree()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drawing a map\n",
    "\n",
    "Cartopy optionally depends upon matplotlib, and each projection knows how to create a matplotlib Axes (or AxesSubplot) that can represent itself.\n",
    "\n",
    "The Axes that the projection creates is a [cartopy.mpl.geoaxes.GeoAxes](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes). This Axes subclass overrides some of matplotlib's existing methods, and adds a number of extremely useful ones for drawing maps.\n",
    "\n",
    "We'll go back and look at those methods shortly, but first, let's actually see the cartopy+matplotlib dance in action:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<GeoAxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "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": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.axes(projection=ccrs.PlateCarree())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was a little underwhelming, but we can see that the Axes created is indeed one of those GeoAxes[Subplot] instances.\n",
    "\n",
    "One of the most useful methods that this class adds on top of the standard matplotlib Axes class is the ``coastlines`` method. With no arguments, it will add the Natural Earth ``1:110,000,000`` scale coastline data to the map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fc448ad7e80>"
      ]
     },
     "execution_count": 4,
     "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": [
    "plt.figure()\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could just as equally created a matplotlib subplot with one of the many approaches that exist. For example, the ```plt.subplots``` function could be used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fc453f68c10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RAPr3749Dhw4VYHKdndjYWPj5+SE5ORlnzpxBbGws6tSpg7p160JDQwP16tWDhYVFodv/FE5OTggMDESDBg3Qt29fBAYGYseOHVBWVhYd84H3PrgaGhrQ1NREZmYmwsLCYGNjg379+iEzMxOnTp2Ch4cH9uzZgz/++AOvX7/GyZMnceLECVhZWUGhUODx48eiC1haWhoGDRqE9evXY9SoUdi/fz9+/vlnrFmzBsnJyahYsWK2165FixZBR0cHDRs2RJ06deDv748LFy5g+fLlsLKygpKSEqZNm4bmzZujatWqol9lfvjgPwy89xFdtGiRaJMuDlatWoWZM2fC3t4eL1++xI8//ogNGzYUW39FRXR0NA4ePAiSyMjIwLt372BlZYVmzZrB1dW1SBauBEEQv/cFCxagatWqGDx4MMaMGYOlS5fm6f+6Z88ezJo1C+3bt8fu3bvFxVMAqFixItLS0gAAS5cuReXKlUESixYtgrq6Op4/f/7ZspcEt2/fRsOGDTF69Gg4Ojpi4cKFUFZWhpGREUJDQ2FoaAgDAwMEBgYCeP+7/WDGi4+PF23TsbGxiIuLw5MnT7B9+3Y0a9YMXbp0gYWFBVJSUpCSkoK0tDTo6urCysoKbdq0+WwzyD///INevXohKyurcDZdExMT2tvbY/Dgwfjxxx8L1HlYWBjmzp2LK1euIC4uDm5ubtDS0kLbtm1hZWWF8+fP48WLF7h//z5iYmJgYGCA9PR02NnZwc7ODrq6ujA1NUVWVpa4DdXe3h62trawtLQUFxBI4u3bt9DQ0MCrV6/g5eWF58+fw8bGBtu3b0ejRo0wdepUnD9/Hpqamrh27RqsrKzQo0cPtGjRAjo6OrnK/8cff+RYMADeL8RMmjQJJ06cgFwuR4cOHfDrr79i165d8Pf3R0JCAoyNjUUvhrZt2yIyMhIXL15EtWrVAEC0n8nlcpiamqJSpUqoXbs2VFRUEBwcjLCwMDRt2hStW7dGxYoVERgYiKysLCQmJmL9+vV49OgRgoOD0b9/f2zevDmHj29uKBQK1KxZEyEhIUhPTwcAbN26FWPGjCnQ95offH190ahRo2zHnJyc8OzZsyLv63NIS0vDqVOncPv2bTx58gTKysq4ffs2unTpAn19fairq8PS0hKvXr3Cpk2bALz39x0wYADMzMw+q+/IyEjcu3cPa9euRXh4OB4/fgwA2LJlC8aOHYsHDx4gODgYlpaWqFSpEszNzaGkpIT69eujbdu2WLFihdjWhg0bMGXKFCgUCvz555+QyWQYOnRokW40KWkiIyPx+++/w8/PD7NmzYKdnR3i4uJga2uLmJgYhIeHo0aNGkhLS8vX1vH4+HhcuHAB586dQ2JiIrS0tKCtrQ1NTU0kJibi7t27qF27NrZv347Q0FBkZGSI37+qqirS09NhZGSE27dv49ChQzAxMYGZmRkqVqyIzMxM6OnpoUaNGrh16xaGDRsGAIVTuhYWFvz999/RuXPnfF2o+/fv4+zZs0hJScGff/6JDh06YOrUqbC1tc1z1VAul4tbSV+9eoXg4GAkJycjPDwcGhoayMzMREBAAF69eoXXr18jKysLHTp0gKWlJY4ePYqMjAwoFAqYm5ujffv2cHJygo+PD1q1aoW4uDj8+uuvCA0NFW9aY2Nj3Lp1CwYGBnB2dsbt27dFdyYdHR2MGDECPXr0gEwmQ2JiIgIDA+Ht7Y3w8HDo6+ujUaNGePbsGU6ePAkA+OGHH7Bz585sYzI1NcVff/2FTZs2iW8JXbp0gaurK9zd3bFt2zZ4eXlBS0sLiYmJSElJgYuLC2rWrAljY2McP34cr1+/xl9//YW2bdvmeDj88ccfGDp0KIYOHYq9e/fm6/sZO3YsDh48iOTkZAiCgLp162aLuVAUJCYm4tSpUxg9erSo3OvXr49Lly4V+2aI/BIdHY3Nmzdj69atcHV1RcuWLVGzZk0A7zdI2Nra5qizcuVKLF68GK1atcK///4La2trmJmZwcLCAq1atUKXLl1gYmIilpfL5Xj37h0sLS3Fmeh/kcvlCAgIwLlz59C2bVvUrFkTMpkMAQEBCAsLw9ChQwEAdevWxZs3b6CiooI2bdrAy8sL4eHhGDVqFNatWwctLS0cPHgQgwYNAvDxyYJE3vj5+WHgwIEIDQ2FtbU1KlasiIyMDISFhUEul0NNTQ0uLi7i4tyMGTMQFRWFtLQ0aGhoIC4uDo8fP0ZERARatmwJT0/Pwi2kaWlpUVlZmSYmJly2bBnfvHnzKTsGAdDU1JQXL16kIAhFbishyTdv3nDHjh1ctGgR7969m69+BEHIZg/Kysris2fPePLkSa5evZr9+vUT5Tc3N6eVlRXt7Ozo6OgoHm/SpAnNzc1paWlJKysr1qtXj/v27eOqVavEhTRVVVUCoIGBAf/991+SZEJCArW0tLhp0yaqqKjw6NGjtLGx4cmTJ0m+d0QHwFWrVony7dq1iyoqKtTW1qaWlhZ79+7N+Ph48Xzbtm1FG/Dw4cPp4eGRbXwfw8PDgwDYsmXLz15Ye/nyJceOHcvmzZuzTp061NfXp6amJlu3bs21a9fy6dOnn9V+URMTE8PRo0dTX1+fP/zwQ6HlS0lJ4d27d+np6cnt27ezd+/e1NPTY+PGjbljxw4qFAqeOHEi2wLPtWvX+O+//3LJkiVs3749dXV1xUUn/N+GDH19fTZq1IitWrVihQoVuHbtWpLv793Xr19zy5Yt4vcOgJcuXSJJhoeH08zMjADo5uZWZNfra+RjC25ZWVnct28fe/fuzW3btn20fmJiIv/555/PW0hTKBS8cuUKR40aJX7ZXbp04aBBg3jo0CHK5XKxw6SkJO7du1cst2LFCmZkZBTFtSh2ZDIZhw8fzh49enDo0KEcO3YsZ8yYwZ9++onKysrcuHEjSVIul/PcuXM8efIk586dy/79+/PHH3/kxIkT6e3tzatXrxIAvb29c/QhCAJHjhxJTU1NTps2jS9fvuS7d+948OBBAuCGDRvEsqNHjxavo5eXFwFw/fr12dpLSEjg8ePHuX37drZu3Zp16tRh//796eXl9dExAuCvv/76WdcqKSmJO3bsoImJCefNm0cvLy/euXOH0dHROR6AmZmZDAkJYXJyMsn3N7UgCJTJZPT29uaNGzdK5B4JCAigra0tJ06c+NmLiZs3b+bKlSt58eJFhoeHUxAEZmRk8OTJk9TV1eWWLVt48uRJAmCrVq0IgJUqVaKpqSl//vlnnjx5kjExMSTJhw8f8saNG9l+RyTFhaPciI+P57Nnz0i+vwd69eol3is//fTTZ41NomgotNJVUlJixYoVeerUKd66dYurV68mAB4+fJh9+/YVv+jExES2adOGlpaW1NPTy+YWNWjQoFIZdFnmyZMn1NbWZoUKFWhqasoOHTpw3759ovJRKBR8/Pgx//77b964cYMA+MMPP2T7IQYFBYkzpEGDBjE4OFj0UNi/f/9H+27dujVHjRqV40eeF97e3vz111/5/fffs3nz5tTV1WXnzp3p5+eXZ73/ukRVqFCBvXv3prGxMU1NTWloaMi6deuKq+3F9VZEkn/++SeNjY158ODBj5Z58uQJJ0yYwDZt2nDMmDGiUvtfBEFgpUqVsrn2GRkZ0dnZWTymrKwsuhIePXqUc+fO5eLFi/PlBlVQNm3aRACsVasW9+zZU+TtSxSOQitdALS1tWWnTp1oamrKNm3a0MPDgwsXLmSlSpU4cOBAHjhwgElJSTQwMCAArly5knv27OGMGTM4fvx4AuDff/9dKgMvi/j6+tLCwoIVKlSgoaEhdXR02LVrV3p4eIivNt26dcsRa+HmzZvZ2klKSmKbNm1oZGREAFRXV6eJiQn/+OOPPBVYUFAQ69evzzVr1uRL3oULF9LW1pbjxo3j1q1befny5Wxmjrzw9PQU5W/fvj09PDz48uVLhoSE8O3bt8zMzOQvv/xCADx9+nS+2iwIiYmJHDNmDO3t7fN0u4uKiiIALly4kOPGjcvXZGHevHkcNmyY+F1Vq1aNANi9e3dmZmZSEAQmJSUV8YhyRyaTFetDSyJ/rFu3jlOnTv0Qn+Xz/XRjY2O5f/9+9uzZk87Ozly8eDE1NTU5ePBgnjhxggqFgpcuXeLkyZPZrl07Nm3aVPzBff/99yU7+jJGdHQ0W7RoQTU1NVpYWIgzpSNHjrBjx47ZlOvx48c5adIkWllZ0dXVlfXr16eVlRX19fWpqqoq2tWHDBnCXr168Z9//mGvXr04YMAA8RX+U5w+fZpOTk58+/ZtnuVevXpFKysr3r59u1DjFgSBv/32mzi2/v37c+rUqZw5cybd3d2pra1NFxcX7tmzp0id19+8ecOVK1fS1NSUw4YNY0JCQq7l0tPTuXPnTlpbW9PIyIh16tRhnTp1ePDgQaanp+fZx927d+nq6iqa23bv3s2ffvqJd+7cKbJxSJQvPph5Fi9eXDybI/77Y8J/nO7Pnz9PfX192tjYsFOnTpw9ezaDg4OLcahll7S0NE6bNo1qamrs1KmT+EOePHkyu3btShsbG9G5XU9Pj927d+eqVav48OFDzpkzh+vWrePp06e5f/9+HjlyhJqamtTU1GSfPn1oY2OTbRHGxcWlQLKtWLGCpqamH40QtXr1ahoaGmZb3CssoaGhvHbtGvfv388VK1Zw4cKFPHv27EeVYWE5fvw469evT0NDw09uxvD19aWZmRk7d+7MPXv20MjIiAcOHCjwjDEyMpKJiYmfK7pEGSIjI4MrV67k/Pnz81U+JSWFZ8+e5fz582ltbU1dXd3i3ZEmCIJ4o/r5+REAz58/X6BBJiYm8q+//hLtjF/Cq1JycjKbNGnC3r17MzAw8KOeBd27d6eTkxNPnjwp7igaNWoUGzRowAkTJrBTp07s2bMnGzVqRFtbW65bt44LFy7kkCFDxB1fQ4YM4YsXLwoso7e3N42Njenj45Pj3M8//8xJkyYVuM3S4sGDBzQ0NOTZs2cpk8k+Wd7d3Z07d+4kSUZERFBXVzffZhOJL4Pg4GBOmTKF3bp1o5ubG69evcqAgAAxNICrq+tH60ZFRXH+/Pl0c3Ojtra2GEJg4sSJxTfT/V8eP37M3r17U19fn9999x3DwsL4+vXrT9Y7deqUOGN7+fIlBw8eTACsV68eFyxYwPPnz9PLy4t3797N14+prDB8+HB+9913BXptTk9P571793j58mXWrVs3x8PH09OT7u7uNDQ05J9//ikeT05O5tatWwvlBXDs2DEx1qyGhgYnT57MS5cu0dLS8pMLZWWJ169fs1KlSvku37t3b7Zv356hoaGUyWRUUVHhggUL+Pz5c54+fZoLFizgsGHDOHDgQP7+++9fxERA4j2CIHDbtm00NjbmzJkz+ddff3Hnzp3U1NQUdZGGhga9vb3p6+vLBw8ecNu2bezXrx9/+eUXfvvtt9TT0+OoUaPo7e2dq1mvRJTuBxcZAOzbt++HKTbXrFnD8+fPMy4ujnK5nGFhYSTfK5j/Bnnp0aMHdXR0RFvnpUuXOHPmTLZs2ZItWrRg9erVWbFiRTZu3JgHDx4s0Op7aWBkZERfX99C/Vhv3LhBNTW1j7o2TZ48mTNmzGBWVhbT0tLYqFEjWltbs3///vny1c0NQRAYHh7O8ePHs1WrVly0aFGh2ikt5HI5NTU1RZOFIAj8448/OG/evFwf1m/fvqWpqanoc/ngwQO2bNkyxwLmh7+SWhSTKByPHj3inj17uGrVKk6ZMoVTpkzhwoULuWLFCrZs2ZJ2dnYcOnQonz17Jrq//vcNLz4+nnXq1KGpqSl/++03WllZUUVFhUZGRjQwMOCgQYPo4eHBtm3bcsyYMYyOjs5TnryUbpGGdvzQ1oewffPnz8eSJUtylFMoFPDy8kLbtm0xdepUVKxYEZUrV0b37t3zTDmTkpKCq1evYunSpYiLi0O7du2gq6sLLS0tDB06FFZWVvmWtbjp0aMHvL29MWXKFDFJZX5p0qQJVFRU4OnpmWuqmLt372LgwIFQU1NDcnIyWrVqhS1btqBnz54wMDDAn3/+WWaCOX8ucrkcw4cPx4kTJ5CSkoJ79+7BxcUl17K9evVCcnIymjVrhnPnzkEul0NDQwPt27cXw0oC7xNgtm/fHkOGDMGmTZvE7bIvX76Eg4MD6tWrh23btomplNzd3ctMQHiJ7MTHx2Pu3Lk4fvw42rVrB3Nzc5iamkJZWVnMht2iRQsA7+8PR0dHmJmZITk5GRcuXEBkZCT8/f2xYMECdO3aFTNmzBBjtKxbtw4qKiqFCkNaaqEdBUFgZmYm09LSuHPnTlpaWtLd3Z3m5uY0NjYWZxErV64scLs+Pj789ddfuWzZMg4ePJhNmjT5ZDi8kiYwMJBGRkYcOXIkx44dy/379+dLxoSEBPbp04fffffdR8sIgsBjx45lc4VKT09n27ZtOWrUqCKQvmywZ88eNmnShB06dBDvl4/5uyYnJ3PXrl2cNm0amzRpIu74MjIyyraYu3DhQs6YMUP8nJGRwW+++YYqKio0MTEp8KKkRMkjCAJ37dpFMzMzjhs3jnFxcQVu4/z58wTAPn368Ny5cyTfL/rq6+uzYcOGnyUfSiu0Y2BgIDdu3CiGTATAunXr8p9//uFff/3Fpk2bcsOGDZ+9IykrK4s//vgjLSwsSjT/Vn44ffo0t27dynXr1rFHjx7U09Pjzz//zGvXruVpIvnnn3/o4OBQ4P4uXLggbjv9Evjll19Yp04dxsTEcPbs2eJmnLw4duwYnZ2dxd1gjRs3ZsOGDfnkyRMOHTqUZmZm2dzgzp49ywYNGvDp06f86aefeP369eIelsRn8OjRIzZv3pz169cvtIteSkoK9fX16enpme14UlIShwwZUr6UrkKh4L59+2hhYZHNJlazZk3u2rXrswbyKUaMGMEaNWqU6R/Nu3fv+PPPP9PFxYXm5uZ0dXWlu7s7jx49SpL8+++/OWzYMJqamvLChQsFbj8jI4Pbtm2jjY0NW7duzenTp390d1VZJjAwkMOGDRNjYjg6OnL16tW0s7PjqFGj8nxQnz59mi4uLqxatap4H6qqqlJLS4uzZs3i4cOHefr0aR45coSPHz/moEGD8lypligbyOVydu7cmQC4efPmz1rXOXv2LJs1a5btmCAInDBhAnV1dXP16CkIJap0nzx5IiraESNG0N/fv0RWfmfNmsXGjRtzxIgRNDMzKxcr7y9fvqSfnx8HDRrEefPmMS0tjcD7LBOfO2NPSUnhX3/9xVmzZtHCwoK3bt0qIqmLn8WLF9PIyIjLli3jkCFDWKdOHTo5OVFJSYl+fn5iAO/c4jwEBASI8T8+BPDW0NAg8D5TQtu2bamtrc02bdqIG3hGjhxZLu6Xr53Y2FiqqalRTU2NAwYMYKtWrejq6sqjR49y/PjxVFJSyndbL168oKWlZbZ7qGvXrtTW1ubSpUs/W9ZSMy+UJB92g8yZM4c7d+6kmZkZnzx5Utpi5YuTJ0+yXr16lMvlvHDhAuvXr1+k7a9Zs4b6+vrFsve/OOjUqZMY3Kdfv34cMWIEAVBFRYV3796lQqGgm5sbBw0aJAYW2rZtG/X19Wlvb88WLVpw8ODBTE5O5t27d3nt2jWGhYVxxYoVPHz4cL537kmUTWJiYrhkyRLu2LGD58+fz2a+zG92EkEQWKVKFT579oze3t5s06YNjY2NP7kTMb98FUpXLpeLCycymYxz584VfX/LOgqFgi1btuS3334rhn0sKlJSUnjo0CHq6+vnGQinrJCcnExzc3N26NCBTZo0oaOjo2hi+O+mm9DQUALgsGHDeP/+fRoZGZWbh6xE0dK7d28aGhqyR48eopkuP/zwww+cNWsWGzZsyEWLFjEyMrLIZPoqlO4HUlNTSb7fbWJra0s3NzcuXbq0zPtZJiUl8ccff6SNjU2BAgSlpqby6dOnfPDgAQMDA5mUlESZTMZnz56xe/fu1NTUFGP8lgelm5mZya1bt3Lu3Lk8c+YMMzMzsyUPXbNmDSdOnMhhw4aJngnm5ubFvl4gUTZ5/fq1mPCyTZs2HD9+fL7rfojgN3ny5DxDaRaGr0rpfuC/tmUA1NLS4rp164r84pYWMpmMZ86coaGhoRh/4cMruLKyMi0sLLhgwQK2b99evAbt2rXjoUOHytU1eP36tSh/o0aN6O7uzlatWrFly5bU1dXl7t27pd1iXzFXrlxhkyZN2KVLF86ePZuampoFerstrq3fX6XSJcmwsDAuWLAgm/K1srIq87vZPkVERESOMVWoUEHc0bdkyRKSZPv27eno6MigoCBu2LCBrVu3JgB27dq1lEdQMIKDg9muXTs6OTmJY169ejVfvXpV2qJJlDIzZ87kjBkzxKzdhoaGZWI35VerdD8QEhKSLTXK/8amLU88fPhQHMeGDRsYHx/PP/74gzVr1sz21A4PDycAGhsbs2/fvmzYsKEYRlJJSUk0w5QHYmNjCYAmJiacMGFCoYL7FCcvX77kqVOn+O7du3yVl8lkn9xGKvFp/vjjD5qbm/P58+fibyI1NbVMvPl89UqXfJ89oFOnTuzTp0+5DTUpCIJ4c/13DIGBgVRXVxcDBQ0fPpwAePbsWR48eFBMB/Thr1KlSuUqeBD5PhLd8ePHWaVKlTxzVBUXvr6+dHV15dChQ3n58mVGRUVx8ODB2Wbff/zxR55tvH37lsOHD6eZmRm1tbUJgNu3by+hEXxZ3L17lyYmJnz8+DFJit/ByJEjS1my90hKl+8DmlSrVq20xfhs/msaiY+P54YNGzhx4sRcg7ScOHEiR/3Hjx8zPDy8BCUuWh4/fkwdHZ0C1Xn48CEtLS0J/P9kjgXh6tWrNDc35+LFi6msrEwAnDFjRrZrnVfeOYVCwYkTJ1JbW5uTJ09mQEAAZ86cyRo1anDWrFkFludr59q1azQ1NeWBAwdIvp+MODk5sWvXrsWSgaQwSEqX72PHfu7WvrJCZGQkd+3aJSqSD38mJia0tLTkiBEjytVmiNwQBCFXE8irV69obW2dZ92oqChOmTKFa9asYVRUFNevX5/tWhXkh/nq1SsC4G+//UaSvHz5cq4POHt7+1yTkZLvZ2UAGBkZyaSkJLHOunXr+Pz583zLIkHu27ePJiYmvHjxonjs6dOnoifLzz//zBEjRnwyc3lxIyldkuvXr+ewYcNKW4zP5uTJk9TT02OvXr14+PBh7t27l0pKSmzZsmWhQ0mWNXx8fOjs7ExVVVXq6+vTxMSEdevWpYuLC+3t7bN9j4IgcOPGjezatSt79uzJ4cOHU0dHh9999x0bNGjAbt26MTw8nE2aNOE333zDVatWUUlJidOnT8+XXXXIkCE50k0FBQXRz89PvNZ3796lu7s7VVVVuWLFihzuiUlJSXRycuLZs2cpCIKY8iq/jvxfM0FBQZw2bRrPnj3LMWPG0MrKSjQpkOSlS5fo6urK3r178/nz52JGld9//70UpZaULknyu+++47p160pbjM+mcePGrFy5cjblGhcX90Uo2w+sX7+eBgYGlMvljImJYXh4OG/fvk1/f3/6+flls0e/efOGAHj06FEeOXKE69atY2hoKKtWrSrOQCdOnMjk5GQ2bNiQvXr14rx58zhmzBgx+NDHYhDHxsZSRUUlWwSrp0+fcuPGjVy8eDE3bNiQbbu2n58fu3fvzu7du+doa8GCBWzatClTU1OZmprKSZMmEQDnzp1bIinoyxsKhYJTpkxhhQoVOGrUKDZs2JCzZ8/OsYGhbt26dHd3F10iHR0duWrVqnwvahYXktIluXnz5jJjZC8o586dy/E6+yX/UL29vVmzZs08y6SmpvLnn3+mk5MTBw8enO1cdHQ0LSwsaGRkxJiYGPbt25dVqlThyJEjOXLkSAJgv379eP36dQIQZ04KhYJPnz7l+vXruXPnTvr7+4vXu2vXruzUqRPNzMw4YsQIzp07l0OHDqWenh47derEadOmceTIkWzQoAE7duyYQ16FQsGhQ4eyffv2ouL90Hbbtm2L7uKVE+Lj47l3796P7gLbvXs37ezs8sziTFJ8w+jTpw83b97MkJCQYpC24EhKl+TcuXPzjE9bVklJSRF/nOrq6oyMjPyiZrW54efn98mwlgMHDmSLFi145cqVHNfjg1udqqqq+AofEBDAsWPHikkr//sA+/AqWq9ePfHYhyA5H/6OHz/OAwcO5IjbEB8fz6NHj3LZsmXcsmULjx49+lHPkKysLLE98v+vuBdkF9WXwPHjx2lmZibOTrds2cLt27czNjaWycnJHDduHI2MjD7pDUKSzZs358KFC2lkZFSmNv1ISpfvN0r8N51LWUehUGRLZ/Q1ZZzt27cv586d+9Hz8fHxBPDRwNWJiYnidfvf18wDBw6I51asWMF//vmHurq6DAwMZK1atQi8D5K+cuVKAih0+qPc+OB5ERQURJLi9uavxWf3+vXrtLGxobm5OY8cOSJ+D8OGDaOOjg6//fZbHj58mAA+uij5XwRBoJubG3fu3Ek1NTWmpKSUwCjyR15K932ekq8ACwsLjBo1Cu3atUNmZmZpi/NJ9u3bh9evXwMA/vrrL+jq6payRCWHlZUVDh06lOs5uVyO7du3w8LC4qNpVHR1dXHx4kV4eXnB3Nwcz58/h7q6OmrXro1BgwYBAFavXo2ZM2eiS5cuWL58OUaOHIkZM2YAAB49egRHR0cYGRkhJSWlyMZ148YNODs7Q0dHBwBw7NgxmJmZISkpqcj6KMsEBwcjODgYP//8M169egUnJyfcunULu3fvho6ODvbv3w87OzvMmTMHkydPhkwmy7UdmUyGzMxMzJw5EykpKTh48CDmz5+fa2qrMsnHtDG/sJku+X722KhRI27atKm0Rfkk//sq+jXxYXHM2dmZbdu2ZXBwMFetWsUFCxbwm2++oYuLC69fv85jx45x3rx5/OGHH9ivXz/27t2blpaWtLGxobOzs3j9PrgZffi8ePHibCaJ9PR0mpqaiufv3bvHrKwsDhkyhB06dODGjRvp6+vLBQsW8Ny5c0xLS6OHhwcnTJjAK1eu5HtccXFx7NWrF42Njfndd9+JCRLv379fHJexzCEIAg8ePEhDQ0M6ODiI11sQBF69elUMOC+Xy9mmTRvu3r07Rxvp6el0dHSksbExu3fvzvDwcNra2vLBgwclP6A8QEklpiwPPHjwAG3btoWnpydcXV1LW5w8Wb58OV6/fo0dO3aUtiglikKhwPXr1wEA48ePx9OnT6GsrIxGjRrBwsIC6urquHnzJuzs7NC8eXNYWFhAT08PqampaNmyJVJSUiCXy2FgYABHR0c8evQINWrUQGhoKI4fP45Ro0blmBX9+++/mDx5MipVqoROnTpBX18fHTp0wObNm3H37l1cunQJPXv2xI0bNxAaGgotLS0kJCSgTp06CAgIKND4nj9/Dm9vb2RkZKBOnTpo2rRpoZIfllfOnDmDsWPHomvXrujfv7+YOHLz5s1YunQpwsPDMW/ePCgrK2PRokV4/vw5zpw5gxcvXuDdu3dQKBSYO3cuGjZsiDdv3sDa2hqCIJSpa1hqiSnLKocOHWLlypV5/PhxpqWllbY4OVAoFFy3bh1tbW1LZctrWSEjI4PVqlXj4MGD2alTJzZv3px2dnZcvnx5vnJjhYWFUU9Pr0AzSfxn8ezkyZM5zv93xjxr1ixOnDixQGP62pHJZNTW1s71uh07doza2tps27YtdXR0ePXqVXp4eFBbW5vGxsbU09NjzZo1efr0aY4ZM4aDBw+mubk5f/zxx1IYSd5AWkjLjkKh4LJly9i4cWNqamryr7/+Km2RRPz8/Ghra8sWLVrw8uXLX7ynwsc4duwYa9euzUGDBhX6GnxYrPHy8ipQG3FxcYyJicm1zsOHD/nHH38wJiaGVapUKdS24q+d69ev08DAgKGhodmOh4aGcvz48dy7dy8fPHjA3r1753CVVFdXp7GxMVeuXMk9e/bw6dOnpTSKvJGUbh54eXlRT0+v1J2pSXLTpk00MjLinj17qFAoSlucUmP37t20t7fn6dOnP+uhIwgCt2zZQltbW1aoUIHOzs6cMWNGNg+WjIwMjh07lubm5qxVqxbXrVuXr3th9+7dbN++faFl+9qZNWsWe/To8dH7fNeuXQTAHj16iMGBhg0bxqNHj5aLDCGS0v0E48aN47Rp0zhkyBDa29vz8OHDJS7D/v37WbVqVdGd6GvGxcWlUJmQP4YgCIyLi6Ofnx+7devGb775hmfOnGFaWhp3795NV1dXvn79mp6enmzXrh21tLRYo0YNdurUievWraOXlxeHDx/ODh06sF+/frx06dJXb/r5XNLT01m/fv1cF8vI92+jlSpVIgBu3LjxsxO1ljR5Kd2vbiEtN8LCwlCrVi3Y2tpCQ0MDjx8/RmRkJDQ0ND6r3aSkJFSsWBGqqqpITU3FnDlzEBcXB01NTaiqqmLkyJEIDQ1Fp06d8P3338Pd3R0//PBDEY2qfEISOjo6CA0NhaGhYZG3n5CQgA0bNuDy5ct49OgRLCws4O7ujk2bNollEhMT8eDBA0RFRcHT0xO+vr7o06cPrK2tsXr1ahgYGKBGjRrYtGkT1NTU8tWvTCbDsWPH8Pr1azRr1gwtW7Ys8rGVJ1JSUqCjo4M9e/bgu+++y7XMsWPH4Ovri8WLF0NTU7OEJfw8pIW0fLBs2TLRblS1alXOmzev0G09fPiQrq6urFixIrW0tGhqasrBgwcTALdu3cotW7ZwxowZVFNTo4ODAwcNGsQNGzawf//+RTii8snRo0dpZGTEiIiIYu/r9evX/PPPPwu0k0lHR4c//vgj165dy7Vr1+a7np+fHwGwdevWrFChQrmbuRUH7dq146RJk0pbjGIBknnh0ygUCo4dOzbbNtCtW7cWKLVPZmYmR44cSQMDA+7evZtyuZyxsbH08fHh/Pnz+dtvv2WzUQqCwLS0NJqamtLHx4fGxsZlLitCSXHmzBkOGjSIDg4O9PHxKW1xPsp/F3fU1NTyXS8jI4NmZmZ89OgRJ02axL59+350R93XwJs3b+jk5PTFmmgkpZtPatSoQQB0cnKio6MjzczMuHbt2nwv5uzcuZMtWrQo8FZjZ2dnnjt3jkOGDOG6deu4Z88erlmzpky6sxUHnp6erFSpEtesWcPY2NjSFidPMjIyRKVrZGSULS38p9iwYQPNzc3p5+cnpg338PDg48ePOX/+fM6dO7dItx2XVQRBYJs2bThmzJgvNnCTpHTzSVRUlBiFCgB/+uknqqurc8eOHfmqv27dukJFMjtx4gSdnJy4Zs0adu3aVex/9uzZBW6rvOHp6UlDQ8MiXTgrbnbt2iW69FlaWhYoo+zChQuppqbGOXPm8N69e+zQoUO23XC//vorX758meuDXhCEcp9UlST37NnDevXqfdEPGEnpFpCzZ88SAKtUqcK1a9dSX1+fnp6en6w3f/58jhkzpsD9CYLA77//XvzR7dy5kx06dCj16PclQceOHblnz57SFqNAZGVl0dXVlb///juHDx/OUaNG5btucHCwqGBjYmKYmZnJgQMHEgBtbW3Zrl07WlpaUk9Pjw0aNGC9evVob28vZnw2Nzcv18HPnz9/ThMTEx49epT29vYF2kZdnpCUbgGJj49ngwYNCIB2dna8dOkSjY2NPxlqzt3dPV/KOTeysrLo4+PDrKwsdujQ4auIubB27Vra2NiUeZNCbty/f58AeP78eRoYGHDFihX5rrtnzx4CoI6ODi0sLNitWzf6+PjQyMiIM2bM4L179xgdHU1vb2/6+voyMDCQr169YkpKCjdu3MjatWvTz8+vGEdX9AiCwL1799LExIQeHh5ikPkjR46UtmjFQqkrXUEQyp3tRqFQcNy4cQTAixcv8vHjx7SwsODZs2c/WqdmzZoMCAjIdx8pKSn88ccfxZmPlZUVR40axb1793L58uVFMYwyS2pqKjU1NcttZmaSdHBw4IYNG/jmzRuam5t/MuD2f3n58iWfP3/OwMBA0WQQEBDAuXPn0sTE5KOLiVlZWdywYQONjIy4devWohgGSXLv3r2cMGFCkYY+zcrK4oEDB7hw4UI2b96cLi4u9Pf3F1Om6+vr55oH70ug1JXutm3b2LVrV27atKlcvRplZGTw3Llzon3t4sWLtLW1/aiLUbVq1Xjt2rVcz2VlZfH69evctWsXf//9d6akpLBy5co5tjl++Psww/4SbHi5MXfuXLZq1aq0xfgs7t+/T1tbWx47dowjR44ssuh1PXr04JAhQ/IsM378eDEiV0xMDMPCwgrcz/79+zlmzBhWrFiRwPvEplOnTi2UzPfv3+e0adO4a9cu3rp1i6tWrWKdOnUIgKNHj+aBAwfEe1mhUFBdXf2LyFn4MUpd6WZmZvLVq1e0t7cnALZq1arczXxJiilWPrbfe926dezXr1+O4wqFgs7Ozqxbt67or/vXX3/xzp07YjAWQRAYFBTElStXskKFCmKqbwDcsWPHFxeDoXPnzpwzZ05pi/HZzJ07l8OHD+e2bdvYvHnzHEkpC4IgCFy2bBnNzMw+6TqYmJhId3d31qhRg5qamuzcuXOB+kpMTKSSkhJXrFjBP//8k1euXKGvry9tbW0LHAx81apVNDEx4ezZs8V7dsKECbxw4cJHJw2pqalf7ISCLANK97+8ePFCtGd9LK1JWSU8PJwA6O/vn+v52NhYOjg45IhOFRgYSFNTU5LvZ8tVqlTJ0x3szp07H50BW1lZsW/fvrxx40bRDawU8Pf3p4mJSWmL8dls2bKF33//PeVyOQcNGsRatWoVWvHeuXOHOjo6+cqaQL6fzFy7do1XrlxhjRo18t3Pxo0baW5uzsmTJ2c7LggChw8fTlNT03zPnI8fP057e3u+e/eO69atE9MclTebc1FTppQuSdrb27Nbt27lTumS78NCGhsbc/bs2TmiJJHvXaAMDAw4aNAgzpkzh127dqW+vj7XrFlDQRAIgF26dMmzj7///pu9evXixo0bGRQUxNq1a4tKd8GCBeL/16xZwy5dunDWrFnlbiaclpbGpk2blnu3oadPn9LY2JhPnz6lIAiiGa2gvHjxggYGBrkmtfwUwcHBNDMz++Q9IAgCFy9ezOrVq/Phw4e5llEoFGzQoAEXLVqUr75nzZolpqi3s7Njw4YNaW9v/8kHz99//81Ro0aVeqr04qLMKd3yTlhYGPv37089PT3269cvR5Ca4OBgTp8+nTNmzOCRI0cYHh5OkqLSzWsxjsy+JTkwMFC0jX34+/777zl+/Hhu3bpVdG0rS0n58oMgCNTU1MyR6LE8snXrVpqbmzMlJYX37t2jkZERb926la+6UVFRXLRoEQFw+vTphepfEARaWFjw1atXHy2TkJDAb7/9lrVq1frkLPb58+c0NTWlIAhMT0/P896Kj4+ntbU1T506xTNnznD+/Pl8/vz5J2W2trYWs3t8iUhKt5hITk7mvHnzaGJiwlq1anH9+vV88+YNb9++zQ0bNuQIQff48WMCYFBQEP39/Xnz5s2PzggSEhJy2LzS0tJEBV7euXXrFu3s7EpbjCKjR48e3L59O8n3C8fNmjXLs3xUVJQYrNvNzS3fJoW8+j948GCu527dusWWLVuyffv2+XrIvXjxgioqKrx8+TKVlJQ+uah348YNGhsb89SpU/mS9UO2Zj8/P+ro6JS7t7T8ICndYuaDbW3w4MHU09OjjY0Nf/jhB5qamnLDhg1iuVWrVomrxNbW1qxZsyZNTExyzVDwJSMIAqdPn85vv/22tEUpMjw9PWlhYcGLFy/Sx8fnozO4hIQEHj58mA0aNGCvXr0+a+Htv3h4eLBZs2Y54tOeOnWKOjo6XLhwYb5jPQQFBdHOzo5WVlbcvHkzjYyMPlnn33//pYmJSb5cJjdu3EgArFu3brnIV1gYJKVbgsjlcvHJHRwcTHt7e06YMIHnzp3L1X45Y8YMdujQoaTFLFUCAwMJgNevXy9tUYqUixcvsnLlypw2bRoBZNv0ERISwrVr19LU1JSdOnWih4dHkfqoyuVyNmzYMEcs6K1bt3LEiBGFblcQBBoZGeVrd+ScOXPytXX99evXoqnMycmJv/322xcXZyQvpfvVpGAvKVRUVMQEedbW1vDx8YGamhrmz58PU1NTWFpaon379mL5jIwMNGvWrLTELRUcHR0xevRoeHl5lbYoRUrbtm1x+/ZtnDhxAgCwf/9+zJs3DzVr1kStWrXg5+eH48eP4+zZsxg5ciQqVqxYZH2rqKhgwIABuHr1arbjtra2CA4OLnS7SkpK6NKlCzw8PDBw4ECoqakhLi4u17LNmjWDj4/PJ9u0sbERfyPPnz/HpEmTsGbNGgiCUGg5yxOS0i1mjI2NsW7dOvj6+sLf3x/du3fHxYsXcfr0aQDA27dvoa+v/8l23r17h3r16qFJkyYYN27cR2/88kK1atVw+/ZtKBSK0halSLGwsMDFixcBAD/99BP8/f3h4eGBt2/f4sCBA3BwcMC6devE778oadWqFf7++2+kpqaKx3x9feHo6PhZ7X7zzTd48OABTp8+DblcDgcHBxw8eDBHOUEQoK6unq8258yZAwBYtGgRjI2NMX/+fPTu3fuz5Cw3fGwKTMm8UGzcunWLAHjs2DFWq1aNp0+fzrWcIAhcuXKlmAkV//FgqFixIpcvX87Lly/z3bt3fPbsGY8cOcIZM2Zw6dKlfPz4cQmPqmDEx8ezTp06dHV1zdX1rjyTlJREAPzuu+947do1bt68mX379qWBgYH4PRbXbqwhQ4Zw5syZ4ufBgwdz7969n9XmzZs3CYDOzs4UBIG+vr60tLTMZr+9efMma9SowWXLluXZlkKhoK6uLm/cuMGhQ4fm8EMv7y6EH4Bk0y17uLi4EADbtWvHpk2bMjExkSR59epVent7UxAE+vn50cLCgleuXGFUVBQ1NTUJgCkpKbx+/TpdXFzYvHlzGhgYUFVVldbW1uLNu3r16lIe4acRBIHjxo2ju7t7kS0olRXevXvH+vXr083NjaNGjeKOHTsYEhJCkvz+++/5yy+/FEu/z58/p5mZGeVyubjJ5nNDhJ46dYoAssUDWblyJStXrsypU6dy/fr1tLS05P79+z/piTBs2DDi/zI0Hz16NNvOSxsbmy9ml5qkdMsgT548oY6ODtXU1Ni0aVOqqKiwZs2aBEAVFRV27NiRFhYW/Pnnn8U6bdq0obq6eo4bOzw8nK9fvyZJ/vXXX2zQoAEbNmxYLjITZGRkcMiQIV/0Pvz/IpPJaGZmVqwJSOvWrcsdO3aI4SIBcOfOnYVu74PXjaqqarZ7av/+/QRAR0fHfAUu6t69ezblnZWVRR0dHRobG7NevXoEwJUrVxZazrKEpHTLMOvXr8/2evUhoPW4ceP4559/ZiubkZGRryhQgYGB7NWrF01NTXnz5s3iEr3IePfuHQ0NDRkZGVnaohQ7+/fvp7u7e7H2cf36dQLgiBEj2KJFC65YsYIWFhaF3gFav3590dPgvwH9k5KSuHTp0ny/pdSuXZseHh7i5w/RxvB/AZ6mTJnCPn36FErGsoakdMs4crlcnDXs27ePW7ZsYfv27T/bvvX7779TTU0txyaNssiSJUtoZmZWoIDmgiDwxYsXvHXrFv38/Mq8k31WVhadnZ0LHXO5IERGRlKhULBfv37ctGkTVVVVC50MMy4ujkuWLCEA6unp8ddffy3wtf7777/p5uaW7V7874SjU6dOYvtfApLSLYfo6ury5cuXn9WGTCbjhg0bqK2tTR0dHWpoaNDd3Z3btm3L4URfFrh37x5tbW05fPhw0W9TEASGhYXx9u3bjIiIoCAIDAkJ4dSpU2lhYcEqVaqwQYMGtLKy4pQpU0p5BHmzceNGtmrVqkQfDmfPnqW6ujoHDhxYoH5jY2O5YsUK7tq1i48fP6ZCoWDjxo05a9Ys1qlTh+PGjct3W+fOnaOJiQm7detGMzMzMQ5wVlYW4+PjefLkSVH5filBzSWlWw4pyszACQkJfPr0KRMSEnjq1Ck2a9aMHTp0KJPhNZOSkjhw4EA6Oztz9uzZrFOnDo2MjFi3bl3q6enRzMyMenp6nDp1arYQm/Hx8TQ2NubatWs5YcIEVq1aVVycLAs8evSIxsbGpRJPujCLU48ePSIANm/enMbGxrxx4wYnT57MadOmMSkpiVZWVrx79+4n21m9ejWtrKx49epVZmZmisr1f3n58iX//fffAstZVpGUbjlDoVDkKzBOYcnMzGTTpk0LHbC6uBEEgWfPnuX06dN54cKFbLPyly9ffjR+gJeXFwcNGsRFixaxUaNGrFevXpmIZBcVFUU7O7tylwtu06ZNrFy5MletWkVjY2MuX76clSpVYnR0NFu0aJFnJuSYmBhu3ryZVapU4du3b0m+jz3cuHFj0YvjS0ZSuuUIhULBli1bsnHjxsXqs/jo0SNaWFh8MX6R/8uHyFvPnj0rVTmCgoJYo0YNzp07t1TlKCx79uxh1apVeeLECVpYWLBevXps1qwZe/TowfHjx+co//LlS3p6erJChQp0d3fPNrP/8ccfP+mlIghCucyZ979ISrecIJfLOXXqVAIo9ld/QRDo4uKSI5D1l8KbN2+oqalZag8VQRC4ceNGGhkZccuWLWV+ke9jCILA1atX08bGhj4+Puzbty81NTWprq5OANnCPp4/f54VK1akuro6d+/enaOtD9HFLl26xFOnTnHlypX09PSkl5cX7ezs6OHhwcuXLxMAJ02aVIKjLHryUrqqRbOvTaIo2LdvH7Zu3Qpvb29oaGgUa18kERMTg5cvXyIkJATW1tbF2l9JExISgsqVK0NVteRvcUEQMHz4cDx8+BA3b95E1apVS1yGokJJSQnTpk3DyZMnERwcjCNHjuDWrVvo2LEjxo8fn23b78WLFzF37lxMnz4dKioqOdq6dOkSAGDHjh04fPhwjvP169fH06dPAQBXrlwRjwuCAGXlLydiwZczknJOZmYm1q9fj+3bt6Np06bF3p+SkhIGDBiA2NhY2NjYoEWLFli+fPlnBUcpS/z111/o1q1bqfQ9c+ZMvH79Gjdu3CjXCve/rFixAj/++CMCAgLQuHFj9OjRA0+ePHn/uvx/VKpUCY8ePcpV4crlcpw5cwa7d+/G7du3AQBNmjQRzwcFBcHFxQUtWrQAAHTq1An37t1Dly5dYGJiAhsbG/z222/FO8iS4mNTYErmhRIlJCSEAPjgwYMS7Tc9PZ19+vTh33//zbFjx9LIyIgrV64s9/EQJk2axDFjxpRon4IgcN26dTQxMfki7JL/y9atW9m6dWsKgiB6N/z+++/iQmdCQgIrV66cq2lh7dq1bNiwISMjI6mtrc0ffvghW9qp/+Lp6cmwsDAx5oOTkxN79+5NVVVVKhQKenh40M3NjW3btuXRo0fznUizJE08kGy6ZZ/o6OiPutOUJMeOHSMAGhkZ0d7enp06dWKfPn3KXaLBmJgYWllZfTSbQnGwZcsWmpiYFOsW39JEJpOxdu3aYuDxCxcu0NramjNmzBDL/Pbbb6xTpw4nTpzIrVu30tPTk2lpaRw/fjzXrl3Lv/76i/b29mKqnu7du4t1vby8uHz5cg4fPpwAWLNmTTE41Ie/Xr16sW7durx06RKPHDnCFi1asGLFiuzYsSOPHDnyUQXcp08fAuCZM2eK9Rp9QFK65YCHDx/SwMCgRJVEbgiCwIyMDCoUCgYEBPD06dPcvHkzLSwsOH/+/HK1IBQQEEBzc/MSycwhk8lYpUoVXr16tdj7Kk2CgoJobm7Ov//+myR58OBB9uzZUzwfGxvLnTt3cuDAgezbty9r1arFKlWqEABv3rwpZgPH/8UYASDmdpsyZQoBiHEYALB69eocNGiQ+NnIyChHBL20tDT+8ccfYvqjMWPGMD09PVuZt2/fskKFCgTAK1euFPNVkpRuuWDmzJmcMGFCaYvxUSIiIli/fn02atSIEydOLFMbD/Li9u3bNDExEX1Fi4slS5awffv2xdpHWcHb25tVqlThhAkTuHz58jz9veVyOY8cOcJbt26JD+yWLVtmm71+8803Yvn/eu1kZWUxISGBycnJPHbsGJcvX87GjRtTX1+fo0aNYkRERI7+YmNj2adPH/bu3ZsymYz//vsvN27cyBYtWhAAlZWVuWHDBvbr169YPYQkpVsO2LJlC1VVVXM8ocsSMpmMZ8+eZevWrfnbb7+Vtjj55vvvv893SvHCUrt27RKJqVBWSExMpLu7O1VUVKirq8t69erxxIkTTExMpKenJx88ePDRreYzZsxg3bp1GRoaynbt2uXrTSQqKooAuHfvXkZERPD7779n//79cy2bkJDA5s2bEwCVlJSyKXhPT0/GxcURABs1avTZW+0/hqR0ywGRkZHs2rUrW7ZsWeZf4Q8fPpxtdlLW8fb2pqmpabHuThs5ciTbtWtXbO2XRRQKBc3NzQmAAwYMyKbc7Ozs6OLiwmvXruUrMt6niI2Npbq6urib7d69e7kGPX/37h19fHwYFBQkhpHcvXs3t23blu3tbNmyZaKs06dP59atW4v0d5eX0pVcxsoIpqamOHnyJJKSkvD777+Xtjh5IpfLoaamVtpi5JumTZvCwsICN27cKLY+atWqhUqVKhVb+2URZWVlhIWF4Y8//gBJNG7cGNbW1rCxsUGdOnUQEBCAli1bQl9fHx4eHpg+fbro99u0aVM0bdoUSkpKuHbtGoKCgrB48WJUrVoVN2/ezNGXoaEhMjMzYWVlBQDo2rUrgPff7ZEjRwAA27dvR82aNfH999+jS5cuGDx4MA4fPoxBgwZh9OjR0NXVFdubPXs2BEHA7t27IZfLMXbsWLi4uGDx4sXFf+E+po0pzXRLhXv37tHCwqLMmhlkMhlbt27NLVu2lLYoBcLOzo6XLl0qtvavXbtGOzu7LybzQWH54E525MgR/v7775w+fTpbtGjBvXv3ZlsMw39mxdOnT8/2OT+hSE+ePMlt27bx+PHjrFy5MitVqkRjY2Pu2rWLJDl8+HB27dqVzZo1IwD6+vrm2Z6Hhwe1tbUJgM+fP//s6wDJvFC+6NKlCzdv3lzaYuRAJpOxd+/e7Ny5c7mL2TBy5Mhsrk3FQdOmTblkyZJi7aM8IwgCk5OTqaOjQwA8evQoZTKZ6Mnwsahln3qQpaen08vLK1suuMzMTA4bNoxaWlqiMr99+/ZH23jw4IEYz7dJkyafHfpUUrrljDt37tDCwiLfTt8lxfz589m+ffsyGRLyU9y9e5cODg7F2seZM2fYtm3bj54XBOGryI6RH2JiYkTFFhQUJMZP/l8+ZJewtLTkqVOneP/+ff799985lGLt2rUJIIeCjo6O5sWLF7lixYqPRqf7Lx4eHnRycvrse1xSuuWQ/v37l6nIVF5eXjQ2Ni63Yflmz57NXr16FWsfN27cYKNGjT56/uDBgwSQLQ6wxMc5cuQINTQ0CIDdunVj06ZNxYW7w4cPi+ViY2MJgFWqVOGhQ4dKUeL/j6R0yyHv3r0jgEKnWClKUlJSqKGhUWK7eYoaHx8fVq5cOVe/zqLk8ePHdHZ2zvXcgwcPqKGhQWtr6xLfnlxeuHfvHlesWME7d+4wIiKCAHjw4MFss9oPCTfDw8PFY97e3qxVqxa3bdvGbt26lYmErHkpXcl7oYxiaWmJZs2aYfPmze+fjqVIfHw8jIyM0Llz51KVo7AcPHgQo0ePhpmZWbH2o6enhzdv3uQ4np6ejoULF8LMzAy7du3CnTt3ilWO8sju3btRt25dXL9+HZ06dYK5uTm6dOmCAQMGZIswtnXrVmzevBnm5ubiMWVlZaioqKBZs2Y4deoUXF1dER8fX2Sy9ezZE//++2+RtZen0k1KSiqyjiQKzp9//olz586hZcuWePToUanJoa6ujszMzFLr/3M5ffo0OnXqVOz9nDt3DvXq1RM/h4SE4Ndff0W9evWgpKSER48eYeXKlbhz5w4EQSh2ecoDWVlZ+O233zBy5Ejcu3cPZ8+exeXLl7Fs2TJMmzYtR/l27dph8eLF+PXXX8VrGBkZCV1dXdSoUQPJycnQ0tISI5kVBSdPnsSECRPyVVahUHy6749NgUnCzMysNGbmEv9BLpeLgVQGDx7M169fl7gMN2/epJubW4n3WxR8MI0U54aTwMBA/vTTTzQxMeG9e/fE4z179mSjRo14+vRpcYGnTZs27Ny5c7HJUp7IysoS4zIUJJj+w4cP2aRJE7q6uvLatWvs379/th2S06ZN+2SGioJw4sQJAmBMTEye5cLDw+nm5vbBW0Ky6ZZ3EhMTOWHCBNrY2DAgIKBE+165ciW///77Eu2zqBAEgRUqVPjkD6aweHh40NjYmLNmzcoWDlMQBKqqqmYL8SgIAo2Njb/YKGQFxdfXl/r6+hwxYkSBQ2FmZmZyx44dNDU1pYODQ7bvNywsjOrq6kX6oP1UW4IgiK5pe/bskZTul4IgCFy6dGmJz5QaN27MCxculGifRcn48ePZp0+fIm1TLpdzwoQJtLW1/agSdXR0zOaU7+/vTzs7uzK/zbskSE1NpYuLC1etWvVZ7cjl8hzuY3K5nBYWFly+fPlHXdGKg61bt3L+/PnMyMiQFtK+FJSUlNCjRw+8evWqxPoMCAhAYGAgXF1dS6zPombNmjXw8vIq0uu2ZMkS+Pr6IiAgAA4ODrmWqV27NgICAsTPBw4cwMCBA6GkpFRkcpQXsrKycOfOHfzyyy+oW7cuLCwsULdu3VzttgVBRUUlRyofFRUVXL58GXfu3EGtWrVw//79Arf79OlTuLu74+7du/muM2bMGCxatOjTqbY+po0pzXTLJG/evKGlpWWJ9degQQPu27evxPorLmbMmMERI0YUWXsrVqxgv379Pnr+6tWrVFNTY3R0NMn3wWEqV66cLTvu10BKSgo9PT1pY2NDOzs7Dh48mP/880+xmXv+lwkTJrBu3boFfrv44LKJ/8uOUVAgmRe+HJ48eUJHR8di7yczM5Pr1q2jgYEBU1NTi72/4iYhIYE2NjY8f/58kbSXnJxMAB/1/R06dGi2LcE+Pj6sVq1akfRdXsjMzBQV16lTp0pFBoVCwYoVK7JGjRoF9tN++vSpGBrywIEDBaqbl9KVzAvljBcvXsDR0bFY2k5ISMDmzZvh6uoKHR0dLF68GL6+vqhYsWKx9FeS6OnpYeDAgUXmSqStrQ0ASE5OzvV8lSpVEBcXJ34+e/ZsqSXKLC0iIyMBAMHBwWJUsJJGWVkZ165dw+PHj3Ho0KEC1a1WrRoyMjJw9OhRdOjQoehkKrKWJEqEwMDAIle6crkc8+fPh7W1Nc6dO4dff/0VycnJiI+PLzYFXxoYGhoiOjr6s9tJS0vDjh07APx/5fu/dOzYEdeuXQNJyGQyHD9+HN98881n912euHTpElq3bg1ra+tSlcPNzQ2+vr5YsWIFdu/eXaC66urq6NOnDwwNDYtMHtUia0miRHj27Fk2B/yi4Mcff8TTp0/x7NkzWFhYFGnbZYn69evj6NGjn9WGl5cX+vbtiyZNmsDb2zvbzqj/Uq9ePWRlZcHNzQ2hoaFo0KABmjVr9ll9lzcsLS1x5coVhIWFwdLSslRladCgAa5fv45WrVqhWrVqaNy4canJIindckZISAh69+5dZO3FxcVh//79ePfuXbYgz18ijRs3xvPnzxEdHQ0TE5NCtfH333+jRo0aOHXqVJ7lNDU1ce/ePVy8eBG2trZwcnIqVH/lmV9++QUAcOfOnTJhWqlatSqGDx+OM2fOlKrSlcwL5Yw3b958dHZVGFRVVSGTyaCjo1NkbZZV1NXV0bp1a+zbt6/QbSgpKaFdu3b5KquiooKOHTt+lQoXgGhDdXZ2LmVJ/j86Ojp49+5dqcogKd1yhI+PD2QyGWrWrFlkbe7btw+urq5fje/oypUrsXbtWly5cqXAdcPDw/HPP/+gefPmxSDZl4exsTHGjRuH5cuXl7YoIj179sSFCxdKVQZJ6ZYjQkNDUbduXaiqFo1VyMvLCytXrsSuXbuKpL3yQNWqVbFp0yZMmjQJCoUi3/Xi4uLQuHFjfPvtt2jRokUxSvhlsXjxYhw+fBgpKSmlLQoA4NGjR6hWrVqpyiAp3XLE27dvYWBgUCRtxcXFYfTo0diyZUuZev0rCXr27AlDQ0Ns3LgxX+UFQcD48ePRuXNnLFiw4Kt5KygKjIyMYGdnV+BwliSRkpKCyMjIIg1t6uPjA3d39yJrrzBISrccIZfLi2Sx68mTJ2jSpAl69uxZJhY4SholJSXs2LEDs2bNQkZGRp5lMzMzMXjwYISFhWHNmjUlJOGXxU8//YT169fnWYYkbt26hS1btmDkyJGwsrKCqakpnJycUL9+fUydOhWvX7/+bFm8vb1Rp06dz27nc5CUbjlCTU0Nqampn9WGTCbDyJEjMXLkSKxataqIJCt/qKio5KlwU1NT8c8//6BJkybIysrC+fPnv4hNIqWBrq7uRzeRAO8Voa2tLYYOHQo/Pz/UqFEDFy9eRGpqKuLi4rB06VLExMTAzs4OoaGhyMzMRLdu3RAUFFQgOcaPH4/U1NTSD8b/sa1qlLYBlznatGnDY8eO5bv88+fP6ejoyCNHjjAuLo7Jycns168fu3btWu6y+RY1H0Lx/frrr7xy5QqnTJlCR0dH6uvrizFeGzVqxP3790tRwT4TPT099u7dmyQZFxfHefPm0dnZmaampqxevTo1NTW5Y8eOPK+zIAhcsGABnZ2dWbduXQLIFkP3U/z777+sVKlSkSV7TU1N5fnz5z8qM6TYC+Wfp0+f0szMrEBZSkNDQ8W97yoqKlRVVWWfPn2+iFgKRcGOHTtYvXp1Ojg4cNGiRWJuridPnvDt27elLd4Xw+HDh2llZcVq1arR2NiYPXr0oK+vL0NDQxkQEJAt31lepKSksE6dOuI9DYDp6emfrCcIAr/55htu2bLlc4ci4ufnRwBs0KBBrhmeJaX7BTB58mTOmjWrwPXu379PPT09vn37Nl8pqCUkioO0tDQ+ePCAL1++/Oy2njx5wlmzZlFbW5v169fPkXb9f7lz5w6tra2LfLLxww8/EACbNWuWI4GspHTLOT4+PgTAwMDAAtdNTEykrq4uvb29i0EyCYnSQxAEVq9ePVuAfUEQGB8fL/7fz8+P3333HUeOHFnk/SsUClaqVIkA6OzsTB8fH+7fv58KhSJPpSttAy4HJCYmol69evkKPvPgwQOkpKQgPj4egYGB2LNnDwYMGICmTZuWgKQSEiWHkpISVq5cib59+6JGjRpo1qwZLl26hLt37+LQoUP45ZdfkJKSAgcHByxbtqzI+1dWVsbEiRNx9epVaGlpoVevXggLC0OPHj3ylpt5+MC5ublRShdd+jx79gyNGzfOV1ppCwsLREREoGPHjrC2tkaPHj3Qrl07qKiolICkEhIlT0pKCnx9fXH16lWoqqrCzMwMu3fvxtChQzFu3Lhi86t+9uwZnJ2d8dtvv6FFixZwdXXFgAEDcPDgQSgpKfmTdMutnqR0ywErVqxAaGgotm7dmuv5pKQknDx5EnXq1EGzZs1w9uxZaauqxFdJREQEtm/fjoYNGyIzMxMZGRno379/kfdz5swZnD59GjKZDKdOnULXrl2hqamJzZs3Q0lJKU+lK5kXygEBAQGoXr06SOb61D506BBGjx4NABgwYACqV69e0iJKSJQJXr58iYULF6Ju3bowMzODp6cnunXrBk1NzSLr4/nz5+jTp082P++bN2/izp07+ZpVS0q3HHDkyBEAgLW1NYYNG5bj/H/D1O3btw9qamolJZqERJmiSZMmaN68OcaNG4eQkBB4enoWqWnt3bt3GDhwIJYuXYqUlBT8/fffWL58OZo1a/bRgPb/i6R0yzj/Nf/Y29vnWubcuXNo2LAhfv31V0nhSnzVKCkpYe7cuejZsyfS0tKwevVqqKurf3a7YWFh2LRpEzw8PDB8+HDcuXMHhw4dwrt37wocoF3aBlzGefXqFTQ1NeHs7IwGDRpkOyeXy7Fjxw5s3boVU6dOLdXAzBISZYX27dsjODgYCQkJn53iHQBu3boFNzc3JCUlwdfXF/Hx8UhLS8Off/5ZqEwr0ky3jPPHH3+gc+fOOHbsWI5zCxcuhJeXFyZOnFhqif8kJMoihc0Mkhvu7u7o3bs3Fi9ejPPnz+PkyZN4+vQpjI2NC9WepHTLMG/fvsWiRYsAvA/AoqWlle28qqoqIiMjIZPJiuQVSkJCIidLly7FtWvXULlyZVSvXh2nTp0qtMIFJJexMo1CoUDlypXRrVs3bNu2LcfKqEKhwOjRo3Hu3Dm8fftWivP6FSGXy6GkpCT5X5dR8nIZk2y6ZZiHDx9CR0cH27dvz1WhLl26FP7+/jh06JCkcL8y1NTUoKqqinXr1pW2KBIFRDIvlGF0dXWRnp6ewz9XEARMmjQJBw4cwJ07d2BjY1N6QkqUCo6Ojnjx4gX09fVLWxSJAiIp3TKMtbU1VFVV4eXlhdatW4vHw8LCsHPnToSEhOS6YPDixQvMmTMHGRkZiI6OxqFDh2BlZVWSoksUM4GBgaUtgkQhkcwLZRgVFRU0bdoUmzZtQnBwMID3CvWHH35Aeno6ZsyYkWu9VatW4ciRIzh16hRu3bpVZpICSkhISAtpZZ6IiAjRF7BNmzb4999/oampiTp16kAmk+HixYs50shkZGQgMTERKioqMDIykuy9EhIljLSQVo4xNzcXM6levnwZGRkZOHbsGC5evAgLCwtoaWmhVatWuHHjhlinQoUKMDMzg7GxsaRwJSTKGJJNtxxQr149yGQy/Pnnn9ixYwd69uyJGjVqiKEer169+tkJKyUkJEoGybxQDklPT8etW7egoaGBxo0bQ1lZemGRkChLSKEdvzA0NTWzeTNISEiUH6QpkoSEhEQJIildCQkJiRJEUroSEhISJYikdCUkJCRKEEnpfsH4+/tj9uzZ8PT0LG1RJCQk/g/Je+ELIjg4GF5eXhg+fHi24/7+/ujYsWMpSSUhIfFfpJnuF0B8fDymTJkCJycnUeFOmzYNmzdvxsOHD6WZbhlFoVAgJCQEDx8+RFhYGARBKG2R8kVoaCj279+Pd+/e5VlOoVAgNja2yPoNDg7GlClTkJCQUGRtlgbSTLcck5ycjJUrV2LLli2wsrJCx44dcerUKTx69Ag1atQobfEkcoEkLl++jF9++QX//vsv9PX1oaenh5iYGCgpKeHPP/9EmzZtSkSOoKAg/PTTT1i7di2qV6/+yTpxcXFYsWIFdu/ejaZNm2LixIno0aMHxo8fD319faSmpiI0NBTe3t44cuQIoqKiAABNmzZFnz59YGlpiVatWomxQp4+fYpDhw4hKysL1tbWSEtLQ1JSEkxMTKClpYVnz57h7du3kMlkSE9PR0BAAN68eYPw8HDMmTMHcrkcWVlZSElJgUKhgCAIUFVVha6uLmrUqFGkadeLEknplmN69eqFChUqYMCAATh+/Dh69eqF0aNHw9nZubRFk8B7xfbo0SNcuHABaWlp8PT0xM2bN2Fubo4lS5bg8OHD2eLh7t69G8OGDYO+vj5q1KiBAQMGwMbGBpcuXUJAQABq1aoFGxsbmJmZQUdHB66urp+MrSGXy3H79m1s2bIFPj4+UFdXR3R0NOLi4qCtrQ0XFxc0adIE6urqSEtLg6qqKtTU1JCSkgJHR0fIZDKkpaUhNjYWqqqqGDx4MPz9/WFtbY34+Hhs2LAB3377LTIzM1GxYkVUqVIFLi4uOHHiBGxtbaGpqYldu3bh1q1bePHiBfr16wdDQ0MoKSlBJpNh6NCh0NHRwZ07d1ChQgXo6+vjwYMHSEpKQrVq1dC2bVtoaGigQoUKsLCwQNWqVTF58mT06dMH6urqUFdXh7a2NlRUVKCiogKZTIaoqCg8ffoUDx8+RPXq1cvcjk1pG3A5giTu3LkDQRDg5uYGFxcXLF68GHPmzEHt2rUxc+ZMuLi4lLaYXzQk4eHhAW9vb3Tv3h1dunSBiooK1NXVkZSUBG9vb3h6euLJkyd4+PAhtLS00KFDB+jo6KBJkyb45ptvoKKi8lFlKZfLERAQgLt37+Lo0aMIDw+Hm5sbmjZtiufPnyMkJASRkZF4/fo1xowZgzlz5uTajiAImDFjBnbs2IEqVargu+++Q5cuXSCXy2FqagojIyOQhJqaGhQKBaKjo1GxYkVx9qitrY3Hjx9DW1sbmpqaMDIyQlZWFoyMjD7r+mVmZiIyMhKCIKBSpUpQU1P7rPZyIysrCz/99BM8PT2RkJAAFxcX2NrawtraGra2toiLi0NERAQGDBiA2rVrF0tQqLy2AUtKtxyxbt06bNiwAVlZWahfvz5iYmJw4cIFJCYmYv369Thw4ACGDh2KpUuXlraopQZJXL16VZyxNWzYEABw//59hISEwMbGBlFRUXj79i3S09OhpqYGMzMzmJubo379+jl+gIIgIDw8HF5eXnj48CHu3buHiIgIjBs3Dh4eHnj06BH09PTQo0cPnD17Fvb29ujcuTNcXFxQo0YNVK5cuch/1ImJiVi0aBGOHz+OkJAQ8djOnTsRFRWF6Oho3Lt3DwYGBti/fz8sLS2LtP/yREREBB4+fIjXr18jODgYISEhUFNTg4WFBQ4cOICsrCxUqlQJVlZWGDt2LJo2bVokZglJ6ZZzUlNT8fjxY8ycORNDhgxBgwYNxL/Lly+Lr0+xsbGoU6cO5syZg7Fjx5ay1IUjMzMTsbGxMDExQVRUFM6cOYOGDRuidu3aiIuLQ1hYGORyOZydnRETE4MnT54gODgYQUFB8Pf3x+PHj2FkZAQtLS3ExcXBxsYGMpkM7969Q82aNXH27Fm4ubmhWrVq0NTUhEwmQ2RkpJhSW6FQIDk5GYIgQC6XIyIiAtra2mjevDnc3Nzg6OiIzp07Z8vMHBQUhLNnz6J27dpo1apVsV+jpk2b4ubNm/Dw8MDIkSPx6NEjDBs2DLa2tnB1dYWJiQns7e3RrFmzYplJfimQREhICCIiIuDv74+9e/fi1atXaNGiBXR0dKCurg5VVVWoqqrCxMQEjo6OaNCgAezs7D75IJWUbjExefJkPHnyBE5OTtDR0UGlSpWQmZmJUaNG5UiXXlh+/fVXTJkyRfz88uVLyOVyTJ48GY8ePcLEiRPx888/i+cDAwNRv359xMfHlzlbVl6QxLFjxzBu3DjRPhcWFob27dvDy8sLKioq0NDQQOXKlaFQKBAUFAQDAwPUqlULtra2sLOzg5ubG6pXr44qVaoAeP+a+eeff0JLSwvdunWDhoZGjnxzH8jKysLly5fFhS0VFRWoqqrCzMysyL7LomLSpElYv349jhw5gvDwcCxZsgSzZs3CpEmTytV3XhYJDg6Gn58fUlNTIZPJoFAokJWVJT6Y/fz8EBMTA39//zwXH7/qKGMBAQGoUKECnJycoKSkhJiYGGRmZorZGEgWOI31w4cPUbt2bRgZGSE2NhYXLlzIdj4uLg6jRo3Cq1evEB0dDXt7eyQkJGDWrFlo1qwZatWqBZlMhg4dOsDMzAwaGhoAgCdPniAtLQ02NjZQV1eHrq4ubGxsYGlpCWVlZbx9+xb29vYwNjaGjo4OlJWVkZGRka3vqlWromrVqjhw4AC+/fbbz7hyxc/bt29x/Phx+Pv74+rVqzAwMMCpU6fQuHFjPHjwALGxsWjRogWA99kwPig/knj16hVsbW3zVDJqamoYNmxYtmMfm6GoqamVG1/mnj17IiAgAGvXroWVlRWuXLmCWrVqlbZYXwQ2NjYfTfRKEvPmzcOOHTtgaGhY6D7K9Uw3Pj4eDx8+hCAI4uukmZkZKlasiOTkZCQkJKBt27Zi+b59++Lff/9FWFiYeMzV1RUdO3aElZUVgoODce3aNQwYMADfffcd0tPTYW5unqPflJQU6OjooE6dOnB2dka/fv0QGBiIrVu3IjIyEgqFAkZGRnBwcICRkRFevHgBNTU1jBgxAhEREQgJCYFCocD58+eRnJwMTU1N2NnZ4dWrV9DT00NKSgpkMhlq164NS0tLPHv2DKGhoXBycoK5uTnMzMxgZWWFpk2bonXr1qIikclkePv2LUaPHo1atWqV6fTc0dHRMDU1hampKZYtW4ZmzZqJD0YJibJCcnIyrly5gpMnT8Lb2xva2tq4cOFCrglh/0u5NC/4+vqKU/ikpCTcvXsX0dHRyMjIQGRkJB4/foyYmBjUqlULqqqqSE1NhaGhISIjI5Geng5tbW3o6+sjISEB0dHRSEtLEw3kqampEAQBBgYG6Nq1K4yNjfHmzRtUqVIF9evXx+LFi/Hw4UNoaGjAwsICVapUQd26daGsrIywsDAkJCTg9evXePDgAQCgSpUqCA0NBUmkpaVBTU0N6urq+RonScTHxyMwMBBVq1YVn6ByuRyenp5IS0uDg4MDatWqlat9jiTevHmDixcvYsGCBVBRUUHHjh3xyy+/QFdXt4i+jaLhg2+oj48Pzp07h0OHDmH+/PlYtGhRaYsmIZENQRBw9OhRzJ07F1WqVEHbtm3RvXt3ODo65uu3Xa6Urkwmw61bt+Du7o4ePXogJCQE5ubmqFOnDipXrgwNDQ2YmJjA2dkZDg4OxWLDIomsrCwoKSnh6dOnCA0Nxb1796CsrAxzc3MYGhoiLS1NnLW6ublh6NChRSpDVlYW0tLSIJfLxVQ8qqqqyMjIwN9//43bt2/j8ePHePHiBXR1ddGsWTNMnDhRfB0vDZKTkxEYGIg3b94gLCwMiYmJSE1NRUREBAIDA/HgwQPo6emhUaNGcHd3R58+fT45Y5CQKA3mzp2Lf/75B8uXL0fnzp0LXL9MKN3o6GiYm5tjxIgR0NTURFxcHBITE9GpUycoKyvj9evXuHfvHnx9fVGlShXo6Ojg2LFjX627y6BBg3Dw4EHxc6VKlaBQKKCmpoZmzZqhU6dOcHZ2RrVq1aCtrV0kfX5skeljpKWlISgoCLdu3cLVq1fh6ekJGxsbWFlZwdLSEgYGBqhYsSLMzMzE2bqkZCXKA9WqVcOCBQswcODAQtUvlYW02bNnIyIiQtz617x5cwiCgIMHD6Jjx4745ptvoKqqinPnzkFTUxM2NjYYP3489u/f/9X/MMPCwqBQKLIdmz59OiZOnFik/QiCgL1792Lnzp14/vw5kpOT4eDggG+++QazZ8+Gnp5ejjqpqan44YcfcPPmTURGRsLW1hYNGjRA+/btsWHDBpiamhapjBISpcG6deswdOhQHDlyBDo6Oti6dWuRebEU20xXV1cXycnJ0NXVRVJSEszMzODl5YVdu3bh9OnTyMjIgKGhIfT19WFkZARNTU1oa2ujRo0aqF+/PlxcXPJtF/3S2LhxIyZOnIiZM2eiYcOGMDIyQqNGjfLtcxkfH4/Hjx+jSZMmuZpfSOLFixeYPHkyoqKiMG/ePDRo0AA6Ojp48uQJVq1aBWNjY2zdujVH3evXr6Nly5YYPHgwLC0tkZCQgMTERKSlpWHs2LGFehWTkCgLyOVyLF68GJ6ensjMzBTXbADg5s2baNy4cb7bKhXzgkKhwMmTJ3HhwgXI5XKYmZlh+fLl4rmXL18iOTkZkZGRiIuLg0KhQFJSEk6dOoVLly6hRo0aePToUaH6Lu8kJiZi7969mD9/Puzs7FClShWYmprCwcEBNWvWRIMGDcS3gdjYWBgbG0NbWxtGRkZQV1dHUFAQAGDChAnYuHGj2G5UVBTs7e2RkpICFRUVTJgwAWPHjkVsbCxCQkLw6tUrPHv2DGfPnkXNmjVx7dq1HLJlZWVh9erVePLkCZKSkqCsrAy5XI6zZ88CAM6cOSMpXolyhyAImDZtGq5du4YNGzZAU1MTlpaWMDU1LZRHTZmw6X6gfv36+N82K1asCHt7e7x9+1ZMKz5ixAh06dKlSPsub8hkMvj7+yMyMhKRkZEICgrC/fv34efnB01NTTg4OEBLSws+Pj5ITEzMVtfCwgInT55EgwYNxGMRERHo1asX7t+/DzU1NchkMmhqakJXVxeGhoawsLCAnZ0d6tWrh3r16uGvv/6Cn58fUlJSIAgCEhMT8fbtW6SkpKBSpUowNjaGgYEBjI2NYWJiAiMjI4wcORJmZmYlfakkJD6Lly9fwsHBAZUrV8ahQ4fQpEmTz3JfLFNKNzMzE/7+/jh06BAuXLiA58+fIy4uDi9fvoSVlZVkE8wHJPH27Vu8evUKCQkJiI2Nxbt37/DmzRs8e/ZMdHdr27YtJk+ejJCQENy7dw8PHz7Ew4cP8fbtW5iamsLR0RE2Njai2SItLQ2JiYmIiIjAu3fv0KVLF3Ts2BEGBgZQVlaGtrY2qlSpAhMTE8mfVuKLIysrCwcOHMCyZcuQnp6Oli1bolGjRnBzc4OTkxMMDAzy3VahlW69evXo6ekJExMThIWFQV1dHcbGxggMDERISAgSExORnp6OunXrFiqE2qhRo7Bjxw789ttvcHJyQvv27aVtjEXAB6W8f/9+/Pbbb3B1dUX9+vVRq1Yt1K5dG7a2tl+tvVxC4lOQRGBgILy9veHj4wN/f3+8ePECGhoamDx5Muzs7GBsbIz69etDV1cXCQkJyMjIgJmZmai/Cq109fT0SBKqqqpQUlKCXC6Hjo4OgPcuFXp6elBTU4Ofnx/i4+Ph4uICCwsLcceUg4MDLC0txf3s2traiI+PR0REBBITE3Hnzh34+fkhMzMTz549w6tXr2BoaCjaJiMjIxEVFQVDQ0M8evToq/dqkJCQKB1I4u7du/j9998RGxuLyMhI3L17FzKZDBUqVIC6ujqSk5NhZ2cHe3t7nD59unBKt3r16nz8+DGioqJgZGQEQRDw6tUrVK1aNceMNCIiAg8ePBDtjyEhIXjx4gUiIiLEFe7k5GQYGBjAwsICenp6SEtLQ0pKClJSUhAXFwdVVVWoq6vnSPHRoEED3LhxQ5qdSUhIlBmSk5Ohqqoq7nRNSUnBq1evEBISgm7dupUdm+7HIInIyEiEh4fD1NQU5ubmBQ5EIyEhIVEWKBdRxpSUlGBubp5rgBkJCQmJLwVp1UpCQkKiBJGUroSEhEQJkqdNV0lJqewG05WQkJAou8SQzDUqfp5KV0JCQkKiaJHMCxISEhIliKR0JSQkJEoQSelKSEhIlCCS0pWQkJAoQSSlKyEhIVGC/D+ccGc3pLoOKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "ax.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Projection classes have options we can use to customize the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m \u001b[0mccrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlateCarree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcentral_longitude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "The abstract class which denotes cylindrical projections where we\n",
       "want to allow x values to wrap around.\n",
       "\u001b[0;31mInit docstring:\u001b[0m\n",
       "Parameters\n",
       "----------\n",
       "proj4_params: iterable of key-value pairs\n",
       "    The proj4 parameters required to define the\n",
       "    desired CRS.  The parameters should not describe\n",
       "    the desired elliptic model, instead create an\n",
       "    appropriate Globe instance. The ``proj4_params``\n",
       "    parameters will override any parameters that the\n",
       "    Globe defines.\n",
       "globe: :class:`~cartopy.crs.Globe` instance, optional\n",
       "    If omitted, the default Globe instance will be created.\n",
       "    See :class:`~cartopy.crs.Globe` for details.\n",
       "\u001b[0;31mFile:\u001b[0m           /srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/crs.py\n",
       "\u001b[0;31mType:\u001b[0m           ABCMeta\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ccrs.PlateCarree?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fc453f99370>"
      ]
     },
     "execution_count": 7,
     "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": [
    "ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=180))\n",
    "ax.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Useful methods of a GeoAxes\n",
    "\n",
    "The [cartopy.mpl.geoaxes.GeoAxes](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes) class adds a number of useful methods.\n",
    "\n",
    "Let's take a look at:\n",
    "\n",
    " * [set_global](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.set_global) - zoom the map out as much as possible\n",
    " * [set_extent](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.set_extent) - zoom the map to the given bounding box\n",
    " \n",
    "\n",
    " * [gridlines](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.graticule) - add a graticule (and optionally labels) to the axes\n",
    " * [coastlines](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.coastlines) - add Natural Earth coastlines to the axes\n",
    " * [stock_img](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.stock_img) - add a low-resolution Natural Earth background image to the axes\n",
    " \n",
    " \n",
    " * [imshow](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.imshow.html#matplotlib.axes.Axes.imshow) - add an image (numpy array) to the axes\n",
    " * [add_geometries](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.add_geometries) - add a collection of geometries (Shapely) to the axes\n",
    " \n",
    "### Some More Examples of Different Global Projections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZXXLLlAkIRIIz9hkETQaP1GplnDbzT51pl5CdMAs8JaYM41tZWNhzkyWU5TSZlSOMfkH2dpZT1fctM3MrkVeditGHYSbCLiNVzFeZ9q/+2bVbrE4IC9up5A4Z+YMQUO7pIujtQoUBcRSilMZxHVzXQUgXKR20dBFSIv0cfqERMLAFWoGQKKWJ4willGXciQytMS2NWUhCgiOl0TTtPCd9IdFmNRanq41H3zVt7ikMpxN9ze2VV3zWCutLb7WDtesS5mfGbFVSuMZJ6gVQlkYyIlBk/xepfVyHhSYdTV+8j3hfSVno+1tGgSCVr7VjInuL2ppJaTKhOZF0o86DkoFSjKBJTPy+DEkpTZ3ipLPPrtF/zTqtv14KgXQkjuPgSGuRW6ao7JxorTK3staqhRmklEhHEsXmc9bntFK/Mu9t3lGn85Mw72Qsvq59DdOtScCahqWtZmHNrOzE65WmOB2c5PpkQqQwZlBWgtRhkuliVJZxZ59Xe/G6QaH+qxAgEUihUSq5n5GQlt33wdogwaF11sxEmGt07ZgkMWcAZKot1NheX7ma6Z/WdCyeT8/yRRRaBtDTsZzZH75GqWM51d4uoqCKUgqtFM3NLfSWeij1dCMBP5fD83yE4+J4ORw/h+/nyDc04je00jxsLUZN3IpCywD77JqgEOiE9xhFPQ5xPR+lwXUEc999noUzp7JkznTaliwEIXEdSRhUmT93Dq39+pHL5fA9H9BEkWGgYRQRxzFRFBFHMeVyCSklg4aNpN+QETQPGEJ3by9b7PdNRq63kZ0bM0rJe2qo4ZYJzWlNGIY13C9tKl2EKSMTZsSN6SktbdXoqe8cKKWI4thqvdlZq81ZIhgSBpiYscnz6jQbke1N7SZSZAR+phMrP3EVLctYk/t/xUVZOEZn1kv2Vitdk56XWVt9YJJ0HWYOZiE1M3r1/pQa7qpxtMw8I/M7Gdy6j1LVd67qRJN9D0dKXNcwXCGNiquVwWb7al/pG6XvCGCYdBxFti81GNLwiFUJUF33n+EL9WObUcu+sn2tIy3FXDOMNjvzKQaW0fCy2rwWEKvMpFk6UoBQZrpSCaFrjhwp6gwTQKMt46xz2GQW1aqa1prY9jnRoFITIcsdE/UxleKittjrsJ3aQlMZQsrib8loOFbEv3zfzcz6+C0cIaj0dtG+Yhme5zN69GgWLVpIFEYcdcyxDN1sfZqbGmkoFnEcB6UUpZ4eGpsayecLOEJSKpeolMtUqlWmTJnCvffcSblcX5BrzAabMGDAQMq93VTLvYTVClophIB8QxNrjR7D9C+msmDOLFzPZfSYdfj80ynp9Ud/6zguv/BnxrmmYoSUbLTJpniuR1ANiOOQsBoQK8VfHnmI83/641WO/ZwZnzNnRq3eTbmrg90OPJJlbe30drWjSl04UlBsasZvbGHd7fZB+w0kcIFSCqmM6aeUYaiu56a4pxAGcxVSpnBGMueGf6gaE0nhDssMpLSLvl5E1kEomWawQ5kRtAndJ9EnKzMre8M6KC1VUTJQhBCGVvKugyshiDXl0KwFV0pcRxDGxrLUGXwsq/1lBVOyJrOCo95IN4I38ccJLAxj+5XcRwpBHKegRh1zNo7AmoPPMNbMWq3jEyL1F2BNeWEmj3RdCmF8DbVhy44+MvOOUpq5cFynhrmn7EDU0UHKRbN2hj3fCNzk3LoHfgXjFJkJzny1/CNRJr+uff3GdULUvVAqsVaBzRh6lJaxkUqWOhzVEkCNga/0tmZA0uN9fq87MXNfEmZZ63ad9zMrZUVNczCMWGbOr19wCSHWPtc0guwzk9/CSg+fP3c/f7n1egAKhSJaCCqlXhqamtl8q+3Y6+Cj6NevHwUHtPVcDxs8kI0324pyDJ4jafYFTYUcjpQ4nouKFdVqhWoY89HkKbzxwj+489bfp/0cOGQYHW3LicKQPXbeiS232JR+/QfQ2NhEoVjEdwxzWrpkCbNnz2bjn57LhA02pFypMH3aF8ydM5tHH/0Lf3vicTaaNIltt9sWrSGyDoYojokVOH4Olxy5ghmHw478FkGo8HyfYkMR33Vpb1vB4kULaW9vY/bsWbz26isAuJV2pvz9YQrFAo7nM/vLL5k29ZMUz9109/c48OyrsKYEjnQQOMjY2HBRbDRpHCx+K4i1Rkdxqlllo0gSRpksCIFZ2FJAUCnTvngelc42Kj2dNAwYSm/HcqqlXpoHDKZ50DBahowE4Vh6xQpvozHUDN6sE6WmlSar8aV7buKLt56n0NDI2A03Yem8WSydP5v1t9mTvb79Y1wJA4surQWPnCuJlGZBZ5VRrTlcKZjXUaXRl+QcwaLugErUR0dOHVK1LiSaqBRQ9AXlUNWtwXriNh9yrmTxjE9Y9MWHLPniA+bPmsGKZUsZNGwkIzbckg13PYihY9YnlVP2OpWEH6qaNayVph7oT9RDs/ZcKevWVPK7JOveyjLg2roUUqRMF2oOR8jqTcn6NnesSbrkzhmtN32Vf8EZVqesmQenVtDXXQurTwMePn6i/u6vH1rpuMFEEryupnEmHsVElU9Mt+SlVKJ92BHoqzWv7gUT7SDVa8UqrrQaSMIIE8mjs2YgGhOiY03QVIvNQCDJ/7rutgCoUheomFxjMxEOWmsaPaMpdAaaey44kS8nv8Pu++zP9ttvz4477cwbr7zIhx98wMcff8zixQspl0qrfNUp02bTWMgRRxGVSoUXXnoJ1/PpqUZM/fxzli9dyrQvPmfxvNmst81uvPvk/em119zwG/bdex8G9O+fEqXr+alzKUtkqfDEaORCSqN0ZEY0cXQlbiSRQESqRsEagVY1LT8lZjuaWivKPd20rVhBU1MT99x3D1ddflH6jPW22oXh627E4LXGM3jMuvQbPAKkzOiQaWeSF0gXZSL4lIrRWuE4LtJxDNu1izMMAl6/5wai3naWL1rAyHEbsGLJApYvmseKxQsp9fasNAcDBg5ixfJl6ffv3/IkA0eOSenawVhpK+scfXpt//v1ybvTuXQhfdvoDTfntOvuRWlNGGsKnkPeFUTKQGGh0sRKE6l6ploPhfW5qWWErgStBXGW7yUCaVXXCIjCgF8csjEAEzfbinPPv5D1x49l1swZPPP3f/Dko3+mdego9vv+z3F1QL5lAH5TPxJNPblXov2mjtE+A1UHg2QZ4tc089qytrYdiZNYHulJog5PrnvNrBadHcPsQGTGqxZVkYVWa/1OZIpIBEDK9gyFXrr/Bu9rrbdY5bv8M0xXkHFykJlIrS02K1MzPFG1wSxaoW1QssVakjXruQ5RpAijOIMLr6qHq/xYf3aG2Rrmj9Vuan9SWimZiMNk+HSiHZjvAmifN50//uR4ers6GL/JNhx92a1IaYyCyw6YUNe9jbbYhm32OYyl3RU+ePJeBg0dQVdXJ3Omf0ZQLnHkcd/msEMPQQlJb3cvd919J6889zfA4LPPvvQGOUcweOAAig2NVKtVytWAd955hxO/ZQryj5m0JX6xEY8YoWIcKVixeAHz5swCwHEcvpg5h4GDB2eIXKd44kpYOFkiypohfYiOGvFpsELTstTUbO/rvKo5WLXWRHFMUA0ohSHvTZ/POcfszbpb7cY+p/6MfENj1lBabdMqTrubuG2EMIJfANJxUmGitHGMhNUqVx6+GQDnXngZPV2duIVGJk6cxICmHPfeczePPlxTKoaOHk++sYl8YwtursCI8RPZYt8jyReKRNm6Z6tc1dmxtIIhGRGt6Vq2EOl4NA0YbGgz4TY6S3lZcbaqJlb7NeVhdYu0zwlJ3/sMfNL9JXOm8fTvLmfRzM9oHTCI1oGD6dd/ANVKiSnvvkFTcwvFYpFFC+YDsP6WO3HkRb/FcVzL8PpGvGCZ4crYcaKMJQLdKFa1LkpZi6xI+QuJ0mD6HNc54/q+eP08rDymqziSkWdZVpEq7jp7x9p9EmsI+06XHfDVTPfr4QVqhJw+M9EOExVf1/DWZJBVHCG0MkA3AukaE9GxvyehQ6Sab3r3lQYkGcq+QyvS77XJSDFYIXAc4/2W1rOptU4nqbttKXPef4lKVxsqCimXetnz2z9l4Wfvc/OPj0ufPXPKu5YSwBGwxW77894//sbIdScxf9oUJr/3FpPfe4tdv3k6c2d8ztwMhjl41Fje/vBjXn3tVVQYIP08i76s/S6EoLFYIOdKKqFCVSPef/8jHnv8Mf72yIMATNppPw4555dcftDEullpbGpiwoaT2HG3vTjvp+eRLxQolYPMGTXcLaEGKWqavbbPl5lFkkI/manQ2d9q/Lzu/zqrSte+h0FEEIY4UhCEASsWzaXY2MRBp56LbGyq0UsSu/kVi0PFseFPmQWmUTUiEPWJHiiFkBLX9zn33td598n7uOW3v6GnfTkHHXUsJ594Am++8RrvfzSZCdvuQeeyRSyc8SmL50wHwMsV+NnDH6S3q2O4mbGxo0zCQmuUWGtJfHDr4MyuPLXVmQ6g6HPfrDXXd1VIi3/W6yoC4wTOnLiK/tZNFIZ5OaIWSjl09LqcdM09REGVrmUL6WpbSk/HCpbPn4V661U629vo6e4iXyhSKZeYOflt5n74OuO23JlQ26iBPu+RJKwk9JYxvtKupp8zizth0jUsu8+49X23lYKKa/SUjFWdrMnSTIbO0yu/Qh9NeFztTHPzfwLOtd38Wk334dRUSAmhr2PJ9i7WlgCVQusYKU2YkJDGFEjCN5RWVKsBKjamft1A9KWauhmp+4/k3VPPsnWOJIw2iZqIM7jPtJcf592nHmTGpx8yeMhQli9fZhY1cOjJZ5BvbGHq5A9x8g0I6TDj3RdZa5312O07F1BsaCLf0MygBpdqqZc3X3yGaudy5n4+hbalCxm1/sa88sT9ZNta66yHn8uxzW77cc/NVwMwYPAQBg0ZxrTPpnDsqWehlaJlwGBGDh/CeWecAsCYjbbmuJ/fnmbWBOUePn31GZ646SL6ttseeJwgjIi1xvM8mooNrDtuPE2NRRPSBTbw3obZZCyA2hxSw80s5p0QvEpV4NrIJ06TmjasU+ErBARBSKm3G9d1iZDstOWGdLa3pddf8tfP6ieVRH8QKTShrSMvYQ5aK0xMZK1/KQ1Y/DaFoKSk5nDVxEHAL6zWO3z0OrQtX8K+p13ExB33ZcWiObz9+N3kig009R/M0LEbsPakLVerAqz821eqlyvd4SvVenu5FNCzfAGd82dQ7ukkKPVS7elg/vRP+fT9tyiXemnuP5Czbnsex8ut5pmZW2fGKvmerDehqcFIaEorFhJXS4gooGHAMJbMncGiLz6m0r2CD15+lrVGDGfTTTfls88+48033wRg6Mi1GDR4SEo//YcMY/D4jRm+8Y40DRyxWoYkVqKB+j6nwiejBcfVEpUVi+itVEC4NDY2Uug/FOE4dbzUSc6341vXj1VP8Nc0sar/SKzmpF22/4R/D14YMX6i/u6ND5sT63DR+gdpXfPkm8FRdRqVNHmnJuMjComiCKvzrmxe1j2iZh73NVn64jJCChzpWI+mOR5bJmB7igAuO2ADAPx8gSFrjWP0uhN48+m/EMcRm22xFYsWLmTRwvl1b+m6HlEUArDlngfz7fOv5rMP3uamH58AwJ7fOIgN11+f3918I1vveyTzP3uPUk8Xy5cupt/AIey5267cf9899BswkEKxEYQgiiLCatl45qMIjcZxPUZP2JTx2+3N2E22o2jDvrLU0b54Pp+88iQz3n+FcncnrufjuB7ScUFI4iggKJfoXLaI4WuP45vHnsApJ3+bvO8awYTx3GPhIrnKebVJBDrzZJ0wWDvfSdqk1sRKmUgHe4LWxjR0XSP82lcs4/e/vZE//O4m1tt0G753ze10BIl2ZZmY1qg4NkxW99G2k+eL2owb4eqkHUstHCltpIbM2Iq1ZR2HAcvmzyLf2ETr4Mymt5kXXdWSyIaG9ZmSzEnZ31ai7FWcp9P/5n/yJkumvse8zz9i1hefIqVDZ0f7Kh4C/YaOYu2NtuaAMy5LhfLqmgZyEkJV65oQ0DH3C6odi1k64xO+/PRDers66e7qoLenm0EDB+J6LvPmzmf8uuPZdNNNKRQKjFtnHQ466ODUpyPQlCslHn/8cdYePRohBLFSzJw5k3vue4BZM2dw5h+fpf/w0V8xaP980xoaPJgz7VN+d9bhjFprLQPJBSE93V10dXbQ0NjMgJFjGLX+JnQvncfodTeg2DoQvCJrb7Eb0v0njPuvbSvzQaBuHV36jfX/fUz31F8/zOopjVXSV2q2WhghjmLCMLTONVl/QR+zoMaA61+uRvf1WhrCOscQSMdoPFmGoZXixf/6JUURIoRGx7FxBDS2Uu3t4ZkH/kT/ISPx8gXmfTF5la/Y2DqAfEMTux13JhvtuC+VasCMD9/gg2ceZPYn75IvNLDWehsxY/K7VHq7AHA9n0JjEzqOGTBkOJ1ty4migCgICIOAptZ+OK6H5/u4rsv8WTMYM2lLTrjyzhRTXdVY135JNENSh1niSArKvXz43CO8/MDvGDZqDLf9111ssN44nMR5mDinardJmajSmRTUjPDUWqQB7bEF57NhQyb5ISYMA6rVKiqOkQLCOOL3v7uJO2/9HQA//9unRAhrNipUnOyqnplv2xfzMTHha2FQOu14Qg8YLdikBqGVRjg2jCalxdoTEstHCjBJZ4mtnrHZRR/AwJq8Nc32q9o/o0KZ55Q6l/PJcw/y9F03c/r3z6C9q4f3P/iIubOmM2ydDRm1waYMHDmWAcPHMGD4aPJNzV9xv5VfIdVuARUFhOVeHNdFCLj7olOY8/lkdt55R9Zffz222nILBgwYQLGhiRHDR5LL580ttUoVH0tddr1qK8TNcaWhVA0548wf8vILzwOQLxTZfL9vsvvxP8RxnNQQ70N2qdNbqUy/xcojpwFXwOxP3ufhX/4IFUc4jktX27I6eGnSzvvTuXwRcz99v+76s259jn7DRtVP0T+t4a6urcwf/21Nt8Z0/5WHr3w/mWR9WW1XWc0omywCNTw4+xoJnGE+1hZXBt+owR3WYSaFNFpuqkloLrca7hWXXwrA8uXLae/sYt78Bbz4wgsATNhkS0ZssDlaa4qtgxg1YWMcDH427ZXH+fsjpuLaRjvvx6Hn/or2mR9z79XnsmLxAvr170/bihVsvPN+fPnxWwwbMoghg4cyY+YMFi1cwLCxExi59jrMnfohSxaZXcnX33hzKj3dLF+6iKbmltQ5AdDU0spW+x3F9t8802gzukb4Kw97YpIndQVMzLSwUM7rD9/GR88+yG133cdmm2wCgCMFjkigmHpHRw3CFDb8x8yB0sLMW1agWQYcRTFxFNLd3UVPb4kojghxePS+23n1pReYNvVTALbY92j2O+1CENLg/nY+s28lSeCgVS27+uMJE05pxfYJ6ZhxELVzEvFR73yhdr8sY17VYvwq7XaV8/IVGm6m+21zp/Gb7x0EwFXX38Tfnn6OzyZ/xM7HfJ91t9qFQlPrqp8rIKqW6Zj+Pv0ac1QLg8gPGIHrCMqdbRT7DTaZYAKEVtx82n6sWLyQYrFIHEdorSmVSmy97bY8eN8d1LT7WgRPQk+JEEMnyQMakSpJmiiGzgDaq4Ke3hLfO/ogFs2fRy6Xw3EcYqXxPJdqpUKxoYF8oci4zbZjzMQtkH6e7rblPHvHr2lpaaHQ0AhCsv7Gm7LhHkdQHLF+/fDbd0o+dyxbwHtP3s/rj9yWnnPoD3/BRrsfisZYY5XeToJyiZYBQ01kTJY//o8w3FW31THd1ddesH9qdYRY13T9R3u+0jWt1HElnqiZlEZDUjVtqQ8Bp7G0yZ/MaC52IdUFhturk2gJDbx+7w0MGTqM+++7k7FjxqAUvPDG+/ziiiuYO2Mq+XyetdZai86lC5j60buMH78uS5cZ6VkoFFiyeBEAQ4cOY/HiRbQMGkHbwjnc9INvAnDsid/m1JNPYqedduDjl5/C83NstsUBPP+PF5Buju0P+zYiDqgunM6vr7+eUrnKWWefSWtjA9856wzGjl2HN954nQsuOB/P94miiO7ODl64/w+4QQ8Tdj+c/qMNAfYtBVD7LGqmgFapYBLCYYcjvkNOxhy89y48/9o7rD16NDnPRUjj8Y8ST1FGO0oiFRL/hUZTSwapMenYZgEFQUgUVnGlwHclLvDd4w5n9swZAGyy+8Fse8jJDB49zvgIVIzj2vRNaw0ZGEmgkcisJp6hhZQQlUo1/iSyxnj9BCBBWeYgElLsc6+sFZXVvCwBJZjn167NlRhxHxjiK9qUp+/mL7+9ktNOO42tttmGs37wIybucgCn3fwYfqEhlS99n6+BqFLmsZ+fik+I67m89+77bLHHQQweMYqn7ryZ7fY/muYGn7CnkziOWTJ/DudcehXHH30QDZ7AqSstoDLjYZhtMmbZuFmTy6gBE7ONMMoNQtCah4FFgejXwJuvPU+yxd2Ktg4+/eRTNt5oQ/L5PD09PSxYsJCHHnqU7s9fp1yuUK5UOOvM09lv373p7ukhDEIe/ssTXPvdQ7jg4Q9xc3lK7UspNLeC4xNb7VYIaJv3JR888yDFYgNhGBBFEc/eehVvP3IrheZWmptbaG5txW8awIZ7f4umQcNXgqz6zNp/r2XWz2pPW52mK6xIm7TTvhxy7vVfgbt+RctoQ2FPO8tmfkJc7eXL91+ms6ODoWPWY9i4DXA8n/5jNqS5/xDAFiNJnHM6MWszMYZpHIeuc+ilnnddMx0Trefm0/ZjxYLZjJ0wiR1235vZs+bwypMPGQ0rswfim++8h+vlaCwWiMMqy5YuZcTIEZx11llUg4Db//QH7rvvAe544BG+cfY1LF88ny/efIEv3nqeQUOGE8Yxi2fPwHEEhXyeUrnMRrvszx6n/Iwnb76Yj1/8K988/ttcfMH5rOjqQXt5RjS5qSn/m9/cREOxyKSNNma9dSfwyaef8uprr/GnW3/PkGHDAcGuR5zEyA22pHn42mg3Z989E1MsatlFKq6lmNx9wYnMmvwOAOtttBnHf/t0jj7kAFzXw/NcHEfWmaVJNmWW+ao0GiI7zZqgGhEEAb09XXT19iKkwx133MYDt93MqFFrMW/eXHY66FvsesoFacqvk2gdGQ+Ptl44KSS+hGoUZbKGsiFsSR8yqQkJx8w6COts1JopbM7JcB57C8+BUmcbX7z2N2IFQbVKqX0JjQOHM2LCZgwaswGO669aAU8I7qta5hytFJcfuCEAx37ne0xab21++uOfsN3+R7HDMT+g0NRa82OkXNe8y7SXHuXZO65n552257prryJWMePHTwIgXyhQsdmJ3z3zLEYMH0LBhRtv/B2u63H9A0+zTj9Bq5+KK2rOy2T9JPCSQmmFQCKFJMH/k7kyWWxunTKWFpnR9QJxlQpbKuEEwtg26dxOnvwphxxyJLscdBQtQ0fz3N03I6WkuaWVpYuNVdjY3Mz8ObMB+Pjjt2huaiYIQzo7u1jR3klnZxfdnR10dnQyddoMHnzgz6y30WaM2XxnWgYMQlVLxNUSYaWCV2ig0H84wyZug5ZOPUxDlj7rXyGlp8yRL997kft+8QPiKPz34IWGlv6637BRTNr5ALY+4Nj6p63KmsrQf7l9Ke8++GumfvA2PV2dTJo0kWKxwA7bb8uwYcN45dXXmDNnLlOnfkF7ezvNzS1UqxWGjhjF4BGjKBQbKLYMMCaolAxcewLNQ0fTOmQE+X6Djelozefe5YuRnk/P0nnM/+Rtls2fTevg4bz11J9Ze8LGbH7QySyY/BqRzDHvs/doHTSMcVvtzqgNNueqI7dc5btf86trOfLQA5PxTlsYBFxy+ZU8++zzhFHEupvvwC4nncdHf3+EZbM+o9LbzdpD+nHF5ZfhSM03jz2RJctWcOwVt/Hb0/YDYL2Nt2DggAG4xPTrP4CBg4ew9rj1kI5k8cL5zFmwlG232pw9dtiW5sYGFi1eQrlaZdmyZdx15x3MmD6NhQsXstWu+7Lzdy7C8fN9ocia+WwFUxzH3HrmgbQvWUi1WgHgvIuv4Ec//CGu46RVlrTWafC/UjWmm8A1WmNrkBqurFQShx1RKpUoRzGfTJ7M9088om48B44cw/dveRKtDGN1HIck9TsRmCkR6QRrrfUJXZuHpBYzWaad0mFi9WTSQzMQg7RhhI4AFfQy+90XmDV1CsvmfUnehWmffcoOO+1AQ1MzOd9l0KABzJ27gIcf+DMAB59+PuO335diy6BaOeFVEVC6YDNQiDVGlIKe9uUs/nIqbz72X/S2L2PJHGMRHPD9iznim0cz7fMvWLxwAU39h1DsNxi/ZRCxhv8650j22mUbLjjvbJK4oT/dfjdXX3ktSsX8+oZrCKOQQw45EJJID6vMLC1rGn2HJk+gUGk0iEDaIkQujhQorYgSnF2YzEAwVd60Ld6hVGx/c1NBat5NWasotspPViIldAOuY+4ZxXGmbonGcz0EDsuWtfPQnx9i8pRP+P73TmHUqJG0tbczcuRwujp7WL58OePHj6dSLtPc3JyJytAZ5UvhSgcQdHZ18Morb/DiS6/S091NsVigoVgkl8/T3d3NJ59Mpd9a67LnaZfRvWIxOqwiXY+GfoNBOriqSr/WVj6ZPJnZbz1FU0t/iq0DcHMFwjDALzbROmIdZn32EY/e8DOAf4/ptgwYpA/+wc8Zs/nOq7Zz+rS2uZ8z/6OXiSpl3n3hr+y68/Z899STGDlyJJ7rkspCkb2FJo5iurt78DyPOXPmMWfufDo7u1i8eCn5fI4wDJj6+RfMm7eAhQsWEEYRk7bagYb+g/jw5WeJgipxrBgwcADbbL8N48auzZTJU9h22y1pa+vkzjvuZfGiRQgh2GijSbT268eHH35EY1Mzw0aPZdqnU+juMOFMhWIDRxx5GLvvsTuVakhXdw8zvpzNR++/z7Jly2hqbmbSxpsw68svef1F4zDYYp/Dee+Zeuy7qXUAN/3uZh6+7x6e/NuT5rztd2bQ2A0ZtN6WfPjsg8yd8jYNxQLdPT309vYybOwEhq09nqaWfnz82nO0LZ7PFdf/ht132o5BLc1p2FQUax54+FGuuPh8Nt39QA46+2rDtDLFrQU2dz4TVfL0LZcx5eWnqJR70Uqx/gYb8uob71gvdK0AiSl/aPhZrCFWiihWqNjcq1INTEhYuYzvmagIbTPDOju7efml5/nVlZcQWOY+fPxEjrviNnLFhvQ5dcHkaY/tJwFCayJbDCcpAZlYN25CSwkzrr+8Tko6slYzwZHQkovoaV/BW3+9n5eeeIgJE9Zj8y02Y/31xoLQrLvuOgwfOYxKGJr3VsaUvvv2B7j9D3ew+Zab8/GHkxkybDgt/QfQ2H8QIzfcmlGb7kyhuX/aERVHlNsX0zpwMJEwRYVGNSn65TVftDmEUUT7nKlMf/8Vhk/cjrXGrksUBXTMnUrH8qXcdfX5AGywwQQWLVqMkJJJW2zDx++9Q8eKZRx4+OFccfGPaWho4C+P/Y2fnGPO/9kvruQbBx5ApDRDisrguoiUcSZMKYwjXMdB4BJEVaJIEccaV/ggFOgEzlNptTfjWjA0UI0CG0qYpO/WmLMQNl5XCMI4IlYxjnRsaU6Bwjhcg9iMMWiKfgHfdXFwUcpBa4HrxLY8g6AaVimHVfJezlhCjldjIlb4akx4qhHWmljHoI0jFwyjl0Km+L7SKu3Xhx9N5pwfnc+ihYsYNnw4hUKeajVg2dKlxHGM67qss976TP7AOOe+c+rJtLe1U65U8T2P9o5Opk+fwfLly5m42Za8/8ar/x7TLRaLulqt0tTSwt5HnMC4HQ/Cax2avmud9aZr2Vr9+vfn+uuuYqutt0gNF2NO1vKlgzBMF5GyDjbzmwKhiVSM49i4SylwnaTYiKBteRuvvfomSxYvZZsdtmL0umvTmGvAySw2pWumtTGFBK5wyHt5QwxhyJw5c5g1y/xNnvIJTz31LADN/QfieZ6tYOSwZP4cADbZdFPmzZuHFhIpHRpa+7PbYcfR3baM5x68g672FTiuSxxFNDW3cNvtf2DrLTano7ObbbbdgfPPO5dLL/s5J1xwHU//6VdcctFF7Lnn7vSUy2wyaRJ7nHgOOx5+ChqY9tJfePCGi/H8HBrYbJsd+M2vrqS5qQWtBSeedAJvvvE6nu+z/V4HMmz8JNbf7TCQbvreOomvzozLtH88xP3XX8wmm2/JlVdewZbb7IhWGs91UEob5qo0SotMzYUkx97Ul60GJjph2owZ3H7Hn5g/ewaVUi9tSxYSRSEj19uYcVvsyPgtdmLIWuMRsr6It1kgNRw+rW9rtfLEiaO0Jo4i4rimMSVvIqTE81xc102VyiS6QWlDb440QkfFGikU1e42Xr/3Bt596e/sudcenHT8MYweOxwlY2IdmToTStn47gSOkBlzG9CCSrnM4vmL6e7sZuniZbz5+ju8+eY7jFh7LJvueSjr7nI4X7z9Dx78xZnpuJ949V205AULPn2bWZ+8z+dTJtPa2sqC+capmi/k8TyPsWPXJpfL8/FHk/nROWdy8snHorVm/oKFvPLymzz37PO88cZbANx11x/ZbvttWLxkOfvvfwQdbSuYOGlDHn30fmZ0aIYWY4pexh9SmwF6KiU0At/xcaRJ547iCK0M80xi8x3HLPIojhECIm3iwR1ZK7CSMFgpJK500FpZBitRNvkpVjFKK2KtCOMIpRVBHKKUwnc9GnNF8q6PigVhaObAdc2cVqPArFkV4dq1V/QLRCEIERPr2JR3dEyacKwMNBLpGIkgUkbrdmx/NBpHOIQqwnc8y3gFYRzb9zSc3JEOnnQRWqBixZNPPcvzz7/Ijjttx7Hf+iZxbBJ3tFUAlJJ0dbXzwQcfcPrp3//3mO7EiRP1I488wLvvvsdTTz3N/fcbE2uLHXYmX2xi7c13Zew2e9uK7BCUe5nx9t95+DojdY89/SxOPvEomgoOvmO85WEUU6pUiLWtNCRM8H3O8YjjmEoUEGtFVYVWOzGT5zkuedcn53qEyiQzFNwcsYqJUeliixNGo3W6tYlMM5Y0eTeXmt5SmvsKIVBRzMUX/4KOzk4aGxspFgo0NZm03Lvuuo8f/PQ8zvjOMag45vXX3qQahEyZ8imzZ89hwIABCCHYb799CaKIY485nocfvp8tt9jMErkAG2513s8u5PHHn+DoY47lyKOPwsv5vP32e1z0k3PY59SfsfUBx6GBZ265lHeeMllp3776Tm477wQuuOgCTj7xBJQyTKyts4s33nyLnq4O/vrEE6xo72TwqDFM2usoRkzcOmOta9CCKDL59T/+6U85+eQTKPguja1D8X2DDScMNgwVYaSoVkOCKAQEvaUSURSj44jOrh7+9vTfuPvW37D5PkcxZuOt8QsNtAwaTrG5FRBICY4jCYOASudy3GIzfr7BVA6TAlTM0plTCCLF0HGTcL0MVprg80qb+OikhoeuOcCSimNCWpjCMhYpaqhCEt4mBLTN+ZwHf3EWu+66I2ecfgrNrUUqUYVQh2hhzOFI1VLSHetkRBuBHStNGMeUI8kTDz9NtVxigw3Hs9666zBk0ADQirdff58Lz7uMPU78Ec0DBnPP5WeyyRab89F779N/0GBQId84YB823nQiG206iZbWFmZM/5Jyb4kNJ03A83xrDoMKYxzfxXNcHOEQx5pqGCCFoKe3m+UrOhgyah16urs4//wrePdlY3WdeOK3uPCin1IJQxDgWm1TaWWTlRRKa8phFaU1OdfDEQ6OdYo5yDQKRlNLRjHvb8bKKEoKV7p2jMz4O9IBrQniCDdTKjGIw3TNRXFMrGvrNed6hHFEznXxHA9He8QxeK7AcQSlsEIlrJLz/MyuGoq8lycOQQoHx9NUowpaQ87J2wxUI0grYZBCSwnf8F0PNxMxo5TCdVwrGLRlyoY3ONJBaGkjJxJ/UpI/kCSAJf4OM3aOhHXXm/jvMV0ppc7l8lx2zbUMGtCPL6Z8wDXXXMeNN17LE0/+nReeew6A8x54h7/88ixWzPuSaqVMc3MTCxeYIh/77b8fv7ruFwgUPdUyURwRaZXqEY6UuMKht1qmEge4rosnHaOdaVAoAps80OQ3UHR8Ah2lzjWtTTFtzzGBGLEyiyeKI+N4URrXkcSJAweJIhNehcZxHApuLmXwtb9EQzAmUTJ5lqdnPOAi/bdW1ax2PNIQRNAVKFpzkimfz+T4o44gjiPyxUaGjN2AiTvvz4Y77ovr+Wil6Fr0JSsWzSXf1MytPz6OI486gp9ffgl+zkcIyfTps9ljjz3wcwW22HFXfvqjs3niiSe47fc3c+g51zBxlwNqCyLpr4R7LzyJ9casxSUXXciAfi209h+UZg5KRxLGiiCIqVYjekplYqV5+dVX+Ojjj/hyxucsWTCPuTOnMWqDTdn5mDMYPi6bnmzjpS1MsWTWF9xyxsEAJqJj+134/MN3EFKi4pgRI0Yw9bNP2fGAo9nt1ItqY0eS1WgSJrCaixCZUoDJljS2tGOC5WpVD1sIAZ+98gTP/PEqLrzgXA7Yfy/KUZneqAQoQhURxjHTZszn8YdeYOGcuQxfawTHHvcN1h470kYxCKJYpRrNMfufxPKlywEoNrcigKbWfiyeOyvt06Chg1m6aAmXXnMBs6bPxvc9tttpG0ats5ZlghrHaoUmAkfiOY5VGgQ518dzXDzhEMUxkY5wpUOsFa7j2Gsd7r/vYa678leMW3cdTjr5WA499ECU1nadxbjCxMeGlvHF2kImJAkwmka/iCNkbT2IJETRYDep3xJzvSOlXRMm5tOxgk9KibRrJWHEQRRSjUPrlDOmfjUOyVkFypUOpaCK77q4QuJoF4GD7+ZxXYiUUcISXhFFBkt2pQtKopSxyLSICeMA13XwpIEePM9Bo6iEVSpRFSGkeTfAFWYMTUknK2QRRgA5jn0vQ28mTLLWh2SDSykFnuubVHdHWL+DYbzrjPtqprvakDGtNfmWAdx614MsnvkZY9YZz48vvIT3P/2Sd9//mIk7fYN1t9oVhGTetKmUezr57lln0X/AIGZ9OZPu3l6efPgh9t53N7bbYRt6gzLSkQS2+HUWAjBmiCYMY8paI5EUXD/FeoI4olKt4HoCz/XSidBaE1WMiSGFgxAgkbh4SCkICKmEIRpNzvWJtDEhpJTGbEgAfw0Fz0c41o+rFZG1W7Nha7bOUfrsbNC+FqSxx4aAzdmOgIJrJOU7H3zMWd8/A6ViGopFwrBKvwYPV1dxrKn88FVn8tlb/6ibiwMPORzP86yDSzN40EC22247PvlsKm88/xSHvvx38sUmDv7h1UzceX/LuKxZnsZBCfY9/VIeu+4n3H3v3Zx40snkimUaGhoRjmEsUajSCBClFL/85VU8++RjrLP5jgwZuzFjdjiQg8esT66hyQq12DI3U4XfYK2A1nSvWJr2f8utNmf/PXfgmot/gpAehUKRxpZWfv+HW7jp+usYuclOrLvVLmZcVQRWQ9Uqtvi0tHGWNt42CXGyZrPSBofMyDqqpR5eveuXzPzoDf70p5vYcIPx9IRdlOMqCMOIgjhixfJ2zjzmTHY+5gwah+V5/rEHUJUuLrzyHCKLd0eqhm0++PQd3Pq7+5kxt5tSdyefvfUyTa0tAGy909ac9/NzCLXGVZpCscjWu2xN1WY0VqIApRWhUkiLNQohcKVDpAwzbsk1EVQDIhFQkoauHGkYRiWq4mqXnOMhpeLoYw7i8MP3M1W3HJdSUDFmvMVRDfJqtPRIx2k9jnRjAQxdRhZ7VWhim3otbb/QpqhSZCECIQS+46ZMnISp2vWRDccL44hQGfzWlU767FjFlIIY3/HwrFO1qkJ8ATlHEkYVwkjjui6OcNN4ciUM3Og5LtpJHHeKahgQxwrP8awyZv8UeMIDj3QOqmFAGU3By+FIh5zrIYVj+ZFCaNdAbcood4nypdFEcYgS5jlSSKI4REhJTwi33X4XSxYvY/6iJatjq1+THDFuov6OTY6odHcw/f1XmfrGc6yY/yWb7LI/L/35j2y0wx6su+3ejNtyN2ZNeZsZ777MsrnTiaoV5nxmioYccNB+/PCCH5DzfII4IrATrLUm7/nk/Ty+9IijyBYrdqwGElGuVhASXMcFbU2VhJ1ZLSRhiA4OOcdIzwQn1ChCGSGloBqFKLR1IGB3txEEcYQjRGpO5FyPnOObRAu7wI3xkGi/Mp2EZFIMn9HWBDI2bjUyWVfL2jo443tn88UnH1Fs7oeUgs7lS7n217/mnvsf5uO3X0vH/Ijzb2Th1Pf57I2/U2juB8Kht2M5QbmXoFzixZf+wcjhwzjzhz+mu6fEEYcfzl8eeZiu2GOv712GX2gkqeJk3j9bBMWoLdPfe5kX7/gVd911BxM3nEixodEkOMSKKFL0lip0dvXw+eefcfb3v83BP7meEeMnmfsmaUOJMo8tKJ3E22boR2vN20/cw7N/ugqAHffYh+HDhiKkw8cfT+bLLz6l39C12PrA49h494NIkiRq+KOuexfzCjLVdJOQsGyIYKw0vSsWMv3Vx3nxkbvZZoetufDCH9G/uZGuaje9UcUyUk1PqcKzT73BI3c+RKVcpd+gQXgy5lsnHcy2O29FPp/L1O0wdWCFzab8YupMbvjFzcz4fCabbbc1O+22FbOmz2KXfXZi/IRxhLFh0mEcp5CXKUjupPhoEhmSc72UjoM4puj4tJW6aSoUUiHjOQ5FL8+KUjc510BtnnUMaas1Jy3RljXGex/EcepMSrL7IhUhMJi3Y+8hhbBrwzDhyFZwM/VThO2/azF5Uo3alTKN5HCkg2PXg2dN9koU0l2tlTOVVlNu9Aup0813PGKlKIdVo51bbTJRiHzp4TuuIT0pKHoFPCeH67iEcdXgvghc6SNtnRGt7MYKwjjVjAMvQlshIoXAczxc6VkLSdlKhNJCU5EdR41GkfN8S6Nmnce2ZkviqD399LN59tnn2f3YM3nhnt/8NzLSbrBe+T6RC28/cRfP3HpVeuhHd71KU7+BLJ7yKvdedQ7NzU2MHbs249dbh70P2pu11xoFwtQc6A0qeK6HK4xZj4JKUKGQ86zZElMOA5SGvJM3cAGRkbRKp3GDQRwazA1hpaiR2nk3h4MkDg1jdxxBKCO6qiUc16HBz+M5rgHPo4hKVCVSRupivaGOcNKiOXnPp8HLp4s8cRAkIaTSLowEC0qYQG854OfX/p7HH7ibsRtvyxE/+w2en+OZP11N+6K5LJszjf6DhzJzyvvkio2M2Wgrxk3cjPGb78iXbz6F39hKy7DRxOVeYuly3y/PRwrYdNudmDPtUxYvNE4YP19g4PDRnPLrR2pTlITr1IWLWA+vELzxyG28+9e7ueba69j3GwciHQelIApjKtWAm359Lffe+Se2PfRktjv05Brr1plYXfvupp5obbPr5H8Vm8XZuWwRHUsX0LVsEV0rFhOHIcPGTWTUhE0oNLakhWrqwtyEwJVJ6FiCodXKNtbK+9UiGKa/9TSvPXQryxfNZ6edtuc7pxzL+PXG4kmXclSiO+ilq1omjGOmTJ7JNedfy9B1JrLhjvvw/O1Xc/ZPv8OOe25HrGvvZ7ToJEJC4AjDSCuh8f5Xu3uRrkO+oWijHRSe4+DbbKxSGKTMK+e4JKFxvk2LrUSGGUoERd+nu1rBFQ6VMMD3PIqeT6QUDX6OMI6pRhFSCJrzeYOBWgdWAoHZKSLBwBWmvGaoYlxpYLY4NccMxpqsG2EFiyNkWtMhLctqoYKkUpgZDmNteFbr1VpT8HyqUYgUkkBFKU4eqdiOjWv/nJq2bHHmUBnhkHN8IhXhOS491YqNMjC4qiMk/YoNuLg42sURPo7j4lhLEgsxVcMKQRzgSsNTIh2ZKAthohjyXh4VmyqIYRATqxghIe95hFHIjb+/h2q1yrlnn4oQiryfw3NrlqbWCmUth47uCi+9/h7vf/wJLz/9Vzq7uqj09vx3Sjva2emzpLY+8Hi2Puj4Wg47gsUzp/CHn53KH/50M9tutyVBFBJaiSqQZi8tFdNUKJq9k0KJiCXLOpfz0pvvsteuO9DoNyGcHJ4jCaOAKIqROBScHPiCSlg2dQOEeWY5qiKEiytdFGYQq1GQOgTCKEQrowmEUURATKCMRp13PEP4cWAmJNHUtJmYMIzxpDE7KmGVvOunBK6FxpOuxYVq2mRsseKOrhLfOvEMvObBnPbbv9JvyEgcizvvc8p56Xg++IszGDhyDLufeA7LZ3/O3/70K7ZaMIfSgs/ZdONJTPv0DfK5PEuWLGXAwIFsuf+36O3pYbDMo70iS+ZMZ8Pt9mS3Y06vcYlEOxQJVGJnTScxjJptDzmZURtsxrnn/IBcocjW2+xIb7mMUDFKOyxbtoxNdjuAbQ8+se7alAASYZPBUxMC0druVWWLTrcOHk7roOF1FJRqtLLm0JBJVoftt7LcLorN85MtWsBoc+medcCy2V/w6I2XcNkVF7LDttvhuRqcmN5Kj0lQwzIbzCK/65YH2OXYH7LZ3ofR07aCZ6JfMHGrSVSjmKy+nmjP2v4vrXMXAZGKcRuLttSi0WYNLhsT2WM516EoPKrWmZWk51ZsOm7WUgoSLUwm6e7mmCONQ8mVDgXfRWCcY75jYlBTJmjnNogNszMKiI9wJEnqrnAEOoZqHBJrw5AhYaBmDUh7T8dGnUhp3tlzaozdsXMUqhg/I3BDFdqKr8qqIUYImfcRNpY3ItRJBbmaEoNWuFJaZulYZ50R6CZ+3NIvximvdIQjJNUwQISWKTvJzrxYTd4oap7j4rseaTU6BTO/nMcf73qIeXMX0NG2jO//6EeMHtjI9Tf+nndefYn1N5iA9+PT0Bg4pFoKbNikYOmydm6/9yHeeP11Fs2dbdL819+U0VvsSrG5Hy/dezNf1b52N+CaC7zGeF1p4jcTj57WsHz+dN7+y58oNDbz8KPPMGz02vRWqkwYOxzP8QiqVTzPRwsfCQRhiHQcnvj7a1z+03MB2OyxP3Ppb2/g1b8/zVrjJ7D1Dtuz1eYbk/N9+rX0Y93x42gqNBGFJXpK7UgpKMocPWGFIA6RUtIkinRXSwQqrIH6SiA1lMIKRbdAEtMaCYNfutLBcxxCC30ITDKBeXNJb1A2ZpqJUUEKY067UqaM12jOySZ8cP6lv6Jh8Noc9INffHUlKAFHXXBz+jnadAfGbbkbQW8nr9/1CRdfcl7qkBMIXnn1dW75/Z/4/PNp7P3dCznq3GsIYtBBL5+//ATr7jIc1/dNyUyZOCJJTfQ62EEIRqy7MXufeiE/OON7dLcvx/V8DjjsKLbdfidefP4ZDjvv1zY6oJbAUAukqjkZUwzPMlvT5Yyg1rVY4QTiyBaWF5bU0tAxrdIwJJtLgWOrQ2mlKLiKnkikpAkar9CAn8ux9XZb01j0qUQ9tJW7iTBanrBzE1oPdUtrkWlv/I3xm29DY+sggnKFh+97jl322o75c5fwxWezWLJoGUEQsvGWE9nnwB3wndr+YWbOa5su+q5DNTJQQlLGVAiMA85udhol42iFuytNaJpjmZoA8p5rQ9xqmn5jzqfoeQZ7tVCFFJJKFBLGsd3+BiJlHV1C4LsuvuMQKnNOrE3YW6QURc/Ddx0qYUjOMxpnKQyMjwPzjpGKCSIT+oUyfU2mL4m1dqwlaJQrM2dJvVyz64UNA5Xm/T1bFS6IYxwbFmpJ38JGpM+IVGSgClnbO81zpIUsIgIUjhbEQqGkhhgiHSNio9y5do9BE1XhE0Q2gkILojjk7gf+yp233samex7OhN0Oo9TdyYU/OIPQxpb7vs95F/yUF9+cjJ/P8fa7k/n4/XfZaNKGfPH5NN5941UmbLcXOx33YwatNR4/X0Q6Trptz+qY7mrhhWHjNtTfveHPqemYIGcp3oZI6X7a2y/wwM/PAGDDHfZm5gevUyn18MNzz2btMWNYb4PxFBsLtLe1MWTIAIJKlcMPPomli0yUw2Y77Mqn779DtdzL4edei1doZPbkt1kwzVT9Knd30LlsESPGjufoY47hgL13BsweXkLGVMISPUGZfoUmqmFIb1QhJgYhcTT0hOU019+xCz3BsjSa1mIj1Sikq9xr8GEbveBKJ/UAB3FkA7Ndgy8hrDfT4HK+ZbxSSHbYbj+OueT3DBu/UZ1joTby6YDWDllFb+kX73HrT0/i5VefYcCAAZaQEyeV5vxLr2VpkGPnb52FjmMeuPhEetuXMWz8Rnzjh9ciHZdkU0alaumVCRbXtxsJRlrqbOPVP/+BFfNnMmrDLdjxyNPMTOtsH422kmi5KTSgEkYp6zRig3Wn3kUjrGzMdbKYEqcdVqtK7lPbONIIMs9RhHFNmGQjT5VSXH3k5jz5978wqKWRFaUOHn/yRebPW8o3T94f17XeeW12mVi8pMS5J/6AnY45k833OZols6fxzB9/zuwp761yLTz80r14OTcNrk+YIhYXFZiaE6UgJNmCJ9mPLckcS+YYbSwi12KbniuJYpX6C8IgItQxvufiSTOennRwpaToWwdxpOgNAqNF2nUQxsYB5jkOUazIey4Fz7NzqCmHYVrcKMGJe4OAMFaUoxA0eNJEUCRhYVFshEfiuAYTM+9IiW+3Pk/SwxOnIximW3B9WvIFa7IbhaYchakjG2r1nSMVpxAdwvTRtb4Cw6SlcWYKSd7z8DAares4VIKQz6fN4YNPZtO2vIOOtjZcCYWGRhzX47233qFj2RImbDSJE04+hkfuf4SnH3ucU351PyPX3xitodrbxb0XnkBP2zJOOeVEbr/zPtqXLTMOY60Yu8l2rD1xS7546++0Dl2LnY4+nXxja7o3XLKYjBICVx2+6b+H6Q5bZwN90i/vA62QjiEQx/VIwnqyzSgcOl2YAC/fexMvPXDLSvd9+d0XePPNTzjvrB+wy+HHot0mmgYOZb0tdyHX0EQYBmgFrpc8y9w7qpaZO/UDXn/4VoLeTjbeYksKxTwtjQ0cctDetPZvJtQWUBfGmRZrZcJuVEx71I1wE9NNWieBwpWCnOPSXSmjpSDneqANoTuOYyCG2BC1lMnkJ1qeTJ0BCdZc9PKc/6PLmPLRFLY5+ES2Pfy0ugWX4RWraJo7f3oMjb7gjv/6DYVCPvVuJ7+///Gn/OScn6GkR1guse22W3LpRT/ju987myjfyjfOvtZWJktSd2sFhRL4JPtoKWrpsUII4jipeWA6mxivBkkQVts31yTbXhus2BQOT5hsX5pwXTOeka00l26fpJNSkfY8u42yQOFR4pEbLmXy6y9QKZU5/aZHGDxmg5WHTcDDV56BCDrZYNL6vPPm+wSRwPV8ttluA757xhEoZWCJjz6cxiU/uIKNdz2AfU67ECEcHKkIl0/lihOOYJ311+GHF36XrvYuQqVZf+MJNBT8VJsUQuBZLb3R9wBNEBkNN7J1hatRlDJSKYWBGTzX4sKCahSnNTdyrkMUa3rDiFhpm6BR264mEWJF3yPnGgdQJYoJ45i8tQCU1gRxbdwDm16b/PmOg+8YBqa0+T22ySAmcyxOlYxKGKUk6jom4aEShYap2nt5jmP9K7V5DpWp1SCEoOD5FF2PnOPUnNFaE2FCrZJEFA3EcYTrejT6OYIoTBNalIURE4ddks3m2vC6Bj9He3sX115/J6+9+ApjN9mOhtZBNLT2RwhBtbeHarmX0RMN7/vzlWcxYPhoGloHUC33ctzlf6LStYK2WZ/w93tuZvfdduSkE47jqG+exITt92KPY88kxDNKp1UYfN814xeEqdVS43+1pX3NUZv9+0z3hKvvNZqNlKAVnueCcGsFpVONbWVtTmtNHIVorZj26l954Z7fMHKtkcyZPY84iujt6gBgt+PPZqsDjiW2WoKULo7r2BexeGEdgA+LZn7K4ukf4ccdLJw1i45li/nNTdfR0JgnUgHUIE1KYcVgPyqiKy6lxbWT+Luc51GNDD4Ua5VuuOm5Hr5MYvuMZh8pQ+yOkCY+ERPEn9R4TTQI3/VYsXA53znhLDbZYTeEdBm+/maM3mwX8k2tK+8skkyI0JR7evjbby5kVH+fX/7yYtA6dRRqrajGET3VMi//4xXGjVmbTTfaCE+4lMplTjrlLNwBo9nne5elDF6npTQTj3DfZ9ZNWurIys5qgoUlcIBxZNl7ZrTw9NYWjgCTveS6LkKaimZJjGMyp1EUo+LIFCUX4DimCJAnY6a++AgfPfdnGhryvPOGScH87k2PMnSMLfuX6XtQ7uXT155l+byZLPriA8ody1i8YAENzS386r+uZsjQfkRKc/9tj7G8p4EjTvs+HRWfahjzxDXf49N336LY1MKgwf34/f2/Am12ya1GRngHkUplpu9IfFfiOZK851CNFA2+S6w0QRRTjRTlMLIROg45z6FfIYfW0FUJCC0+XLAYdTlUFH2HjnJIJYzwHbP1es518FxJOYjI2XPDWNsIgcw2WhoqUcI4jfBPoJ5KaLzwriMoeh6dlSqeI6mEccpsc65DpDS+9erHOtmQwMxfKQhJNtbyMokPSQ2OWGkKfo7+hQYLFxnmqDMIpVCmzznfI1IRsY7xfc8IC0wih1Km2nYQ23hiDdJiyXHijHNdXC146OG/86ff/Rfrb7cXux77AwqNrdRF69RIMVkINZqXktkfvc6dF57CnnvvxqGH7c+2223H4kWLOOKw42kdOppjr7wrdY+A8Snkcz6xUoRBmBaFqltL9v/VMd2vd6RZyRnHRsoFYYzAelwtQC6lW4/h2f+7l8xm9kev8srDd7BiiSmPmG8eyP5nXkEcVHn90TsYv9WubLDjN6iWKziuSb1FWJPUzlbiJdVJyJaUjFxvY0auvzG+VHj08PSt13LUEcdw2z1/YsSQwXT0rDD4mmMYY2fQa8JhFBQ9j+a8TxhFeK5hGEt6IpJKR1IKXOlax4atTYtIvdOOlChb4i51uJNkvwmCWBGqmJah/Tj34h+wYukKqtWAT979G3+95RdsufehjNtqd4attznSddIBkwIG5BWdopHN9jiYF26/klJQNiFAAnzHrcMMd9x1e4QQdFV6afILeL7L8Sd9kx+c/kM23Hl/Rm6wJekmnQmurGz5Pm0K2ySrInF0pBhHQkBC1ISrqIXnaRtjraWTMvPEvEogjYTZSgvBqMj67tPoA5NxFkdRmlmW5vjHEMYO7e2d9JSq3HDzRdxz1xPcdet99Bua2fEhQ3B+oYFN9zyUL954hoUfv8Baaw1i8YIFDBo5mhsuvpGzL/oed932BJ++9wHfvOgWlpcLCARzJr9O2LOC+x+/mSceepYdd9+GnGNgqEoYUQ5jcq6xaLQy+G1T3sWRkmqkCELj4omVohzESCnIew6NOZfAarsAXZWQfkWf/g05SoG9p52CvOfQWQlpLbiUXUlvNaQl79NUMB5zz2Kovt1rMLmn0toEsdnvodJ4UhDGyjrKFNXIJVKKII7Jew5S5lFK0ZjzjXBzJHnXYUVvlSCMaLBro18xR3vZaHSeq1LN1hHCaLXClNCUwsF1Nf3zRaQy8xyjcB1pmJM0PgahjbUTRAGRioi0JrL1D4SwrjetKUeRDd10DK2QkK5xrk2fNocrLvsNPRXN0Rf/geHjNsxEv+h0LBKGn3CP5LgUgmqlzMPX/JA/3vZrttt+O3pKJbrK3Xj5HJttuz2zZs0jDiMcz7Pp9CYJIwgjtFZpWF5fszVl8KtpX6/pXnVvbQWKGopmcqOTmMnErAS0YvprT/DkbTfQ1Vbbynrw6PFsfeBxTNplf+IwJAoNhuS4JpUzWZiJVEly8xOMJNGKatlIZjQTHKzZ6+KhX1/B3M8/46yf/IBJk8ZSCasYX7SZXGEZl9meXNJS8NL+dVeMw8GRWMzMpMQ61qRJnQLaxOua/HLTj6Swh8bcP4EaktFKMm8c4dC2vI2H732UD9/7mLblHbQOHEiu2MhG+xzHBjvszZfvPM/0t59l6juv87MrzmWzrTZNw9ucBC+FVCsPE5zZdSm4OaqVKn9+5HHu/q8H6T9sFEPXmcjE3Q9j0KixhtHZAjJKKbNVkFV7HdmXUGrjW2O2tcVumG6yd1ktw0+j0bFKteJsKJjrealDK47jWm0Ialqv75kEkZ4l03n74d8TViu8/vwLjFhrJIcdtS9/eeBvrLXRdux6ysW4rr9Sl9Ew7d0XefvBXxNWy3R1l+lcsRzpOAZqOPBYBo+ZQGPRJwoqtI5cl7cfuYXOhTP4y19vNNBAaJinY4utJJYNQKiSLCzjMFIair6L6wi6yiFBHNPgGRO0MefRlPcohxGR0jTlXOI4cTRiNVIjbINIsaS7moaaCWKGtuRTiyOIlHEkZepYJMEbiU0iMtZnwhMUEEaKMFZEykaACKNALOmuIHFozDm05j16qhELOktIKWgp+FRCRXcQIrQpe+k6pjpbGGsqUYxjlTDfcXAcQYNrmHgezyoTJhytGgbgCLs+THRHrMG1UEkSNpbQVVKkyTFR+7jCWKVBFHLeOdfw/pvvcMgZ5zNpz2MQ0k3hF5HZLMEMSUaJyJC3EIIZ773Cx4/dwoMP/oHe3jJt1U7yToGbrr2FJ598joN/fB3Dxm6A70qamhoIlCCKjYCL0pKpNcvbc2TKl6I4/u9huinTtXdPFp7jSFzPVANyJLhC44uY2bNmpdXwN9rtIDbZ/RAGrTUuow2ZGEBhS64lmqxMmWkyaDLVvrSmbuEnjDhh+Ah49d4bmDXlHQaMXJeZ773IJddczEaT1qej2mNqMwirKwuBqyW+4xNqRSQUEOM7goGNOQY25olibdJhI0UpiAiVojcICWNVJ9sMzmkmNcGnhBDWqaFSR0mSMw4mUiJJm1wwfyE9XT08/uBTuAMnsP3h3+XKwzdl7LrrcPVNV9DU0mSYt0wclto68gxG7doKSdXIZPsIjIOmFFQpVzQfvTuZzz79nOf/9ixHX3ATw9bd1NzFwg1xBldMFqxOaNUy3UTbTfDeZJ+1vsXNsbGcCTSTWCexTcv1PQ/Xc1GZ+gbJfHiuoQUhTTLJR3+5nucffoATTz6EmTMX8cHk2URhwPIFc3jo0Zu46hd/pL1XcOzVD9Q5KeMooG3BTFbMmcqD117A/gftzt8efwHX84jCkA232BIhYOann7DBpPUIg4DJH37GoUfuw2FH78OkSetSscLWtfV+E4bVVDAxsVGs6K6axIGWgo/vSAq+S8419QkqoaISxqn26kpJdzXEEQLXzqPnWrpIoRrDHBd2BJQqAYMaXFobfOM3EDUTNqkCZ6A+K+wshJBsCFqH1Ttm+6ow1pSqURrOWA1jO12C7nJEHMUMbMxR8Bx6w5j57b04QlJ0PYRw0njbnO+Yte4Yra+9HCEwmrVJsdXknbyNUlDEGDggUahcxyGOIhtlkfgMatBcUu3OcSQ5z02LL1XDCM91CcshB+5mNg7YeMfd6Oloo9rbxXaHn8rMyR/wzpP3c/Hjn9TGIDt4yaoVZhxXLJzLPecfy0svP0a1aopvnfq9C/nsow/Y+oBj2WL/48nlClSDKnEY4PkeXi6PtGs3gW6y9r3W4Oc8pJRcftCkfxNesIIi0XAdR+K50sIKwpqoGleZUn+d1QCn2MpZt/3DLjxpPOlISAqIWGLLakfGq67RcaIdCbBhNjVQyLyY0gIptIVnNFIrhOOweO4c5kz9hLUmbcvux53BpT+9nOt/dx0jRg2ipKpWu0i0VfCQ+MI1IUSuTzWuIISg6NtsNmX+1wUTnF6NbY685UQrSgEdlSoSCCOzF1jCsRwpiJUJu7HiwzAFZaCLpJjGwKGDGDJ8CFvvsjUP3fkEux9zGsdfcStv3Pcr+g/oZwK2BSgd2wUDeRt9AdJUabJjF0VmUcXKeKK167P1Tpuywy6b0a+lwD2XfJdjr7yTYWPWN1EHdkh1FKdzkcpvLRDCxGsmoVuJJmI0X12D8rUBX7Qd2ATXrdupwTpCjAamiGODMTqOQ6FohFysFMJuCTPjk0/4/lnHcvK3D+bUU69i6LhJfPj3R8xi9F3+8uh17LvPWTx/84/YYM9jGTFhcyY/ez9/v+MG+vVrYsjQgRx4yO5ceNn3OeHbB1MpVRg6cih/fewFRowcymbbnsPwgS02VCtx6hjYIFKKnI0m8C30FOskwkLjOoLGnGtTWCUNOdemtda00SBS5DyDZ1bCKMU8c7amiLRKg9QmRTwJ7pBC07/Bo1/BZqhpUHHNbE2tPkSacKE1dvOHBMDXxt+izZypCKTFnaNYUQpjmvIGDlzQXqEcRsSxoqcaUYk05UhR9PNUg5DuIMCXJqSsOV8kiEKTtORrWvJeyhdKVRODjONTjSJwY1zXSADHkcaysvRlUoKNUiOlpDFvYuVjFZtMOy+ps6vwHOiJY2IEeenQ0Ozz9KsPUCqX+cuDzzJg0LqMHD6IKy+7huVLjFW9bO50Bo0eT1LREMvQs+Cr1prWISNBOsyaNYfFS5Zz42/uZM6XMzntt38jqJqqZkhBrlAgdFxUFFLq6TGV7TwXz3Up+C6lMMG1kzkUxu+1mvY12/UIinkXRyibixxDVKVctWX/oshIrjA0EluasmuOm69tq4Oo1VhQhnHElpAMv7XbtKARDsZm0glzM5pxYkJpa0LUxs9oRlJpDv3xddxyxv68+udbufDRj1mxZAmnfPNk7nr4vxg5YjjluIzj2YQLoekMShQdn6KXR8VGG60Eka3cDToiJXwP4zBJ6sAqYEDRozHn0BtEVMOQyGoeQazQyjgzIosBh9qE8gRxjDD1OmjOF9NCzjvtsjVPPfQUj/36fNbfZnc62zsIlBlTrQyDSwLWYxXRXQ3oqVbIuz4aY/YJRFq8xUAfAeVA016OGTh4iCHI2dMZZh1QRiAKXLAarxnghAHrDKSTyL4E107mLhGFQmS2PVcJEdbwM+lJU6UqMDtBJ2mlCTNP9iwTaOJKD/Omf87zMuThR1+jo62DYeuYKmg3/uHnTJw0HinhttsvZtutjqGs8hQbirxw9408/Nj1rLvuaLQ2jLISxqy/wXhyrsHZT/jOEQSRojHnkvOcFFKRQhCJJDZWpA6SUjWmNzTMNY4VOc8WRZHQr+gbmhAGZohis+pcCQXPMGvHEXiOIIoMPXtS4jgZ7VVrtDLV56SEwQ0+WG1PamztjKSPGLoUxpmb7F2X/EkpDYPWAqHM/CmliYEojCj4DkXfw3FcQqWpVBWVUOMKD9+RVANNOQ6J44ic79LsmXoCsZD4no+jodH3ySuXrkqZZWEIwoSUFXzPrG2tkY6Z+khpHJIUX0HB91CAqgggTp25QZTsJK2JHRuK5wgKvkOkoFE69JRCk+QUx5TCiJZ+TRx50qGgFP0afNZZfzyVcpUNN53IsvkzGbTW+FpaQSa6oM5CFTB20x34y6PP8uYb7zJsox3Z5ZTLCCpV/HyRMFLEpRKO4+LnfJy8b+k3pFyq0BvH9LgS1/Px83m0NiF60pWQbrS66rZaphtHESuWLQeZYIE6XZhmsQmDlbm+WXAAoh7fTTUi++o6s19aGpaURCiopLhEejqCRKJnEgy0RosEyQJtTZjv3/IUcVhBSskmex7OlJf+ysU/u5LtdtyaI448lH7NRSqVKsJReLZsnFIKVzgWt3MJAoWLROias1MKYVQS+yZSQNFzKPqO0YqUh9bGabe8u0xHJazV/6WWSOJJac0q6A2MB7noGQF13jXncP8fH+L5/7qW0845xaZSkuK51TgiDKKU8VWikHIYUilXaV/eSRzG+DmfhtYmCnmfpQuXc/cfHmLq+++h4ijNE7eDn86jkBLXMxpLFEeEoc3RV5oYs2iTeFlzWVLcx46/1dwTx1gy4do668BogWZ6E9jBFESPwohq1USaSBtvipNj+6POoGvFUsavvwljN96WR679IQBnnPIzPv3x91mwYAm9PSaXf+a7/2D+x69w4UWnMnbc2gSRpqNUJYpjfNex2qyH63hEcYjv2DKFkaKia1vQJIwzyZ4KhGHUDcIwziBSeNaRlWSQJXxPa2Nym4JLJn43qe1sMtCMdaItraZ4oIV1EtjAF6CFKcSUVEtLYouTeTM4rrFEtDLjn8ByInkRbWEL6+TMOcZkX1EKqVYMg6sEMU4scRA053KgYoTvocLACGPXQTmacmAZpDQWTITCcx0cNLGCBk9S8CRaOHQHIZVIE4Vm7HOOoCXv0eg5aC3pjSJacw6e69EVxHSVQxsKBgXfoSHnIQTkHAM5tZdCOnoCpNA4jkcUKYRwqIYxOddBKcmMLxcx5YNPyBWLVEu9zP/kbTbYYb9ES0gxl2RjJ0GNL22z14H89pzj2Wr/Y5i0+2HoWOP5eZsGrkwh/8Q6dXySOsGenze1Y2ITxVCtBqZujMzsiLKa9rXRC0K69mEuwjMakrYgYOJEiY19gIEOVIrR1G6S/CNqDpdk0WNNUWG0ymRoEm0gxoSQmNqetnKQ1UATnFZQiw31fLN1dFAp07ZkEW1LYPa06Txy78McfsK3OOjgXXF8h1w+hxSCcjXAtVu7VEPorcT0K7pGXIvEvCYFzYQALawgkNCYc2wNBiOYZHORatxjHBdCpxk1Sfxlgnsqa4b3BBXj7S4WOPns43F/dJK5RpkqwdU4SJ0PykYuCKFxNLz5xhRu++UtlLq78HJ5wmol3Tm4sd8ANtvnSMZHis/efROAT156lJwnGbf1Xjieb85VCp28JwLPNRESyuJpkCgLGTgIMtCQ1cikOdGu+dTcSorkZOkhiX6Io5hKuYrnueRyvikaLwSb7/tNEt2kY8kCpr5pKq6NXG9jnnn2HVzHMK9iYwMXX/JdDj9iL8pRTHtvFaFNjGfBc6y4xhxHU/A98p5DGMX0VkxGZBxrAwWgbciUwHMkDTmPBotJS0u/YVxL4ACj1YaxxncM/us4ZhxdKdF2lw0TLdJnfy0NlthJvGqpv8fgSZZnGAZcW0YGFtIadEyqqKBBCWq+AwtZCG0cuxqN0ILmnEtPXCGKJcLx8JTAly4eDtVKgOc6NOVy5PI5hCMpxjEVL0Y4Do4wxWKEAAeIEURK0Fpw8T2ftnJIJbSRDELgaU2L79LkuWnhcceuWceTNOSNMtNVrtKYdxnUnENrSUepSsFPCvFbB7gWlIISKlL0b/RpyLv0RoJqEDJnrolOaWxuZb0Jg/nL/Y+z5UHfpnXIyBQKTKSWIAnv1PiuZNi6G3Hmrc/aYuQax89ldsI218RKEQSBwffDMK1DIex9pWuwfsf1bMU9a5Kspq3WkTZk7fX10ZfcSpKvnzSzCyipQyRZsFkPt7Zcsxa/WoMFRGZRgiXijLMJe6+EuA1BZu6dSn7ziokTLk0ltI6f7rblfPDsn3k5k6AxcMhg/nDfTfiOpDHv0lMNqdrtYHzPpdH3GN2vAV84VhMUFlDAgHCYY1KKdPPZWCXvrwliaCtV6a5GhvFa/CrJugptULhrNdhsrKVvU4sTVd+xBUqMGR6bzJ4YFi1q47RDT0nfacCQQXS1dyIdj2q5l12/dSY7HX069196Cr3L5nDdby/n9Vff5bUX3+TDdz9mva125eiLf2dpK1NX2FbuqgQBYTWobZNjVYS0+ldCyxY+ko6ZL8cxaZ5KkTrMlGUK2govbR132UIqUkpyOT+1cKTr2rk0NNG5dAHP3XIBHcsWs3DOHADOPf877P+NHRg+cmiaktobGCdYbyVACgffs9i1NnMQRgrHkTT4xvzvLIV0lwJA0Vz0qcaaINT0b8rRWjQ7ziZb0SRJAEoZrVZA6myLlIHMZLrGjaNRK/P+wq6ZJE41pelkPSTmgfV5qDg2ayzZMkckC9n8HtkC5WkatU0DNjHYKoWkhDB5xIZWTVlCrQVBqChVYmIl0bGHg6E7FVbxhaaxoYgSEuE5RNgavBa2cjxwpYnmcTAxs6EWLO4OzC4QUtOcN85eT4IBZGSanON6LriaUMCyrjKVMGBIc96ExiHoKIUs6zW0JwV0lUMEisa8S851bR0K6CgFEMfcccsDtAWtfPrKk7QvW0qhqYWtDjiWnY46vcZnMrH3Qgp816QIt7e1Ue7uJlcoooVrZaC2sA5mrVsl03cluUKenq5uY907JqJCOp7xVcS1mrsg+M23d/k36+liQ3uSWyWEIrMJASqVKKkGTCKpQa8k4kkdB+alEiaqMppUrR5mwsiTFVmnM1kNU4ENLtEgnNTka+w/iF2OOYNN9zyUuy44kbbFC1i+ZClfTv+SLSeNY0RrkVBruoKIhZ1lGw4Tsaizl5GtjQhbdEPbWEK0sHs2JVqcYYiuI9P3LPiSBuXRWU4SPYTVesyOCUbqG4dSZE3PRJeJtSYnJZE2jLgaBOnYKhsX+eX0+ZxzgjG3v332iey41y68/upknrjvCU68+l4KTU0AdLctY9r7b9DU0soN19/DiiVLaGpuBGDauy8Rlst4hUI6rolDLNZ2sVpJnjDJVN+y/bX2SMooTDiatoIRkMlmh8nWSdrOe61+Q/IMpRTVSpUkq02KqtkHzWoQfqHIZ++9TXO/Vo467mDO/N6hDBsxFAH0BjE5zzHe+VjRG2iEkulWLUpLqoHdM0tpSkGZ5qLL8P5FBjTlaMg5JgnBc1BasLSzwqK2Ej1ll9EDG5DCCj5pMsl0gl9jMFPHEcyatYhPp85m803WYdiwgbXxsqteConUpoaBqYAV091bZuCAFmqqhq7BWdq1GX8OCUqjrLMsjo22ZTJEbfGl2ECByRY7CXNWxAjXWKtxDBJj3bh5A48FQZlIKcqBIohcJA44xjHruy5RrNES4tgKAIx14noidehqwEMwKOcZBI4Y38bdCkjLpxolyh6Qgo7eAK0iBjfkKDiSOFI4rqQx77CsR9FTCSm4grUHFMhbx1Q5VCzuqlIKFNVqwD+eeZ1/PPMKJ1x5F6898l8A/OBPzyEdnygMUmGWwDSu4+C5giCM6OrsICiVaWrpR6RrlneysWayJpI63+VKiOf7OK5LZJNNzJKPjKVoHQGJArO69k9UGau1ZAGm4LROZQNmCSbl9ozWl0IR9oyE/yYasuXUaFEPKdh1nNGGBTV9OYNWUHs/nUxuwshJNCpT5Wq3485i+it/prEhR2trEwMa83iexBMOSElnJaRcich7DgpY1m3KQDZ6buowSRiU6ZrZbijx9QFpWqvnCJoKLj2ViDDWFDzjvACQShHGIGRS0MWMn2uJuBrHNu2xVqsTpcm5RtInDPc3D97I6LWGUw5CNpg4itsXzOaab27NiHETGLnOeN5+9gm+eekfqPZ2I4C3r/1xOo9N/QchEg+rNoJMxwa+iKKQMAgyW58nLfvZHsloW1YVtgKlpsEmgikJGUwsGB1TF5tt0o9N+J4Sgjg2GT9Shvh+jp/c/xazJr/Nc7ddxQYT1uHkE4cTxlDIGdzSEcIsnliD8iiVKvSqkCBURDoETGUr15Xk/Rw9VZOc0FTw8F1JEGsWrqjQ4Ety/Yos6yzR3hvQUjD1Wd9593POO/9mxq87hqOP3INJG6zNTy/6I59O+YJFc432feNvzuPww/a072SI18FJMdr5S5Zz+bX38Marb1KtlKmWS1x1xTkcf/SBZmzsedJJ6iPYzMkoxnFyKBUZpm8tC22TEACiqsEbXN9Hx6YYt8akw6ON+1cpo0V3tpVwhKBfayNNxRxFGdJZqlAKFD2xKaaed01UEEKCpQ2EQDuCqtYIJ6kra30cNmRRKUwUSqwyeLNOHetIQwc5EVPM++SEUWZ0bMLIXFfQmPPoKQUMb21A2hjg9lJIW2+V7kqEVpoZX8zmvj/cy/G/uJMha62T0uRzf7qafb57sRFIAkDiuQLX0URRTHd3hZ6uLgSCQlOLVRkzDuQswxSJ4mj6HlQDUyMZUxDL1IyoKaMiy59W076W6aaKtu2QGdg4xWUNg5QIXdNaddKJZHeHVd8xfbHsL3UdFtlvlrWn+ccrX5zEAZuvNa6stWDo2uvxxp+X8KtHbsaJFIV8EaRHR7lCpVzBsd5WYm3SHDVUAoWLMjtV2JE1FZ3itPvKer0NzzHPbs67FH2H3mpEbxDTXQmphDHVKMaTJiPOhIAZ4zVhPJE1xQPrKGjIeeQ9SaR8BjR6dJYD/vLqg7aik2JZWycvP/sWixYsJ7Z1WRfMmMqCGVNN34Iqk3beD7Rgwx33BWD5vC8pNLfg2t1UNZooqKaxu1EUp9mHYJ0ISUC+qMVHZyGd5P0dYbR/TWISY/erMpZIYuIppWqbjtpJTywBKYRJ2rDHlTKbiLquy/pb78rAkWO4+idHc8y39qFUjegNFJ41bZQWBGWIql00tTjkPJcg0MyatZTvnXop7cuXc+kNFzNgt81NIX1f4rsG1qiGmkoQUQ1MFEXelURhiNeQ4+XXJnPGGVex6b7f5Nn7f8szTzzDqacdzfNPPpeS38WXns4RR+xpVQ+ZKRBj4qJ/fMkfeOLRp9hsryM45rJbmfvWk3zy1ovMX7AUx8lZFSQpY2i2tJeOi1IRjueDNBqwE3tmCyOtUcJG2qBxPQ/tmIQDMAxR5B20dAhj6OrqNFtVxSF5R9Lc3Eo+Zwr1hyrE1wLhuTiOJFImDMzzBGEU2rR2B9cVxvpTMY5SNRYjNIlO4uJADBpTZ0NLhcL4CISUaVRG3nVAkW7xo4WxYcPIrLHmYp5yqPF0DFrQmHNY2m1W/IfvfcZNl9/I3qecz/Cx66G1YvCo0fQbNoZ1t97d1BEREt+FnGtw93K5SldXD93tbeTzBXKNLam1WuMTKrXWRKLlWiexqRkSUfByFvY0dK3iGJkUdrc8YvV67j/jSFvVdxXbfHvL3OyDhDADm+A3CeaXQlYkCyzBtWqmbR3/zKjpQog0tMeu+9p1qcproQcNwsYvJrsQJAMblrtpbGxAaUm/gkeoNN3dvURRSF5qYt/F0YK8EPhoIqAaK4LukAbXp6cS0JAz3vDeyOBZORvUn3TMWtWGeViPZ943sZthbBh1Q84ljBXtpcjWhNX0b/BxHEk1NM62ahgRxMo4fZSkt1ohjgN6KoExjbpKvPn6VB69/S46VyxPx62xpRXheIzbYic22+twRqw7iSgMjZZpIzz6D1/Lxk7baYljY5pah029NZJOna0KlTBaURO6aREco+kbGhaQIWYpQFkNNwpjq+06VqNWOK6bFoaONPi+h3RcwmoJ18vbxAxTXjCfL9DY3EQYxzbsS9LVW6VSDSj4OYRSdPV085tbnmTAwH4cf8zuTP5oKspGv1z6w8vxbriAbXbcyjDZUNNc8KkEMc05SXODZy0bk8gyZ+4SjjvmHPb5znls+Y1jGLneRtxz8Xf44+8fqFsXf/jjYwwcPYY9dt6cxlziWzBW3JSpX/LQfQ/z47tfw/XzXH2Ugfouv+hH7L7zdnhuHsd1EI6L1gIVBzW8O46QrksUlM3WMMIuam0cQsIxgls61rKINdrRCNcUrmlv70ahcKUmly+C61HIewhHEYuQKDJ7BuZ8FxHEhFEIUYwLhOWIQj5PrBVaScJQ4zomE9ORLjnfTWseow0U6dodmqMwREUKbcJfTHikB5EOCcLA7IeW1L6VycI22YDLugN8qQ3tl0IGNeVY3BlQqcY899dXuPf393DQ2VcxbrPtiOOYZfNnsXTeHMpd7QwdNYqZ777AHt86ncLAoXT2BnS0tVPu7sJxXJqa+4HrG+jEFD7NYLgWGkvUS+tXqO0Mo1J4LVGytIpTZ3TCpFZZVTDTVs90zewigDgOjTbrekjXBVt4OOlwQghYHM+o2wmClzBNUcOwRIZdihrfNQwzcaIJ68iyomdV/RPZD8aJY5xatlanZRZLvpzCyLVHEIYh+BIP8HwP5bh4vqRRKbpFGak1xbyP8j3KbT2m+HkY0ZjLIVCoOMbTCiU0jiMh2TQhlfYWghCmgIf0JL1VgVKSpqKbTmpDTlLwTKxnEqSvcyaUSYscYRTTVY3pqVYJwypojzCOeeeNKdxy1c2M2Wgb9vz2+URhwOO//hlrbbg5e5x4DsPHTzKRD3FEpVSyjNGYmGFotrX3POMA0AhUHJkxijWxzSvPSsAoNrub4pgg++T3pHZqMrPJtjHZ2r0JbJAcU7GpS+A6bpqKrBGmX1JQyOfRCDoWTOPxG3/GknlzyRcLOK5rdtPVmlKpxJZbbwzawXMdqkGVO+98hnfemozvCnLFPG+//Drrb7sHH37wNp9P/oxLLz+dJ//6Emr8+gybsAUX/uhKDjnmcI49fn90azPVqmHgUjpIkdSKgHIYc/nVd7HZ7gexxX7fBARjN9mOM37/FJVSj9mUU8DimVN59rarOfuU8znixG9x3DEHMqhfE01NJplo9NgRrLX2Wiya8QljN9uBCdvuQXnJl+y66y6sP34M0pE4+TwIh6hapRJ04so8Oa9otKkwRAcKFcamUH9OEMsI1/eMThtL4+R1IazGaOEQRKBCTf/+LShh1q9CUqkoujvN1va+G1Ht7kE4Zt9Bx86Hkg6OlrS0mK2fHKVQcWQztAKQknIYUq6Gdu82TS7nUSgWDUwUReRyebDOOyUVsTAp61EUk3OMkoGwjE8aARMpTUc5tA5pRW9QYeaXC3n+yVdYf9K6vPnK+3z8zkccfckfGDRqHFEUs2DK2/zjruvZcfcdOOm0ozjvrCtYumgpQ8dNYvw2e9LT3oYjJU0t/RGuh1LW2Wh5W8JVaoqaLY4vkpoSdksktLEA0SacTgtrrbkpXdc2M1i9rrva6IXBo9fVR1xwi+XyCZQgUjM66Wwapy1lBlOt8cK0uLc9pmEljDTRFO3bk9aPFQkTJdWwsoIkcbylnm8pM4zPYMsgeeZ3FzBp/QEcf/zBNOdcPC0pSBcF+DkTqiQds9FcFJvao3GkKQgX1zWDr4UmUiG+72Acw4mGZ7Q8x61p5zUvtUkdVdrEblZD45is2OwlA1kY872nGtFZiamEMXnXaOrlICYII5NYIeAn372cJYvbOPv2f6Tv2dO5wmgxmFCzKDI1FZKQr2TslLUCXNfB9U0hD2HroUZRjIpiswh0Oitm/qRMw7kSc0zaWrqOW9vWyEQskEYtJI6d1FEaG1gkiuJ0Q0ulDK24jqShqYiUDu8/8wCv3H8zT714D72dXTT5DkuXLidQRoNeb/2xVMOYaghz5i/i+MNO55AfX4tA0L1iCRO224sHL/s2S+bPZcCwUey4y1acffbRnPbtiyj0G0HryPF0t6/g01efZp8D9+QHZx9HIW8cR9VIUcxJGgs+3V09bL/V0fzo7ldoaO4PAjoXzmTe5Ncp93ThFlqYsOM3yDeZ3xZO/4RXH/wdLX6Fy68615QEVRrfcbn7jgf5x2uTOfKS2xAIXrznRt58/C5O+95p/PzCs8gVCoBDVO6lUl2GxMEVebsTssFi4ygkUiHSE3iNDmEsaO8wCSdaCJJQbAeB5ztoWaBaDYnCiLBaQiqFdM160SrCcyQ5J0drS3O6fbpG4LqCcjnAy+VRsUkYCoOqCVd0BCpWuMLUodBxTGNTA34+j4oiIpXEq7pEcWSgBRmjdExVBURhSN73zJpBIxyNEpKOsqHvZd1VKpFCqJjfXX83zzz2FJN2/gZLZ09jrYlbsvl+36KxdUBKg3f99CgaGvJ88fHHbLD5ZixZtIyJOx/AmM12Il9swfd9pOenPqUadms014QDZflg4hyXtopgbNP/Y63I+x5xbNaY6ziWnhVZ2FQCN5+6x7+bBiwyarNVubNQrI0csDp1WpAmSYtLzjTWt8F9NbVYz6yannTY3qp2uQFy0wGTxnuTaspGm8rqyboGaAudZvuMWHdj5sx6i0qlStFz8IUwiRC+QxQa51UcKXKFVvK+ItTd+J4HgdlBIiSmqxpSkBpfW68yiSDC4o/aChlzICkAnQTMKwU5zyHW2gSY23cIFZSCmFKo6C5XKQcxJQkF36MSRnz41mQu+eHP66YmjkNc1ycOA1zPaMbaMrrEVE+rLtnRTqCCIDC4red7Nt7ZmKZKmB11zZvZMuFWg00cB0AKMZgNKZ2aELTzbywe49hJcFxHSqPtBKF19IDruiQZibHSBJUA6ToMHTOBAYMG0eg7DFlrEI25PCNGDKFUCXGFpJD36S31ctH1d/DqS28zaad92XCHfdKxqfZ0UCmVaGhu4aRr7uGpP/ycQw44g6223YzGHDxy/+/ZYte9OOaSW7jtJ8dx5GF7MHadkXhS8+Wc+bz86scM7N/E1puvi1Ixv/rW9my6/Y7Eccy86VPZYaetGNy/laXzp3HzqTcxZsONGbb2eMo9XQSdy1AteQY1N4OO8TyTznryyYcyb94CHr/mDA45/3fsevzZvPn4Xfz+d79nyseTeerRO9E6QmhNTjQSxWUUVSQStMnoE64021A5MbFW9PQo2ts6aG7xQPigXVwnhxYuPSVNV/sSGvyYpkaPpv6NuD44wsPzc0RxSBhootBAPUrb7cVjjdYSz/OplM3+ZHnfpVAooFVsilxZbdAXRtuLEXT2tiOEJopAJFqsBiEVSiiqUQCO0QV9Ijrbe/jrc++y1x6b09DSSntPBa0NvKBizbNPvMAbL7/DCb+4iyFj1rPr2SpX2vAYRwq8QhPTP/mQDbbfk9EbbsV2EzbFyzWa4kp2T7MoziiWSSRNaiXXSpLW+EnCt0x6fWK1aUjDImOLURNDzTGh0nWwuva1mG6iWtfibms5zSZu0zDjpFJQDc/oE2qUUbwN9pk4DLQ1M+zzrLZMyjQyjjkhUEIk8TMpdgqJ51sSp0iuSPuYy0m62pZArGhp9KkqhaNCCjoHWqJiRRJk6dgtRaJIUSp30+TnEI5DVxDSWQroJmRcY54UpE7gDW2zj5ICONoE2ycWgmNDjsyYZnBqDdUwpL23ShBpGnI50FWaCx4F36W3V6UM13E9zrr1OYot/UFpgkqJOI6IY+NNNYKphqUmVkXyj077KhDEVMqmhm1SatFxpNE8dTL+yu5HlzBiUrAoOd9xHZJC6UKbMKc4ilLmK6xmEUVGI0NTdw3UdkwIwoicEOTyOaKgiusZ4dpZquAgaPAcqrGms7vE9374K+Yu6uIb37+UiZtvQXdFEcQGSy13ttHZZiqLda5YxsE/vIYls6cx+YVHeO0fz7LPAXvwzF+f470XjSNs8uQvGDduJMccfS7TP59uFoZnwo6SdvyRu1CtBuy2y09obsgbgYGmrbOXV197n1lfzqNp7HD677whO++8DU6sEMSo0KenEjJscJHf3vhj1hm3H58dthmX/G1qeu/xY0YSlntBaeOMjM22M0IItDR/wo9B2MUuJELkEKrKsCGDkH6OMITOapXOrhKoCr6EgY0uA/v3I1/M4UoPUo++qeCVK4KQLkK6IBxUFBEGAUI6OJ6DV8gRBCESRRRVTQy5trUzpCltGUYxQmiKXs5kackApQMTMx6G6EgREaNlRMH3kQIe+9ubXHbJb+hsa+Pp7bdgh123AynYadctKAWaP958H2++9CZ7nvwThq0zwcyHrUiGSGjdhIBJYo4+6Qge//PTHHz2NXR1tJvxsZteJtZW4uRPFKPastWk8eeGrRmNXkoDvdU4nGHgQZWSLUMLJlqEMLShtdJa/P9NpitEkpBQY2JJEkJyPInDNJqRhQIwmG+iXyWM1PBMZZijZRAmGsVqVUqjpQBtPNiJ1pxAG7ZX1lzOxvAaHDeFKjLqchTFTNr9KG75/jeolk5h0JD+dHSF5JRCKBOmJTAxib3d7RSKeXQMTTIHStAbVOmqhujYQQtJtRqa0n62ihPKZAQZ4WGKkSQQSjUylZaSlFSBgRES6eu5koUdJVM0BAehNJ40E7zf7t9m8YLFAJx77xt4uVy6Z5mKTcC6inWqTSZ5gEl9imxeTJrkYL6l5elEnGi0AsdNtvmBFEYX1gy0Vk7isHEcB+kkhdVtsWkpQEdIoVFS2Hj+WjnJBCZK4yG1LaJkt5RRGjzf5c2//IlvHLgrDQXP7FDgGSHoShcpFJ0d3bz6/Itc+OAb5BoaqVarhLGf2Dc0DRxq7hfHNLX2x/Nchq+zPsPXuYDRG2zOw9eZjUF3PfYshqw9nl9fdx4TN9kwjQD53k0PM+ej13jy9l8z5bOnbYEix6YBe6YusFTECga29mP/vXaiXKmg0UjHVJlyHZewKpFhRByHhEGR6TPnky828J3rH0IIwfrb7M7nb73A+eeeaTIOowgl7I4f1nJEaSJdNQpGTqKdHEuXV+juaTPbpzuGJoRwCCoKzxUM6ddA3nfJ5RrxHMNQzXgkCQLShoM54OSItUMQxoQBOE6eUiVECAjCiFhBuVol70PR1xRzOYRwjLYbBTiOQykO8ZU0W8sDaEGoINYxQRxQKAiamgq88danfP/Ma6hUQvY69SIevvqHvPn6e1QKI/nsjb/z2cefscW2m/DCX5/hpKvvZK0Jm9gSlYZuoji0lqtjSsIKiIIKgwf3o1LqJQyrqDjC9fIpjWV3t6iVHSCjmtUgQTDb/3i+ZwswCVPHQpPCn6WKeWelYoIwxFGxKdAvhBUGSYzXVze52l9J8NukgLKLa7Mxkk3poFYL0yx4myGG1ZJIwoRq1azS1NAEO7RMM9GWk79afV2RZviYP2WdNXajP2WcNCpOasUaczj5HMWKfHM/Nt/7ML7znctxtKax6FKKY3p6e5GxhthgYUhFKaxSLQe4tnCNdhwQHg4RORtjKjIDq60qbjKxrHauoaMc0lmOWdxZZUVPYIp72HcKYhM21d4b0FWOCJXRuMMwQKF5+JHnU4Z7+E9vwMvlQUjCMEr/4jBOs8aSPdFSxmsFXXb6dfrXJ6zFnhRHkcF5MWX4TIykKUTueR6eDSlynIzQjSMDF9jqYXFkCth4UuMl8aRY7SHR9KUJ6XFdN2XeYRQTRRHVakhD/2E89deXcISgUjV1I4QQVCJFe1eZ2+9+mqZ+AykHEZVA0RvnSbZUEQK8fAPHX3Erp1x9B80DBtO+eC5XHrEFd517eMpwdz/+bLY9+CTGbrw9exx7BlddcQuHH3s4AG3zptEydC2aWlro6TYxrXGkKFVCqkGI77tEkaCzu0RXTzdhGOD7ksZGj6ZGiRQxQRATRmakW4rNVHo1L770IYNGrk2+wTioDj/vBlr6D6AcVIiJKAUVSkEZLWOENIxdSW2rrwmEdij3BPgyYEA/Sf8Ws9iDaki1GuLnclRjl4VLA6RXwHV80ugiGwYopINwfPAa6ay6tPVqlqzooaunDNLBz+VoLObwPMf4EqLIpLhKn+5I0BnEtFUqaGnoKAgrOI4p4yhcbZIxfEk5quD4goZGl36tBTxPsnDBYpYuXMTOh51AdfkcBo9cm1Ovu58Dz76SXKGBJx//O6PXHs1RJx7O87dfbRIxrLJidnoB6bg4UhMHFV6+65fElR6ef/YNdjrqNBPOFisTk5Dyj4SnxNay0inslqwNaaMrfM/D8z1TeD+jtDmOcbI6jiSfL5DP50jZtTY7neQLeVzPTUMkV8tVV5sGPGZ9fcylt1mNlUxJRtKXUbaaSWJpy7RKU8bMT/X5mqZTBzwkmqllDRllP2UQiZqfhG8kqHCSKpxI8PSe9nOiQTuO2RDx3otO4OjDd+b73z2YZW0VepaWaPI98vkckYrxC6bUnA41Db4pRlOREW0VU09BK1NhqiHnmrhFR4C2mUqOSQkV0mDdvdWYnkpAR6lKvwaf1mKOrkpE3pWEStNbjekqB6kWqawTQguBA+y79REA/OyRDw3uaXebTcJzslppdqZrQE5NwtdCwVQGGyMz7qR1e6U1rxxbBU060lo3wkI6ZjyV3UtNQ1qlLA0ls7GZcfpsbWOADcTguia0qFDMG03VRjBIKah0d/Dr7+zNsNFjGDZ0INf/6hweeOgfdHaXmfrZdBYvbWef75yPKrWzbM7nlDuWUS11E0aaLz/7GNfzyOVyzJs1C4CDf3glf7/9Wno72+H/R9p7x1lSVO//76rq7htmZiObyXHJOecliEjOKqAgCEowAEYEBcWECKjkIAZAxYAERVGCAhJElBwlLmFh04R7b3dX1e+PU9V9BxW+r8/vvl6zMztzQ3eFU+c85znPAbY9+GNsvf9RFUbn8g7f/9jujCyWvx93/i+YvsIq/Oa80/Cjb7Dttpuw/lqr8Mc7/8FWG89lh6034oprfs9LL73A7rtuzc7bb0gW6IMeOfg73ZJO16K8YcLgEN1OwcJFi/jCGd/hwQcfZc3Nt6XZyLjzhl9y4/VXsPpKKwuH1Xu89rTb0rG3StZi0ElGGZI4lh6dDry5uGB41KK0IU2FkeJtzjJTJpJoTyuDdisjNQnOJ3jTYCwka62VsulWQ/q8dTtd2o3Qat2WdHu5eK/GkJeWtKHIi4JOt0PDKNpG0wgiNdbnUs3mS7ROGc27NBoJzuZMndxCaxE++vWv/sxnP38+kyYO8cILrwDwpd8+xouP3s+vvn0ywwsXcNvff80JR5/OKlvvz4bvOVCkUgNzwJUFFxy/N92Rpay13lyOO/kIPn3M6ex98rmk7YmS8DVppRQnnqeryrmjTYg7JU0M7YE2znnyoqjgBRsUFFVIcsf1733o8RYooEppGs0MHXrGOWspioLzj9j2/yZiPnPlNf3hX7uqwjyKohDRh8rwuj7MRC6gPwsWubrjPjBuyoDTRkMcuZ7xBOkXVokfEDm/b/97HIhobnSffkF8bZIIBrXwlWe49isf5UdXncUmG6/JKy8upuUzGs0M50rSVorzjjwvmdQYoEcZMvyWN0aXUtiSmRMGyTIT2ApBm0GBMppOKajyWG5ZONrDaMXkdsqEdkZeeuYvHqOZJigUi8d6JFq8vF6RM3mwQel8aKmiOe/83/DUvxez20dPFTjAuzCxLnj5LmDuxMEM3oyEqM7aKpno+ubEaFlsNlCE6t5zNYSURA5yMNBRTN2EjhDSJl3my0VednjY0O46Mhj6Qz0QSKHRyGg0GpRlSacrmXG8FIokieG1Zx9Bac3vLzqT1198DqU1y6+xHgOTpzHYSnjh8YdYeYXZbLbJusyavgwDAwOMdTpsufkGdLqiHjc01GabHT/Ax75/PdNXWJWlC17l0b/ewjo77E3aaOGBNE1QwPOP3M+frjqHydNmMHfTbUjbQ6y47uY8cPM1zH/uSf79r/tZY4t5vPz4gyx+41XSZpvu6DBrrrkKd976I7RKwQlbRlh1AistHh7Fe4PyApXkRY+nn3qOux/4F3lRsM7c1dhqow1EQjBNKK2lcNJGqtVKSZoC4ZWlwTpNnhfkucVT8taSgiUjPcqyF+hunjQbIM2aNFvtqlqskSVI/zkTNC5MiARDkVPw/qS81Ya5CN+R+ejlBaXt0MgSRkaHSbWj3WyIgfIFRheMOItWDm09OklIEk+iPRMmiGdYFAIT3fLH+/jM585jlU3nsdY272WFdTZBobjvpp/yt+su4Oe/u4xLf/Az3uhNYqfDPsVbrzzHM/f/iYXzX2TpW2/QUGPs98E9WW31lUnKgv3eewSfuupOOiOjJFlGGUWCfGRMjbdR4p2aap03Go0qJ6KUwJFFUUgRWLQ5wegqpRhot/B4et08vCegNGnWwFnpVXP2Bzb/v7EXlFIkqbR8tkVBUeTjPBdxWAN4rGJZJ7U3Gwxwv1mvDe549yxijiKfG5Iw0RD00znC6Mnv35YtDJ8ZUZWYfCNAENZbJs9Zhfd89IscdfSX+eznj2HX7TZj0sAEbOEpSrkmpx0dVdDwOQ3ToNAFvbGcZdqDKOUCxBK9GuHooVUQEfd0CkcvVNY0U027kVCUnkWjOdKZVTHSFTbA4tEOzQQGWgLMt1LBbC+65Cb++Ntb2HTPw6W9ia09XFfJNCqcqpOXFb85RgM68mpj0iqMoI9E9hTnaslHY0SWMxZCEN4vtkPXOuCzRe1FxwO5jkeEy1yW4omPL56R90iShDRLRY/ZiWhKfVKL0PeK62waChQ8e+29E2+9NUK30+P1lx5jr0P2Yt4nDmSj9dYGcZylg26a0i0K0iTFe8uCBYsYGBysdGknTJ/DlvsegbNlhTN77zFJwsrrb85KZ1/Lmfuux2N/uw2Agz93NlsdeGzociJr/TuHbSMGa3SYXd5/FHMaizF6CLxCKweqRKUJ3luUKpg40GbpaI/CFiHZqFllheXZeIP1cBaStIlJM7Jmm87wEowtyJylM7oU1WhQ9BR5qejljrwoGB3riVZFr4NSJa0kpznJ0C0ceWHIMkNnbIyyLBmaMIn2wEAw5JJPMdZjjFD30jTIEMYGkiHJajSo0KAyNnDNmhrXK0kbGUOJptPr0bU5iesxUuQMthSZFiF4l0j7KsGVdZ89EE/4hOPPZMNd9meXo77YZ2xg0pSpdMY6nHjM13juiSc45EsX8ODvr+bmS79N1kixhWXazGXY59gjuPX39/LLn9/O0//6JxvtegBLF75Ja3ASIWlUr8nKGYkOmiQiXSgWcs5TFoU4GcpQlMHOBcdG7IdGCr8UGk1RFGijSdMkeLiiLtYPvb3T4x2NrnOObmcM7xxplgGhLxRiNGNPItEcAIWr5eX6tuJ/BTkU1PXRcYCoyvB89Rae2NY5Glx5ge/zwkLGfdyJBOioB+Gp+5l4Vt1kHgvnv8j3Lr6eL37uW3z965/lfTtuSytrorXHqByankXdUYp8hFQZMu/JfErhpHtwdV1Q1Zg7L9zb0nlR5PeOxZ2SgaZgPUXw+oZ7krBJjGLKYIPS+9BjCR546GnO+Ow5DE6ZzXuPPY3Za2xAGUJBF3QZ5DCq2xnFZp01UlsbWWMSCpuHIQ+qZsEQRu3ceFqLgIuu+NbxNYkRb9YWpRjRvlUlZ6WvxkG8sJgsiwpqqjK4aZoJudz6KgS0LmbkZe1kacpL//gzt119AZtssBoXfv/LdEYKBtI2qfMoZ1FKY3tImWqWkiUpptVmYOoQKKE+3XTrPYyOjFCMLZFxC9EZSqMTjTKCdbqYDDGKL9/wKCNLF+Gtoz1hkmxU54InqfjQ165k/rNPMHez7bn8k/ty6Fc/Ay72Lgv8T2vBGBw5aZrRSC1l2ZU27SpBmQZLlkjEo3WKtWNyEJa58HKdo9kawCRNOmNjvLV4lCTN6HS72IBrTpwwSLc3TKvZYtLkFrm3LFjcI887ZM023inGxkbpdjtkaUKWZRDKgNMQaTkLsf0MWofClbQSdep2uyjlydIUbRQt3aK0pZQKo3l94VImtGG4LHlz1NFqJAw0xGlQwGBbcNBoAhKjUM2Ua649i8MOPZ3N9zmSCdNmxSXLzNXWY4N5ezNj1fXZ/oi5/O3n3+ehv/wJgF6nA8ArL+Scc+Z32WyPQ1l2helsefAnGZgyk9eef5b2UK3ZXKkahmIeX+WnwnoP69cYLVKW2gjMleeVwyBXrakEZh1YLEUBiddYJ7S5JE0l3g3rKCZk/9fjXSvSylIykr1eHuqPbYUZ+sqgiaauAowJZPpAJxGhDNlcsYgC+goLKnS2DnP6tVrrEYqbN7xe96ldVZBFxIaj50f4WVU/ey8M1C32PZKtDziK5x++ny+d9km2umk9Ji8/AKUm0yntxJGYDmWe47xCeUdqMhFB99XJg0mke3GstU+NohNaeMQWO96LRF0nL6tOAxHkT42mEQ6UH/7wD/zs8qvZ5YjPsO72e+C8Jc/LkKCyfUY1hjxy7yYkG0obE2qExJ4kt1ylgREMaRjX6MXIL/t71xEMr4Rf3omxJ7JLKjpfGOeQHlbekqYpY3kx7jn9Hq42otrVTGAsL0HVSQoTBMSfvedm/vqzC/j6l09khx22ZHixw3ctRUN4vGVpUcqSZoZGM6uuQzypIoyQ5eB9d2HJW1/kvB98kTfeWMCM2XNYc/Md2WCfj9IcnCC0PVVw55Vf5aG//ImJkyez7rz9WH3r3WhOmCrekNKYNKm6fMxceS4zVprLY7f/itVXW56dt9uMYniEtN3CY/FKoTBgUpQucWVOq9HEW0de5oyM9XDWMKHdEBGkTleipKIMGhcK5xVjo2KIjZZCjdFOh4FmQq/wDAxEzWjL5IkTSRJNomC5GQ0WDhe88eYIaTIpyEqGZFJZ0mhk9fpzNsAW0n8s7mnrYqUgotSlIS9y6R5hNKOdnmC1pWfp6BjKKBqNBj4RLrpSkCaGKUNSbpznnjTpgwVRbLXl+hz+4T358+VfZe9Tvhd4vTBh2ix2O/ZLeK/4562/5KG//InNtt2ch+7/F3m3w7rb78EK62/B9OVWZdryq9JqNfBA3uthTCIRjBaTZr3QFUUfuoYotZJ1lqaiYtdoNEizlF6eh84QlphkE+3ufuUwWcfiJaegvBxMPRUOmv7c1P9+vGsi7f2nX1rpvRLc7XEgdfVy+UEHnpExNUaHj90T+gojdPReVe3ZBgNOCAUivBCJ+KpiTMggxGup74Z6k/cniOqsUfVEgU7kcPjjZWex+jKwYMESHvrXo5x60jHsved2dMocvEdbRaYaGKPJyzxgQRqVgNMlVklZcGxO+dZozpJOTuGk2qyRGLqFhPGlE2+4lSakRuCE4W7Br359Bz++9Fcc8qULmTJ7hZCkkhC42+mOM7oKVUcXSqPDWMss+FAkYaHyNKtlVL8+RAiVZrGObAUT3lskKMFLx5AI0wRvLtLU6kHWZFlCked0u71xHRJi9Vqd6IywhRDzG5nACzqR3lNXfGofvv6F49h8s40YHi3ojYwxbdIAjTTBK6kWShLRilWpwQepJ9NqhOJ5jy9LlJK/eedYtGgxz73wMt8473LeGIVN33cIs9fZgsfuvJnH//ATrrnqfJ5++t+cf8GV3P/AQ0ybNYfJ02ey0gZbM2OVtXn4tl8x8tZrmOYENn/f+/n1uafyyY9/kKMPez/KiXoceMiSwH3VeO0pbQ+sxboCpT1l6Xnl1SU000EG200pRggCQLYsaKQNCmvJ8x4TJk2g1W7hbYnzJXleUDgtergjw3ifM332oEQOqluVoy9YnPPyayVlJ2HSxMlobcgyEWopy4JGlsneCdBSq9WMKweloNPp0el25RBQov081u2RpQl5WTLS6bB46SKmTChYYc4krIdOzzE8VpAX0o9qmQkpUya2wEnxT1E6Jg0klaOwdLRgr70+xdydP8j6O+1HvTzDKneW8z6yE0vefINdjzyFVTbZgfbEKdLUFXG6mlkCWhg9r7/4PEmzjTapqOaFmoGYZ49dqKO3YoxAXMbI2hPIpif7rCIBvN0OBYYMMW/k6W86WzsinvOP3P7/WJFWQSLRc5TCgSi+HavVKoSv8mRrxX3JY8cL6n9v3+eJ+kAvI4iEqXFJnVrgpvZoInhRlRyj+t56fOVbvEYVqSLaSDgQyvs23/coLvvUAcyZPQMzOJVTTv0Gu+22BSXSIkfrFEWBd4mchBrQGuU8DukQ4cJ7+zA2pfPSEHHKIIXzjIyVXP79axheOkrWaJClin0OmMfrr77Jplutx/13P8SW+x7JlNkrVPF+WYi8oXibrj6x33ZPLkIyIZwQHq+t1lgVR/gaSohKe9Hj1dpUwjKR8ldxgEOfunGao74PJ1Y6eE8uVOHVUEf//EWMPWrFZkkozDCSoPNIVnj+iy+w4grLs2jBElKlmNROaTXE09KpgbTeAHiPShIRfjHilfuyBGshS4OMpGPyxIlsvOEULvve17jo8qu5/cbL+Pm3P8fEZaaxy/ZbsPIqq7LSiiuy6y7zWLJkKY889gQL3lrCLX/4M7df/mv23nMeyy27Fud//xr+/ovv8Z6dtuHQgw9EOQleSYPmrElkfQUsvJE28a5kbHiUa3/9O5554RU2W38N1ltnLZaMFLQbLbSWCLKXl6RpQ7oGNxpVNwJbFmggU4pSaZYseYt2M8NajXUlWtkoKI3WimUmpfTygjfeGmPxqKXdGMJ5JyWxocV7EtxS7z1F8O7yvEuaNhjr9BgdG0MboYlaXYZEaMJYr8fwyDCtrMNaq8wMRVMKoyxgGBnrUpQlE9op7SwkrUrhty7piHEW7VnHE488xpq7qHoxx29emEi7HfU5Xnz8H6y1/ftI0ta40nav6uXtgbTZkpY5SVY5A1HbQ9a8qwymOBIlrmvFQw4NZH3QZYixt6zfes/FzxOHIUbWtQOKin0MecfHO7MXVprrP/CVy+qN5KUbqvPUG4na4YlQQ5+1Djfj6r/3f3gfHBBPwP6vuFFre6oCrSnedMCE45OiCE84nWI4UXnY0cuOhkXryjO//7dX8ocffQ+AGcuuwEF778CnP30oSzs9ugUMpQnGQaJMwDEtSStBGU/upY2J4Laehx99gZNPOodXX3wBhaLRarLuphtz1x9uZfdjvwTAkjfn87ffXMWk6bNYb71VWWvd1fjLvS+wz8nflft2luEli1EqCeG7DxlVV91XBbUECEbwR1/DAXG8VfRuVfX/qrW7FqUurTUq6Aab4C2WwYOM+GysIBw/jwIzqaAHnOc1uyXOcewMIgk58Yj7/e6BZiKdCozh+Xt+zy/PP50bf3oJc2ZNp9nIBFPUsYURJC0p8VQOfBGFjTwY8X69D0UKaHxpcXmoLDMGr4PXk2W89OoC7r3vQfbde7egXUFAvI2EqrZAK0moCFWvYM5KW3HgQfux7MxpUBZ8+tjDKTsdsoGWHMRanBNlksDvlijigb8/zI677s/K62/Oy088xN23/4JWow1O08gaWAtLRzsk3jFtymRskHhstaQ6ryhLBoYGGR7pUhSjNBsZedllYLKWarMYSZoSlcjht3S04PWFHYZHSvLcMGVoBu1mO2g1h7ZRtoxV9bKnTMJot8tbSxahtGKZoclSCFAWDA0NMjLWZWRsMUMTPCvNGqDZSLBWMTxWMtrpUVihNa40a4g00SwasYzlJZPaCQtHC0a7JZnRpEnCsR85ldastdn1qM9DgBdjtBs9/7HRMWxpabSa5HmEjiSSzrI05BFEA3rha6/SmjAZggPgXXSy+jjiwVBWLdT77Uzfmq5tVJ3fiJKlQXOschTeDmt67zn/yO3+r50jRIkrcR6tpRKrDK2TVQg/PcELQk6vuvQuGEbAq8Bho89tl50S76wy3opQsaFqnIRQpVOFuH34rK4GIuBGwTOvTiDGG9z+e/POiTKQ0myyx4dY+tYb/O2mn/H6yy9w0aXXMGXaFPY+YCdKL62j8Y4sSfAlkKQsHRtj4mCDVpLiNXR6OSeefBF/ufV2dv3IZzlsh73ojQ3z1vwXeOaBO9j1yI3YcNf9Q2ii2enwkyjyHj84eh5HHLkHP7rk5yx89QWmzFqhMpRygAQ9i3gPIRNcJca8DzUoQUGpYjeEhdWnQRyRlmAqKz0Mjw+C7PJcW7rK64iaDlXZdqwkjJektIig22piiXBOdahW2K7onGbao7EUZLSM5ckH7+KxO2/kpSf+yU8u+DZzV1uZpJmJEfRykKAQNk2SobzGdrqUZSk5hGYmuqZ4lLf4PKfMy4rEr1XQebBBLa2bs/ycmax40D7gLK43hm60ZMx8gTIKbcRrckUP5R0mG2SH7bbkh1f+WDZPknDC0Yei0wZog2ok+KLElwWqLCExqFRKq9eZuzJTllmG9370izz0x+v43OnnceYXj2PqpMmUZY9up4ft9UibGb2iQGlDUZaAp9kQ7nhROEZHR5g0tUHW8DR8A+NTVKnDfBQy42G6JwxkDLRShsdyFi7NWbT4VRYucWiVMTQwmTRphGpAab/jncOVOc+/+iKl6jA00GLBcEmiUoogutQpemTGYwvNwqU5Q4OCseMs7WZC4TQjXcuikZKBVsKSsZxGAlmqGWimlKVntFfwxktv8Oqrb7LJJiuP35cBajHKkZfCm221GhSlYN5FYatchA3NZBXQHhhgOEtDRVoq8I7yVdTbHwnHnVEr4UXoIXJx5Rn9ZkNWccDHYx4rbIDKaVHjkYH/9XhXeKHXK0IG0uO9FW2CyqUO3mJQuveuVqHyXkJYrYVoLcdLPCN8pdQY9QDGVXgFA14RkoknUq3gFVv5OC99rByxzv+/3EaFy8QBDLFJ7DulJBH474fvB2DFFZdnJIfvnvcjhjsd9j1wN0Zzy6RmA22kzNcoI4LNaPGoPPzk2tt48pn5HH/x72gOTgSgOTDEnNXWZvaqa1X3Il5jSZplpI0m7QmTGBnusOW2m/Ls/bfxytAkRhYvZO7Wu5NGDyyG9H2QSj81Tnqw2fC8qBRWuZv0cS3o50TL7cdBk4Vmg0oZiGF1oSlmP3zhiZGLAlxlXOOG/4+FFzxebRR56el66bybJIonHrqPG75/OscdfRj7f/l4ZkydhGmk6DQVPLPIEUqWQSUGlaXQyUGDaQh+qhKDzUu8L8GXaKdEw8BrPCU+AZWGkL2ERa++TnNwgKGZ06tDXcbNitHMWjhXyKY0CcoYnCu5/AffZObKG8vctge58Q93svO2mzN5yiTpulxabOFJWxolHQ9RjZTGYIvPnPxxvnnaRzjw1Au467pL2GXPI/j0J49lo3Xncv3v7gDv+OInj6I9MEiR9yhLiW7SVCCL4eFhvJVr0rqJtpmIf3uHx+J0Kd8Li0kCjKNhymCDiQMZs6Za8sIy2i1ZOvwmC4cLSqvoFpaxXk+qv5RncKJm1TkzGMkVz76ySKRNs4n0rBRPoBSNRNNI2uCgnWny0jPcLXBAM9U0UumgbLS0nX/5rR6jvSIozHm+/uUfsOw6W7LBzvvWu97aSua00+kwNjqGs5YcJRQ2Iz3SbEgCxt0cObdpJswYnRiRSEXErip1vaCQFo1lbXN8FclHJ6f2gEP1a5/XUmG+wUmMUaD5f6hGg/8HT1dacTvK0pMmKY1GRl4U4YSUEMUYLYRiD/2iGiAektIa7aRiSxgPEYqonkWNmMhA+j6j0Z+AGRfYBly4ThbVDnFs6ld5e9XfanpZ9MBiiHDIaZfwl59fyIN/+CUAW+z1Qc4/53IGB5vssOu20lpdKZppE2UMTdoU3RLbKGk3UvLOGNOXXYHGwBCuLOXs7BP2UToeXtXgorRh+0M/xdfOOJv9DtqNP955H0/cezsAS958g20O/rjQxaLRq+5dJP+kStBViI4nRgQxeA8jF/4fvewYZtUjHhZQSOBV8xQ8oAgRRdaFt5bIXokebLWQlRjiuso8JO5CcQUojIKskfLyw/fw82+dwo8vO5dtN91Qukp7j84aqFTjiwJXgrcOk5lwYHhIDcqWKOuxufCoSTQ2L0L7Hk2aGRLl8S4BTJAvdKjEMDhxCDMgni1ao0JjTNvNwZaotEEsYSeKxRhDknU4+6tf4NKf/Y7NDz6ez3z5c9x8zSUBVxeNhqzVxFslL7MO3y2hhOOO+RCLFi/l7FM+wI4HfYRtDvoY11z1Hc45/1JGFi8EYJlJE3jPTtux3OzZRGek1+tVrII0S0lNW9aU8jgv8oleWTk7nEOhcVahlAvQTmRBaGilTJ0IbronL0S7w+MZ6bmgNuYZ7pQs6sCLry8gaSSYpIH2nmYKxojqmFaGwnp0UZAYQycvGe0WoDUGaDcSUDDcKfAIJJcXlkRrbrrpDhYv7bHfaV/ERGEaazFG1onNxxhZshSTpLTaA4hgUom3JUmWUHRKIv0rzdIggu+YMHkKSZLQ7XaxviRRkjgUh0v6/0Xc1fUZhRoole9KKTTiiEUzFLZun5VS9XoPyWUbuNDv9ngX7QWBBKJHE5sleie9tKSk05GkCVmWBHjBEh162dVB5VsnaCMDJDX3sWosern9mEj9+fj688ddWb9L6+tX19nKvpC67/3jdVXYTAUueloTJrPb0V/g0NOle/BTf7uVnQ8/kbO/dTn/+PsjjOUFY0VJt+hJaKNFdX+40yV3jkWLRvBobFFQBu2HshA9AudFcESryKzw2FLarqyx+c7MWH0jvvftS1hhvS3Y9xOns+qGW7LZ7gcSeaLOuyqsr7DbIHzjqqRWHKc++leEZBCDLFiXrfB3D+JF6MiprMu4Xb+xj/+3ZcUXrhpOEhNsfVVofXPVj9NrLRS3ZlNq3P92/VV866xT2XHbLVBKY5otkqFBiYQ8eB0w+cCkkESVaA54J1VUXhtUmpC22jQHBzDNBiXS/qiIHXgTIHQBtp0u2YQBkmZDDC4IRFKW2NERfKeD742BFajAdUYpO6MowKQZ7z9gL3QxxsiCV1h5o2351oU/pJdLO/lY2aRNitKZvG+Sif3Ocw4/9GBsWXDr1RcxtMxMDjr1AtbaZCsAJs+Yw29u/zv7f+h4Ntj2vex52HHsdsjRbPGeg9hil/1Zb8td2GrXA3ny2X/jKXCqJ95+KgwW60tQWpwbJXPjvJKuvy7Q0ZxoGDunSbTs23Yzo5EaennJwqUdFg7nLFi4mFYod5840KbVUJgU0kaCyVKarRTrCnJbsmikR7coSY0i1Yo0UYx0pFrOOUuvcHSLEg28+tJ8fnjhj9juoKO4+OO789xDd6G8I0uFB93r9XjzzUXScTfNMKFKT5axl84jEPRXxNkwRu6/LC2dTpduTxTtnC1JE1MX+CgjtEUtB7AONMmYbzDakCaJNJpVURM8NGUIe9AHydIKOgumpU4y+yiC+D8f76oy5pzDh4ok2YDi1ZQloOSC7GgHcFKHjRjN0rr69QS7G72h6HFSYstYQVYb3KiSVdkQZ0G5yout5CWDt+zwIfysw+aA0PSJoPdrNtShsQpWN3rBzntmrroOy6+xDi8++Qj333Qt+5/yHU7//Gf4+CePYK/3bU+7IVllLWc4Re5Z0ulwz73/Yu3dj5YQJAx8OFwxSsnCShKhlvXyYFXEqO3+sdPZ8D0HM3u1dbj2jGN55h/3MHX2ipR5l7GxEudUfUCoem6qE9rXhDL6nhaZBFWiMjLfqTnW3jm8lkUbSeQVQyEa98jf7IMuqtHzorImAUQkp6sKwagPOYkqjBaxm3t/cwULX/k3u+20DR5PMtCWzaQVlCW206PodoX7bTQqTVEmEe/RO3QmYbcD0iyjLEUbwyhNY/KQVAiOjpI2E0y7LayGXkkyOIRKU3yZU46O4sseSXtAOgBkCRQO283x2qMbDUiUiCIpOdCyJOF73ziVnfc5jEO/cgm/Ovtklp09i48ffiDamEqjRPS/A20pTcA5Vlh+NhddeDbHfuxkLvjorrTabTbZaF1u+NllfODok1h+tbns8METGZgynbfmv4A2CQNDE9FJwqRlpvOPW3/DkR/7Et875zQ223gdUArnRyhdESK4yNOmEn6JMkilkznTBgzS4ly0yz2DrQRtFEtzj/M57Xbg2ZaOMg/i9hiWjI4x0ExJWg2SRNMrhaOeJTDUkvD+9cUdvJe8ZpoIv7mVGm658S9cdO6V7HDoJ7jjJ+exYP7LDC9cgC165D2hR44uWYJXGq9EQLzI8z69FdFgMEZXVFGRNk2JWjCd0RF0kgUbJO3jTdARkbbuGp0IPxmlSEJxSBJqEKLkqo2KZCEy1iF6jG3K4h7SWla1c9VOpP+n//Z498aUIQyWyVWSJa5oROECg4teFAUxoRW5mdaK9bdlgTdZ4P4RsL+Y8IrmQkKhiu0AdSJGxw2uJJQKgHcVTquY/ewbEl2NG6rCb/sD6uj/RW8Y2VStQT741av485Xf5N6bf461jsO/eiUXf/loJk4cYMdtNmHqQIt2klXCM2M9x+JFSxiaPC1kPCPLALJU5OJKX0XPJKn0vbr9x9/gjl9Lv61lZs5ktY23Zp3t3sdya28qoVpiGBho0ukWVdPIKlmiogCRf9s9BQNLH3ygVVW5V1edBRaDqhN2WvdJ1PlocOufq4RlDLICXhzpg7XB7Qu/wsMBOsAR3U6H3//wPP5xz++YPDgIpcOpHOU8WIW3BdiC1ABFAT6BJIWyBKOEIqZFaEh4yoqylzM2MszghEG8h6zdktDbW3ynhy9KTNaExIArsZ0uvU5O1gA7OoJOk2ApUoEZfKAXKo0ru9jeKK7XQ2nFeuusxbLLLceSV5/nmPN/zZUnHcxO227CBmutGZwMYdI461GJHGTKiJ7FoR/cn6232pyiO4bPe6y4/LJoDNdefi7X33wr1371Yxzwue+x/JobhdLTgEFqw0bvOQjnPYccegInfPKjHPeRfVEBWjBawuzI4LEBfips0OtA8MfEgjOQBdpY6Tw/ueZW/v7gE3zgqIOw3rDMxAbTJzZ5bsEYSlnppIxFGcjLgqWjJY1MOicMtFKajZQ0lbXUaqbkhWUg1dx48908/fTLPHjfP1k8nPOBL19K2mxx4w++TLM9wOzVN2LxosU4K8YRbUjTDO8cva6wNlRIVmUN0UfRgaKoFFUZr9ZS6KCVYmTpsBzuqbTHstZhraw7kyhxmAK3PUa7ZeAsey8UzcjYkh0V8WFTVbxVCTSlSFNJJlrHOAfyfz3eXXtBK6yLfE2Fsgo0aC2GVzaqUDGs9diAcVjnUUpC8Cf+8iuefOCvrLbhlsxYdQMmzFyeSMVQSiQV3TjDGzHdPs/XIdU+AS70ffw5FU54FbkvBIEXF41uX1GG3Fiwvaoyt3X3iUiBMuxw+MmsvNEOrLT+Fmil2OdTX+f8s09j803Xod1MSFItJbWhz5l3ll53RMJoIwB/s5FivQiVv/rkP1jy2vMU1rPGFu/hwhP2Yembr1f38eZrr/HmTb+Em37JMrPmMHuVuSy/1sakSUKr6YEGY2Nd8qLE46vTu59L2Ddoch0qjEnwUOt26dHzDaGXHk+/00rhta+8afFi6wNQGyOmNSZWK1jBEzHj+mCtDzulFMYo7r/uEjbdeH2WnTVTZD6dDZ5EOBxNKNu1Dl8U+FLCU2/aIk1oxBtWOgrQa7LBBiZTJFV7efFcbddRjHYwmQiPlyOj2KKk7Ob0uh0azSFhHSiBWmxeCn0uM0E8SDxtW4pehgMwCZeeexZHnvBZ2pOWYe0tduChh59gy803lWSjsyiToIPGc4w08OI1rrTiHFy3gx0ewZcW02qw9Vabs8WmG/Psi6/w999dw15rrCdQjhJHxSiPTlI23+ODLJ7/b876yrc49shdefrlDqkuWHHWxDDH4LXQ3krr6FlPK3CbrYNe6dEefvHLP3HPvY/zu+t/T6/bZXDSZJwyHHTEAQw1EiY2M4YaBT2jKcqS0juct4xaJ5576RloZ5QoHnr4eX5/0x2ssdpyrLLqctz+l0f48+9v54VnnmHj3Q5izV0OY62t3xMgMZGLXH3THWgMDErZbMSTCJ13y1K80bBrrasFZkyW1pRIL+p1SSJ4f7PdAhQjS5dWkpCVAwHkeYkKewekRZdzXiCFmBvy8nfplybGN+K6UkUuxjgNYjlJmgQ9BuHTR2rb/3q8s9GFgPPVlWhKSRcEUXKX55WlpQxD1t+C23tJ8qyyxe7MnrsZ133jRBa9/m2WX3sj9jjxmyRZs/JYo7mrS0dD0qYvjI1ea/9B4r0UJGhCFUoN6tZYcIjxa4hhHKBRGfnoBKvgMSdpxqqbbCP0FOeYsdJavD7/Vd54/U2mTRnCJ7J4Gz5lJC/Y98DduOP317D8mhuTpal0SEBhS8tlJ+5Js2FYdsXZ/OUPf2Vf02LPE79OkqZMW34VjElY8sZ8nrrvz/zl5xez8PVX+cnpxwKw93FfYoMd30vuNGmWkGYJ3U4vzIevjEI0mPE0jiWPQv6ueb1VR19tQuujGtqpKWqxHbWSg6U6lOITkYqwYLTRoJwNCaVoaMUbrfqpBTxboXj+yUfZafONhFJVOmwhfDOTSmGBDyIiqtlAt9vigcqCRGkjtDgH+FjbHzx4E5qfhohKGYNpBN2QLBFR8F7JorcWUxYF7Watw4tSEl14wcmliEbWtHWgdIbKLN7lUFo2XH8t9tl9Fx74+53Mmbs+v775t+y5+05Mbg8yOHEI5UtM8J5ltTm8L+uoTABJdJahsgYGxVnnXsL9Dz7M+79yufRF8174MdERCdqw233gRBa8+Azb7vhR5r8yn0232JBrf3wWWofNhCVNFKVTZEqTBu9N9kNJr9Phcyd9g3mHfYL3HfcVBqdMJ2u2+O13PsWBh+9DEg7nZprQ6eUoLYUYZcBQhU0Arczw4x/eyJWX/oINdz2Qn33xOwBstscH2Pbwz3H4eptXiXDv4sGfcPKP7sQE9bLC1UZRKSmQidWdYjBDPz1UaPFUw48eh7NiPFX4jPZgW5TRuh2SNKsScVFrxIVozxjpfViWVnSSQ7GOeLu6glYrq+A9SkVNExFSx2gK60TdrBSp0ySI8v+vx7v2SNPhRPDekWghJGeNTLA+PHkvFxA7qMz7vtcqkNLSJGPijGX5yHd/yaJXX+Tykw7ksk/szVHfu5EkETym7jHRHyRTldnVKkV9lUiqfrLkyn3FAouwgQap7Y7QQ7Q8AgMTs49VBVwY4DrZJYpUKEVndASAJaMdhjs9civtU9qNFhNbCXvvOY9fXPMpnr7/Ntbd9j1EA7j49RcYXbqEb//mB/zr/kf454NPs+zam5I22jSzFI8nTTTLzF6WSXt8gOXW3Iirvvjhahqu/8GZXP+DMwFYZsYMZq+6Nmtuvw9D05anLOWEzRpNMcJBiDri52VQ1NJaB0WkIHPXZ0CViRVU4C1YXQ0OhIorcQL6qDThu4sZWxXXS5/nHApZtIlsCRWuybH1ISfw4zM+ypmnfFSSgaUlbQ9hGk28DXh3llWJM5lJjy+6uM6YjG0ZQkKn6oPEg81LnNakWSYb3jrSoQngS7y1ZIlhyA4ysniJtKDRGp1l4lX3evg0RWUN8aLyHsp5lNfoVOGdEPN1kOScMW0q6sXFrDtvP55/+AH2P/wEfvnDc2kNDZBqAIdyGkwKiWT0sVbE3/MS3Wxh2kOYtM39f7uH839wKQd+7jymzl4hiBvF9S//OO/AeZoDQxzy5ct46Yl/orzlzivPEClCIxhj9Mitg+GuhL6JgiWdkp/+5EauuPhattjrg2y130eqLeG8Z9m1t+Dog07kp9edS6s1m05eUJSWzGgmT2zy5nCP0jqpuOwV3HPnA1xz9c0cfe6vefpvN5OmKUVRsP68fZi16jqVZwsEj1YSoEmjQZZoCeO1DnQxoVzFasrKDvgaTqtoWcHqeudkf4cozlqLNmJ38EKjlLJ9OUBt0KMoShecwhjZSRupshT9kLwQqqx1rio9ttYHUaCgMKYNtpS9ZUtLmRd1y653eLwje0EuVtgGjUbG4GCbZqtBnud0u12KvEBr8XSdV1WyxflQFeUsNmQRY1fRyTOWZdL02cyZu0Glj1AX9QYjEM2C6qOIReNadUhQdbItGFRPpDRF4ACqstMq3I7+Ws0VrnDPaMjD38VDjNQpy9CUaezzya9x8glf5dzzrub5F+aTlyWLRkZYOjbCMpMGOPHTH+Km75/Kr7/9ae78ydn88qtHc9lJB7HXB/YA7/nWyV9n2nKr0GgN0MgMSjlJlo2MMLJkKUUvp7P4Ddbecnve99HPcOLFNzJrxVWrOXnvHjvwr7v+zM/OOpEX/nEb9/38uzx79w3MmDWTKVOn0GikFHlOr9cLJ6+Lsa0kBEJhSwyldIiZopcQx09OdV1hiZU3LC/qG7Xx60WHCjfRL66r3XR4D+8k4TFt+VWYs8KKlM6iE0020ELlOeXosBRgmASdtlGmEd5dstXeO4jZ+agpnAgzpjoQSkt3pENn6agcBM22YMBpC90YwGQNBqdMZJmZUxmcPAmKEtfr4Xq5bNqsIaFiWaJCUQZWWAyutMKW0IC1bLfVpjxx3x0sfe0F9vr0N2kvsyy/uvlWur2c0ZFOiPwEAvFFju90pYquRO4xa6CTBk89+SS773Moy66xPnPmblQJJrlqTYeEZ5CjLIsSYwwrrrspE2cux6KFS2TOfIAxwn4RepSjKC156bn4gqv56U9u5qAvXcIuR35OjFNgGGlteN9xX2Hi9Dlc+7M/itC5t3ilaWeG0klj1USDwaKN4sknnmPFtTdhwrQZ/O23V7PS6iszddayTJ61HM4W2EKEZIqiFPgxtKl6+Z93csvFX+HxO28MVZC6ohSq6DT1wQKE+xKura3sQDAZIjsQKjfjmjdJSq/bI89LiiJ2T/SU1gesV0qvi6IQSdaOVC72enlIY0XbQfWVJAkTJkwgbTTkM4KUY2xUGeGMd3q8o9F1zlGWJUmaYBKhboyMjNHr5iK8nReCkcRQvvIW62oPqccPavfWUlrHh7/9C/b65LfEPQ+4bn9iRsK9t9WRhJH3Qc+3QoBV/Ko+vgLZRSKhP9nUhwMHTzaGlfE9KyyZcBh4AXMiHLH2trvz4W9ey6PPLOKwQz7NZ085h2efewllEpwybLjpBlz18++y9kqDzG4v5f0HbsvPb7iQ/Q7dm0YigcWGux4IXjrj9no5SZLSHhxi4tSpTJo6lUVvvMyj99zBTZd8SwoVfG3gfnz5z1h3+93ZaJf9UEnGY/ffzT9uu4let0NrcJApy0xlYLBdqbBFWMZZO64cu9qXfb+LYxsZFRXGFcjlqAAVxERGhYfHz4mnmHgDJia8oC7cCEI5zWaLpx97jM7wqFC1ul2KTldgA+tERm9sBN/rgtdSJJF38UGQxFuHInjZ1klyt7DYTk7ZywVzyxKUztCmhTYtFBrvini8Y1otTLuJbjYgySBtiIerwJe9EOGYoLzm8WWJz3u4XgcbPKHVVlqeU44/ih+degRFr8PWHziBc39wJXfddx+jY2NVhECvh+/0UCrBpE3xcFtt3liwkC+eeibrb7g16+64Dwd8/vwwMRJtxLJrKX6p175zvmplP3GZWXileeHFV2Wuw/PEmEEr1by1aJgTTvwmv73xHg457RJmrLRGHwRHtdOUNuz9qW9y4y9uoplCZhTKC792oJEw2MhYacYE1l5+KqvNmsx+H9iD5x++h6fv/TO7fvSLzFp3Rw459QLag0MBJxWvsxoHBJ646ZJv8vLj93PDhV+lkaUMThgiazSoeN8qtnWKHG8pHa52cOVYBYct5oSC/TFaC621KDDGMDDQEhnHNBFoNHLm8VVy24PoUASvOuay4hcIbXKs06HXy0UP2tYtswil+f5tGilvf/w/UMY83aBlKZ6Q/D5qFySJCQr0SLkpsapDNmPMfFsvVWR1YqfPK60M5ttPifokk9NP16pY1IITwSIEY68qY+mDZ1UpBBE/OmbXx39O9VN1DWEiIzUr3PPEadPZ6xNfp9cd4/7fXsUJHz2NyVMn8d4957HjDpvx6htv0Zo0la22XI85y81kcbfDjIEmDz/6PAAzlluJpfOfBTwzVlyDbGAoVH45Sgdb7nsUG+x6EM2W9NL60DevlkRl0WVkeCmDk6YJzmgt686TsmLTyCS0UYpWw2CtMCucj2I4onCmid5ukHAMG7yGavqr+mojKjzGGlKI0YmKJ1XEeMNLyrLEaEeSiPZwtxRPRAcYY/6TDzFt+lSaQwnWFeIBG4+yBeXYmGT6LZhQheYRh4xSCjMiI8boBGUtOkhQYsCohGywjW5k4K1wVAG8RZumGBsjZd3e9sTzVHIwqUSDzaGwAk0YFRJ9suG0koqqwsq4NJophx2yD7f+5R4ev/Mm1t/lQOYd9km+dd7lXHvpd+l2CtqDKV5rirKL8l0oLfPnv86HPv45HnvkEabOWYHDzrycWauth0e6Rtd9B1WI4ny1SsV7lYMnSUSbY4W56/LgQ0+y6opzUE5hlA4aworHH3mKM796OaNli0O/ehXNgSGZ576dVkV43pOmCcNLl3LX3Y9xxAdPYtc9duaLZxzPlKGUxSPSxDVLxKCuuvw0vvjVT3HaSV9gr098je3ef1wlg1mWOVmWkOd5YDwJWLLwped487VXueCHZ/Opj50mNLsQWWgtDS7Dzh8X9dZSpH372wuVrc4jRDsiUWrWyMA7hoeHxZDasCb7xjPKFcToWZK/UUiqr4oUwHmRd0Xsl2iJZMGBhLxX1nKp/+PxrmXAeMGHigASywYOIhIeyjJyQH2oiOl/aX1jlWhKH1jTnyiLnpksBvU279fXAi2qD3bw9WLxLohzE21HH080XJEPkG99hap/6Ktfe4TaFBemD6FNLMVViDRcc2AC273/BLY+4BhefvIh7vnTL/nxZT+jOTDEyhtsydWXX8sHjzyQvffdlV7X8ve7HgDgx6d+mGYzo9FqsXjRUt53/Bmsvtm8kDySe82aA6HAQjCpNDV41WJwsiQfnZX7sd4FQQ+5eOshyVLSUpF4wddsn+xRuC28dxiTVFGKLctgbMerJ8VTX+AWQd6ds32iRzUTRKNBO5xTlFZhdEKqYbST471wL0GMxrP3/ZFNN10HZ8RQSvNFJ/ejQmJUexylMGYMUKiq4sorJwyHROO8eHw2GF+tDcqX+NILrKUTdCJVZbFzcTxgXNGVVusKVKqkT1mnFHwOT9qU8cY5sNLFulAlZYiFkiQjbcNhH9yLUz7/bSbNWJZpK87l+u//k1JZut0urXarggveemMBN//pbi696lrMpFmceMWfabbaciiWMlfWu2ijgs1V/X5BNZ/xAANYaaMduPKH17HXHtvTSkWrIbeOP/zxHj7xia+z2Z6HMW/3D9BsD1arP56uYmx8HRZ5z6Tps/jkJ7/Nbsecyl+vOZ+xXCKMyYMi2J0EZbjSGdZef00+e9ZJfOuLp/L+My5n5kpzJQlYljgFRV5U9sIjrZEAvvW1i9j+/cdJR+HR0Uq+NFqFGInqgMmmgSXgIpQQJ9L32RcvnnRnrIstcuFvW0tppVEqiNMgzUW7RHZN7EpdBIaKUsI9j2MjDAdD6UNUFaCMqDVjwry42OXlHR7vUgYc8BEd23nUTqANfMAkSYSLWFHA+jOL/V5qvdljwkr19y6qTjWAul13f+uY+sl10q0KLcSvpw6T6neLRQAVbSxaFFVfZz2Fql6U8XdRpShaca1QXpgLHmE5rLjuZqy4zmbBu3SUec5me32I6846npdeeIUtt9qQ939wLw754N6kiSK3nkW9Htdd8Uv+/rtrWX3THYKhkyspi4L6/NWUpasOpdKWknwKYX/U1VVIkipLE5qthLKoK8pi0iAebs6LpF9sNBmbd4rmaigrVjIXqFq1ycaqnDhLSgUqn5JCgLBuvPah9YnDeV29h1aaha++yB+uuZxf//p8nI4C6apvLjzooHDmwr0p0WSWzVY3BZRSaglhrS9xWCkL9SW21wEPxmT4VJK/OIvXYU3bEucKXOBUe+UwLsEVgJU2PmWvK9oizuNC19uSEotDJQqb5Din2GnnLfh6/mlOO+MLrLnt7gAsGVmCSwcoi5KXXnuD3/zuNn74k18wc5U1WX2H/Vl/xz0xaaOCcmLFXiVf2L84x23Mt8NksP7O+/PQH3/BDTffySH77iz80QRee/0t1tl6F7Y58FgZ2Xiw9r9d5eVCajSTZy7H8RffEj+BO68+n0VvLiSZPYj1npFuScsZxgpLt3CU3rL+Rmuz29478+Rfb2bacqsGDFTRy0uBZ6IDphQrb7Qd8C2ef/oZxrq/IE00K2+2M84pkjQLhs712Zw6z9DvxMU96RFGlaqiOOl6UXTGSJKUqP1cxMSrt3Q6ZZVcK0MXkzK03rLOkiZJn3aMI4ll4nk4EAM2rcP+c7Yk7+WhLyHv+HhXyljMeusIUIdQ0iPc2TwvQvLEg5YutihVnVcR+6twqoglAEECqvKUfBXy1yI349ZceF1dTDHuH/GA3raB+z3afoAhLrxaEzhamRhSS7hZ41GC2UTKk+9fuMGIR0qdQpM2mugpMzjwSxfx95t+wkUX/YqzvnwejSxj1dWW5+wffJlpaZORsYJl19yoMmTWOmnrQ0h4BV88tj/HC2OiLEtRurcxUVbPm/Uhwx7C1LhgYyeBcONy30ETmDDeURe4iihi5BLnPMIPqhrmPi/M4b0J0IdUYxE2kFJIFt+XTJqxLFu870Buue1B1tt4LRRUnUdANr7xBChEAQJrqUQqfxRixFUojVZaSwvw1IeCiZKK7YLDui5l0Y2zjLLgSx8cgLiz5R6tUXQ7Oa2kgdMlTrvISpMEnpdQUydgMkXhFaNdYSJssfl6/OKKc/jAMZ8B4Pob/sTLr77FHX+5j7feXMCqG2/D/p85h+XX3qjOXThHr5Dr1TGach6vdGDp+T4jq6p7wFNjh8EArb3t+/jLX//FfnvPIzUa7xzLLTudJQvu6nPufbV+41tWCWUFjVTR7eUohINuTMJqm27Pr677E5d+73XuuPUO/nTXtcyeOYHSeRZ3cwaaCZlJ2Gzzdbno4huqPSoqaaoqptKBojVp+uxqz7zx0vPcdMk32W7xQtbZcX8pgAnrLbamAgLcEh0nX2GyOsqFpkkozVeMjSxl+K0FDAxNEMcxQmxx54c1LhCYrsbFWul/WItsxQSd6KeUZTTqvuKll8EzLp2U5Mcc0js93jGRFl+q+/QoIdDAqk4DQSW/ekHkgAovM9pYE/mgocZZKVEN0sZUeGsd+sqg+CpDGVkFfRnNvq/+h+o3otR/r5W54n3VBRiq+qrhDzlwTBV+CKCo+j+oPjzi+zrbJzQu2dOJy8xih8M+zcGnX8oHz7yKlTbZkfv/9g8euOdBJrQbvLlgIY3WoODlQTQFVFiggpk3mxnl6CKuOuUgrvz8Ydx6xddxxRjNZkazKS1YjBLjlKVJEPNQVUWbDtxW+a6rOxX9hH7NBI+3vt+hryCdOOdGS3cJHa8zbIL+OZPEQ+jp5kq8t/XvnCzYqXNW5h//eII3lvZY2rOM5Z5uAb3C0yksvdJReiddObTDKflSOnjB2uESS5GUlCrHUuCNA20pXZfSdXDkYBzOlHhV4FQevhdimJWIObnQ+cMrR+lylPZYU2B1KZ+DwyYFpSmwqsSkirRp8NrQKyAzWtTLtGXW7GW48vwzAPjW2Rdy/xPz2fXjZ/LJy29lv5O/zdTlVuKhm3/Ks3+/k8QYJmYFGltxwSUzT3UI9C3YcOhRGeHoBMV9sMK6W3Dbn+/mzcXDjPRyxvKCv//jSSbNXH7c6RiTpoSv+HpxKGw1nwrxIFffdAf+/cwL3HX7PdjSsvvOH+HyH/8Rg2L2hCapMRQOerkVHWOl6PVyrA8Hi05I04Q0NTQaKQPtJvMOORqAnQ/9GIMTJnHnzy/lj5d+hbwoRfsj7tFw1c77KrKL3HJjpNIzSQxF3qUocha+8RpvzZ9Psz1A0miG/Rjj6PAVIkOtTWUvjI48XAKu7DBJQrPRoNlqkiQpzWYmpfxa92n0KoqgnOYJCMD/H3gB7ymKnKIoAEWaphLahglKwqaOpOOqLYv3IemmUQGjEzsQg8+3ObyVp1r/W5/qYaCCexVLWX38a4yN1PhIrOKhxs/wfQY5XECw49X1xNdVdGjdXyLbL8wT3zNmk1016DWEAVEvWOmEtJGwzJyV2OXIz7LeDnty0vEf4pvnfYGXn3uBlbc6EI+uvcwwjmmS8I8bLuPPP78SZ0tGR4Qn/MJj/+TBW69n24OPYfk11mPynJXJl77Oa/9+kpeeepRme4DVNp/HtOVWDQ66RhMrxoI3X7WpDos6wBBa11i4CgvbeiuwgRbjLns9bodYCONQHnSElxxBqziOhwdlxPv0nvV32o8rbv0NH9j/U3zvoi8xY8Z0NE6wQiPjXjopMY80P8HZvEA7CixSAFF6H0Q2RV/AKI83MfoQ+CFyYbT2lJaq84D3ITz0TkJU50lb4gsSkjHeuND9xKFTWcfWKxaNWvCahtJ0ux3QCp0qpkydDMAmO+/Fhrvsz6KXn+KxW37Co3+/B5xjZGSEjXfak2XX2YLFRUGSJCIBGfYTNlAj6Yv0+nyLGHl5LbSnGAFNW3F1VlxvC87+7k8549SPkCSaZ5+bz8xVtw17RlWRThXT+bi3ZGyLUvio1lmyhpiHoakzeeAvdzN9uZU49gc3Mf/ph7norONYa+7y7Lz1WqTGcftfH+aCc3/EGtvsSZT+1ErTSKXvXZomlMEWnXHAZnzg9AvY4PVXufUnF1b39fSD97BdZ4TmwAT6H9X9x0jSy1zbIg/YccHIordIkoTWwBADEyYLRS1KQKq+11Z3HuBLY3Ch+KKRpTSyTNZtEQt9REPbWqHEpmmK8yUS1XmJtGIkrZQcYv2T9V8e78pesM6JDqtSFEVeTZ4OHhTB04kYrNQhOzA6SBIGIxaijP6QP16b8Ovo63emgm5CjbBWqME4g1l7tZWRJQzs/wRW6r/FBV09NRgdef/gaVMbV2NUCFmpjGztfYRPDqF+WN7jP1krEp0yc5V1mLPK6nz2E2ex4WYb8rffXM4K62xCa2CIlx//O2jDrNXW480XHuOvv/kRZ5/3eabPmskhe32UXq/HfQ/+lgf++STnfONiXvvXn/j3My/QHRtjmenL8PqrUlbsdcrUZVeRzwxVPNZaSXxWoub9B0iY79DCSGuheyVasF7xUIM3HpXK4iCC4Lbj3rI2uCZUN9VVcpq0OYGPnvsrbrn4TD538jl8/8JTababeK3BegrnpEWN0WgV+11FmCiIyGgdKD4Khwv3pSiVaAc7V6K8wniFU4CzmESisjIcHEYTPCuFVaoqLoi7vEoyEb1++UsnV4x2cnCeHpAlhrJQmMwwe85MHrzrt3z42M9zz4+/yXprrc4B8zbgwtOPYbk5sznmlK8xMnFliqLEe8HrEx38MSW91XCqYveMi7Cguh7nQuPYsIidtWxz0Mf58ecP5TOfPpyXX3iR39/wR7bZf7nxhsCr/1z74WfrhSqVJmlVcdoZXgTAejvujVKKOauvx6Z7Hs4nP/Y19t13B4amzuSHl1zNTh8+mTW3fg/WWhpZRpqIwXXWSrcKZbj+vC/SHRvBFgWrbrQt/7ztpmqfTZo+C5t38e1Bol5uxG6TRJNmqeiC4KX01hYMDA5QWk9zYAJFoIdJUYNg8oLv5hXToB8/F3sS9TUUWUhAJkki6nUhF9Lt9PDB6TRa1gGWCv4DgVWF19/P/vnvj3c1ujHcVcjpV3s2VGIUgr7Jh/XyclxiRyvJJvrg/kROnIpO4zjD23fBvv5W28QYdATD2Ofd9sMM1Wv6vNe4qFTf36r/q5pnLIak72fq5o3xoOj/3Og2+Eg76cNX+3123fciYwx7fuIbXPnZw/jXgw+zyhqrcvah2zJ7xRV5+bln0Vqz2kZb8MbzT7Hvvjuxww6b8Mpbo6y74Tr88+//ouss2223PttvfyGp1rz2xkLKvGDOnGn85sY7ueSiX/DEPX9kw133oz04UbKp3tNqSjdjaakzbvj7Hq7vymU0FarSbKh0LJQkE1Wg8Rhfh641JEMFNUW1KRvk93RoobPjh07i9xedwTe+dglnfvPTtdKTGt9hWbgNEhYbDU6F8mEnOrAxw59oUbhzzqK9w4TEjJDXjbAqVIRWRMCuZsuISE1kT5jIDlAiCqO8CGF7D91eGSI7BYEYnyQaZxKW5KNMmtri5mu+h7YenRrSVhPvPb3RHrfdeRf7feFQkfZEIhGvTRXXSXNQLyJAvj78oS5Xj6Gwsw7StHJOpi27EkNTp/GrX/2RM08/jykz5/DXX17BhGmzWXfefuOdnvA+sUmo0KmyCjKKGP9bLz8LwJ9/+j02fO8htAcnsvX+RzNp2gyu+PZnWWbOCnz4W9cwcdpscGUQmpGElEJHKTOu+8YnePSePzPvsBNZdu4GdEZHSZtN0jRjyoxZvPT04zxx101s+N7DKoOmlaKRpUHrI3CP04TEKMgy6VKeJCSpwJwusH2arZYUNhgVNMBLEq3Ji5K8KPsUCUX7Be/JixKlPGUpTobzoSWPEhuQFyWp0STGBAEhj7I100pr0ajw/dvnvzzevQxYaVqtJh4YHR2tNqLzXhShIJTJ6Qr4jhtARCXkdxZAG1Rw5StDF57rVShkcLUo+f90VqOpCCpA4//kKyOpxr1BTW1CUS0sUDUG2gfcV0a8gjKCgY6npFbjLZbWVZhB9fzxtDVf/RamLbcyX/j5vdz72x9x86XfAuDl555lYOIURpcsZOUVpvDBg49h//3n8ebSHolO+ca5p9HtDDN96hAWkYtMDCw3cwrWS9nnjrtsS3vmHH70/au46ON7cPCpP2DZuRtKNY2RDWCSnF63R212a2/OewXeSTVUabFh80nIH/QTtMFZeV4UB4ljJrSgGmqpdBci/1LJYU1JxXGcd9inuOQT+7BgwULMwAC6VzKxKZ5WasQTteH6jBIxauWkh5sJRhAFha0rk9Kw8XulYiQvSRONLUBrMdo9GymMCusdeejK20xMaLWmyEJb8URTYfXRM3Y4RnNP4i3t1JAYTe5Set0OqVEkOgEsTlnQnsJ20Srh6hv/xKRZKzK4zJzKmCrnRb00VIdpraQAz2hwsZFjvS/i0tQq6EuosLq8wEQ7f+SLnPm5w9lo531Y8saLHHroblx15YWsue0eQYsAKv4WEWKRpKt1TjRDvMyfs55H/voHAGYvN5sn7/6DFPcAq24yj3mHnchmexxOoz0gRlKHqCj0MHRemDiNZoO3XnuZPY47nfV22IteXjDy1uvknTHyzhgThlrs/4G9eeSpZ+WwC9FVlqYkaRq0MBSusDLW3S6NgSEpYLAW7wvSrIFPDKJyLR0oJIIJ42UMvrSkaUKWJhRlrRUizRZkd1aqeijyIlI2EWPrYzItFgZJAUrsS6i11C280+MdE2kAaZqSNRpBFb1ubR75abHsN1KPoguplAo6DSIULRtPVd5B3IzRGFVCNxUXt/8qVPVVc0ODwVAyCCJeISdef+Ivel39WGvF8w3haqSUxQVYhSDBrY3wgQ9haO2m911d38kZYLPq3uTUDAOuVNBcFWhmk/d9kM///H72/dTXANjxsE/w3qM/y/yXXmObedvx0uujjBUFWmVo5VlphRmgoGEUqYYlXc8Li3v8e3GXxZ2S0bxgpZVn8/lvfYZDjzuUKz93OG++9AweKCp+pw5VhoGZEsQ/dJRLRAVPQ4m2KaIg56yrMNAkMWSZbAgTKnySJBEvJ85F/AqRjjZJFZV4J9niXi/HNAfZePcPcMhex3Hzb25Da8VIUdItLWXYAKX1dEvHWOGC5oXMzwvPv8a99z/OPfc/QS+oUsnK0IzmojXQyUvy0lI6S6+wjPZKeqWnG94zL4P8qBdua249vdLRLRydwtMr5TDyQZPUaEWzkeBtTrfbwbuS0muWjHYoXEmCJBp9U0FDQmxVenqdLude9GM22uPQ2nKGNRk70kauqSi5CQNFB/U84asLnh17AERPtYpAUSy/9sZ86so/896Pnc6EKcuQZA1GFi+mO7K0z5mIaz1GI3Lw5r1cvEhquO/QMy5n3mEn8vLzL3Hrld/igRt+iPOeRnuQbQ/6GI32QHUvznvQJgjFKMrS0mg2eeb+20nThOXW3AgPNNKU+U88UL1uxZWW5ZdXX8+/H/1HpQGMUlJUBTRSoe85IO8VqKRBEapc45zbssCVhYjoFAVjnR55noeKMRHSkUSYkchFKdJMdINF2EvjfL3+a6U8QkOCoko4S5cLVa1nG8WEQoLunR7vCi+AZ2xsLGBH0j3XecGfKpggGOBo/KLQifcOk6SoUhaFD6Fbv9HqrzKJIe/4sFfR71nW3up4HzdyHOP79P2lCtvqzw53FuGMKvJXNXYQr8HX71P91nuiAEv/86sPrtxbXwP4iiCmHOqzEf1h7+R0XHub97Lm1u8l0utuv/r7vPHq60yYOMjEoYn0xhzaW9IgKAMwmjteWtiRuUlgtJuDk/5V2sO83bdHFTmXnXQQsgFKVlxzXdbcdnc23PlAbF/mOnpJiTF4pfo4jaoaH6N0NXcVqyHQZ7wOB5Rz1TzgRWfBax2qgKDI8yoRhBcPz3vYbJ+jWHXjHfjx2Sfxzwcf47jPHsHEwRZZqZn/6iJemf8W02Yuw9SpE1m0YBE/veoGfn/jrXiVMDRlGiOL32Kn92zL1848FqMUC0YLRscsDSUNIru9QooGAv9TRe/VB9U8rRkrSgpRDRL4zHkaicZ6KYfVgVuVGkU7gWmDCaVNcdazaGSYdkvRzBJUIl2j8QanSwg89kuvvYFsaArLrrlptQarrLnWlTiLc5FPLd6dDnKCFX+vb83H9S7OQ5B9cp6BydMoi4LVt3of533rZOZuMY/WhElVJNbvMgg1sGb6WKfojQ3zwK0/ozVpOrNX34BtDjyG5/5+O5tvtgb33vYL3pr/PO855su8/RHbzxulKYuCZrPBMw/+lV+d+0XmrrM6P/3SEezw/o+z6e6HsM6Oe7H8hjsw/6l/8pvvfg6AnT98CtZasiy01/FeohtbUlgvkptGJCqVt5UTZdIUh5JCFx+ryfrrCxxlKXhtlhkRrzFSzh7x36g9AirkL6IMF5WzGD3h/gq44J7xdqmB//V4xxbs01dcwx982iVE1ajYHiY+oqZlzPL3h5NigOtqDxsawrmQTasusvIqowfaZ/GUqr1WBf3UMvq84zgoKgxu9E5rtoKvJqf2qKmNZOWdQ/9yrP7W91uxO/0efxWgV7hw/yurMESrqnJMOm/YoL4k46oRbFIkBBXnHbkDV1z9PSYMDTE0OES3u5hlZ03AGENuPaN5yetLeywd7dFshJLfcLhMbwvGt7BrIVEMD4+yaMFCpk6bwsMPPsqvf3Q9Pp3IAV/4Po1GU7w8ayWxE4ymDUpPUWwlXndUL4tYvQs84WpeoxHvGzmTSHKjKIogJtIPH+lKn9cYgyu63HLJV3nzhcfYaoctuO+vD/DWgjeZtMxMFr7+ClmWkec56+64F+vtuC/LLLsyWZZS5B2+/9H3cOjR7+f1V97g3rvu47wffptJEwfIGimdwjKhkZAZRW4diRZYqZFI54DSeRZ1BWdOtSZLpDuBJoq8KFqJpplqMq2weUkxVqKzTAomAilee0/DGgwG00qq1khPPvMyexx0IgefdjETZyyPLQOu6GNnllqJTZq9Bo0LHbjWfWMm8F3N5200xFMrrVQZFkWBUooilwSS9B9LqkUcczLxZ6NV1efQWmn7/ubLz3LB8fuy1vpr89LzL3PAF77Pk/f8kekDo+x3+F586vBT2P/zFzJrlbX69kqNFcf1f/9vLuFvN1zNJ087kVXXXIn3z/sAAKdcfTd54SoHa3jBK9jeGCusuR6L3lqE0qL3YrSU8jprQ7PJ6NOoqr1UkiRkoXej6FFEJo4cXNIlQuYzOj6ilyuQplaC18b1GoW6vI8pVR9gNarfaSWJ2rKUfWJtqPwMkOwPjt7x/9aCXe5NEwUoHBqtA8bpJalC8HB9APylU4QL0EFSGVGTGFxh5aSA+sSNYbmv86tx4UWYos/k1U5y/Pw+v9jVxxqg+jZ/7FvkiK3jA75QGcngv4Vn14tm/GD819+G39QehFIBMw73oYKRj4dGaYXDGu9HDqY0YIyWLMkYXryYF559kc22WI+xzijLzhzCK80rS7os7OTkpWBbacOIqLl34UI87UaTKa2MYkmPjnUMDAwwMNAGpdh0m01Zf/P1+cpxZ3DHT7/HLkd+JhhUuRaRA3T1oo4iNcHd9QFmQmupROyby+rA8X0YpPJgCeIgMQSr6Xeq7zXOWpRJ2fWY03n+n/fw0mMPsNmBJ7LiuqLJaoxmwUvP0RyYSHNgouB+3kmSJG2y3ynncNcfr2PK7BVJ2pP5zMdP55KrvxsgA8sboyWp1tJu3DuyxDBtoEHp4I2RLloresEYNhJDI3aZDfqx0fvvOUdSenzX43o9TKYwxqJRGDTGK1Sm6PkeRineWjzMwUd8ju0/+AmmzllZtAigdhri4UaQIo0iRKJLKvMT1Kw8dZInznc87JWS9jUuQoFaB8J+FHUPH1d7CsE4CUSUhyikKEpmrLA6e3zsC9x13aXsedD7uO7rJ7D53h/mjpt/xwGH7sG0mTMYW7Ko3gPKY5SjdGLMOksW8eTdN3HvDT/hm5d8g+Zgk59ddT3LrbEuux//VUobvXQxjsvMWYlmQ3QkBidOpDvWJU1TWZMxrxLu06tYVSmryCSCtdrSihMQnbLguTrrqv2nlQoMB1N9dqQ2qv6drERJTkNfhaDYKh0cKbFx8t6GiIO/e0fgdzG6AduJm6668VjbpYLMX3WpFc1E3PlCwHQtNe1JYii8r3rWxBMrZvbjsouYq64tbHVFYuyp3PrKeNJ/gkc3X4W27L5+mg9VTZGPGl/dZ9z/u7Gl8m6ru42efrC0pqKbUSXWKg9a1dKXIrauqxBGa0UZMMpEK37ypSNYa721WW/DDRntduhpz4JOQrfMWdQJ3ot32FJSVkrLZrfOYoyh42DUSva9LBwWRxZajaAhyzKO+fyxnH7cV5h3+KfQJgnVasHwBn2fIjYEDHASuoY24njqwL02WteHnqoXp7OxeWYo6/Sub3H7aDcCBU1FVIJl196M5dbZHBDeKLak6DmGpswiZtV1oO05HLaE5dbaiOXX3hitNTscciznHjmPF198hRnLzZTneU+nKCrvvXAuYMaSqLOh/WvpHUXPMqYUjSShsI5JaCam4YBMAo0tzcSrtIYkkSy40grb8GCkQ8Qz/36dQ488nVW2eA9rbbtH1Yy03jFhzfmwlcJadYEy58Ka1eEA0DGq6HvUZe5C07TOBYnH8H59eyRiguFPAq0YU+cdFOR5jnOWTXc7gKzZ5s/XXcTJp36c7379YganTOesz3+HiZOGePr+W1ll461lT3mFx7DotRdZNP8Frv3a8ayxzlxO++6XGJo8EVsWdEdHWGbZFRiYNC00tQ2RkZJrUIGFosph2u2MwtYRqfdg0gQVmVJxvYaIRXtfzWscQwVVWa6N3YZj5KxFKyQefEnoKBx1EypIM3i5EXZQSmwHTtauCH652kFU/8V+vO3xrmXAELKkLuJGYeOoiJWqMOERW5L/a6XD4rKIhrGXTpxKFlL0BKtkmqdaTLERXe0JBX+qL3r3vsZQKqOpVJWU7TsbxkOt8ali1StPq+bVevqHLeKx8S/xfeIzPBHjFi2CiLtFOpUOuFI8OqTLQr9/qKoxBc+d136Pp//5ALfdfwtLhoeZNDFFacXrw90KHqkMmIoHna4TmngWjuUs7pa1MpKHdqpZ0i2q+11+pdksM20Sf/rh2ezykc+hA6XLF9LaOywxwI87bMYNpI/GN3qvceAJnoAShS/vQgv4MMLeVnOplKFSl4v4cLgPfL3Y68gojGO1weo5dtaTJFIIMrLkLTojw8yYM3PcJVczETyoblli1PiKy/710C0thXUMJIbcpKTOYQyUSmNNicbgjSdTKcp5vJGyYYvjgktv5Afn/4itDzyWdeftJ6FvgAX6oyYdogpHv0GUJpsOKszVAVpZKv3jOA19rpVzIshi7fiOzONy5n1RpUMFgZ3gDChhqZSFZUmes862u9Nd+ibfO/sKvnbO57jmJ7/l3jvvwztHWdzDLkd+Hp1mKOD5h/7CNV89EWctJ37xOLZ77w4466WVjVbsuf8uHPf+TzJ3q90w3vLWy88ydfnVUTrhxxefyVb7fYSps5bnh6cexeZ7HMxOH/oMSknnldhw0wfKojKyEOoy3sCiktO1wqdF70NVgxWnWQGqeq2vIrhxY/b26DvavxBUCqRThuIaefN+yPN/Pf4fEmnxQ/uSTr4vzIwbH4XXqt5soW1PfK5UdBS14IqLHmJfPXV1WgQPddzF93u4wUtSMWQXEnONT6tqA4OvB1rFLHDNjqioUko2eeV/RI+V/kXt+zZnPNJMFByKz6h3QxCISXSQn+szHv2Ee8lQKx6/43ruvflnXPSjC1g6PIIZ0Cz1nk5Xei7VjXhjnZ5cY5H38EbRyoQpYJ0PqnBUdedLemXtnQOZ1ixZtJTGay+xZMErTJy2LDpRpEqhirw+1LyMYyTqx60cnPdxYxl/ru7S15l3r4RTKqGyHL4mMVW3kXjgyuELEeuPoxo/c5zFD5tAe19txkQ7LIbhBa8yZfo0UZZSUATnUIcDpWp/Q52biIdLUnnt8v9WmuEKS54ZlA2C20CpDK2WkaarOjTIVKJl8fETv8NDjzzPB864gkkzl8eWdSv7foMbYQClwNt6fevAU1XR8/UisFSR+qt1Vo9T7CsmQvH1Og7PwvSJ6cSH4JdKBJZ8nyMVDqKxTo9t9/sQj91zG/9+4XW++p3Pcv/fH+dLx3+ZzfZ4PyZLq/X+3D/u4v0fOZjDjtoPlGG0J/ogKM/EdsbdDz0JwMO3/baiofU/Zq2yNotefQGA5x95kGYzw5Y5RW7RSYK3VmoDtEgJSK7AhGFwaKNJAJVQ6XdrrXHhNeBDN2HZi1Eik+jVxnUXoh+ZKgWVGm10/nxFj1VKdEME6og24Z1JYf9P7AXJpspCid5Pf0VHFcoHI1v2h+rBo1DxJOnzkj3grbAi4kYNCKgsln4sh2gI68Uql1exQuXKFJWxDX/uewRZQOLl9f3R939O9Stxiir3iiqM+1+Pfk87bpqp7R6jZYPcGVzAfLyP1DFZ3N3OKPfe8GPOOOuzzJ07l1eXvkZTtcmLuvIrGuy4ISFsxjSKdEjnVxuz2PjKRsVMrFIKo6BjB9lsp+156YnHOffIXVh1vQ1ZeaNtWXPbPZkwdWY4KEMZJS6UdtdjFiMavA9c0f7DqO6iGj0oZ2PbIy33njYk0pCBl0RPmGulLEoZkfqrwuMwx+KiVMY9LjUdDFlhIckMT9xzC1vvsIUkHgMWO5AamknKcF6Qh5CwjEIq1M1RbSjlNKHAZ8AkTGmnpEZ4wUbJ3BWptG7C56QT22gc1sCpX76cx555g/d/5Qp0kgXJv5jJqA+SOnchOLrqoxrFNQReSunD7kAL5alqVR8qBQUTd1XVYLVgY0jh/bjIp1qvIbS31omAfLhKFf7mrGO049jp8E9w0TdOYtkVZrH+BnPZYvstmf/iM1SeGDAwZSbPPH0/DkMWPrPRECpiYgx/uf0+9jrhK9x13aUArLDWBrzw2EMAHH/BDSyz7EoMTpwEwKobb8vr/36cP//4XF584hEa7TZb7nUYW+z5QYrSSYfj6PhFv9+7UJilaGQJICI4vY4IJtmyFCgtHHwubHYV9qM4QP3yr+GwisbU15YOJfCbjJ0I2pe1V/RfLEP9eFeebv9pmiViIOqWGkFoxTlK58b3SKPak0BNwYjqPPSFhiL2IVirDae0rRZIX4VT9aYhJPAS2vu+/8dfxEq68Z5+X1Dm67cbtwjH3/h//CgOxvhB9cHwR0Pb/zrv4fXhhOGOpdvLKfI89F2TpW2doygKRha9yQtPP8U6a6/K6OgopplSRJJ1nGwlVJxK6azyfCLB3wY1JkWqxbgqREd0QiNlcitlSiul3WiidcGHTvwwXzjv22yz83Yc9/F9aHef5/JPH8DffnMFw4veIGs0SNKUNBOhj8QQMr+RzytG31q5hzzP6XW79DodOp0xET0J3pdzoEwiFEIjXUjKopTKscB0iQeQtBCSKCDiwxXfmrr1knzFAhmP9yI4ZEvpUDIy1pWkGQLJTB9sMnOwyZRWg1TXjVXHrZC3za9RigRwpfRtM4pQPAGpcrQaioGBjNJZHI5zL/glv77uD+xz0jmYtFkJ/ETPvD7xI+UuHFI6qe6p0r8gNoONVL0YxlIdVnEBW+ex8WALCzu2M/IBuupnC9XVnWCdDVKivtpL8jcZibFOj1XWXocdP3Acl573I8rScuhH9uWpv9/Nk7f/jIbp4RF5yUcefJTfX38rw72CgaYhyYS58tOrfsubi3NW3WQHRhcvZN77dqkM7nuP/BSzl5uJIWdw8jQOO+27bLHHB7FFj8fvv5sJkyewyhor8LsrzsErxaN3/JqrTz2UX3/jYzxz360UvTFAoKVGs0HayGi2mrTb8n3i5IlMmDhEs9XEKJnDfu5+HaZ6ksQE3nlMcsui0DoK3QTjrAWGsY4K0qpdzXd+vCNlbMaKc/0Bp14YjBj1Io8YlIsTGz0UubgqcdVn5OLpoJSA9y6E8lH1iur51K/p8zBj6Bd/1X/ZFT4IldFRMebvW+cVoyBCB302uHYK+iaDGumlCqPle3946un7nHCN/R5AHRX0/b2aAflFd8lbfOfD8wD45R9+QdH2UqFE2AThNZF4Ed89eiTWe0on7JBEa9LQVG8gS2kYzVjhGMgScutJjaZXlvSso5lkZMbTTKR68K4//Y3PnPAVJkyaSHNgiBnLrUDSaLPJXh9h+opzq8y59zVFTDQ2XIWNxUorE4yoDY0J4xxqLWwAEVEnePwynlKyLGvKVdBG7UFEQ9Wvlyzetq7m3Wihnl36qf045lMfZvc9t2OkVzJ1oMGUZsLSXkmntHRLx6JuD2HCqLDOZVGIjipkOmGZRsJQ06CMot0w0sJGJ4yOdOWaXUkzM1z5o5s455wf8cGvXsnkGctXqmFCzg84bDCSsodMaB1jKs50tSy9q4TitaKKNGORkfMCJ8iilWKXXiccMvLC8ctbSUFCXpTjXAajpUy2LC15Ufat2do5icWXQ4Mpl570ft63z44cduQ+3H/PP/nmaeex0Y7vYccPnURpNXf95qc8dtt1rLfxOqy4yrJMnTmNrbdan2+fdQV+0lymzJjN7Vd9C2008198mbmbbcfuJ5wF3vH60//k9X8/xqLXXuHvt/6WmSuuwvuO+zKXn3JYdb1rb70Towte4IgTD2N48TA3Xfd7nn3iGVZeZwM22PkAVtpoO7JmmyhYZAPEdd03PsWjd9/KB079HiuuvxVZsxmirjpXFA+sIs8r2hkQkm9RE7pe+865kBgNsqxxD3jP9z+yw/+NMiZZwrru2+gIbsRTNrIafLXJ0kbd8ljh6wQcEDvMxvDHA0TwXutxi67y6NV4I+Uqr7b/9zHB5GuIIRjcuGhlIb2NglZZQRi3Eut3rXUXwiKOm1IOm36M9j8GjwoB9bVYS2XGo4FHPLPu0oUMTpjAKZ8/gZwCrdLgecjopcr0iX37ahpceI5RikbASiPUkhlDp7SM5CUNYxjNRfsz1ZAaQ2oMjQS6pYxdZhR77rEtO867iSRNeO7pF1nw6hu89toCzv/68ay38/5sc9DH0E6ogZ76wKUyuH2LOPwuZpkhQCqqziJrXQFKRGMkkjaif+yNEs1y1R/xhM9CPsMF2EIpjfbyfkmjzQGf/z4XnHkMQ+0mu+62VTV/WSjnmtBU5M7TLYRK5jw0E82EZkKnsIzkljQkQm2AVyqNVycNG41xuBx6vYILLriWfU8+mymzVgj6D67CUN++xJSSjHmzkZCkmTSOzItKvlDU3eq95n3tDPhxY0XV86uWQoUK9mF8csfH31cOQOgSHQReoncXNTS8d4Ee6BjrWPb/zHlc9YXDWXbZmbznvVtx3ElH8vNf3UenSPEe1t5hb0xjgOf/dTf/fuUpFrx8C+d/9Qe89fobHPHNgxiYMInXXpkPyOH7xH13ss0bLzNh2nI89fe/cv/NP2PWynMBeO35Z/nt+aex3va78687bgbghUfu54xzv8Iqq6yIc5ZtdtyK0hbcfdvf+NNNP+U355/G+ju8j50/8gXpBu1lbex69Od58oG/cPVXT+Cwr1zMiutviUlrXZkK10V45VolEKI4VJ1gi9TZuPZdUNfz+Aojdv9hDN5mV96tOOKgUy+qPDwdvLJ+yooxRtooK1HpabVbsthsWbXWeLtQTZx8bTS2lNdKm3RZLJHREBeVfLzuM5h1BriGMGI41Lc4fShPVnV2sV/oYtyj/iAUVN5UxW5QkU7lq/eO3ysjg+p7n+j9xmsKC73fxQ6L3DvHfdddwJz2Eo46/mheHxlGN5Mgni2L38bNHq4vtjqCmp9swqHSP+lKSdKnkSRyBHgRCVdKkWnNhKYhCcUAIz0pIIjoY8MYNGC0Z8Ebb/HhD59G7hJmr7YOG+55NEmjDT6wKSpDLDh2DJGdd9V86qDpW0mAhqRqTCbGCMl58DaWxboqIx+9RZMksg6DmHs0QGhpi20S0UJI0oTXn3uca844lp9e/wNWX246OkRoo7kjMcLZXTjWI7eukixdfnKbXikdAvLS09DQDuW8qVFkRgGGTrcnycCy5Jxzf8rtdz/FQV+6uII3Ip5dH0x16K+1ptHMSBMt/PdA25IqRVdFlS6WucasfYgyY+mpMYZWqyHdh4dHpLhGXoCvFmMQ+zYaW5S164rARVmW0suLvgIMgfqiCFXcHkVRMDRhiDeee4xffecUtttxM5ZddhoPv+CY9+HP/ud2UuBsyeN3/5EJy8xizmprk6YJP/ni4Tz1UF0CvNpGW7PPyeeIHkLelTLd0EV8dHiYKTNm8PT9d7D49Zd46v7bWPDSv9ls+6055Mh9mDRpIkZpGo0GWZaxZPFizj3rQkbyBvud8h1effpRXn32UR796x/49yMPoJRm5w9/mvlP/Yt9TvomaSq9BU0cEiVJxbhm8bXMZvzZBSinyAvKokDhqwKw6On+4Oh5/9PTfUdMtzJxAawvbe3JeFRFVfI+1PMnBuccvW6PIhRCVGdJCBcjmB8pZkkS8GEXgPyIRcWJI0Ia0TMKV1UZ4Dp7Xhn08Goxnn0LIdRNR4egwnPHnTtizCVa7YMjwvXWWLILQht9Bi68WT1G8TP6xKYjJBPGDScJkNbkmVx79Q00tGPawAAqd2gXNkv87HFX6avvcSQ8QRimbzziBiz6OIgADSOJtlZqGMgSUq0ZzJJg6Ag6xZFD7Zk9Yxluvv58Tv/soYy9+iSP3nFDWIRxTD1yGLoK57WhSk0hBrcK0+JIRRgqriddH7zeS7EE3osB9i4Y1FQ0HBQobB+8NX4evZf7WG7u+szdfHtuuO6PKGNwXrGkW+CQLrlDDc2UdkYjNcye2Ga5SW1SrWkkhtRAOzNoLcY3VkT1nGKsKEhT8QBHeiW33nIXW+59eHVvcTnGVkNx7TYaWcCqZaP3cilmcKVs3ixLK2fDxdZWfWXV4xavEn2T0oX2Tr4e07c7Bd47yqIMEWSM1mqYI84zcT779qqzUnSQmITR4RFmrLwWx5z/Gx55Yj4XnvtDHvj9L/jhyfvz7H1/pDs2wnMP/oUFLzyJtxatE9be5r0su8b6UnHoPLNWW4/Zq6zBJy/7A6tssDnr77wPSaIoygKPppcXokGdNUizBs1GkxXX3pBbf3Q+G+32AT5+we8Yzlv8+MJrGZg4QDbYoEQ6lRtlOP7ko7HDr3HGPhtw6SmHcuNFZ/HvR8TI73nCGayw3lY8etcf+NHnD2fBy8/jylIKlvqgsjwvyPOCoiwqR8t5SS7bMqfo9SiD1G3c6CY4jvH7/3q8M6a70lx/0KkXVRMplBxdqelEL815TxrFZhJphqhiG5WyoCzrmuY6ARI3ow4amdU6QqtamT0Kesffe+oGdETP6G1k8eiZQ00Divcpugdvc3UV1cZQfZul8oyrjdxPwH67RqWq9kX9AvCxFc7bAswKYkAM1+iiN7jjR2eT2SV87/vf5JUlbzIwocmYs0FouYZ5lOrDj+pb6NtQkIRiiCR6mX0QS2YMQ42EVmIYbJhx9+icD73P5L2y0B9Ma0iUhAw/+/VtXPLjP7HnJ75dtSmRcDQkQMOwyPzWlLi6e0WAGMJgC5+3nkOPCl0nXGUwYgWRYK+u8jS8iiXowolWodpIsF1Ns9Xkxccf5FffOIE77/0ZSicsHOuSGU27kaDw5KVcp9HCZ/YournU+jsURhnwJa1URNgTY0TCUGmWjPRQRvOV077Ho8+8xUFfupgySE5GVkEwcYAnDWwKGRtAJWgtLetRoIxoAAinNw6ICytMxG60ESUrrzStZoO8LMk7HfKilAKPfkeEflit8iPEKdKKLElI0pROp1NBXjGhFl8buWxigIX2J90UEr5/zHsYXvQWO+22A3/6/e3VZ85edjZpa4g9jv0cQ3PWJGsPopTGO0ve7YqoTsBUS+vxzgbILJacy5qYONSi9JrO2Cjf/OB2nHDx75g4fQ6v/ftxLj5xP26879eoRCClXq9g/iuv89brbzF14kSOO/REtjnwozQHJuDKgjW3eS/toYl0xsZ46JZr+Ot1lwGw9X5HMmHaTDba9SDSLJWS49DBIl5HzF1YKwdk5TyGuU1MjOYkOjn3iO3/j2XAvtrJVR241pokZJW996AVSQhdoqE0WtqX1MawpJcXVP2DArRA8IB1MOSRUua8QzlJspnYUFFpkkSayEWbGb3OeEgTLlc2fQzvw8iovoUYrruyWP1GQlEn9+oX9P0rP1QZ6QoyoJJ9jB8xfijHu6qVhxesZTYwif0+812u+uz7ufG3v2fredvS7RWYTPfhtAHi8DIfNlC5ZH5UpSeglSTRVJDbTIPEZtW628NozzKYivHTARrxISa0VpI4zVT+VvoSZ3WgAkqDP2PSit9ahaB941jdp5djR/V1KFCKKhEWNWt9OBxsH91PiiZC9Z7vez8bWxvJ7+RnD8ajvXQj1ki5ubOW5dfciMEp0/nrXf9iky3WYyDVNLME5yNvVSCc0no6OBpGIrhUG3pWsvu4Ep8maGcpXGiTjia3lsFmymprrMhLi96el5DBSI2kP7u9gsJbYfwouW5tfIgCxYFJlCdNU3EOXOzDFTCDqguCD46OqZwA52pjG6NKVTkFVPcYYZlqRanQ3TmU7sc9qwKnV7aOCt0kQg8wgsyr9xx9/m959oE/88wDd7D83HUYXvgmqYGVVluZu277Kz846QgA9j/xVBa++hK3/eIqtt7nUF5+8l9473nxiYc56qxLGJo0hUf/+nsevP0W9jr+Kyy35kaYRKO0A2/IBibxxV//s3Iqrj3jYwCc+KEvcNDhe7Hjbtvwx9/ezg+/fxWDk6eiXM5WO23Lsw/cxhFf/ylps0mv18M5x+DQBLY/+Fi2PuBonrrnJl5//jl+d/FZPPj7XzB3y53Y9uCPia3zXjo0O9G0EE2SsD5VgAlUhUsEVoPCu/8/PF0FSSololqJRmk/Dhezd1VCJIgVKwVJklJWrrm83miRevNKhRsxQVIuYCpeiSxc9CadxymHdlETQGOCZbTWBwwgepxUC8QHKxrFmZWuB8HjpXKI2l5WNlZWG5FnGpM2lWctuz6EYLWOAL7OLsdHxJHHm+1wBcEA+b4/KKXodLrsfuLXuPSMY5g3b14Q3lahKilW7skLsqBvWwQv02hNMxV8PbeSvNKawDWtXzOay5wMZAndUqTujBJ6GV6gl27pSbScCM6K8QlYCJ284JprfsecdeeFYVfhgFMESm+1duJ9KaXQSVLBICqOo+qbn5itj0ikFuHa8SyZADmE0Fgoiq6aJx+gqdgQM1bwaa3ojY1itKNhTOX9x+FPTe0MlFYOFRWWl7clWZaglfTbUkrhrcUpxdLOCM1mC6U0a621Mldc9ht8WNd4Xw2FYIB1YVB7oEW30wmi7BZlkmqdxYhGKYVJBdtUNiiMqQATWA+h24ENB1DcM1XkOs7gRjW/0PxSBWZJoD9ZG0uzZRzFcPsq+RuxzEjZ83hU0ITIuz1WWG8bVt9iF9JUOuYuePFZFrz0LHusNY+3Xv43rzz1MKC47RdXAfDQn29gdOmS6tqeefAubr/uqur/7YlTMGkKSpOjKu0F1be/djv2VEYWvcnAxClcdM5ZXHzOFRR5wS5HfoZ1d9yLm39wGp3R+QwNJpx1yGYc+52fMH2VdYVL7bw0lNSG1bd8H2tu7Zkye0X+cOU53HHtRay+2U4sM2cF2VeBWRMhtMQY0QYvI7QYo2Exvs5Ja6J3erzjX7XWQXRCcB8XlY0QDyIq+qSZZC69FeUsk8jbmiTFO0cRFqu1dcY1brwobk3YvJ4YLsoCE+MkTAFrPV5Ln9Kqx1VYoHED+ypu76d+xUcAFnQkRMfTvzbY1WuCN+KDByGLLy7sEApV3nMI/kJIrrSqe8PFgoJqLyj6cIXqZ2MMZZEzZdZKzFplTf54y63std9evJkvwiUKjK8+I3rmk5oZizrCH1VK005T2m3N/KUdrPdkxpBqEWyZ3E6xDnJraSSadsTSEaOQGrn/XtBaTMNhWDikC6+X5MExH/86Y7bBOjvsLV6Tk87F1REWjHcMVQnj6p2TZGnfnKjqVbV36FUdzhHmKFJyqgID7ytvvPY05JAWg+3wXofD0eJJWXn9LfjbXf9k++23oJfneDxl8O4UoT2QUqEsFlJlQEndvtFCe9NKrk8OOkuSpCjtKa1ls83XZ8aMydz7m8vZ+oBj8Vo8TaG9SQEQXoydc6JK5oMMI8pV691aV1VSiWNae68CL2iskn1RFAUmSbBlWeHnYVtJJBqNKDUEV5e7Cmz0tvAtGGGL0Sb4FGJmk5AAjapaAFmWorVibHSMfPES0tAeZ2DyDCZMn0Ma2t9Ext8pW+6JK3p8/5jdADj4c99hrS134vS9N0BrwzHn/oxlV1uH0nmiQ+m9x5cCsfU/5m6+c/XzqhtvzytPPsSSBa+y7g57ovDsfuzp3Hzh6Tz/4L8AuOikQzn62z9hxspri5NnEpyzpInoPcxYZW3y7hiN9iC/u/gMDj/zClCKNJXS4qK0KC1jbbQmaaYV/quinQgRdfIuRvcdMd3Zq67tP/Ltq6sMclH1DQqhezCKLhDQFYosS0iypsiyGUOR53TGRnE2enZ1CihmYnVU5gn0jriRKq8VCZljw8J6kcS25xEKCEbQj2cx0Geo+pZY7Rn7Otytcd0QAgbh7ohzeWdr1S1V83Xj81E6lAU76j5PtftX4WXRY/ZROyJ+lqOzaD6/OPPjXH/DzxkpOwy7UZJmildRAUy81sI68VK1VKJlRrPc5AE6heXVpR1aqaaZGJyHRIuxWNztMbGZMbUtbVlSEz1dTaJgcTcnd9BOE1pGU7qSZiqwxZlf/xF33vMke510NkanQDQSdRWUipa0b95U9KrC4V3huxGvJ+pJqCo5GfHbuMZkgUdPzmNLV3mShMMOFXmsdbvvSGNc8MJT/PbsT/K7P10lSVA8eemkFZDytDJNt/B0C08WEsIC5XiaqaaZQOE0vW6PVrNJp9MVzDzIPhpj+P0t9/CDi27gkC9fhsFRlD5UlopBM8bQG16EbgwI9h02qon5ES8efpImoQgloch7WCuetwkeeahWrXjneS8PxQ2qb4xqgx0jAaU0SZDYRImH1h5o0ev1KHoFKhjVmPTTWmOSRCRIfU1Bi5FHmkhkJdl+SZRG3rExBmulMKYsQhl7ODTSNMXanMEJk2k0Eha++hJDy8zGZM1xZcqt1DL2xjOccfg+TJg6nT2OO50J0+aw5I2XWXWTHevEK3DpJ/Zm/nNP8YVf/oMkibrJnrHhJbz23OPMf/phNn7PQTQHBkmNCh0nHGmjUdk1rTx/u/Fa/v67azn0jMtIGy3AMzBhQnVwKEQ8p65ZEDJAnpfj+ONn7rfh/429EJMjIaLHKIKqfJ2hjYhY1D7o9gp6vV5Vjgf1KRBhCFOVv+rKMEmYaarPEwNWe0CVR6liZlcFvlzMhBM9fKLxrUAE3xfnx30awloPaBNwaN3PxaUfExj/OlV7x/ECIzZbY5xvf0L/e/R/Rh+fM3ggk2atxPLrbMJll17FpNZE0tLgeuB7mnizeWlJtGJiM2XGYJOJzQSt4a3RHt57WkGRqbCOni1pp4ax0LW5CPQoaStT5bLxClqpeMfOOcYK6bjggD//5SF+cfX17HLUF0mSBrVRjQPl6gOlChZCtBDmIHJGo9cmX7FqaryaXaw6S9M0VMDV8+P7weP/DGb65klXidhn7/0Da8xdBWcdjcSgvSQ/XKh+bCaKRiLvnxeC45XWYRTkpaP0srFazRQd1kySNfCoqkHrhAkD5N0xcVCsQ/mCRIt4T5okPPfAnzj7w/NYOP8FItmevjHx4d5sSLTZMq9EuSv6VhSSCg5HZBTVhjUOQjwEA7yjVNXBAwic+1r+USlFaUUi1OOr9uYEDe3SOnrdPDhWKcYklKVEu9J1RIVqrlQEwRX1XIdOEp5Aj0sSJkyaSpKmKJMycdZKqLTxH/zWf915C18/6iAANtlwNR658SJ+/pWPcM2Zx/P43bdUzxsbXsz8555i2szpPH7X77G2xAbes06bzFptAzZ676F43aDbKxjrFoCWjhc6cpzlWjd578Ecfc51NAcnkWRNaa8enTIfVQF16JVn8d6KIHpgpEQo5J0e76wyVuFttspQW+sk66sc3Vy6zJZFiaduSVMWBd1OlyRLKXLpxTV+b8RFEgyYlsaFgpsoHFLvLH+LnqiW8LTiEkbYSlGRuqsL/48f3gY11NcT16gLkEHEf72vDcD4QRTDr/q8OTHUlUgvkZtbR9wxAVbHfy7yOOOTvHgixhjyXsF2h53E+UftwmEfPJBpg5PpWQlnrfGM2FFKStqpZnI7o50KG2FhJ2e4V4rgdqoZyFKMguFejtGKic0MrWAgS8i0XJdWiqKUsF0n4nWakKEv8wJjFK+/NcrHjvgCe3/6WwxNnVmfKwQ1Cy3kwOgdjo8wQjJG115XzbcOB3jAD5WOOHioxAsJHhuwSEfNk5Q56OeihvmoTuz68epT/+KhP/2GG266iMRAbI3TL/s52rM0EoNSsVWMRBUJEol1OpGP2UB5S2o03bJEaUnAGe2ZOmWI7tgooILUYYIKerYL5/+b677zBWbOnsE/brmGeUd8PgxRFXNRBWwQkjYER4R4Ulf85Ri+RZplWFbj1noURo+MbqU0zpUhCkir6EP6j2nRRwmeWlEUaBMijb4ozDoHTgS886IkMRIFGS9YZrfbpSzlfZKqeUFfdZdyVSRikoQ+x5bXnnuc5/9xJyusvzVDU2dy9Vkncf73T2XPvXYMhSmOq6/5Had94RxWWDs6kdI4c+XVVuHgQ/fk1zf/nlU22pb20ES00SKa72NOxuMtslaR+S3zHFsUJJlAoVqBThOU0hK1pwnOSl816yFSVz0itxkdukhrDQuXd3q8C6GMcOJlwplLU7TSDA+P0euVCEvC9YXtNZc17/XodTpVaIEKQtk6tOHWpmpdE6XXkkQzONhmcGCAZqtBqyXk8STRZKkhTUQ8o9r1IfSsNnLlfvvaS48LuQ8WiWaxWqXU4Wn/zfv/OLVq76HyInAhuVj53PRv+sjLjayB2BusPhHrFAbIhknSlMbgJFZZewN+du0vaKZNpg5NYWJ7kGntycxqTseNekbGehQhcTZWWEZzR7e0LO7kKO9pakVDe6a2UjKtmTHYZPpAg3ZiArQgrAATElnKg/ZSZKEVJKmhkaa8uXAJ7aEJrLzh1mF4fWUExKCa4DWZ6hCsog+5qRqf83GMImTjMYkJHmktABQ3SszKOx+lDXUVJcXEjuobyb5jVjw8o3nyrps59IN7MW3qJLQOnpwWSaBESxK0tIJrDzQSGomimShaqaGdGUwoxmk0GnS6XcZ6wkFvJpFZIvc6ONRm8YLXZG0Q4ZQUoxx3/fwHTJ06iRM/ewyvPfc4taFV1RBVZ1XYdxVlLqxFBegkwQSdhoBT9Y1rHUVFmKzPLQjdvCFNUtoDLZrtFihVYeXVyIXXl4Wtxj5cLWUpVL2ikAOxDA09y1LE5IVqJW2f0iwjSfS4faZV8IiDKHvcB/+69ZdcfOJ+jD13Dz/7ykf5zmHbst2OW7LXXvOCkXJoFD/58Y3sc/wXGZy8TN/1CvS49Rbr8MS9d3DFyQfRG11Kkqa0B9q02k1JhgZv1FpL3iso8x5FntNoNsLduYqjW5QFo2MdbFny67NP4YXHHqyi7CqJ6wEdo+06KazMO2O672h0e6MjklF1IpuWNpsMTRwiazTodArywlEUkU9Ye5vKSw29KLZL9VB1+qoQmqUB0zNy6nkUpYVuLwelpGe9NiiTSIPLLCNNEynRq+hmdRgVDWm/QfjP2PPt/nb8le9bnLUx8H2GojbYdUZcOOuGqIUbVoA819ViIxXRvG9zjNt0MaIIF5UkhlazwS7HnsEtd9zHhz70Ue6/5x6Gl4yAh6F2k0nZALZQvLiww9JuSW5l/JtJyoRmxkAjpeZoB5qfrytm0kSTaB0qzgTzda6sNoH3nsI6OnnOQCsTQXqoICIxjAqi5oHSRFaY3G/wenXdEy6GszFaiEwRgph09IhEECnSmqJhkbGpMHf6x7Kazf+YM6MNj997OztvvznOi6PQTBQNA+1UMZhJUrGVaLwLf0s0qVEMNrQkFJ2nkWqsLcIUezq9XGQiHWGTlvzpT/czZ5U1g7FX5MNv8vAffsrln96fqa2Cm/5wBT/7yfW88swTorrm+69X9HO11jQbWXUvcr7L+nIVFCEJvppVIypkWlFRAN/OL4+whfMi+zk61qHMi3EYapzb2PG2KAvpmGvrsRb5yKISB/dOPFnnIe/F/neqPjRcn1OEJKaazRaxNVVZFNx7/VVc/73Tuenmy/jxlWdx7U++zUknf4Tjjz0IHU5vo4Wyut22G3Pb1Rdy/w0/DKp1irElbzJhqM1ac1fkn4/dxNBAg5efeiTYHcH02wNtBocGaAT5Ux0oo81mSwyoc5SlJ89toNtFiAsev/c2brnsGxijKsNbnZBh0Ws83pV4W/DaMw/zTo93NMm9zijxJK1DHEmKiDJ55Vr2TV6dxNKxWwFCk6pwWSR8rzm9YnCjKn6v1wv1z1HvwUtliBcJwCRNKQmLFh/CzLhw+hbruA1Z83THgwb/zTjXd+L7/qeQ0DT+L5r6CA9UHxzDvfiqvr/VoXUfTFJdu3QlSBKDdY6hZWay9+cu4PE7fsunPnMqW2y2CR//2NGsuvrqLD9jNvZVT7coWDyS02gmTG1nFKWjUzoWjeW4pmGwkYROuNBMkpA9dyK+7T1axyZ+vrqGxEvPKOc8SsNLL77KlBlzKj1WSeQoOV19qRRZngAAgq5JREFUDEEl3I+BVWxpUi+bvg7NEWpAjKcYoCivGA2uD+Gwrzy+erpUZQQijixLK/JKwwwpKIsuCxe8zrprr47XBTEJ7qlFZoz31XwqD6mWCjrvPL08Z6DZxBGEmpyjVzoajZRu4VjSK3jt5df42U9/xx9vvp2DT7sIYwxZmnDjFd/ADb/K6ad+hHnztmIsLyktrLzBliGs9uMhMi0ebpImdLpdnC2JRToeJbzbUFZd9hnCKscQEtIKxH33ASf3fbokFS8ZfJJIxwbZkH3QRuDremEyVPOoVaBKmQDmq2rr9DNVVDDcAwMD+FaY3zCPA+0WOjEUTjiwP/jorix64zU+dOSBrLvmyijvWWfuSqy7zsrInvWUXlWqg1/8wlEcdMDOnPzZ7/Lo7b/lPUd9nif+ejN77bEtCsWkwTbbbLcpj//1JlbbZLvaCfOyBrNGA9UIHGkd+dEyPqVwM6tCh5hg2/uTX+f6cz9PZ2QpjYFJgAoUUU0V+TrH2PAirv3aCbz81KP/1Z7Exzt6uhOXmRE2piF6Zd45irwn3qkXwL2fshV7WTUaaWXPYhZVRJ41oZRewkfvKUsRWG4koYea85R5Sbfbo9vtUeRysuJcpayUGEPWSALfUD4nhrSVp/o2D/btxrCuTHtbmBbeqM9XEOMiKw+UwVOHFbXD1WfxKwMftkEQTunH3SpcOzw5MZpGllGWpXgNztNotlhn3n4c+vVrGMmm8rGPf4KH//EQZWFZccYslh2awkCZoXPpADylndJIZPO9NtzjzZEeiZZQWCkJMSUnU4K3ffcYsugonPWBkwqthua++x5l+oqrhz5dshGUklAfAkNB9WGS4fcueHExOqgLB+pxj16e7jMc0p5e1zGMhxonU9W4KmSuogdOnPPwWc57TNpAKcXixYvRGAwJqU5EiU2L+E2iNYJaeVDCAvHO0e3lNBtN8XAIvbbCgVQ6hwVu/vWf+MgHPsOCfBIf/vbPmL3KWiglTJGH776dH171dXbdZVvw0CtyXnj2ebY/9FO1N953+MdGlXmeU+Z5HQkERozC1Y6Pio6NQutEeNAqVv/Vcpfx38rz9VKm6sPpVFHJwnw5J16ltWWfb1DjwlUirzbF1ZdE25o0SeRztBZZ0GaL9uAgA0NDJI0GvVKYKK89+xhld4QDD9qdz3zyUNlRfRRL7yw4j7aeBKmOTAzMXWNFfnnt2XzsI+/jF9/4JG/++xEO2G+nSov7qKP25193/E6irbAeRA9GonKHRNtQa3t458jShFarQZqlNJpZ1cBylY2353PX/o2hiZNqWdPqpKd6z7M/NI+Xn3qUvY7/Mu/0eGf2wrifJKQuy5yi06kqutIk8AlRFTAvm9tVGy4xAcOJJ6OK2K8L5X9CVu7mktASY0w4qaXmvchL0aEtrYDftsB7T5IK/FCxD/qBsTAi4zyC/3jUi0b1vy6e3IhXV/kHql5gb7e14z3b/veXl+m+8Ym/i9qcSaA42UDN65dQ1ElCe2gimx/wcTbY5QAuuvgyRobHaGQZA+1BMp2SWMeipWN0C0eiNRMyQ6phLC8IUyQ4rQ5wiI/JlRCSauFhayU4p/OOZioh961//BtrbrOHdDCunPZw3UlSsUCqe1L1dvc+iqFH2MFWizy2GAIFuoYtKknHSosjxCrBMEsIXRueWrHMyGYNp5tWkCaGLXbbn1M+/128jb3DMjwavEajSbUmUVIGnIa23EopkkwU81xsqOkd3V6PXlHS6RV89SsXcvnF13H413/MjoedxKTps4gHDEqxzKw5PPPsS5ROEs33Pfg4WbPNxOlz6ugnOAkoFbr6GjG4vl49xgjcppMMnQgnPjZYjLh1FYH24Y4+RGVVKTwEZ0l+lyYmNK6UcYsFUC5UYMWHDbodESpABSy9bw5QYnAbjYxGs8HgYFv4+4T5CV9FGMu80+GWC07j+I99kPO/cTJTJkwQuEtrdNxDDrz1JGgSLxFYagT+GWxlfOj97+ORh37OfXf/kKmTJpJojfUwdeoEhiZO4J5fXkqR59UYRRqrRKw+lPlKwizLUtIsqaJ0D6RpGuh7hryw4WC3RHVFmb5YWON437FfYPuDjmKtbd7zX+xM/XgXwZvo3Uqoh/fkPRF7sE68pk63h7UOrfueH41PMGRS4qvRRoUFPd7bjHqjzpbSSYKwabSq/FGvgqCzVpXIilBWyrr9dcSzoHYp+wxfDDn7RbCjpai4vn3eaO3Fvu1kj7p4fdBEfHkdUtfYYxVKq/qrb4Srxa61Eg5lmMSo3CXlorIxVtpoO156+RU5yHQmKI/V5LklS5IwdpbSOxqJZuaEFs4r8konxVbGNpanhthAPAOtpRkiCus1D/3zWV554SVmrrhaaNUd2tUEjxdV31+/zkWc/SikEvnNwoCxFbe37mYQvOFxSR017v/RW3HejsuIx3lNjCFNDY0sZXCwRavVxHrFth/6DHfd+w/+/eQrUnFmc8qS0M1Y5jx28tUIMyVVAc8lhLZlIYU/WlGUlksv/AUPPPgsR5z9c6bOXrE6KCqalFLMXnl1HnvsGbQyjHYLbrn5L8zdchfioR3XTdQd9s5i87GKRyvjJQdPNU5O9po2wcnRUVIqrrnxUWd0hORg12jl5aA30nm3OnhNjcPKhcWIow/K6cPtBBqMmhr1AYySooms2cSGA1BrRS/v8eKTD/O3X13GFZ/ch/M+vC2brbcKJ37kICjAdxy2Y/GlA+tRoS2a9gplAQuu9Liux1tP1WTDaYlglNiKoixpJIaf/vSblPMf5JIT9+K1ZyVxGWUXfR/sInsvrTQ8olrYGy89x0++fDRvvvh0kKuFsdHOeCdO3PEwHoaNdz2QrQ84BpM2eafHO7MXqL0aKfu1dEaG0WFwI5AvG8kHYxg2na8NTCzlLMsaf+t3OiV6FCk9FweE8dxFiFUkIv6RGEMaGl3i+hJVfdeu+v7jQ5jU/1B9X/VvohHqe140mhUUUBvt6vdxwSMhsnyXAYhc1v5zQIcFmyTxpE2E6dGX7IofLlGnx6QJWaPJ6OgomTGMdTo0MqH+5GWB9dLBwygRp2mnsjLzUkJmo6M+KIE5IhsndlRVSleQQCNR2CLnS6eez85HnEJzaCI2SClaJ/xEH3Ai5+T0r7HXvtOlxhyqEa7Gw7kQQsqBG8d+/MqgwjJdqJaLxk3mQeCNRqNB1khptlo0202JnLo5tixIswYb77o/373wWozLUF7oUYlJA3ShMToRjWGtSE1ClrTEALsSXAk4nAKvod1MaTYzJs+YQ7M9WF1X9OzB8/Kj9/Hw3bez1+47gLUMd8b465/vZrUt3lOd6uLFaoYGGgxkkvkXj6rO+HtP6KriQ3jcL3xUOxPVeBhJjsrak6afsattfF0cf2tjYRNE8Zlq3UIlNBX3QJwSYwyNRkar1ZR25gFSUIAtSkZGRhkdG5O1knd59u9/4aJjduOm73yC5PV/8dVTPsS9f/ox533t02JICzG2vvD43OFLjy+sQA0eGfcKcvDY3AXql0I5hSvl8Ey0YjAVr3iNVVfk8ku+zLwdN+aG879QNzeIDlaAP5KgXxHrBKQgxJA2B3jhkb9z6UmH8Mjt1zM4IJrHRPZMGPMIq4GjtKXUDvQv4f/yeFejGxeSArqdLr2xEUzWrIxtRe2hzjpXMEKYZFeJEMSN6SuPN05o7R3X4eF4wxPwp4BjldaGjgQinhP1WXXgAOpQxliZ1eCB48dv637rryIy0A++1qPwNpPQ979+W66o8DLUeNqOCmGrGAoqr9sYRd7rBU5jPDwUhPsxwUvxztEbG6XVauG9Y3i4w6Ilw2Rpg9zJWJjAYWqlwlgo+5Jk3juSVGCYROsKxqn41Q46uWVktMvoWM4VV/6WV16azxpb7CRjErB954Sbah1VZVUsy61U2Igebrh7HbEwqipCgVv6tJNdLR3qIg4Xk3RK1wdeIPYrFemH8h5ZJnhcWVqKwlZhMXi2PPBY/nr/Q/z1bw/jSlA6oShKyjK0kArhs6wPh3c9fMC/U21IdFIZoXYj4f2Hvo9/3fkHli56s/ZwQ7bee/jXH6/jYyccxqRJE/De8egjT5E02kxddpVqyYgEoGa0WzDak1JTH5gcNQQTON22DMprjNt7cZ9UnHrGw1hV5NUX4scS4Ng2qoruwmuqbVc5EQpCoYkJlE2todFq0Ww2yNKEZrMhf1NSVZh3ulAWfHX/jfjhqUfzjdM+zr23/YQLv/t55m23ORMGB7GFxRUlLjSR9NZB6bC5xZWaIDoi3alVxJTDPrWRYQA+95Rdi7dCd2wmhkQJFHHmVz7Omy8/zyUn7M3Iwjer/SuQjhSXFMX/1957x9txlOfjz8zsnnOrdNWbLcmWZMuWZUtyx9jYxjQbQiBAjGmBhB4DCRB6IIQeaiBAgPzoHZtibKpxIeDeuy3Zsq3edXXLOWd3Z35/vGVmz72SCPl+8pcWrHvvKbtT3nnf5+0laxNezVRTZ87Bqz5zKYwx+Nln34d7r/sdevt6MToyQnHegfR0X1Kcb7tToqgiQDzQddDGlCGw4d177NmxFc7lShgBpJYJijMmsA+FJLM4IIJGF8jfFGpD9juvBSNcgriCol2JhmCHTQAqcCGUQFKDun1yeimYeRvABJEpRnjABIQtlYKMoJQgOeoB4EMWgpBfl5lBfihnFYHD45CPM5owaQSH4SI/vkRZVCpFo7oSDw3VHKYSmOM7NuCIhQsolREOY+NtVL4N2AqDfQ2yx3pyXufGosNlNdtlif4GIV9nLToVtGoUmYcsNmzehR9dchUu/+lvsenR9cibPfibD38TWd5DZmBOSiCvr+XGkVwbWIhFEBrS1u0SVWBrjEDVYBa6Yi/2zAgkLE0cTvS5GLOa2nSbzQayRs5xpB2AGYv81zswiOOfdAFuuf1enH7q8eh0xkA8wgBUhA3BG+1QQR0BgjKbdqdARs0EYC3Q398LACjGR2GGZirdkQ24Qv/0Odi1axc5f33AH/9wO5aceBafJTKh+cBVKzyfkSDZZSTQydZaEaPkgkKG4R/5SHQhuWiTEDmZjcS0IykkjIUJGAnzUnr3akoQ5Oe5rjWFV4mdkwpgtcbbVHhHzH8I6OntwfhYC957DA72Y2zfbgDADb/9DhbMn41irAMvpgxrEEruGhMCgTZD9luDGGnkfdBOz0pZbBYybGIJLDCDB1yPVc268tSa6nvf/xSe8+zX4r7/vhwnPfMlyenls2qjZkHRImTTnnX4kXjb927Eult/j8UrT4HLcuR5A2OjY4Cn+hTGOW0VZQ04Ffp/UfAGEDXYodNuoWy10TNlOjyfEsPpm5IsQAiF8z1YLQCimi2TisHzHgAZv03wCJzFYgzgLdcMhdE0PTDhWGPFls0ZTEws4vlUdT8oQxWUlV6q9DLTFDWLFg8apmJEvYmyP/JeZuQhZfLCcfkNw6UVjZB/EM97gA+G+ixBUIqFlC00RtbDaFGgPVs34IhFC2GMQ1F0YK3F9r070TeNUnPbzGRDRbG3zYyEU7skbaW/maP0QKes0CooemH3vjF84XPfx89/+HMsPHYNzn75O7HgqOMpndNThpaqobzCwQvK8mpOEsaYMsTIWKOjTBZJnTESOZDItCBLCNSeiyBrI9ENgW3fXj8lSNmZWHjaZQ7NganYtnUDQrsEfIDJWX0WTSpwGUoH6jVmLSpvgKpCVXrk3JxzdKyFn/74KgBAo7cfCIGKx8u6mICFK0/H9d/+CDJnMDzSwpW/uQ5nvvTtiuYphMsjoKL29KoVBEh7J3Eyhsqrs5DSpq2WwDSsKQFRgyBbZex6bfgXCU0TZOuyjJpZJgeDBGNcS4UbJsDJvlKwPdGtAYd9Ujy+dRaDgwPImw3cdc0fcOopa3DY3Nnw7Qq+VdJcc/LvhNITgg0B3hIoQcZM3rK25wMq7mUisbMoOKY5tzAN6olXtAsE42Ero/q7OMpWHn8UfvyzL+LlL38nmn39WH7G+Si5q03WyJVepcgQROsCAJthxRlPgxgcpZhNLGyOGm8CYujd/q6DI10m7LKq0DM4lRZXnBhG8F+Ih4kJHiYAnlL+hBAFsfL2aiaaSSSp9wFZlsGiohqrouqYmDApEhn6tzBlyRJjezATXECAqYQBg3kmSXwJv1HbtR7oQP2V+LLyfEETNIyEeQoqkDxuXnwTkYDAbXqOU+9y5pyWDfSBuprKMlHESMVOQoPtGx/FqjVLMTzeoTKOnXFkeYVGnmsuvjPglNmAZkZS2Fl6b7hVwsJgtFPCmoCxdon3/8uX8ej67XjdF65A/9RphOZczEfXguJQA1BUaSUV1wRF8gZgB06ssSwn3UhcKC+gAdguLIKYySoEtjUTCjFBBCdJWyF66doavLSJjxER3nugApwlm+ucZStx47d+weUnDdl2EeC8Za2mQkCArwJKb+B5f6VIz56RMdx9x3144+s/hIXHnoC/+fDXMDhjNspOERtL8rrMWLQc+1oeTzn/tdi8eSvmHHks5i49gTQapukQAlXmQ/ybRhbo2aIZGVZfEOCNhbWRNuX9IMBFXmTmaq1B4HOm5gjE6AcnySuCLgyVAxUTBqVFkxrf39sLD6DVLtQ8RNtXYXRklJoYWIdgLUoP9E2bhRtuvBWbtmzDvBkzkeWUWh4Kj4oBSgAIsXLVO8q3MWw3oPVwgRXAingKigqW+8IZ7xDg4RoOsEBVBlSWnHfjnAzSzB2OP/5ofOf7n8SLX/gWDM05DDMWLken3UHlPXXzMLHcrDMGlSV7MeD1LNNPSe6BJn/I0kUkduDrIEyXJa33aLdaLAliiIgxEtoVkyfk4Sqlg3in6XNeBmit2hJt4Imwvc46g+Ct9rOCMTFIHpHhKjpVLCTRE0aRmCIndk4IoydBwRtfVhGJi/MgcEFiHrzRBwZ9pjjqIgoU5m3hwVEYAPc3M4wEMhh4RfEAta4nBmQoVpHVFM8IToRCCB7DOzZj4YIzEYxFqz0OEzzyLENvI+OQsGgzzkRoBUJg40UFmIApPTnGO5SdtWPHblx/5TV401evRqOnF95XsC4DBJeHgFCVxFwDt7wX1VUdNBGlCtONC0/aTLQ0BC6JaXR9VBgnSFfiwIPYpIUJaFH7oKphs9lEs9lAVRUYHxtTLUhUZ5/Rnh129An4wSOPUluXzMA0LfIMCKhgg2TOGXhQ3d1WWUKK9ztHnvNf/eo6zDh8KZ779v/QU1IUHcTCTaTaZ3kTL//4JXjkjj9iyqz5mD5vIcbHW1QdTSIEWMKkoYx11MlRP8KMwHWPwajYSCsges/zH9pjjZmiVsVjepNUXIECYFMCRZtYiA9AfTEhoJnnVNayw52dOUNMzH9STSxrNJFlGRo9Dn+89P8DANxxz0OY88RpcE2nAM5XHjZ35JQXYeABz6UFEMBmBDAD9orQM+fQ6OunuiylRygrhCwAVMcGxgaUziBnoeNg0Ck9li5ZiL/4q6fjoVt+j2kLjqKIhYrsyZHmoNobLQ2bQMFZtj4KIRgSzOrD4N+7O9l0Xwd1pBkQU2mPj6NixAVI6TzOAU84vJfzJ4Y6eTdBpiFAw4aIEbJEBqWCliUXNQms0mXR027jbfXmMdYgOiCSJ+t79YkZjdULydsaOUGcCgZVNEHIO3KY+buBEyYkkF1i/YgXWU6FjqiPviee5UyZv2cChrHqkBH0B5CTbO/OrRiaPh0jo2MYG9kFg1H09RsMDTTRcJS+Oth03FKdic5KV2OqKYAA9Dccq5iU4pv39BLyNlxeMVHZybxQqZee4nrpsDu2z8XDHx0eceVDRFIpHQC6VkYdoFJQBIrIVFXmfYMBjHOwnJ2YN3KMjY9jeHgEVakwEoLIfeWpDmrewFGrT8V3f3w5CzqKv6wqqF2w8uAmjRUazsTUz+CpiWQAjlx1WtzrEIsXCTCQn8YAy046E3MWHgnLHn9lhCGKcjEl0Sv0n2XEmbkMjjPHrJE4ZzJL0VJK+6mgqF8YbtS45DnEHMThVnE9bHFIEq1IDQw631mWoa+vFy5z2iMuyxyHh0X2Icy33W5hdGQEnXYbz33rJwEAy45eyBEIADID13CwGSXiGMsosgoIpUfVKuE7FXy7hB/voBov4FsFyrGCyioHAHkD+eAggiUmbXI+L+xlNCAzPaUw0WI7BOQGmDrYi/E927mUQOAcAjaVRCIjISYafUjT+D0aeY4sy5A3cuVNxMNsLf16f9eBkyNYzSzLClVZRIkodhiWRFIXFXpIoh2vHv4ERKkuxBfUSyuTKyvqplqUHu12gaJTKvFEJIV4ciEHO8SFk0Onk4kvp0xW9VnmrBqcHwKkLgCUgZrIFPl1HwxSFiLfDSBbrtZ35Syhqiqj7Q0R2SRLpJuLIMyKhMHuxx9CKAvMXbAQ+/bsRG/uMXNqL3cqoJCZprMUf2tihETmLBqZwZypPRjqayDLgEbu0NvI8KvLrsb8JcuVWVZcrq8oCk7flG60nLLNNmbrHHp6mmg2mxgYHECzpxdZ3iDhk64zUQg0bVBNUkCs2AQWcMJqk4s1F/ULGArvcnxYGlzJTvL+SUh5XT9laIGQ/5kv+Uf8+1e+g+tuvp2dIcQ8Kh+ofGG7DQSZrwOCw3irhPcVtm3biWuvvgkLjjkRwjLLsox7zv9pG3UrHYvJ253lDa0h4JzhjCcu3sMMQuJds8zB5YxG2S5dMUiINRjSjsMJg2WzTTyDQm/kn5ECR1XFFcfynIvTZLCOuhT39Pag0STm0mw24Rq5tnqnXQ0wodIWVdZqfjWKosT46Bh6+wfwhGf9NT7z5e/DNKwiSpNZ2IYjZmkMkQxjlhA8fFkREw6M0iuPqlXAd6hBgssaMLYXxhMdBgFvOZkYwPkAOZ8F54AstyiDwTnnnY51t11HvM1QzDh0DhE8yJF2jiJ2JBElzxtExpay1cQHpBrf/5bp0lkg04IvCsA6KuPINr6KbUh6HlRVMgnziAdQUz3ldz6cJTe7s6wuSbYSod5Ki+dU3kezAa+LqqX8wwvzTV5Ua0SIIU1i2pBU0sjKSZeT8KRgYs3eqKIh3huJPZsf0s1I07RUxDtDIyX43mmChpeF5auqKozv3Y6jj1qK0VaBHG0M9OYobBtDAz3Is15Ya9DTYKbIayytaXpzcop1yhL7xjsYaxf4/e9vxfe/+RM88+IPJvPjdRQtIMRxNZoN9PQ20dfbRE9vD+9dhU5RktTPc7LZ8gEUM5KYo0griQxBEKRY62P4miAzmT+Nzpoo3qj6nfSt8rV1RrLKETmTQJ8+/wg89dXvwQc++Z+wlkKMfMmaS0XZfJnNEIJBWQaMjlXYs6eNDVt24iUXvQ1HP/FZWHjsScTgrGgFEQUBZJOnli00R8dpsUWHygw6Sx56LbbuYmt6aR3vnNNQvLKsEq1M/BxB6xlIoX0xVVjxBMu62UhHirQBYrZsmunt7VEzDe2jhXU0jioEOhOylyFtjJoAGwVbJIzGx8fxhBf8PS6//Cq0TQHTk8H0ZHA9DZimI8brDHWDaTiYhoPtyWGbGWyPhW0SIgYMMmNgK7LnhrJAZ99eVJ2SzU+MTZP03MDas7FAnlPsbWUM7rxnHWYuWKQ5BQC0AE40Jxrln1FLIMqTJqnqFE60U6HcPKt3uei+Dsx0DdXVHB3em5gS5Ewk3JSZGDFajmg2EX1KVkwA1CvYHc+mjhpmTp4RsGQwkUQHZ9NEG6HEywk7NIm6apJXg/wm59+Ioyse7LrwkHkCAjc16oJxoRxvNQsIS+i6b/w7itGQfCfog8RuGtMMlXMjoD28HdOmDaIqO5jS38DAQAO9vXxIjEd/w3JWWhSABkAjc8yoLKzNMNjbxK69Hh//wJfw9Fe9G9PnHQ5flhqn6BLhSYfbI88zLijOHX0NO2JclsR68oowEnYuQxIgphmDstDRSeU09lb2Shc/SOx3dE5aS4RdlaV2p9Vt4v2Rr6ftxEvuSnH0aech6x/Cl775A7Q6LRQVlSoMZQAqqj1RdjzanQpj4+Mwto0PfeBLWHTCGTjj+a9mQWEgiMLEp+r8s4zm4xytfeUrdDqdaH5jCZJx/LK1Bi5zXCCcCsaUVdJi3sT1CJwqH5K1lVZaYpclNO303NG6xQxESrclbYNMbSW8L3XdtKOyONukC0eCdmO8vWEgJdoLPbfolHCNXsyadxjuuvchNPoc4AzGywKdyqNTVeiEgE4IZLPOHWzTwTUtbG7R8h3sGhlmZtwAVMiVqKoOCu9RtCuIBl1VJdl5AYxXgcPTLFolFaIP3uO2W+7H3KNWET1yckSnU5D/KAjPogWTerkuo6SsoiNlLQumuaDPhmhsNcYx+XXQ6IXxsTF0xkfRNzikKKPyQQsU04skZaRKGNGGGO8j86xDb1MbG5WBjLGFKkG48aDXCAj27PO9bGoLU6mE6BiwXQtgUGMEUZ1NgsL1EEfjPWrfkBAp+Rz/I0guAFqcRBBskNlAmXdq94aOnX8yY5cOAxI2NqWvlyIpGKWJr6zTGkPf1CZn3kC7IsDQGlEHX3Im3nvferz+NR/EslPPw9ITz0QIkrRAokRC8Gi83B+trDBetdBoUN8rX8W+ZQGS6k0RC4D8LsKL52RZg1A6kMNLaE3QmRGhnarFLMRlTztFQczeeRSdAto52AiyEzORNLT0AAiRw1g87fUfwMcufjZedOGz4LxDMdYGLJD1ZKgMxS23qxKVH8fb3/EZPLppGM9750d0r00GNSUw8FTQYABORHC6Vt5Tl1+qaOVVWFTeq/2WegQSuq1K0iiDODVT+1NCq/RagIH4EySChxlsqJQpyg0Ma4LGOXSKgmp9SJ2LVodQn6NiNZaZLypiPlnmUJajHOYIBHG68XhERZehGgCn/sVL8B9fvhRnnHECGsbCtwNaRYWo41B43y233oO771mLu+5dh2t/fwv27t2LLM+x8PAFOO2UlfjbFz4LRy5exHTtgQaAVgdlQU4ukxvY3KJkOhZJQK3CAnaPtnD3Hffh2Ke8FAJwAIpasGUF8ssQXwpehDmFbZYsqHwQR6jYfHneLHCyzKEoCxzoOkgL9oDxkREqfsEbqqYCjT7gwXvPcbkC2wnaG8QYW2Gk3WYPuaepPMBB37UwFwh/5sOTwH5RaQIAEyQhgxEvgOBL/phVm5ciDUYQVu8XlFKCD+RcEcaHiBwF6RoZfDKfBN+qqUFriurnIwYPCUPmk6t3EPUtY3uwyxpoDbdgsxw9fRV6B3J0MiKQRm5YdTUIGTA2XqInc2iV5EjLHDmjqrKD977rMzjhKc/Hqc9+Kc+z0gwwQMwKITGVpELQAIbLQYJRHYdUAWSjTG3rGukCEspB5m4MKCohqKAIqj0ley6MG+T0CEHC8jy8ZXQjgpqXV/q10d+S+hyLMGV5DlhHdUTKEsYZjI+Po9nToM7TNmDPvn344w134LOf+y6a0w7DX73z83CNXoRAxd0z51QDg+pRkREbGKUjMPqsgtfkC2n/QqFb1A+t02lJD046Jy5xVqlQt4hlUpM42hAQfIXAjMMYUJ0GZ0RRYzqTQi9Om2AKspNzQ4kFFapyHIETaEgoc5v2quRnJsAq0DnpaTY08sY5h4H+XjT6BjEyMkacqQrIcgNTAZkNKGFw0w334G1v/Ti8zbHo2NWYvXglnv++12PGgiMABDx8+x9w5++vwJlPfxn++N/fxuzp0/F3r/lXvP99r8XCGbOp40VuYXNK6ZWYafJvG5jKYNdICx/9wBewb6SDhStPIzpwXOY0cxQS20jBA5e1NBYlp7pnjQbS5CXrHKxLagazxlIWXSit6zog0/U+oGi30ezpY/RI5CUOAlFWxBtqRC2WG5ApKLHf6VmAxFtpd1zacY57E7Ql5gMhihDvg6AB5CTQkgLZoDAjtc8ACL5KHFgG0pjcgFtlK3M2rE5wGEnaEh7MrP3ERU0TROQZwngF6WpFLROFl8zD8KGge4gEtZzy7JFlGR665Vq85uXnAzagpzdHgRIIFg4VmlwhKQRyIPT3ZLj9nkdx8Svfi3962ytw5plrAOvwD//4SfjmTJx8wUWaBiqHLGhyCXTtk61EQEBRlMxgI3FKE04qbu0TxMvAwVjdb10PFjAhUMEN6gIc9FkplBNHrRzyqqLi2gBQcM0ODU2sPYN+r9isYFQTMxiaNR8A8M0f/hzLj1yMK6+9Cdf8/kbs3bMXvX292L51O444ZgWOe8qLcfQZ50Nq+zYbxHBLDjVKG5dKaKkw+qitsPoffExUQGzS6TRsCvwc0QwTrc7IeUiQpK5jPEMBALwUkHdKU9YahKqiIjS8N9LsE5D4BxZ8iODCWAOJ54Ehkw7NgItUBbGbk+e+0WxQdTa2N8M6/OSz78UTn7gGxhrs3juMz3zhh7jiit9jeO8wpk6bis0bNuOpf/d2rDz7L1jDoeQOBEoKWbLmTCxZ/URMmzMfL3rZO5E3Gnjwvodwzblr8JK/fhZHABmUnjJkm1R8BM0GmTN88Lj++jtx9W+vx4s+/C24jIrEV55rlbC2oT36hEewXZ3Mbpb5nINFQNFuYf09t2DJqtOZzOhM+6qaqF1384oD5QrPOWJ5ePY/fhzN3gEg4TXWCJMzkBApsa3K/dLPigdVzrRYRoUhisooaE892kKeCbIVpMRUx6jJclfhiKQ1dg7E/ij5wOo4RVJp3zITkzUgKEKIsyz1AGjev7DslCGn6Fs3gmerJpN06hH1qf07kEofTQ+UI1/t246vv/tvcO21l2DLhlHMm2XRM+iIOF1Ab6+LETzM+B97fBtOPvFC2gdOSjj29HNxwRs+RHOvgjoqNR1UVw3Jb+n82FkjyMka7iPF3nAj+f2CZgEkBVyM7hndVzLegITRJ8jVGLIvhlChKMRLnzSyNPUOuEJz0B3ivZUxgapKNZs5br/yUtx02TdQlQVOOOeZOGLNkzAwfTbG9uyEyZroG5rFKaJGz2Kz0VBQUJYFxkZGIckJQsOZ1iJwaicsizJqa54Ke6t9lJ2BVUmRLeIYVN+EOsVMMseE1oLXOcJA648YQEFJ8LzPvC6WzTECYIR/p/H1wpADYlRSCAFFp837FqLmwWy60WhQScdA6dBZnmPtjb/FlV//JPr7mti7ZxgLjj4eJ17wMvRNm4X26F70DU5Dz5RpCsrEYddg56zYVtutMXz7nS/GhnUP6jqse+gXaDYadL55nCYAtmGQNx3apcF4u8SFF70DQ8vOwHHn/hUAo0AnFdS9fb2wLtMY8TT6SmmWz8CmtXfjy//wAixcfhz++t1fQN+UaSRcOdzyQN2AD8x0Fx8dXvCuzyPYDILipIGgMErnrAb3W4DTVaOWaAw0tpZNpAhSowARTVkj8YNR6tIzxFbHVB8YbYr6xlJR+5QJw2YaFY935YM6AISQgIgwnarILOE5nElqvoaECSh6SOJH1cqQCBlRu3yibqdzpvvVUbF+hm4FHzxya3D9D/8dS2dVeO1r/x7ju4cxdSCg2WfQaGYoTUBPj0Wemdp3YYC77noYz3zWm/Caz/0czb4BNHp6UXbakNKYwnBjxIepjSDRQ+pMl9dKnDPS8Vkz9xCZhszZANQRxBq1bUomotq5E/ozgbPLjFVHkVTaUlrSvYnCWQ8Lo+m0awVADivnLPfqonjfEAKqooOqLNHpdGAtV9KrolCQnn4y/9b4ODqdDsRxZNmvYZ1Tx6NH4BjQUDOrEQMrWWDEmNBKkTMYGCT94BL0np5ag6CJS5a/U0+55lY6qRaKaJqIOy6ASbSQGAaXqtwAa45cZJzOIVGKMH4pnWjYqVeVJfZu34is0cTgjHk1UCT0UQsvlflbo6FsxlD/wPbOx3DPNT/D1T/+HpYuW4TLL/sMVR10sfC9cxb7OgEbd7dx/1334Z/e+GG86IPfRt7TpzSrtM0aV97Ikec51XFxtqblxkgFmpMBcNnn3oNbfnUJFq1Yg+e//dPomzIkChz+9S+P//NasFtr0T9lCI1GDmk6KE4KqeMJVi8cFypnmqBFs0aJAYkC0+3RBqRQMofGeHG++RohiOlBGKGUlpTvxlTMaIuVcoWEMMjzSd72eAipE0RMLaV4Ai+cQ7kOPSvwczjEycdWRcqQtaBIRPJpXK6sRryEVOj3kPwdQsDOxx/AVT/6Jp7z/PMxum8ffHsfqlabzDsBoLYq9CSJCxY79KJFc2AQsGfrBmK4RUfDkMqyQiVFxQX1iLTk32s2XUHrwSfB4iEyh/RQJ/OVqINoGjIsjK1+Tpx8crglxljpDlAHUBQQIaEzo9qXgkD5JwQOnKe9DRxm1Wl3UHQ6KNottEb3YXx0BOPj48hzCo1rcBFrCScSjcQYAheVZKwZw7WGpZIX/+7IDGdAKJjq/fLBNrLUMS68ksSKhAFJuKQWFgqizcXwrWj3jfM1gnwRtD5vjGiJ37e80EFXLPmckQy1RHAKTVsL53LYPCd0KGvEtCIhiwJArMswff4RGJwxj8aHAPhKmbckNui+Mp2VVYVWu43xsXGMj42TZtE3C6v/8nV4y3eux959bdxww50wmUVpDNoB2LZrBI/uLnDPhj248YY78L1v/QyLjz8FjZ6+ZG6xT5qAjk67QGu8De+rePSFJjm6RCJ3gjG44PXvx9SZc/DoPbfiG+98GTavvQf3/uEXELvv/q4DO9IMh7GgAaAgh0eIQdzGEMFIXFqr1QEQ0Y6oio5VuwqR2dQQbgioGLoIs6JIIWqJkla+J6ZlAONrjAxa84ARmAFIz5CDHWsJGBOVzyCf5fmGYDiOX+A5Q1ipMcBMI3XWQBC2kWeLoyzw3MV8AK44FiKDQ3TK1bAm38MCKFqjWHbMMgzNnY0w3EFfj0PuHOXm83xEAN1111qMjoxh954RPLh2A7773V/juDPPx+HHriEVsyxRVp6aFKoThacvIs7U4zto2mT6gGr+SdZfEFqxieBK1pCRUDSjeA0dsyGiJMeqtgGtNxixggVsxYI3GWl8loiqKr5jkg9IFp3QkNh5KTjec/NFr+FrRkKq2JEkUSQilKqyZFoSxkW/O5dplwJiuEELhKvmI6AhxHMgQt8nNCbREN4EGOtYmEQnGiBaX1LoHuDYX074kOp1SLU0/SNZqLjnJlnfYJDskTxXfDdyFsFZbFEzCoFQr6xz8BV8VWpFQVFDJfxOGJtqQMlZEBiCIMKuQlGU6O2tcOZFF+Plr3gPlixbjNNPW4nfX3sz7rvnIbzno2/Dd756KfaNtDE09zCc+pcvZbo2CgI0RCwBRd57tMYpQSZrIGrSMDWN1RoDmzmc/uwXY/ihPyBrNnHJRy7Gru078A/fOB0Hug4aMuaZS4mdQ4hLAuBhqBiytYHRCi+6jw4T0hCF8KCvxZ+AhLgYNqBTtS26j1fUGJGQoAHH6oym/rJKGwDuUiqHnLPm9LnM9JUAgTRrSjywstmijsglUQcyh+CpY7JW8q+hAyjTDkYcIlGlj1pgGrFL33XOYeqsBdi0YTPGWgWaVQeNZo5GsxEJKAC+Mmh3PJ73/Ldh5/adOO7UMzA4cz5Ou+itOOKEM7hQOBEroacqqnhBDm8yv9ovwowTbzmrld6Q+k0HxkR4mSCuhK/zIeXC2l6+K/InMJqNESnOWpQFO29CUFtVjXcgImr6nQWf48McyKxBhdfZdZpoL6JBwZDGluc5rKXQI9lL6YhNArfibrjiRKJ7OUvaHjVVJdOQE/NLiA02fQgoiypBgrFIT8q0RHAJMFFhmEoTQZ+CQI3EerNAgSDb9HvKsnWrRAhG34PpUqnBoIjqKafPivtgaj4SKmIk5wmc4RfiWEMAfJoIJOOxIBOkYJ46bcpn260O5i0/Fa/5/C+w7ZF7sfb+WzB/9VOw9OwX4htfvRTHnfMCrHn6heiMj5M/gCnZ6RmUVUiRh/AZesVXXv1IkppPGpjB1sfW4Zf/9Qm84W2vw4pVy/HqF16DlWc+FT29/TjQdUCmq3YkS5WNXJN7RvmgKIbsbl6rt8MEKkXH73sPwAZYtvuG+hP0IDsDUO9J9vKaOAZFmAz3Je0hRdMxWoF2KjJFQALAxYseNzISgKpfOjZmiEB9zPG2kMMkqJUAiEeA5UI1jPq9VyeizAmIjE4InQguMhyK+7N48Ibf4OynnYXRdomhngaajSYc4xHjDYb3jeJXV/4Rx608EmOj43jZR76JxSuiOUlaIXXabUpbrcQ7Dp6j1bnp1GtYUpeVHeURS+p9pGiP7LGQCB/UVL0VZuslUkXsgRyWKJ1oaTlLjf222k4maMV/QeGSXiRCLgpAsCbg0Ww04L1HpygFxyW7Tcwty7OoEhsqulQU1I9PYmspRjlwAoiNdlSXwXFdAl8WHBYWNKIjBOichYFTVTRaSxLUla6t8FLPC+94j4ysve5ZtGfL1ogzLjoQ4zyDidqq0p+unYEGgqlWyVLRxs9oRFIaMifgxIhNHUoEhgFPMCIx5QxYGAVo8TjK/quyFJEJxEErNmbrHA5bfhKWrHki2sPbccMVP8QTL3wDfNnG2uuuwGHHn8034jgM7xEMZ05KokliOkEANcNlc0+eSe89Xn9r8OCNV+H+G67Gmqc8B1/7z2/jzLNPw6nPeB7OfcU7kGU5DnQdFOlmWYYQPLclpsMhnkLvA8qigIWFh9fYTZe5WIuWw4l8CDBWMq1SYk/QnYlpoJIdRQH1vOmCnOQgIxKLquhyaxEM6cFSRkPMXhBVjdkaJmepqWCYGQZm7IIuEeJPDtGhYueWY/3ouVWgzVbUwGFP6tMDiKgT5i8oJwQKqN+07l6sOHoOfAk0+poo2iW8NXDBAaXHJT+5Bv/yfiousursZ2DW4UuS1SVPebvVQqdDIV0+ejT58YmdnRlgN8Pl28UKVjLO9AyZQOFfspmGMAstq+FzGU0xmrbJB9aLrRhx/lFQkRByXGjJOkLARdFBWdEaaht5IMlwozhnKRbT6aT1Y43+MICm5QJBO04EVmelngJAzjyXURlD56wyX2OpQE3wnmuVcAEUS7UxYADjuGi3ZGbCdI2DgxmtoPdo/goQ9Tj9St2BqJEEjB5jtIhVgWVYg4IKF6rsi5hMSmcRvKchGuNEM1EnnGWTHCTqpgaJIokbBmQ+nmEZn5WHMv0ZBATrdZ4xqkjEDX+Xx+cr6qAMa7D18cdw1bf/A4uPWoYZM6fjlj/egDd9/RpY1+TvphyHVlRipwXcBUBDAp2jZrHGxNrLuzY+gu9/6E0oOm2d34033YeXffQ7cFme2Csnvw6KdPM8U9VNbtVg+2lVeWSZQ/Ae460WHfBAkNyxgV6afIsBXxaupg7G9VR7cLK6+ocwV4mGSFmq2BuFYJJHKRoWVGWS+4qZgxgr382m9jsekwERn6BwHjShfqvD1KIeMk/DDiOZEHu6NZyKCVUbD+paMXi0Brdf8xvcfg0wc+ZMLH7uU5B5A2QO7U4bzhicdPzRumcXvPGjLGnZEVMWqMqSs45iuyRZrBTcaijtfgkieY9NIwjgbrTJvkKBEZdxTO4hB1g+FAICI2JJcIQwFuW5bEJSIVehCrEIkjVSElCYAZm6nCE1sCgKdLzseYTgMTyNUJ/LSP0dH28p0xWkKlXDTACtr43OZHEki1ZWFW1KDsgy+ITuJZRNTBNgVG4ssdq0j1cUDBFYyHopw0GCzohwJmxZjBJIaNxaoi09fzJGJOsDjcNX2y0MDLwKhig0GGAhfT7fU80JxLzT+G2ZobWR9uNdo51V+UOiSdGfUQiRI6yD+cuOx5ErV+M5L3gyRnfuxC1/vAFZRhEJUkpA1inGKBPjlTh1oikxqZLW0Mjok/t2bcMlH3szXvLal6IYHcXvfnczzn7JP2DRihPR0zdAPCEBlZNdB2S6AVx9nQ+OZ6QTuE2L9PUKnhoWSvEJwLCdl3Lki7JS5ELvx/Q5oYf6OE2s15A4rCRULIAIwnCmVuCDq6SodJagWZX+CeoS5xgMMQO9R93mI4uhpgTl6J498OCsHa8HQngKkWlgWk684GpPYuYnKllQmphQJm7egtn6/aLdRp5lyJzFb39/CxYtX4mn/G1UbUgdLoFAXvqU4ZKTI0aW6GGRH6FLqMkeecDYUNurYAyH40URKMzRV6CCN7Cw1gNq842dP2poWVCwBqkj4ZGBNcQKhaeU4Vr0BI8ncw7GSK0FGkPwNk5A6KEW7kf3KNnmLXVABGkL0zEgLQ5GuiVQaBjZbb1qZ957ZI0m+z2EUUSJptobz4MoRGy4kgTCe2EMwJEZMOL4TYCDMuCEVkJS0IkZac0sJCSP+mcYVtKyezGXRURJtGkRP24Y7Pia1E5pWE8l76siSmHIEOYZGbegYJpvZPcCwgRYydmX10Og5J0nPO/V+OwH34ROq4VzX/z3ZGM1QLvVRukF3QOBMwJlbKJJGwhDjnHgwzu24pZffAc3//KHuOAvnopHH3gQV/7yGrz561djysw5ZDpSRjZR+KXXQdOAq7KAN4aLTJBaRJk4Ff9d6YQDcxpCd9HbRw4sYZ7UbbUEhXgJM0pIQNabPm3r1GF0r6LUh6FMFFHNkUxb7oWE6IPcy0Yng0g3KUQiXnKR9cIEDKKzwcAgcLKVtHZWUlOYrdMCQNJUHMHkxAtxKgjKVIWgd295HHmziZlz5+KY445CZgHrAetywAd866e/xHd/cBme9LJ/woLlq6EPZTtaxYVspMmjOO/UTqqMNyGVFEnxGsSwOzKRGNkbLwJHvmeSaJMuUwEACfQXhiYH2sBwrk/Q/QiBIw5CgJMDbbgqndJc4Jq+lCUWIJ2puwU5CWkV4sx4Y8hiVde+JCSO18BaidPlEn+ZQ57Z2GEDoLRdBDR7+wB23FbCuDk0Tw654TFQZizRoGXnrXYuMXVFWMKwHCNVhFDXDBFrYaQoEPxZIUcJ61J0agKSB6kmFvTJBFAiIw01cCEtWmRPNLQuZbEG4q2jDh+CMBWQWJbqYMdV3KNuJg5I0hX5gSK1UheLRceditd94QrAWDR6+uhsVtF0hYCI9C3ZhSnd2crqEO/zHplz2PLQHbj0E2/BWec+AV/7+sfxta//CFv2GDz3zR9G39QZWhc8dBPcfq4DI90Q0BpvMcIQ+yAXEOHDJwxQGSFPRlhfUJWJgvfV1ms5XkHQbzBxZWsoUBiewK0o4Sh6ATBgp4mIQJH9JiKEWGEJyhwMpDgKkmcIk48L2D3HGnOVMQokZMkrQwlKPPxplrDBU084Y13MFUdcApG6d171Uxx3yql494ffiSnNgguqGNjc4cP//jX87rq7seaCl2DZKedMtoGJuh+UkYggkfRZKiRtGUHRWFLbqtjg6F6x2pvgyxAC4APZ95KltOhqRAmTEKbhIkn8eoJWpBmlIFwASdlHYnQU4QJ4T+2MvOWsLGOJUfsoYFRwi21dwh0VMUP/E0cukj0R2iEbrmMGbDh12qkwJtKyun6VL/Xv4Ml51WzklH5qyFZMvl2jhWPUVs60KPRpjEEjz9Bo5jDGkjMwRIEm0TyyXwkR6L/CvNSsUqNvZTeKHkXFD4CYpPVcC9OOglnGKQLVdI0jBVdGB0OvRA0ZIWhlNqUTPhgm+ZvMM0nYKP8TAjk4p0yfqbHoRbutwEO6hAcGJcELgGCzhxVNiHhHnjl8472vxBPPeQLe9MaLcMN1t+Pq312PV3/2Z+gfmlFbQHX418TgxOugcbohgNtUV7phUc2WZ7LMNIkqzwtj2P4LY1EyeqQfVFxYvZzKTEVFFyloIh+ULTPJYRBGKcxVkTAH2FtT2xijkoz+Sc0n8QlMACnj5cX1cjp1mesxo0LcCKIuxgyUgJTxkerklWjBEhdUk9YY7Nu5FVd++wsAgEH7Dgy4BqwcRGNgXY59e3ZRVwTOJ09nQL9Tbn9Zph57Rl/y9CrAGE/WOg6BIobCqrtzFGLGa0BIXBBr9+Gip1hhZoAeqJROZN8CgmpCYvYQYa22sQAJINIzK8/XmFEeB6n8OcZbLaCoM6JgqFgMQAy302px5axMd9w5C3jEmHSmY2st8kZOXTJACQfGZQhliTyjbsGGU+KFkcSSlVEI+PFxVW89Ry5EQShkHAGCJLtkeUbFZJxFWXk0uKEiGAgVEgIYkvWWWSlB1O3YKUNmf5l+38Ak8epMoQqQutgKb7+Yhmo8vSZ0Q6J9KwuF2LdhhJZMLGUpDzCI9xEhZyysMLwQmV5Vlmg0e9DsyZF7j6LVQrtdKHBLyAoSfy3aK5ktxRzp0Ww08eQX/z023nsTnvHkl2HuoiPw/Hd+FgPTZiZjovGbwLWbJ6nNkl4HcaSBC8XQgGIWEmqLoCEkgB4auUEITHCGES8Q6yewXU3SfIn5UYtnssvFW4naI2q4fDdtFBefKwclCgAItjW6TBr0buLXFGnITjKI000RphWFTlB06z1UbTcGsOL5teQR97oZUW1Oi8zQb4ZaRJmAoZlzMDh9Jk478ww0GhlQFTBZE9Y6+FDira+6CDu3bcOVl30Lpz3375DcBKKa5TkdzlBRymblQywsI0ipNh9WsQQlIqaoihC1ut9ckFvURt0Cw8JIBCkhIEH8tKY+5r9z+UGp4eorDrGLbEWFsuytHGzjrN7HGO7UYAqqCZwZVJWojLTWJC8sfEXVxpwgHEDtmVLEhejSalGaUJWAYc2EnZ8+VOh0Cpq/jbZoA1D2owIRjvaJcLNOs0KhRgJvqI8ZxaETw4c1XOiHK2DBaEy6VMySfazl7sQVUEAQBSDTggEkCROIayy0rJqs+kFSVK2cNDaWDOmr9cuA+tClaFceImYFfXBkazze9EZsqhAgwPSsjk8QA3Q5pXe12x2dt0iawALeMh0bnRetUxUCznz+qxDC31HL+YCk/ooQOH1BQF6w/wumK/av3FmUQZwwE5eQtwMAIO3D43gkTCXZFBV3HGLGXkLQPCEN72IhcsGUMjD5elRx4uwt9VKzNiJQ9ZgaRclAjFOUmyoSVvQhDClBtypxorgXerSGfEXCrCs5X+KtNvE2kssBBI3XFTUtBLLV7Xh8Lfr6evG2978LA9ajUe0j5m48jMnw8S99AzfetQ7PeevH62hTkIqh1i+NRo5mbw+qskR7vIVWq8XVomSOCTrjQyRxpCJQfbAajZAG9VvLcauG8uLlOIpTKb2HpA8LVWtRJEuoKlSes59CcpiFaKKwlXXzim5lvoztAhWYsZlDlnMfM0+NQIMURTdShN0pZaljzsr2EgJ3znEND2rpIxErYgLxvPeSMAJFXWy+6EZqCdSKKJdPS4jUTvUhMkjKbllF4fevz14JAHjq374Vpz37ZYTgFcl5pUGl4oBES4vCX0cVmP5sZLgaLWIjQhTmHXmjgA6j4YR624QZAsIc+SzKMTLE7eXeWl5ANOaEnlMApnVSEju57F9VVei0Wqj4HDWbTWR5zh046PMSx6z357EIqPDeo5E7ZspBtXQtbSDrmqwvDMeeVxUOdB3Uputcjr6+HrTabcp7BvQQ1T5b+8kJDCYhZnl3gvgT1CILHZGvsy7aqrhQukE8lEQYYn9jxiVIKF3R7ks2NEUhbGetBAUIwab0yQs7UfJECUy+aB4XHx4JsDHKPITRma6bp2to0OgboISGAsiaAc2sidxZeF/inR/4HC6//Ld4xSd/hGlzFnSPRu9TqUfJwGU5egcy9A4MqGMHknXHl/TwMpIRGAJarRbFQXqvRV+q0jPaA6WoWrJxijrsPdQ8QJpOdJrogTVS+MUjeA798iaxqwEmNVMoc6SmmU4OCm+LCPwO1xpASJAjLIcmSSEYA2pfaLRThzCIyrOt1ZCG4pyBLwtUxqHyJY+nhNT2MMbAcHxvpEtEYV9jgLzHKlySJAWeG/kqLIpOgU6nQy10TEO1qGCAmQsWYeemx9DTP0ho3FEomg9AqKDPU3MgbUQX3Yr5JUVJHG2TCAJTxTMktt30bKuAlEPAGgLx8KBnCyamYptEI5ZhyROt5d5pibCKexYSZm1gEy0sHgCjFdW8Ceh02mrWM1w8qcYDmP9YG2OMe3uaKKsqQcAxmy41MQogI02K4rNrDH2S66AhY1VVYXjfiOaaK7vQOZpETQWr1ZGhOF5AYWTWWQ2zEmeNolgKeyBiT4sFszcXvHEmQTu6GQndTMbc60CwjjzSZpIS3J8eCpVk0NMEIVhdqyDCRdS41PMsiBvioI2AjXk4vRYJy3uPwRnzYGyGm264Hk89YxX6egOM9diyeScuvfTneON//Rb9U2dM2Lddmx/F1d/6NMpOGyN7d+O5b/k4ps6cG9fJGLU3qpYra8SqczCAR8VlLQ2yLE+KkgDNZg6X5VpHIHCasQGVToQx2uSx8gmRI0EptYNK6xrYJGPE9GMlRCmatsShRXGXZIoqtTYsYC3FlstcM2eQZ5bb38jj5AACLkQ69iEkabyAcxmK1hhFgvAzok2aZmGlLq7LdCHlMEpfPwrh80pTQkbOiB8ixOiSQCi54ga17Ta1E3LOUvNPAK/53GVcYMowDYMyR3kvjIHSp/DEyUEE9BctSi4HeRL6ZZ6HxCijETnxG0YFoJqJLCVjSNQFupC3RGykODwFKrRe9Pmol8W11GmxAKzKUgvaQECPRkJYXR9Ct06ZPXW2jq2dMgsET+njE2KiE8Y7PkbdNBJ9fL/XQUPGOkUnqtu8QBWvshQRScfhJLvKBA22jw4vaBSE4QLdMQLCKAEQUXMSAW+DNY68/UBXqJmgZNqttIZATVLK3kt1oQSti0Co8VTB5lGoQZ17SF7UpYpVuowsBl+eiSfEG0UnFWK4lbQYglElDotWrMYl370Uc+fNxNFLZqGzcwfe995Po2i38JuvfRJ/+cYPygj0eX+85Cu469pf6t/tsdG6phG8qrH0/2jjkxED3Lrck0Mocxmc40iByiPLM2SNHGVZwvrAGWLU+qUoS232J157UtsjuolaDWNYSweSHIV24t4wY6BAAikIH2CcZWFganRgbWyNDW5bbg209oWMhaqBxEaCITg+ZBUya1G0WwiwCHAoq0L31oSgFaeEwsQOK86qEAJMFfv8IYTE7BJiPRBWr6NKL+QVbZQhSLimVzr2FZDnGYzNED/NldlQkW/Ai3AJxMR5bEbPNDvMlN55P5SqIl2YhKxT5MwkRcdeTptJ3cs8NtVaJyt3Glt6SWsuMLgzynBTgEBPcjYyUE3ECQbBKcWjqnwSpRRNpAL+5PXMWVRlBwFUrtY6Cw+qNUHM2egZl+/5qsT42BhpfiEwaMABr4MwXZZiEGkUD42oLJ49m9Lpl9ZHvN7szUs20jpmo56qwkuwtTC0NAJCnBASSymqnBNPsI9FRAAoM5XLCKcVdCebYiKTrEvYEKUqoJuZyvzazRU20XPIWWRBEflBCdYaYay8buJsE4O85sCbGnFVVYVzXvpmfPJvzsUr//tqHHfSiXjgzrvQ7OnBqpPXYPemR9BpjSPv6a0N7+mvfCeedNHFsNagp38qM3lipCLN6XmcYx6CNh7VcBlLNteiU8JzGri04C7LSqNSgifGLPHbwQcUvkRZeEa8gGSUyXYwrkr+RURixmjtBcNrJA5PtTFCBK2DmKKyzCXrGIhxVgW8xObK2vNzxA6oWRqI9kaqVVGh8tSHLViHosN9r3gtJcvQB2LZzlLkhA/g0CRmXBzTnDsLZwI6nYrnQ+i44nTTgOiclGgfoTzDK1WxPdlYi2CkAI/V50gHD6EpB6/t2uErgJ1LtUJOBlyoKOhZERAcr6i+q69D0nkT6KxoFEnxdBXuwtR97T4i9OWz6fdSNCrfNyzwPDM/y9XXUt5EH7extVcX+kxrTsgMaC8KBOMYkHDpA1FJjYmRRyy0qJ5Ji01tTE8aqrj/66DmBVrOBJ0YsaFFgoi/MNGkmwCRWOl3oWFREhqkoWZy767n8FEhhsAeeUENVHW/UMYtyFHuQ7w4ph/qnNJbM6OTMUc1NFmNdDEZmnSvkdb9rQpyOrksOvUC245lueT7bPOU4GzZv7Ks0Oyfild+4nv48psvxN033wIAeMcnPoUnPeEovPutH8CHn78Gi1eejHNedDEWrjgRIQBZsweDPT0RFYAZTkXl9TqF9Maig+xcho4vELznnHwWCCDbYmapjmhf7tDjgB2jBbXYHm2hLEu2nwUNi5Koiarinm1S1UsYKqwmoejBVyHuSVsKdBSlTkFtv1hQ+5Lill2ec2ZkySiY1tbDwpes2nP7JWXYIvCkGBJ/L3AFMYBr4Vqn4WNg4clT0ThfKcDvfSwZWRZpyjWNp6pKGEMdEVyWoTU+TrTrAWNo/Sy4sE8XYKqF5gkBBWm5Q0LQOjeh9oXWmBZtoor1T1Qx4EnFqAMBWVHjMAJIfHy8amZCZKo5gWm9fmYUBAHJ0aO7xdTZpKCWrECSmKGINin9GQtaxfs6Q9qLOm5Bawxhs/Ky4XRx49HpeAhCNyAtQsYdQ2UDqk4bnXabBLGuj6195kDXgUPGIAwwTlwWzpjoxQsJQzKKaiKj1QXR77LE4rqlWs5P2LURlUWInSRO9IaLPZilDwDHNkcpfk6hH9RSpeSQEOcyOEZ0QhBaGDrEg19jiPy7/qo1EuL7ljxAqPgFX5VaGSqEikJIUgSuBA0upsHRFr4CrGPJT/+rKo9pC5bgn757Pe7/469x55WX4t/e/jZ8Z/ERGNk3DABYf9dNGBvejdboaCwRmMIFXlNqpiiB9ITAxX5Jjg6alRTmDj6GwflOgXbLoJFZBCYw5wysyVCaElVRAMajKCu0Wi2UjSb6p0wBbAYTqHMAVLWuEILYI8WWKIvjIrMyBiRooTVWDaJjyFcVrMtQSelHEFOQsoYSM0pMk5kL055nLcuxQJTOIZUnhC8NG6k1fWQISuOInXyJyZYIiIVyUrKRg+g5g7MsR8ADoZAmbnMOPm8ixCJyMxQj7GzS7SOAWhhRnYnMOloHEZiBfjrnOA2cKqMFZq4CgOSwyZlMkSyHkkCaaHoukpFGN0CK33jP9XGjj0RtJgntC4ONDqh4xIyR6mhRI62haRlzCHpGAgQ1C0M1qmVIP8OUhwlql5IFdPaAsl3A2EyRNkLA6OgYevt6EQBUZYWyLFF0OlQASYck4Z/RRJRA0UmvgyRHIBYmTjJm5KbCDy0oxkbL1rHqZGo2k3jPRMeHMHBl0hTsxjvhUXTafLgcgrVcuCJRFVlV97IplpsYaphHNHEURcHtVaS2LxN+KndZ0tcRrhBGIsbFVAEWAJJpZxiplFC0oV5nJRRSazTlV+4Nw0koHBwv6dMGMMbhmCeejxVnPRNl0cbmtXcjbzQxe/HRPH4WMzwmywxUiNZ7j9JX8IHvH6Cfp0fzQeW456osQWFLsVq+Dx6tDjE46sOVAw7k3OnjQ27JgTa8ew/27dmNwalDsUaGIJoAriwVkVE8F0b/dtZxrHOAhpshhgbRAacoAmKwlFwSQgUp6h6998JkoCqqcxEVlyGgkdOhI6exCCdMpGGhjwB4eHQ6bR0XTFq0KY4VKVKq5PBzA0beH2sdh4XVa0+IPTfLMvT0ZvRczrAShl1K+m8yT7AwJ9U5CmN1MvLEyEMvZG2UThECx++KqQyqDabMWegZPjJdPSf8mRj3G5L9i8wSgL6fdu6WuF1JOBV0iwBm8hQrbZnGjCENh86ARZCy3yAnrRG6tyIYPYV0Osr0M4HaK1VVBesNRvaNaASQ9PNL/R+BAUyMtonz3t910NKOMbbV1l5LF0x7DvGGGmVkEumgeiFvJjgGX5itIGqj9yZ1NcC6nCoABSqqY8oyxvIJGRjByYm6L9qJSTfU6KaXZdW16YmapGuajEcJTYghroMiIT7cMAZ5zrHHRmy6QVU9k4wjjRWOagnPo+K2IYljyXsPYzMsOHo1fS4G/FLR7nRlkv2wUmWJqxFbG1GCc5miCOk+4OV5IUCs3gZJ51rEKk0QcwALn2ajgSnTp2Nk7zDa4+No9vSokNLMJyYCkcvSbkeOthCvFJH2VdQvItEHZkBcQS1R7QILZumtJtqTMeSwk/oFVsqOGmCsoCQHPWTY/6VjMJE2ZK2rSWK4haaFxPTQJtqOIHI5c6lw95VHq9Xi1GdK7LDWoqxKdDp0XxGQmtUHwGYWPZbMPZ0OmYVCEFqVNfX6HOlxpggzIBECzF67C/IEUvnB2qfMV45SqnLTenSvrJwJAWNQ2qNnRIEhmk7NzovIM6ygbuYlztoEvYogCxpv3m530NPIo4wwxJBtsLGzio48ImUSqPU51N/f//UnFTEXeO4rccaw+AhxUyIAVHafCjFaVHEuGHGQsYRlRkXr4aOklJ/KAB2kertCDX1QvA8NLTk4IX4nPRwpgUQJnMRsytyDoNgUmSatv0Uq8yY7tgMGRh+ReBJkyUjCV+IQkbGSSUKPpjGwjPKkAad6Yg0VYNFohAQdaIysSW3lVJcAxiDjTCZxqkh/MevSjhlcUMWz8LTUJLDi5pCSZ6+GIRNNFg1j0OxpojU6qtEIEdEmiIvH4KvYrpRFmx5uFfJdwhDMVCV0TCjBOQeT0aGBj+ifttRqam8IUugGOo4QKBVYGism1BuJWUcJUFNJJA8A09RE3lLzjQQktAeecXxGKlhCAKpAAthz7eCiUyjystagp9lAs5EDVuKalXPRfjcasFmGslOgKBLmy48kZha1ssj4hYGn7ihJfknnGPhZ3aa6CHoECKX+m27Rlt4vfaJkqerrCnz4WRXRvufEFmNAxdoVgdIzvRS24c9kWYZOp4DLORMWgbRNkD/Gk71Kz0Ms2AStH6M8T98/8HVQpCvNJgFQAWZjYj0FWSFDoU9IrAlRPehixDWGW1evpbVOfR8S1CDnLmGmSJ4HVZtSYkaCNFD/PBALoySIVhhwOl495D5W9o+yXMbC9YRLvn/X2zUQDWh++YTGlgwJhTnJHDxC7NmV2OXE8x184KpWoQshG4gqK5Eg1lL4Fz1fokEAW1VsguFstmYOGKufJfunVZsWvHSvJbrIHMWzlkWJsijQ09Ojzjmp4hYYfSq6gDDZdF+iJpKaTdSMA1GD0bWvsWsCoQ4L4yINKSnW0EjcbwlJoytGo04ELyaSWyL7AwKDtchCg/6G+JuJL4kgUhrgXwyjS72PIDG9keUOEA4uy1AFUNlVWQheKRHq1lrkzRwSnuXTPHsVRLohuv76goKaENc2BTlpuKaRhCAhaVNbe6NjTIVMus4T0aKCFUGryXmNBx+Q9G1jpXmC0XNgbcwkhDHIGw1451BVJVWM8wFkeaS1sN7CWzYtaMHpONcakOK1qWe6TrwO3piScixrMxdHVJSESAgVingA1FTj9DYifcSMULd9JY9TRMHSBdHuGpF2/FxqrggBsWW3SKEoFeJhVWKLaqhMSQubm9RWBQAxCiEuuEGQVjgySf0Mbb5le1Vcj4TQRfxCDlxk+OlahxAoT94KQVETQjq8Yl/38Jzqm+eUyECtZWhNvK+Q24DANQTyRpZoEE0l/rLT5loDDj2NBnyoqLus91zKMK5f1SlQWero0Gm30MgdrMuJoI2BtwbS1ke0BISU4cZ6tDKUyFYTM09Cb6kgk22IBzhGrkjdENnMuPbRASIrH3hOyl+UTlC7JrIFQNvMJB+YhF/HX2sMNwUmtvZaHJPR71vWEo0BFdxBoUwlIFZFi8Is6Bo7V2/RHgeb2LGDmHsCA47o5ErrEcvCy8zFPixAx9p4NmtAxJh67QSVrKlADvF1iACk+8g4I5oOko0BMMPXzOzg9fwZQ0DH8ppmWaYF7NXenNQnqSpPtBuSiAweU55xZwl5fogNQvd3HTR6Qf5R1YBjYckNFWqMIaSLpIyI75UwDvlZM6iH5FAY1F9HPBRpaAl1Dk3+DnFPI2+VpAXAh0oGU9+oru8k4Frtj2LGSrdfbLUmGW9qv5Z/dX2QSOuu9U2pUYgzPROpsT6+RrVjU+ZgDLWnyZo5GnkGZw1aVeD6D7R+PlBdCO/jAYI01rGAy9muWxUoK8rKQvAYGx1houIOx6WEVzGD9BWktq7LyE6mGVkmUHpqhLY629S2LgdCCDu1WWvR+eS0C6IUpqQMSZiAMbW9sYxw6DZpMf3kIKe7kTJIdF2skaTfqSHYbiaT/BvvKQy3znRpDoiMFnG96BxCGZ0PgGF0X/F6CdpKaQ9SUhSBnZ5QzSECAZMkGLGQ61I+VRnTvTOcph9XTlChxkPXJ00/lEeILZhXiJYVotOkjLUWeSFrEmVp/E6QtmKGTA/WkMM/6Y1ZA4R8Q+89AS3H/MZTIhACN7qtKjVDidYvNmTtdDypNI7XQc0LaXGHNASMBmrig5PP1Bwd8o4yBUEXKaONkkU+WzMG8MpGic1MjgP+YwhYFHQRDaUqCCBEO5mbpAbWa290fd6I/TnU/k6vKGBMF5FB10dWySTrKT+VsLhSWk2r0M9AC3QgeEaWGZkfQsBjD96N+677LY5YeTJWnPokjHUqKiqTWaCyVBWLmXCnU8AnTFMkuoFB6QvKROPTZ6q4z3JkIGi1SgWhstJkf+uIs36cxTad7J8wSGVswoAik57UcRGCNicV84kcCqEHw6hM5gGIkE32q77smGSnazwl/h7PhhT0l/jfroEicmqmlyRe1irqjQ5Y7arSNbx0bMGTHVhsmJpWbSgDLmp6hO1FCzQAmyOinV/b2oj2KCalVJjxmKVou4REpkIlADWzkqrrstYqXGTPI4qSt6XYUlTrE+2ne7P0T6MMFIgFtVIzQ0jstrWfhk15MDDOAjlx7ar08bxwOvbBzApyHdS8kEpM+amosIZyYysYGrMwVlmwiGIEyUwWGdC1WvGApIxPxG+CHcTGl141XhvhRzfYiO+mc9P74uDCK2EytUgNvp8VwjHpZ+oMNA0fSzUCkxCiST/Lh1lC86w1GN62EQ/fcT0u+fS7a8PbsvZerDjlLDr4XkKSoNJbqsmVlYepEkJmJhsg/E+IN0DapUtaLe1ODdLp5WM13AQ+yt4FXegaekmvOgAEAoUDNRo5epoNeB4TdTqO3V0924WNQS21WMOGFFyZ7kfRY9SeWt8rGKgQMUBSqrR+8NS56ixsAMUGp/cIUCQfks8LOifHrCRkMD3AIo2PN7w34qGPzDiCk7KqYCoDY6qaMI2AgGnLxhRsp1EQcbwIMVRNaMcAHEVB2YhO5pxRSybxP4TAsc/Gq9mBnisoPCgtpCVXU4GaOvli0XYDKTkQ+QDNP8o/o/QVArVwMkZMj1b3Tr8V6hSoUTq6RxauYZEFqlNNVejiWoYwgYJr10GQbqhtjBJUgjbpZ5qDXTcXpIw4XroC+jl9Xeef2pbkp1HJZGBVVdKQLVNfsOhVTG4fkECY+nNVWQ3x1YkoKv1OOjh9qG6icxa+KuE42qP0CSFMQK1xneOTjIp4ORTOWgqGd04rd43v24s//OCLuO6y76IsOgCAZ1z4t2jOmI+FK07GrIVLMdyuEG0ktHYUNZDYqYIQs+xtZI6qfsn6hmR8QIKcUuaZfrZrD1J8VkOSwIQl71pjl1n09PQgb+SkInrq20e1HiIDc9xRhJpGRmaoqbcBCCwQICg4gdWxRx39tI5Crxo5HbZOp6wxMQEhwriU4XISUNqlwFqDotPByO5dGB3ei4Gp09A/NFNrN3u5J89XHKXGxLC+KBgDnDBRiECIIETRIUCV2QQxC7rl9zNHSFhWe6Anw1Bvjs17xtHmNvKZs9ixfQs233crFq48BYMzZpMWYSPztNZRDG1VoTU6jL4pQ1AQ4mPihyLhRODW6qIkbCMgYPfWjbj+p9/EWRe+Dj39AwQAEpgrtKm1dBPGWzPRCIBjNME5ehCNUsVpyucS7TaA9kGeZ61TgBJrPOz/OmjtBdkUmUyE3TxgAU2CVENCuMlEDW9ISO89AdPIJBNmJotqjMYo1iQRIzC1LU2iaophH4hoUyUrZDJQGRlBMTNtHRci42KHlea+1zaZ/qXeSRLuFLSPmzJ5RmFpIRhh9Ok8DKs4zlnkmQNgUQHojI7gj5d+BTde/l3MnbcAT3rS2bjyt7/GN674PbZmc7CvXcIAKAMigSoHTOaqPa6StTBxjHKY6HXEfUl3TQ5OssGKOnQ9Jxp1JuxWsvURYdoEHVJccQBQcNiTpJJT+dpMy9fY3FL/NE6E8Z6yuWygzDMAGv6oPokQuKzjRKForUEjz5HlFDZnmZEK3QmilqaVwnypCai09QnYdM9N+MOl/4UH77gZA1Omon9gCnbv2gkP4Llv/iiOPOG0CF6UTgzgIgKmTrUBBi7RGLk2g6NFtMYgc07NBbJlzgL9jQybH3sYP/rs+/H0v3s7Zi9ayumwQFF55JnDqYumYdH0PuwY7WCsXWLvnt347pc+jUu/922sPvlU/PyLH8DxZ5yHU555EVzPAMbGRrFvx2Zsf+hO3H7NFRjevQM+AAuPXYNjn/AU9E+fjSOOPxWNnl4YALs2PoI7rv45fFVh+pz5CEUbYyP7kA8ModHTi31bH8Xozm0oyw6qThvrH7wXu7ZuQmvvDjz5otdhvN1BWZZoj49SDRBOomr2DWDe4qPgGk0VNoHXUWKhlbZD6KLBwEWLoGsqYC9mowofogOhGZSJKetAlznQB+YtOTb8zUe/rcQUw3VkePGcxKpVemIQgjQQ7L5zZGIhYQLqeEgIRCIcag/kBUOI9sO65zfGdcaX04yY+s10fCFoWmPK+GIXXSRIeXKJRqat+grFe5kaM4le2+RuBhpOZxU1UeieZOPBAEfO6MPH3vAi9PY0sWzRYfjVLy7H3/zdq7H6xJNx9nlPxbVrd+L+LfsiQhWByQczaiG8bUiIkdciIl7sZ826NlbWN92sSYXrwS4+JmIOAACt08tIg1m6y8hZWCVMCszoMucohE3SgbmjgBRKEmFmEMPMRKWVdU+FkLUGfb1NVAEYHx1TASqFscUDnmUOLiNmuP2xtXj09j+i7LRw343X4uF7bsWMWbPxrne/F8/9y+dgxszpqMoO2mXAZ7/ydXzhkx9Gs28Qcxcvw/FnX4Cps+Zj3e3XYePa+zB70TIMzVmA/qEZ6OkbwNylx0aQw1d3B+myLLDxgbtw26++j8fvux3NZhOje3diz+7d6O8fwDHHrcS999yFPG+g3RqnanJ5hrHRUSxduhRFp8DY2Bh27dqJLMvw/L9+Id7wxn/EYYcvwr69e/CFL34Ol/7oh2i12sh7ejBv3mE44YQT8NznPg9HLl4I12jg69/8Jm679VasW7cWD95zJwamDlEh8bLAX/zVhRicMgXbNm/AYH8vBgenYt2GzRjZtw8nnXAclh6xGI1mEz09PZgzdy6WLjsKb/3Hf8RNN91AnT+yHD19/YTQjUWr3caeXTux8ZGH8NrP/RSzFi6ta+smalYhZZIJuVK2JCXQVElYHZkmvNI4485ojks0iw89b/UtIYSTJqXuAzLdpceGv/3Yd4lI01qfCVrT2qj7vU3QSUVWFT2+6aFOoXlEV6bO32qMmieRoHFVxVN1gu+Vfi+1UcdccGGQ8T6pecTrZwDA1N4DImgUtipDkvAcZsGceRWjLSJSrx965yzHuEa1f/NDdyOECkevOAEff81f4m1vewf+45MfxoqVJ+DiN70FK44/Ae3S45LbN2J4vIx7oGFYAWrSEcbL6yOqbWrzVfyfzLVbCKZIOK51bfXjB/enfYU491Tg1NcsmkLkmQIIUi+5mJWktxgx3IpteLSOPnhI83NB7t0OmmguoOf3NBvwAbjpF9/H+rtvxvJTz8XRp5wND6DZbKIzPoZN99+KR277A3ZueBiPrb0f/f39OOfJ52FoyiBWnXgKnnnBM9Hb00DmqEC/sxG0SMGdm2+9Fbfdeht+8uNLsHXLFpyweg1OPOU0PHD/A3h8w2PYsX0bNj7+KM561gux5jmvhDHUVaJMY5+Dx39/+9O44Rc/wNz5C/Dil7wUzzz/AlRViTmz52DGjOmoPIUTel9h+7bt6O3rQ1WVKIoCgwODuO++ezBlcBBZnmPq1GnwwWP69Bm6jeRkkypz0DNOZzkKALLRUiW2ouhgy5atqKoK8+YvQJbl3OkFXMs2RgQJsXRKz+nRgjZDnV6MMNCAovTIMoM3vPEN+M2vf419w3tx2NJjMXPeYRicNR/zDluI0eG92L5tC5af8QzMPuLoeB8+H5raLEhYKhFqfDkbLxT0Cd3RmJ01+NfnnPBnMt0lx4aXf/RbjCLAhZ1TJkPEnYZp6HuJehpVtXqUgtpjEvvNJNaBCVeY5Dmy+M5Z3XU1tPOzAdoYab8iiFQqg6XjlftqDB6CeljrtsoULYqkI4YpjME58gBTELuNGVo+StRUcARe2wBa89JTy6T7f/kt/OaHX0Or08G8pcehHN2Ln//sMlTtUXzh85/HJT/6AZ534Yvwwte/Db++b6vakGuhdz5xeAoD0jloHAJSlSNd7wgKQpxzlwD9E3ZQ7wV0ZfNpXzDJkHOMRCtmUg69jQzGAONcB0KYrDS4fPjOG3itcyxesRqVD9i2/gG0d2/BrHmHY8f2bdi9bTPa7RaMcZgyYyZ6p0zH7CUrdI+tMRrXjLHd2P7grdj08ANYd99dGNm1Hc978d/i+9/6KtavfQA9/QNYfvp5eOSWa7Fs6VKcf/75WL1qFVasWIEFCw5j7YXWs6xi9qC1Bo5DvwTPCI0mUXNwHEEghbW379iBj374Q/jlLy7HVbfcB2sMtmzfic98/j+xY8c2tPftxpaH78fcmTPwjW98A/PmzU/QXdwrkqn1eAzZecNmNWFAFIGB2lmWGsUAUJQeRcmhVNYoA5V7ydnMpLocXxqlAwE2Slr8tzTe9CjLGK3kHNFLnolDOfIjY6BruGnTZtx7791Yv3491j38CB5/7DE4l2HG7Dm49IffR6coMG3WXAzNmoc151+IOctOQNboEWAcaZ4zH2WoEpFSlRU7ilPwGfCR56/585ju/KUrwt99/HsoyioxgMeNS1hpbde0ivxEXqz2FeaB8SbJ4uvn9cOTX+nHjI2G+RgJQDeoRV4IU03sqCkTAozGGzLdReYgoTxdjEguPahSZCRERpRGGgDM6GW9EBJmptgSeeawcGoTO/fuQz68Cd/69w/ir573AqxYeQKe88ynYs2ak/Czn1+BPMtQVSW2bt2Kc88+C6+4+M3oX30BWqVPFlvm4icwyjj/uK5RbWJC11HVFz9FyhGtRzNKTUDxWMT2Lcw2zzLkjYwC+X2JamwYs2fNwsienXjwpmux9LjVOHzpcrRHhlGN7MScgQaWLDsaj23eggfvvx8bH38Ujz6yDuvuuxPrH3oAU4amYWCgH3t278a8wxai0+lg59bNOHbFcfjdb36JlSesxpJlRyNr9qDVamPPru146MEHMDh1GqqqwvjYKEwgG+Gu7dvQPzCA059wBlatPhFLli7FM57xDAwODACggvp33ns/rrjiCpx20io89bzz4hIxYhM+lYasiUMm7SkIQGPPYwQJUFYV1wohe+tfPvNpuPWWG/Fvn/kPvPhlf4v7770Hb3r9K7Fw4WKcsGo1Zs+ehaVLluCUU5+AZjOP9D9x+5KzGRJNCEyjxGRcLZ41OZSB7MM+kDDpFBU/x3BNjXpEjmVHsBTRsrbrfkp3HI3ig1ZUE6HjuDi+tZSN52ys26ERUgGQpETVHA3FaFc+oGDUbA3w+OOPYcPGzbj91lvwjW9+A+vXP4KjV50K2+yFy3IqEG8teganYWjeQsxfthLT5h6uvMHTYAETQ+msMfjAc/9MpLtg2XHhNZ/5EVfR8qi857JmKdOJCDGGcHTdqPYIWWQT/5zwYVP/M/1q10cVIYpUTTYgMENLw4WAqL6n6mR8TxhlitTrD5ch/erLH8a2x9Zh1mFHoLd/AIPTZ6MsOlj9tOfDNnoS23DCcELXckwy/cCCYqgnw82XfR3f/swH9O2bb78HZdHBu975Ntx/33141atfi7+/+A1AAIqigwceeBDPPP+p+PBPb8W2kQ5EZMsUjDWxawAzP0FeKQOWLgWyW2QKSdYFET3rnsvfKq0izxfGTB5wLlWYZXpgMmuw665r8KUPvYNsos5hy+bNOPfJ5+H3114D5xwazSbmz1+Aqqrw8Lq1GBqahhXHHYfFixfjiCOPxJo1J2L58mNw2OGHa7Ww733nO2j09OLpz7gAvb29NAauhlaW3BMPQFUUuPqq32Fo2hAGBqbAOodGnmHevHkYGOjXRBihHx8CWu0SeebQ05TGlQkzA2KYo6y37EVC+j6IjTlqJWKf9sxsok06oFNUeO973oFvff3L+NLXvo0tmzfj0x//CN7yln/C6y9+A5eqrKvLRIPpuYp0KMdYGX1SwyKEUGNm+hNRkIRACTqVjxEXcgblPBkjmYHsbFShXNdsdSw+oPRQ34PY4AFo4oY1iAiXNWUR9okyHQGiiXRq5AWlX8CYgLGxFh57fBOuv+FGjI2NoCwKVBVlYD6+aTPueeABPHDX7di3dzde/ZkfYdbhSxVWWGPhfaU08L5nrdgv0z1ocoQPoCBza2GCh3UG3AlED1M6sYny1KDb8VRTaEwsbJK+G+Kf+l3JgOsOkBZvLg1TmgXS65vX3Yssb2LGYUfCGIPRvbvgywID02fRYeMC0DKJ+G99vKKyWANsXf8gPv/6Z2PqtOnYu3sXHrrlv2trNjvr4KTz/xqPrV+PPbt2YPZhCzG2bxiXfPFjWLjiRMxatAydTgdHrj4DA9Nmcd+rgO2Pr4MtO1hx9FIY61BkDn0z52No5hxkzmHH1k04adUKTJ8xAwMDg7DWoigKNBsNeF+hKC0WH7kURy5Zgo03/wrNY87pWsc4NWuk3GLg7ELDWokgLEYYVeA+afJ9ObBC6OC18RBjjqSiirqThk9N7+/BlP4mehsO7d3bcPu1v8Lae+/ArTf8AdOmTcMPfvhjPPGJp+Puu+7Crt27cNZZZ8F7qm3abPaojfXR9euxaNEicl4h2td9oMJM1lrAZXjRS16q6K3WLghk9jFMeq7RwJOf8jQYJNXgDKvJgDI+Ikda1bIKcJYYlRezU6ItRDpKnLCIyC11Zipz4TXvLupdsr2y1S7xrOc8F+vW3o8vfvbTWHDY4bjs8l/h+OOpOzC1VzIiJflMpqIgImgdUsKkUh+HCAChH9EipcxigBSiEUaYnElBuoj2XVU+Ec1+aQlOLaXJ86i0clxcSyNFq62hztsVpRlLaygfIsuQnRZfhdBAvQhO/GxPs4GlSxZjyZIj0SlKNutU2Ds8iqIoUJQVPvSxj+Jnl/4APf1TeG3ZBCX8g+nzQNeBzQvLjguv/NQP6aAl1ZiMMYqEhBBJRY5IIJ1wIl8VLQnyictZ+yeRSoZr9pIEzZxDltNBG9u3F1vXP4gQPBo9fRgf2YuBoZnIe3rQGhuFb43iS//0Up3P6rPPx8N334K9O7bGOS49FkeffBaGZs/Hri0b8MhdN+G4s56BVec9B512C4PTZukYEaiR4PjYCD70vBOxdPkKrDj2GDznuc/DI+sewlf/6yvYto2cBNOnz8CRRy7B9BnTsW7dOuR5jpe89G+wecsWPPrYY2h3Clx71ZUYGx1B3mxi5ryF2L3lcUyZMhWjoyMoywLHHHscZs+Zi4ceehCbN27A0qXLMHfuXMyePQeHLzwcZ5xxBs4551wAQLvdwc7dw9jw+ON41zvfioH5S3Hqi95cF4QH1ECYqBl+mIRoBREE9frzmTZRG5DPyN8WQG8zQ5Y5FFVAM3M4fsEULJs9iIaz2LlzO1YuPRyzZs3CO9/zPpx11plYvnw5046gkJDY2yOCopY1EXkKUpSMOaXBlAkAbBM2igajms8qrTJDwCRprN1akqyWCGHnJJYiMpN430SY85zkuSrABN2yI4yafUIRZFkFLaxuXYZmM0NuAnp7cmqhpJpU1MycjehShQFzSq9Hj09kiGhcO5/IfgrTExJKNKeAiCAFZVJRI264aROHcir0U6oTQenjPgDUNaXyUThJjoA1hoAfX47/Vg1ScF4XZUe6iD9D+gYLmaIkx2BRdLBn3xg2bt+Fm2+8Edde/RvcfdvNsI1eXPgvX8bA0Az5GkRjFD+RMQbvvWD5n2nTZaZrRBVNsm3SeFmV2IlnMZXUatsLXWvOSCl9TWyfm9fdjW2PPIA5i5aiPT6KTWvvxejeXaiKDvbt3oFtjz6EseE9OHzpcjTyDJ3WOKYOTcPundvRbo2jr28AQ0NDGB8Zxs4d2zEudV0BaiTnKwwNTcOTn/YMTJ8+E5s2bcDcufNx3KrV+PdPfAwP3X8v8kYDA9NmYWDGXBx+1LFoZBl2b9+CfcN7sXvLBmx55AFapwWH4bY770czt2i1xmAtFXoBxL4U0BA1lFEgSfKATllh165dePDBB3HUsmWYNm0abX5R4qrf/QbjY+NYsnQpVq5ciTzL2F4e6xVUZYX1jz6Kq373W3zqE/8G5xzWnHEOVr3gjbA9/VEoCiEmix1DrBLtI1VCUkLpZsT1W3UB6YAZfU08efks5M5iX6tEb+4w0LB4+OF1uO3mG3H1lb/GJT/8Pt76tnfiPf/8Xu3gQOFXVg9HWXKreGMUdYnTLUbORPu4YdQhYzOGmJfQVQASZtR9RUYez0Xd/DTZcRG0X7sCWGjQlxRtQf4OlKqbZDvJsyIjiuq55+wu6yycIUeSPDtl7LLH1qLO9PiS8NJuIRlVe+leHJlVIjZ0DQygDjPrurRPkK01aqIT1ywVcpVPBJ4KnqgFiEnCB+6S4epmOzPZA/Z7Bd2bqGFI3zaPTqfEpm27kTUa+MGPf4KvfO4TGJw5F0eccDqWn3Yups9fDJc3SLjJLdXWEp/yL8865n/JdBGRQ/oMoAs8hZgdprGtAG+iT0iLLsulAEkNooM2rcdix9q78L5XPx9nPeV8bN20ATNmzcbiZcdi9rz5mDm1H3Nnz8ZRy4/BkiOXoCezaOYUeE6RCRbcOSRBJYEr59dVKxlr5uhwi603hIB2p4C1wL33PYAH1j2CdfffjSxzmD17NgamDGF0dAzbtm3F1k0bsGrVGlz4whfR/cnFq7ZKOuj03NxFhJ9mfgkBCvrxIaDVKtBqjaEoSoyOjgLBI2/k6LTb+OUvLsdtt96CBx+4H4888ggGBwdx0qmn4ZWv+Xuc/sSzUFQBe8cL7BrrYMdoB7tGO9jbKjBexNCbbnqJeziRDkL6W6jvoiBjBKAzPop9Wx9FT3s33Phu2M44xsdHsW3rVjy8bi3uvecuTJkyFSefcgrOOutJePazn4NZs2ZpbntRliirWMc2RVLp3snj1R7P3EYdVYi1yZShhFgjIAoPZrImMjs5xPL9ieoBXakNNECcRJERBD7Y6XpqjeMQtG1SVQWNvRZ6dTaGyEkUgFxpam601yaMN0S0S4g3zkFMF7KuuqsqDLrpMdn/ZB5iR5U5i+NaTX1KF1GYR2QZEbVPmLsImgn8KJGmkQ/JnFJiSD/fBeZC1+8Jipd1rNhvNdousH7XOD760Q/iruuuwrkvfROOOvnsOurX9avThlCtgfnfMt0fybQmzLP7pdowEmIGgNG9u/BvLz4Tpz7j+Wg0ezA2vAdjI/tw1ElnElLdtQVbHr4P99xxCxYsWICBgUF845vfxqJFh5PTo/JKnKmkkiD3zMUWH0LwwZPjQQKcjTL3aMvpFBUaORV+do4cLEVJ1YR6ehq6KQlIqREJSXWjdYdrQfohJT45hKEmxUV1E+cdN+HAq175cvz4kh/qus6dNx++qpDlOU459TScc97TsOzo5Vh21HIMDA4mVC0Pi6jQB/LYjrYrbBlu4fHd49jbKtAuPUbaZe0w0NrEA5ySk+6xCOCyjeEtj2HLQ3fisbtuxO3XXY0Fhy/EYYcdjgXz52Pq1Gno7evFrFmzsWTJEqxYcRxmzZ6tKMkYQLpN18p7Jkwkc0mRcETEI+NNRyeIiFbB6MET9VlU35REZX7dJrHI0Oo+hig0o9AW6BfE0ZEyIJt8vwtlhhARnaxrCFBB7TT+OD6cwAJq36ExU8xvSDikkkQA0mpo9fkHvW/axFLGGIWeABlEZio8IBEY0UlGc6ibZ0jA+BBjbmW0hm+0P1RsVbjXzR2TM5/0fkZvkpowZG0oHp7OeKvVwdhYCyOFx1PPPQNnXvg6rHzSBcit0ep8+riUqyfnV571/r849s9zpDUyi3lTe9AuKowVlcYOKoKdYC+ImWm/+8anMLpnJ6bOmI1jTnwCVp18OoL3uOvqy3HOk8/DXzzraciyDNdcdSWyvj4sWbgIz3vGOXjCaadj9pxZCIEOnCyW9x6doivFLqHHKikxKHYpCTlxHGIileEJFUcmW1VkiK8qQgIwVEdY4w6RELcQaQgclhLgWShYsaMhMmXowYrIyQAoihLWGpQVO4AQlKi3bd2iRcPlev0b/xGvfO3FtddUxUkNVbo2UkqSPtNwDo0+i+n9DRwzdwoq7zHWKfHFr/wXLv/Bt7Dx0XVojY1i1oJFWH7K2XjyRa/F4JQpcMagXVaoeD5Faxw//fS78dh9t2HPrh04/PBFWLX6RDz76U/BZz/xcUyfMVMhaopKBYWOjnVU4Mk5kt9JdbQamhfRrddTUgVOb82smg7KKqm/q8g3Mg8RfiSoI3M16T/JmUlRjCDhyGwj/UVTQFCGLozMgBw7ReWVOasKboHM2RqDimCCacREJqz+En40hWnVC6uIQ9Dw04XBSURRemxS513EB5Llx6ZAk9Rp0DVFHCcL9hT5OWdqiD21b9c0u5oZskay0AgZE9cyBKACtJJZFHDpFU+XCgsDiG1emLxMQ80LPqACabrtdhutTomGNXjXP78f737rG7D+xt9g5tAUvOeDH0OzbxDBUopw5YkWK+Yz7dKjzUkcZTqpSa4DIt0TVp8Yfnn1H1H5gBYzXeH4nh/qQ7TJVF5qWAJPXbUIY6MjGBicgpF9w5g1ew5+dNmv8N1vfg2//dUVaLVamDZtGqZOnYqhoWno6elF/0A/jl5+DFavXoPjjjsBjUZDIXt6KATppihEmKPEv6ZqjRbvTj/Dc3AarM3qoTZRhG5WJJzIxCR0RVRBRVAh+cG/q/0uePiKer212m2EENDX30/pvQkVffUr/4l/fsdb8Pf/8BasOfFkTJs+HSeefBryPI/OLB6DZm0la7Fn9y48cN+9OPnU07nuQLyE8B95eC3++R1vxfZtW/G6i9+KVavWYGhoCh5aez8+/9lPY8bMWfjIpz4HA0ZAoL294Y//jRc++2l49nOejzlz5mLfvr0YHh7G+Pg4LnrJy/HkJz9V45SdjRuW2u6MIHrEAyq/S9k92ds8t8l90j2JXnP523vUGiwKU6yqeMhjqyNBw5GpyEEN8WTGfdS/U4YVTRdRA4pIKr08ozURFoJoa2m7CapM6W7iCTX6+kRgmFJvirCFyaUMPGFCCfMMyVqkd41j7Hoef1Z6s6XIVwBIQNQM4rrRB9L91XC5Wk2XKAjrZhFM+reAJBVciPul64cQO6dUFaqqRFWW+PSnP4HfX3MVOp0O7r/vXr33Zb++Giefenoc9KRXfG/u1OafZ144YfWJ4VfXXBd3hAcsP/b3aISATlHgist+imuvuhJVVWH2nDl41/s+QGpMWWL9Iw9jZGQfdmzbht27dqGqSuwb3otf/+IK/P7aq3DU0cfgyquvY6mVGM4hhyUutFXoRKXlUoRn9J/4eZmF1m1AsklJWnOiFdWYcPeBSomsezmj+khvaMSHEVQubcktrHMoigojI3vxox98B5/46IewcPERWHDYYZgxcxaOOHIJjjr6GKxacyJmz5kLANi9aweOPfIw9A8MYNq06Wg0Gnh43VoAwCte+Vp86OOf1rFs37YVp60+FqMjI3DO4aWveDX++qKXY/eundi86XFsfPxRPPLwQ7jqyl/j6GNW4MdX/Bbdh7goCvzHZz6BBx+4DyPD+2CsRVWWuPI3vwQAfP07P8J5T31aLUZVGIvnw5QedLEHOhcfEzOyAhq5RSMnVFhWUTB13yO1Rcp+AvE+2hJJN01kbcL0J/ySXKH2A6J+C20Isz3QVWMQtceYSRmcOE1rQ0iledfNu4etSDEZZ0SE+xlfKtBMfYwAauYZ+o7YyOsMQYXRJM+ZqFUwiFP7dj0KRL4SC9NPMu50pF28KRU4ksDgvcfYeBtFUUIiPD79iY/gphuvxwc/9kn09PRi7rx5mDlrdg0Y/KnX/4rp/vqa6/7HD0RIt7eOZp529hNwx2231D7e29eHxYuPxKZNG9FoNHDiSafgwoteinPPe5qiivS+ShAiTRO+IOufgqOIrrq8uQY1JlyzKUWhOGFq8pLv/kxI7EYc1hRbzE28kU8OkA/19w0M2p0O7rzzdmzfsR07t2/DIw+TM+qO225FT28PFh+xBH19fbj1lpswvHdv7ftz5s7FV7/9Q6w56RR9beuWzXjFi/8a995zF7IsR9HpoLe3FwODgxiaNh1z5szFwsWLcfwJa3DCmjW44mc/wW233oLRkRF47zGybxibN23E6OgI5sydjxkzZmDq1CFMnzED02fMxNDQNFx40UswZ84cGO7HJgJQ1oXmTvvXyB2HW5EamAo/cW6IUxSIzNiHoEg6CsG6ylxXnfnfLlKfBErUrwStdX8uAb/7vYPYp+N3EvhsEts5JtJxXavruntyz/QrNVPERDwaLQQJw0tt2fGYdjHcCUyum5a7x0vnrdbeKjlr3TOKJRmDzpciFOiL6V4qwOlaJ2H8AXEdA2JUhDBdoROhKXKal9iwYT3OPuNkzJ+/AF/86rdw8imn1aK1/qfX/1Omq3AfkzNV/tAECSefabVauOO2W/DTS3+Ia373W6xb+xDuvG891q9/BPPmL8DMmbOSzUlRdf33A6o+8aN6iOufTZg3UmKsb3Acex3F1GIfEdUnw7/Xwqv4H0n3TBFxSpN1Gkq94ILg6FPO0WubN23E4489iuHhvdi9Zzc2b96MTRs3YO2DD+D+e+9Bo9nEWWefi1e97mI8/thjuPvO23Hfvffg/nvvxuZNGzFz5iwcsWQpFi5axGYLYGx8DPuGh7Ft21Zs2bQR55z3NJz5pPMwdWganLWYMmUQhx92GGbOirHLJpmLzoTnbq1Bnlk2LXCuPgfNS8ysrK9EegCpmhq5ptyeYlcJ8foAVKVkKvHnQnSWpF+ejMpT2p8AIFMuhYTG0L239avGiCccgomvpbQVJhnrZGdLv5j+Pclz0nHWHF/6GjABIUd18uBXQpuTzUOcuJNl86UZajVnZVBsruYmoO5A5YfUJlN7frJE3edM5mZARe699+h0OijKgKoqcfllP8F/fPZTaLVaOP2MM3Hiyadi1eo1OHLZURgamqbjOtj1ZzPd41etCd+55DLMnDkLW7dsQt5oYPr0GVi39iFs4APfGm9h5fEn4Kjlx1A2WDLbgDoBCayXoitvf/PF+O63vo73vv8jWLJkGc4658mQ9sg1UID07/p40w2bIMVFYpsYbiIhQ3TLWBtAN1ygbMJQ0/ED7ExhNJFlMRpCIxq6zkMqeRWRyfsJUXTREeLAEuSRCh2+bwy/oYpINrPYvHkjLvn+d/HlL34Wx61chRNWrcExx67AypXH4/CFi6hdd6DasdQyngYq8cMVF2aJa0VXWj2KUCatQx09pXM3Na0jJHsq65U5QynHJjJjDUNC3as+wSnTvVRd5DypgpvQ1/6u6FiKD1A6M/v5ejez2s/9u0BaMvYAQcDp2iP5fGKWnJQv1sebAI4a8Ih/hAkLGD+fanWh680YKVQXcuLrUg0kFVwm2vnlrHb3PIt7K+OjX9KwtFSD0emZ1FkYabKbv4mNv0gc1T6AHe0lyk4HAQYbNzyOm2+6HjffdD3uuvMOPLp+PRrNJl712oux+IgjMH3GDKxafRIGp07B3j170G63MWv2HE3i+LOZ7pQpU0IAkLkMxhiUZUnhSQCWLjsKg1OmIs9z3H7rzdi7Zw9WrDwes+fMxezZszF//mFYfMSRmDl7DganTEF//xT09fdh75492LFtG0ZGhnHXnbfhzttvQ6fTwbq1D+KxR9djaGgapk2bjrzRwPbt27Bzx3YMDU3Db6++ATNmzawRUmqs76ZQQU8pqk2RpuwNhSvxz4QhkJe5nq1T19q6ix+nY4o1KCYeLCjjqDi/XPLWtbEDU6Tw1fQmEiEhphK5L0VBEAHlmVVPr7UGHS5YJN9LmZomIpgYiiRzCCGJYVSBEw+UjiNFS0zoEvkhDFfjSxGdjnK4kt5++obhtE7I3Hh/aip499rGIehr9f4BEwAsBBIa1N+o3+NAT4M+RT4cE6wnGWeYbPh1NhwZScK8JpnfwQYqd+sGPqk55kDSZzIhJjdWhjwJwEgFpJwZYbYhQOlPHWuI96IY3wRU7Ady1x2wEz/XrZ2mC5m+55OxlmWBoqgAUEVAYwxarRbGxsYRYHD/vXfjZz/5EYaHh7FzxzbcdecdKIoOmk3qYjI6MoKFi47A4iOOxK9/efmfx3SPOuro8Ivf/h5btu3E/PnzUZUFHl77EI5efpRGA+Q5xbJu3LgB995zN3bs2I5dO3di48YNeHT9w9i2dSuGh8nDPTqyD1OHhjB79lxMmTIF4+PjGB0dwejoKPbs2Y3MZcgbOXbv2lUbx+o1J+Gnl/8GjUbOkRMembW6jho3ylBAbIhRhaVNlApghLh5w1VSEwdI7yeMJoR6SctaoQ39LhhVR6IQlOarSvtEKTECnGMf0ZNjRk/PgI6b70jP4nvWSYw8sRRuZTmawtTQukmYtDEpoQa1kdUON59FRWCYCBA1ZA2Rpr2P7IYyqQILiFQAxvXv1gwkYUDXVWcIZeKTMSK5L6+aahUT3UvdX8KEuadRKhO49H4YfroW6VjSx2hYL7rn0MWJEzDRTU/1p8qnDgja93OFCY/dz6f0F7E8pN/rBhXdyyQha6kw606ySnlm1EoRhWH6YUyytul7k4ylCxHQz7QejJxzCAjyaLdLlJxQVVaUdNXIc1RVCYOAqiowvHcv+np7kTebQAjodNrYtHkTNj7+GF5y0Qv+PKa7avWa8Jur/pB0BfUxhVL6ogUaZFGUKMuSoXtAo0G9pIzxABzGWh2EqlD4nec5YCyyjFXb4LF9+zZs27IJg1OnYeaMGRgc6EOeZ8ikkDcXVSlLmjipBB5FUcAHUpMtFwz3ldfIAO89FbTOsljLVte6y+FS+zs6dyICriO8GBpl1HMuTFHChCplMHqXCQzCGFazmQuLqq2OIYBij6XluhJcvE8dySexwUJniAWl0wSRbqwj8qsekkefkHhmNcskgkkYjzIVTHYQ6zZzU38zvp6ooZWPz5LPVyqt4ncD0o2sX5MAwQmXQSzk1H2w66qrnIGQvBKRZdrLQdcgsTHpewcb0ITRTTIHc5B5mdqPCVdt3/+EUdSXdj9j6v5sCF1rJH90wVGDGoOWj0xEsV1mk5Aw8vTjcrD2o0noR5KHadSE7pWU3zQTnpGCN+k8EkBnfvb0/j8vOcIYg9zJqeSqTCp9MkVTOYDeJiIBCAIEfc8HoL+vqZNXJOrBeduUdz/QfziOXHx4rcuoLk5gJhuAPMt0AVwG5I1GguJMDSmkCxuLpse/QxJkHgKNRzZLQzqZSdVSi9X+Fl+T4HcxS2f8bBcmqpq1A8vDStG02LYMS2XpjODsRG6WMs94T1MjNWW+8hkJWkZdoKQLltrr5L0Y6M9twW0awB/v0y3L9TWmpZRfym6lq2SSeWXJa5FhMYo2EdWqnVeFTWpvjNxuAvNPFsp0SwxdYHmujd9J6Cz5s7b26XpqAYzQ9UZtg+J84qMncs664N0P20s/8yex1D/tSiPh4/7FGU/EcWYCAapeEOrLEULtY5qE0U3rwQg4qPsT5FOy3PGv+nAmHWcItbbwQkOUJRrjm6WggZx1A6oFIt852EoftLRjTaLIwPj2oYpvBQNqDJjOAckidU1QS8QZAPC1vvfSSUHvzb8RujW6OZIdJqozbQLt2mQSXMcmXCQksbyg+4ldM7U9AcSkPKB2XgkZTZ1EqdokwinNYQdi6nBkdHUbtTGhtmjKqBBqxCKl84Q5ppfhcXbr4ZOtaTfSSHcw4SG1t9NPyPgs/RMPjP4eCZTGHhTFykhUswiCwhNGEuLY6/ww1NBwSDpFp7gq5WcTeMEkwnDCR2or8qd9TgTMZNER3WX/dIsmHYtBYiWKwsMkL3ajuJSgu4XH/7NrshX505h6Kmy7JdPE1+Kc6+KYPyjvsdCPPDfV4kws9pQOArJMNbE2cUrJoVEIGDAhhrp7dAe6DtqCfRKxNdmnar9PZHL7H0wiWJRIicfwIU2kmJFFNpwHrkxDoFl9LKZrpY1ISP7b1pO14oj1Q8kGy7dCFBi1yYb6VwSvuNozJW41eVqNaUfxEhlvOoP6Oka+VGdQk94c0dJZu2P9Eendaq/sd/+6NztdMps8VQk9nbCMS96ODKSb7EJtHYSRx8Nbv239IIX0j/S1/UKeg1yTMHAZvz7b1IVVyhpTfmlqAjUdZJjAiGtMQu4fEsGjXw1dSA9di4EJ61F770+5/qfM3CRAA5ikt6up/agP609j6F1fir+EiS9Hq0NKeQeeVJeMmFQUTPZX93VAm64x5uYDfvvQdeg6dB26Dl2TXTtCCE+f7I0DMt1D16Hr0HXoOnT9v73+/Dy3Q9eh69B16Dp0/Y+vQ0z30HXoOnQduv4Pr0NM99B16Dp0Hbr+D69DTPfQdeg6dB26/g+vQ0z30HXoOnQduv4Pr/8fZnHkcPY70wcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RY8gS3HjmTxm36pqM32wHSkOW5o+/OYp3XmvOMHOjvUcPq6SsRFF03qxZs35nrZ31tb6o/5LxrXD+nSGEKBeLxZ1b29rPrFQqA5K4LqKoQHd3F9sefjrLfWdr3n7wRp658zrGLbcc2223PRtu/D0GDRlOPU6pxQm1Wp16PebPb7zOH+64hZeeeYwBo8bS0rMPPfoNovegYQwfuxL9h41CRiUcbANx90Jq1QrlXgOQ0puYTt064UxTpzmt9ZpREAYKFYaYJmGWCKQUpNpp34mvP8N9F57MueeeyVprrEIl6SYmRpuU1Bj3HaG8KSww2lKLUx6670+8+5d3qXRV+Msbf6Grs+uvntfw4UMRUjBq9Eh+dODe3H7Lndx374MABEFAS0uL6d2n9ztTp0w9OU3TB78N0/zt8a1w/o0hhBg2aNDgy+Z9Pm8za61M4niR7WPHb8yW+x/FjSf/iNGjRnHuuecxdtnliFNDdzXmzbfe5smnnuSTjz9iyqRPmfjRu/ToN4jlNtiKVb67A6X2XkgpscDtpx3IB68+g1SKlddan0kfvktbWztzZs2gu7ub/c66iqErrN3QhtZijcHoFGOM8w29HymyMIpUSK8pTRMKVOlYwFU/3YGzzjyJDddbk456JzVTR9uUOE154vGX+dODz9OrT2+22fY7rLjSUlhjMRa23fCHdHU6F7JQKlOvLor7rLTailx0xa+xAmpJkod2QqUIVMhnn05jl+/vBcB2O2xtXn/tzS5ruX7q1KmnWGsXfG0v8//o+FY4FxtCiEFDhw67eGFHx7adHQvVmCWWYMInn+Tbtz7sF4xbdzOmvvkMD17xS4752bEccPCP0dpSq6d89PGnHHfckXz43lsstdoG9Bu+BL0GjWDwEuNo6dnHacDMNrUwe/LHXHrotgBce921zJo1ixVXXo233n6beZ9/zjln/oL1dtyfjfbyVSqtBgRGa6w1DvWR0gM/4EInDgiyHsHNXnH3grnc+cvDWHW1cZx27OFom9ARd2KEJTUptSRh+w12ZcWNtuXjP7/A/FnTePz1eyHzl61l3sx5XHvpzTz/9MvUKg3hPPhnP2bz7TZGSYmSkmqSoITwZjUoISmoCGUFXZUKvXq2oxPNnx58gltvuKM7jpPLP5v22WnW2o5/w2v+PzG+FU4/hBD9h48cdb4Ioh2mfPpx0RqXtdXS3pN1f3gwq2+5q5cpwTuP/Z6X776WK6+5jlVWW5Ou7hqPP/EE99x7F08/+gCrbbUr637/AFQYOgHxccfcNLVO+yEcuPKL7VcAYODwUbS2tjNt0gRaevRi8FLLs9z6W7Lkahs4SNVdZ+5N5uCrNz+b45XGuu0CmPvZRD567o+8cN+tbL7NFhx99CG0FkIW1BaysN5Fagxaa+6980muOe8yxm+zG5+88ji/uewUBg0Z4BJ/hcAY91si+dODT3LpudfQ2dHJTnt+n+1324aWtpbs9oi1A6siFeT3GkiFRJIaF78tBCFKKtCGJx54kjtuvqs7TfTF06ZNO8O6Gkv/0+N/XjiFEH2HDB16TkdH555dnZ1q3e1259m7bwBg4Kix7Hj8b+nVfwhZaP7zqR9z40n78vt7HmL0EkvR3V3hsB8fwPsfvM/yG2zFCptsT3vv/nksUnp01MOmZBCNMf6I/vnHtQoLZn9GpWMB/YYvQUuP3ggpXRwzQ2S92VoMLIkGbYXXnqAClWvJ5mz6By88lo9ee47vbboJ399+K5ZddkmkMgghqOgqXXGV7rjOKy++yUW/vJJdfn4lbz52F90zP+Ts848kzWgLTXHQjDQhhSDWGilEk4/rwjahcpq7nqT5QqGkcrFd6wCqRGsiFSCEoKACAit5+J5H+cNt93bFcXLujOkzzvlfjpn+zwqnEKLXkKFDz6rX433nfz4v0lozaoXxbLT7oUx698+M33YvhFQNwAWwJuWsHVdl7fU35JfnnM9Lr7zGeb86nT4jx7LtT85CBVEjZOFBGJGhpH5SGmsg12ksFgrJmD4CqaQ/SuP9CEAb4xk+7rsyUA6JxZJqS/PbjGsVztl5Te5+6Pf069WfYgApNappjRTjwB6rqSQxb7/xHueedgm7nn4trb36c9svDmblVZdknwO39drYMYqcsAkCJfKwjou7SgKpyGBlbTJqRIPokBjduG5v8iohMcYSKElLFNESFUjqCXfe/oC99w8PdMX15OyZM2eeZ62tfZXv///C+J8TTiFEYfDgIWekxhxa6e4utra2MnOGK8g2dOkV2O83t2GMdiCIYwlk38OalEeuPIv3nv8TQ0aMYu7cuWz6o+NZcrUNvOnX0G7S+4FuUjeRA9zRcmG1jesikBJkgyBgjWkCc8i1t1jEhBVYaykoQ6IFqW0OosCNJ+7Jrrtvz/c33ZBAGT6vLqAzqWOFJdEGKUFbi9aah+78E7+/9g+sstGmrLjpHtx25k+wus4aG23IZxMn8pcXXsjvYMf99mCvA7YHm7pngyAQAm1NLrDZ/eWCat05MwMiUNJZFkJijUUKaCsVUf4ZdnR2c8/tD9qH732so1atnzJnzpyL/5eqRPxPCWcQBGv36df/vjRN+iwzdileeP5Fmu9/99OvYuTya+RCaZtMNSklUgmssUx8+2UqCz9nzKrrExXLADnZPPsNoiFcTWwc66Us+y2lRCkHojjN6va3NjN9QXrrV0iJ9HyCroXzWThjIsIk9B+zPAQlfxeL8vBefeAWpr/9NJdedDrWJjz14qucd8aFDBwximNOO5hePYoO6QXSxHLVJXfw8fuT2OUXN2KBiW++wKNXnc2syZ8QFQq0tbcyb848dj9oN3beZzu0cZaAEAIlBcr/dgLqmFB1rdHaoo1xcdhsIfJ+bKRkTtJItSZQkkgpylFIagwLO7q5/Zo77fNPvfLJ1ClTt7TWfvw1TpNvzPifEE4hROvQ4SOu1JadhEnUmb88hbfefp/zf30BAEFU4MeX3k9bn4FYTzZvCCZeCzoJMcat/Jl+yjNApGwKXTRlfHhByzRfsyYOAoWU0lP5TB4GkUJgjHEAjN9XBRJjIJCa6qyPueRnB/L5bBfLX2/73Vln50MplHv4c5KTFpK4xg0n7EXnvBm09ejBws/ns9kBJ/D4Db9l0NBB/PriYwlDSaoNEydM5Yi9jmWvs29iyWWWopIEICSzP3ieS4/eH4AbH7qWwQN7kmpDorOYqKCWaALlzNpQCsqFECUg0RYlBam2dNZjjHXUw3KoKIQBkVKk2i1ixkAlSYm1RglBoCSFwIWErIU333yfi8++qlbpqpz32bTPfv7fHiP9rxfOYrG4Wc8+/W6zmB7LjVuak079GS1tLYgELrrsJu6/9372+uUNtPUdiNEao00O/zvBcMfJ6HEiNye90OZmqF3EVLW2ybi0Nve9lJQo5cEbaNKOIj+ZTlOM0QipEAK3PwJtDIG03Hb6gay95rIktQ6uu+I2gjBkx2N+zdJrbdp8sfmwwJwpnxBXu3n/id/zwStP8/mcufToN5AlV1iBn/38R0iZkqQp+211EEdccT89e/emkgTUqxUu3HdDCsWI+XM/Z59D92DnvbdDCIgCST11GjFO3YIkhKUYKIqhQkln7kZKIgXUU0OSGhJvUZTDgNaC046xNk6Depe8EmuMtbQWgnytkUJQq8fcePVd9pH7n54+47MZWyZJ8peves58U8Z/bZkSIUSvYSNGPjxg6IgHujsX9tjvR7vz87NPoNBSpBQUOPn03/DMC6+xx5nX0aPfoDz9KiN4C3z6VZ68vKhgLqotnQlscKilMc5stXi/0RikEIRBQBC4DBFjG4htRkYwWpPEdbROyfxSkfNjXfAktYK4XmfSlJkccvherL/R2qRJwrBxq/71Q/CrjLDQf/gS9Ow/lJcfvoerrjkNgDAMmfTeu5x70vl8/tksTvvpecRxgpURlSRsBGuE4txLT+PUs3/KjtutR6+WiJYoRHqfOk41UeDS0vyzJ/JWgTFue2rcxUgJPYoRbcWQlkKAtpZQSfq1FigGivZSSI9iSK9yRGshINWGYqjoWY5oKQQM6tXKMUftKX532UlDxi239CsjR464xBcG/68b/5Was7W19YcyCK4HCmustSqHH3kwffr1BqAUFOiqJWyw+obsffaNjFp+NYeCau1TtgxgcxPVDQfASGfDeSQ2A4mcGFrrzdemNCvjkdVM+DIN6kxaZzK6sIpxlQwyde3jllIIojAAIah3zObN+69m/uyZzJ45m4/+8jonnnkMCsMD9zyC6jGMzQ4/NweLWPy1CrDGcvGBm7Ld9zfjmouvzDepICQqFlh5w61YYs1NGLH8Wsz65C8E0tJz2NLcccYhFFWdW27/JUpAPbXUU0M90ZQiRSVOvXnrzGMhnOmaTS0pBKVQUUs1tURTDh3C3F4MkVJQTzTlKPA+NyjlpLyeaDpqKYm29GkJnMALUP4erTZcc8299vrr75v72bQZ26Zp+uJXN4v+8+O/SjiFEP0HDhp0f2dn5+rdXV387roLWG6FZZAe0dTWJTR3VDU/2GRbtvzxqay08XZY0xBMa7N4v5vhuaZEIFVDk0rvS2ZPz1iLNY1nmQmmT7HEGIO1zs8MggDhU74ayc2OrqA8WSHzZdNaN6/dexlP3XU7m2+5Pn16t3H5JbcxZqU1mDV5AiusNI6LLzySXXc+hoVdCXv9+vdExcXbnmTmteHDlx7n4ctOY6UVl+Dll9+iR+++1KoVho8exaqb78Knb/+Zt555lB49W8EaJn86hZ69e3LOb49lnXVXxlqoJSmVWBMpx0pKtAVrKITOnLUWOusJ1VhTjhSFICAKJC0FhTGWWuL81dRYepRCsM7kDYQgNYZiGORWSmoM87pTdJoypJdHcjP3Ag/QTZ7OccddmHz22dw/TJ48ef//ltjof41wSilXa+/R45mOhQtLG22+IcedehTKs2oS7fyXaioIVZFbr7+dWy67klPufQeEQOsUnehG2AIg0440lJADhlwwPfs0D/pDDvyYJrQ/+74TVkEYhshAOt82z720KCUpRqEzjY3FeuGf8voTPHDJadx8+zkMHzGI2297jBtvf4aBo8cy5Z1X+WzCR7z+9l20tRQ47NBzqJVHsNYPD0cGztJLk5jXHryZT199nJmTJ9C5YAEAr719D6+9+havvvIu48cvz6RJ07n7zsdZY+1V2Grb79BvyADKkWT29NkM6NcbVSyQakMhlN50twRSYrxZWk1SuuopLVHgSQmWRFsSj772bSlSDN37MJ4KuKCa0Bq55nAdtQQsFALPxZUCpRz6qw3M7KjTXpC0F4P8hTi3Q+Zhr5tueZDLLv/DtKlTpq1r/9n+JN+g8V8hnH369Nk/TpLLujo71QlnHsv6m6yDkjKP39W1JjWaUljig7c+4bRjf8EWh5zKMmttgrWWNEm81rS5aSpwfmUgFcb7lA1DN/PGFhVmmhg6mSnstwBuUgrptXjqEqWlFIRBSKlUwAA69WiwlEhhmfLcbfzp1mu49+GreOa5dzjt+F+x8b7H8vtfHc2Pjz6YDdZZltVXG0uoJNOnz2HPPU9iwseT2eP0yxm2/Fq8fNeVTHvjTxx6+M4MH9qPUaOGILCoMKKeGh/2cGZqFhapJU7jtRYDZzF46yHWjterpKAWO+aP9iGn1BiqsaYQKAIlUNKBQEnqnlFrMfCaVuR+aOgRaON96wyhDqQkCiRKitxvjrWhZiwFCZGU+XMOAoXB+fbCwp/feJ+jjzmva+rU6VvHcfzU1zPj/j3j/7RwCiGCoUOHXjN/wfw9uru6ufL239F/yAC0tRSCgNQY6lpTq8a89PRr3HL5jVQrNXY45jcstfoGgNNoOk0x2njTM4uhuF9KqcYJnZrL+a2Zfye9ejRk1Lkm5o9oMIWSJMFagfblSMBp4ygKKZeLpMZlm7h4pqQyfxa/2Xtj+g8cQHd3ldY+A1n7+/vz6evP8sZj99Des51b772ScWP6EUiXL/ra6x+z03aHcPBF91JbOIu7zjuO2+84hyWWHIbyaKiQkq5a6kIYxtBeCrBAoo33JQ3lSFEuOJQ4UB5FNg7EktIJXSVO88+ttXTUUyLl0NkoUESBCxOFShJ6wkGoJALnTzrOhaCeGrfdfx4It78QzVREX/tICtAmdysEWSK5zYkZM6bP5ZDDzkpmzJh30vTp03/9fzXJ+/+scAoheg8dNvTp/kP6j5v0ySRx6R0Xg5J0dleR1lIul+js7Obe2x7mkTv/SOIToLc46GRW33KXRbWkhTiOPRi0qGBlP9aahubMQykZb5aczG5Mw1TNjpGlc+k0dWZ0mrr9bCacAWEUOlRXqjxEY61g/qwpLJw1nR4DhtFr4BCMsTx720U8dculAGy907aMXWYUH300mY6OLubN+ZyP3/8Qk6YsPXY0++y3Hdttv5FHSwXVesr87phAWUIVIAWEgaJcCLBW0F2LcyS5XFCOQWSa+LXejDWeMCGFIDUWrS3dcUqgnKZNUkNbMaAYKs8CEiTaEiiRl1sRNBa0LD3cGIv1sVHvOLhnbptLqzSxpiwIK5riXm5xq9ViTjz5YvPqa+8+OGnS5B2ttfWveAp+7eP/pHCGYbjcgEEDnt7r0D16r7HeahhjmD13IVddeBuvP/00QkB77750LVxAa3sP5s9xwfqfXvcE7X0GsQilzhqs1qSpJx/4uj7Ch06yIRskn0WEVmb0HRyzx+gMvcWjs5ksewDJC6/2WRsN9pGkEIWEhYhAqlyTLY66vnz/TTx8+ZkARMUiY8dvQrVzASOWXZFUC+ZOfJuJ7/yZhx+5lNGjBqGtYEElphobrDVo7UzyQiippZZSKKmnzpw1xlCKAsKgAfQkiaZcUCTGEkpFFAjnLhhLagyRN2GVEBiccDlr1Am0tc5MlVLkwqyN8Vxckfvk1jg02VpnojphzJ54Y3ibxa8T3t2wfotoaFuExQq47vo/cvW1d0+cMmXqOtbaRYv+fsPH/znh7Nmz5w/69Otz3U9OO7Q0fNQwKt11LjjzEl5+qsH7jIoltj7sVJ679WJGjhzEy8+9AsA+v7qR4cuuRk7P9JNFCKjHCUmSeuqe25wJp8zCE2SfNwQzD5X455iZeO7wmZCa/IDGa2dtjNfGAoSbvIVCRFQskOVpZgW6GoguJPUaU95/nZ59+7Fw5hSiUpk7zz2ehXNnM3jYELbY6jtst916jF16lBMGH6vtqqcoCXFi6a4nlMLA1RIKJHGqSVLtrwtC5dg5SjjVZKxkfiXBaIvAIqVFKom2UI4ULYWQcuR8TGMbxPYMbdUGQilyrerJVg0WFo31zVqnObOYs0A4zeieaPYGFgHqrLZI4XSv8H6qMdqFvaTk2edf54STL+iYOnX6d9M0feWfnXv/7vF/RjiFEHLIkCHnDR4x6MdH/PzQoNxa5o2X3+UXR56W71Nq68Fup13J23+6nQ9eepyfHHcQG2+6Pt/fbB/mzJzF8X/4M1GxjPS+SRZSTJKUeq3uQBrbEK5m1o7Iwip+dW4mKCgpsRgHbhinjU2TkJLxa3N0ljxDxW3yx1Gu8JY2BuF5qlIFSBW49DF/JRPfeIY7fvkTVlp1earVKlMnf8aZZ/+U731vDUAQSOG0Lu6YxlqqsQN8umoaJSyxFhQDQSF0C0Mm/kmqPcfVmaCthQZpYl5nTJKmRIGkvRwhhGB+V0xXLaVHS4EeLSEtkUL5B2uszZ+Vtf55+kehPP84q9GbvY9m68IYZ+4qq7xFY/46jisAL7zCijzNzhiNMQYVBhhrmDL5Mw75yS/rs2Z/fsSsWbMu/+pn6Fc//k8IpxBCDhk69A+rr7fatjsf+AOpteHUw3/Bx+9+SBhFFFtaWfl7P2D9nQ/ijYdv54HLf0m5pYWefXozfcpUevTtz3o7/5hVN/uBO6DNMkicIKT1utOaWi+al5iZrWTcgCaN2WT2Npu8xlhf1sPmWjLXpIsRFHLCbcY+QuQpZa5Il3GZG0phhUQFAVIGzJv2Kdcdvwet7W1sutXGHHfUD+jRGvoiYs6sXFBNaCm4MEWiLUmaoo2ls2YoBJI0TSmEkiCI0EbQXUs8S8rdey1OiLWmZ1kxrG8LSik6q24fKaEYuX5JBsmcBVUWdNfR1jK8bwu9vOCm2hHdVYaY5arO3XOgHFI8b34XUz+bxzJjhzlZM3gf3eeqohBWYFKLEC4QnXGbs/fprtu5B1hXKUKnCUIJpFJondLdVeWnx5+r331vwpnTpk37+Vc1P7+u8Y0XTiGEHDZ82F3jN1xzm+/vu4MIpGLH9X4IwGEnHsLVF9yAikoMG7s8n7z+Ij++/GGmvvcaf/jVUfkxVvnejmx16GlkpTv820enKWkcYzwKmfswTYAPWcDbC3OzxswC5dmKb8Cv2l57ah+eaDKThXR+aR4nxWsIT67PUFwnoAIHTpr87zAMCKMIhGTWpI948sbzaQvr/OHW0ykUw5zjmkVak9T5mVJYuhNJmhikkGgtSOoJ2oAwiq5aN1Ip4iRGo0lMQqAkg/u20FoqoIQkDJxWDqRj8STaMq9Lk6QpLQXJ7I46tThhaO8yrcUIsCghmDdvPjfe8jhLL7cMm31nOZR0VMXrb3qEBx9+kVeedxX7/vzmnQzs39PHLa3zBrQ3Yq2zPN55fyIHHHYG5faejBgxlAvPPJzWUgQ4m1r6xQzjTFudJnn4KolTklTz0xN+rd95b8JZU6ZMOeVrmrZfyfhGC6cQQg4fMfzu8RuusfUP9ttRtBYLGGt54/V3Gb3EcF545g0uOv23+f5bHHwyq2+xKwiYO/VTPvvobXQas9J3d3RWpda+KJYmiV1sk6xwcubfkHFZm4QQPME9E073BQfUZgitj3x6EoIx3l/T2mtJV7oS4cI3Lri+aK5nFrzPtLXJNZm/QFxgPlCSIAxRUYQ1hrt/fRRDeiuuufxoEm2pp444HkiJFJ6pBMxZWMPaAJsoFnYsRIuYYhgSJ5bEpJQLCnTMhxNnM2jYAFpaCvRqLVCMHKMplIJSpChHgYt1poap82rU6wk9W4pYAXGSUqkn9G+N6NVaRAi44ur7ue6GB5k3ZzbFYoFBg/qx1Wbjufq6P7LG9j9i4lsv8+HLTzB56qOESmB97NNqXMkk495NnKSsvs4ujNtwO4avsDZvP3kfhfoc7rz2LAIZYI1LUrfWeKAOhFBo7TS+TlO0NRgLh/3sTP3OexN+OXnylJO//pn8z41vLPG9WTB/dNiuon97K8Zaps6Yy5/uf45dNtmL6VNm5vuvvuUurLr5zh5MsfQdNpoVN96WVb63EwJI4zpJXCdNNXGckmpywVTS5Rc66pxYRDClAKlkQzBlZtrSgP7JVWtunjbdR64VjXH9TQKV1QNyGlR6sCTPiZSNCggNo9rtay1o7Vo3mCRBCMHqW+/BpIlT6a5bumoplXpMdy2msxrT0R3TUUmo1HyMNYFqpZtySTN6aA9GDW1n9LAWxgxpg1oHO+9wOEfu/zN22XQv7rjlERZ2x1QTQ6Vu6KqnVBNN4vu5BFJQigK0gc87q8yd30l3tQ7GUI8TR26op9x158OstvkPOeam51li9U147613kcU2erQVeejyM/ngxcewxpAY4bNSBEopVBAglHuuFstDjzyDCkJW2XxXllhlHfoOGsqrL7zIDbc/hAwiwigiDAsEQQEhFDIIkEoRFCKCKCIII5RUBIHkd785SS237BLHjxgx/Bdf+2T+J8c3stGqEEKOGDn87o02XXvrH+6/o9BGMGthF08/+Q4X//yMfL+7briDNbfZndU234WeA4ei47oTBqWQKsDpNUsS19CpztFSYxoTPfMbnSnrhCYzXZ1Jm2k1FtGkUrpk6lzzegvEmWPaMZR0AwjKfEwl3XVpnaC1bqSPWbDWAVKpdlq0ELnMkCwm6r4vybi9SkrCQNI5dwb9Bvajs1ajveT8vcCzaBZ0p3RVqvRqLTm2j7VIaWhtLXHXfS8TlFpZb51l6d9epL1HG3G9zj6/uolrj92d+26/nwnvf8xZvz4K7UMV9SSlu+5SuQqBA18iZenXXkRIfHqY9RRHyR/ve4L33v6Q9fZbCYRgnR33p2f/QZz/60sZtdSSRKW59Og7kLmfTWKVFX/AqmuvxtEn7sfw/r1pKypHgreuMsWgQf1Ik5i21hJYeOKWSwDYYpP1ESiUDFCBBBm4EJl12lKpAG0ThNQEKsBYjYoEF593sjrwsFOPHzFiON9EDfqNE04hhBw5cvjdm2257tZ7HvRDMacz5uUX3+T6S27hswmNBPgtDzmVFTfexgMHGh0nWIFP+M0IdhadxL5blwWtF/EB/Y4uyC484b3Jl/ShyoZvmQmmEHkGBQiwxpHe/XedSZaS5XRmJPgwDJFSkeiU1E8a4fmpOjX5QhEoRblUJAzgjcfuYuGsKfQaOJwwilBBRFu/IRRLJbq0QYYRH73yBCsuPQQpJcUwoBxBPTF01lKs1kihiCt1Si1l6mhefel9br7+ThZ2VjHGcs4pczn918cxfo1l2PC76/LIJSey5ynn02Poslx0wKZccuEw9tx7awotBRJtqdZT4thQDKXnBEeOyBAFKOWeu7EufW7FFZei/8ABDBy9DMZoWnv1Yfx2e9Nn6CgWzv6M7U/5AUFYoN69kCdvupgXn76frR56jK122YEjf7o3/Xu0UAxCpIxZbfVl2en7m3DF4dvzw5MuZKXv7sCbf7qLZ177iN22G+aBJunMfSRxpQMplV8QLUlaA0lOmywWAy6+4AR1xFFnfyMF9BvlczpTdthdW221/jYHHrG7mDKvk9/f8RR3XXcHWx58CoXWdv780G1suv8JROWWPPQhhPQrJURRQBAETb6fA0R0kmJsA2xpOqdPgHZlMzJiQLZf3t4gzxYh54DmJUdsQwtngJIxGqM1aZI0CaEDibLcUdP0vWIholQqoq0zpae8+yo3/PxAVltrNZZfcWmmTZlBmiSkScq0qTNIkpgoikgSzaAhAzj73ONobSl5E9lR5D76ZAonnXAREz74EBUEGJ3SZ8BAOhd2sNYO+7PapjvyyUsPM2/OLKb85Vmuvf5M2kuCl156ixN/di5bH3ISvcasxiNXnsnHrz3DSuPHs89+OzJ2qUFukRMCpaAQhiTa0BJJwjD0y6JLwn7soWe58tqH+cGp14BUfpFrhIWyBS0fFp654zJevf9mBgwZyve2+h5bbro2pUjQu1eZQCkefuBxLrzgRvb81S3Mnj6D235xMBtvuimH7Lczqy07irBYAqlIqp3U6wsphq1gIK3XiZMYIzWqCAhJai0LOlKOPvoM/f77n3yjQKJvjHAKIeTQoUP/sPnW62+398E7i2qiufWmB7n6/Cs57IqH6TlgqIfHU5dFkgWqPYBjPOskLES5ALk4mvEZJ41iXc2zQWaVCTKmj0f8s5StbJv0Pqe1Ok+SNj6SniG9wgu3O41pFH72pqkrHOb+dsIpsnunUHDkdyEVCMHkt17m1jMP5YmX73Q+c6IpBZbWQpj7wd2Ju6dyJOmODd2V2KGzRhJrzROPPMGjj73Bdw/5FcXWNmqVLhbM/IyeA4bS0t6Dd566h7t/ezKrbrQZ3ZU69YWzOePsoxi39CBeeultjjvyl6y88dasuuUeyLDIqw/ezuuP/J5evXuy/yG7sdb4cbSWI6f5tbM8aomhFArCQFIIFQ8/+DSXXH4fO59+PWGxpWnhc/svmPoJtVo3QVSk7/CxGShLmsR8+NLjPPf7y1lm3JIc87O9USJ0tYes5forbuThR5/lB8f+hqBtIG88dhevP3Qb5/zyVNZbZzVGDB0ISOqV+aRJN6EoeVBIkyQxIjKIYsCCDkO17gCj4477pX7vvQ/O/Oyzz74RYZZvjFk7bNjQSzbadJ3ttt5zOzF9XgcvPv8WV59/JX2GjKRHv8Gk3jxNPYunmf+aUeWM9zFQCqmUy9FMTS5EeZTRR9yzeWKbfVAsUnqBVMpB81Lm1E1rhYfmG4BQNpw567idWeXzNEmo1+Mclc3ipc3a21p8CpkLMWTg0RLLLksASDRRZGktRvQoFkmMRgpJLa47xDkV9G8tQVuBeqpJtGXqzM957tk3KLX3otDSigoCWtp70dLWM0v0IPYdzRbMm8sup17N64/cwQF7HMUW39+S3XfZmGtu+g1XXnIz5+27KcYYjr7xadbd6QA+evVpzjjxBI445kC22nJdrDX0bCmysFqnVovprgn6tBVoKxfYbtvv8MADz/LAb49mrR0PYeCYZQnCiClvv8Td5x9PoKBHz3YWLuhABgVW2XQnlllvS1p6DWC59bZgiVXX44rDt+fNtyaxzprLUgwFRlc5+PBdWGrsGH5z2o/ZdI+D+c5uhxIWSxx88OEAPPP0Q6yy4rJEpZ5gLLHuRlkX10UUSXUMWmNMTGdnF1FY4KSTjlM/P+W0EwcMGDD9m0BU+EZozj59+uy1ymrjrjrmjCOCi39zHX+8434ANt7rSNbYajcnZD5nLy8nsogWdOR15ROZpQdZtLGkSdr4DlklApkzTUSGrXrzSvpAtpQSFSiXlWL99/DpTX5ByPixxmeTNJc3yRoNpUm6yPVmxZ+11r6cSVOIJHAxukIUsWDmFG4/4yAeffwqFFAshBRCRSADklTTXY0R1gFEUrkqAQAPPv4Kp/38AubOnsPy62/Od/c7jkK5FWjiAFh3jU9ccxYvP/h7ho0cya5n3IiMinTMncUzt1/Khy8/QVuPnhzyk/1Yd82l+PmJ5/HmG++zzKqrs/4PD+LdP79O2PkJp560H3GSEEiXjF6p1Lj59sd4790J9O3Xlx2224BXXnydC89tVF5oaW1FKsUpvzyW9ddeCWnds/rg3Y+4485Hefapl+g7aAgA9WqVuF7jwkt/xdKjh1AIFYmJKRUDAgnvvf8pRx91NkOXXoH19z6J076/en6eO39/AxutswbCaKyuo9M6rhaTS3EjSDEyZfbcDkotJZK0jVnTZ/HTo46vv//Bx+v/p6l+/3HhDMNw5SWXHPXCBVf/ovjCC29x6tFnEURFjrjqTwSFoqOTaeNAF68hMwpcrnxsI4tBSIlSQR4OsRlTx2YFnZuEE5u3SMjim1IppFSEgSIIA6xXrw7gsb7oliBNk0XYQLZ54YC89Ig2rg+nUio3tcHmIJGxi1ICgyCkra3M20/dy+SX/8jlV56KwqCUcKa8tigBLYUQISQpLtNEGMub73zEoT8+jXV/+GNW2nh7iuUyoQKBoVpPSXTDv1NSct1xuzFmaA+qlSrto1Zl1e0OQABap0ip+PClx3jp3hvo0V7imOP25b7fP8DdfuEcN359pk/4kPMuOIkllhhKS1Hxm/Nu4pbr7gBgxLhVGTB8NO889wiVzkb7k4uu+jUrjRuFEIJSsUAoJaEURGGI9YSLemJ4/Y33kUJSLhXp168vrS2tPoxlkaTIMMISYOmmoDQ77XQ0b7/zMYde9gAXH7QlAK898wAjhw9x6DYGncR5Sp6WoIlJTYwMAiCkEgdMnTSbWsccDj361IUTJ00ea61txOv+zeM/KpxCiH7DRwz78IwLjut1yXk38PrLf2GnEy5i+LhVSZMUrXWeBC282ZkLZiacNqPF2fwjV0rSAUMZeAM+V3Jxdo8XjCyOKZXKS4l4zxJrfT0g370LY0i8RtTGIbXCC1xmchvP9LHG+MC4zdk/2eKgfI4jGflASMrlItXO+dxwwp4cf9KP2GTjNSiGiiAQaA1JYlBCEklBKBWp0VTrKfUkYa8f/YJ+y23AOtvuiQoUBaXpG3UyrSMi1gJNkPt8gZS8+/S9zH3vGVpKgs+THmy4z/G+LKfOgSod13n+rqt568n7MHGFHn37Iwtt9Bk8nKHLrMIjV53Nby77NSstO5TXXnyVI49wHbm/f9SvWHmjLXngdz/n1Ufv5ZGnbqZPr1akCFBCUAgkga8Sn6b+HStH1IgTR5yvxwmV7hqp1hSKJUeol5ZARSRxjLBQSer07BUwf+4C1l53N46+4Sk65s/jnt/8jO122JZTfrwLUejembDGzwE3UVKRIAJBbAXz5hu6u2OsVbSoKpMmTuTIE86eOGnylGX+U+lm/zESghAiHDZ86LP7HrFHrz23+zEvPfMyW/z4VIaPW40kSUlTjdXa+YCiQXXL0dEs7WvRrD8c2OI0V5IkpL7MpG2q6eMKNvrvGefjCSlys1gp5TVmBvZoxxEVBtI6wqaNPE5cGCYLu1gf03Qa2eYCnJnA2VporUV537hRhc+Zu68+/HtWXXMlNv3eeEqFwPFrrQuTFAsBVlqfB+lyPotBSDGM+HzOLPr0H0AUSoRN6KhoJn4eUNcBZjF4wVhLv9HjeP7Jp7n/rodo6zeEKFSEoSIMApQnXoTFEhvudhhLrrYBqTZM+3QC393rJ2x+yOmsuPH2bH7gyZx05Gm8/u5nrLHumoxddikA1txoQ7S2LL/R9rT16sOTz75LIQgJBARi0SyUKAgoqohQBkgrEB6Bl9Y982JRIUjApmRtZ1QQIVG0qIhKl2XAgMGsvOryTP/gzwwePZYdfnYut954I4edfB5BsYzBVW1AKnLOlJaQCApKksQ1avUOZCComDLLrziOg/bZedSoUSP/IJpRrH/j+I8BQsOHD7/pe9tvtNSpR7qV9nv7HcvS4zchrsd+IhsvRM0I6KLP6K/FsoF+Wg/1Z6ESv0OOugrZOFYjhtnEELJOoEQu/sYRB6wEa9wKLgXWZInHJvdLXRzUaQMVOOEzFi+w7rzZeeJUN9LIvGYtlFt58bFX+OTjaawwbjja+GroJs0LiaVYtE2pxymRsLzx9qfMnbsAZER3te4XCRAyImsLIZqMJGMtfYaM5sDz76ClRy/K7T2RUuVV2V9/4EYiaqAKzJ8zk1kT3qLa3c2qG21G/9HjELjY7LLrbka5pcTJPz2Z3159ASeedTIn/vREfr3fNhx87vUMHbsSY1Zcnfc+nEpdCyKhkcLVAarWEh8nhUgqIhVSqSdobanUKhgfC0YYhLQUi67UZpLGSAokKDAJhaBAvWJZfZUVePFPv2eJNTZhwKilOfSyB7nkkK157IXt2HCNpahWKwjjhNHiTHttLCKFfj2LKCpYWaejFjBjnmCHHbbmrfc+2qJej48BzvlX5/yXHf8RzTlw4MDDllpuzPertUQADF92VVb87o7EsdN0NmuHYLJiWX6la9aauW3rhvMZGyT1LPs+DBwNTCpJECgH8gRZGpb7HYQhQRgQhK7+jdUpOm1kaRjjzElNhLO9AgKhETbx5rZDaSWedqaUR3MbWSyFKCIKI8IwpFwu0dbakmv4DKwKlCIqRKy93V70HzqciROnEqeC7pqmWq+RpimBkLREBSIVuiyNJOWaWx/hqGN/zWpb7MaIFdfCWIEVATIIcx9ZCEXWZBersTpBSsHAkUuQVDv5+KVHWTBnOtYDJvdd8RvuvfZyOia+xkqji0yf+DEjll2Z9XY/xvlofrEUUjFq5fVZY8tduPS3VzFmeC+mTJzCnJkzeeelZwmlZdXNd+WpRx7j5NOvwggX5tHaYIBKHFOp16klseuzIgPmd3RSjbvQUmNFilKWltaIQjGkpSWiEFqStEo9rpFYQxA4X/2g/XclMlXuPONHTH7tUcJimY33PoojjjyeBZUqMlJUanUWVitoobHKIAML2hXC7t3eTs9WSWvZIlTA3E7L6accK4cOHnBGqVT6zr9bTv7tPmehUFhniaXHPH7aBccXdtpoT4KoyE+uf9ahl2lTdX3hQx6Z3rKNsEWmy7JMJBejFPm+zuIUeYcvB/J4Op6vz5MRBpQvOuV6lRjvb1lv+IoGCptbNg55FT7Ibhp52+46TUYba8RWpXQLgpKCMAopFgskcUItTjJbmELkKGfaurIlp2+/Mv2GDKNerRJGBQ49Yk8O2HNTJk+bx/2P/plUO59s0oTJPPfM82y01zEsudq6qLCIrsxn5sdvUVkwi9qC2ZjqQirdXYiwxOezZ/OXF5/FYhk6fDhhsci8WTNZaqmRvPziGwCccMcrTPvwbd568l4+ePkJ+g0cyNQJn7DHaZczbNwaOVHfGlc1UEpJWq9wzXF7sPKaq/PIHbeyxmbfZ7MDT6IQhUhh6Ozo4trj92alNVZj7NIjWHeNsYzo15PbH3wRFUVsvt6KDOrTRq2WcN4Vd7DCSsvQu1cv1llpJOVi2KBZSgcaGWupVhLiWKO1orVUBgIWzO/gngcf5qZb76WtzwBGrDCe+6++kJ8eexj77b49hdSRSQyGoCBoKRRQgV+8ZEiiU2pxnWmzEip1KEYhut7Bvvv/uGvCp58u9++s6vdvFU4hxJDhI4a9d9bFJ7Tfe8+LvPHGBLY/5rek2rFpslAF+Mx24XxCLL7Km8g1ZhQobP6Z9yMz8jnkGkupINevjukjGzQ8P7GkdPxNrY0rSWkWLXWZDesZR9abfi6c4qTTGGfWGpNlolhvRjttGgQBURgSRoHLLaxUkaLRbsHohCTRqCAkDAOmvv86WEtLzz5Uu7u477yj2W3fXbjhipsYOnZFVBgBEERF1t/5IHr0H8aCzz7l6Zt+w8R332T5FcYxaHA/Bg/sQ8/2VorFAgs7umlvb2OnHTah3FLkhZfexFoYv+byGJ3wnY32JSbigIv+6PMiLVqnTP/gdf78yJ1ssMdPKbf1whEwnFURBAolFRbLvM8m8tTNF/HBS48zbOwK7POrWwCHtEshWTBnBk/fciFptYsJb7/BmKXG8PbrbzBmpfHMnTqB72y8DtOnzWDy1FksmDOLuFblow8fpbUUIpEo6X67xHaDsSlWCCrVlK7uOi2lVtLY5Xx2V7q576EnePfDSQwbOZjtt9+UVhtSVs6fRgq0SWhtLVMsBxg0kggrJNVqzOz5dWbM7gIpaGvryZSJH3PCiSdPmzplytL/rrq4/zbhFEKIocOGvnnkyQeuIKJWTjridHY/6ybK7b29b4f39RwYQEYstw451E10OSkcTS8DUtx33f+yDl/Sa8yMQZTHNAVeezrVKaRE2EZ3L4SkufVfczg1TZLc/82Anyy0k5EMMu3u4p2OfRRGAVEYIZUgTTX1euzr6DQRETw3NzOLw0LU1KMTTtlyGQD6DBrKZnsfjsQwZIlxpCLgqZsv5u3nH6dnzx4ccMCu7LvnNkRhiEhd/8sMAnNURoO2OtfiGeSdJAmvvPY2+xx0AgddfC+lXgMgS/wW7kEkqUtIb168nFXgkp+NtxjSehWd1rn88B3YaMc9KLe2EpXbGbXaRoShi9Mu/HwBE998lpHLrU7PwaOZ/tHbfPbeS1Q/n8GLj/yRTX90HH/50x84eL+t2XfXLYhUCSFCl5+JQaC8p2PQNqVWr9NdjTEmoBQWPV85pl6tEaeGRENrGFAsFFxnOGuJkzoWQxQJym0R2gRUqpbuakytnjBzbgfaJBSLLbT36MuDD9zPjddfd8ukSZN2+/okpTH+bYBQr169Dlh97ZWXa+nRi6MPPpXv7n8CpbbepJkpK1yaVNaDhCal1fDLMuJ5ZpIq1/DHk9QdYQCfKe+HF8Zm8rrySGqqNZldajMhpnE8481qJSXWl70wed9O00CP898N8zdbHFSgiKIIIYWrRpBqVCCRRrqkY+vQ3jR1GS7uvK6urRAuleytJ+5mybFLctvNZ3HuhX9gxmsPI7A8fuOFdHdV2Hn3H3DTRX+kT3sLRnvzXRsCETqmlNa+tV6AEJZ6JSHJSRcOiApUxAZrruZ84epCbM8BOcBmvO8chCFGOi2PtxqyYmZCCpT0dYnCkMqCeVQ6F3L/tRfnc2DJlcez80kXY0RIz34DWXXzXTxwJhi05Diu/tmujfnSfxBj19mMRx5/lX132wohIrQWQIDEgHB+obWuXm2p4DCJ7mpKLal5bS6wRtC3vQUZlBDWYmVAa8++pEkd27kQgaVe66ZYEFjp0vGS2JIkloKyaJUQhjFdXQvYcqtteOzRR3cKguDif0frh3+L5hRCDBw5asQn+/5035ZfnXQ+G+xxJEuusXGuMRvFsjw1jqxtnjOrsuB9NpkEoIIgD/gjssJRDv5vdP7Kji0bvTO9FshI5xnZHRqhUyf8HmFVCqsd00d7bmZGIHDxyUXu0/2WAiWcCR0ELu1J++5lboHReTHlbFiEN+UtQRBQKkaEYcCsie9zzfH7cudd57PcMqPQqaZe0RREgUKh6BYnCybVCITLV1QhVhtSrdFk6WoQFgqoKAShsL5dnysF4n5+dvKveOGND9n5tGsQQYFFADe/IGqdIvxCmVUrNNaBWVJJFvHNhaDSsYBJf3mBYrlMz0Ej6dV/MFZkaXKZYLvF888P3srrj9zO8ut8l9W3P4BqVweXHLw5773xMG2tPRFCusLU1mBMAqrZ108xJiVOLR2dFVJtkcKFZqSB1tZWpJBE5XaU71NT6+pAYIjrNaQwWCnpqqbEKVTrMfVajSjSBEVNd6KoV4ssXNjNkUccOnvKlCnDv+74579Fcw4fMfzuDTbboOXsE89jy0PPYNDSK6PTtBH4l54l4zWP0RoplRegRdgGXsM581BkpqoEK1xAX3kaW1YMyvmdIqfMWWvyYzi7qEE1cmexucBlBaWTVDskAl+NXeCZQZlAN9cTcsihVJJQ+XvAkSKyJkkmdXHK5uyMDM4KpKKtpcS8qR/x4t1X894rz3LmGT9huaWXRBCB0SRxjUKhAIkgssr56iZjN0mSRFOr19FGUyoVKRZdg1wrBCIIUcUyMvAhFuvyPk1aZeiwocy89yE+fe4+xmy4Ux6LdM8z4wYLhFLeUHatEIQxgFu4pI8TS+GIF+X2Xiyz7hbkQJoXykUIGbiFbfUtd2P81rs510BJ5s+YzIDBQ6h0VQlkkbZyCeHjxQDWZcyDChBCYYgphCGFKMDU6qQ6QaCQVrGgoxtlNcVaTBAqVFjwzaO0s1LCgDCIsKZOEiekcewWDRmSxp0M6F1krkqQqifb77Bjv3vuvvN84JCvWlaax9cunO3t7T9ccbUV1rjrtvvZ9shz6DdqnNOYmeBIxyd1yGbDhNW+QFMumpnppDL/JqstK3JQR2WsHiGQMvBgjBP6XFPigI58smXMBBoV6FyluyyrxIKQvhC08bVVbV6jRzaR2DPTL/BlJxGCSFqiUNFZqXsL2jozUUrf3dlNUK01UkraygWevvUiXn34Dg7cb0duuuB2ioUS9UqClZJ6R81XRA+RWKfFfHC9Xk/c4oJFKUGxVPZJ4RZjNUL5GK9SgM839ZpLKs2xh+/DhmuswA/2OYLBT93PmFXXo9DSSu8BQ+i35MrIQksThVL45ABBiLM20jSlY/qnLJj6IX0GDqJ14Bhs1ObfibvXLDsH/1meFeTdD2stVhiEFeg0QUpJoSwI0aS1OqoQuflgPPigfdX3QCGNu/+2llbSOEFYTS1OgQhF4CoSJjHUDFEh9fm1DjRMUku93kkpkNStRljtWhpaSaRKKC3o11MwPe1mm+23E08+8dh+YRhe/nX2B/1azVohRO+hw4ZM6qrGbevufChLrPFd0iQByAWqEAXoJqE0Hrm1thEyIcNqmgQxZ+HkIErg/BAhcoFRvqS/9sfLhDCrStBszrpziLzuqQM7JFEYUKvXqdVc+Y0sD9MNuQgVUElFEDleb2ZKFgMBJqY7BinDpnvHI7WOUmesoVyMeOfp+3nprit56M7f0qdHL8dswZLqgEq3Ju2uUi4VkAhCCWEQorVrfCuEywsNQrUIySIr/SEDiSxEyEJxke3WpG4nDWmcEtdrPPPia9z/6DN0dXczdepnvPveRyy7yhqM335fei+xKkLYnOQhhEAJeOH2S3jhvptYY/WVmTljJp9MmMRW+xzGUutsQbG9j+uv4mPHuU+ulMs/zf38RqG1mZ++zwO/PYbnHruKUEfEXTEtLS0UCgWM1VhpnTEjA1ABBFBPFiCMI4fUa3XqSY1abKhUoaACWspFTArlcok4jl2oxLtN3ZUKUeDSDhd2diMk1GJDW1sr1ern9O5XIrYx02YJPv14Fj8/6cTPpkyZMtJ+TR22v1bNOWLE8Ju1VG0rbLw1y6y7BdVK1eE1nlvqQBLPQfXop81obs0HEiJTTs4sNY1ylFnxYm/wNmlTAdaQel8vZxp55pDNtG8zpU4YME6AA1+BvV6vU6tW84p5TV5YbuJm57TCgSQmq1guBVUD1qq8jpAUgjB0/N0kTT05IiD0oYtX772OXxx/CH3b+jqhCkK6u1MWzu/GVLvo1VaioFwZEhBo48CelnLBA2IOQRZhlJscIjG5CwBgdUzO7bBg09RrUIWSUCwU2HTDdfjuBms5hDfVdHR3c9+Dj/OLM45gv9/eTbFnPwLpXRFg/uwZPH3ndbz2zN0MGDQQGQS8+cbb/OrcS/n1ledTbimz8Q6709pvKKJQZvCya6CiNkdNlJIZEz+ka+5U+g0ZQcuAURgEpR69mTd7FtYEBMUyYVBCWjxirLFSIAKFlRJpDcIGREEbSdKNlIpyS4nIKNqFpVo1zJ7bRZgWaCuXHfJsrWslkaQ+1BWRJDGt7UWGtLeRpjGVah1tJJIC0ga0lRUjh8YY3Z/Nt9hy8AP3//FU4KSvXnq+RuEsFoubDx894ntJ0M5a2+9HvR774P2ipHNrtTdndZ49Ak0Tyb/8LOzhhNGJmxTkPmtGIM+Ob41LM/OGKib3WS1k6WeLLwI2U65uFa/Xq9RqiWfXNLcI9Iho5rvmC4f1JrvMPwPrKyjInKUUKFcVIQgy9o7TGJNefYxSJPjehuu55OlqjVrXghy0KYWSQhg4c8w4hk0YhhTLrgwlCJChR729RWCMK5IFoBQiCBwvLEsEz8u2WLAClHAWQt6JWmADRe9ePdln952YPnMOlx6yJUuNW55lV1+XoSt9h/KgUS4tTSpmd8QMGeL82JVXXI5br/gtKMH7H0/g3IuuZuG8icyeM4/bzjmWJcatyPLrfJdpUybx1tMPMHbsKG585U2SOGHL3fajUChQiEIKURGLq02Ekg6U8hXe84XbP3MlImQxwpqUJK5gjUAFIa2tECearq4athtKhTBnaKXa1eAMPZqdakMxCEFrerQUqaVQq0UsnF+nT6FMKdIMHmTYefedxNNPP3WUEOIGa+1HX6H4AF+TcAoh2gYMHHDr1CnT5T7nnJsTv4EGkio8/zTz4Xz8LCMRmEwo8VoyJxe4FT5PiBYNAXGHtXmr96wqXkaxy8xVt42cnZMtAZlpJf22OElzmDJbJBqt2Gmqqpn5nSK/5lyDK0nohTDvJ4mveC4kji7nuLldnQsZOmwYc+Z9TppAmhhCqylFIUmiaSmWKEQFJC4vVAmBjAKQNiuA5M9uPWHKOoHMmgAphVCekO92cz+e6IFUCBU4szDjFRvfWsGHt0469lB+/KPdeemV13nosWe59oTr6Nl/EMtusLVDuYVFBKETdGMQEaAk45ZblmsuOcc9t6hIpRrz6KOPc899DzG4VOKSp+9gyIAS7374IZddcidDhpSYOWsu9/z+GoKgFZ0k7mKTxC0wIlvIF10g8Yu+jAoIGaKSBGEN7378MQMG9afcYqlWa6TdKS2lkisLE0iq9Zi2lpBQBKRxQhL7AtoYlNZIpUjqgmp3TKld0Ls9pKtS4SdH/aR41uln3i+EGGvzPh9fzfhafM4BAwfeNW/u3O3X3HYv1t7xQLQ2xHFMRizPJi40BCkL6DcetBcFIfPKd3gAQSqnich8UMEijBZrMyHy5/AzMRdMk6lIcuF0WseZnoHvxFypVEm0z7tsaq/QSDvLUFqZLzi5D+q1fJYWZnweIUAYhQRKkmqNkEHe7u7jZ+7jk2fu5Prf/Zrujk6sMZSKBQKfxlbw3xNKYa3GpMYBIQWFUI6Cltn7wgpIGj1aUA1QxoHU7r5NEoMFFRWRoStWTZJi4gRbr2eOMSjpfrwpLIQDYaxS3P/g49x0y93succP2XrLjUAopAyxJsXq1H1HBj4Wk2KFRKcJJDVkoJChN9HTlDSpImzAJdfcyb777U57qUBSrVDtcmhrFAToOEYWFEFUwBiLUBKhwpz9lS28QikQkltvu4cfHXwkACutMZ4/XH82tWqKFIpAhBQKBSrVmDRJ6VEuYTHIKKKlXEKYlDiuEhvFvHmzKbeEtPRxPWas0EyZWeXcs2+0Tzz2xE9mz5594VcpR1858V0IMaxSrW6jtWbVLXZt+GBNAXrRJCha60WSlDMWUEMPecFp1pDuRHmB54yOl3FiEdlLys6R5Ybq3FxzajRrmEv+Qq11zXS7uivOH7aLC6X8u4IZBIpiFOakCG3xnapdeKZHwWmVMAiJfEev6X95lnsuPYu9f7AdXQsXUohCevXqQWtrCy0tZcrlElGh4BOD/R0InI+eGhfWEAJhJdIIRApWW0ysQRtEYrD1GiaJsdqA9vmtQYgqtrjQSmqhFnsf1E1ukdnHFtflSLvkdQvoJEUA2269OXfcegXbbPU9QIFJMTp27kAQ+kXPo+AqICNrBFEBGQZYbTBJ6vxgJTFWc9JJp7PjD/enXq3zu8uuY5d9j8hDUWmthqnWsXECaQJJjKl2YSrdmHrNLQD+GqxOGDigLwCHXHwv87tjTjv/FlpaIsCQ6Jh6vUYYSKpxzJz58511Y6C7u0K1Vqejq06hWKBUbqNYbMEkEqEjZBoxpF+RfQ/cThSLxdOFEMWvUpa+cuEcPmLkxV2dnWrFjbYlKJTd5NRpUyzQ5ra+40c2EpSbtXieddLkgzZAjexYXtiFb4LrkcBmDZlVXm+ET0wulHkc1S8aGf0uqxObB9SbjQvR9HnDscz9TyklSirKoSYKoL0lohg4X1sFij5lCbruJ76gXu3igXOP5M7zT+Tyc09ng/Gr0bO9nV69etLS2kJUCJFKOHNUOp/Q2BSsQYYKGQWOWGCFE0orMXWNSTQm9Qufr08EyoUdAKEUUkUoWUBoi4ljdL2GrtXQSYLBoD1SbLG+Wr5GJyn1riomTt2RfIMhp4519gE2jTFpLXsy7nPP7JFKoMIQWWhBhi2IsARCISwEqkih1MqZpx3HC888wx1/fJLf33kfTz/7PJVKldQaVKnkhFwbhJJYr9WtSbFJjKnXfCE1l4u7wbqrscVmmzDtpfvY6bgLuO+u+/jDQy8jhCGOKyRJlXqtgiSlu9JFR3cngXKhqVo9JlCKro4ut9goRSALCBsg0gKBLbDMEn3ZcttN2vv37//Tr0aK/Jz6Ks1aIcTIvgMGfjx31szgkEsfotDSM9da0pt5GQjjkNnU6cUMmBFNSK7Ij7kIYT37twtHuOrd1uicgdOsBfOgfyagNIpsNZelzAkJNLI38cfI2UrZQiBpWmgEWRpWc5gnDCVKGAoKWmWVmlGgCqSEtFCjKgp8+vrzfPzqk0z4yyustery/OqkoygWCq48ShR6LSM8XSmrm2QckJPxjKVAhhEicBxiKQKop6T1uo8Tp+5ZKhcDVoUIGYXeOrFgdF5+BWMgdRk1Ls5jfCzZCTFSeuqhSziQYYj1uagyDFGlItLHmTEGG9edBVQoIqTC4qmSGXDg48fC+qqE2mlro2NUoQWsZNDQ5ejq7mbU6NFM/PRTfnXW6ey29YZExQJYBwZGrUVsILH1BNI0ixs5bR0ICCUYy5w5C1l7k53Y5bhzqdgSt5x2ABdfdDZrr7QkoQpJ4oRq1SHzbS0tRGGLu04paO/Zjk3rGBQLOz+nVz+FtAWEUViZYIM6cxZ0sPn3ftw5deq0gV8VMf4r1ZzDR4y8ZO6smcFy621Oqb23I0I3lQbJ8jBdezadc1SzTJQcTfWTR9BEyRNZF+lFs06MF8ysno+bwzY3p3VeJ7apXqzfF5tp8UbzIdukxW0O8DQ0dLOuzD9HeE5wRkO0LiBfTZneHVA3imotppZoFpgSr957HQ9fcSZbrLkUl/z6ZM4//TjaWsuu6kEgnZYMFLJYhCBwPh8uZ5SsB7QlRyiF8uajX/CE8BBXIAlKISoMEGGALETuJhKNTRKMSTAmBgxo48zZNMUmCT74DDp2oRerHQAmLEGxgApC0lpM57z5zJ8xi3pnlycXCBDO13PcPu9zGoNNY2wcO3sc4UxPm2KscXmnKkAFRaxx7JyP/vI0ABM//ZSlV1uPs869mOvveZyO+V1Uu6ue8VQA4Xm0VqGN16TCYhMNiQYB/Qf25eILzuDms45E1OazzeFnctjhJ7DbgScze34nKggoFAqEhQJhIaKlrYxUbhGpdFWQKnSsp1RQr3q0WArnThhJ315t7LbHFq2DBw865quSp69MOIUQS9TT9LsAa3//R3nqVI6oeYGzTVX0oAFM2CYBdQekscp6oci0XrOPqX1lPvyEdIW2dC6QjSCmF7hMCGlcR5aDmZe5zH1ed01C2FyTL3bXbrHwdW2VbHSETm2AUBEyKFI1ESZsoVgsMnvC2zx15w08ft9NHLzfboxfbRWEEMT1mCSrApE1QarXsdU6th5jE+PNVQtWIWwWK5aZ+sda7dBSJQgKAWGpgApDVODyWU29TtpdoV6tkdRj0lqCrmtIHVorVAAqRIYFpFBIL0QIX7un4JLWkW6hlcp14I7jmLhScewbz7oSSoJS+WLrLB8vsJnWzIEpiQgCJ6BBiJQhadJNuaXE8w/fQbFYZIcDj2bnUy7ll7+5gCf+/A71OKW74sxX4Rse6dSS1DVpbNyCJqUDxRI3rzbfdENuvO4i7vj1sUS6m8OveYLisOXYasf9ue+JV7jlrof4zha7cPZlt5HqlNb2dorFInG9TmdnN3Eco42l2mX8nPc4h0ue4sD9txOFQni0EKL1q5CpryyUMmLU6MsnT/w0GDxmLG19B/kgr+ei0jAPTWaiZVA/OQDoQD3RaFrrgvYZRW7RZrbOXHWMExcvpcHeWQTE8UnaTb4o/nzOVBQ5wOToYw1y/KISmV1hk+4UmQZvbqbrdBsuYoGSglIhRCnJs7dfwrN3Xc/vLjyHIcOHoRNXO7VYLLjr1xqhAmQUOZPWkyRMnGKNIE0tQqaOHqhU41J8S0MChTDGkeCFM+ccGitcBg6uNXyiNTZNUcIVtg6DRnfuIFQuHJUmCKVcVQUtEd48xLpwBmFI0Fqk1bYhOyVRuegQWTQWJxgiy4b3EQYHXmkIHT3P8ak1oBtMHwSgUCIgjhey5NJjOP4nB3Lb+cez8y9v5vvHns9Rx/+Uu2++ikE9WyDVRIXI3as2hIUIIUJ0YpChR7CtxdbqkBrWXWtVHvzjTWy93Z78sO9gxm+5C4PGLMu5F13GwtkzWH2znbjm8sv53nqrs+JSYxBSUY9jAu1IQFK69oJau4JkNpsXRlAslNhv321aL7/i7hOB4/9VmfpKfE4hxNiho5d8a9qnH4f7nnMTbQNGuVQwm2WTNGKNjXbr5EBNHl8UwscvMzM2m/DOTFEePVTSIXomczCF8BqwIZTNhaczwc20KkLkwpn5j1l9H9tkNufhHn8OZ1JnJm7m+wrfnUwShL55kj+vlIJyMUIqxZtP/JEXf38pD9x5DSOHDwWjSWs10nrsUq2U9CEeiWwpIqLAFT2uVTGJBiNI4xh8orcMBIQKEQV5KAHA1r2m1f66PdG/VnchDBm6XFFdT3zoIvUItlM0hYIr1yKtQRvherpEiqBcRIWRe2Ze85kkRSfOSgnaWlGFyC9qgdeSXqUJBTol7uhAao0sFhDlstf2sbdmpANcZECaJIRREaNrxNUKuhaz424H0zJoNOP3OYXn77qGCc8/yC2XnUOPchGJbzBsNG2tLQTFAkJAWAzc+TOihTYQhciWNo494UwuueRqAI695QUKbb0wOuWGE/Zmynt/ZsjI0ez6g+3ZZtMNGTawH2EUYY2mXouJjaatvUhUSADhqIQixeJyRDfe5MDuCRMmD7bWNmqC/hPjKzFrh48cddm0Tz8OAXoPWaIp+wMyHH4RdDTTQJmQNB3LobepM9Eg16BKBR4AUnnFdbcgN6rt5b5iRi6wxnN1syThJtAoQ6Dwx6CJ1+n/3eiJ0gjsuEsSi/w0tG4jH1UpRckLZjU2vPvUPZx84pGMHDnMPYskxqYapUJkGCKKRWSxiAykMzNTD5J4f1snsXtSwqJ9KZW8qoN09ZAaaJjrpqYT37ZCSIJC5MzTMEAFTpBLPVoptJZyLCAInFAZI0gdUofrJO3BI2khkCCVE+56jAyVQ08FXsibuk772DSASeo+xKExtRq2WgGb5kAdOsHWq+hqN7pewZrEL1oBMgi46sKzefeV55jywn2std3eDFpmdX58wi8JoiIycGyfJE2J48SFeIRE4J+LACsURAXnW1cr/OSIA/M5N/nlB52+DgLSusNyhi+zCk++MYHNtt+NDXfYm8NP/jXb73U4m+38I5ZfYyMOPPqXgAPOpMzqM0EURfzoR9u3DBs29F/uufIvC6cQYrmWXv3H733mVRx7w2N5f5AMtGgOmzS0dEMohEfuFp34mX+SsXmciZkR3jOiMtJXvtNpbrY2h1JyTWot4H1QPxHdOZuWD9v4bbJMf8DSzLzJ79ovGg0Cf4Ye49vDR1GIlJJ6apj65lNM+fBdvrvRui4kEdewSGSxjCoWEWHgJn0UuEB/mmCqVWxqSOsJ9UrNhaOkRYaSoBA4hFZJx+iRypHXs67Y0gmQo+pJtLWEhZBSa5lCsUiiXYaK8oSLQrlAsa2ELIUkxlCrxyRx4iyDQoAsFqAQORxHa+cLJwkqClGlEqpYdCh79jxtgom7MXGVLJxhvCUllJsXulLDJgm2HmOSNF9MhciqGqb+WbqKEL16tnPF+Wdw16W/Yt4Hr7DR3kfxwXvv8fir7xKnCdZqwtCX9PQvzy3IAqFc+NEKsGGESVMG9e3LFZefD8DHrz6DkmBrnXw+YwrnnnM6sz5+k80PPJHDrvoT6+52JF3lQSy5yc6svfvRHHbZA/z5zXc478p7sbYBgGUWyE4/2IwwCg4SQvT8l2TrXzVrR4wa/dxGB562zpIrrkw9TulY0PkFrJoGudyNBn8n05t/FeCXmc/pQxQ+rahZ81qBM2fzmrDedMUHyhsqsqGdvS+ZERwatD1P4fbgU5a1IbLMkzyEInKTVkjXht39W+XFroIwIFKu7OLbj/2BJ266iCsuOYeN11sDm8RYIVHFcuYQe63ktUyS+h4exgFEcd011A2VQw+hIXyFyBW5BncM40ItOeDtY5CpNoSFEKTCNdB1NYERgrhaJSqEzhQM3DUn3TVsXCMIBSIKUS1lx7YxBuoxthYjCyVEqYQMC27R0wlY483lBNIYGYUE5Vb3frRGaGcG2jhFpxZZ9H00BW6R8q/HphoRBKhCC9Zo4s4ObD3BaM2Tz73MPocey36/uZV5Mz/j7vOOY/Ott+WMn+6HtNpVOYxCCqUiQRiQpikyCDFpQhBKUuGaLGEFstzC5/PmM2LUSvnMXGX11XjygZvZbc9D+GxeF5vs9VMKg5Yiq1/lqlMo5k6bwPUn7sfNt13LOiuNQttuUlP1EV3LTTc/xCW/u/2iyZOnHP7Pyta/pDmFEIOKLe2rjF5+VbSVviWfyLDZ3K/7a/lvxDGhoX0cMsoiRbhErpFEbuYi3KTLzdXcnPXAg8nnp/ubhpZsuoRc++XHzj/O/MoMgBJN19wApYTImt66s2QHcWmGlpkT3+fha8/lkQdvZeMNxrswB663CUY7092k7rqNdT6RMZg0RdfrSJMQRYogUi7c6ahGztQNPKHA5lLorlJJRND0IyRRqYgMXev4zOjs6upm4eefY3HnS5MUow1BIaLYs42opeyNE+HqOmmNiFNsnDpucxQ5YTcuXGK1Jq3USat1dD1F11OEFKTVCrpSceaxT14XgUKVC5g4JalUIevSJr3/LKXrpRrXsGnshN5owkKBDdcdz9JLjmH+xHcZs9oGHHrZg/zl/Y85+bwr3Pk8SJgkKeCtLe1qP2mdYRS+hIIV9OnTkw/ee4FXX3mMCR++xB9vvxShE66+6JdssNpy3HjGETx35c9z4CdLkO87bAzf2eMIdt9lX1558x2skT5G75LHt//+xgjBXkKIfxp0/ZeEc8DAgQdvvM3mpdaCRmubE9SB3EYUTf9BjqvkN5nxTTNgSIgs8yQrYSnytnACkXeChiYv0GYxyiZtbYxLRhYuz1JJlVfCy7Wx13h5S3nR5E82+ZmLxDdzdkTT302fZWSL1FhevusqDj/8IJYaOdQLnvPLTJygazWsTrBp4rRdmkJcd3Q0kyKlRUWBS32LE0x3FVutYuLE+U2Jxtbq2DRxwi2EM4ll9uOvSTmt7xxpSOOYWnc3Sa2CFFDwgpsfA7xvKhFB6K63WsdWath6jE4cdzlP+MHFEfEgn/Yt6WUhdNhA7Ijn6AYJQYQBMnJxVxEEfqERHuF1iwg6RZgYk9YxqSZrDmWFYIdtNuPFe64lnTeNUnsvdjjmXP706J949q1PkIF0josPl+XNrYLAHUdIX52iMX+GDh3EMmOXZED/frQUInQcUywVOeXYw3n0zmuZ8cm7fPLoDXkh8CyJY9Xv7cj3Djie3fb8KW98MsdBAn7BLhQiNtxojdYgCDb/Z+XrnxZOIYQIwujgjTdfD2ESxzQxzmfI1P8ivppoTPIskyTrBpYBBxl3tgGOylxIsyrtuSZuBmKEcBrFNSlwP07tekTQo6AZqBRGqCBEBaGvfaOaFoXsehvmbOM2ZJPQZka58KfLWELu8/rcqXzyxots/p3xpNWaD0MIrAd6HGMmBWWxymKVwQZgAxChcOwWYxpmr06wcYKtVkkWdGIrdTfpPbPHXZ4P/uNXM+linnm4MnAhkaAY0ta7Jy1tLchAogqRq6CXZXoYFyeUxQIgSbqrxJU69VrdTfxAOGCnHrvFQWvSekJSjwFJtVJBKOEohEmC93EcyJWmGAFJLcZYgyoWvFuQpfiIHEEXQriwkAVpHSKvogIH7r8ne/9wOy47amfmvvMs5bYebLLPUVxyxbWui5sStLa3+VrEFhW64tpZaAprfXZOI4nE2jQvDGeTBF2vE4SKocOGc8Plv+XJu25k1vsvuzcusvKnhnHrbs4y62zG1VfdRGostaSRz7v9TpvIUaNGnPbPyti/ojnXHjZ2xZ4qKuUXJJWPU3nEMBMox6TILESRm6qZhswcvYbf6U7QoOm58EkW72yuxNfg5LrPms3TfD/pOJG575qdX31x2KbR3MhNlEZWTHadNK4X8n2lL6NSq3bzuyN+yCkn/pRllxwNMuN/CgglIvRgjS80la3yvgNKDlihJNZPMITLRnHaBRAWK3ELk1IgQ/c6szw2B+w6cMTgNBSgopCotUxULqKiIC81IpXwE9fxYwVuKZJB4Pwtgfd9XfkXjMEkCbWFXcSdFWpdFZJ6QrVaJanXHe81e1bGsY+s8NrReM1vrNdG2QvLCAo4re3fcliMkIGLJ1vjNPBuO23LFeefyY1n/4xKx+eMWXltPnj3Xap1nZerqddcpolOEnctvp2j8ACjTeK8ukXme2WlUl1oThNEIUstOZpNNlyH2typOeiTofkCGLPqejz9xHMs7Oji86rCWge2LTN2FKVStIwQYsA/I2D/tHCOHD3m5FHjNw8MinIkKAbuYrPM9mzyZilTbl5Lz6RRi1DxXMK0cprNazLpgQ7Xh4Sm8EaGCDZCM8Jr0gbmS0MTY5uEvQGo5ICTL1/Z4O+6CeXaOWQC2KAQNmh6Ml/h84Rvv/pP/+Rdhg4fzv57/9CZcMUCCAPKr9hKuvq1C7tJu6v5Cwe/rmWwcWZqeC0mWorI1hJBWxlRjrBK5awa4RcaMr+4Sbvn1r8zW5BKevBK5JNV+mdlrcmFRQiQgSJoKXmzUyGi0JnMvg6QTizVrjpJ3TXtXbiww21OXfxUCHwtYv/coggsBMqBaRk7KQcGszIXSqETDVYigwARuRhr5o8HSrHWGquy6Ubr8/b911Jq68WwZVbi2tvvxwpBtVqjXqtR6exGp5q4FnvlQW5YOa3vgCz3jAyETc/SWucfhyFdlRoiLNBIjhDenbKMXnEtRqy4NvscdDarjF2fT6fOcYItJTv98HvFgYMGHvzPyNg/JZxCiNbU2PVGr7gmUQCtUUotTkhSnQtA3m6PprKVsqkGkPdxcq2a+QkZGuuFJSvQ5bZltCnhAaEsNzNjAZlm8WwAQjmP1mZKuimG2qj+JmXWXj5bUHxStwcp8qa6qoEqNyd7S7/YlNv7MHXKFB558iXf/k97NeZq9CTdVUg1QamIai27OGcWK2s8ZTeJogjVoweydy9kr57I9jZESwuiUHAEBJ/IjTEO6Y1T8GUyrVOtzvRPXa/QjDxP1ijKO5BuoXL1jIQKnGb3KW9hsUBYLiEKEVZ6DY5FWY0MJB0LF7Kws5MFCxZgvJkrhIDQHcdhVh6083WKZeAdYW9JZcKbvRyTukwmFRWxMsgTwQulIkEUEhVDCsUCK41bmnmTP0Ji2XT/47j08qt59uXXmff5HOK4njPCosgxnzLrLZtjiGw2eDRWCW/ZKEQYIKQktpY/PvAoLX0GkpVMzd5RRv/cZK+jmTJ1FgCXXv0ASikKUcA222xIFIU/Fo1Upn94/FPC2d7evtvYdTcvRWFAtRoz5dNPeOW+G5j2zsvoJKZe6aLSMZ9adwf1WhVjDXmVNZnVO3WTQzb5SULkCU1eoyrysIWQSJGVvWw2ZTNLKENsyY/VcBWF9z2dj5h9PysQnb3A5gwTkdVhzUxrJXLBVN4/lXkYxQm5NoYk1fQeMpKN9z6a8y68CmlTt0InGlNPMUmCNSmyGBGUy6gwQkYlx2t1XEVn7hYLiFIBUSwgSkVkVESGRcd7jQouG0UqMBpTrfi4aOrjqCk6TjFJ6sIx9Ridpp7c4BOgjbM4hDFYbdGxIU5SF9LBLUxCBd5f1KhSiaCl1VkHnmxBFKCKAT379CAKJIFylkiaprkfKcLAE/l9PDZx2spY687lFyZrLDp2gJPJskusdyN8t3Inu5YwCimUy0gp2WDd8XzywXtIUnoNGs42R5zJ/occy++uv4u6STBYx/oSAikMCIO01hPyM5PaLxTenAWBCENEGCGiIu9+OAGAcp8hDeUhnAOSAZAqKnDQRfdwyKUP8vB9D1GPXRShvUcLAtsHWOPLytk/J5w9ex83dq1NRdfCz3lzSsBDt9zCozdews1nHM6EN1+k1rkQhUUJQRrX3cTwI2vH7tBZ2Qiz+BXUmbqN+qZATih3hKCM5tW06i2iURtgkGvroBBCkdlpWQfqnLRAZj46k1CqhoaUWexSSY/4eo2a+Wm5Py2yd5sTXkhq9OvbC5OmmEoF3d2NtdrHqyUkHqn195M/gyBwGjEK3QTxXbrzUI3w/qUMEUI1aBTWYNLYIdv4si/efrN4ZrZt3Lt7+77fS5y4BILUYLTPxck0TPZOAsd2cilqIUQhKIkKJS292+k9qB89erRRiEKUEM6njD3DyVe8N5Wqi6+mLoxEoJCBq4GL1l7rpnlShJBAGjtBwiJcm263f5Jg0pRRI4YxdPBApr/xNBbBEquuywEX3cvv73mEa//wMImNqVYq1Gt1kjj1cfHYmeXCNHz0LIUxSXOQTwYRShUYOXIEbW1tRC3t7rHJrOh5s1S42Hq/YSNp7d2fx599G2tcx/Wx48aI4SNGnPFl5exLC6cQYulSe6/BQakVoUro1BJ3L+DMc34GwIRXHmfwsKEMGjaMnr17UygW0WniGP2+Wrp78LKBd/qVzXqqnPRmkxA2TwvLyRI+bOL8Pe8L5gKYCawvTSlEA0Rq3AENw7ZBJ3R+ZharyogPfvWnYcYKKXz9V5WbxO49Nah/Qkrae7TRp08fkIE7dqiQgSQolwhbW1waVxJjahV0XM0ern82AVJF5L01F7MU3A2ZHEBC4usICTJ6TNYxTQSeRRSEXrB8LDF7EoHvrpZq6l01uud3EdfrzoWwIMLIpa5h8msTUQkZlhCqgFTORyu0tlDu0UrvPj3o1a+Pq9igfWparYburrrk79Q4IZQSGRQQMshDX44hRK5JbZpg4jqmVvGdwj0g5imcWfjtgL125tm7riNSbkq39+7LD068iEsvvYZaqqnVa75HTerTBTMAzs0MZ9kkLr0sbSDWwvOttTF0dnYy7d1XgSxcl+EhGfvNt6dINePW24Lbfv84WDf/Nt9yA7q6OtcXQrR8GVn70sLZr1//0+YvWBj161WiT0/Fr/baiDdffIETf+Z6i34+fSLn7f9dZk76gEKpRLmljApDwN2IWExgMpQ0vyCZIa4i56tq37UrSzfLErib+bRZ89ps5MfMIUOaTFGRC27WzSxHQCA3X2UO62c/zvQVvhizE0h3J81kBCkgFSETJk1zJlUYOooeAmEMwliIU9JqnbhS871AfeoRAmsFoJxwSleoKmMCWZNgdRVrYh8T9Z3RslQunZWE8U/YI4oZausKb7lV32rrJqRxGEEgBGl3lbir2z8P1xYPGeZAkxAhggBHbhd5KRPns0rCUoGovc0VfxYiW7lycMlKiQ1Cv9A1gBcX5vDNq/JFxZuPibtXV9nB+o4ADmwMw4Dtt/oeSbWL5y4/iTDtxljoN3QUY1ZZl5+fey1WaBLjzPa4FntF4CtipL5WkufjykIRVW5BFooe16hx3bU3sMo6GzBg7OqAaKqtmyUXkM8HbQwrbLgNLzz5NHPmV8DC+uutRBzHUalU+uGXkbUvJZxCCFGpVrfd8uCTCaOAOLGU23vm25dffwtmTPqUuFYDqZChc+Az4kCGZrpZ1gh6ZKGExosy3pLLUrwyP9Hk38kPAw2wyDbiqznftQl1bWhNcoHLzcX8JhuBTSElObU2M/Myc9g2FgZrMtPa5gBYvdJNFIaYatX7ej7OlySYJPGkdO2I6N50lEGIlAGkCbraha51YNO6f01ZArNf+dPU/WSxUHBMG1/3SPhFwsmoyU3/nImUpthYk1ZjdJwghKuBG0SKQqlIliEiRIAQIVKVEDJLbLZYk7gaPSbN82eRKl+I8oTxQgERRRBGEBXc5A8Lzly2Gmsc6SLLKkJkneGkR00dAiF8sraJ65gk8aG7xiJ629UXUrQ17jzrEFpDJzTf+9FxPPnYE9xw71N0dS2ks6ubWrWezxtHR8wEUzl/PozyH2EFn378CedfeAXtg0YRFooeVBL5FG7GPwTObSu392T0imty+93PYI2lX+8e9OjZSp9+/U/4MvL2ZalF4waOGB2MWXFNutMUbTQ/vvxRXrr7asastiE9+w9mgz2OJIxC2tp75JPWhUVEPpEbfrhrBNSoR9uIL+ayksWkLN5nsg0kWOBr03pB8nLWKDTd1GKh2YQlM6GzZU/QWKesDzFKWotQiSE1rm2EwKKN7/PhqwKSH8GdU0kX0pn14RtstNJSmDTOECuX4macf6u1hkCgjMZUKg4BDYKGX+zLcpogdpNbejPRZmBJEy3SL2hu8fBFtEUumU5ohUUIlyCAdmastppEu0LegVIoKQjKRRfuACegWUdsN/Xc+a33GXFlTJCqsUCAm8BR6Mxu4RDjpFrD2tSVOREWoUKkTV2mCu7xZ4Ah/v1abTF1Zx0IFbiSurheM0mSujg1bl706dWTc047lq1+sC/TX3mAnqtuTam1BzufchkXnnogY0YOYfVlxhCnhjS1qEg5Di+OUWVSl+JgU43BWXDV7m4uu/pGevUbwMrb7ud4uj69T4iGteTTMPKRpprlNtiae++9noP32oxQCpZdZgzvvDdliBCi/I+WMflSmrNnr15brbDxDkHqbew0dXHNtXbYn35DRyNVQLGljWKpzZthFmt0A762GdDjyz1lgpb7jzYXziwsAeTV+5q1npuTDe0r8KRzr6UzjYptmCANc6RRAUFnDYkyDeiFOE1TajXH7RQ2JVAgheN4Gg8kZTSwZtMZBJ1zZ/CX5x5j6y3WJg00OrRo6RYZIzWaFBsaRGhIdZ16WiVOukniCknsfqdp7LjDSYquVZ0mrVec1tWpX1icj2OT1OVwpilpmpB4EE7Hift+PfYoqWMaaZtghEYqCKMAFQYEhcClkLWUcwvHcX+dUBkTY0wdo11PSylDZFBGqgJSRcggcprHx1wJPH81cXFEVXAmrLGubJgUgizX0kWZfD6txANVrtK8y6xRGJ9uZrX2CLMmiVOSJCGMAorFiEIYcMpxh3PbpecRCgdCDhi5FJvseTgnnvQrVOTKkS5c2I1JDUIEjrgvJNqkJPU69Tim2tnFk0++wAZb7MrNt/+RFb+7E4VCGZd+lkUCbNP8a8wx4RfhJVZdlymfTODTqY7Wt9SSw+nZt28BWPcflbcvJZwtrW379hs9zpubWQkS8hBEHhLxPl0ojZvU0heFUoowDAgCxw5yAkSeFmZ8MrTxk08qp4WcXDf3KMH7mIsOp7lcyEb5LPysPlBWEbM5hGKzcEITQyTbP9WGrlhSy1ZpIYikJ6i7mdvkZ+avCIulc+5nDB46iGEj+mOl01o2s0yVdLxrabDCYGSKlilaGbRI0DbFCFf7LtW+zk9ax6QxOo3Ruo5Fo42rlmfSGKMTtE7Qcc1NstSVe4yTmDRd9McYV8okC9sEBUXUWkKVCqhihAhkPiusTTHanb+hKaWb1KqAlAXnE7uiuJ6JFSJUmFc9sMalbglhCSJHn3Tmu8tOoa4h9iGmrKxNZuZLh1477q/1BkgW7nKpaGmq0SZ1rVJCyeqrr8CAfn2Y/MpjZHTLFTbegbC1J0+88BcKJWcsxnHqFYwmrjnCQveCBa55UqDYfb9DGbLKxux/0X2ssOkPc0525lY0UBObudV+PnsfWwYss/Z3uePuZ7AGllpqJNYaMWLU6N3/UXn7h4VTCBEtXLhg5MS3XsYY7c0KSUb0z4psZSk11ro+FHHqcgYDH4xOtHbVEJpuT+DMyCzmmaV+JUlK3qaBDDxyD0DkAuLtfeEELvFEcOP7ehpr8riq8cXEMkE3NsNGctyuYXIDSgiUily3LiHoqtYb4I0HOQQNTR8qF3p56/G7WWv8Sk6DC+OFwWCUxiqPrvrfOg+vONM3p+Ypi1EaIzVGWAzu31a4dus6jdGkGGmwSmClQAuDCCQqClCRIipGBFHkyODCLQapTkjTFIPBSIsRBitcjVcT1zBx7OOgqSvuZVKMTZyWAxDSExwM1ibuh5QMRQfysIQxmlTHuaZ2qLJ7XrauIXHJCmlq0XUXC7ZVB/wIYxG6iTDvyRJap+4eSP1xY6xwz0WGUCwFHHzgrtz5u7PonPZezsceOGZZ3nn/Y2QoiXXqHB7rSDMijMB3ICeNmTtzBt1d3ay8yXYUS6XcBVO+Wn5mPTXAioZ0ZnFPaywrbrQdd93xR6qJZt68BfQb0J9Em02/cuEExpda24JhY1dyvTs8UQCbJVO7lTWjsuUrnZB5ZgBeoPBxNFdyxCNy1qAC6aqh+8x2kzfXbWg3axvZK4hGPBJwZo91RaGz6nsOrPChBd2olWvzn0wDNtWy9Q89DCXlSNAapOh6F3GKLw6dhSoa+HBGzn/7sT8w4Y3nOeGYPbDSgDRYmaJlgpEpVmkIjPO38RM2i7UJixEpRiQgNSKwaJF6IUyxKsHKFKtdVcHUOhNZixQj3bGkBBVITwCXvqK6C+O4bcoR1dMETUpCgka7KrXG+PBFBV3vQteqLsSQpr75UeKAIFPHpBVMUsWkVUxSweg6YLG6jk6qDigSGkPsrQRIbB0jEg/uZO/FJcvH9ZhKV7cDe6oxJnZCalLtkrF9FUWNIQ00qfXPMzRomZCKGkalBJFgi62+w/HHHshVx+/Hpy88gBSuheO9dz9AYupUahXmzp1HvVp3vGMBYRhQrcVcft3v+c42e7H6JlsTlttdJpPndjdSGUWuMJpN2+ZhrGHYMisRtfTkzgdfYpmlRjF/9izK7b17CCH6/yMC9w8DQoOHDP7B7NlzxIARS+R1aDNfTXvUMGMBZaBGEAhS3zQ2IwkLqXCNbeUiIZUkNWgT5y3xXBvzCKWycIoTLuGD6s3mZHMcNC9F4skGzv9sUnM24+BmGSjWhy4caNTg6UKSpCT1hC6vYckLUJG/mMyMsVg+fv0FbvvNiVx8+Vn06FEC4Ytc+0wdmxUTczqarEastcaDChlZ3JcRzUphelUulHPWjS/5nHXszmIV1mQ1kQRoXCV1D0a5Z+omV2AD6kkdtGPbSO9LkVkZ3uoQSJQMESZFGFfyw6GcntCgjSPnk+Iq9EUY431bjPcvfQE2gRNMcCVQ/MJtfVK8q/frEsKN91MzXw5hMKFbQI0wTtixWGn9Imcd+Ovv3aaw/XbfZWi//hx5wtksmPw+G+xyMJcc/CCfd1RoK5cgtsz7fCHtqaFW68YYwyHH/pKPp8xgkx+dyPAV1/Udzo0H1PDgpnDpkX7+I5pZaX6xtjbHx8Zvuxc33nA799x8Gp9+/DFrbrV7NPH9v2wC3PL3ZO7LaM7vD15yXD5Bja9AYDIYHev7ZMp8/lrrOjpneZkqCDyY01RnhuzG3OeNcibN4RKP1nq/tpmD6j7OMluyrAzRCN14/8XmoRQ/MfLveylvApkyvzTVltSGaBFiZdaVqrE9B5W06448ZqXxrLvj/vzh3uepa1/6FWc+awuptU7bCd9z0xr3g0ELg8l8QXchNAKU7jpdQobF4KwMKbMi2E1gFt4ayP/dEBLriQvWp5IFYZZk4DNdlPULis0XAEOC0XXSpJs07UYn3ei0gtY1tI3Ruoo2NYxJSJNudFpz/rCJMTbGVeMzGBs7kMdoDCk2sMQ2phbXMEYjA9er05oUIzRaJM4aUK7KnSsQLbCBt2zCho+P559YIYh9hQcELLH0KP5w3QW8/9ITTHv2TsaO35DrbnkEFQq66xWqtQqzZs7g2ZffYpeDT+L1t95np1OvYfSqGxCqjBjTqHVsssUvj8U3z8KGxeVen5sXY8dvxIT3P2BuZ8wqqyxLqRSJEaPG7POPCNw/JJxCiJ7G0nup1b/jY1GCKAoplQqUigUKhdCZqJ45Y7RBCrfCKEnOtMiQrsx3RCxmDGTaz2b0OnLTtLFnJrzNdn6GyjaaImUgUBZWaAjjos57c6yyEb/M2kTYXAMbrXMLwPgyjLmg+mMESjF+m9148YnHWVC3dMWGWmpIjHH+t7bUtKWeWuraEBtItPFC7ItEWovOCBXSupVZ+L8z/1hqkMaZu74KYQY8GeFEykiNldr7en4bKZoYKzUqEi6XVDgBdIisq8qQ8YndJMyehXaILTGamJQYI2InaB4g1HmRaJ+vmomPpw7mVoEj9aLRpCZFBGClJbXOxLaBwSoDymCExgiTx5uzBlaucgKoSLhyKlISG0u1bumupNSqVYrliGKpwBXnn85T995Kv56tPPTgn1DWIE3CLfc9xjG/upLDfnYqLcOXY7dfXEO5VEJ48CtTBqkVLok86/Xa5GI1JrCfsE2+qNaGsFBk5PKr8+Bjr7H+eqsSL5hBd7W6xj9ChP+HzFohxHdmzZgZbb7Mqh4QgMg36hEidUTnTNAE6FQTBgHa8FetFizNoZFFR2YSgLPZJVkZEB9J8jcvcHC1x2MXO4C3Xr32kPiQTqZ5s11z19cdxf3pNZE/X1bHAdtYE0XjEv1xFoXUO+bOYtCwYRiTUk8hlQ5YcvftzFnptWG+aGRFCLzwSXxVCQRZBTzPOHbX5s1kb4OTAVmZlsWb/e4upH9n1vNuTbZOenDJIvA5o84Gd7FLf3zrArQ0PwGTC553D77gOXgjc7H3bHMTX0hy3WwlILQvS9qIzzoN7r4jm1ZzXykVoQQqcGBcloUqZUB3VzeBsIRhAZRl4IB+/O6ck9ntwJ9Rr9d54vn3+NOjT/LIY0+xwU4/Ys8z9qPXsCVRInsGDTdAAeXQUKtbqtouogiwvpTqIoaezf+fldAZs/I6PPXUq/z0wC24484LGTB8ieKsz6YuCfx/e3r+Q5pz8ODBBwgB/YYv6UIKgWLCiw9x7wUnsnDOZy54HQSuPZ01zu/E/TY5L7Vhekq5mGlqmx1rQbZMZlX0GvmODXZGI/lZ5Kv8opXwvBPfeF5Nzy+bPIvIaxM45J1MY7BWN03cbJI2/KEsHQ1coeEe/QYxZ+YsB7hoTTXWVGJNPTEkxnqikMBY4YsDOFS7nhoXIDcmN4UzcMoKGoR2r0WsyHIk8fFhbwZLC9LljhplSaVugEo2dWatMBkm5zWjQ2RTk2BI0DbGov1+ToOhtEdFU/eZMCC0EyoyEnlGG3QURHe9XiCFFzTr0OvUOKK7lAJtnYbPzummhg+dSYuVBiMNWjogCKyr4BA4yqCVwnVdsJJQQbEgKbe2oK1FRiADwXJjl+Tmy84G4KCDfsr7U+bxw+N/y8rb7EPfEUsSSj8fpAQV+vi8m0M9i1AOdFNs01tafm5lv/N55P+XWV5LrrY+f37+RQYMHcK0KdMYstwaUXt7jy3/ntz9Q8LZ1d29bv8RS6CiiDBUkFS5/syjeeXhO7nlF4ehwpBisUChEBEEiigKmpKphSctN5mR+b0s6gc2T/ZMkwmhUEHoqGg+NStLxnYhjIYwNjc4Uj5p1hHpJWHmW3kVlcNC1mvKJhNX+Ke7CDUvE07TtF92C4j8pbX17k+vwSP4+Zm3M62jQFcMsbYYBKkRGKMc+KU9CKZddbwshQ7cupBai7agEbm/qn1sNzPXfbqmN08N3sP12KsTci2s82elEwqnHH3pEumfqQ/fCG9GZ8KX3Z/1gmezmpN+IcjC23mIRJLHc42wpFhnpmPz63FJA2BNSqAkhWLghD+nSXq/2LoFyCoLgUWEOPKG1H4RaNBBtRFUE0s91igpaG1rI0k09ThBBg4mEYGgVCoDsO8vLuX7J1/OkGVXR+FAQyMUqAitNS/ecSmn77AKsz95m1QbZnZoFlZ1HsazmWYVjewU5yY1W7VuvzTV9Oo/mP4jluSJ596lrbXMgGEj6dt/wK7/snAKIcIkScrDll0VIQQLp0/ggoMaNYtmTvoInSZEBSe42kq0FVhfs8XibO88o6NZVTXJZvZBZv7p1LE/sr6aGcdWZIW4/Df8OpYfyC0KbsVy+EoTPiqyUpuZD+pzSpsuRfw/9t473pKqSht+dqg659zbmdhAQwOSc45mQMUwiDqGMY5hdIxjnFHHd8yMOuaMgVcUM6CAKKDkJDlKpnNOt/vee05V7fD9sdbau84FGmR0dHy/4tf07XPPqVO1a6/8rGe141DRki1hlSehUgwt+YcMjv+7d5yCq357Md7w92/AgwtX8YNTaByPpUdggIBLX2wMQf8IYUL35SMlpTxjH9iYU40WZL8psURWSZ5mSAJInkMAUAcScHErQwyoPcXC9FnqtolaJ8ENIAsnDAoBgFcg9Bc/z6go2eUjXQfb0XR9cn8RJLDUyBYIA2EVlI3QhhWDZsEM9Gyi5hIPQOMkdOTPKBI4TQTYEwOPQe15LxgMBjWqqkKn7BLhigkwHYveSBfdbgfLbroUoqJhKNG36Jpf4fT3vAifftnRmFx0C/bea3f0Vz6AuqowOahQ+5ZbraQxv7UvkmTmjSlNGzFG7PeU5+Css36HPffeFajG0B9Uuzya7D2WmHMX74PeYc+DYbTC2Z97L/bYcxdcd/UN+IdXPB+/u/QGXHjqx7Hnsc9GM74G91x3Ce677WaMrV6B951xFcqR6SQ0xtDcDwSecu7zN2QT1PIzScBqR2l1oRDRyqTBPDEqaBaewLGilCsomaE5uM+1SdJ2ikPYmJqCJQhN74NA2HI8mBadY6b8bwJIWGtQ+4itdtgZb/3mr/H7c76Pf37Z23DaT76InXbahpgbdIADuetFYWgKG7v5YhUD44+FOgScLVQKTI9C3mtk149cWg4FgmCZDZFHq4gYFQssrZWGZlAG3bNGhDVElEaOC3WoeO4E0lpD/B2jC7iG+0MLUsS+FTaktjlWWEJ32yYnDbz+lIeQPZAVYTtPQbEbPRNtchM9F8zgo0a/iWgcAO0xPuFQFl2EOMkK0GO0UwAemD9vB1x/0c/w1JNehd0OOhKrNlZYfOPFuP/OWzF/x+3xjte9CE974uGYOW0UH/jY59DfsCYpVvCzid4BIJioDwSVpj3D4ZbEnHyzgkbb88in4cLvfAaveMWzsXbZPTBF2VNK2RhjbnaecjwW4dyrrgZqy+12xKbVSzAxtgFv/vi/4NVX34DXvv7vMTJ7C9x43S34ySlvRzU5CalxAkDRG+Xxbjqjg5VYOnmg6ekMbXbFAaFuNUvrFFeqJFSSMIoCigcS6AAhpE2rUrJEQRlOKPEXZTdb3jKcSBNhVywQkX0YxWgXBXA9VpI+9KnDnvtKrF3yAE477Vf4t/e/Gl1LsXjjglC4og4E5LaaLJoPubPF8hT5mC5C5XiGXwsAtbABGKZxocSbZEgBEnBjNCFtQoZrB5UFDKAsOz0IFgQfmCFB0ySvpkZRKLTohtjS8yOMVHfUKlt8ep2uJUByBmRxh5ZbklVAqtFSDVPnpCN/Z4hA1VD9e7Lfh4bCSFnAe2ps73QKIBZovEPPlCiMxcj0rfCNL34c7/rAf2LuDvNw8nOOx35veyX23mM+TAwoLFGTxBDR6xSs8Ok7Q4iwhWVCbuQYm40DIafyrdDzpNBlZPpsbLH9TrjzroVAOR1bbLeTXfLgvbtgM0mhRxXOLbbY4pi1a9dixqwZWPaH63HwofvgskuvSxvrTW96AZo3PB/nnnMpdpq3DZas3oSPvffjOPgZL6SySqBieYp3gkLIwQwkM6H4ZrKp4tdVFkgSUPmZNkCIAILw2zIBmI6SQQHYnY3JEtLqaSjIJDDhIaL3ymZWyaBLl4tK3y0KhOJZcZUb54cejlLAkS94PX52ytvxvvd8EZ/9r7cDIOBBUBqOzxGhUPmYXFbmAIOiXTxVV9Dm553aeMAqcvcEEsl9NwAoAWW0lHsgZgwhRiLZYgim49jacjhCMFdFnSAcIlCzguPHY1KcRaEfCbVgmBUnhFzIIUiK02ilkTPImfVCBFC8CMPCGmK2stSAL7GfQWECuiU1pscIDJoavU4XvV4PznkoHdAZ6cLoEiEChx+xP6789Rmw2mDQn4TVBkoFgpAC8K7B+g1j2G63WTTlO+QHICR1knPQSiFoQPlWRrvlaUlSE8pgp30Px52X/QKHP+mJ6MzZxgLYC5sRzkeNOUenzzgKUAhr78QNv/kpnviUw7Bm1WoAwLbbzkK3U2D29C5e+tJn4alPOggnPecovPOj78OaJQ8my2d07hgZHemmbG3m3+H4DZkvCEo4f/gGvRB8xQTTi2xSJBscEzUkn1PzvzmJJKPeFW+UxJ+r243Xcm0UU2m+TvBrtrAwlgD8iAQVdM5hcrKPiYkJNNUATTVAPZiEqyvM2mouXv2J0/HgfQvxvTMuRKfU6JaWkwW06Q2zLWgFFMxS4DhR1ARpIpeyEsWbPlKWN3L9MHDcF6KC8xGDxqFhiJlYLq1UgqlKn6vnbHjdONTOY7w/wKA/IPZ+FUiBGMsAEm4qsBYBCrVXqHw+p4IiMr1IysCzrhXX17MVjVHBB42qCXBRwwXJTCM9cxHmCBqq1Hig9pT8kecOALXzqH1I2XlrNApbQisaG18NJtHtjqJRGmvHx7B+41rUTR/eDeD6kyhCgFYBViuYQsErCrf6/QGMKXh9MtxTNIh0oKSqgMouLVr3EBEZ6qew0z6HYP3adViyYAG22mFHtdXW2xy6Odl7VMtZVfXuSgOfe++7sM22W+PE5z4N+x+4L0488SkoyhIFJzJMpA1ljQWK6Xjg5muwYeUybDF3e3iloAO1lxljUHYK1FWTgPLpaP2Y5mRC4pgIHRSCCBWoQ0JcvATVk52odKqH5exRTIIJFkwBK8jrbe6iZNJbfwvbt1Kg1i2lCaDgGkApBEO9mhGAtQVKALbbxbP/+YM484vvxz++6nhYnV2e0hANRhMCSkOau+HGAB8J/JJ6VpVYRfEzqB4aoHKJUyk4L61ttFEMewPU8tmO/dgVjbSxNPfEFr0OoXVAnSbOk/WsXeD4M3CDuE7WUZIjliFs0tMdAtIzVkrBKS4j+QZGR3hHyT9rgMLq9GzZUcquMigxpjSTN/A1Nz5gcnJAI/oi4H3N59BwzQAjIx1Al9g4th7eO3RLmiEaAu0niXWV6UCXxO0bKo8HFy/D7tO3ZBcfLY9OFAhND6DnH7gPFa29k+NOMTDz9j4EANCbNgs77rwDps+a/aTNyd5mLadSStV1NUM2/Fe//QnUDpg1Z0scceTe6X1GRWhFwjLWd9j3wJ3w9Oc/G2d++h3Usa4JiG0sPfCi7DCmk62TFrcxN0ln5gIkNzcE6rN0zElE08Vo7IK0iQnI3gpPjsgVyBW0RQFtZfgsfadNJRgu6Mv1KGSws1IwhjpuNDeJa1NQTIyc8yXZ1TCmpBF2bKG23mk3rFmxHGs2VHAMCZuoHTbWHusmKmzsN6gZTSRJH8neJmEKZC0b/hMjoLRJGGeyuJFiWsWfBwl3CJEdCRL6OgADxwmLSLtea03T0WyRylLOR0xWDv26IWa9AFQOqL2G87QDQySrNmioAyyhoJqIxkdUzmO8DtjQd1g3UWP9RB+TdYPG09g+HyOaoDBoCEUl0+K9YFWSxZX9ll3cbqFgrEYdFHxUaAJdszLUmjg6bTbGxjbCao1et0QhcFINBEZYRQQ0njye2HgMqgqLly7HtK13SvG6mHVheo9QSUHoduiTJDPLkWRzuyNEIXT7DTdg1912wqA/2P1xCyeAuVvvMD9939z5O6KqCSfZ6RZJuzUeqDxpFqsjpk/v4J/e+xro2OD+m66CUqBNrVqbH9zJoWTkwpSapWoJqKxNElJxjYhPiKBlAhYItAlBzGdMrZMSEMl9YtG3mh4uYYLFvVXJhbOW8MKFMSisgeERdpl7iIvraCU/FM3jcN6jcQ6urqFtB/sc9VR8/MPfQL+OpLGVxsZBg35DQ3+aGDFRR4wPHKqaGOey647UTyivC5AhRrIqjgVWJnlbXl+5fx+BQeNROw/naYiSAqiJmBYNqesiKkw2wMZBwMBFVN5gUEf4qKCUxaBymBjUqB25mj5E1D6g7wIqBwxcyIrEU39s4+l76XWFytHvQlQsxPlP7VtWk29bBFTiT3IrFbyx2DhRYdM4EUhbbeC9w+i06dg03ofzDQHqQ4COCoW2NLWtYwELqhdHD+dqDPoTuO2OP2Du9jsg2k5KbkocSbXvHFaRFY3MbYy0t+UQ40BZdYV//sov8PKPnIptZpeAUtM3B+N7NOHca9ud9zQAsPt++2DTRAXAYOb0HvcvUoyxYmONpRtrjPUbDFxIbTb7HbYf7rrqNwiBYptGej7ZTdQmz0ARCyXWTEAMEEFSORaFNtzdQjGJbNDseZACsNagKArq9DdkUcnCUmxirGUrIcOM+DVjGMRvUvO2NlTqaJxnd09nTSk1Uf5D2Fuqzza1Q1XVqPqTeNpr/g2LF63Ey//+3Tj/olvhQkRhgB4PLKqdR+UcxTlaUSzG5xf33bEQNoHmctQ+ZGC9p2yvVpzs0UBhSEhjBFlmL7M8MrxOQbDCArFU6NcRkzW5ssEH1FWNuqkRo4diRJAoiH7tMHCEEVZsUYjITqw8FT60kuZ5jiVDYHAFAy7YwrsQqcmdlb7zuV4tyyF5AmsUZnYNSgtE32ByMIn+oA+jI1yggVI+GkxWlM3VIE8jaO50KU3KxOugoSNww633YLvd9uUQaVh2CCUksXxMezMZGSUJzpgUiGTKlVbYct4TsO2ue2PjQGPWVttYANs+LuEcHR3db8b2u5j3/eg63Hv7nUBw0NBsORSMUtg48NgwcKi9x4bKYayiMXdKKexzwF647dJfoT++McUgktRIJNPiPtKdZ4upsnZs05JMqfOypWq5wAxXUZpoFCEJplaiqd0PKlY0ygVgSlYxWSphAUTqrhGC5SEXRh4eCyx1rUT4oGDLHl70wW9gn6e/BF/95Jdx+qlnolcUKIxmVBCVZFLHSWyBD8DN654EpnIMqvf0s/ORvYWI0ipAaaxcO4Grrrsb5194A8674Ab86jfXY9HiNQloH6C5NU+zQiUY3KZBwPggIAYFEyO1jOnAzeFkASVRFaKHC54tY0jrCcW5AL7+BABp5RHAVpoSReJ9xGQhfSAB440BgIRNs0ek2HXvFQYzuhbdUqPgX4QYMd6vsHbTONaObQCx7iPHtK1nTcKkoaFhoHHFtbdg290PekiWXFxc2cOCZkuoM8kmy55WEQL5y+5uhNIG45XCNjvtIhnbhz02mxDaepttj549dz58NYne6CgKXcDVDbqziNqw8hEb+jLaHHCNy9o4Ak867jDceOWTceYpb06b/OBnvhh7HftsGEvZzkY0uNywSJ2i8W3eq0SNCZbDmBYkLZtIsXjB5DVKIkIraHYBU59g+0YlUZTKNQJapgQU1zRAE8rIwpIrKawOvHfayQBIXZT/pTQiW+cDnvocPOGgo/HDD78eix9chre87x8xbUaHkgxaoTQKBZdoIjQQA4wWi8TfB3IllecBvpGmVS+4fwnO+vkFuPDCa7Bq+Qpsud08dKfNpE9ojZUL7sH/+fzH8aQjn4DCRMRIbXrGaARorJ5osH5igJ4pMGoVqroPXRYw2lJiKtDkaWMsfAgYNFRDl9qlgCiyYiMrwvqCXPGWa04l/awIfSD6oUIrNAHUJBCp5kvxt8R6CqWlkk9hNGb0CpjYwOgSTeNQu4DxwQSABltuMYpp3Q4lz0xEqQ003zcQOCQKiCAleeVVv8drTn4XvG81VQCtEp4m7K2h7RSSws8IOJlrKhIbmCsLDBccG2iMbjXPdHu9fQH87o8WTqXU3O70GVi3fAG23W5blEUXjR9HYbvQAMaqgPHJCs57GBggREw3BjUARNo8b3z3a3DBuZdg3eq1mL/nrjjre6fh6rNOw0s+9E2MzpwDow2kYVUsY+BaHN2k5qyuIIqyq8vXmOIkTiUBbPU0Z2wjF+O1BiJneOVceS6luCsK+euFvY/wutayexsjovNwzkF6T4e040MOqXXJNRuMzpyDV37yB7j8R1/Bm1/2Dvzrpz6AvfaZhxgJsqcVja1AUJgYBKxYsRarVq2HLUrssvt8FBY0XgAKPhS44Pwr8YPv/hQrl63EQU95Bp7ymg9gy/l7Ey7ZaHQ6xNh+1zUX40Nv/Td8//wfYNdtSgARhaEbHqtqbBw4BKVR+wAbGgRNyR4TAxrvUAdgtCwwo0OdK6GlBDOcLaQQg4YlkdLRSsHpgKomy2s5GddhTqkmSAkmImhwDgCc3eZzIDIpGw0ChgJ09DChwYxeiagsKgQoo9EJwLTeKEY4C0vC7REjo6zYc4IKUqzFFdffgu13mo/ujNloGnlmog4ZJKIouQZ26zUHwZRHESOguFUyckM9U+Pw5zcMADMyB7NmzZ73SLtms8IZoeZ0e9MwtmEFZs6aDtc0GBkhl6vxEesnK9TBoVMUVOw1CtoAM7TCuoFH0Bq6W+KEFxwPww/wyCcfhu9+/ns4/6v/jhf821ehDQ23JTeG+yU9INlZY4jKREorbbeW3sX/S0kfnWpQEg+k5imluOMjZMvLmytXT1ggOU6TxJWSxuYYmd+oyVxI/GFBpU1dxVY2AQA42weUnS6e8dr3Ysd9DsZH3v4hvPD1r8VzTjoWs0YNqkHAb869Buf89Dzc/4c/YHTmbEyfsyUGExMY37AWozNmYJ9DDsbW226Day65HM4FHP3CN2DH/Y+GLTqQNrM0hBi00fc68qm4/dIjcfq3z8Q73/UidG1EESz6kwP86jdXY5c9d8c228zCZFNjoIGRTgFtYssVjnCBfirY9fYB6ftipJY3KOpUUoqUDUBuqg+UvfWRLKJWmhJiCii4XiiY31KwvgFwIaC0Cj1L9+QCvT/6CIQGFhbwEbZjYTqEAusWGo13nLWm9TcNN1BbDd21CD6SEmfP4jvfPxt7HftMysQHhxgdYiQ3OrUuStjDCUgfqC2QMNuZ4YI8cZ1pWLkmTdtAo+yNotsbecTxgJsVTufdtO7oNKwcH8e0adNQGGDW9B5CBNZO1thY1cR3aiwaxzyrMWB6WWBTrcDzpJCxK3S88s0vx1tf9k7cfc0F2PPI49gacewnFibmba2NRsFS6EMmcxawuchATK5lK5DnIrKWhu+WQ9uuSaGdBgdSckfQKzSzETTWnBuvSRpjlsGH5g/S90jmOX2nxLQA9jrqBEzbYi4u+8Hn8YvTvof5u+2Ce2+/E9vtsjv2P+7FeOY7j4UtexznGvTH16Ma34CFN1+BpSuX45iX/gu2mr8XjDEoijJtgMDJHhUYcM8louNe/U589z0vQzMYR/QeN13ze6xfsxaT4+PY44AD8flTPw5tFHykuHyktKgC0AR6jj5GTDSOhAkKtadySGloPk5gQ1SwEpfYq/IBlfPp+uoQAO/hOCM6vSyglMTZlNgqGCIXYkTjFQpWwlUTEXQEXED0EapRcFwvVYEQU1rRSA0olVBOcAACdbtQq5xPJGALFy/DVdfehDe9+uM5QdnaXCEAUqJI+0Lq65rwtSnBKZBKpQB4CJuCYloapRTK3iiMtVs8kvxtXjgb1yu7I6gnN2F0pIcYKxjbwZKxPtZO1glSN6gGZI18RMcWmDFSYjIqbKg9JSqiAJXpgk2hcdJLn4Offus/sedRJyS+lZacJFSLS9xDHOt5JGJperNmIaIEBmnAnEVNTdkxQqlAsyKTm8KLJ4KYzWeKkUW4BKcagnTEiyUFpgp9NuXtV0Tj5M8FBNSe5ndstcMuOPl9X8KaRfdgw/IFOOKl78PInLmJklGscowe02bMwqwttsSsbebRtOnok+vtGY1CTgTdWwgB0megtcaMLebipf/nG7jryvMBpXHiW0/B9K22Q29kGk5954vw8Q9/E+/8wBswezptjyZqNL5BxYTLzgMTdQOrFXrWonIeDcP/COFEWdSRUsMFAixU3mPVOLGtl9bABY/Ge9QupDJR5T1Mq8+3CSGBXFSkjOigIdpU5xWq4Am40UQoF+FqB1M4KOegtUETHLmbMcAAsNEQQY7RiJY7bqKHUQbaKJzy+e/i2Ge/ADCdBFiXLZEA/Wk/pHCSYm2S3Jz6kFgT0pCdTpT2UtkdAWKc/Ujyt1nh1IQ6R3/jaszZYjaMNdhUOaydrNF4D2s0GiH3YtrAkV4HhTVoPFP8Q/hOU5oOEcCzXng8Tv/q6RgfW49pM2fnzT1svpBJEwIU03UMiYLEelOC8ijJIEV1OWoji6nbPspnYk4OKXbTclaq9WAYbhPSU2lfKGNdVUbfKPGrW/dEOkCEPHIzlHw3rd/s7XbFrO125Y8R4smzmwhmCPQOGPT7qR1JoJAhkpVUOcBKlt37iBhdqiNvtdNu2GrH3fhWSTH1SoU3fvzz+NEX/wvf+spP8L73vwI+RGyqmO8WSDQtEVzqIOwdI3c8Bg19d8cabKopiaRDxMAFVOxduRjZAwIcdw1prbCpamC15uRXRGkMal6X0hp0DdAED60sSguEQEoocMKtW3ThBg1UoNgShjpnVASM4pJJydJTIHXtGA3ccff9+N0l1+E1//XepIwJw8xJqGT18prm7G1stRrKvqOQKnKHQpT3pRCKLGcIYfpDBI+Pzdc5tSkAhQdvuwF77bsHNtQeGwYNBeyIaDz1+umCWiyMpTaavo8oLHJNTfGkLr4hzVm97ebPx+pF96UdnEDCcYqMsksrxfU2ubS8uZ0pS0wG7c3P/9MKLZBDWsWhpJIMyZFWKcH15iwkfy/HMZKEyleUFU1+mSwLIhOjCSk2y5CMRkxxN9f8hLtGkCkhBBql7sitVhAe2SnPjhWKJNogDcxiEWR9GDNqjCY2gRk74ckvfTNuvOIyND6iZkYHJ9ly5O4gBUEtZddUVt2FiA39BmODBmsna4zzPBatFHs+LS+F49jKefQbh4m64TJKxGTjMNlwqUhR+U7WjLwpIpOOGiiLLrQy0KWBjQWMLgg8oi3NVNUKzgQEQ+UgDXHDHd79/i/j6Oe/CrY3AzFK7CxJStpjaY2j+EZ5vwnXVGyte04mpUeS1ygGlN1RuBBm4hGOzQpnVNrWkxtx/x23Yr+D9oZXwETtk3CI9TGa0tIhRmysHDZWnpIHHEAXRqW4U2pAALDjLvOwdsn9aItiW8imHknrpMVB/mzaazHHr8iWLNGj6GHXIwEJJBHEr2meniWoJci5org1PjF/K15sIsAm1nJhJqRry+l68SIyKwM/YHbVqZe0lfkMWTGkqWpy2y2L3N45uR4ckRYDeaOJEgwtWkqluXFAW6xfsQhzd5ovjnteo1YJRGIoCbRTfZK/x8eAftNg4Byc95zlzM3ukjxBa4OHGOC4PEWJI4dB4zBe1ZhsPGqn4ByVuKimS946DU0CmujgfETUikYbAtBRUSUMMVOUMuMDPQfgPf/+NTSqi32e/sK8NiF38jxE8SELZjIqrbUVLPjQxkR2uBS/v+h2UdfNI44FfEThVEoVxlo1vn41Zs+Zg2nTe9BGo+IpVmgLidLc7AyMVw1WT1QYGzhiSgMwo0NTiROzXARCDDjgoN1x9c9PxeTG9fSdLTSO4EKniqnYt9RK1nJf+Q3ZKor6UgKInyr4WVG0FYcS66rzgibLJ1k3dnm0jlBI6ApoFaEjk1nLOD7kjZiUT4wtlgdipzOK4IQkmHyORJEiwIYpddr2IrU2xpRK7tAGyywPNDyoWxqoSOBtazQevOkq7H/oQaDaX17rOGWXkrC1kFLpymjzubYyaV1HzqbnWJ/WX/OeIYrLxgfuOqH1GVR9KFPCcXiASIwKXinAKjSqoe8yBlF7YuCPBqphYdKRh5hRXT5q4F//41u45IrbcPw/fwLQZVKC4rnkvZVNgQxQTqopp2BzLoQNheQ4FNjj4/UgDmYNpMnJDz02Zzmnd0enq8k1S7HdDtui7BhUrkXezFeqIlBVVQusDVTeo2pcik0makoYsHhANuzJLzoeM2dNx/oVi9PNg8sWbakR+0eKIGuiGDxbZ37QyWVNuycF8OI6pkZsPp8w1VNGMeSFlME5yVp4aGQBskanuDKEVjsRn9kaQ1OetU7TsSlm9Rmwr2JyrbQCFelDZNe2hYZKgfAUN/0RjrxE2XJm4Rk6DRQUah4IRC6rxh1XXYiDD92LLKAnyxc4sZPRMJzwYBpRgDat5WcnCjQmq8o1bAh5mTzPLJRTPZr2pZbwKAuLChoxOHZ76b1VrTBwChUAO62Eix5RB4ozlYHuFMxdpFNyLQB4x3u+gkuuuBV//8GvozM6K1nJ7BE8zNoqxUYks2ckA8AejuzYPDOWXHERSAlPOF/wiMK5uYTQ9O7INHXv9RfjiKMPwyZ2TVz6QhIwXzt4HdFRBtoiEVUGRHrgGug3HlIXkvjMKGDZohVYtXwVZm29PfuGJEzGGEjaJvhAwAGFZP00b/IIpBpS0mD8SHOPKC2y1oYZ1WgRKYEjE54pzpV2sNT/qTSslfiDuhl8oOlX0gepFKCNRcHUKVrRSHoZhiTs4BrS7cL9plozaL9F7plcqBSw8PWkX/M/HyqaQ58QlzjyNmm7zzG/RfoQfVDcYUNKZ2T6TIyNjUHpHaBAiKCE7mm1nakpNlopOifxFgNZrSLFbe2QJLLy1LR3uVSTa4ii8K1m0L0K8NFBlQbWKJTC6Bhp8K0HYDoWvl9Da4toSPARA5fZGCivgE996jRcf9M9eMEHvo7uyPSWAomtdZriKaT7VHTO1oJKZpwANaQQ216d0MxQMjArcqUfp3B2eqNqcuMY5myzOyZrR1ovJQb4YjVZq26ngNUKfUc4S/Dv5AFJXCTub2E1vvedX2C/I4+GVo43ukpn1kaxy0MaXVwhcvcihnZEWtAh3y3vBABJBGJk8eb3KIqzVCS6RXF1CYpG2TdRRc6HFEeWNq+p5nS9bzy5VDDMk6PJ7dWStRO1BhplJ241830oSL0MaY2hpOk8i+ajHwJqb3kg6XVwE7lCUSjoMIkAC+gOrNGY3LQJE2PrsMe+e0JFwLIAGWWoXMb/OU/AgVIbWB2IbiUGxGgS4CTbEN4H6cayR0P/JOWrW0pEjtIUmNUpUU+Mo+hZdLRiflkijmug0O0VcM5gEGpAK3RHepisKtjYYMSWvI70Jyjg3PMuw1ln/Q4v+ch30R2dDiEHl3idL7GV1JFbYMOiwTQvOq92NpDkZSgC1mSjROvRuMydFSOgjVFKKRNj9JhybM6t1doYNTm2DjNmzRxyqmTzAoAqNIxRqH3AZMNZPZVCYL6InEY2SsNqDaU62OdJJ2Ldsgfx9bf8HU7/15dg6V03AsGlb1LGQBseG5eaezNGNYPkM/tBfk02JtvGKdlEeV0UR1QKUIbdatNycYhfFdGzFdEwKsI7mpM5GEyiPzGOunE02o7nPUrPqLYmuUKSeDEMWzOKLJXRZHVpPGIBJWl/lePuRxLJITewFftJ4V8hN0WSG5nfU9UeE7VGBLXFKaVw+xUXYK9DDsXsGQZKAd2Spll7Tn6JG1sYgt0VVmO0LJL1E+s5nL1uXy+9LmASKPrZMWYYEJYGDaM0urZEGSPmzJqG6Z0CMzoWpVHoWI2OIY+EYn0NbQsMao+V68exfPUaKKOIWlMB0BFeAyvWbMAHP/BlPO8tH0Fvxpac/4hDMabEwWjdgShKsnw+z6VhBUthk9iEkHIFQ3sycga4ZVzYcD6sHG7Ocm6q+hOhP74J3ZFuilWUaHgMjzuQhxAiI/5bH8galOpEVgMeGrvtvy8+/d1PYGSwCcc/8ZX44UfeiN606Tjsea/GQc94MWxBeNAYAtWuFKBYgzMVWtJMrcef90Xr9TaJdUJ/RN7EKX4SaIEmKyDumaE4wTc1mqZO57VlB0WnR72qxpBmjpmtTtrIUs1MkyDm72LrET3SgCX282SN2TdNiJO2A6vEJLRitPwecTvVULuVmBF5PMaUtB7868t/+nW84/3/jFIH+Eg1ycbTvJXC2CTgc3olZnYNJhqPgQsouM5qGTUUQ0jJPwBEgo3s5aRrF6+A95JJO53Wz9U1YtegW44guAoehD7q2my1tC4QoeFjjf5EH5ODSczdehagNVz0KNhTigr4j//4Gg58yrMwa5cDSdmzROXyTna/s0qMybLzpYkgDB/sbWnxhnjtg+J7DK0Jd+zduKaJMcYGD3NsVjgHE5viprEN2HKrOemRJx7YlqvlY0QMHqUhl8ZqnSZGK5BrBAWUmmZOKF2idha9YgJ1UHBqNvY5aH986esfwKJFq/GlL5yBr531HTzjDe/H7oc/nYTSGKK18AEqKqLiNyr1JsYpGzRfXxaCNuKDtFmOD8BCHlvQD6HdBH8+hgBjCuY+kgFAhveYuOERwQVO+tAVGB4aLC5qLkXF1rUw5UoIzPWT0Bd4SAyKbJjETW3tKN7o4p4L7aZoevbkRbnyfYVAz0zbEhsmPGovbqfnujR5R0YDI9ZgVq/A9EKlPtKR0kI3mmrgkg9o3aNOLmC6A3bpVcow69Y9KgbMd6wFlEE16MNaqepmlBVx13rE4NE1NA5wq9nTUFiaYm0tPR8PYNGy1fjdRdfgzV87F9bo5M5KBjmv9BSgS/LK+Ge5TlEktMFauy0bg6EzKeTvBMB174e4s3I8inCOAwA2bBjDNltOb7lMCnk4siJq/QhUzqfewMAb32qNOSMFAIXRwiAAqLxGMzmAVhG1H4FXXXz+tE9hWldj/zlz8I3v/B9cfc0d+Ne3fAiv/dzemL31XCjFzGha6oVUPBZQO3HRZjdaViMJQ6TtKWDlrLFzsJDuD6CEkNaEzeXaI7W1IFkqAjtI3Tdl30AlBsNTrKVOGofG3aVGXbRiy0BII0lYiSeSLCXES6GLlZ5IOlFOMkjDb4xIA18pniL3I3rFlJQSyxO3j9IKx73mvfjO5z6MI47+MnpdA6OoITxC8eBjQFnD16IxraQM7aZKIURqFi8sK2c/NVE3fETeQyoOvyhNy1prak8LDaguWZBgsMumVL7XwhbYVDc0WKvbxaCaJJgeSHHfducD+Md//Hcce/KrYHsz077Ie+CRjyHHo/3sQYkyhaxwKcpqGTDebzGGVI4iDyKwsp4KXs3HI8acMcaqqauwfu1afPCt/85Xlr3VCObk0TrhIUV31Axm7lqD2b0yu11aY7wJqHyD0ngY5TFSVNhyZBJW02cmaocQgScfsx9223sP/Pzjb8Jp7zwZ1535VUysXUJzMLpMLG0KbomyUMak2BO80YRRwWiKXXMsymx6ymQNxzFHiFx/dI6ysjzezzWOh/I6eNegqSu4ukJT9eFdBe8I5q80ZXqhqfYbA2ecAziGFmY/sgEhxQr0msS+5HrnERJyP2k3QLEiIMVAIpupXhTYXRdgBLtUwYecJQ6BAQIhzVjd87AnYpv5e+HLnzoNIZLgjXYsulZzUzh1lmQmPQK4G0UZ+JndAt3CIiGYFVuadmw8vNF4L7E7DIprG+dIQGMgtsOyQAI+8HppkHATYwWA4FCWHQTviH+WdgMWLl6ON7/5Ezj2pFfg4Oe9NnkweS+LQo/icg2FSpJBNtyYTvF5HqQrQpv/ZiurSAiV3Kd4VvIaIgJZmIc9Nt/PGYObNnM2dttr13QXiv1nuXHHHR/Ki1FX6BiDJniMlhYda7BmkhjRBo7ahaZ1CnSsYvytZzeYUunCNdO1HRz9xEPR65bYf58d8fOzLsNX3/ZCQCk0dY2tdpiP577tE9hqxycgRi80b+zmKtB0ErYg8gR4kZIL0raiLUuGmMsQ0skuk68lAdVyoFnQOWHF7nOyw0Zi2pgEqt2vSteYfG1oEdjsPCXXMH2fGHveT9ro/LfcI1ufBCkDAzFYGbVJtmOI8NFz54rGs9/yEXzn3S/GL35yEV7+ihPgQ0S3kLIFrVXjfaa+5Faw0ih0C8pUE0VJSBaF9g7tj5A2MlLZpVdoHvJEz9Faw2WQiMY1KGwJgKaJlYaUj5SuACA4hxCAuq5hbYG6bigbDY2f/PxCzJ47H0948ku4UVoEUzyKHD6oh6y3hHM5CUldVNKzyg8jtD0b5OcPUars4aWskYKr+iis7T9U8ujYPDVm8O7Qpz4DO+/QG3IVASTXLMQIC+KpASIKrVFajQ40XIhYMT6gdiKtMYhAqTW61qAJAb3SQsDFtQ8wQXHMSvw3b37TC9AED6sVDjtiX9QffSPGxyYwfVqJX/ziMnz6g6/GU1/2Zhx4wt/nTakVI27IvZBG6/AQ9yULKbmALEApXqM/gb0EJURZ/IBEUHNrkUrdMClDp6UQLw+OXWYtDd0SD+cAUgHCdZ3WW8uGER3TcueSBlekTIRNTzOkUjCsCdLHFozWveXLR8A1BELvjkzDSz70dZz+wdegLAxe/OKnoSw0ul2DMQU0PqLfBEwrDcWE3jOiy6DDLm9hSjQhYLLOLWIKnI+IVBbrGANVEJter6SmCsHwBh/YLaWyWwwyDhAIkcOLyJ4BAuomYlA3mDGti00TE1AI6HZGUFU1zv7FJXjaa/41sW8MwxhbiaC0NdoRZ4ZbEuEbKeEQkZgSKAxSQJA4OjIJGVn4pKDU8HkHE+MorBl7JPHbrHCGGN36lUtw5GFH87kl9lHp5gAakFNoKpGECIzXDbTiWRsAutZyIy1pvPGKYFaF0bAa6BjJ9CkmvSJiLXKRaUqXUgpFUWLOFh10bcCrXnEijjxyP7zmVR9EUw1wyHNeDWMilHeJxBcxpP5QGdUgIVqq/LdcGBEssZTeCYQuEm8QC0mKN5RqxXfUGpemq7VcOAFe0EPPPDvpdbSyt4jpM5EFCZLIYlkiJI9KMU12ybLFJtxsjqvFHWRjRBYc4rIBUtd1jYM2GrO32R7/8JFv4wcf+kf0Rmfi7557GKZ1NLYYKeA88RQppeFZEY6UFoPGwQegNAqlMZjZLYHYMIwPNFWMIaASLs/uFSg0Ud6URgMFxcMGCoUOHK8ZcqcZkyuN2BJ+a5CrOTraQ6dnKXEYaPjR1b+/GbY7DdvteyyQ5vRwvDkFgJ82xJRDKzq/MRplWcJHBTPUUhYpQSh7Q+XnSqekc1qj0QSd7r2pJqG13vRI8rdZ4LsxZtAMBig7Jdr1salHjAQ5IwIqQhI1zApHcRBdbOM9Ku5w8JHe03CX/GhZYFppscUIfVfD4wCI3hHoWLKmClTU9wHYef5cnPGjU3Dj+Wfg9gt/CGgDbUvYsoMYMxsdh1xI1nLoT76HnAEVayNtQ7RM7W4YlawOC5PEkbolmLHt3CDFIZFfV5RRS58R/Cxa587udsZoIkaOiYW717NFNvmdERw/B57yxveV3Kr09HL8BVkDem2r7XfCi/71i/jaKZ/FXQ9swHjVIESFaR2LDieaXKBupI4hZRsjMQTGqDBSFJjZLdExhoitI2f2QX9P1tSkPVKSjegVFluOdjC7W2K0LFBaoCg0ytLAWEoOlUw8LZPNqKxhUDcNYvCoGw8Hqp2OjffxyVNOw75PPjHF6TmcyQ9HDQlTy5vgME2UOhQxzNNXCmUNvSsRpLdc5Laga61RFJYmy/H61v1JKGDDI4jV5oXTGjNR9kYxPrYxBcVTHIB0BGQGtXSb/EPjAwbOtUDvedPJhu9ZTY21SlHzrg/J/dFKUuh538booRWw87yt8cMffRq3XPgTfPMtz8EvP/VWLL716nQtkpiQh5I0ZltAIbGHPBD5DnlYU62UWMXUazOcIOD7TuNaeDGy1Y3pPnJCQW5OPhwTi4MoFOok8ZTQ8VIIF81umRZELDogWcJkWR8mlkoeV+s1cuPIldthz/3xpL9/PT7/kc8iQKPvCIyeWvvYrSeQurAykgdllcJIodErDDpWJ2Uot2q0BlGVEKHZSGFQGEo8Sd8pjMR5BtYIBy/HupHyFJX3qFwNZZhgjL2OSy69Dsr2cMRzXsb9lnLD+Rln70aedQ49ZM/TtVI+o+GkIK2RSZnZvKaqpbNj+j5jDD1D75O3VvcnEYJf+zDiBOBRhFOpuG7m1nOxfOEyZrPDkDUYvqqpYXS+5alAbhESBYpVS6tR2tyrR9O4VAJIS+KBfHvmaouANbQRdt91e1z621PxlS+8Dy/8uyPxk0+9G4PJ8bQ2SQgDUnwhyjNCwPBIMXRgT6C9cxXHdykxIIgfodsUdxJy3mwI221IEUxFyZaWFzqrZmCoN5DiSI/gHCID0BUUjCU0kbHE0K6ZXpPiUQZPBE9KTTGYm2OptsfQ+tokv7Sf6JqM1jj02S/HA3ffheUrxmHZlbfWEqLJaAxqh37jkzIV9rzRUmO00JjVLTCtLBgNpTCjW2CLkQ5m9Up0CgoXOiKUihoLCo3U+9l4jwiD2hEfUWCr2QSgDhoTHCZpQ8iuqokYrz1+ff7lOOTpz4YPKm1eCRqSM6taAqqkM0klRSeaxJrcrha8R/SeKTo1K15k69layAgpnyke0yGmABhMbkI16K96JPnbvHBCrfHRYtPYOLTPQU/bcIsWgqIUuJbft3xuic3EShqtMFpYijG9R6k1x6R0s9M7BTGyQWgQZUCrStlBwb2GyPVBAIcdvCf+4cXHYfe9dsfyu25quadtC4l09SpxV7aEM7R6J9Mih6xNtU4xptwb+NztOFysMC8kZOK2FkJsSDcLgQBE8MQihhiHkUwC/eNBQkobLh+pdI8KSLjTEARKKVafgXypQRstQczPVNbAtSyzLUrsvN/huOj8yzFSWpSFhYvE2F43ARsHDeqQPQyjgdEOEav5gMSyZ7RG11rMGeli7vQOtp/RQc8qzj+Q8DW86XulQbew6BQGERGTgxpV0yQX02gGJ3pi35vWsSg1hQb9wQARCjffeCf2PfxI4r5VD7PVRSmqtpBGWEvrLL9XHB5ItwrFqr7Vk5vlQbEwiCWWRgeiEs1KEQCqiU0Y2zC25GFED8CjCOe6tWuuLUdn4aorrkOsXcsran2DWIlkFekKxQ2VvkzqfZTGVKC0GnNGSszslmmwj1KUmbRaw6rI3C8RhUKKNz2n543mITKsi4zwvIaIY590KBbffg0vTwtpE9sCJNNNWgvGmzibE7aIsS1IHnnqFCWOMn4yM3+LpU3oH0VZZO8aNAwD9M4hNWDrVmM3f3/geiIUCSfNm7FJUyevIHgQBNChqWs0TYNBVfE5ck8mLwjEeibJ5Ico4UYIUjqSUkjE/P2PxIXn/BoTAwcfCWFEeFgq8o/1G2yYqDBeOVQuUnO0j6gD0ARKhkzrWIyWNlmPjqUYEmzd6fuETDqgCR4emrh6lUevJASaS32ygAKx6xH1iUe/cuiVBbqlxY47zcXFP/2/KRHZWoAhd1bcWyk5dcoCETEzPmiN2gVAwOzSnJ4UaMZ2pyOFFhke6l2T9ppWCtW65WFiYvzOR5K/zQrnhg0bbgr1ZBidOQsP3v1A0qSyaduOgrwqJ5X6k0kbVMGIigIwWQeM144ZunPoTRueUEDSwmRNBjkYFVFYCyidwdLKUJwRqaB+/HGH4b6broQP0rqEtBFFOci2nFoYTyEff0TGt2Poztuxokqnj/JfUgI53gvepR5RhEh0GjwGImOTW5lXOWIcEn7xXCTLqtlaORfQOE94WHa/SIjV8P0kZyE/OfpdigFabnhIa3Xws14GZTu4+JIbWl4F/U4jotARM3sGpaX4s2pE0AjUrpSiurch4Ru4gLrljXlmkJBd5UKEUiSMpe2g0AZl0ZGHgshTBSyP9KD7MZgcNAQpbDxO+a9/xQVnnknhkVit2L5yFgL27AT8L9cCfh3tGjdEIdJrRkaKtPZRalhoJYhEuafHioh1Sx9oAPwBj3BsnkMIuGvdkgeaHfc+BHfeek+yErkXOm/T3Gibv1wBLCA8lYu7/KXMMlnRhCdEilGGzpsQMpSZazy7WGwhg6sRvYPjaVRUFKeU9nbbzsFgYjxpwyyYwzfXjrvauZi8gPlPAJK7GdobO3uurT8UK8osDyJUFjoQnhPKAPgkwMkdjnz7OQ6SbGLkTdb+rjZmWJJs1uYxF5ovso0hlf/S622MKfJrhGwKDCuM2O3wp+PyS2+EUgqTDTXRew4peoXF7JEOZo9Q6yCVWWj1HKOvVGTC6BBR88AisdSNJxdYBc/gA0PzW2KEgUY1qABobmELGNQBgzpisqIZL51yGjZNOgwqKt2UhcbFv70KxzzjBAhrgpiSZDM5ntSarGVZ0OBg4b7SyZq2dwQdntdMa5Wm2hnBOMtG0uS3USwcUr+wrO+6lUs9gEWPJHyPJpwL161Y7Lbd4xDcfOPtMMyyLTGkbgXTcpRG6p0xuV+ayyEyUsDHgARvYTc4eV2yEbn04VxAv6EhOWIFPKOBrNFQ0YNZqEFNXRGLl6zC7K22hrVmqPwhi5tUihqWVq5stDKpyMmZlrXgFQZdEZcptEqDmejDmhM+KoHph0AL8tAlUGF3M3euDLtIQ9YThL+NEBZx8jTSkCgWOsVrnVMQYj1kNTIHcLL0jMlTKlsICU/mH3Qsbrj695ioNTb1Gx66FNGxtDEHjoQmQojAKWnXLej50rAiLiWxgvaRFJnRCoWOKAsLqwuiTYkkJJODCShDiDIfAIsIIpx30HAoiy4m+jTEaObMHkZ7HVhbYtWKtdhq+x1JkFpJH9ln4n1A0aS5JDxcCgLvXRG4rODyiWRCnrHc1ih7Q55xiDAMvGhvxegdENxEHKbQGN6Pj/QL+o4Ygmsmt93tQPzhtjvgBw2soYsSBETJpL1GK3QLC6prIsVqWmnUriEKTd6NPkTUzPo9cAH9hoDzjmFg7btwnobZND6gceQqNZ7eb5RCIb5+oPg0Bo8Fi1di2pxtON7MC6lVvuEIZPe2FS+LZQcrHunGJ7fH0nxPY1PDssSXAJUQKZkkSpldI1vAGMvsCmJbc/JIsfUAZ/vIVecElMSwUKndSFzOxEEUkWgykWJBRRPRpngLcp/tcBNDApo3oNDMKEWbtBpbg2bQR2nodQ2VkGEdq1EInlnlPAPYWqe2NdrZCZcbOL6s64Y8I9tDVQ3gvYfVlODq9roY6Y1SM4GmEKYsO+gVHcyePh1GG6xetxrTR7volF1opVE5wmg31YAWM7ZtZssrAcMXnUfDAAylCJU00usQ0zuofzdvpZjIAcQ4iWAmxa4UgnOp9i1IJHGPx1YvQ6/Xe2Bz8vdolhO9bve+8bF12G7H+Xjgzgd40i+tuuUOFKNUSxgJwaFATRxQkWdbElSvNAo+Uq8fWdKQ6PclntPt6UyKXD/qjiD/34AsdBoJHwMLAmWMzz7rYszb60C4xqWNRlqSMogK4OGyOd6THJAL7bae7LMKgiaC0+0p2dJ+6HHK3xJHh5bFiul86eGyJ6K4E6Z9vURxIvxHVJvRbN6DDwC3S4lA0BdTWWW4hS4n8UX46WqG40e53wRPFDdNAX+4+rc4+aUnY2YnoFtQs3WpgdJalNZAgZ5viJ7QQmxADHeXJE4JFaBAjQ6D2mHjZI3GR5SdEUz2JyHEXUCAshbaWMTIzAcNUbNWVY216zZg1dqNGNu0CbYs4WBR1TVNBFfA048/Crddew04rcEdU7lxQFxbYxTqus4gjhhQlMScL2FZIn8DHlInb7u7EhqAydEAuu7oM2tliBHrli1Af2L8qs3J3qMKZ78/ce2GZQux476H4Q+33J1oBpUimNXsXomRwpIQKYXZvU5K4CgoFFol5M/sEUZ+GGIBiJF5Q3nz+kAXbljgNW8dBV4gRHZ3ZRAvIJUjpanwfNHFN+K2W+7C3k85mRYndcxwPIds6aTILG+QmCyFIxhO/iBS90YQWpH0MIetvZCMpXoiC6W4kSpmvGbSCvxX6lhQclEtxj+WExkWLI6+TOb2npBCQbiS0PIQJIpoJX3oZ9lR+XWEPPgpLU+IeMJhT8Zlv7sSTYiYVpbYYvoICQ5IcVU+EPdtzKUOJbkI5I3MvyHLFoCgDIrOKAaVg1ENRnodhODR6ZSt0MIjhAZRBRRWY1DXUIYA+xvGJxCh4YJHt1MmUdlr392wYvFi+HoAY4VdAq28CT1h7z0rTFoDw5lxx907lGtQCQI6LIzMwpDyLaLUwEqSS1PsxYh+Hl+1OK5Yvuzqzcneowrn8mXLrt64amGcu8chuPH6m6C9ShsZINxrE6gTvtAavcJiZqdMVBxW6wRmnjNCED0F6kKY1jHQGqmLXitJHABWEUTPR0JiCPjbRYWJ2mHgPFwkbSQwriXLN+CD7/ssnvFPH0LZHU23p5R4cApRZXCDMMLJA1MKyQJJAVoK+0mI2s8mDuVChx5YextGOR//LfC6DL5mDlnvGGcogpJjQoBxppKoiXI/tCmrukGAQuMo5msnJoYUDK9j5vcdsqfZErClSmAMBex+6BOxfOlyXH713eiWJXwAJmuHQUNYVq0UKucwUTk0Tlx2pDCmZhghp8tYeVJDtUFE3VRQpkDtKAsfAqAREEKDEB2UBsrCYlAH+KhRFF0U1mKk18O0kS66BSVeXEMuaLfs4KknHIurf/TZzFGlOK/QyrCSkqU1o1q2gXcOdVUngUo6rEX3KfVwcPycvY2YGPwVr6n3LilDpRXGli9wMca7Nid7jyqcMca7xlcsbLbe7QDcf98DmNgwkW5Ug2pOQhjcrz3GBw3mjBTs3lLM53zARO3R50nMNcdJPWtS4sCHAOkCCRyo14EY0GtmQtdKo2MJLB9j5IunzzU+4L3v+zz2ftKJmLfPIZztzcNa1dBdZaEBKB6SWAnt90fWpknAMiKoTfKcY5ok6SQOQ24lxzeiOvP6cp0ydzgArdNAXEwNICfa+NLZ3QUKa1Ncl7tUWvedfojpu/Iv8vXIZwHaiL5xaT6qNhZPftlb8fWv/ggaQFVX4IQs43dVspg192MCFFMGjjslmSh4bK2o1ulDg6KwiABq75PCMCrAGur6MIaEiBgONJrGwTnipw2ehhKF4KE1DRFW8DjxxGOxesmDAFcF6BExn4I4LaKYTUEN3tagaWq4AE4EyRKRwmyapqU0JXZuhx3M8St7hQEd8lw1gHXLFzoA92Azx6MKJ4B7Vi95wJmigz0OOhy3XnszNCt3FwIGjlwpyrx5bKpqxKiw1TTiHSqMwsxega7VWD9ZYbwirTZwHn1HvZyFyQNVraE5jT5GdI0lahBNmdjaa0QfUDBOEQDP91RYuHQdbrruZhx+0utpI8ecW6UG2Sm2I2Z7EcBacMgAkhvadnDblo5WWid3Obbd2/TQopyKNjoTSAdpfJYHqsj9jlHx9O98LVTopk2dSKPSg5NUvoEwzk9VQ8MSKrpcpXvPri2/XdF9CZA/AohS90PEtFlboHGBejV9gI6AQoDRFs4BdUO5BGsMnPPU2BAjRkuN2SMGVtO+qRqX2P2c8wn15QO5xGVpUxF/pKBJZ1oZ9AcVFICRbgfdbgFjNfrVAJ67h2LIw5Sisnhw4QpsOXdeq5VOoWMFxJCkk1vCNLrdLqzVrGx0spKSyAOIRlSWNggQg59rS2+mz4XkfiHti7o/UcUYq4eVOD4ei+Uc1P2JgfceOx34JFx1+e+BJgKBNmPN6efSKIwUVJecbHyKK/sNzb8Y56lUFGST1pyoXWuIqkpajBJGEYWJ6BUapaGuhcqROztZNynbG0Ba+ZxfXYHdDj4KRaeXNJ1kXhXAowpVy2aGLEiyMVv/i+LHpKWWn1gDSpwoDybmWqO8TxIDEMGJfG7k2C9lb0XodKtUpYnxQUjBEkmZsLuxqIUQkhWaWh5q3dmUexy+L3GAU3ZYqQwZtJbZBjSW3HUjdtltV96UtJ6FMeiVBho+c/cqgRPQc5jeNRjtEN7WKIXCEk+uD56ggkCysEbTKlcNDT7SRlPGvnEIwWHaaA9WKQyqChEKZbeLsixpHgrXwZW2CDFgxbJVGN1yOxK0EKDhEVxFllXlPWKMRjOxARd++xNo6jqHBshhEdKWiMlroebtAFdXdH4ld53RcN5nTiUA2LR2JbqdziPC9uR4LJYTnbK8YdWCu7HDAcfihhtuhB93QE3fZtktmdktscVIB1opbOhXmKgd4TDZn/choldY7sOjmxbwO28LvnlpAqam1tJSuxFigFVUr6qdp2CbNdn6cYcfnvZz7PPkv2OBQHL9JEuZ5nqy6/mQG2/lZcAxg9EmBfftrGb7ScUk2PlBiuti+OHLd6JdB+NrjOwSS5wrn1e8KcuigDHIrjdbNFuQG5u0sqxZy117OCFsW9EpRpWXTLWsRD4igMW3XoVbf3c23vDa53MZhxRcYXTqexV3UYEsYeO4ldBFVE1M9eoQPBrnKGSR5wKp29L6+Uh2YLKmBuumrjB9tAutDQb1AEYBnbKEMQWB4gOx0hMTH2V866pG2e1C6C8br1AHAwQPoyPH8Q5FUeCyH38dV/7yDIwtu3fKGqihdZBIhn4T4RryFoUeRrwospptRUqezvJ7b8b4prEzp27BqcdjEs4lixd9b+VdN8SiOw1P2P9wXH/l9RjRHUwzo9Caut+7hcbMrsWWo2XKzs0oLbqFwaxeiV5BgIBeYdG1BGoeKSzXyQSDm/17AQkTuVZIQXxhNFFCKAAwmKg83vq2T2N0zrbYYZ/DkrBEXo40BoD/5Hhj2FVVYBiX5jkt/H2SmAEjXCjTiiTIJFS58Tc/Mn6orGSydcxWhb4TZKGMTpaKLCS73BzjkOWNyf2KIaQH7znubLurw4d6yCvpdYkx1fAGpB+ye7528X344Snvwre+9XE8Yf7sBK2knAFB9IQZRyND3Sj3AEy20ETU5h3SvWsuH0k3kGNvSkHBBwXnIvoTE1QS0ZZcTkQUBe0d1xBe2TBLvzRLKESsXrUOvZlbsSKkPeGjRlQWrqmpXm8KBO9w/01XYpfddsbaB2+j1UnheBwSVsm3SGtd9jgUoM1QLiJyvBnZa1JKY9XdNzRrVq8++xEeSjoek3A2TXPRktuvqQBgr2OfiYsvvgyjtoPpdhQdVXI3AVm8aSyQAl4GqB1oZrdE48nsT+8UmNUtMbNbpNHl7eDaucCuAG1ik4J25ggKTLrlAiYGDnfceCOe/tr3Q6VIDMlrHXLpUpsXcjodSKAJEdLIF5JrgyKJanifs5mT7Ksa/mWOR9m1FesIqOS+Sryjk5VmhcDC1ziPEOmhJ7MqygtorV0cEsss/o8kmPK+9hoN35+414DCBd/6T7z5ra/H0UfsBUDgmhFQ3IwAJMBGov6IMa2xC1QGKw0xpotCkE4QKTvlNaJmBx2BmkEJZdkB4W0jhSlaUxIoehhLwlG7QPFnoFzIksXLMGvbncB4IAhYnYZqaVhr0d+4Fp997fHY+Qnzcehh+2NyYpL3YnzIKgrIBryH6HnEtKdSqar17GNbwGPE0vvurADcstkHg8conDHGFePr12xsBn1sucv+uPOOO6FjxKjtYE4xG80gpg7xwAKxqXIYGzQwmnCzM0qL0YJKIrO6BbYYofpoxtPy5mB3wDHCQ0egUMTsRtDHgE7HEsNbpMRR2ekwkbNcMG/VGFOxTiDgit1DSasbY1jj5sWN7HphSOCmPCZ5n8Qg6RaUiHO6DsHZaq6GSzynTbsLhc4i7rf0hQJ5HCEgnSr0DfLZ9jhB8Qwe5oofekxRNKJ72uuktcK65Yux5N7b8fpXnUgdIhFc7/Vc7oocjiDF8YIsgiIkl+CSgcg4asJMU/8mqJ9XK3SsQq8UuCfQ1BV8iJg2bRo6ZQnXNBjU1Dpmeeq34ufYOEe0KeB0j1JYtmQFZm41Nz2j9l6zRQlrDVbefyc2rVuD933ozVixcg2602fmdIMIIX9W6/y0y8K2WskEqgpi+9cGUvoSBa+UwtqlD2Ck171jc7A9OR6TcAKANfqCpffcjN6MOZi/1wH49a9+g07ZxfSyhx562DhRY2xACZ5+Q4LaBIpLGufhvEPHKHStRs9aWI6XIg8vstxaZjQF50YjTd8SXKT3gVxEY6CthVLEZbPz7rth5YN3DT8A1TZ07F5G+T3TaCqyRq2cEOdssrC0A/k2TQitbYB00NDLw8wFyRooQw3R3Dkv+Fr5lqnCM+RWskst5NnSaJ4tsE5Ip6l/8k5sZ7we4UhKKd0sWQmtseDmy/CU445Dt9AIUUNHoFMQxE3rHFuThxOhNVAabvPj19LzUArdQtrHDDpGoJ8GpeU/RkODhgQbrdHt9qC1QtXUmBgM0K89JgasvBHRLYgakz5fwGoDH4GqrkmBt+aTJBSa1iiswYaVi3D+qZ/AZ756CraaPYI1q9dhZNbWYifys085jPxvbQycc5LnQ1LLHI6lzH7MTs/SP1yPlcuXnraZJ5GOxyycSxYvOmPV3TcGADjyBW/A177+bSxauAiAwszuCCYma6wZp7oXLbiGNYYpJ1i4okobyXOQnAD0HKfKTVhjCZoWidpBJh0jEt628eT6Flphtz2fgFUL7soav71BRQSGXEO0XKocd0qBWYbK8ppnCkZF4b40ZJPbJj2cYrn4nEoxBy3HnO3vQ8ylG6D1EGNyzDM6CZDgh7yBlPRNmcYUM/PuGe6k2MwRke69/RqdXqWa3x2X/xrPeMrhxN1bV/C+YnaCgNJG7ryhyzQKSQl3rUZpqDOnYzRUJJdXsydUmowg8iHCKoJp0pobbqujVsDJQR8Tfao8lIVBv3aY6NfwjUNpNawy8C7AeYeqaYDgcdaZF2KXPfdCMTILksxQWqGwBmVhsXHlYnz3fa/Em972GjzvWYdjwYOLsWjBEvT7ky1GvqkUNYQXL6zlpWIPIXlS+VnS37m8AgDL7ryunpyYOP+xPJ7HLJwhhMsW33HdIISIbXc7AHsfdRw++pGPYdPGTZg5bTpmlDMxOaCyidYKI5zwERqHsjAcj3hiiPcSY1KGzXBwbbThTRdpLiT7852igExFRiQem8p5FEbjgH13wvplDzK7ueIFfOj0aHHa4tSdK7FeihlavXeysgocs9JcFGI1sMyMkN0eKA0I+iR9M8VgnmdzBm6ylnYpGb2Qyi8gJRMi1dqskaxfi3+p1R+oWsKbrlcya+mMyQdPRztKlWwpfZQ8A6WA1QvvxaY1K3DyM5+IwpbodkoGiUSmE6HBTlYDXaPQKzSTsZHAdrVCz9JrWilQ5S2yO6vYQ4roWoWZIxbTewViiBgfnwSCR6cw6FpiRhjtlhjtlSg00CmoDc8Yi6rxGNTUXDFoGsQYMD4+gS9/7nQc85K3kFIGgRh6pUV/3TJc8M3/wDfe+fd413tfh1e+7DgopfCqF70F4xs3Ycud9h6OgdtrpBTK0qDXLaluzRlrxftD8OAp7hZDAeJb3rByyUSMceFjkbnHLJwxxono6pUTY2sARBz6wn/G4hVr8NMf/RCuCZg7ayuEClizaUDUhZrwsU2I2DioUTXEX+pCSAgQiY80F+ApAxYQo0vuUWa0o0USZIlrHLwnDTt71jTU/Qk41yDyaDpZ0aktbVMCrSEXMDmYHDAqdlUyDjULA8Ue7QbadllFTk9CQwgRh+iIBjCLbctpluwOsrApttRN00Bmk4p/KNlCsqYPF75MFcSc6GrdffJU5Iq0QmIAMFpj4R034LCjj6EkH8fQpVWUobcGVjGZtFH0ulFpOrfwTqkolpi+V0olWoGYLoxGpygAWAzqiPGJPnqdErNmzkBZFOh1O5gxMoLRbgcCmiiMQVFa1K7Bpn6NTf0aSgMuAhsnJvHWN34Uex35dGy7x2Ec75NAXfLjr+Hr73wJnrCtxTm/+hpe8uLjUBqLib7D6LRRPP3lb0Z3+pYMKIhpv4kVNVqjKEvUPqAaTLY8Is1hB0BzTHMdGqB9vOK+29Drdi9/mIf1sMdjFk4AqAaTP15yxw0IPqLsjuAZb/ooTjv9x7jw1xegYwps0ZkOVReonUe30OhZjdp5bKoc+j5C60is7iEmPhzZLz4ITX2uc6YwO2Z2Be89XOOhjUFZEAql2+2g6k+iKAp0Oh0qhQhgnHZhK2Zo2YuHicESXYjKkX+C7clnVF722H6PtA1xEgjSC5mYHABoGbWQjVuye6kpWKU/3gfEmOetAFTOEWufykL83e3Rc0C2tODwVWIi8BXJ54GWAMs6aI11K5dgu3k7AgCapiECaRAe2nLzs4wpUACiyo33RuJRnb9Nq2FFQYrOIASgX9UYn5hEr1titNejqdohwgeF/qAm5RsVHKgZ0BgDrwzqQPC+sYkBfnz6L/HCE9+Ezpx5ePIr35vWxhiNqj+B3//qx/j29z+HD33wDdhph7no2BLBefzu8t9D2wIHnPDi1Cggz1aen1KZLaGpawxXy8VLUUzCBlpwDmliDFh5z01x4YP3n/bQXffwxx8lnKtWrjx77b3XN1JgnbHNTjjpnZ/Chz/yCSxbshRzZ2+JrTszYeoCWhnMGS0wUhj4GLFmvMaAraewHsQQuKiuoECdB+0jgqB11tgkpK5xKMqShAcBZQHMnjUN1eQ4bNkBFIGMqTzBFoii9Zx1a1vM9Lscr+aEUMxCSE+HzpUA6YCIqbjcqhXPKp1ra5GTOEokUtL0irONWsNomqiVHzVfs8rrkQRPqUy9mWhU2pYXXM7IwqhUvqso5NZRQPDDSazIaJ1ZM2cC1SbUoWEvwUDBQEcDBQ2tLIzSLKAKBkhzU6S1ra0Ym9iaIK0iCmsQQ0RdV+hP9jHa66Hb6QIIqB31eNZ1jRADfY/KRHHk5lND+UW/uhQvfOYbcNnlf8DJ7/kvPOV1H4EuyiHFdelPv4XDjj4ch+w3HwgBpSnQ1B51U+MH3/kZjn/FWxGRXdMozorsGnYDmqaBc01mT0Rr/6r2ftGyvRAjsPTO6wYhhIsfi6wBf6RwArh+4R9uHnhPxLjGGsyctzv2PfZ4fOxjpyCEiFnTZ6DrNTZsqNGvHEZLgxmdEiFELB2rMKg9OoRchw+c6HGO0f1xKHhWyKPRhZ1bMZTP+waG+YWoSE8ETDSSj63yFPItadNqNzAj7X0lXmhOunDWTQYbERuCXFl2R8nyh9wtkmJC3uQcOwdPXA2Jc4Yzt0iJI5UN25C7zKWVkDOAgu8V3lSZz6KVeBsiqGitQcvfbwltQlWJ9mfhCTFCmQIbxzZyFh3JQko4opWGToP5VFJSmruUBJopDQgiuKVR6FoqpTWuQX9Qo9cbgTU21RGNISyuNQpWWzgfsGmyRt00cM4xQbnHL398Hr7+xR/gxR/4Ep71zi9iq10PgCkKQOXRGVAaS++5Fc9+1jGE3jKWmRk8lq5Zjztvuxs7HnhsVo4xQubJhAjOh9CF1dUAUuYSJgSlNbQtk8JuK6YIoL9xPer++NoY44bHKmx/lHDGGH1h9EUL77gOklgxxuDwF70Fi5avxnnnnk8xQmcEo1pj7VifmPYMoYdCCNhYNXzTMWV02/XFwN0pmdeVXFkNGkWnAXhPGbrCGDgfcc+9S7HN/N2hQB3rMUQeES+bk51Exl6Ka9KOvvKP7axqy20F91F638q1sA1qJ5BayRqiEuFPRy7Sc5Yv1y5BLnyyKFkIKQOY/Ohs+ZXi2qxK10ZonayAwJEtpggnAQtapGJJGVHKP7EfgMpaexx9An7zmwtx+dW3Q0VmZHCOPQAgwkCpArkurGl+Kv9HfblIltVqpAy+JESNMeiNjMIYSw5rIHqaNFYPQN3UmBg06Nc1t6AFrB3r40uf+Q5+9P3z8A8fOw1zdtoHRWGZsyd7NKJ4Nq5eju3nbQMXKNnofIPae3z3O2di/6OOhe7MQG6Zy7tDa4WiLNApLbSS8pg8C/DkOJ6sFgU6g9b6Rjxw3e8wmJj48h8jb3+s5cSihQtOeeDK85r2DEhtSxz8zBfjnHN/BUBhdGQGoteoKiqFaH4gox1qynYpU5uTELmBCznTmTKl9F2F1SgLes0z20LjIrbbfguMr1uVPqsN8cJmtwOtikYLaiXnhkR4eUOLu5pQMsmqZoKvNvqHaB0ZRxml148VDl9DFnaVNwEvRJoh2sriJuTPUPyc3aYQc402uYryM2J2r9u6hyGCSWvJ6+w5WKOJL7ZTwBiD6VvvgGe/6UN45wdOgfYWrvYApD7MrWxKI0QFB53uWSxze6PK+ED5RiCvpY+O6Gw8dbxoTUmvGAnwYEBr2+VuFedqvO+tH8Gd967GKz7xfYzO2RZaGxKQENNyZTI2Tp4BKVljtMHytetxzs9/jQOf9crk3vNGAtg7UKDSiQsB1aBK4Zgx1H2jpUQXs+cnIAjhZbrn6vP7a9eu+Rb+iOOPFk4A1y277451/fGNyZdWWmO7/Y7BLbfeiiWLl6GwHahQwoAQFJofgjU0YcxoyuLWnjd0oA4H4qRRQOKaldYsjpTYKmil0HhKFChlMG1kFBNjGxBcw1YjQ+Xalq+lz2j5lAijGtr07Tqhav0t8CsI4VcUwEGO3aTjJrZehwhzK/bMlxIZaiZbSA39vl0yAV+rJB3CFA7d0BLqtlDQfWU3XOaqWrZm0nbW7ZYYGe2h6JSI2qDxdL6dDz8O69aux6KFK6AaelI+UEeQjxUaXxMGtmFlrLLriwhoTp2krC1r9ciAZ6sowVRYwyRtigbvNpx9j4GABUFh4+QAG9aP4RV//27Y6dvhxHd+GcXo7OSFtOuRCTfNJY66PwlrwAx+FhODBmd87xfYdf9DMLrNLi1PSmrIMYUZMQYM+tmdDd4TfYx4R1EQUvyYUk5CYfWie6CDuyXG+IijFx7u+KOFM8YYm2rw5buv+k1yDWOIsN0R7H/kU3Deeeeh2+1RR4fXGFQORSpQc3wUiatHKSS6EXqdBIaA5xyNKwVXO8hULB8Jp9kpC/gA1I3Hry+4GjvvfxgtitIoioJHPGhYS50tacOzdhOaTgWkTZprfe3ySuTL0Ck5Q9C6mOJgEWgpPic/QBRPSsVHIArcS5QAxUOtyHcoiQL5Dal8IHhKPiRKTWKpy/5p9kDSM2s/cAasFwW1gRWFxUi3i+nTR9HrjUCA4ym2ZaW651FPxw/PuQRuYgA3CIhRMQaa7tc7Bx0irLayzOyik6KwWqNjaGaKAglqoQt0bQdWW1hjOAZVCN6hcRT+eO9Re49BCOjXNXrdDi48/wpsveNuOOEtn0JEQGIkzIDqfOccIt1+yS8xe84s7LfHjrDGomkcFi5fgTN/dB4OP+n1QFTDShCUiLTGolNolGGcmAi1To38LnB21vv0XSndls6lcP/V54dFCx74j81L1kOPx2M5sXr1qm/cddkv+8SPypegNHY76pn4xS/PxeTEBGbNmAUdNCb6NSrnURqFwtDGGDDbnmU3UzY4NZ4rFlDmAVURWiYlBypREPM3ZfiaZoDf/voy7P3EE6E4kxdcg8Ggj0FV8TTqBgpEKiWWjDrnQ/rjvUuC1T6UYppKlV3HDLzjIzVB85/ok6ubYk9mO4iB5mwMHa1NkR5p22Tzb0JsJYR8pgSlgjcJkmCbW4loiv20QbdTYnSki6IoAG1gywLdXhdltwMfI/qDSUxOTqCuG7jGZZ4ipbDHMSfiF7+6AI3TUI2G9hal6cCaAt556BjRKUpQgzLSRtdasMs0lcuwUrTaorA9WN1DhAG8R1PX8HVFTP+anyUidewooNu16JQGG9aPYc42c0kxcX2xPZCYllTwvgqDTevx61NPwSmffjdGixLBe1SuwY9+cA72OOQITNt6l5YwkcEorMGcmV10ChpNsWEycJIRLIesUFlB0nMISWlLTsbVFRbceu14COGixyBaQ8fjEs4Y4+rQDG5fvfDeBGsDgC122Q8bNk3g1ltugVIGvc4IlNdoXI4hpJ1MFj8iEuwV8SGUIkSsx3EbZ88kseC9R1loTBvpoG4cujO2SNfhgkJUBaAoBqldQNM4NDRXMEHnIgeB3gWK31pCJy5JyyFMSSWjabS8MYQWUjzWXnMNM/s3Od5T/LBywkVwsjn2jPJtIoDstkpSh0D6NsfkQC4VSI01+e+tpIwhGJ7S1OEfEWGtQVkWUJqy3RMTk6gqhxAVg7l9um4ohZ0POAK1j/jVNXegLEsUlgABTdNAI6LT6QBa89CqkDaw0jrxHqkYgZhLEs714UIF7wWwHgjmx+umDA1sKgpL9VOupx5x9AG495brIT1EGWFFYPmsLBVcXeHb734ZTnj2U3HofrugqmpE77FizVr84qfn4/CTXk+Eba3kE62Vga83oq4GXAkoZPfTd7VCFtmxAv1sK8f7b7wMRuP7McYpGvnRj8clnACweOGC/7j78nO83BCgoKzF/sccj1//+jcYnxxAqwIIFnVD0640xxa1pySKUHJQn6aRrZAD+AjJFiFCo/HEEj4xWROHLTTGNlXQSmFi/Rp2HykTam3miFWKGnCVMtlxVRptoZBDBHYI6MyLLuULycFC3Hopt7TKKUqbVtY0R7yCvEkxl3yJxEoiwBKztiy5Qqv/VDFZ2RTkkOwK+b+UjmKgYTxKa3S63SSYETRkFjEmMutcw6WnQR0iGk971TvxyU/9FypN082ami0XDLHuM6dPCNw/G4itUOaDZq9bXMgARJ4vqnIoYZWCFSAIchJppEOKqdvr4d7bb6dkEU+XjoHiPJo/I+ECcNul52LHnbbHxz76z0SBYg0q7/H9087Cvkc+Ed0tdhzKS1hDxGMhRqybLNAEm4W/FTnkdrCYkmOBPSIlcUgE7rvyvGrZksX/9Vjlqn08buEMIVxw3w2Xj8fg0gay1mLHg5+C8y+4kFnkLDqmi7GJAQDK2vYKGvXWZ2xs4wPa0C5psvZtTh/mpXUOmBw4TFY1nA8Yn2zw1a/9DNO33A7z9z2MBsmCrQlkE+d4MSK2Nr7EfVP/tDKxfK9inST7FjyVVKTe2A5VRLEMlS90TmTRPXpC20Di6tTgSJ/WOQZNnS5y7la2mUpDrZkrckO8MSWZIRdIzAoUa0rcBEQER0Oq6G2iMJAbuvnedj30KZg1dz5+d80tiB5QMEA0iLCoqybPnpRYnCeaJdZ0CNwwL5hA5MjS0khHY4gvKAaiS42BIIKknAL6g4pXOQytG3lEw7HjgpuvwnOf+2QU2qJjC8QYsXj5apz9s9/gwOe8Ln0WAIdRLXcVSJlfaWzPeQlWqPLcU+JO9pXC2Jrl6G9YvSTG+AAex/G4hTPG6LSKP1xw0xWtjLzCzB12gzIF7rnrDgAKKhgiBAMhgypHrotWlOmquJ4l9c0IxbNPQtK+NNHYoHYUl9x4ywK8771fwNOOeiG+983TsesRzwDasU4koiixJJQAEuY8JDY5CVOGqgoqP678MHixlMDlAKkXGk0cPwKyTxnddhwZJU2QrV7me5X7z2x+CsIlhGT9kzWIFF9KLRgABPoXOT6UYcOZDJnKS52igLEWMV1nwGBiAjFG2MIiROE5YgXGCyTnUAC0LRCdgy1KKGUQIjdDO0dKlj6euo3w0OVAdrmBxvEAowgoaESl0QRgonZs/XL9l6hPgFtvvhtHPfM5LeUreOBWjZqV/Njaldh6260Qmga+qVA7hzN+8Asc/MSnYHTLHSBVMqOyQk65w+TZtItheX8k4yHrxR8W5XD/VefHdWtWfeyxytTU43ELJwAsXbLks/decc6AHgKDEooS+x/zdJz3q19htNdBaTpo+pShNOwG+hjRZfr+iIjKkfXRnABqPHWpy2tRKQxqj/5ggHN/dQU+9K6PoLvDwXj7ty/Ch86+DQcedxJi9EMWJ+0MIFuQ7DMj4VhjjjFl8+dDpV/mXC7V9gQa+BCPMsrGbgki18FkJINo59D6qgSd458BsfitEgF/QGnaAN45tuA8la3lGgoASL5Ca2n2zoLmvGecs0kIKF4Ivn0RYhIMAJgcW4t5286BVQTk87WD8oGG6gJQEVwqaVlQ/hOk/sjn92yNxFqCXVka/+cIGcTXUPsIF2nsw7lnXYQd9jo8x95aD61bXkdKwGnuoNFKY9GyNfj5T36DA5/z6qSgAKZ6gVybzJ9hxZBw1sMuv9TnpS5NMNSs+O659sLJiYmJnzxmgZpy/LeEM8Z474ZVyx5cv+zBtCjGGMw79ASc++sLCa3D1nNi4NK0KeEwlXLGQIYVBUrREzGUx0TlUTURazcNsGjNBvzL2z+F73/vXLzgPZ/BEX/3GnRGZ5Ll8T6XOiBFcLlK1brgh3ltOOJMgqZbfzJwYcoMzfbvwQXrVOQfZjhIX6vydylJ3IglTq5zthat8BapdMUeggAMFHjUwFATt6xF5NjPczwooGz6vLUFU1MixdCt5zsUawPkybi6gp/cBFd7NBMVVABUVIi+pekCoCJB+7KSy0YoxAgXA4ICotZwkHk4Dk3dJBQTlX4Ul1gCzjnrQqxZvQ7b73NEOo9MQosRkClwiEAzGGDFg/fi8IN2Q8ca1M7hS1/4Hg588nEoZm7P0MrAHlTu2/Q8oEkYFUlAke5jaDgWVxugEoQFEQr333g5Cq1+FWOcnCo3j/X4bwknACxbsviNN5/73UZcLK0Upm+zI7aZtzMuueQSarhWBfr9CpWj+Ri1C6gcaSaaWUITqvq1Z3ABoYgGjcf6iQFWjfXx6/OvxcKFK/CKT56B+QceQy1Y0SeXVRtJZrSVc/6JDEA7DkV6Bxn+KQ3O/FuJGeVDMoxIBDILnhpiEW8Fcem1NMGrDffLH8eQKMccTpE1lEcl1yTdLdRjitbmiiIgrXuJkZMzjktG6RLZcU8kZLxBQ6CsbetatQZmzNkKS1esRXCEN870MAoqKhZKBQUDDZMc2JTMAqUjaD4O4KFRh0jlm7pGCMSB6z1RY4YYMdmvcMb3z8Uzn/5afPe08/Gaj34V07ecS6W1mJnX2wm5ECPGN65H2emgU1qMbdqE751xLi679Doc/Lw3wPvItCatGJLDAy+ej4QZvKmChA7IVjo5Z0riaQDB44ZffmewcMGD73wsMvRIh/3vfBgAnHOXzdtp/r1rF9+395Y77gaAAPG7H/Ns/OTMs3HYEUdjtDMNdbOBA2jS9iFS0sdaTfQWmhZ0w+QApSF0SuMdQ/YKXHH5zTjsOa9EUdIsjOCpbUxJ7SmQe6KAVAYQNyXFk8kKMUZUeyh+wFHeF7M1aoPFExQwvcYCz+ZNXOq20GeJi+l3Mch0bllBSfxkgRGrKpyoQhUdxSpKQqsV70jJQj5LsEmTvkEgexKfNc6jrqtkFek+6AICt/RRrZbidK1pxPvI7K3w4PJ1CIqRRp0CztU0r4UHTamQkysCIBdWCImPCRzErWY6wHQsFEqEqDBoaqzfNIEtbQ/XXH0r3vOuT2H7J+yNZ7/tU9h6twNBzQMawRNT3xAaC/k5Tp+9FXbY8wCc9Px/wdrVqzF3lz3wvH/5JEx3JiPTpG6dn1mmnGkJLK9iUt1DjhevWyv+vPe6i4FmcHaM8VG5aTd3/LeFEwCWL13yuhvOPvXSZ73904X447sc+lRc/IMvYOXK5dhiq61hfQfOBZQdSw8flM0D6CGHGOF8RN9FjJYGLgYM+h62p9EtgJWLFmHf41/JVkGSHxqE7Aiye1NPqHATZSMiFpOFhNMXWiWJTRKqGRzR9ivl//m5KHZlJKWuk2uTHmcSJolNskvUts4YPiticm8BKBmZTkoEyRWOrevLpYnk1EqsqInt3lqLTqeDsrC01k0D1zQIIW+2trtsxI3j50M/B2w7f3c8cO9diOGZ0FDo92vyVbVC1GBFRxlmbSxd2xSlpbVGoUiJRk8dIkbrxJgeokOnsAgBeM87/xNPfekbscfT/gFahXR9UIqZCHzr/pEUJwAURYEXvf8ruPnCn2HLebti6132QXANzZNJAJGYn0DW0Cn1oFrrkDwixbxxSenSvgPIut7wy+8OFi9c8N+ymsCfwK0FAOfc1euWLrhrxQM0l0VrjaI3DYc87bn4+c9/DkBBhQ6qfkNN0wwkKK3UGqWORN0k41XD1lQTmVPlsNMu87D8vtsB0MNXLS2cyxZ0PSpGFjqJRwJhNdmllfEMmRiLuVYx/LkcLbIgsKRH5oYRXOsQ5pYfUnrYLe1LsqYT418CLSiVXFTFLrpABcWtlUlWwoAgG1Sa0lMMymUbYZAwRqNTdqgH1ijUTYXJ8U0YTE7CM2OiKK8M0c9oF0QMlYxmbrsTHli4FOX0WbDWQrkIeBqg5F1sLVMkzla5LtoFPPtCSMECQiBX23kHGmFHNAlW07Ndv24DRrfYLilRmVXjmoaY+XxI2XcRl+QE8F7b/+knY/s9DkizWFhLpjg1C6kIdralis9quD5NzPeqJchtVwm4++qLAN/8NMa4/I+Vo6nHn0Q4AWDZkkWvvfEXp9aynZVW2OupL8Avzz0P45s2Us9lxVOQFXWpWMPj6BUhhwqrYRXQr2toECeN0Fk8/0Un4OqzvsMtYSHHMLS6nA1kFgHNYANG2ITk7kknO5Lg5I0uWrJtkYBkIXNQiPbDQ3p8+RP0FnFXZS8IMqh1Wq2HYsdsXlqaW/5wUiq2kkLZD+bkUuoJzee11sIW5CD1J/uYmOijP6jgmMlQ8MXtEDht1khJER9y+9bonK2xdMkSwNdE0clJJVMUiCozN5BuCymzDEWv1Y1LNDPUBkj4Z7LOGo0nki5bWFx6ye8xd9487LDv0bKp0rNo6jphgEW5BN4H8jCUuN4yBCnmIb65W6X1jFuPAJHmoGlWcLawMNYyh1WEtDlJQz1ArYw3nnNaf/HCB9/9EAF5HMefTDidc9etX77wtuX33Q4pSYxuMRd7HnQ4fvfbC+GdQ1cTEXBpqExSaOHiicRva8iazhntolsazBjpYMZIB9N7PRx08N6YOXsmlt1zS4rlCGPKU7CY3sMYA2tt0qTt9jNxFQVxIsBvaiQ2KdOaAQdSm5QpUm1N244r1SMvDO0QRgyJpdTiHeUEVPJ/JanFgtyKe8UiDX0Y2eJK2UYSU3J/IQT0+5MYDCpu12MSayCB8BXAMWtIlw2+pJzYArbdeXcMaoeLLrsGrh5gvD+AshqhaVBXfWbGbyG9Irh/MmKy6sMz5lhrmoxG09HoHpomoKlrasjWCt/46o9x3CveDGXLoTWOnH2WsAhyiS2lIh6SMOV751J2uh2b5j8cU/J5JK6lvSHDe6ncJMz28n6JXe+68jdQofl+jHHVY5WbzR1/MuEEgMULF7zuxrNOrcWgaGOw2zHPwfm/uQDeA6Omi/54YEp+vgCOjQASzJHSYKRjYTTQsUCHX7Ma2P+gvbH4zhsg058QIoKPVCLwHEcww50CGDkDBltnl0WzRSnLAoW16HY7GJ02iqIsYWwxzE6AVqKBBaQtMC0pyfGOCDDanm0b/sXkT0oiMkCsOJ1oCOVLAivnan0f0rXkrK2Mqs+N7IBzDXzj2JrlGFWsZRuFlLGv9CcBEJRilnWDE177XvzTuz6MFWs2wFoLgCbBlaaEj1RjDUAarRBBszed9+lZCGooRoUI4prqV30oTfSg11x1E1avGcMOBx6HPBaDLriqBol0q50IEi9KKyJ8BpBY8yWkkM8lcMYU9BZASDZrbfoDDnUQCYGUvSVxh4nN46bzTp9csmjhvz0WWXksx59UOGOMN29cvezGpffcmty0bfc6DMuWr8SyZcswNlGjnqxR1z7xfnasTg+s8SH92zAH0GTVcNkloBrUUNoMJVzEmkgdz/PsT21auS6VG2MN9y4aa2CshS0KwNDPlHYvUJYd+rwkZeT+RHaUSkKR4QmZLwjI4iNZ12SJU6zDVJmtzGuGDCoWSJUeEemKXK6RDSIAet1SGFopFIVFUZKrWVUVzz9FyugmN5tdP4lt5ZCuoFQe4nUOMWK3I0/ADvsdie+eeQFGegWCj1DaQhmb2gW1pdBCOmiapkG3KGGUYTefrzxqVLXG+EQNaw36lccZPz4f7333Z/DsN30I2pbJchtDlrCpm1YMEVv/pxizLAksn2rFrGid93BNnZ5hEkymwtG8jsYYGEvZZ+n08TILhsEn6et5ze+64jyo0Hw3/pE9m5s7/qTCCQCLFj74+pvOPjXNHdS2xBNPfjW+ceq3MckkwFZpWN2hrceLRDVPzqRGqkHVjcegcejXDUIMuOcP92HH/Y6AgsrFfmRhIenhWKDlJlISxqIoacN2OyWstTQ/0jnUVYP+ZB9N00AZam2SLhPBrgLgWFeOYSF8SOAmv2spkHaNlWELQBIvEuy2g9xOciXTyd8hw5BEKUgqR5SPcAv5xpGrydw4SeEkCzp8P1AkBDT3pBU3t25PKWD/E16EX557PopOgZGRTuqfhXfUdAtAGaSSRa/TSeMTBGrpmoiqUdgwNon+wGPjZIWPfOQrOOMHv8bLPvxt7HjAkwC2TpriH7iqaiWyxJoyD67RKMsCtrAUuiiV7iOGgKaqkCZcAwk0IkJKMEfq4KEJbhxv87kg+GXkPECMNGXs5vN/MLFk8aJ/x5/w+JMLZ4zx9o1rll+x4Kar0mtPeNJJWLlmHa6/8VZ0ooVVJlFCUW+fSt6aNGSLOzqzV2DL6V30yg463Q6CayBlArQ0f85uZkypcAUZo9HpFOzGaraclpMV4E75vODt2SntwUeUYW1RnNAdD/01jLHJDlBbEaX6rFhADjoVX/9QDBsjkIAAMcVD2XtgIY7UTtfpdlDIDA/v0emUVBuWwbppQ2el0YYRanblQ+t1wdkGoUuJETvscSCcj/j9rXeiaipU/T76E5OY6E9i4Cq4xhFiyEdYZaAisRv4QOeua4/+wGPdujG4ZgDnBzj1Gz/C7bc/iJd++DvYcsfdEME1V16jKH23si6STeU9YGzuBALA1Ktk/arBAE3jUrZWMtuCrIpAioNlMnVIXo5KijNGCSNEOIFbL/xphGu+FmNc/xhE5DEff3LhBIDFixa+/Moffm687hNyyRQdHP38V+Gsc87FoAbggLoZQCuD0lqm01eJkEsrJu+1loasKo2B78B2pqEZTMC7Bt7V8N6nZIppxQ3kMlKxn2JPHjrHWbaaO0rElS3LEpqTVN7l9D8UgSViy1KrtnCq9D/6p3SLaCQhy0ZKsK0tXiM1fI4Yxe0cPi+9lwVTJYlBVgxkWcqygPcOg0EfrmlSV4lzTXJb22cXyy5KCWwJZO5mSwdyyaLV96iAg45/Ib57xi/gY4OBrxGMgjLUWxpihPdA9JQXSLBdH+EbahesfUBEg7ITcN45F+Ksn12EE9/+aZjeDKSp30CKOZu6hmscFPSQ/lIgpVcYjaIkFn5lDEKg0KGpazjnc7zPytwIsEQUW6cDbTRxIztHt8p5C9VShnRN9NkNKxbjtot+umrp0iUffESBeJzHn0U4Y4wrJjaOvfu6n301SBZsp0OPx8o1G3DBJddg04YKrqrRNMwxq3gojlI82o4WIsRAA05BG39s/Tp0R6eDaosGUj2TUgV/eyv+4LjHU5zS1A1NUY70muO5l5GTMRExuTFtC/mQ5E8rpT/1j0hc+kxM+j0nL2LkxJU0NKeFS1Fm2nzslmdJzvU22TRak2dQNzXquuHrpzi7rgZomgbG5Cwl0qlVylTz0qViOl9OKlEJQMCwxVdK4YBnvAiXXnI1VqxcjWK0A2UJpeQqh3qyQt2v0TQ+xeE+BJ5aTgilqu4D2uFLXzod3/rWmXjxv38F07aal9ctskIJlG2tqwoBqs3qmZSOMQa2YBZAVoDONagHFbnFSg3F5cbqxF+lEFEWlhI73qPmidkxsbjH1vPPOQTvAy7+9ieaVcuXPT8+ygj5x3P8WYQTANavX/fNBbdefcfSu26mxSu6OP6178eXv/5NrFq3CZ1AE5vrxhMzuAYPxKEGYhnQoxUVrMfWLMf6VSuwzc57ke+vpbMjb54MFmfh5tec86jrBlVVk2UMRD0RmBrD1TWaxqXkKJBbzCRhICTVqXWKXV4Z4ycs7kBbmMGbp+V+JsEFOA8P8FaQhE/rMpIbSh6AUIaqBLg3lmKsqq553QDEgLIoECMwMdmHLUr+tpZwtixmO+uIfGXJeRYGOYhV5aM7bSa2f8LeuOWexYgx8Fh4dpXBU+aiR+0dJgYVGtfA+4B+1UfjBxj0N+Ltb/sErrr+XvzDx76HmXN3za1zEL4jQ8OCuaE9eQwp/s1JHbTyEJ4z+Jq9r7JD4/7AYI7AtXLDNJ3eezTOw9c1hFFQcf5HeCsAJJAIoHD7xWdjcsOqnznnrn5cQvIox59NOGOMceniRc+77Hv/2W/qPkIM2Gb3A7HXYcfiO6f/DIPaQ0cN54hOsbA09k/KBY2PHItqWKswuWE1Zm89F6boJM5WqUFSYiAkISW3hP6khmF+6LlbX1xf+kNatcUSzxlPyezKBON0DFnVbOmETFhek1BYBDFGz5OoI/JvYorrpG2L7S8nNVQWbiVzOIBOp4QC9ad2Ox2MjvTgnAcUbei6qcl7iAC4pYyvJGWGvQ9cXkD6XgApo4zIs2m8R11TTVkEdcGt1+LuG6/G3vvthkGfxvU10WHTxk2o6wYINL06hAbQHk3w6PsadXS45PLr8fyT/wWdrXfDSf/6dXRnbsVlCrGEhKmWZGHTNIkCZCis559zvMnKhOveMdJ8HgLxtxE/5FmIYDrvEZxHVBrK2OSVtJ5EegZQChvXrMT15/zf9YsXLnjD45OQRz/+bMIJADHGBYPxsQ/f8MvvRsva5uC/+yf8+jcX4a4HliE6R25QQxuyWxJrdOU9miD1OlqavfbeGSsX3g+hvKdx9K0FQwas50yeZBqzLfLs4iYGdxYSagOjjJ/lpAAlITKfT06msKDJBqabBZDFbdhSttaE/5f3V95saorwyPly54icm7h1aKPE1C/a70+kJE41qCD/aCN+hva1/Dv/L91L4A4P7z1zCgWekuahFFn5TreL+U/YBTvNm40AQhPVVQ1dWnJxEaEslSWMNVi6YgV++csL8drXvB+f+MS3cfzr3ocjXvIugJnS5Y/hZwBFguWaBnVVk6DEvOYyKMlawxnakCxv/lvI1toeiCZmvaKgfcCPL3CiLD0uLpW1QxhqRvC4+LufdOtXr3xJjHH8scjC4zn+JMD3zR2rV636zL3XXPj6XQ972q5b7rgbOjO3xFEnvginnf5T7Lbr2zAy3cL7mOKb6R2LSeXxq4uuw89OPwvfPu1jgFKYGK/Q6Y3QwNKmRhqNEDODQDqGQivuj2wVKih+yuWWGOk1rRURVgUPU5TJIgsySLGiUEOWUbc92NYlkASScIGTKxJvslYHkLvssxUF35coFek6AcdbBTNJ1HXDiCiDuqpQNYHRLNQPS/G0TsqgFZYjKomihuMpuUZZp9RIpyhr6aBgfICOEeNjG7DgvgcwGFTw8OhXNRYtX4bfXnotfnvJtdi0cRx102CHHbbFffctxPimCRx41FHY74SX4fh9jgVsjxqZA32zUgQeKIuChNITPreuqXwiOOL2YxaqFu/FUtLNBskuRxE3UigaVGrSWufsLaR5glFkvB4htr2WHGveddUFGFu+4ILBYHDBY5OCx3f82YUzxuiVUs+9+DufuPHkD367a4oC+57wcnznXSfjzrtegAP22x66V8D7CFuQIzHSMZg/dyYu+d212HXH4/HKf3whdtplPpxrIENrAYKeCbFWznJOPSQTyoF9igfbli4geCAESr5opYlS0zkE51NMaZjmP7mtQCtzw4OBuGlZrHqSWEWbRD6vWqIh5ZKhqxaDGkVrE+ly4MxiVQ0QokaMVFgn5WKgQBlKMgKGukSGTfWjPDCAP0T/DFlhaE0b1zUOZWmww577AwCe/ow34XnPezp++P0zUXZ62PvQI3DMy96BzvTZKIoC65c9iKO22h66nIHO9DkEQJCSR8idNMKl23CmNCKiqSoic5ZrQyIaRAISsKKR3lNyvQMxMjBHUQgOhTV5eHMgwEqMSIKttCJlwYdQvch3Ka3R37QeV/34KxuXL1n08se4oo/7UFMzeH+uY+7c7f7zgBNe9J4Dnv1KhRBx50WnY3LhLTjlI+/FFlsW0Fah09Og9kx6XFdfcydOet5bAAB7HHQodjv8aTj4Oa+G583onKfAP5O58CE/tySj7Ui2pEaSK2ArIeznESpRgFDcFlP9SzRsOp/m7pqWe6pUtphUl80k0DFKRlHczvRTvmb5kYWWaqxcGPeOy06GNxj3TEYFQIrmXK/kGHbqU5Zk0COtmNyj5rII6TXKipalpfkgihJyN/3mJ1i78A845MR/wBY77cUnk66dyN0jnHCDggwLaj8vWncLab/S3ELWn5hg4WkJjZZrYQhmwWMYwGFIGjOfQxkdA6HB+FXnXHKDY8z3qlTef1JOE9SQNhq//tIH/N3XX/bSjRs3/hR/5uN/TDiVUsV22+9w/4nv+uy8OdvtAl/3cdq7X4BTPvEBHH3onhiZpqEKhW6XBERAGRs2jOOII1+Dk97zeczb+1AoFacIpzDEZYuaj1YMofKvEwief5s7DTJJVOpyUYwkUVmAyLWVwjX7PO1YSGpokhuKyGl7cZvEMiFDzBQe5ojDPYwA4JyHVpopWqSXVbpy6GZT3B1CSyiQpE+3FIhcB6asD6a8RymdlIQ2jN3VCjQ5OvfGRu9Scq6pqR4NbnB/aMwtgpbb96jRm0jD66pO353yClwC0rYga2hMWsfgHSudVmePImheYNB6SDF0TOUabXQe8CTPE6QItKaY9t7rLsGV3//sFYsXLnjiI+3zP+XxZ00ItY8YY7Nq5YpnX/iVD/bryXHosovjX/UOfOrTX8KGMYdqokb0EXXDLhRrrDmzp+NzX3gPfn7K27DotqvlXK1kjCQ12HpKViE9fukskddyZheBY8qWZclJJKY/aVk4egM9vJRkilO/M7YerJRZuB/TWChl8gbQkrKXx6DE1PK/IrPCiYbPiSLhThKwv9CP5DhZ4nDFXTFkTLXiyV+aO1jaLnrrEpJwZy+evleSLT7QKL6mAYKD5inf3jnUdY2mrtHv9+G8R1mWKAvCu+oU52dFJlYYEtdBoa5rHhoUQZ03CkIqLRxOSjHUMOF4vUTImRnRGKjU9gZukGiRitNFAMgKW54/WU+aSrd++SJc9r3/2rBk0cIX/HE7//Ef/2PCCQBN09w2tnbV63/7jf/jECLmHXwcVqxci+/+4EcYW99HqDxcDRbQnCF7zjOPwMc+9mZcc+apQARkyrOC4ERbVBUiNi1ZSX8ecuTkgSSkMl6TNw3XHVMNtXVwmmFIuJP2lURVyG6uWPDsukYeJBTbPIv5/Ow2aq1TdlYsvWQdyeojvZYceTGUuk1tmS1Tsi5TbkgSWKJw4tB9Z4WX2vWYEtM1NQYT46j646gHk5icmESMoEbvokDBg2wN1y2NUayfRCjoeYZI4/5cXbPgmnT9xmhWNOxeFxbGmsRCSPeroC33XlqmWgF1v0gG1zFCrH33dN+iMPhZ8vxXV/Vx7ufeW61avvS4P1U72GM5/keFEwDWr1//g7WL7/vGdWd9M0Ip/N3bPoof/ehsrN40gWpTA+Ui6irCexE+qkM+44TDsPAPt8L7hixXymbyorZNnmysqV8+ZNxYCFPCwCdAg6BGkgtlDAprU5dGdpXZZ225fQITDOmSJDubG7vlvFqE2HtmMGjhguXfxrJg5nEVacIZOPlBZ0+z2dK95isdXoaYSyTyZnE3pYFZBF0B3PCeMlRDJRbnAyYnJjDo99HUNeraoa6JptQaA2W4QVwhATYofDA8Pi8rWgrNPVxdQwYgEdgjC7HR9Cw6nRK2KBKjoMA9y7KANXKu4VIQUeGExAApypyeXQvlBfbc2Eqf/5UP+fH1q9/snLvhj93v/53jf1w4AWDZsqVvu+fq31x//+9/i+32ORJHPfeleO8H/hPrxvtwkw7aRVRVQN14rF27EXfcfj8u+O1N0EbDVQOOAYoh8xhbWj5v0IcR0NbvHhL8yKCgSJlXzw8+N12LxGUAOH+Q4p2W1aWviMmSC9Basbue2r+0hrFlqqsmCwuOTZVofB5xECOEKc+HvKnBn0m3MvTPvD7ikibme/YaWkPdhiyVTYNuM5VMYuRjr8M5z4OPOHRnd5K4gUxmH+Dseo5/Wx4FAreDUZks07XQjWjuLJKmBWNMcuWpE0dTD3uk66HZOC7VfaXtKzfMixDyxOpWOAFEblQ3uPrnp8Z1i+45fc3q1d/e7Kb+Mxx/EeGMMYZlSxYff9WPv7xy9YJ7cNBzXofenO3w4U9/Hf2qQagj0JCAnv6D3+DpT3s9vvL1s/Gid38anZEZkIelksC0DeZUqzksnimWar1f5evKBMjeZShZiEy/McU4y/e3Nlnba83ZUAWZnZITLTpvCI7DVNsXV+0kCBXlJYMoLpiSRBHU0HfRd2ermZI+osD4W9oDd6FaTceK3MOioEZj53Ndtu2lSBKOhjMlaDiB0AuLslPkzDc3IkgWOLYw1AAABhsIB7FciyyRZEtlDo5gosXbELCIWMamca34fCqTBXsEKsewqvUANZfN7rr6d7jz0nNuW7J40ev+iO39Jzv+IsIJADHGsRVLlzz5gq/9+2R/0xie9vqP4JZb78Z5l/+eJtt5hdhEvOZVJ+GoJx+D7fY9BvMOevKQBAgvTCruJwv3cN/XMrTyGlpj/aZozraFSA8W+buz49cW1vQG5IQsW7+W4hCG9nZclxxI+ZJWYsdoRWMFWxZQsZsouzemryJhbk/vTvfGgpxiSoBj2NYGFcWnFJUbfOCETXsRWwvY8hLAoYA1lhvWTVI+yY1HXgfdUiwhxsQ5K8oOiprmiaGCrCYxHNA1aEWY2wjAe6JC8Z4HNvE6yDTt0Hp+EutqLU0FmTxIKyqlrVnyAC77/ufWLluy6CnxcUwI+1McfzHhBIAY490bVq94yUVf+2CtoHDim/4Dp3ziK7hryXLAKagGKKLC3z//Sbj2l9/DwluubGUVKfBPYd/UDFD2Loc916lJEAhreEt6o8o/tizkkMuKtsBPPeNwnBdDAELG+cqHZZOyh8dCRBtOlEYAzY2puZUtJuuTZ7OImrCGEiRKBhu1uIXaqqQd8+YSRgueBkFRKcycPh3dTgE9ZdXikGJJN0DuYFlwXgDcj5mfQ3smjMDrvGvY3SWMLMHxChqCXBb8s0VRFFmpgXDKomwyXLNFnRnyJLu8bXLcrxOvUxZMrTWqiU047wv/2l+zctmT45+4R/OPOf6iwgkA4+Pj56xfvvAzV//ky2HbPQ7BE09+Nf7lXR/DyrEJNAOHUGv8yzs+iclNmzC2cjEAQCB0SUyUCB1bpii8pI/xIiJSnEn/bAtjLk8A2TWcaqmH+QyyeOaNoZAZ23QSMIkfAS6PhDwEVzh/Etwv5FhXLANxIZXo9brodjvolCXKwnCmGS3XeZiWgzKmmfkvKmk8p3GMncKi2ynQH/TRn+xzL6cIRpyifugOjNawtmD2CJ3vJ0Ru0XNsjT18oJ7JuhqgrhvESMmvzN1jUPDgJWttgm16ybRy3Jjc6dRLq5IbLc3SyUtoeRKaw4zIyj2RvCng/C+9321Ys+IVTdPc8Rh30J/l+IsLJwCsWLHigw/ccOmld1/6C+z3jFegu8WOOPqJz8ed9y2Gdx7f+MonYazFH665GP3xjfQh2XDGpthMEh6xFSsmEwdgavyZDK1Y2NZDVEoltxJhWDCz1g8JdTfsMg+71tJyRCijKQB95EtMCR+0FE/r0mOLxEtzXCgxXFGWmDbaQ6dbotfroFuanHGVLGT6vFhVsZY0ybkwGqUBFBwaV2NisqKBw0r6GllxSZKMlZZCjtOIzVDDeUfCyLVQ1zhCNnkiYIshwjcNVMzxZKadYZoYViDaaoSmJkx1BDh/nD0EVnaSM5BFCkXYCwAAHXZJREFUm2LUAUS0W/2gVCsjTMJ95RlfCBuWL/jyxrGxn/8RW/jPcvyPIYQe7VBK9bbbfvurj37RGw+Yf8jT8JlXHAMAuOjc72H7bbfA7668EW9++7/jgKc8Gye96zMAIrxr0NQ1BoOaG6fzTD/Fm7c9kgB4iHg+9DrAn+d3i/unjcn2gl1d+YS4ie0yiQDic1wVk9sYBPvKWUtxxwQ3rOQ10DaUuKq92cRyK0QUBXX9K63hnCNLyDDBEICAkKw7CT6dKCVDEGCMhncOjfcITMUhJaVkKyXRFuUiZI10oiSFArMI5Oy5Urn4n7IxkQAEEvOJcNiWQCoOEH1Tw7sa2uax9qmklYTSY7gjRdxnuofEXcwdKdJ/K7VUpRSu/clX4l1Xnn/20iVLXhjFVfoLHn81wgkASqmR7bafd81hJ79+v+333B9fffNJAIAbrjgT03td/OrSG/CFb/0MLz/lh8Sa4BsE7zCYJH4YcXlUyvRlGJpk79vGKH8xMkNWzuekjGe2UOKC5pPkfSqCrNPGEawm31sWXAyXfEQ4fYzM6M6Y2KQMhoWTyKc8ZSlDAzBfPX8Rf56EXIbJpiSSYgCABNQgDHFUlFSJSjJZ5DHIeL2s3th+pvgO2XpBstqhlQeQe5SEEZ2HaDsZPWVswtaSImTtpDUBHZoG2hZQSdFmjyEC2RJz32bkv9Gy8gAngdKw4WE87VU/+Uq87+oLfrlk8aKT/xoEE/grcWvliDFOLlu6+Mjrzjr19mV33Yb3nn4ZAOCQY09GiMAJRx8E4/u45kwqOQk7XlEWWRA5NkvZVWQ3sh2rthOYZC1ZSKSTP9JGc4FH58lcjkiJGZ72ml3UiESCJRYwxladVBIW3D/adpEBElqTLgb8Ggszu29pFABics2MpWRJjCFxHAVxkXmgkLSY0UQ3OrcPEbUHKhfRREXzUJPJlqSK5jphjtWkO4NczkyOFbxPLBNi1SlTSn2gyaVUFNNaW1B8ay06hUGvSxA/m1A9QGTmA1t2YYuSmOWtZUI2Oi+hgyQj2/JAgNbzF1fW0ozUFrsFAFz146/E+6658Jy/JsEE/sqEE2ABXbL4iN+f9c3b7/n9JTjhNe8CAFxz1/0wpcW3vvAhXHjaf2EpM79rbZikiwWU70iwp57dndjS5AnKBpWEloxjKwEUYp5EJTEkb7i27RU3WIukt0zcQ50SyjIKDYegkNI2UjwLZejcQCqRaEns8AgDTda88YGsilIpzha3XCugKAzHqJKJHWbc43XP35pccXGBW/VUxgkbSSTx98TWOQJjltvJI4ltrdFQRkOpiLIwsFZTdhfgLHWA0jYho2zZRVGUKXlDGeCQnmF7bcViDy13y3XNrP4cTiCSxbz2wnOWLFr4/L8mwQT+CoUTaAvoqbePTp+FF7zrk3jnv3wE969YibnbbAkAWL34AdAGImtRdnlYT6KqYEge4z/TqPlWljWw+0NUHbk4T15rSAItmyDEiOgFVJ6TOMltUu16Id8Lcvw4XASna8+fQXLp1FAmNQupCDUUWT7nA1xkgIOhBJBWMcHdAKBpHKqqydnYxI+Tu0DkUOz7K4UEESTgvkECTbRi4tBaB4r52mB8Ws10f+CkUVEw1M7AshmXtQ8AJZ8SKEAz5M7TewI9U8P8tGnuDZCUp4BSAFoDYww6nRKjIz0C3ut8w1f/5Kvx/r9SwQT+SoUTyAJ67ZnfvN3VNQ5+xovwrn/7NFZv2IjPfubDuOT0z2HhbddC2NaMLdAb6aIsO8wQB4g2bXMLBUnFs+kIIsTt+mMKKLPFTZswXyAg4iwSrZAEf2o1NYaYsa8qgwbA1tMYjXb2tZVvAX+EBIMvIl1PzDHVUFeFZJRjLi0pRZQeZWkJRA5RGNmCE45WIIYWUbFggmaFUB+kT0kquZCccJILRhJIpRSXfDqwRqMoKObzIXC7mSwF43adQ+DvCl4y5QHeNTxBjilMg+B7fZ40xiGHxLC9Xgej00by0GNe1qt/8pW/asEE/soSQg93UJJoh2uOfMHr91t2362486qL8IPvfBYLF6/Ehz72eczddW88443/gWlbbE2senWNqqoxGAwIJC/7hc3PcK0rx0sAEiibeGcw9L7hQn12+9A6n9G5VJ/gYPJvtM8D3rwUPxI1Zc4o6pRg4RmW0hScCjBqaIwDfR9S5hJR3PoMuBe28rJDxX3niemehgK5RIMiJ0wlHTaT3lNm3DD3joiigrjR7Ilw4K4Atrh030VR8DQxyubSBZPgW4NEoUn0KRpASF6RVkxb2qISVeBaMM9kRerNpPUw1qDb6cDy2D8VA+qa2P+u/clX491X/+avKvnzcMdfvXACIqDbX3XYSa89IHiHWy/8Ec749mcwWpb47NfPwO+uvhmv+s8zYGzBG4m4Rwd9GrHQBiS0XU4FciEzUoeSHdK0nIRR4j1k1xVDgslnkwxicqv49dZnIcnQGPNIBRZUpUFAcbb8ArwXJgL5XG6qzvdEYiXDg9tuZ87SyrXKWLvGeSB4OM8jFVOkSutBrrJq1XSpybs13IssPWdF03WGAGVMajgvrAWipwlfkDktzG0kSkZL6USejZxbFCpjc4fiTGSPh1WFtbqFUmJlGQIQPVzjceWPvhjvvebCv5pyyeaO/xXCCQBKqd72O+xw3vyDnvRkGK1X/OFafPdrp2DunFl41ov/GYe84M3Y46jj0vuNotraYDDAoN8nQmKBvtEJIVZKWBckcSGBF2l/layeWCGha6R6m05UHpLiF58uJZpYOGOyRoAIs9jDVP5p1d+ExCwB8mNMrwnYmxcn27zIo4jjMCxQq/ya1op7GiPa5aEY82wTiTehhDnAM4ImR0KJgDoSAii5uXRRFOMZDQ1K4FCiWieXHHz3sh6yvqkZQWWF0XJ/0pE9B/rOskNT40LwOOMjb8bTXvF2bLvr3oghYDC+Eb/+ygfd+iX3fXvpkiX//NcumMD/IuEEAKWUmjt3u4/O2nbee7bceY/ynmsuxKlf/gRWrdmId7z3w3jVJ07HlvN2ljenOC3GCNc41IMBgRWipPvzuY2hzvmoMqBaiukAuWiJMgQtoi6tE5sblS9y/EdCzdZZMpjDvm6yDO1pXtlFDXQtrY05hCFFTO6qJJzSWHuVhZ+jSwBUX3SuRt14ri/yZGwugUgmVChVJAOt+MKG4m65nuDlH3JbHNPGJHhCCSIxtbxdWuXQEkICDvB8EtaasSWE6b2tUlRhDTrdDqJS+NhJByAEj2e+9j047HmvxOpFD+D8L7xvsGHNitdtWL/+B49xu/3Fj/9VwinHtGnTnjtry21/tPsxzxi58fwf4ZSP/ytu+8MDuOjqO/CSf/9aKla3zUm2MoCKSHFlhOAqc2mAWo5o8hh1+jMdhtFwzvPGacVkLTfQGJOatmlTq2yWc54pCyREkPJbNFtrynXlyVb0HWzpgZZVAZBwuXyb7G6KghJX2DvpcWwV4bXmTg5w29pQmI6kGAC23Cq9QbK0WrRKJO5f7xoAw5DEGJnVPjHpC+i87VlIwkuw0shKIYacKxBFwl9rjEHZLeGdx9mf+zfs/+RnYfcjjsM9112CS777qfVrVi57WtM0Nz/OLfcXOf5XCicAKKV233a7HS7b68nP2+aW3/4cz/+7E3DNdbcidLfA895xCrqj0wEAi/9wA77z3pfjqJNejae9/O0wZTnkMmZXlgWYcaTeExdOjIrBB3m+ZafbpQ3DtdDMngCUnQ7zqDo0NU3EalszcWupNEGbT7r8W/eWY9FWqSXPTEHawOKOJrccSFShilkenG9ndHWK85JlZ2VgreaBP3lPUMw63KmSY0XxJGLKFGul4OoBAAUfab1SfK4yjtVIB00rdpfEl7jW3ueYGSAlKm6vE2UCGdtnuV7KeQPvce3Z34m3X/Szu5ctWfykGOPqP/EW/LMff7WllEc7Yoz3rFi2ZI+7Lz/nur2OPC7+5uLrseVWW2DL0YD/fMnhuPbc7wOIGF+3EgBw9dmn4dZLfpETEOJeSSZQaqCM8pFNSVSQJcpOB8aWUIaGsnY6HZ6/YdHpdNDt9WCsQcN8t9ZakvNWeSFvtFbMmqxCbkkTyhFwrKtMSyghCScWTCC5xBEyGt1zWUZc1ggBi8tnE8LI0NRwo0jYaGaNTugrWxSJgMvazLxnmPWOBh3zdcaIetAHdAEfFSmFdnZcyRhFw14GCZVlj0O6WSQJJYwTkglXCmk0hEJMVl5cX4JuKtT9CZz7uff6O3/78x8vW7J4//+Nggn8D5BK/zmPGOOYUupIhEu+OHuHXf7JbrWHvexnBO379Tc+jtDUOOzZL8MJr3o76okN2OeYZ6U4RTKSdCLa1GKlAIo7i4KA40Kb0evYxFnTNA2MJVQOET0TPMz5GhXTQcZA06sc86jKrI4peQ2+l2zB08GKI0o2VQWkEYDIcS2/izLOwqiXXGuaW5mTVQAixYOEPw6ZOiR7lfl6JC7OlwS5Ba+Q6p3B0Xi+qA3XJ32y0u1yFNWkqWfTmPx7UU5aA64h4dOGFBFxmFEzNcXCyNaY/QXDkMANK5fiF5/+l2p8/ap3rVq58iv/rQ32Fz7+17q1U4/Zs2e/bOYWW39ryycc2LvhNz9Jr++094F46iveiR33OYQ2WKt4XhRFqrtNTk5CQcEw8bC1ebit9wQyLzjx47xH0/iUcJFx95H7FmUMABATUXKykOBMK0QAkAQAEAFFdmk5Ho38uzTpTLKa7bAz5h9kE2ttKCbkrhMojeDqFDsqJszObirYq5ChThjKiEJJEiggOHLbvQ8ps+ocTwLjJI4wC0AbRvaUsIXlrhGfUD+iLADANQ5FWbISHF5PuQjxeowxKDsljNF44JZr8NtvfnTjmpXLn1VVVZ7e/L/0+JsRTgAoimK/rbbZ9rx9nvzcHe66/gq15J7bhn7/3Lf8Bw46/gUU+2gNhIBq0Kd6YqSZn5qpT4CYWQQ44SAcprM6Bi5EbKwcqqpGf7Kfa4FcViBBogximoomsZ6SrC6DyIVgCllIpcSiuV6Ys8a5FNS2ZgKvi8HDNzXFdrYgd901qeZKDcoxcfqk8kwrC5st3TDET7FgBm68DpESTJCwYEodMgHOdY4JldapJpphkJE7SRoYo9HtdtPYxuECikrhiFKUxCvKAs2gjyt+8Lmw4Oarbl+6ZPGJMcalf6o99Zc8/qaEEwCUUsW2c+d+tDt99tuPevFbuyvuuwU3X3QW1q+m2PM9Z1wDKj+QK0jjyovkeqaaYAgQ1gIilArJ1fPBo2M1ojJw3sE3juIh7+HqBsE3ABQG/QFCBHqjo+j2RqB4nIJrmgyUB5CGKiUCa8nFkksKUNwYlbAZ5FICAB6zR03RJCy5RzGCMrAKMktEwbkmlXVkWnUyTCoLFRjgHjlGtUanTLbMyqTRei3WCfbb06BaBWY2KFgptUi8W4klYWGnvldha6B7FxpLGQVBQ40UbGHx4E1X4pL/+5mJ/vjYO9evW3dq/Bva0H9zwimHUmrP7bafd+6uRzx9l0Of92q1btkCbL3TbtBFJxXBJcuIKJ0ntDsTsx8AgGIkcHa3LSyCyRXrwN4cZCyfUsCg38emsY2IAEZGp1PTdsgxKJ1MMKjZNU3ZVwa0A5lEWsZHyCQtxQw/4pZS0llDaqWR3Uex3GLhQpROjraiQMsyZYtPk6MtmqZBPahJoNolj9ahW7VXAVYgWd+MoErudFJ8kn0lXGybatRai95IN42X6G/agEv+76f9ivtuu3bJooUviDGu+BNun7+K429WOAFAKaW32nrr93RHZ/6fY1/+rt68fQ7h3+QaZUbaxNSgKxtISJ1ls6ZWJUXlByn6s8fYysAinV9xnZGIlwcoyzLFte21H3Iho7jUnIzh/kWtNYxmmx+RGeXkXDGfU+4pcLInsNRSmYI+I4wMbVcZyIN7oHJZQmJhIWhO3zNFMFvONse8ipFIOXam65MscnZcUzzdqn1qjn2LToGS1+3Oqy7AFT/44qbxsXWv27hx40/wN3r8TQunHEqp+dvvMO+XO+5/5D6HPv+Nujd9Zqt1KJfyW1xQvLGYnZzdSShhlJN6H73ZFrYVNxEtRs7UkDsYEVDXDhvXr0On24OxNiVOEuAAUhIQ1iGwZeQaYpsKFLkwn6x2ShRJL+swEspYSs4HnmYtE8gSJBEkEgJMiK3PZjxvfm3Ik32YFLTAFiVDHSUbPJTcaWeFc0JM4IxyXUZrNJNj+N13PunWLXngwsULF/xD/Asy4/1PHP9PCCcAKKXU7DlzXj8ybeZ/Hfnit07b+aBjuWcSQxaHJlwBULQhACT0jLFU1+v3K+pJLIlpLoSYNldCuQiShuNGrYjoeHzTOKJr0BkZJavIbiRlkWMamSI44Ahh3cvxGadU+cay1cxp3dwcDnHQo0qN6LF1v0B270kusgvahgoC2QpOjS8RJUqesubpf/l7h58JWkIJTnIx9xIDIyTmv/uK83DNmd9at271yn8YDAa/fqzP/X/z8f+McMqhlNp23rwdvz99y22POeC5r+3O2+vgoVmTImAxRmro1SZB0TxbzOApIVGUlMJ3XKLolAX1FbqGLQ+SwCgFOO/Rn5wEfEDZ7aYanzGaXVWVEyZBGsJjK2HDMWMUkD3d05ArK9/5cPfOgtrOwIoQUwmyDYKPyG66StcgfkYSvIiHSmb7Z/Uwr7eyv8lSChsgACidXNwFt1yB6888te/64z9ZvHjRW+Kfccz7X9vx/5xwyqGU2nfHneZ/c9qWcw85+O/eUG63+77JjY2Ryg/TRkdQlgWahoYn1bVDVAplURCzgIxt8A0iNIrCoqkGKZEDaKJJ4SZvpXR2iUF9pO0ySppYxplivlIIxjRZTigMQemQhVc+k29U4kBpiRNhUCnLqoA0hiGVNvh8CaTeXrzh+sZDj6m/nyK8Et/mjG6uqSp2ZRbeehWuP+tbA19N/GrRggffHmNcsvkn+rd3/D8rnHIopQ7ccaf5p87YZt7+B5/0hnLuE/aGgMZnTBuBRsSgcVh8922Ys/3OKLqj/MmYLB8V0wn21jQV0UBCtYRRLJS4pxi2biJg7WBOElWtjT2EeQWGNvzDPcf0FarV7cIW13PWU0YHxikSFB/yw9ST57+GrGnr99JDmqGJcjkqW0oRUP70wtuvxY1nnzpwkxsvWrjgwbfGGBc8whX8zR//zwunHNbaQ7fbYcdvz9l+570Off4biq133gOdsoDWCv3JcXzy748AAHzkvD9QKxknfwR8AIA5i0QwMSRBGTcbk5srv86eYRwWOP5H+1QP97TkuyIEkUPEzgE8fyRkVFQiKJMEklbp2jRfk/x76DpYYTySwUxCplSy0DI6vu2WY4pgSmy56PbrcN2Z36jqibHLFz54/xtjjPf/EY/vb/L4X42t/VMezrnrARxgrT1q3ZL7T91q/h67H/r8fyrm7roHpk+bnt634NbfY/u9Dya3U4NjQ8UdFQpUWGllNJM0SZA3/HqKJR/l+sQVFCczO7DZhJWFRW+kB8PgfO8cEW83joQuBBRKJ4ACfXVMnSjthoC2m5zKRyz4Bdc7pduFapM8BUwRXC+GgPUrFsO7GlvvtBti4G4ZMOieWSEW33kjfv+zr1WTG1ZfvWjBg/8UY7znj3pwf8PH/285H+Gw1j5pu+3nfX3W1nN33uMpJ3e33nEXfPVtNHF8ZPpM/NsPr0QTFLH7MYNfSuQkUANy/QCYYvna/4iP8PqUo5WAyUKpUuxJQ3+IM4cwvjERmSlQacNy9wcN0CW0kpMhurlIy6fO8alSKjHeNXWDxlGNlBBWhOZZs/g+rF++EHdecT4euOX36E9sAgD881d+ga3n7coYXGDd0oVY8ofrcefFZ/friY03LiSh/IvOJflrPP5/4XyUQyn1hO13mPfOEPGyXQ46etrapQvNXTddAwB47ivfhH2f9XLY3owEfE+CGZBhbUPAAKQYU3zVbKUew7NIgsm1QcWjIlosemmUO59OaWq5onmbCt75IZe8bUWzleTkjAIGmzbADSawxQ47wzuPpqqwevG92LB8IZbdezuW3X0L1q9ehukzZmH3PffC857zHJz0vOfi2KOPwLJlywAAL3zHR/HgXbdh4S1XRVf1x0trPr9q1aqv/i0ie/5Ux/8vnI/xUEoZrfUJO87f+T+iLvZft25Nd9O6Nen3z3rde7HnMSdidNac1rwOcfvaWdCYjGNk1A6AhxYB8dD0jPQ1pkGyLY5egOqynuNfzeTJaxY/iP6mDdhyh/nojoxi+f13otvtYdOGtehPTEDbEltuPx+ztt0xfU9/4zqsWXgPNq5chEV33og7fn8Zqv4kdtr7YJTdLhb/4WZsv+N87LnX3jjwwIPwpGOPwfbbb49ddp4PpRQGgwEuvvh3OPmk5wEAOt0uyrLjZs2adfniRQs/8P+1dy6xbVRRGP7vzNiesT2Th/M2KUmrNCGu2wIVckIrQUWoQpFYNIgF7BASqAiJAgLBpmxAtIUNEmuk8JDYFEQREoWSRA0koYBS0jxwXiJOGpOH/BiP3XpmLouxxw7Lpk3d5n7SbK3RSL/Puef89xwAQ3eTB/ZWwcR5AxBCqit9vhc4jn9NVdWKTFqzc0GHS8SR599EW+fjcLllFPyudIPtDUBReyQf4nK9yP9Z8Aix0lGnywmO5zA5cA6yKGBm9DeU+3y4p2kXkmtRyJILnODA3Mw0Iv/MYXZqAhxHUF7hQ3R5EYLggL/xXoxf/hMdhx6BV1aQSqXwS/9PKKvw4fo1a8aSx+vFfYE9aG8PIBjci56eHoTD0/i0txcPd3bgqSePQlFku/1irUMwMDIyjKePHcPamvWnVVFRaYqSuJxMJN5VVfWL7dSjvBkwcW4CYuWDBxp37HhnfX2929ANRyaTtoXaHjqM1o4utIYOQ/TIudEbhr10x27s040z5oBCNOQIgVt0QnQ5MDn4PUb7vkN8eQGtra24MnYZ3d1HEY/H4ff7sRCJQJZlBALtaGpqRjC4F3V19QAhuDL2F9xeBY2NjfYZVTesKm40GoVpmvB6PJAkEZIkWpVd00qRHULxZPWCQSJfTDJME6dPfYAzp96HUlZGCUja7ZY+j0QiZ1iB58Zh4rxJEEJ4AA9WVVU9A0Ke07PZyvZAkE+oGhkb/R1tDz2KJ14+Cbmypqg1QjemtkVVXoCivkyCV3SipcaL1EoEj4X2oaPzIM5+8y0U2WttG6OmffHbnmRHYd/VtFokBWcQl+uXUAp7JXs+Otuzi5Bvy+Q2muWdSblzrWGYWFldxUB/Pwb6LxjDvw5mCEfmV1dWemOx2DkA4yxt3TxMnLcIQogHwKHm5uZndcM4ApDyf6NRh0ty4/h7n6CqKYBrnAtG/tIzYEdSy3NLMfXDlwgP/Yh4bB1LS4sIhTpx+sOPsLtltz13dmMKDHtaX7HHIX8rxNJuwQmUe88ic3tuVAhP7HOytVaPQktncGlkCBcH+ujPF85nkmpyTc9mv15YWPgKwDCl9PqWfNhtBBPnFkEIqRMEoau2tvbFVCq1H4Bzz7776YGOg0J14y5S6W+GIFcBgguCIOCP82fx8clXUV1Ti+adOxEM7sdLx19BfYM/93uwB2sBVpQDCuZ0+/xaZHootEZyBSrAvvaWryprWhozM2HMzoQxNTVJJyfGs9PhsJ5OayrP8wPz8/OfmabZRymNb/lH3GYwcd4mCCEigBZCSFtDQ0PI6/WGEslkW1rTZFGUeMnt5uZmN5pkTrz+Fk688TaA/AVoDg4+Px0B0HXTPrPmh1sX1g1a0VXTVKhqColEAmurq5gO/43p8JQxMTGeXVpc0E2TJp1Ox2QsFhuMRqOXAEwAmKOUZrfs4zAAMHGWHMRyfu8A0CbLygOKInfxPK+4XKJkGIZHN3SJIxwvCIKD53nB6XJxiqJAlmUiywpnGCZNJhM0mUzQRCJBdV2nhqHrhq5nTUp1hyCkeV5QTdNcp5QuX726dFHTtDFYIlxmZ8XSgYnzDocQ4gQg5x4FgAEgmX8opfptfD3GJmDiZDBKlDt24juDcbfDxMlglChMnAxGicLEyWCUKEycDEaJ8h9M/uL8CYfYBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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efVHaq008cJNPAUq5iaW0RmmFEsHWsn8t1elf8fK91/PyXx4DYK1116euUKCjo53XXn0F7fnU1dVRLpeIwpAePXrY7t17TJg6dcoJYRg+bDvz3BfLDygi0m/AgAEHFYrFI3r06Nnr3XfeXghS2/20Kxi56s9IjMHzPEQptHJ53crpVhILsbGdCtmCSqrYjnkUevajZ12exMK8UkQldudlo8MACsG0TMe0ziLfNIoITZQYrLEY41h/qf5Ga0Uchc6ablvA3GkTmDfhczvj64+jeTOnRr4fzFLCa5O++fJxa+1YnOe0eOz8F8n/G6UrIsUgyG07oHno6Whv+NLrbZUftfZm1HXrjbEWk1hsOvQFSI2ZVNFmVo6zhJQICktsIZzyKd0GLsGJW49Z6H7dunfn5dfe4pLLr+TW668EYJXV1uDXZ/yGJIm58/bb2H2PvXnu2afN0089OXfWzJm/LJVKDy22ZH4YEZFlllxyqctz+cI6u+21b27zrbanvZqw/koLl5XY/YxrGLnyulhjESV0zJ3Bp4/fwodvv8bUSRNq55352KfOsk0X2rO2GQ3ARvsczYa7HwoWwsRkNyc7M+vNBy4+kY9ecAvy4BFLscbWezBk2BL0Hr4MZXJ/m7WUWs9aazxPYayzvFvnzWbW+HHMnjCOWV9/Gs2c9FVsTdKaz+fHzpw6+bGO9ra3gI8XxxL+c+X/vNL1PG+1wcNH/r4aRquMWmvT/Kh1t5S+zcOdFWItSZwQJyZ1C9PJkrqMSgna89Ba4StonfgZbR0lHrvhfCZ8/jGrb7Mnbz76x4XuV2xopNTWyiqrrcFvzz2fLTfZgGJjD0qt82keMpRHnniW/v37pxaORYkwY/o0fn/Beeb1116dPHHihF3jOH7zJ2+o/yPied4aw5dY4qYBTYNGHnrksf5qa6xDeznkrQ8/5jcnHUlYrdA0Ylm2OuwMPD+HTS1NpRWI8Najf+SpP/yO5VZenY/edd2wwkY7sN1R5yAZtIRQap3PzPHjGLrcaohy383MVQEacpqOMCFJp1e1o52v3nuFd564mwkfvbXQM2+0876ss/n21DePJrEQGXcpa1MPC4iNwZj0Nuk1M4csLHcwc8IXzPxmHPMmfpZM+/qzKCy1VwrF4udt8+c+OXvWzEeADxYn0/xnyP9ZpSsiyw4ePvKObv0GLbvajgd5g0aNcZ/jXLokTkgSQxddm7qF7qfne3iexlqYN2MSlx+4We3axbp6qtUq2+9/FPNmz+Kd5x6hrbUVsBSKRcolZ2T07T+AWTOms9E+x/Lc7ZcCUN+tO1vvcwRBkGPNNVZn07VXpS7nIQJfffE5Jx5/TDR92rQ/jx//zcHWlSZcLN9DRGSJESNH/nHI0OErH3/KGd6o0ctijKG1dQE77bgN48d9zIb7HMOaO/wCLBjjFlrP0wjiFKooSq3z+f1e6wAwaNQYpowby+mPfJKOjUV3dumiZS14GMpfvMpHb7/Ko/fcxnq7HMjP9j2+dqrFQRbGgieGDx/+AxM+eY93XnupdsXBQ4YydPgSDFt6BZpX3Rjde1jtTtBpPSelBUx442l6dW+kZ9NQvD5D0UEeRKgmkCQxcyZ/w7SvPmHqR6/HUz7/qFosFsZOmfD1dWEYPm2tnf0jdsdi+Qfyf07pisgSg5cYeWuhsdfq6+x5jD9o1Arp52CThCiKSZLOBV/SgyIplCAuWKI9DQjJ1I9pmTSOK849/Vvv12/oSGZO+JIgXyCsLFrwy8mxf3iKQo8+fPTiE0z+7H38XJ6oWmb8h2/Sp/9Ajjj+FHbbdnM8JZgk4Y7bb+XKyy5ZMHXqlO3DMHzxB26i/1MiInXDhy9xXUNjt11OOf3s3MqrrUGpVCYOq4wbP4HzzzmTT95+hUOvfpSeA5qxxtYW2jT+mcJGCkmx+o6W+ZgkoditV3oT/o7CTQ+mct2hmzNzSmdZ2W2POocVN90p08k18QQeueQk3n7uUXr37s2YMWM47bRTKZdLjB8/HpPEvPPOu9x2+50cc+nt9Bu1ChYhMc4K9hVM+Pgdbjp5729tk1FrbMgup12Nym4sYI1h+jefMeH9V+z4914OS63zW7TIw5MnfH0H8IZ1lb0Wy08g/2eUrogMHLLEyOtVrm6TdfY4Khg+ZuH6x4KzcuM4Jo5NipulilY5vFaJ1H4aa7FzJ3DWvluwwpgxfDB2LAD1jd3RnkfP3n0Y/8Vn/GzHfXn+gdsWulf3Xn1ZMHcWQM3yHThkOAdf9mcSL+9MFYEkivj4xcd54a6rWHbl1Tn/7N8ycvgwlMCUKVM59OD9o2lTJ186efLkXy12Df9WisW6zfr163fPwYcf3X2HXfbEJAnzF8zj/Q8/5O677+K1Zx6hz+AR7H/BHeTrGh1mn473jKNQ+0vpFEclZSFQU8zpqd8u6QnVcjvn77Iq/ZqaWX7z3Vhrh85a8PPHvYZfmU9Y30Rdj96cv9+mtWPffPNZynywKVyg3M2sYe/9DuGVlzqt4BEjR9I8bAmef+YpADbcdDO69R1IhxTpKFeZ/MnbTPpsLACnXHkHjf2HoOt6YpWmEnfCEhaodrQx/sM3mPLBq8n4j94Oc7ngq/mzZ/6hZcH8R6y1ixZeXyw/oPzXK10RyTUPGXqd9XK7r73bkbml1tiwFmUWINCQJ2TWl2PpMWQZklw9ickCFrWL1NxHi/u8Zc5MLtl3fdbaeEs+/+RD5k6fgud5xHHc9d4gCms6g8e9BzQzZ/pkVlxlNfoPGECghIcffgilFIMGD2W9TbdiyXW3xu8zhGrsJn1UKfH0jRcw8YPX2ffw49lpmy0ZMqAv1hjOO/e39vFHHho7ceKEDay1rT9dy/7niojUDx++xJ+Gjxi5yZnnXux179mL+S2tjPv6G9547nFuuu5KBo0aw2YHnMTgUStgAS1CnLh+MqlWtanLnwVIMyqYKJeomdNgS/MptS4g13MgiQ7cA3SBpLK/33j4Vp6+8QIAjrz4NnottVotTnDzMduzYNY02tq6bhIBx5xwPAcefCD1fqb8LYmxGGtSK1yBFb766ivmzZtPQ0M9L7zwEjfccAtrr70GG264Pk8//RwffPgRG2+zIw/dfSd1DQ3MndW5wYPneXTv0Z3lV1qFpVdeiz5LrkifQcMIS21Iro6OxKcSW+ZMncD4919h4vsvhfNmTC7lfP/Rid98dZG19qMfvgf/f8t/tdL1PH9Mjz59n50/d06vXU+7ipErr4sS0AqS6Z/w/jMP8MnYd/jmK7fzTpJOuu1+vge9BgzCq+/BoDHroet7YSptVFvnMPmD1/jyg7fpM2wpPnz1BcZ/9gHd+gygZfZ0ADbZdFMuufh8Xn7lNR599HE+/uwLeg1bhkmfvosyMVEY0t7eThJHbL3bfgzq04N8t560lyPytsqELz/jhef/yqAhw1h3l4NoWnkTZ/WGVV6462pe+fMfADjhjHM56dijsRYeeehBzj7r19MnTZy4uu0syP3/UnzfX37gwEHP/fLI43tvt+OuCFCulLjqqsu55RqHm49cZX32+M21QLawWqwxmMSk1qR0WXCpcW+1UgS+wsyfzCcvPcHkz8fy7htv0titkZkzZrLZngez3p5Hk1jlAmRdpk77nKncf+lpTPjQBd8O+/1tdKuvo66hgWtOP4qtttqcm66+skYRu+ji8xk5cglGLDWKvKcAg4jCJKQLv0Er7RQvBpNR0ExCYg02jU0oUXzy0ac88OAj3P1Ht9HCMssszSeffMrDj/6JUUstyayZs3n1ldd5/Y03ee/9D5g6ZRp1dXVUKmWWGDGSNTbeghFrbUmcGIJcnnnTJvDEHdcx7ZsvrO97LVh76exZM2+w1nbdImex/IvyX6l0RcRrbh58gSo0HLHlkecFfYaOBITS3Kl8+eTNzJ46kS/GjWPfffZkzTVXZ6lRS6KVZsrUaYwb9znfjJ/A9OnTmTVrNu+8/R7DRizBV198QSFfYORSIxDg9dfc5Onfvz+5fJ6JEyYAMGzJUQweMYrPx75NffeefPXpRzT27kvrHAcnDBgwgOnTp9OtRw8Mirb5c1Fa02vAEGZP+YYVVlmDX595FnOmTODQQ37JbkefwfKb7U6YdsNf77iCl+65FoDrbrqFHXfYGa01r73+OkceemDrlMmT142i6MOfuMn/I6SpaeBRPXv1vvDM312ey/Xow7QZM/lm/Ne899brPHvfLQD0X2I0B1x0F16QA5ti+cYQRXGqpATP99GpNWuxDl4SqKODsY/ewiN338mmm2/IkKGD2GaHLfHzOfbZ9WC+HPcVTQObWHLppVllyz1oHLkGE959joED+tJt8DLkknZe/NMNtLR1sPxyS3PxWb9Z6PkHDR7MlEmTADj4uBPZ/xd70T1n8LXG4hgKURyDUY7CJoLWAmIJkzBVvM4r85QmSd8nsYZKVCUyCYF41OfqU68tITYxWmt85acLjSFOYlBQDUM++uBjHrj/EV564RWU1lhrmT9v/kLP3bdvX6IoMt27d/9m5syZvymVSg/av92xZLF8T/mvU7oiMmLo0CHPRXEyuPfAYWx55LkE3fugBV68+2qeu/MqLrzkPNZbZ0207xPFMTa1EipJSGRjtPKoy+XRSlNqa+fVV95g9bVWpb6xoeZmKhFy2scay2effs6uP3dbqzU0NqZMBSgWCziryVIuuzF42mknU1/fwK233cnn48Zx+VVXcfPNt1JsHo1JYt587G6CfJHzzj+P+++5l3fefZemQYPYbr8j6Ln0WthCN6qlDq46aBOOO/F4Dtp/fxoaexLFhs/Gfc5+e+zcMWnSxPWiKHrv39QFP7mIiB46dNjtSy293K67H3SsvuW2m3jzLw+j/YBeTUMYtvxqjFpzEwYsMRqtXFqCManDbi1JHKdejkWJws8FkCovEcd/jeZP4+7fHsqAfr044VdH0LupF5GJ8ZRGUCQG2ioxk7+ZwoTPP+dPd91Pfc/+fPyOW5wbunUHm1AqlUjihHXWW5NyqcJ+v9iTkUuO4K477+P22/7IVddextFHnkASx9xy6/WsuuaqaHFwV2QSKlFIoH3EClo0iFO+URJjcJYtgjuGO5YYQzWJiJKYJIkpBHk88dB4KLGEJsJi8ZWPFp3CYq5tE5MQmwiTWtZaaQSYOGkKjzz0GGutuQZrrbEmYRjzzTff8PTTf7FPPvlMxfO8V7744otzcLuFLI43/BPyX6V0BzQ1nWStPeeaqy/zd9ppNwD6Dx3JFtvvyGdvv8Ibr74KwHsfv06URIRxjBZFmESY1KJJjMFYQ/dcA4lNUCL4no+xlmpcJUmMS/W1nd5j3g+oliqM/2oCdXVFPvl4HFtttRkNDfVMmDCJBQta6du3N6+//hY/32VnjIXxU2dx/rnn8tqrr2EsbLDnkQxddjXeePBGNDGrLDWYX518IudccCk3XncNDY2NeLkiR938PGB567E/8uaDN3P9zbexzuqrEOQKWOCTTz5n/713KU2ZPGnDSqXyf57PKyINTYOaXx0wZOSyldjIVx+/y5iNtmftHQ+gsXc/Pn32Xj547iF69R1Aztd8Ne4T5s1fwIl/dPtdGmNIoginoNKAqZe67dZhqe0zx/PH0w9kt912YOe9tqFqQt7/eDLvvf0JW22xOgObehMnhueefonf/foi+jYPY8nlxvDKEw8u9KwXXHkOY1Yb42IECJ52dLT6oOiUvjic9rqrb+GW62/m4svOY/MtNiVMIrAQmZgwjjDW4imPgp+rlQG0Foxj79YMgyyBwlrrlG4cEZkYCwTacwuM+AQqcMpb0RkhtJo4jklsQuD7IJbExkRJglbK0emURisPT3k1ZW2MAWt4+513efjhx5O33nq3HfjjxIkTf5cWelos3yH/FVXGREQPGjz03gUtbTtVSu38/hrHFujTvATd+g7klssuYOiSLkPosKN/yfxyK7Ex6JR/KYgbTInB1x6xsVSqVTdgjSURgxKNRhOTEKVZlVm8pBJZinUFVlp5BZQIo0cvhVIKi2XYsME1fHDnnbYjNjHGCsWGbsxtr1JqbyPI5ekRGExUZuzzLiupZeZyHHpkmOJ50NbayoDhbkdvay2rbrUHUVhl3z1+zqlnn8/+e+xCLl/HyCVHcMNtfyruvtOWz2utV0mS5NOfrid+WhGRXn0HDBxbMWoQDb1ZYsnl2fLocyk2dAeEBTOn8qfLzgRgx51HUOzWm3ffeI04itIglsWaxC2i4rlEgxRKcGwBS8uXb3Lb2Sdw2JG/YJNtf0bFVPnw0wmceuivCMslJnzyHmf+/hSMtWyw8do8eM+jjPt4HLMmjwfg1PNPpl//vgxbchgGSyUJMcbhs77ReOIxN1yA5ym00gDs8PMtWG75JVl73TUpR2WSFOc1WGKTYLHESYzFOD4xFmuclZ7BIZ5SrjaETVCi0Ol7gaUSVylHkPMCtIT4uorGQ2qZbgol4GkfXwWEcQQIgRfgexDbmMhEVOMQX7tnEwVipYaRr7zKiqy6+kq6Wql2e+aZFw674fqbDhw8ZMg7kydN+qW19uOfbpT898l/vNIVkfqmwUNfGrX+NmNW3/4A3n/2Af5y84UUG7uz/wW3M++TF/n8rReY8IXbwHOzrTfBWkveCyjoHMq6gWmUUI4qzuq1lmoUYQ14SuMpTZ2XJ4kNSgvlOCLn+/hKU40joiQiMQmhF+FrTcHPgXFZa6G1tawhEYUx8OYnEzlgt59T37MPAGG1wt1XnQ/A0GVWomXOTL769CNWGTOGo0/p5P9q7aVBFKcw1tx+P5pHr8h5px/B1+Mn8usTjyOfyzNw4CCWG7NK4b2333hLRJb5v0jxEZGmfgOb39vogFP6DV9pXQf5ZAVprGMgVEsdtfPf+fgLZk56ihErrcP6ex4JImiRGnZLF6tQgMqsL3nvsVt568UXOPvcXzF61SVZUCnzwstfcu1vzyUsl1h1rZU45Pj9MCZxiQ1as9Pe23LuyePo0683G229IausszJKKUpRFU9rVHrPxCRExhKTUI0iAuOR83yHx9blWHWtlbAkjkmBdanoQGIzhoWhIymRpBanrxSRTRzjwtjUE3OrvacVYgWVsm+MMUQmwWCoC/JYMVSSMuUoRBCMdcrc1x55L6Der6/RJJUSxAgajRJFFMcOW1YasWBSmMaIUI0MDfk8O+24DfV1heCQQ45eq0ePHh8Oam7+ZtrUqQdYa19anNb+t/IfDS+ISFP/QYPfXnvXI5qW/dk2zsWKQm751T5M/+pj1t9oYz567x1WXnVFlltxOdb72dr07NUL3/pUTIyxMfV+HaEpY40iUDnKSRlwGJgxlkD7WCye1URRBMrSbit4WlPwAipJiNhUqSrBUx71Qd4p3jTineFgiPDp19PZfaddWXa9rfjZXkfzyp9voHXWND597RmWXWczxMbM+vpTRi09mmnTZzB1ylRKbS21dz7joY9AqVp6qgXmThnPk9efTVJu56rrbmTYkKFcd+WlPPzIQ8TV8vSZ06cuZa1t+9sW/O8UEenfb+DgjzY/7Le9m5dZCSUqDXYJ1jqOtcUp37a5M5n29af4uTxDllsVz/PTa4ASS2KofdcYp8xevfk3vPfCX9h2uy3ZZ/+f09CjjvmVdp76yzvcdPGN1HfvwfChPTnx7KPdPbG1FFxjIYpitNJ4vgs8+UoTG5NCV50wFuCOJc5aDTwPLQpPa3ylqQtyeKn1GyYJUZKQ83xik2CMpRyHVOPI0bozyCJdRBxLR9dSgbMFxgJJqnSzZwi0s61carFNKXNCbN05eS9Hg1/AlwBPsnMNkXH5Ep728D0PLR7tpSpvfTWLedOn8JtjDmfXXxzEGScfhqeEKEqYPXs2p57xOz54/32CIJhfbm87pFqt3r+4KE+n/McqXd/3l+/df+DLmx9+duOQ5VZNayW4gZ+0zeaRK35NUcVccNGZdGtscKs3liRW3HLH/Vx3ycXs+cvDWNARccDeOzJ4QH+0aEwSs6B9trNATETBz1EKK8TGgLiVXAJFoD18rVJoIsZYF8TwlVezjrVS5L0cOc+vBVz2PeRk6gYtzXq7Hlp7l/kzJjP9688otc5n3Ot/YdLHb/G7353FtGkzuOmm2+g7sJli/2EstdpGLL3aOtigiDUuRdlhaBaTGN554o+8ev9NrLzeJgzs35dXn32CwSusxdfvvPDWjKlT1vq/MLBFpHe/gc0fb3bIWf2GLLdKLXHF0aU6ixJl47ZrUaKsnkbGwSVVTL4yVBLACgumj+eOU/fmoYfvwvgxJVPBIoRJxJnHX8TglbegdfZUdGkSh5/8Sz4bN5VqGLPCCs34Gox1ii+LRUXG1c/IFFwYJ+l7uCw2X7tU8jCOyfkePQoFAs8jSb9XjZOaovS1h0r5xJFx8QZjDHEKV4RxgkljVjUrXtLgYKqMI2NqwbXEmNo4jZOkk7+eQgzGJM7yxVnTBR2QU36aamxJTEZUg8DL4SmPr78ezy477EFQKBKWXbr7mhttxs3XnE9rOeTz8dO44KyzmbugjZW22J2WqV+br959uSWuls+ZO2f2tYtZD/+hSjfI5dbpO3DIM9uffHmh18ChaQqms25mT/yCa4/Yjlyhjmq5g9vuvpURo5ZAK8NX30znN6dfwNcfv1+71ui1NuGbsa/j+T7LrLwGxx97BE39umOJaau2UvBztFRKJLhVP7Ixge+h08h2wc9RiUIXyRZNTns1Un3eC5yyFUXRz+Fpzeorbcjxd7xErtjY+UJd8kCNSbjxqG347RknsN56azNrxiwuv/IGprVbNv7lb/jtdsuz8Y57sda+p4C4tGBrMusOZk/+mgkfvsGMrz/h45eeZPn1t6JPU7P97KXHrpk6ZfIRP2E3/eAiIg39BzZ/utGBpw4avuLaDqdMgzpZMouDFF1jau0sYHAK2SUVpDh+Wl4R3GJtreuEj567j9ZxL3P6b0/gkb+8wphVRlDfUEAQ7r7lMZ59/Hk22f8k7jnnyIWe7dgzjmTDLdd1WK122KZWTmmGaZnGriVBAecFpSVAwyhGa6eEA60pBj6C0FYN8ZSimsSQWrIF3ym+ahKjUiZNOYooRRGCwqQWdWZoiAiB1jWLNrGmllXXq9hATmvaqxVC44J5Kk27MzULGhdbs0KgNDntFHWSJHw2fj6ffT4RBXzx+VdUK1XeffEv9Bk4FL+hN32Hj+bVP/+Bww4/mN//7sKF3v/4W58n39iTjpYFfPTc/faTFx7usHF4wcwZ0y+01oY/6OD5L5L/OKXred7K/ZuHvbLjr67Kd+/XhOriGklq4UwZN5aWOdO5/8Lja9/bdJutGfvuWObPm4vnB6y766EMXXZ1ejQNJqyUCEttfPDsg4x77Sk22347Rg4dzHrrrkRkIjTKWRAW5iVtKJ1l2ls8pQgTx4IItEdOe6kb6awBJaqGFXpKs/f2B9DYewB7nPdHpzAAWaSJL913XQ469GB+sfeO+ErT0trGttv8nLrG7syfM4shgwcTdO/LJkeej9IBxhps4iZa1l1KKeJqCS8IXALFBUdG33zy/nalUunJn6CbfnAREb+pefA7a+96xPJLr7tFraKXNRabtq+DP9NAkqdde+Dq1SaphaeVEKiI9x+7hXuvvpTtjzqTFTbZtXafl+65ijlfvosRxSdvvsbeh+3Dnvtvz7vvfsHZx53DFgefykobb8NNJ+7JhE8/ZK+DdkH7PtvsugV+kHMKzlPU5zyitIRYOYypxgm+VjTkfHydwQ1u/JTChEocIwKeSumInsbXmkqU4GkHfVSTpFYIXYlT0CIOeqhGMQZnyXsiJMbNCKf4Y5QIed+vJV/EicHTHt3zRXIpDGFdO5NYF7BTShFoB38YYwkTl21p03GvDay76lYMW2FNPD+gbd4s+g5Zkg9feARrDMNWWIN9zr6J1x66hXceuZX5c+bQ2K07J93wEFNnt9CtX7MrKCRCHCdUyyXGPnGX/eSlR+e3zptzcLlceuD/I+b7H6V0fd9fpk9T81vbnXRFsWf/Qa6Sf1pUPHNzMlvCl4j3Hr2VB66/ZKFrrL3zQay144HOAkoVolvdFdpTfP76X5j28Uu88vjDvPj68yAJSRKTWEOYRLSbCrlAk1iDpxTlOK5ZWp5S+Cl+Gxo3QbQ4S0wr5e4himMPOJnGnr0ZtvQKjFx/Z3Ldm2rPJwIzvhjLXeccyUUXn81qq41BEKpRyF13/Yk11lyVUcNHcMrJZzJh+jx2PuNGvCCHSRL3z1iyZKhawRaEjpa53Hfm/m3TJk8cZa2d9lP01w8lIiLNQ4Y+tMyGO26zxg77uxh8Zt2mwSFSq0x7GXUpsywdDxdxFC0LfPHifdx1UWdiwm8e/aw2cNrnz+aJa87ks9f/CsAaG23AcWceyhXn3UTDwOVZY7v9ePT3h/HOiy8R5AKeeP0ejLVEiaEaGwKtqMt5eGmNDl8LlchQjWOMhYacTzHwaK9GTnEphbGWUpQQRgnFnKYYeESJS2xQ0rnjhLGQWIvCFU0vR3HKyXXnlOO4hutm0zbnKSpxQiVycITgCqcbY+mWL9IY5DCxgxniJAYrBIFPbCLEc/AZFpLEkJFtLc6K9rVmm0334ee/uZFeg4bX2jOqlF0xf8+lRX/2+l+473dH89CT93Ht5dfz4guvsOJa67LuPicjxR4Ok9cuThEnhvb5c3j7geuTqePemzBr2pSf/3/inMN/kNIVkRH9Bw15b+vjLmnonda7FQGlQHs+iEbEUlkwkzf+dA0vPHJf7bsb7XMs4z96i6HLr8EKG+6An3NBrqygjWTb6ihHlSn4ETefvC9bbbM5m26xHmHssn0i67J3ejbkqfM1YWKY1V4iSXPhjU2L5qR4axbUAJdNFGgPrTQTv5rIV59+xcSvJ/LSs6+wwQ67MnrDXanrOQCDIqcNL/zxSorxTI458bAalpdZIJ5SFL0cP99pP3o0DWXDA08n39gbsG5yJKaGIWrVmdI66eM3ef7Gc8ZOnTJ5pf8mC6Jp4KBTBi+3xrmbHXamclH0TqULLqNMdYESUE5ZJEnsAo4ieNpDa0GL4YK9NmD9DVbhkfufZJNfHM9aOx640P0ev/RY3vmrKxrj+R7Lr70+4957l0Ovepiw3M49v/kFBx+yM/0G9mPF1cdQiWJi4wJQBd8j52uSNKW4GhtynlOkBV+n1q10wU8hMZb5pcgxF8TSsz5HJXKWsScZCNK57Q+471TjhPZqjLXge5rWSkjR9yn4mnntFSJj6VEXMLu9SpQ4pavTZ8JCoDX1nmPaBJ5PHEXgyjngKaiYBN9zLAXBEicOO/ZEpRlvCYcf8mtGbbovI1fbKE19trWiQNkLCvCXW37P5A9f45rrLqaxzuPss87n2aee56jrHqfQo1+684pKsWL35VkTvuC1ey6PyvNnvTR5wjf7/X/h+f5HKF0RGdxv4OAPtzjqgm79hi2VuokK3/cQBQrL9PGfc82ROy30vfV3P5wVN/05nh+gtEZrZwEYF+5NC5g4FyxTvqIU2lZ47f6b+OyVp7n+9svpiMvE1oCCnHhoT6M9FynPaaEh71OJE8phTJQYKmmwhJT3qESI0zz47GcWcJs2cQqP3/8Uzz35V6IoZpN9jyGutvP83X/gkhsuYullR6UBEfBE1SLMWhQt81u5+cY7ePKRZxg2elnW3+dE+g5fBmsS4tj9ywJJznWEV2+/yHz86tPHzJ0z58qfuh//FQlyuXUHj1z62V3PuikIgqCmcGsZZSlsYFKX2wt8p5CNs/eVcsq4oCqMffxGnv7TvcydPReA5sFNjFpvO9ba9Sg6dYTl9pN2ZfynnXVcRq26Nv3690ZjmDZxAmIq3PXAZZRDF3wK44S6wAOBtkqErxV5X9OQ9+moRsSJ84Lqch7lMKlxr3VaKhJgZluVapjQv1tA0dcY6wqUK6i9X9daDtY6towBKmFCbCFOEjpCg00sjXmf1kpIWzV2OK8FpSyBJ0SJIYwdvJH3PXxR5FWAp4VyVAUlaK3cbimSLXCGxLpAZc7zECtUqiEXnXM1LR2Gtbbfm/p+S+D5Hi2tJeq690J5OUfgTWGeNx68hdceuJmDjj6K7bZck313PZBJEydz8GX309CzL34QuERmpVN83uHuEz94lVfuuapCXL1pyqSJp/xfryP9b1e6ItKt/8DmLzc59Ow+g5ZaAc/TeJ5O96cykISUy1U+evWvvPSna1lliz0Yvfbm+PlimqBAbfUVUZm/nQ52qfEzs2iz0poL91iDsFxCex7X3nYtfZt7EZGAWHyrKaocVZsQqZDhvevoWcyRJIYwMpTjhGrsor7tYUxHGJOYxCkBpajEcafrm2rCQHsYY3n1r6/x1KOvUuzRxLwJH/GHu650xW7SiHTRd+6aizwnGAthEjNzbgfvv/IGd/7hTjba8zCW23RPrHVlKpM46YzkixBXy9x3xr4dk8Z/tbS1dtK/o0+/r4hI3wHNQ77c6fTrG3v06Q90llU0XSxdcO6y73vp78bRrgKfFNrkgTP3plsRJk9rIUpg6jdfcs55R3HJZfdw9C0vEEchs997hK/ee5VnH3uafv17M3PGHLR2cMUW225AzvfIF3Icfsxe5IvFFDYSOqoxvlYoJUSxgwV61uXwlFtsy5HzTuLEUg4Tir6DFXzPWbICzC3HRFFMvwaXZWayBVs55kWSWDDpYiK1BkIEYmupxKlStDCzNYQkoU9jgfnliGpoEOtgpnzgE5mIQs7BDmKhHEbkgwLaWpTnMvPCKEGwVJOE2FgCXzvjBIuv3bskxvDZZ+N5+8W3ePKR55g9aw4Ahboi5Y4Sx115Bw1DV3IWbNp507/6lEeu/DU9e/bg5FOOYN+d92St7fZi1e0PJAhyIM6CFmvxfA+lFForoijho+cf4r3H72xtXzD3F21tbQ/8VOPwp5Z/q9IVETVw8JA3193tiFWWWmsTPLEkVojihCgMicLQBUgQlPZQ2sOSufnUOIqCdUo6pefUXBk6XbwMbtBac84OywOw3Hqb0qtg+NUZx1KlisWQxIaiypPTOdqTKkP6+fTI+5g4I6SndxRoqYa0VmM6qu45E2uJUrcwTjHf2Bh8pakP8lTDkCP3OIYtDjqFh648m6tvu5i+A/rU8MpA65SXaWuZdOU4Ioxdaub4cd/w2xPOY4+zb6L/sNFuB4E4Ik4Vb5YCP/XTd3jplvPemDxp0sJFhf+DRERkYPOQ99bZ54Qxw1dYC5femh1UC9e0TesjYCGOY5TW1NUVatH3jqkfcclhu7PnIQdw/+33Umpv5ZTTDmb/X2zLkiO2ZqO9DieaN5GOWeNZd/2VGT5iCBtuujZYw7QZcxGlGTqwVw2qMdZSjhKi2NG1osSQ9x3rIDGWwFMUfF3LdMx2AhYgig2eCDnf8W+xLhgY4wJiddp9nuHVWjsPLCus70wFlylps0QGEbSGyDrLYfK8MtXQ4IsmjBz7wWJIRNErX4/GUoojYmKMGJLE4olGiSUfuABkFBuKOU1koL0SpYrXS6EcKPgaXwtRYgljCMshX381nt59e3HPrQ/w4L0us3Lnw09g2c1/gZUU+rGWJI547cFbefOhW+lonQ/AiXe8TKmjjJ8vuLmcQn1xEqUK2CWOVMvtvHDLBcmciV88N3nihF2ttQt+guH4k8q/NSOtaVDzhUPGrLNy09KrMm/O3FrpPRGF0hqURnt+GkzpytN0rolbL9K/lSCJczfJPs/qetiF2Q/7nX8HU7/4gG49e/LcrZcQx5aGoJ7WSgsilo6wBJ5FGQuxxUYWMenEUqlHJZbueZ9CoOnIueDavFKVltT9rNF5cNZqR1SlLpfjmF8fwYW/Pov2llaMOAw30JpKHFGJq4RxTGQMChekS6xjLUSR4sXXvqajvY2n//A79j3vdrfDhdbpO1uiKCJJDE2jVqZp5PKr1NXV7dDR0fHgT96x30MGNDWdNnSldZcfuvyazuoDEJVCRDZNgsiMvhTnTBI3RpQijuPaOHn67tswxvD6m+MotbtiRKv+bENeeN/tSDP/i9eZO2smDz1xLSEawRIllrpcQJ8+bmPS2FDD6pXgLFuEOLRO2SaWfN4FSjMWgJBmg6WcQLGCUo6hkCW3YJ2n5QOe1mRRUEHStFq3gNeukeHB6fUziphoTZxY2ssJXlpask779CgGRHGEVpZ2A21hSKPvUxcEtFct1oAvhm55TTmxtFUdJJX3FA2+h0WR1+6eCyoRFZOgLKhAo0XI5z3md0QYLSwxylXgO/DY/Vh2hVGcferv+fPVv+eLd19l2+MvQ+cbABeDWXeXgxi1xoY88PuTmPHNZ3z04mOMXHMzhyun29r7vkeQc0HiKHZ9a/FYd99f6a/feX7T8JFbJ+bz+V0rlcpTP8GQ/Mnk32bp1tXXb9U0fPRDmx95vqe07nRRMt+qC+sgi9TXTKEUsyWbrKllK6JSK7fr+V0I4SrbcNLxP685bBvmTJ1AXUMDF113Mf371hPkc47gnjj3b3DvOvo15LFW1fL1EYsVWwtKxCn+WAoTJi/oIEwtpNjYWpaSSellWoQ5M2Yzf9Y8ll95WYBagRFHrHeR8kcfeoM3//oyC2ZNp2nYcKrVmFKpwtTPXVXH4254mIYBI9LMLOeKmyR2QbbE0D5/Ng+ec9Cc6VOnDLLWVn/8Hv3+IiJLDR4xauxOZ9yUDwIHqYjK+s4pH5PYTpxTnPJIjCFJEpTS5PMB+UK+thibJOHdp+7hievOBUB5Ht169KSjZT7HHr8Pm223MfWNDRjjFuZM0ecDnVqVzsvKew6X97UQeLozmJcu5IHnLFVjLX6tjgPYxEEDnXzddF6l1nhNraaWb6ZSs6I1pLxbEbDKjXsA26m7sQraKiFhCOUKBCpA4gRNQn3RR3yPcmywImixjl9uY2Jj6F7wiFHMbKtirJBXhv6NeYqedorf5cQzr1yltRLSuyGHr7UzHixMW1CmGhuiakTOF3o21nH5xbfx6J+fpNTegdaa/c6+loHLrr0QXzmJI8Y+/ScGLrkMdb2b6WhrJ8gXa+nPImnsJjWnHDQHILTPm8Urd14ct8+e+viUSRP2/r+SdflvUboiMrx/89CPtj7+8mK+oRvgsC3IMD3bRYFKmhaRJkh06lunXJWiU7F2Kt1sQLsDKZ6bsRjS7417/Tnuu+BYAIJ8nguvPoelRg5BlNBeDRGl6FXMM7SHq08qklrayg0M5TkrJUkHSphYZrZV6AhjqpEhMk4BhkmCVrpW+9SVjdRkWwVl3MoMF/zj7U9z3/V/WKjNll5nM1bfek9uOWUfCsUC5VKZQn0Dh1/3JHXdemaviacV1TCiWqny4RO32/eeuufMmTNn/vZH7dB/QkRENw0a/PlGh5y9RP/ho9N1Md3WXqtaMDTJ6HGZd5O6/SZJQBS5wEf7CqV0yuUWJn/8Jh89cwfl9jY+fvstVl51WQ765S5sttlaVGJDOUyIY0vguy1xqmGMQWjMa6qRZXZrGV87CMpYYWDPAjqLD4iDtXQamNVKiBPbaY0bmyZoWJRVKJuO22yspuMXEWzixrNKSzdCulGmMW6ftlp5SlMrVC5KQAtxEpIkQmtHRBx7KONSilVcpaG+AJ5Popx3lZgEzwfEoEkQ5TOtPYTE0D0PdTkfz4JCYRKDl/MwnmVGaxmtDP26FajGhrkdEW2VKG2/iJ71OXK+x9yOhD9cfgsvP/cmc2a4+uYjV1mX3U+/1r2nNS7DEwh8TVtrGy1z55CvayTpwlDJWEY533OxjDRbjzRA/PkrTzL2yT/Onzd7xo5hGL7w04/aH1bUd5/yw4qIBP2bBr207t4nF4O6xlrDG2NrO7R2RhLSYEpXClF6bo3HaWwtY8sNUpcn776W/tfJb0kxQqeoR6+1Cb+87M8uQl2p8N5b7zOgIcfgXnX0aCwQeIpKHFGOI6xSJDYDKVRKfwGsU3SOnK4pBB7GOCqXI/I7V9VaQ5ymXdo0Siy49NDsXyWOmTZtHvdd/wf6DujDsWefCMAev7mOTfc/gc/feA6AvY85jsNPP41yexv3nXOIe790kCbWojzHwBi94S4SFOpOEJFeP2kn/wPp27fficNX3XB4v+Gja56I80JUFwspXWRT689tJmm69KMljCIqpSrVcpmwXCKJQ2ZO/IJ3Xnye/j1zXHfDGdzz54tZf6M1KIUJ5TChHBrixBKGQluHpVwV5rdW6ajE1Bc8BnQv0NSzjn7dXRBt4uwOsry2jJOrU+6gWEiMor0cp95P6k1Zl6yBEYghSTRiNWKVM1UTQaFRykMpDUZjEzCRwRqQ1KRNooQkjLGRJYkMcRjjBraHp3x6dWugZ71PXZ3F6IRIeVTCGG3BM4JSPoJPHLn7exLgi6apmKdvIaCofXSinOLHBZhtYlFWyAeee78oIVBC33qfgu/G8IDuBQZ0L9CQ9wHDrgfuRu++PRm+wmoAlBfMxsQRcVgliZznVeMU53Mo7QLlWusaNOYWWUOpUiWKEiStXRHHLhtz5JqbsdnRF/UYuvSKzzQPGXqriPzHF+r6R/KTK92mQc2/X2rdbZr6DBtFzXKlKy8z7SGp/Y9M+VrriN+Zq9eJ/XUCEwtZ7l0u1xmiWBilGLDE0vRtbgagb89GGuty5Hyfgu9hrcsKai1XaS2XSWxnaTyRzqYTHM6rFBQDTSFQaOVWbt9zGUpaCb52W6+4gI0lMoYoSWqZVYHWXHbm7wG49PaLWH2dMfQb1MQzN5zNjHFvM/Gj1xm91ia8/tqHXH22c6MnjvuYuVMnAs4STOKEJIpIohDlB6y8zb4NTU1N5/7ve+5/LyLS3y/Unb7Str/IACDHoU4nX42HWwPjqVm6NQaKyjaPdHzPODZUKhGVcoWRq2/MipvsxOx5HWy59XogulZlrBqCCTUdbTFz55SZP7+DeS3thFGESq3WxqJPMecxvz2iWyHAWkt7xdW6fe/DiRz3q1v4YsJ8Pv9mNoeceBPDBm/IMktuXoMIBFBosEJrpcqZVz3Culseyi4HncOUqbNRVqOslyphZ+lq0fg6h1auoIwYhQkNxBYTWlT6mTVOkYu4hajUXqWjpYPGXJ7exQBfGdorMQvay1QrIRImUI2x1YSoIoSRIgktnhHy4uHjIUbSAHEXmCOBboFHwfdIIjARaCMM6lagZ10Oa8BYlY5XwfM8Vl1nZbr16M6y623BlK/Gcc5OK9Iya5rDpVN2gpaMmaFI4jhVup2FjLJ/xlrCauTmGJYkTYNv6NmPjQ//nT989c32GTCoeayI9PzJB/APJD/piiEiyw9ecplfjt5gR8kUrNAlBzwduLamFLOgWef/sVLDfzIrRDpvsBCe5D7LVK27hoiu6WJJ3b+wXOLWB65jxZFDwAuY21YirEZoa/EBDJSqMUXfJwG0JTOlQVxQLePrdi/65H3F/FLE/FJIJYzxPKEYBGT5/+AKpYRdUkc97dFYLHDSWUdjtEYHAS0LSoxZa3We/tOD3HPhqQC0LVjAsbe+yAZ7HkG5dQETP32Xbn0GpAuMJQpdIfY4xRiHrbIRY5+4c08R+Y21dib/RmkePPT2VXY+rJjLFdLatlJTtiqNeDp4vBPXV6qzuI2CLgufa3eTcsaMseTrGtnykF9z8V5r8dU3M6jv2RNrLJ5AuWQwUYXu3Xzi2PDYUx9w7aU38Lsrz6B3jzpExRRzOaLEUqrElDB4SjCJob0j4rBDz2Xm1Mn8+fa76NuvD7NmuiDdYUfshtKOqugSViyz5ray9yHn0FG1rLbFbjxw5Vk8+vgojj50HyS1LI2JU4YKKM9BJEmcWrMkjksbKJSvUYkzksXXhJFQ7mgnDiPqcjk85aO1IvZjPOuKNpWrEUpZjEnIB77jf8fZqM/ceRekQ0CsQjQYSbCxRXlCXeACdGIBqxBrGJBS1Oa0VWnIewzskSexVdbbbF3uufFITr3vHSrtrXz13qu88dBNbHnYWWChLq/QJHRYQ1gto7EEvtu9w1m6jmGhRGFsAspxkpWkfGKbFX6CZTbZTRr7D13m7fuv+UJE1rfWfvJTjd8fSn4ypSsiXv+BzY+vtftxgUoDFGIN2QSzqdvtBqJJS+l1sSaFmiUsyrlxXa3hbCJq1YnjZjzHjHeU4WS1vH6gbe5U4jBk0MC+BJ5mQVuJwMTEnkYHQl4gEegIExoqCbFEdK/LUY5iPAHfUxlRIqWkCUXl1WqrJgWf+rxHeyWmrZKg04wkY10RlFIY42lFW9Uyc0Er3fv2JjYxk2fE/Grvw4nDzhiYny+wzVFnY5KY+h59qO/em37DlqrV4YV06/jUZTcYUJqVt92vPr7vuvOAA37ELv6H4nneGkutuMYGI1ZZD53SpjJMO8PhHbSdei9dttshHRdRFKG0rmHguVyA1qpWscvzPFpnT6JYzDNwYG/CGOa2lAg8jYkNY8dNZsbMFnbdbhV+f9bFVCsVfrnrkfzlrQcoh4rEKLRA96KmsZhHK8ETzTrrH0BQ34dfXn4Z1x6+XU3hAnzyzTwmzK4woHsRTwHKcu8DT9PS0s5OJ1/Klb/ckoaGepZfZjRBUER5Hogmjiq4Fdsp6jApYzHZUHWFbnxdK5aPhtbWMq1tJXI5Ta5Qh/I1kYqxKHTgUVQuKcLEEUk1RHs+JnZ1fbVWiNIEgZemHluSJMbzPLTWxGGIxSlcURCakIzK4eATwVMO166ELu06F1vynqKxsYE4CkmSmD3OuIbzd1uTd595kB4NHt2aRrDUymvh9xhMa0sLplrFq2+sQYGZB6tSOE5S2p5IJ9yktWsnUa4yWvNyq1Pfq3+vl245953uPXrsuWD+/P8qTu9PpnT7NzWdNWK1jQf2HDgEG7vJI1nRbtOJAgidRV2sNbWGlwyPzUISXVySLOBW4+bWVnLnOnV6q9ZZm8axEEQpvn7jSVZeY0UCrfEsNAYBSueos4ZKpUrO86gqIU6qYKEx7zY89HAK3tWHcPBC9qxKIPCEQqCxVpH3NEng+J15z8PTgqeEiifU5QNKYUJbpYI1hsQannvqHe648kZW33Zvvn73ZWaM/5ztjjmXZdfdCmsN1UrZZSCJEEUxnhfjB6nlIIIVm1KWnCJuXmE93n7o5p+LyInW2nk/VZ9nIiLS1Dz4zjV3PdJXKmOBZBmCmcJNiwiZTtzWpAVYMoqYsWATQ7GQR5Ri/Gv389KDd9LYrZH6hkbqGhqYPGECW2+3EYnxmDRlDrfe/RJRpZ25cxfwzosvoLQiatmNFcaMQroN5s2nH+GG6x/jgP23pN1C4Ltss9goYgOfjZ/G1MlTOfbWP1Js6M5+59/O/JlTWX79rXnvLw/w9B/OY49dpnD+Jb9mcL/udG/MscwyI+i44V5ydd0A2HqHHdlww/VBe6hcARPFhFE7gVdEiyaJQoicKy0BSI40wCpYDyIcVza2Qu++vYlNQmKE1qol6bCYuETY3k5dMY82uHrMnsLzfHzfd+68UoRR7KqdpUZHzlcEQUCS7gLsEjEiEgxYhRLf7XYhFosLANfnNG0VRXslpM0YXnj5U7T2EKWIohilNAdfch9XHbYV07/6hGfvd+n6h1zxAFbnqGtoRAf5NNGjaxDfjYVs8ouQFstx9Evf9x3GrxRJYug5cCibH3Nx/qVbzrt34KDmS6dNnXLyf0vq+0+idEVkqUHDlzxu6Y1/LlnmVlaE2naeU4sSA856TBMEOqOcUgNku8IIWRKEzUr7Zaq5i9FLF1zQWcDuzu8+9ziHH7EbBV8RxQnaKnTeI1AeXlETJwkmiumZCwjSYitREuP7aZTZdAZR3DOQFrKGxoKj4yQpuT421kXDsZTChLkdER3VmJyna+w2rYTXn32Bocutysb7Hs8a2+5L24LZ9G4aSpzExFFSCzjW8vTjhCQxbgHILuT879Ra0Sy/6a711YduOgk45Ufq5r8r+Xx++0GjVhraq3mJWjTeMRVSS8443iY2256GmveQJInD9XCWsNaCaE21o4W7LjmXq288j5yGUluJBa3tFDZcnnU3WIMZs9s475zrmNOu6D14BLHuTt+BzUz8/BMeeeRFLrn8FPbe5Qi2OeAo3nn7Dd548RUu+v0JDBnSn3IloqMc0rMhx913P8FqW+xCffeeeGLp293Dr2g6ZnzBalvswug1N+KZmy7g6INO5PZ7riCMYkaMWorevboz9b2nOfSqh/nTeUey/nvvceN1l7H8MiNANEppkrgM+FjjAlliErAGL6dpaTO0tVdACSaRtB0LzG+FODLElRKBly5aNqGhrkj3xu742nNtrB1OLaJI4hAbx+RzPlnpqDCKCHI5UNrBGjiPzSApdU1RiaqAG8uiLYlRhElaP9gKUyfO4PwTzqDY2IMVNtyejPYpQR6AcZ98URsD1UqFoC6Pny/WMuuy6nwZ1Gdr2wFRG8cZjcxag+95KGsQnPWfr29k40PP9d57+IYTkDdWF5EtrbWd24n8h8qPrnRFpK5/06Bn19j92Lzy/Job3BVWAFurj2DTKlqdyoPUYOtCGu8a5e6i7ERwFp5S2JqFTHaBGudTUsWoPcHEMfliPmU8WMRTJGmNVM+vI58Tqqaj9iwtcRUTx/TwcrVkiUy5d3JAIY5tWsAEtz9WOpiU4Ij4oSGME0rViDCKUUpoay2zx2b71touqlYoduuBl8sTRnHKRzWdwcP0pQUIq1W01gS5IMXCPKyN0yw1y/DVNuWDp+46VEQustbO/VE6+1tEROoHDGz+w0rbHqBNuumn0grP81KaoE2zmToZKaQ0ojBNcxZxvFWTekFhGDJ/+kRGjBrB+uuMobGYJ4wMcZSQ830++2YaV930BO+/PZYDLrqHbv0GElc6OH/3NWkePYa6Pk0cc9SFbLX95tx70xWMWWd9dN/+3HX7A5z660PxlPDSG19SXwzwlPD6o3ejKnMptS1g1pRJjBw5jI8//pxho5dl9KrrMmqpYXzy0hPo2NCtW0BdQfH7i09k/1+cyq6/upQtDzuDO359IJtsth0fvvUsvXv0IKeKxHEJl6vmIBIdaFfXOYkIwxibtJPP1xFFAVifUgkWzJlFv14B3fvU4+c0vlcAayiXYywOV/Y8p3i1VpTKFZRAsVAgSWJ8z0VC8vkcnu+zoK0F5SUkocJkLAzlKu7FEpPEhkKg+fyr2Ygf0L1HvXP1rfD15xMAOPrGZ10RotTrbOzZmy1+eRpPXu/it2ttvw89Bgym0lHCqrTmQzo+sv5Op3hNTLrVkZcyf6y1VMOIYiHvqgImBt/ziUWxyk6HSs+BI9azT90xIQiCXf7TaWU/qtIVERk4qPmx5bfYa1CfIUthTVq9XlSnVWuzGFAnrNBVbNobSjnQz8WvnDJTSiGYWspkTVmbtF6ouIwtayydWyqkHEmlKLfOY+6M6ay8wgjCOMHiYbVKM2McBzROIsqVKnk0Ud6jtRwThhV6NxbpLJSbuUrpymyzik1uny7Hu0xL9OGCBC3lKkqEgu9RCDR1OZ8tVtsZgJ5NQ9jnnFucVV0tux1tY9OFUmc7vQTTmaMHhlKHK7uX4aVWtFuI/IA1djyo8fX7rr0C2PNH6O5vlUHNg29aYat9e9X37ENWmjEIfEdRSoORSoQoiTqL3SSJ25fMmFppRGtNujC7fe2mfvA8yyw9HK2Fto4qRd8jEWHsZ5M48MBTGbr86hx2yR0UezdhgEkfvALWUtfQja0PP4v3nvkzD951JSutvjLvvfIiAAN23Iq5c+ax1aa/+Jv36F0Po1dZiT13+w3FQo6OcoUHHnqWd95+k7q6IlddeS69e3RDmYRSSbHkkkO4+PcnsP/+v2Cfc24CoNTRwcQJE+hezKMwGZyLaFCBYMmKj/sU8z65oIBVAVGcsGBeKzZso1d9nu71DdTV1aNVOn2tpb7OQ0Sh/BzWKsJyBVFCkG90i3wUksQGz6bjQQkmjMhpH9/LExISE4GotEqZC1f7OQUKdt35SErtJe554Cq+mjSHPk39WGGlpQGolFqpa+yJiBD4Ls14tS12JYlCnrn5Il576Haahw+jaYWNiaPE0Xxs55whNcW6mLk1JlJiLJ52/F1rLeVyBa2FOApR2q9tzzRirc3pv+SY3m//6bKnBw1qvn/q1CkH/6cWzvlRle6ApqZzBi235npLrL5pLZUxgxEki/ZatxmeKwaeFgLJgmNZUAWci5XhPUqRsd0Sk/E4U4WbBs6UCEZcVDsLqGVVx1LuEX6+SKVSobE+T0co2MhZylESIxoq5Q50oMgbDxFoK1ephBZtFJVqSOBrV/DcOFqmKNJ3AmuFauRWak8LcWJorbjCKeUwZm57FYOGJCHvKw458EwA1txhP36255GI0jXOcZLYFDrJ8C43ITqxb/eLy9+3ab0KUrqOrpU/HDRmfeqe/fOO4jaz/NGjvkEQbDRy+VV3GLXOliil8XwPP/BTWCZJLRvbWcox5VybJKlZNybF9WuZayJM/uAF3v/r4zzy2NXUFQKqVYOnPISE8y+8iWXX35pN9jqMOLHEqQc0e/I3AAwavSK5fI41ttmdhsZG7r/0NABW3nwXnnr0Ifbce1vXD9vsweRP36XSOof77ruCQU39yHlux2iDRRU99tp1O7bfamOMWJRy2zvFkUIlMZEVRoxYAoD+S4xmzEbbM3/CJwwZMoTYxpAYFG67HazFJjGiFblcjrktCbPntJJYgx80IhKA+PTq04NuRU1dsc7trSaCTVzwTakssOATW49qqtTLpSrGQBhbolioy1uKgY+PoEnwlFAOI8ceMYKxrrZFNY6xNqG+UODhR1+ldf4CVt92L3be5mB69BtItdzB0suNAmD8aw+y9Ca7o72iq7IHxEnM6tvujR8EPH7dudx7yVlsf0grS6yzC9VyBVGe21I+7V9rsg3mcTEXlVHIDFp0ij0nLuvSWER5xOk4EXEZpt36DWTjIy4Mvnjtyd3NE7dvGgTBrmEYPvdjj/N/Vn40nm5jY+PW9b0Hnrjy9gcrpRS+5+F7Gi+thp+k2TaI1DbYs9agsGDTSZdGOMkmpQhWXL1VY01n1lJq/Zo0cQKbpUVkk9nVoM0yfGqJGOLRvXdfPvjgGxryHuUoJqyEKJsqaGVo6yghscXTmlAUJo4pegpldcpYsDV8OkmfJTGWlkrMnPaImS0VKlEC6QIgCC3lmNhImrKbMK+9zFuvvAVA8+iVQBRxnBBFrpiNySx8m5b0Tn9mlDegNmA7bW/3v6wKmeCKiqy52zH5poGD7peu1JAfQUSkT+9+TfdveODpfpALCAKPIHDpniaOnfWeuKLWysYILpiWeZjOG9F4nk8uyLmKa5Hb0mbs84+x/gar0aNHN6LIoJWmvRzy5yff5f233mX0OltSiRUxXg2SWmqtzdjiwJNYbas9efeBa3j5uhN4PHV/dzrx92x24Kksu9ZGPPuc64dC0W10ufaG6zGgfx/iKKGjHDp6UyzMb22jraMdz4eGBp+GeoVJYqLYKcG8LvL5uCn07NOXfD7PVoefiV/fg6dfeIVEEtqrZUIbgjZYZRxH2QgkConL9O8d0K9XgNiYaqWKn8vTUvKZPsel8Tq2j6m1FsoHv55ZCyJmzmujoxKhtEf3xnoaioErimOEUigsqBgWxIZ5YUQioGwMyjqcPfDQ2sEMhUIBYyAfuF7pXxcyZoMt2GSXPTnskjv54J2x1DU08MRt13HDkdsx6f1na2wh33cW6Jrb7MXOx52L5/s8+8c/UJr1NX7gkcQhitSaTUxn3CYdq6Tz1mXuOXjJDwJXxlU5eMr3fTwvwPc9TIr9K61Y5mfbsc1JV/caucJqTzYPHnK/iHTZO+vfLz+KpSsiI5uGDL/3Zwee4ftBgFJSo4dkyhKcUapSjl4noC6dkK91Z9W8DroE0FJFmylX6bx3TeFkKuhvuLup4tKex3LrbcGTT7zEumsuzQJTxlYN9XUFiAyeDigon7pcHlFCTws2F1JNEreFj1GIcXgkyt0rii0GFzwL4xjtPCkWdIT4WjGvFFKNEnKexpoEqxSiFUstsySff/IFQ5dbzQXjahZ8Wj0sa4vORnbhhBT/dO3h1G6Wt+8+t2lY0Q3ivsNHs9TqG46svvjEkcDlP0B3/42IiBrYPPiZdfc5oVu3vv1dwEycdZstTLbTTHe70qa84sQY4iQBa/F9n3w+5/orcoEgpRWfvf0KH78e03vAIFZdfQwffzaFDz4YxxsvvMC2h5xCPOdrJn/1Eh2tLcyeMZ35s2dQLBZ446VXGfvcQ4j2mPbVpwBsesApjFj1ZyDCkDHrcPu1LmO62KM/S41agdtPP5g7V1iO5UYN5d2Pv2HHjVfh8/HT+Xz8JDZeexmWHDYgTeqweDqhoyMkCgXfU6y18kosO3oJrth/I9bdfnficiuPP/UXNlp3VXwU5VJE1Y+oL+bQfsbkCOjTu49bcG1IFCVUqjGlcoL2PCLyTJmXEHiG+rxy3/XyRNantS0itgrRbpGrRIb2jjJ1eU19XqNwno/bvBLKsaG9UqUg1jFCxGJshCWmEPhU45jW9pBNNlmT1169hRtvepiHX3iSsS88CVzI6DV+xvzZM5jx9WfMmTGL5269hAGvPsVmh51PkC8iYjDW8OdLnDdx1JnHcuVZh7PTCb+j2GeEK0eZlmPN6iMvukMMgPY62REOovIwSVwzQIwVPM93xlxaOKdbn/5sedzF/rhXntoxfvimDfP5/O7/KYVzfvDaCyJS339g85ebH/G7/v2GLUkURkSR20235kKmLkVXY2th1QmZ1u2Mo2WBNKeYOwNoqarOWAl0RkU7A22dqaaZaK3Rnmb65+/z4q3n8fJzNzB/fhVdTQtwaAue22670StSEbdP2txSG7GJ6d1QRxAoV3EMi2ghspbQWKqRYV5HlZyn6NuYJzaW6S0V6vMeraXI7QoQxvgacjkPrQQTJWy7yUFsd+yFNC21ooNNEseldJYqaaTQdmk76xS3tWlRHcA6aprT0S7pwPEgVQ1TjSoV7jtz3/bJE8Yvaa2d/oMOAKCpaeDZS6612Wnr7nmUZHBSVsows9rBFUPJvA9n8TgPRCkhFwQEOedSusmW1tQNfL548zlmfv0prz50GwBLrbwWs6dMYNToJfn4vXdZfbUVGDKoiUIhT31DkSWGD6atvZ1TT78EnavjuNtf5Jv3X+Gr915j/T2OBsD3PcqlNu76zcFgE3Y9+jTq+w7hnb88xMevPsuUzz+id/Nwyi1zCSsVorDKzjtvwfVXnIlYDSnn3BDTUalQriSI9dFK8fY7Y3n86RcwFnbcahNWWGY0+VyBKKoSJRG5QFNsCBCtCKuKamypVmOSJGLOgioLWjtQ2tV78IMinp+nUCziKSGX8zFW8D2fIN0vL44jsuyuOIoRDMplOTi6mAhhVAUiwJJEZeoKAUosxlZJ0kCaL1nQ09C9MQcoZsyYD1iWW24ndjv9GpZc7WfMmvA51x25PTvsvTtzp01k9vwqu5xxE8rzmfHJy1x38iEAPPzSvYx9eywXn30N2xz5WxqblkTpgIRO6DCz3rN5q1KILMgFNRxYKUUYRcRRVGPxZHsV+oGH7weEYejGHVBunc9Lt18Yt86Y+NTkiRP2+HcXzvlBLV0RkUGDhzy68nYH9G8aMZpqpUI1rKY83M5iNUpULcoPWQSzixUHjmVQC465j7LdAzLQvQuEW1O4WaX+1MbtZCykSiozo60FkxiallqR2Gouu+5hDt57G3JBzuXC2xAjhjIRAbGblOUy3XIFRKXbZSvrtvJJb5NYqEQux1+J4+ki0F5NuZHW4TmlKAESPC9AIQRa8cEXs2no2ZfWubPob9xGlEm6LxrgeMld2sLdU1zU2FiwcRZ1JE5MyiGW2s4LmZttLfiFIuvscUz9y3f8/hZg8x9yDPi+v8LgkaNPXHu3w0RwbZwFMVVtMRRMHBGFUSd0kvaNTpkNfuCTpNsSZZak0gqtNGN+tjVPjv0LAA2N9Uz69D369+/Lpmstxx8uPIm+PXq5sRIEhInbgDFJYu575FWKQ8cgolhi5fUZPmYdTBI7Kxuoa+jOgRffy9nbL8dlR+8DwCn3vMGq2+xHub2FUlsL1xy2DQCrbro9jY15ROpQYhEbu0QCq6jLAzakoxxjjWaFZZdm9THLk8sX0V6AlyuitaajdQG+TYgqJUykiEIolWPC2NJRrhKGMZJU6VZnyBWFyHhUo5AkhtaWiIbGbgTieLjWJiBxrSaw1k4ReZ4miiLEujq5XpqYVAx8oqiKUgabz1EOK5BUsCQYicn7kA/cThiBKEwCnm/p1687U6bMolAsUEcL1kT0G7oUx9/xMneevj8rrb4iLz33J7546wUa+zZxw8mHsPSY5fh07EfMmd3G9PkwcMRoXrn/VrY59iKiagU/X0diks55mxoPls46KyYNqvq+h+e5oKFJEuJ0D8NsV+SkktYP1qn34Xk09OzD1sf+3hv36hNbxw/eNMHzvK3jOH79hxz3/4z8oJZu/wEDjm9edvUL1937BKU9j2ql2rmRYhqpFiVukKYKNXM1bRaNzx5Muli3LKy0JbNsMwu2y3k1pURnsrCSlHidnt9Jznd54eNefZK/3HoxPXt24483X8jgvgNQYrE6piOu0BqGYIWCEQpBHktCsRCAcoR+0YB2qb3VJGFBKaQUJigFTd2LzOuI0rKN4GpVpzvFameJPv/iJ1z064tYZcs9WHWbfbAISRzX+KmSWqwZI0EyPEHcFu21AZjENc9AZYyPLm3krHuv5hU8fenx4YdvvbJRHMev/BD9LyK5AYOax2934qUDeg4clrkoaf+lAQTBbTUUxcRRlI4P93xeymzomvppjKl5RDnfozLtQ/76x6sJVMztN19EnVeHjo3bFUF7bhFUrui939CALhSxRnjr7XfYcOPtOe76R+k+aAlnVacLt3VZGfieqwdQjRImfvwu+YZG+g4eUfMUTBLz1zuvYMgyq/DF8/ey/YarcuQh+6EBQ+SsNO0RJ2WsEeYtaCWMLWI8xCoCrcjnncLVvk8SVp3CxBIEPqVKhfmtZbcDSaWK6IB8ThNFbTQ2avxCwLz2Ch0lRVgJCPwCFscYCHI5Vzo0rbHs6hpot2hphdgEP/Cw1lCtVvC0QnvaYdRJhBHD3AUtxGErsUpcTWFPk/cdptq7MaCh3gUzrYUnn3yVy6+4G+M1sMWxl+IXe7Bg1jSeuuEcmpdajjV3PJC3Hr6Fp2+5dKFJrZRmpU13ZuVNd6TfsKWYP3s2URi6zVdTxkRWwCoLgLs57IJlvu9RLBQQrSiVylTKFUwS17zhzDoGav2pdLoFl1K0zJrKM9edFZUXzL5h6pTJx1prox9i7P8z8oMpXc/zVh44fKlXtz7hspzn59IiIqaLUu1q6dpalaEk6cR4TRYJSulgmfUKndcAuijYrtloaUqE6jxmbWeJOLoo6loHCTVamZDw2JWnM2pAjkt/dxI60SAG4yW0drQjRkjChFxQIIorFIs5wKQVryHGIGKpJoa57SEdYYQSoUddjgXlkDg21LKxAC91F994byJnHXMWmx14KqPX3oQocoGTJI7TmqOp0k1rrGZb1iRp1Npk0f60bm8mGUMkG4CdStdPyRtC26ypPHDeYZOmTZ0yzHZGZf5laR485PrlNt31oBW32F0yQruTzkVRa0W1UiUKwzQRQqUlN12gBIGcMlRiC2jXTp6DYD59/AZef/J+jj58b7beaku8RCj6Pp4SEpMQBG5ygSCewmuoR7QHNiHsKHPAISfx/MtvMHT4MJZa7WcssfGeBPk6lBLaJ3/Cq3ddgecpltlkNwYutzblMEnHto+X1tFNjKXaPp/L99+I919+iO713cnnczh7XiG+T0zFVdqKDB2lDkqVhDBUdKsrYBMhl3PFdFz1OaFcrSI2IZf3qUaGto52/CCgGhq6dWtk3rwZ9OlTj59XGAmpxgnTZsfEYT3WaDyt8H2fQj7nisukqdKS7l+mPedxZVOhWq0AbkH3PR9jE8IoAhHGT51IsRhTVyxgrSEfePha6F7vU1/wHb89nWNRnHDuuX/gz/e/wE7Hnk3fZdZNJ6vzwp675UJefeCWhcbIylvuxrq7Hk4ulyeX4vXtbW20z5uHVyhicRloqgs0aHH6wNOe287L98nlc4RhRLlUoloNHcxG5vl2jn2tFNpzqc8Z28Ekho+f/ZP94pXHJ0+fOnkTa+0X/ITygyhdEenRr6n5682PuqBHY9+BjgYmpDUzOy1ZJzbFlwRPq1oN0q4uJoijYqFqrvsiucI1V9vVU9VdlCyYrGJOahUDXSpYdSrc7CSlBN/XTP38A16+9Vy22mIzPvn8a3574v4MGNQr3XUWcjaH9hRRHDkrNRCMikkkIUlDeh1hwqy2KpU4IfDcuxhjidP6AHlfU/AVvlbMbS2z+7ZHs/6ex7Ds+ls5RWsMYRgSVqoLBRezxUV12akgc7tsWoPUtW6GhWfvLekkcdW8tOeyoTJF9+bdlyVvPP3Awa2trTf/b8aA7/srDl1q2Td2Pv0PAZJlEpmFuJe+p4nj2FknGRaf1l5wtWU707qVUgS+hyjB8z3mfv4W91xwIk8+cCv5QgNxuUyf7vVo5WrN+r5GtALPFXdRgYcEaU2KOEbQ2Dhh/ITJfPn1BC659jb83kPYdP8TSYIGLtl7Hc487WiiMObCi6/BimKltddnwFIrMnyl9fjkpYdomzOdpqXXIGyfz2fPP8jTD92OSizKJKAVeL5bWDUkSdXxjW2ECEyf2YrYPPWFAkmS4GlNEkX4npdWSwvp3qsnvq8xcUgURYRGUalElNpbaejm0dCjgKUCylWomzaryqxZhmKuG4VCscYYyKzdLHaSz+cwJkGUi1G0d5TShdemrBsH47RXKsxfMJdB/aFvrwbC2DK/LaIaOQ+qX88cxbyPSrm8eV8IPMULL7zL8cdfwshV12f9/X9TW2CNNXz8wmM8csVpJHHMHmdcTb8RK+D5nfS/IHAlLjva22idNw8d5J0HZ7It6l1lQZXCgyLOWwuCwG3cmSSEYVSDGVKNjyVLTlKphS10DTIrpZjxzWe8csdFlbCj9VezZs28/KdKI/5fK10RkYHNg99YcbuDVhu20voAafCmM2GhEySHTs4BqYKkMxtM0uNpMfIMJKg1VhcWQtfstCy9V2pXzyzd9Fw6FW1nLYf0WFrhSpQCm3DlgZuQz/nMnjWbHXfahksuPJa2UgnQ1KscGkWUOIqNHyisZ4jIMCVLNbFMbylhrNCzLqCjGvH15Daefvw1F3WNI7bcelXiOKah6LPvDkdzwh9fd/u7mYRqueLodIlx7nfGPlCSQgxZO9garSZJ4oVd+VqwTWrvplRn/VKVpgsLllLrPO45Y/+56Q4TlX9xDHgDBjV/s9UxFzX3HDS8RvWrWbricFoBqtUwDQ5CZ9F5lUbuHQbpexpRCs9zeyMppfjzmfux3w6bstE6axJ4isb6IvlcgPgaCXRtPDjURRDf1Xu1kfMelZ/DVkJsYrCeZsLkqZx65u95+bW3WH7VNXn/jVeZOulDvBQ/fu7ZF5kweRp/feEVnnjyWbbYfAOqlTLPPPsqPXv24JrLfscm662BwiCZde37nR6ackqvWipz50N/oaGhwOrLjySJFTk/h6+9GmbfWF9PGFYpNNTjacAkkMREaKbPmkvgC0oZuvcJUH66pZVy2wvNmFNi1twYmxTI+/XkcnkC3yWfuMQSx90OfJ9KueSy0FrbKFfKBL6PpyWtkqZoL5eYt2A2Sw3L07dXPcZCuWJoLSe0dlQo+LDEwAaUEsqhpaMcYUXTVo6JK2XWXmUndj3xPEattz3UDAOYMu49Hr7sNHY49ly6Ny1BtgdeFt8Jcm7PtFJHibb58513olQt6E6XOd4Jl3WWhLTplla25ip3GZtugCIp9FLb5l4c/z2Oqrz74A1m6qdvvT918qQtrbWz/pU58E/Nl/+t0u3Tp+/RS6z6s0tW2+Vw5UhMbtG3Kb+uhttmK5BNzV86o5W2BpynDwW1HSG6YrsiXSxfqCmarEaDs3yzTQLdJMzK7WVZaAvVcBBXzUmUpM8Mz950Pm89eR/DlhxN67zZvP7ynYTWUAoN9Z6Hb5xVlpgEndPowBLZJE0HFsLE8vurn+LRex9AaU2P3j1pnTeXaRMmsNpWe5AkEe8+dR9KaXY7YHdef/F11tztWEasuiFYS3vLAqyVtKKSU7w2XeGzAmqqi3I1JlnEoqQGtUC6SadyFaa0UrXatVl2V5wkfPTUXfbNR+88ffbs2f9S3d2+/fqdvMz62/xule0OlBrFrQumnvVbkhLbrV2kT1NsPfC9mqWez6ACUUx783H+eNlveea+mxnUvz+5XIr7mgQroIs5ZynHFpukfeEpt6ojaOU5ZRvFLlHE08568jQtlYgbb76bDdZfk9VXX4VOZeEy48TGWBOnNWw7aBq2GmPffoHrrruZs085HFutEhQLoFWqDD3E912dJeVx9Q13csqpjoZ27AlHcvDe22NiwdcBxgoLFrTRo75Asb6exLpdeZMoxFhLob6B2bNn061bkUqlRLE7eDpIJ5QBz2082VGOmTmvzILWCGvydK/rjdZ+uvOGwdokDUAneJ5HGCXMa2shjEN61HcDazEYgiBHS3sbnl9iUP8CPRpyxIkwr6VKNYpIrKFPtxyNdQHz2hMqYUw+UMzviLCJ5ZnHn+eqS29h41+czLI/2742BqxJHJQYRZQ6XGF45WniyCXIaK0dY0iEMAzpaG0hihOUF9Qok5nhlM1dJQ7qcUN+kSB8TTlkc4FOiDH9XCupxRJEhEkfvckbf7qyZdb0qRvHcfzOvzIPvq/8r5SuiAxoGjL8q11+c2MxyBWwFqphVNsWx1W8ShWEyShjJjX9cZ1tbW0i1gAGUV0aqrOxawrVaeWuz1GzXFVaozQrEZftudR1ondeN1PONaSXqNLGzcfvyvw5bsE7+IiDOPywHWmvRNRpRUEpcipHYgyhJAQ55yJZZRk/rY2jjruMaZMms/1xF2JMzIyvP+PTV59m9BobsdLmuwLQPn8O2vO56dgd2Hf/bbnzjic57LpnEKWodHQQhp0lDB3kkKRWocpUAth0m/I0XTazKroGFjPHoGuR8Ox3z3MbNMaxIayUuefXey2YNmXyQGtt6Z8cA32aBg8bv/MZN9XpIFMIWY3ktJ/sws/sJk1G49Morcj5mpxnCZQhsj5FX5j+6Ru8+fi9zJg0nmsvPJNVVl3RbZVkDDaJsQIq5xSuKUdEYegoToU84rZ4wCYJNnSupxWL0m6jU5eKKuB7+HV1btGPI1RQwNq48/0QTBxikgjl5WnoMbx27Mt3/0q3uiJBMYcEGhtGkGGpvgZP8c34qWyw2e7sfuaN3Pmbg7jqqvNZc/mRCIpKuUqlVKG+vkg+1+B2FhGhrr4AJkG0z5y5c+nVN4/SBiVuex5wmWNWh+AlWJylV6rGtHXEzFkQUinFaJUnHzQiosnncoRRRJTuNv3N9An4BUPP+gbiMCCMIvL5gvsZWIp5oW+vPNrzXLacWKqxw9obi05BtpZC+nbLERthTmuVUiXivXc+4VdHnM5pD4zF8x1ma4zbzVorS6mjRBy5rLc47tyAQCkX2PO0djTBsEpbSwtekHcFdkznuaTzXSlVSw/ODDTXY5lqzWoHu4Lome7IGBEZyK1TT7B19jT+esNvKu3zZh8wb97cu/6ZefDPyP8qK2nQ4KH3r7bToUXt50iMi55rpWousM0muvrbCvFaZ1heZok5F8d9d1EnAWpKJb2+W7cymKDTyrPWYUCJsTWcMzuWXoUMhKh9N7O2RZg3cxrz58xiyJBm6hoauePWe/jj3c9grdtaXbQG5VZnsQ53VgjKCmefcyNefV8OuPhe+g8fRdMSy7DSpjux55nXs/IWuwEO52zo2Yf6nr3pO3RJ6hp70KtnA1+9/RwzvvqQBTMnpe/XWbw7e4saZpW+Ry3Dzna6886176xEln6xsw3TpAtjOuk4fi7PSpvu3K13797H/rNjoHnwkOtX2e6AOuX72MTUMoPcT5PWUTA1ClANCkoVsguQaCxCOYJS4oPWPHrFqTxz62WsvcKSvPjw7ay6yhiU77IATeyqeounUZmiF4POeagUx40qIVGljI0ibOKqd2ESEIMKNF4+T6WjRNuceV2qnFkgwcZRWtzXkJgItE5rGsSce8aJtXf/1fnX0l6qgB9grSKOIIrBKrBRDHHCEksMYdefb8tfb/wtG+17LIceeiIHHf87psxp5b3PJ3HHoy8QxgY/FxD4DnKolKsgmlJHGZskVMoVtPbQ1k/nhsFIhAWSyIIRtCga8j5NvQosNaSBpUc2MrhZU6ibTyWazqz545k+bzwTZn/D7LYpNHYTlhrcnab+9bSZFtpNO5W4guc5wyUxQjV0WXL5wHNKMl3c4zhBKyjmfBZ0RMzvCOmoRCTW4qcBx4XmZebupmO6WqlQrVSIo7AWWE7iiLAa1hIgCnV11Dc0oExUq9UhSqcwYDqOMF1+z8SSVl5Pn0MQ25mYZbooblKjJUmpZ3U9+7PFMZfk+w4bddvAQc0Xyd9mVf0g8i/zdOsbGnZcYoW1Vhuy3Bqu6rsS8rnAVTlKJ1H2YmEUd857ERRuTGcuQ1YIZiGLt9ZwNfSVDMfM3MauYmtn2M6+tl0x3HSjwGz3hjTwVnM70mfoO2Qp1t/tUF6851oAlltnUy48+1KWHDmQEUstga6E9Cpo/CAgl3jEUUhFYgo5j7YF8xi+9iZoLyCJ49q9rTGI6gwmmsSgPVh/r2O58re/5Od7bM2Lj9/FV2NdCup+F95LjwGDO6kz6XtlfGZJ21XSIEPNcrddWsK6mqi1hBBrHWxBFmwwadUu1wYj1t5a3nv6vuPEVSELv88YEJHRI5ddccthK63bWTchtTyytle1xXQhEh+QKlxPpdxRl6BgK+3cf97RNASKvzx4BznPByyqWIAkxkSGJDJ4frohpRJnOoggiSEKI6xyVahsHLu9zQLtaF3W0YdEKaxNCAo5fF107akECXz3DuUKKp9DlAbrWAngJvyB++/OTXfcx5q7HMITt1zKK5usy9orL4/WGk8pcsUCFoXSNoU7qlx4zq844dTf8eCfrmO7X57MhK8+Z4ttdieJIuIoZNxnn/HbXx1Hj24NkO7FV6qEzsUO8ijxnAMhBmPjtDh95/wwxvV9BvfnfE0+0DTWQd/uBaL+CVFqVVaihNg6TLUcGWYtqGLFkC96DobQGq0tnq+pVDOOt/NOk8RZu1Gc9qJWtJZiCjlN4GtK1YiBgwdQqG9ApYVoanBTVngpNQyCgmv3JGUUeFoTRhHVJHHpvp5HXUMjuULBGWaVkDB0w9Kk48hY51Fn86P2cxGLLfN0wWW+qXQTBCOk+iCdIibByxX42cG/9d596PrjRL25gohsY3/g3bT/JUtXRBrqu/W4edWdD9UuVOLeMkwzRKLUJfZ0StC3DlfqSvkSpbDKQ7TvKEHaS+vBpvdYxJbtlIWZEAv/zL6bKfTs9yzElinh1P2QLnfJfhdhrR0P5IAL7gCgbdYkll1nUy666DbKYUgljqkkEWEUonBZP23lCmFiMCjKbQuI45g4cRvrJXGS7mLbpXyldbBB04hlWWOHA7jp6tvxcnX84rwbGTxqBbr37kmGK2aWQJbN56zadF+1DJ9OcVN0ulFbOu4661MktYzAWlBCqc4wpbXoIMey62zeva6ubq/vOw6ahwy9ecVt9s9Zk5DEUY190dWaMCmk4D7vXOBqe2dpB3nkcj75fMALt/yONVdamkfu/QOFQhGvvs7BBakid/n1qZL0fUQ0GOs2cjQW8Tx0vkCukEfncyQixAkuQcMTxHftlFSqeMU8QX0xhUBcANdUythqGdPRgY1Dp+grHcSVEiKClws47fhDeene69lo3+M5/vTfUanGVCuhS18GRHxXoF/SMR6G/Pq045g7Yxr3XHwaa2y3Dyfc8RJLr7gyAK+++gbrbbot62+9O/sedxYHnnI+Ox54AjseeBwb77AvB59wHh2lColUnZflOcZOQpLGNLI2BmOEJHE/40RSxeSR8wPyOZ9u9XmUQKUa0toRUq5UKOR9GgoB9XUBWicOS1cKtBsf7aWQjrLDmbVYfM8lAZXKMVhDOXQ8WSVCrx4NRNUK1bb5PHzB4Uz/YqxLeNGKKDaEYVSb29rzyOyEJElS+NAQh6HjKStFkMsRJy5YbKytJcvU2DxKp2VCU+hsIRZMF0ita/ZrthCYrizJTq9SlLDKjoeqURvstMmAgc0fi0jf7zsnvo/8S0p30OAh1y2/2R7d8o09aq6rTfeqShJIjOPolssV4jimkA/I5wK8NJuEdIcHkbRBUpwxw2kzx6Crwu1MIbaQcVMT0wkN2MzSMrWNHsmu0yXamfm2nUkXKdhgqVG0LNBtwBCKDd2Y8MU4Bo8cRdBjEKedfBmtHVXCJEHEgnFb9lQrCS3lCuM++pRhY9Ykw0espbbDQxB4acEOr2apYhPW3HZvdj31MjY54CQ+fe05Jo37ABFNMe8iyzWWRmaN207opJOK15loAhlXOaPcCeD4oNbYVAm7YjPGdO7MAcKwtbZW3br3OOv7uFWe563c0Kdp5b5LLOOsSmPIavem0FoN6rG1T7P3cQwKlQX4PLdx51+vP5MpX3zIaScchl8oovMBbvtdSCpVovYOtLJ4OY0qBA7qiSMQ8PIBKvAdvmodj9nTmroejY46Zg26rghaY8tVdKGIzhcRa4k72ojb52PCqqt+FgSurcpVxx33dLaWIVbYZtOf0TygNzO+/BC/vjsnX3AVoYnTcpXOs0liwRjBIFit6dWtkYt/7wJqd56yF/eetjcNvuXgX+zJwOZmdj32LLY87Cz6L78BdUNWYKmf7ciYrfZhh+MvZMq8Cvsc8lsmTmlDqQKIT2JTnDotbSriFG+UGOIEKqGlVDWUqhBGtoaJaoFeDQF9ehTxAo8g51NfyFGf91HKkOBgwrZSmZaOdpdRKh5h4uZHIdD0rHfbJJWztk83nuxeDPjyyyn4uQKzJn/N2Fee5/k7LyWuVim1txGGbvfmxLjdnKMoqilFF3WXFJ50pSgrpZKrmxukAbUkxhr3jolxBofnueC41pqc73dyvrMCW+nGA0q7z5yX48ZjFiNJw0tZXN4V5QKWXHcbVv35USMGDGz+TERGfS/l+D3kn4YXRGTI0CWX3nGJ1TdzikUyxWI7uZd0uvZxnBDHphYIkzQtyW3BkjhlrTqpZY550FVl2jRQ5JSPTVJgXFJlmTZ+rTanSNc1C8Sis7/EBVcWVujZzy7PjpAvNvDLKx/m0v024IlbruCUe9/kkct/zcUX38rppx6IEkVjkHdlFAXKYUxHayv13Xs7BWNdeTrf0+RzPrHt5C1rTyNJlY8euZEHbryO3v36MHrFVVh6ne0pt7ejfR/leTQ0FCiVXdGTJHFOVSdHt1PJ1papmuUuiHTSxkgDlzWKDdTSg5USktit+MXuvRi+9Ji+06ZOWQt49R+Ng4GDmq9ZbrO9fJtuKy9K1cADdw+pbTia8SWzcn2dAU133FOK9x66no4ZX/PaX/5MXT6PTdI9umIc7cvEeCTYMMGqAsrgmAoi4HkIJq2kBZVSOyaJKdQVXbnEhiJJyUI1wiQG5QVIEEASEbWXiMKQXMEjKbWjfA98jdWaJAzx83lEe5hqBVPtwFQrKM/jF3v+nNPPv4Ldf3szD19+Krf9+XGO2HeXdAsipwiSxKL91CpXml8evA8rrbgskydPI8Cw5iorUldXxzU3/pFHn7yL+eWY7U6+ikKj83S0AkSz7VHn8tc7LmXDTffg6BOO5JB9Nkcrm1bncy66sYbEurlhrNv01C38ruiSS6Bw46Ncjbn2hsdoXmIIQ0YOw8fS3KuOiXMchlyNI3ytiUzC/I4O/HR7qSDneNMWaCg4pkkYJ3QrBIz7eiavvzWO266+ma2P+C2NfZrwgxxfjX2HubNn4Qc5t7uJ8pzHYy1R6DDcrJ6C53u1uamVe69qpYrve9Q11BNHMdVKhXKpjFIaK0IYRmSGhtbOaPNSjjLW0eokrd/tpcfj2ixy475z81Pn/TlIykeUMHS51Wjo8duez13/mzdEZIy1dsJ3a8l/LP80e2HIsOGPrLDdwdsMXnaNmg8vtayi1HpVgk5X3qQLZpJN8umfvsl7zz7E2jv+gvp+w2o7LtgsGm+7TF7bmYnmrkHtGKnll5VVkPQEZzXXvlGjWDkjqwszogbAS5drp9dPg3wzJ3zBvOkTWXqtTUmiCtcevg0XXXoyKy47km6FHEkCbVGF0MSsv/puHHf7y3hBHsGltXqeJrEQhyVapnxBtVql5+BR3P6r/Zj2zbhvbePtjziNZTbY0dGiEudClstVqlVXoIQ0a4cMeoB0lUvt29RaqFUnE1JXy5XQ1NrrtDSxaaYOmCRhxhcf8PJt5z89ceLEv1uTQURGjVphlbEbH3FBbfsMnSaoSMpTdZQrU1vsav9USllL01DzuRyzPnuNu88/kWcevpMlRwx1gS1PAenzxzE2NthSya3wvo9uqHPbwmiB2NWnxXOKII4iSMv8OQ6tq1IWtZfRvkLlCiQJxGFIWAnRylCoy7mygr4P4grZi6/RuRwg2CjCxAk2ikliS3upyjGnnMUnE2ayzu5H8PY9l/LsQ7e5ur4CKq2EpXwP5Smn6LUrVI6BpKUFE8WofB7xckSVKtvt+UuiboPZ7ODT3P5wNTqdBwJfvfMSfzzrUMaOfZCgrgetlZimbs6rUkpIEJIkIk550sW8X8NhLTBjxhweefJd/nDFjbS3thLkC5z6u5MYs/LSLNfcg8jA5HllSpVKCk/ERNaS14qCpykWfXxPM3NWC6+98iGrrDgC7Qc89Ngb3HnN9QxYYjRr7rA/y663JUkc8cFzD/HY1Wey19k30mfwksSxG7PGWlcVTInLTEyhHZHMYBDy+cBZs1hEOUpZppzjOKajtS1N99a1BKtMshR4pVxBdU+n2wjZzthIHGelZbPZ70waXUvM8SkUC1TDEN/zmPrlxzx51a/nzJg6efn/bZGof8rSdVbuMpsOX2GtGhaIuAf10lRNi+M3pu2briUpjSN1Obs3j6Z52WnceNLe5Osa2P+iP+EV6tOYltQwya4UkYxo37XEeXZ65spbcfuPucBVas1KBrKnNqDF0dhSJe32rfwf2t47TrKiev9/V93Q3TOzOcGyCyw5Z5AsoEiOggpKMmMC01cxICAGQMWIoIgBJKhklCg5Z4lLhs05TOruG6p+f5yqundWBPTz+t3Xa1hmpqf73gqnnvOcc55jg6EICMzdxGozNmS1dTZ0IuwJa6y7Iffc+TDbbrUhJAqlLQ2bUuaaXfbYkefvuoYt9zna5R1KUOTlB67jqp+dwaTVJjL7tTns+v6PscX7PsCWwKa7vo/+JQuY/fzjPHXHdSx45Xmu+eX3uOGCs9lp/8PZ7QOfwiajRegjisiyjLwUisUaK1Fdf/9Ui03hgpX+QPLP5RACeJfK62DIKE1aZzOitLmzUmqctXb5m62DNdde+0cb7nVkw89N8Ex8TrDSYb6w7qBbhVYQ5CEi8Lf++QK++uXPssHG62K6Uh6sXdDLGtecMo3RjXHCNWrkwZSWdAFjXRcGK56NbzujFNYXX1iIe1qoWASCsnZG//KVFEXOxMljBKEaXHNE153WdS3GVfypKAWr0Lak1Wrwqx+dwTpb7oZOGrz62iyemvkS602bSm9vL4nWRLEOOeTYAmsjt8Ols61qNNBpSpS2eHn2Ah5/8mkO/tJHXCWbo5QsofvxWpu/i50POYa99/8cU9aYzivPPc1DD17OuDG9AjYw6ESjSiX7D42llHGyJb/8+WVcecUNbLbbfkxZZyOmrrcpP/vBNznngu+xbFwPq4/rY3SzIM8zutb1RjPSDKBAKEOVd/nksacwbo11OOe0cwDYbPf9+fTPr2bimutVZecotnrPoUzdYAvGT5mK0pqhweGQW1uWJaomcmWMJ6IErReFCBWJV1bSzUS8RiOe4pgJ4ynynDLPBIboKOhPi/cZUZYFzWaToijIXXNOXwHYaER0s1z2iZU8eOnRR7AZRVnKz5VmyrqbssdHT5l450Xff0wptZn9PzR4/a843TXXnvGLrQ46vuHLbqMootVs0tvbotFMhZvxQRwnUG1C2lDh+FdpKLfFXofz+d/eCrbk0tM/FvhJYzzoXyU85lxUaqdVhYKtP8aCIam0aH3AiVBcIO+3yicEg1sF1OpBL1sKAp/z6osUVjHUyVg20GXZYAerIlpJwoePPpi7rriA7sBy4ZLcB7708B186BMf4LQLziBOEqZtugOb7Lo/W+15MGmjyZTpM9j6PYfw4VPPZ8Od9gagyHPuufYKrv3VmZx99I5cdPIh3POH01j88uPkwwO0+5eTDfVj8g5F1qXIM0EFji8uyiLU3ytPSHrit+ZJRFHskLFDvpFm01337evp6fnAm60BpdQ4q5I919h422pOfNcCx4sb1+1D6QgdxxIoTRLiJHJC9oJyldaUpWX1GeuzYP4CzPAQJs+l9LfVkkBZFKFaTXRPD7q3F93Th270yFrqtjF5TpnlEmgpCmxRyIFUGHLXKlxpiSPEfX1EaYpKYxqjm/SO6SGJHI8YRaAl19aYEpIEhaLMMkxeoBxvqrQiirUI82jN5MmTSNIGe3zk8xx+9Ik88tSz5EXh8qcLMLmsTefaUhbYLBNU1WoR945DJQ0+/6VvsuZm27HOljtTY8arAKoRQ/2+j32d/T57Juvtdhjrb70T11x3p9sDJlBKAENZyUCnwFrFcLfg9ruf4e67H+eQL3yXw758Nrsc/jFmbLEj2x94HF/62Nf50+V3MJTlkgeNoMWJo3oY5SQVu6Vh2UCXi/98E2tuuh0fOvV8Nt95D5RS9DRjpszY0IkC+e4OQomNX30tojilkaY0mk3JCIGQp+/deRX2o0eiZbU/jYGycGpq8mNTGtI0lYPZxVh8/nnkhKSK0tJxh3iko2AboEozk7J4oQEjl5ImOcVRaBtVFCUKmLbhVux81Mmrr77GtEeVUqPeic18s+sdI12l1FozNtps7/W22VVuNkqIIokUdlw/Jq2kZroorROrtsGIeaOqtaSDKaWIkgad4WHGT1sPH1vyHKX1oRflRWKcxfTIlkp9asTfBTrDi914WkNXCLlufB17XjG8tuIZbOnkJ5UkeEcRB3zmdK499+vkNuajHz2YViMiy4doxLDt1hvx3n124ZqzPsNW7zmENLK89tyTPP/I/Xzg+O8w+8XZZJ0Ok6avRxJrlDIUWUFuJE9Q64ht33Mgo3obTN1oGxo9o7jloh9RFgXv3nMH7rr9Ae6/+UamrrMhOx/0Qcqszfo77kfU6KXdbpNlBUVREMWxnzOC6I9DIF4izx9WXhaPQNtoVt9idzXh9mu+AFyw6jqYMGHCpzbcdf8e5TtyeqMe3A4VPltKL12gxP1MtCPc8Lr1kcaaHbbZXCLzSQKloRgeRjdS0BE6bobJsraUfFtrxHNyOcna5XLiKCpTlqJJC7TGjEI3mnJvUQKmJI4KRo1T9LQaoiiXFdiilEdqNNBJgu12wKU7URbYMseqBJ8jqhTstvMOvHrvtex87Ndp9Y7iOz/8JTdceh62tLR6ExfDsJDnUBp3AIowjk6Eurjl9nu4/977OOSk7zlkJ8amij543RFLnuesvdn2rnzdcsM/ruG4D++HTyW01gVutaJTlMRKs2zJCr5+8pnse+JpbLzTex3/Lhtnx0OPY+yUqVz4s28zbdoU9t5zc5HQdF5mbzOhMCVJpCkj+fzhZfMpsw6vPP0E6260AXNeeUGUvqx4uaXv9uLmOI40L9xzNYMrljF1y/fQ6B1bowBLGeKwjwEnfo4HVW5LGlOS5QWNVNLbigKSRgOlNEODA5I+ppT0VCudBnNZupZNHoyUYlBd1xo/XtYaokj5HEySRip7v5RqPmPFq1xn610osvaMR6/8zf1Kqe3/l9L5d4x011xr7bM23+foplflQon83NDgEHlRkmcFWZbXaqAJbnqFYo1rqW2cSlbEJ3/1d478xq8I1jQsNO+W1TMRwktql+cL3aZWhP9XzhPVbsDrxQXOGod/bP3tA2KuGRFXkz9jix054ZzLeeih5zn4vcdy1jmXsHjFAFbHWB1x3CeP4tjj9pdFNutxdn/XOvzuLz9l2lrTuPkq0YBNvcuTFyitafX2MXr8BMZOnMjMh+/moZuv5eqffYfecZNYtnAeAH+99FoWLVjE6utuQu/41bjyF9/j6l+fw8xH7qbR02LshAmMHTda5sajdutamLsqQC+Agq0KKawJu0NQgLX0TViNvjHj11RKTRkx0kqpRqvn5HW2e4/TRHaVbqqaL1XbPVXUuOL8Q4aJWyORgqcfuJO1Vp+ILUpMp0ve7Tr9AQNFSTk0ECLXNu9iux2XuWKhEBlCZRG+Ny8o2xlFRxBz0kzRuoGOelC6gQ8ugUJFMXFfj+Tkpik2SiBtoCKNLTMpAw4ZIBZbFNisQ9npYMuSSCs+fewHmfX0gzx29QVsstt+DGZw5s8uZOVAv+QMI5yzbXdQVqPjJjptoNMmw8NdfvSz8zn80A+y+4dOZOOd9wnzFrJpLFXWDjhxHEHSG++8Dy88/zJPP/e6aEC7MY21ppUq0kgxb/FyTvrC2Wy+16FstON7ZI1XUVgANtppbz5wys8485QfkreHmDS2xxk26GvGTB7dy9qTR7Hp9Al8/JNHMHlCyhXfOYH9P/0t9Kip7Hzkp4kcDxpys90+kqCq5bIfn871v/sF91xyDo1GQu+oPuIkQasorI1QQFWjEK0DRQDKitec5YWsc8kNIWn4rhEiINRspsSRppEmQmG5+IbvJl0aE6rhZGaVC+oblJUKuqGBIbqdLkVR0s26UvkGKBWx0U77sOV+R282bfqa176ZrXy76x0F0pRS6WrT1lz6gTP+1OcHxfhULXyQRjaWGFbfxcCX/LrAivUGscqzwxJUxuqBLI90vTGoTGWFpOR7HG9G4BetdTSBQ1+VOIauaIbwYcqh7MpQoLyhNvhMBGuMS2+LsQ6Z9i9ZyF2X/ZLX/vUAW2y3FQcf9l623WoDZs1fxtJlA2y/7fpkhaGgZPzoPk479Xzadix7Hf1ZyqxN2juauNGDsarKvS1L5r38LKPGTaR3nLRt0UqzZPbLxK0+Ro+fIkpk3Q4oaLV6aTRTWewU9K8cFE+jLAlhNsdRehV+HelK+jES1Bsq1Nx9vHLn3+ytl13wyeHh4Qtr62CLrXfZ65HdP35aOiLIGebCeyMqiOzEsSaNpPwWLN3Cho2ZxDFvPHIzj1x9AXf+4yK0FbqBSN43imJBcEYRJbFw0c7A2lyS6os8l664WhMlkoBvC5GMjPt6BLXqBKVl3kTQ3tFgtgSbY7IONi8pugVElriZoko397FUHppOF5vlKCU9vXLjkKVSPPvSyxx61Il84aLb6Q4NcP4XDuf3532fbTbbjHHjRhGnKUVXqq9IUvJul2/84Fdce92NLFu8gIM+dwYb7byPiPxE0pbGiXlUThdec0P2SpLEJEnMXZf+grFmHj896wtoC1EsWSS5MQwMt/n4J7/PohUFH/zWr4mSNFBo1U5zZkcp/vb9E/nEsXvx8utLuewPV/Dnq37BtCmjUCqimxWMH5WSl5aBTs53T/s1jzz0LPudeCqrbbCdSF9aK/MRadrtDl7bQGP5xaf2Ye99duXeux9mz+NPYcPt302WF2SdtktfLAOVYp1NkU4YHu9bQhuCOKGRpmgtB02axAwNDdMeGqTRbJE7oSixTZLJIMa26inoBbQkLVMHTlm7rCr/wtA0AZy4voxhURTcfsF3ypefuP/o/v7+v7ytEa1d74heiON4vxlb7tITRbFwHGUZNrP3C6S5nJdNY0Rve29UwR2wtsTYKifXD4q4TH5oZTN77Kuc8Q5lwB6dunvwFIaXdZTvV6ES3IKttfOr7kn5TyEcBMraEPzzRt44hfsojhk7ZQ0OPvkHDCxZwEuP3MlPf/QHBlcsQaEYM3kqZWeAH/zkG0xdYzLt4Yzbrr4egMdu/ivdTpe0kbL7kR/lXe//DEFNLdKsvt6mlEVBnks6TBJHTFxzfXHblKBFX/FTj9oaNK2eJlkunkSWS4oU3muyolsQRSoEKUxZK1pxsF9Zy6QNt1eTJ1/zUSAY3TXWmPaZNbbZM7XGu/YuTclNgP97WdAGaxXWiqB4pEqGOgWWKi9b25wHrr6Iz37i/djYiMJrpDCq8EkROJEpSlOgIostNNaUaEc7EYOKJXBXOFQfJWLwsTk2KzFKRGiUTiqk6z7A5F2KvCObO1FEVkOnIM8KSlOStBpEOoJSdBxKLBk5JRalIU5SNtx4TbbdZjPu+t13effHTmXGplvx4KOPs+E6a9Pbk6LjhMIYyiLn1Vdmcf4l13Dl367i6O9IxB+lKPLC8YduL7iVb6kWsKm3snFzts1+H+L8zx3CXYe/hz122AyLGJd2J2ezjQ5i/W135X2f+FpVIRYWvF/vjlqyhumb7sBXTvo+Y6dMY8zqMzj/13/jpC8fzTpTRtFINLGW3m9xpPjKNz7NX/92C38868vs9eHPstneRzkPpqQsC0pfMm8NRsHgimVc8xdZ/2MmTMaYUnJ2XXZO6I2mbOB440hj0JQu/c0RwVhjKprBBRnjWOyCKfJgYONIURQ5cRSTJJrhdidkz8SxAMWiLEkjBVaFTjbeNihHl3qAkefSBilxiP5dH/hCtODVmRcqpe78b9TJ3hHSnbHueg/udMzX3zV5xkZVLX1tSSglRsgaG2qjS1t15g2GMKwfS4WLKtfJI2a/MDxNABrC/3sUW7OmwajbcDKGiDk1VOzdYFUzxrWTzKN4f4+Se0wNKePe27uALi/C+oULKxbPI22JePbM+/7BA3/7DZ//6ifZasv1RePUoQqaDRYuXsKnDv8sH//xFUxdb2M5Z7Qi73YpypqL7u7RIwL/r0LRaDRotBpEbgE1YkNWaIo8p93uVEUUQFSnYbRE1v2Ye+Hr0kV/TVnyt29/eOXsWW9MtE4BZo3pay4+7Nu/m6hjUbDCuVwe0QSvwgUjtI6IYuEerZWyTUHXEY004dLTPs6S159n5nPXSK62RVClm8vIRf6F85N+dCZ3no07l7XVaCKI5NDuZl2SNHEqW1Ipp3VMFAs/TOmUjyPhSY3JMaZAqQhjSrSJsKWCzBDFiYjkaFwwq6CgpEBUx3Sk0GmEMZolSwb4+tfO4bmX3mDCjM0olr7GJRf+iMRIcOfZl9/gt5fdwB233sq6W+/Mjgcfy9QNNkNcaBsoFxGjB8+xB2gQ2DdZF2kSkzYbYC0vPHQ7N51/Ovfc/QcmjOpDaVg52Garrd/PJ35+Lb1jJoT4hw/urrIhJQMp0vSvXEHSaDG0Ygm/PfkI/njlz9lhk6lkJQx1cpppxHBWMtgpWNnOWThnASd/9OuccPZljJkyHY1UnRWlR6ayX1YumssFXzgUgM132p11tt6Jtbfei8JGJEkauoPLPhb7It1+Y6Ei3R7zexAd02o2wJahUeWSRYtJIg1RGqoD/dhJwE7GLXPduSPXmVyeXwfDb1wpsjGGbpYHGgIFSaRJGymFseTdjNf/dT+PX33+A3Nnz9rFvsP827dFukqpsdPXWX+LyWtvFAIwmhrfhKTrmFwCQdrxicpK7p3Fi5b7E7o+Eh5GWgdsdY3DqRtDb6KrRVjRAeFHzpbXP6NOXXk4a6tv/T2NeK0Nb1OWzrXRQnBol48sCKFqF07N1R41foqnSNlkt4No9o3j97//E0vnvs5GW27BwNLFfO6kj7DF9pvTWnN14iRlzOSpgeu0opPixk6QiI4i0iQGZHPmeRG+bI3mEaTvZBvTmKJMKVzyuc9JrHsfyoBoC/hxsA6pSqrVuptu1Zg9642tgEeVUuttscNuo3ScBJ1Ta90h6+bK50Eq6+/FSDEL3i2uypaNMWy150HMfyJiqLTo0jrULFcSubVjfV8895yRlLuGICpVYNaqgigFIiPtc7CgwVKIBrLBzZtyLeVE1EbGTqgwEyk6nYxmlGK0SCfayI2NpypLQ5QodKLJShhu5zSbDc468ytcdtn1/PiXF1EUBStWrOAXF17F8y+9xqsvvsD62+7K5y/4Bz2jxoDyBRSlS49ydJYRWkOFteonV1VrE+ElHfHLBjvsyb9uu5I///WfnHjCQURWChl23+NdvPzwbWy59wfxGTzBW69tBRAeWNuMnl7R0R03ZQ222OtgfnDGbzjkkN35x3V38d3vf47p0yagFWTG0NuM2WzjGRx13GHcc+lPOfCLP8a6uVW4tEF3qE9cYy0O//JZXPXjr/H0A3fz9AN3A2fxmfP/gSldhgmiq+DBd54XIae7KEsiJ4blDaAUUPhDK6c71E/cN5o4kZQ5f05ZCPuzk2XELn+3cP3zIl35FcYBR2MNeVEI2vZ8emnI8pLSZEIzGcP0zXdk3tP37zA00H8c8AfewfW2gbS+UaM+tM72ezUjl2AM3rapkFoh7mJcIVFkU2hVSfhp5fscIRvb52tGkqrhyfdQkhsWm60sp5JX1FPBbBDJkff1SBa8YfZfzpA7l9eG7ytT7rNVw/dO/ayq7Kpdq3BjFkboDiiliJOUDXbYk6NO+x0nnHMFU7d8Ly/PfJGTTzwVVeT0piKYoxWOBtABGUQuANVIE1rNlCXP38vl3/koz999LQMLXqPZTGi1Gi49RgTUJYEcJKMgIqmpM+koxmcc+LEzRsopjQtsSvK5PJbWmknrbdUcPWbM3gBjxow9dLVN39XwwTOZs0gQBJWr6rCCmxtxyX0KkRhq+VJAXuQkrV6WDRUMdEuGM0snh25u6eRG+HBrKZXFKCMGUFvp1KwNNi4xSUEe5ZQqx1BAbDHk5KZNabugC2xUYHWOVRlW506hK8cqQ4l8GWXke5ujlMFEBaXOnQEvKeOcIs4xqiBKNXFTU6DICkUSRVKVmcJHP3I4X/jUMQDs8b6jePrVBWx5wHGcdOHNvP+r59BspLxw97UUeUZPCq0oFxfcmNByyR/aI+NdNniA1hJSMf0633rfD/KHC//GguX9DHa6dPOCKatPYmjlcrcDfBWjC3SXq6RVIplFUgEqEfu9T/gKr8x8iXvufoyH7rmf/fc6js+d9HOW9XeZOrrJqGZCbuGAQ/fmxccfdJxugXErQkcxsdNJTpOELXZ7H3Eq6d0bbLMzAOd9en8evvoCodKCp+kEsKwVfttRi9btuciBkLLMybIOQwMDLJ47jzRJidKGsxw67OQq1uDuywXFtaPIJMNHutjEcUzaSGk0GrRaTRqNlEYaV229lMhMlqXLb7ew1SGfiHpHj/2FUmrqm9nQVa+3RbqTJk3+2Bqb7qS63W4Nkmu3cXyrm5jMtfSQ6iTteFyDy/wIbTewGqWC/xSAqedTKzNWR7tu2Bz56kFZMKcumOONrDcu/nWrQOHa+7ufehDsp0i5T3buslR2qX9TN6vQvgkZG3Uqwy8iHWlGT1yNLfY8hE13O4A7Lv4R++91HBdd8VMA0p5R8vc+ou9U/yMy7v7TOcx5+XlefPopAJ5/4hEAdj7g/ex5zJco8py4t0X/gtcZHhxgcOVy0p5RTNtom0AjoHyuJ/gqrxojIq6dFS7ez4JSinEzNmH8uPH7Az8YP2HCB9fYaFt81Zl0Zq4GzyIuuMKiqctS1sdcglj+vtba7F38+jdn8eMfTeErXzkGZQ2lMsSxIlGiVFeYWgCWKtdaItJWRGWUpChGWlNaE2onjK1V7GkLVqG0Cxi6WzdhDUp7GIUhafo1KkFZGxnJnbUGFcuBVBjFymGDtooE5VCZJYoVW2wqZfqfPPtPjJ84kZVzX+GJK85l5pOPsnjhAtrtNsdPWpPJMzYJe8MaGc8oiqp0SzdydafVG16jJJc1TQW+rrfNbjx915ac8o3z+PmPT6bZiOm0uzR7+2RtKXco1rFDDUgXRYmOBVEaNzhJ2mCzXffhtmsuZpfDjmPnIz7FVT/6Cj/84Z84+7sfY/KoBivahodenkdRlGSdYaKkIYAs0jRT0f7VkSDLN555lB32fT+jV1ubm37zfXpGjWZ4oJ8H//4Xpmy4A2tstPWI/amcZ2dKE8ZFKSXiSqXEK4ZWLCOKIkaNHkvc7CHLiyCe4xtJVDSlvK9VVSWbrz5L0gTySoM7dp2Tu908dB8uCkNBVawClkgpmr2j2OqA4/rU9RedCXyUt7neEukqpdRwp7vBqMlT8b24pEttERojRrGLKjufQGspf9WufFF7tOhEzUO7jIBkPar05brO9CnlpOpU2DgBuTr/fwQHW6ct8IM8Eo0GQ+yRcP3nMh3VBPnTwHpRndKbFqCKsnoEIc9RcZre/R0xnlqaL+557FdpD7f56ue+y2rT1uCJG/8snXCTiGzFPBItDRwfu+4iOotf4cgj9uJ3l/0UgA8cdTA3/vNiFr/yL84+dg9+8ekD+N0XD+dHHz+Q804+ij995zP84ZsfD7oHcRyHL8lV9py2q+hSHv1LMLQohVtUwLjV1yTL8w2UUqqb5etPnDaDOInRcb31z0iBdOVyJWtnk3D+yiedOw45jpgwbQZf+M3N3HjNLdx440MYW1JYS24gNyI9mJXSX64oJWhXGshLFzNAU5SWopROzEVpRMmqhNxq8tKSlYJKylIaKZpSuOXCyt94ysUilVdGaUysKCNLGRls7FKNEC0NFSkMMNyFoXbGcLvLYLtDFGsx/I2Yvd+7C7/8yWlc9I2P8vtTjufZmy5hh/UnceGPv8WzD/ydiZMnE6fNkDopxkEMhVZeJ8M6VOsRqvtyra2MSx2TfSgu/QEnnsrD9z/GU8+/DtZw2y33MGfmv1xVn62h3YpK8/PjqSlrLLGO3IjA5BkbAzB2yhr0jB7H4V/5ES88/wY7bf8h/nrVXfz58rs445Qf8YFv/IK02YMCWo2UVjMNiLIspUjlsu9/kfuvu5Tt9/0ASmuGB/rDGtG2S9Ft13ZLRUdJ4Ksq4hEvMKHV28fENdakb+JqRK0+4jQlSRLSNKW3p0USxQ7ARETO05PSYGdXXFGPcnm7aZoIWi8KyazIcvf5kdP0rWIgYlpcFhCWtbbYGYM+QinV+reNv8r1dkh32rgp0xrejRRRZxF8Fs4RcqcbG9x1RD9X7kt2XqSVQyGuHt+CtQo3J1jvogp8qU52W78Vh5wcqnWmdSSFO+L1hI2vWMXwUrEDKrwu7L6AzrzDrHQ84h0Cm+G+jFJO/9cb7BEfjyfxPehLkoR9P/51brrwhwDMeuJ2XnjwNiLKgGj3Pe5z3HLJBfzkZ6ewx/t259mZr8tn65iJU8ZxzT9+jVaKlctX8sAD/2LnnbZk1txFnHn6r3nu6RdY8PJTrLbe5rLInChPk4Th4Q5lKe69rd2Tf7KqpFjSbUaPHdeaN3fOZlOmrZ16FS2cO67xxKB2QuAeibmxcWPhN0ocR84tLqRti9Y0ekdxyMk/5OzTv8y7tj+PUePGojXkhXW8mUXF0g3ZWuF9ZZJkvnLHyynEUEdaGoFmeYEyJVFS9dtCaTLjEJRVolFgK29Ku9Qhn98daSldRymKUg7k2BndzJWpapfeopz+RKk1/dkQ+++/K/vtfgPNKCJqNSTZ3hhee20+3W5G38Q1JCrvhj9yY6u1lnLqQPkICq+8PgL0FdU4QxIlWFOSNFrsfMgx/OLnl7HvPruwdPFSli6+Da0M+37uLCkBdxNu3dRJtoCmKI2U8kdOq8J5CWMmrQbA3Zefxzpb78r4qTM4/od/5tm7/853T/sepVEc+/0/MXHaOmBKkiRxrXRKV30m4/Po9X+iMzzIoSediTEFx37vj/zxlGOYtv7GLJr1Olef+00O+OzprLXFLg4IyPqRFk6CD6JIEccJtrRoHQnPG8c0mg0X34BGM8UYSFPXvFJBu9MlTRKG210ppHGGJ3L3JsLpFq0Lh3QlUO2bnuZ5HoALGJcnjZt3kag01rLODnv2rVj+16OAt2zy+nZId8dJ62yaxnFMb28PSdoIO9S7PZVEoA7QPEgOKif64mvYnbEOiJPKgHmjXZUEEj5nhMWrLbrgwisVNnn4V1FD1v5kqzIXKpTsOeRVmAi8C+r4T+v1So1T6PJGxQl1RJGriKk/l+eYq3f0z7vDAUdx+vXPsObGW/LMo4+xZO7rvPj0U4ydLLRQNDyLs879Fu/Zb0/a3YI1p6/Jby/5GZ848Uj6+pr4RntTJo3lkIPezYQJY9lssw356UVnccAHD+LP3/kEt/zmdKyyxElMI01IGw16+nrdqa0dAqjJLCqNReaxKKWMdo0Z6yfAERPX3rCBNY7v9boJkuKnleQwRm7T+lxgL0gSRZFrYS7mwxvFrJtTloa1N9+eaRtuxv0PPE2nKFk6lDHYzckLdzhgKQ1kDuFKsAbp5KHEjbQocgPdwjrtm4ik2cComP6OoVPAUAad3JKXMJxL65nBzDLQtSwdLlg40GGgWzLULRnKLJ0C8tKtcyuI26NMi6GTi7ocSH4sOiXPOiSRJVaQNjQ2KjEqpzAZFssfrr6FdbfdzQEPJ4dpveaxknxjVywQRdo7IwROaJW1b1ygETeuW+97FLPmLuPbX/sBex1zEjPWX5ds+Vyev/tavJfoiyNWFZOqqhnl4C2DBjL09bW480/nhE254Y7vZcY27+bo037LpOnryuHqAlBCA7gef4VUhj1805Vsuus+bLLb/mRZyaRp6zJtvQ2Z89LzZN02Bx25PzPvudZVCwq91kgT4iRFaUdpukrEIsvQSUKUJPiWVmkjJUkTp6YmUqFJmgjd5JCpdvxys5EETy1y5fpaR/jANUBeGLpZETpZ+FhN7NGutzsaSeHUmnXfta8aP37iqbzN9ZZGd/qaax04bs2NVKvVdHXyZQgqWSoD5PlMz/f6NK8oiqTe2tW1h43oAjHKSdP5kxUYkYcb2IaAKRVV+pY3/ja4tnEcEyfVgAazN8Iw+/9QgVtvdfHr2p/0XjYyrFdnmL1rTvUZ/jYhID35X1d44N7fE/LaRf2P/d4fOfEX1zB5xqYA7HXMyWy6y960envZfrddmLtogMxYbBmx8SbrMG2NSSigIV2DWDxY8urSDrP6M1Z2cgpTcsLnj+H8a85n6evPcO3ZJ0tVmjOkSmviJCJO4iAg7o1vFMkC9FrHBsXoydPTOI4PaU6crn1ajze8ohEci0ZwMK7SUNLPRRLH0h5dOCQqNTkZlzwvaA+32WyPw/jR987nsktvEaRrLO2ipDCCbksrallDmaFbiKaqiBvBsuXDzF0ySLcQC+nFjtoZrBwu6XRzOnlBUUozxcFuQacQQyz/CqVSWshKoTS6paGTG4ZzS7cgBGBBhPmbaYwpunQ6w1hbkpWK5QNDZGVOgrjCNDU0nCBRXtIZbnP5X66Rpo11ntZnLTiKB4d46+JAChz1IPSDdobYl3n7t2v09PHh717E8T+8mG3edwQL5s7nwMPey6uP3TUiQF2nK3AufDdz0pfeKlhYe7Pt2evYk1m2dCVP33cHd110GsMrlxGlDQ456Qesvu4mfiu599ZYtz+MEZ3bJE7YcOvt2GznPYJHYLMh5rz8AgC9fb089dizvPLU43SGHOWghM8vjbQAMkVGWZQiUpM0JYvAqeNhDabIpeVPJjq9Q8Mdut0MXNC8LKWIQkei+qedcllPS2gQG7x1DyTEAFljKfJSxHVc7EM03h3taQjSs30TVmPCGmuvrpTa8j/ZVHgbesFau/tq62xEnhdSpWEJp6qv1ArIzkiwIRgUv1B0LcfVubLabWwPzyWvxxle42Y7XGqEkfXWTdXfEEGdXmvBG84qm6Lu+lewOSByt2CUm+wa3Vy7lRrctkbu2bvidpUXBwtcVdpphdMh0K4KDHLXaWPC1DX54Dd+IUgjipix5Y78+Njd+dSJHwZjaSa9tDs5sS6JNYHa6e+WzFvRkY2SKvqtRVtJYh/V28dJ3/kcF57zWy46+WCiOCZOUtbafAc22u1gJk1fn6Io3OPYcDgkUSy5ui4y3jtpqi6KYssxq60FviTWwS/tVeUMaJ+SZj3J5N14GS5RodNk3U7g5i0WjEgSrr3V7nzw27/m6nO/zisvvM5nvvoR+lpNklhjMhgc6khXiEiT2YJbb36Qq6/6J8888SQKqeg76EOH873TPo4FlrVLVg4XtFSJiTTtrKAZx3JPTk2sdHmhkUOVQ1nheF7R5c1LSyPWGAuNSAnPCiSRghjG90QUjR6shf7hQfp6Y5ppjI0tiY4wpcaqEhzyu/CKGxg3dW0mrrlhABmRrtTtrCmxSlE68RasyxGWBlerOHw+uOxVsggBwrTRw7QNt6AoStbe4l2c+8ML2OGgY2rUnP239/FaB2VpiOKIoSXzWPTa8/RNns7Oh53AtA234E/f/Cg9qp8LTzqED556PlMcUAiL3q8LJbvDaEUjTbnvqt/y/IO38/At17Lb7JfZdv9jGD1pEh8+7QJGr7Y2l5zyYd549Q0A4kYT8agqHQ2lIrKixCoBdcaCcutTK0BHRHESZCLFCOpgHRS+r5vLsS1KybzRcpgnSSJrwUghh1aIBri3Gy7eFDmaSXjdmv3QOmQfrb3TAemyea99DTia/3C9pdEtjB2dtkZVeYQqojTB1kg03EqepLE2DIQxBh1pSjTdTted2JJ/+u9cgZsyf7LUloJy1koHVFlPB6OKkNvKVtqyErIIbr3jroAQyVUqeGq1RRNuRl7rf+7/62axLi/pOQmlKia4ekcVSpQ9wvf3X5aSYyvauJZIGZIklrLGZjMog8VKk7dztBpk0vhRaK1p54bBrGDxgLTGTtOUPCskkZ+SSWkiG3X6VL79s2/z7JPP0R5q09Pb4vEH/8Ul3zqBvY4+kS33+bBrZW5c7bnUn0unYLl0z3gARk2YUjv8/AGowEjPKeO9kVrptJ8UHclzF0VOXkhxAmF+VDhwJ05bj2O//yduuuAMvvKJ0zngiP156rHnePze++h2OsRJwuprrc3KZcvoHTuZLfY6jF0+9l1Gj5tIkbX55af2Y5MtNmJgxQCPP/IkX/j6Jxk7qklDa4ZzKYtuxZqsNMSOy23EUei40Ha8YBxpMawostLQTCIKo2jGmryUeTFZSS8xuplQKkNfU9C8tpaoC0YbVEOHjggvzV7Eub+8iCO/eZ4IzpRlyFrw697aqkLQRpVXVNbUw5TygMc1GLWVCy/Iy3+vKIuSfT/7Pd7dv4JR4ycRKAoPFNzw+2g/SpEXBTpKeeruG7n5op+w2rSptEaP54Pf+T1jJk5h1/fsygZbbMjF3/88nzn/1iBmhCLk/Xo5D6UjbvntGbz4yN1895dnMDw0zJeP/zJD7Zyd3v8pJs3YFFAc/d0/8eKDN7H5bvvQN3Y8/f3Dwt9GschjAnGchD5vjvyWQ19p4jiRZ1IKa4XiUG5sjS2DQltphTbwwX2LpIiVpsQUorhnnaF3sUQ8PPBUjvd84zgKWQzSkEEAzOS1NwLUjrzF9ZZGV0dR4kW/vQsT3EOLC3wZ9zPHvTkKwgq8oUQRI7yictyv5CLW0ScjLKAYW/1vm7yOdiuWoEK8oSRWCOVwTx7d4OgC7RddjRNw5IX8XbUeR17qTX9KyGpwhsTzyWLc9Qg+GRDZRVd4IS/zuhUWMLRaPVhjeOOlN9hokxlkeZupk3spgXkr2yxv53SLklgrolSTFbmc7u4MGNUTMypNyFd26RjDZttsjr+5jbfejL0O2JOvffT/EbXGsMWehzi334IqQyBBaB7oHTsOgJ6+MW5o5R4r9Tc3rvjzR9XG34b3822CbDikanOtwOcNR2mT/T7zXZ65+wZu/sddrL7+Fnz4+ycyevwUOkMrmf/ys6StPqass4kTYxexkjhtccBnv8NFv/4NY6dM47V/PcR3vzbAub85jcxIFkN3uEsSSdDIWksaayb1NsmNZclQB5R0QlAKGlFEw3W2TSOJcJeloQtkxhIXJXSh7GZEDQ2REW/GdYcmhq7NiJXIgB7zqW+x/cHHMXH6elWDxZpjVE8RU24zW61BRShl3cElqDeIdrt9I0UqEUp5QamyqjSME/rGTXL7InxYBSA8laF9kYGgwu0P+DCv/utBGjpn7JgWF3/9Q2y514Fc/sdrOOPc/8dvf3IRQyuWMmqCtA/TGFAWY6NA2T1/xxXMfOh2fnTRj1Gx5a8XX8/41aez7QHHVAFMa+kdPZZ3HXgMSSoHV0+flEVHSeLOdeuxjawrz2Fb1+JUa1dFaSrJAWuwWrQ8TFlW8Ry8N+48bLdHJUdZ1qIxPgupRjf62VEEz13XEG7sqjQbvaMprR33b0aidv1Ho6uUiqbNWD92+DCkiFljw8PLP77BoTeizr20uI640pxPTgdNkVvpZhrextEC1nNCFWe8Kj3gl6gCF3n/d4fL369fVZLsXZs0K26Yn4AAcOvG/d8GI4xJ+J0FyQ92rp13t5W/b+UntELxIirj1Iy0dlVoghQLY0RoO9LceN63mD5jTaattSb9Q23KBJa0EzpFxtJhCcgYazClz7302SHSVHC4hLgU17QsDAYj7q4VPnDqWlM5+YwvcemFl7L5HgfL89S4QZ+naa0l7RFj67sK1w9Bv2EttsqLrBaBc/v9ZjBhM4zMJrFuHo0oNrqfbrzL/my86wFhAoqiIEpaTHO5wsGoGzAYygLW325PNth+T9mEWYdfnXgAr7z0GlNnTMMirmSeiYdhrCUzSgJz1qWPYfENP/PS0C4KEscBjmlqxiReOMgJpiSKIiugkBLayMrhahoWG0uq4aLlbY7/5Jn0rbYe2+x7lKPpgvXDgwMFjrGStWqM5Bv7dEzlkKhWlZqYfxfhTq1Deq6c2JbOO7PhoPOTZt06FmqlmkN/I1k3I04iPvj1n3LThWfRv2I2Bx22B5f+/irSVi8X/fIypq21Bi89fAvb7P8RoSWsHAp5d5gFrzzF5d/9PH2jevnWj79J0kgoy4J5b7zBdvscRtrsFZQYDJqV+IJL4eptGroaCuPzseX+fdqXcfSUGEmhiTx6t7YaQ+klKODGlCU6jgPfj0uf9FWViVJO/9sG2yNeqsUab9+q/Sz3rFBElMYLrssab7R6U6VUy1pbz4EL11sF0sY1ekdrb0SsM1bGnYaCqnwJpU+6rpBimHBX/17kuUPF8tpIi9vpsxt0LQjnsw68AZaf65F0gJ8w6xXnbXCx3EfXX0pt2VWv80haqWCwPQb3X25eK/fNvZOqvbvfqD7LoTReNakaH1ygRLm8wBHv4JCH1oqZd1/LAzdew69+ey7tvEOzJyJtJiwYbLOiI+16fOmvd9M1CuPuw1jLkqEur61oM9Atwji0EheNdp+51bs2Z+nc13n42t8DIuYcJYIAvF0Vj8Np8+owaA6hyPuYMEi1cVKOw46UW+QiHqR8rZItwZYoxyNWRRbIsxnJiVbu/0Uz19ESDs1TG3M/38YYt3ZEmGh4YCWT11iN6piozaESIzycF2SlH5WRa8RYSzcvGOxm5HlOZiQYCZZSabqRgUaMSiHRCZGNUBEQQ6kMN9z2FHvt82mSSevz3o9/U1BXkNMMu8TRCNqdP/5AE26ydHRDXZtB2UDmBFTpYasYM0PhysTFu/BfnmP3inLiAZZOc8MbQpQizwqGOwXv++hXUc3x3HbLg3z+qx+lp6n4x9+u59UXXuH6876HLX3ZMsx/4QnOOXonrjjzCxz9iQ/x22t+w7QZawIGrRVHnXA4d1x6Pt2ViyhWzmP5K48QF/3QWcnNF5zKK0/cy9yZj/Odw7bjqduvkXx/JyOLO3z8AS8UgcRJ/OURrKpSPuT5sRUt4fasQoBE6BTsQJP/nV9UfmjFTtiazfF6DTYgX//eoydNS4C1+Q/XW9EL43tGjQ1PpFR1A1jhnvyNgGx8Xy7kpRxDUajMbKgA8aezUjV/HK8u5JBu7e+re1Bh0/nqmnoRQqW45ThWfyIGpFoJ53jDEjat8oirjtgrREf4zAqhyJtE2CJHjVBxCjdEaQ2xFu1g/1z1l3iSXinFG4/fyRXnnsYpp30NFWmiHsUQiuFOJqjRdWSoq4Ippci7GTZWNJOYOFKUWCdMZB2/pejvFgGdW6CZpqw2fRqLZ72MKXNUlKIjRZIqVJ5VnHlDyjZ9yahRDjn4MfKo1towdx4XWCuIUEViTPI8w1rJB43dgi9dGbfxQkVWuVhBpSlRHXXVf8OxaN0zKdn6WhmMjVixYBZ9Y8bQdFqrvnuQUlVx6Ih1Y22ghGLlqSp5tlaSYoqSbhkFr0IDJoqJY2l+mGhQkVT/oeHsc6/gkj9eyz4nnspam72LsqgyFIKLhUdUrtOBU7qxfg+5w8oasFqhrd8vOJTmV6Ws8aIsXQ6prdCwNxBRLMEf7/X5Z3d8ZlHkYOXwFgQp+zQrLB/4ypn8/LNH0hw1lkuuPZ/rrr6dX5/zW7bc80Bxwd17PX//zbz/mMPZ/9A9WW2N1ekUhjzLARjTk6KKgjzL+OeffsLMh+9m1Wvj3Q5h6fxZADx26zXscOCHMWVOWUq2gSmLIPbvRZoiRwFZKxQMgIqlws6UJVEUUxaZsxPisevQvcJ5ZxqUqVaXpyhkuP14e3NcUWa+eEv2mcthUtA3aY0YWBd4/t8ekrdGun1Jq0fJxBiP28Ni9YhC7s6EL2OMS/Px+ZMOhbh83rL01TGCuopCpCKl7rxWm69kg1TIzoTHVu7zlTvd/Ynk13KVlWADfSUvM+G1lfHym9c69yBg3OqEhGplWT8G1ab94Ye2DxJzQU/HEiKpk1sdWnFJpC2hKWMUkSQJjUZKkqZgDY/f9GdO+c4X2Wf/fejPhiiUYjgvnMtfYfCQ0oIYK526XGilJFhQlq5iy9ZOfGegEKPSLZs0mk1eeviffPfQLbnjN9/g1YdvwZiSOG2ECqAoSXnPsV9ySFeH5w4aGcbTNy6QoZQcMG5he5Ra5l3++YdzuOPin8phEUnRgnJpapGrHoojyVCIk8ShkIqLC+6d1sH1U6EfGqAURSmI540n7maHXbcjjiJnzGBUI2Zib4OWy+OMtQ4HOGGzSZqScfOsUfTqiPF9PYxKFa1Ek2gl7byjiBVDbQa6bQpdYrWhjOBnF1zHFZffzDE/uFgMrlv71NZW8MwcUiuN52VrKZlWURorBUkuR1VByAf3QVpbusi7o67q/cbu/NNPuOvinwS07Nd03UuQCtLIiXzbsA9Rsm+HOvDuD32aH532c154aQ57H7QHW+ywFQPLFo6g3JLesSxbPsSUNaYSSx02jYamp0e0DG696X4O+uyp9C9ZAMAG2+4c9tBHTruAdbfZhWkbbAbA9I23ZmDpAu7/23n85bRjue6czzD/uQeYMK6Hvt5EAlnWjPAARDNCuig3Gwk9PU1azdR1CbYueO2QqlKhY0rwdkPcZaQX6uRiwq73c5SXxklKOsTtzFHcO1YBIxoA1K+3MrqFKcvKjUPy5fwHeDohcjCicDlsIxiA4EWpatI9z+LXn3PjpPAAt8hq7X5qCCq8qRsM4TYZ4d5jqr+pHVHuqiHnmnGs7YMRtMGb/a8gbVv/FsC1Kxn5O/+M8wcS+odLOp2MPMsoCuf2KSWlt3nOwLJFPP3wQxyw/7tpt9vEPalzZfGAzqWt1JTObIXaS2vIypK8FM400dKRGSyxVoxuxIxtJUzoSehrNlGq5Ju/OJNfXH0pvX29bL3RRJ696Q/87IR38/Ijd0jr8kaDNE3Y6dDjXG8z3IGh8a24jXWi3nlO1s3otjt02m06w22R+CtKcV1RLHztBRa+NhO0pigLUUDzSk+Rc++c6xjOC++l4I2vGuERBclCR2UZI+pRixbMp2f0WArjKyANk3oaTO5tMK6Vkugo6DjUL6WqCVdI+XKsFCbPpNWZki+tLGlk6OuJ6elJKRG0fdXfH+C8n/6eI77xS3rGTAyHkoPkBG7DPU/QiK4Z3Kq1lJ93l/Xi1jr+HmugoTRQFpU2Ms64LHntWRa/9hzUXOx6qqRSkr3g0xfrXri/h04nY+vd92TbfY/kwp9fjC0Nhx65Ny88ch+LZt5HKxE0u+Veh3HvrXdz750PM5QXtBoRcSK04TVX3c6zz7zC2pvvyPJF85g+Yy1efOx+ALbf5zA2224bUpUxYera7HP8F9h2n/ez+I0XuOWSC2kPDTBt+niu+dWZoCIWvPQ4t/3mG9x/6Vksn/sKnhhMYk2jmRKnKc1Wg55WQtpsMHrsGEaNGU2r2URjiF1RT/CSa2MWx9VaDMtP1cp/dW2OlJShR1qhlDsQFdgyB3hTPhfeml7IbVlYr+huFE7EVyx67EQsrLGgYwkq4csZbQXNsaGWX9xSEYnwaS8+EOftI1QqWHVD63Nw5dtq0fgx8ylh3k2tbxy/zkOebm2z/du+qxt4KqYXZ9zqobSw/pGMBE8x+Lp5wl+74gBrsVaBsVhK117dG06Zil3fdShX3/o3bLN6QHdu1Z5XPjhS3uvQKCuIyFj5uXYroDcWpaestMRK0SkhjS09SUmnsIwZk3Dd/X+hEUd86ITD+fvfbuF7p36ent4eNtpmO6assSZRo4eN3/thGj2jHQoEbY2IzRgc7+o8A1PjvGy1qcOTWEuZ58LhR5X+Q3DjXMofWkmJbWg3L3+v3LMFXVhrsKWhUBpKhdKCfHY58tP87ksfYIddt2XnHTdmKJPCh940oRlFjG25QojhLqAqdx6XZ6oloV4p12XXKYmBdL9WWtFtZ2jlXhcpLr36Lr51yo854us/Y+zkaS6ZvgIE9bWmFFRi2pEE9ExtzWovKlRb926te7nLwpZhnwhNUAQPy69UpXBC/IY0icnzai7ETZY3j2MRraqvubL2PktWFOx06Ec4//NHcNuN93LwoXvwrbO+xtnf/CwHHf9Jtj30U0yaMoHdPvBJrrrkBpYtHWCd9dZk1PjRbDBjdWa9Ooet3nsY/csWMGbsGNbfdGNmv/YGm+68F+8+5qssWd5haNk8Bha+QbPZw72X/4KJU6dxwg8u4venfJT5s+YAcNufzuXJ26/jiOPez2D/AJed8UnSZpONdtiNrd57JOOmbwiILKSipDQCEG79/Tk8eN2f+fx515FMXoPIH1o1W+VpB1UaIkyI0ytdZTNgXGaW+xsRWPfclbMYRdfyPxrdoizymqKOCgjXo0y8rL83uFqTpAnWQpEX+OR4D2k9TWZMzakzVcfawMH6lVdboH4lSGqwjyX6n6vgNin8IrNhYDzXXhHeI1Gzt8yrGmBdy49E1TITAuVRGfes0yZOm9Ufu+e27nOqTV0BZw9YjDVoDKPHjuHgw/YjQ0S1wQZaIVJRjcuV+/FFDaWVgyzx7rh7klhruqVhOC9Jo4h2blw0WJNEEbG2NGLI3D6MtObID+zNtjtsythxo3n0wX+xdOESZs9awAWfP4QDPnMa622/p3BrpQTG6vNRSQW6IfWau+5gEW1SRGZS+w7EtXH1nKXolMkcuGaB1gaVWfE2TMVvS0aNHODaCgXSO2YSh3zxh3z3y1/j7F+exg47bBKQbRoLcBjVSOiWlk5ekGhXBBFrRjViOkXJcFaSuiR9CVJCiqioYUSAJ5amEpRlybk/+QMHfeFM1tx0OyccZEYa0triUEpEXJqNBB3HFIV0+rBGEFMUacmJD9SXf3oHHGy1DuI4Dt6kB79eQwOwSiyxfBMmp9o7svd8Nw3CHhdvyogmRWnIsoj3f+1czj/900yZPJ79D9yDCePH8POf/ZXND/oMABvtsh8Dy5dy/VV3MNy/gsHlS5m+ztq89MwzfOCUn9LoGc2COXNZMGcuAM/efzt7HLOURu947r/2z/zr9uvoHT2Wof4VAEx84D5WW2cjFrw6E4BHbvwrp/3kO6y7/jpgDUccczjzZs/h7lvu5c+nf5o4bbLjwR9hq32PkQNdy7rYeOd9ePC6P/OLzxzMib+4ionT1yNKnCSBpxm0P4AitIpDdhGqSiPVCqwDAj6pQDnbo5X83BbZ/2x087IsbHB1nIWwbiLKUj4ochJpKKmzT5JEfq4IpL7nV+sGz1ob8uuUsui6gXJ5v25tiVH1iNAvYLdAgksf1pIKA2ndICnFCOM/Qr25flVWfIQL6xGWv293m1gs2gm95J0hGD0u/M5zAsEBDAeCxQfs3DuCtTx99/W8611b8anPHsuC/kFQGqvElVWIWFC4fSqD6++pcIZXgHQtGq+06xgh6VGltdi8QCtFEmkasaY31aSRpp2XJI0G660/A6Ushxz6XhF+UZYjP7gPH//oqcx/9h4mr7M507fdN8yX59y9JkHdiFrrAhcWEi2Z2iilfKFIzZSCQ5jWKucFiHFzkCwYd98Y1VrRlJWOJYKOK0/RsM6WO3HoyT/gm188lcuvO4+1Vx+PRQ6jrJBxndLXZPlwl6w0wfvva8SkUURfaskLWctSoy+3kofoC1j3uT/55V9pjBrPRju9lyIvIKBzque0VXK9BOQkOGqKUtKWkliaWToD71uaj7SS0hbLWotVwi030oThTjeUm8u+UVLooqyxcq9RGbwQFd7OGuM6+Hoqq1pTfnkad8C1O13Grj6DQ7/4Q07/f19nv8P2YfNN1mLsxCn4LtxjJ63O3id8NXhnZd7lvisvYq3t92W9bXej2WyxzZ778fgdN4Y1eu1Pv8FR37mQfT9+Crsc/jH6xk3EWhgeWMnyxfOZuta6PHj9JRTdYf515w384Fs/5r2H7sd+B+9Os9lgyuqr8ZFPfJgTPnMcLz73Ij//4fksXziP95zwNYaXLWLlglm8/PCd4fNefPRu3nj6YXY46JjwyKHyuQ6yogit5VD369m4oTPWBsVF7xmZUvoL9ohRGOY/XG/F6eYRxgRk67gyP7HSf0yqmYx1fGMsaKzb7ZK75m4+00iQvA+6+IWII7mdi+p6r4UoPWJgArxXXh/WWVJPSAT3y33vqQVV/U4Wj0OOJngW1D6I+rsEcl058XU/Dh71uFQmXyueDQ+GvF2Psr2x9R8Ynt+a6hQ1orzWN2Eqt91yN604YlJfL1Eh3RS0E7axI+6xujx69tuyrHMeyudjSnFA4WvVkYR/DfQmMX1pTBppelwHWMkIlHfV7rm323pjbvr7eey86UTuvOzXzH7ukWosSlN7Ljn9y9JUG1mBK7IRBQOlw7D7w8vixzkcRfiYgk97UloTxXHIt1Res4DaXPq5dl8b7rAHM7bYgeuuvM1VIWkGugUGSytRjE4141spjThm9dEtpo1ukUYRzTgi0YpWKlH/vLTSvwzolorhLCNJFEVhGMxK/nrZ9ez7iVNqcFYJ0NI+t9NJEqZJyBWNNGSZCLyb0h2EaRKQlW8TXiXo2GrhOnCSpgm5MZR57sbUhr3m9kKhMKUx0jSyvjy8x6qc7CZurYbgqPu9tIgqiaKI4aFhpm6wFZ/42VU88sgLnHXmBbz69KNcd/Znmf3U3ShlGVy2gGxoOQpLlDTY/YMnsvNhHyVpNKXstmc0AMd973cArLftbiSJUE294ya7qkVN75hxjJs8jb4xY9nt/cfz4PWXsv8nv8GhXzmXRx98jksuvJLe0T00+hoUlAwPdZg2fRrfOPOrPHHbNVz4xcP50XHv4bdfO4F7rvojAHt+5GSmzNiMf/zmh9z7l/PIux05dNzeMGXpUH3uYhJFyIoyjsIr8oy823HjacMe1w4pF1nH8BZI962M7nDZ7Rjvb3gRlJCq4ZSAtI5c7bqLViOK8bqm3OODXSHQVvvXZyD4TWI8MvZukvKfq8JDVbm7KgQY/v3yyLTa2EIVBgsd1rDbIiGwEV5TuyoE5Q2pfOUrFwPQHlg5wijaVf6vsgfVh3pEY1BMXHsjpm2wOZ/85DeJyoisnRNbKPKS0iWJh7dX1Xt5AOy/95SFpDVJhD7SWtCa81oipelJIsa1EtLIcZhK0Ygk+JZqiIMmAKCkhHL1qZP59Kc/yL4H7Mn8l55CUaVBiXtV9wL8Y/o0OQOYEmvL6jFqyeauw7LPS/XHp3FrQNSmxB20ZYkpcrrdbkg58x5YOBhdpowxhi3ecziX/fFvdLOcvDR08pIsL2kXhnYhoza2lYqRTfwhJ2CitBalEySQrENH2dhJb3S6BY0kZsLEMTxzz42MXIyKkGKmnTKb2xdRpMmKUsbWdSHwlYBeNLsSj3LrnUo1z3uZ1lonxlKG5VZfe6YsMlsWuQ50jhj/EVQVEtn3YKcOQPwc1vfE8HCbKGly5LfOY63NtmPl0iW01DAXffNTnHbgJvz4uD05++hd+fPXPwTLXwaTo6iQ9HtO+Brf/OsjTN9oa7515eO865ATqpboCqeJG6GxrD51csiuABg/bV2mbbgl+3/mNG676nqwEhhrtlJ0UzNc5tgk4rQff4vZL73ADgccxdbvO4Kt33cEnzj3Srbd74OMXW1NVl9nI267+Fd8/4htefr2a1k693Un5xiF1DNvI0xZkmcZ3W6XrNsl73YxpQn2QgZSOlUkcczgiqUlsJj/cL0VvTAwPDSA73Om3KaVyhFVcUjGEMf19B7ZwHGjge/LpVRBt5ujIxXQUUC43o3yrldQsnIpNO5FYtih8F2GrQ3ccN37CtSg+7lfgD4AU1nZwF3g30DXULX/G7f0HAL0XLYPICo6y+ahdbRseOXS8cItei5s1eG04eMCKHP/r5SiNWYiR53+W678wee5+OIrOfT9BzKcZ0QpJFqRGUlf0srx2sJV1FC9EkOJuM8+i8FYSxJFgePz6GWgW9CbpOEgA/HkFZrcufTNRPCuMZJ9ULhqoMKhWuPRbY1yCd6Fe35jLdq68TBlrpTWSpFIgMinv/lxiIIra7G10uiqVRRYClM6NSwdDnRlfAPJ0qnPygFTFiUzttiJ0ROncs+9T7L9TlvTm0YksWi9+lQurRR5aRnGkkaCMtMooVsYjC1RpkQlMdqKwU60pp1Lq/Nm3GDTLTZhZdRbGbOwGaGRxJK1UkqnCT/m0o/LB8LkuZM4JkoTCT5al2/qDaobXOOCjXEchYaOxlTr2IMYjaXIs45FaWvKXp97a6sW18EbK4syeEWervEAQ2uXWVRK+qJWrsW6Mbz72K8wfZNtmXn/LTRaPXTbw4ybNIld9tiFf1x5HacfcxAAex55PJvu+G5uu+w3zNhse7RWdNpDLJr1Otu85wDW3WJ7XnnyYV59+iE23Hk/1thgK3SzgY4ElCS9Yzn1+irt9bqffg2AH3zr1xz5kX3ZZLP1ufOuJzjvB78kzzI2224r1tloPYr2Sg763PfRkabT7WJKy5jx4zn++39gYNli7rj4J9x39UUsmfMaOx9yDBvvvA+rr78ZsdYYU4aO3CJj6kWSxLaInjQBHGqXc7900fwSmLeqBfDXfzS61tpi+ox1TRRHsqCVCsnIAn5UqKkHlzcbxWGz6TiGogxJ2lqLUcgLp6RkbFWxZIW7tFZB7LMfrPB4YsUxRjm0ILmYRjFC6Ud5JOj+rfQOKpdVnqtKV/MGLxS1OARN7QSzpnRpOhVCDm3LjWJoydzSmPLuYnDZITY49CNpjeqz5Qc+AFdHz0op8iznoC98jwu/eAT77L0XyeimFCNYj9BVqENPIkVCFHJKY61JHRIrTNVKSasqKNWINENZTsdaRqUx7aKkkcgSiN0YGCNSh83YcbJYUh/kVCWLl67kpr/fwf6f+wEeAVlM8IaqWmDvkfgofUxZFJlL4G35sVFONU1pQqWjshX95LU+fEC3LIXWQamQYigJ+gZrI3yeqTZglTt0tFBf/cuXkUYScAQo3fTEkTPuVii0PHelys7YxmmCJg6fVWYFRJrhbk6j0cAAY8f2MX/24kCB2RqvXhSFxC4cbxt7A+CCMDLuNhhAr4WglMQ8qFgaaTGuBI3Ve9yN6BXors7QSrTWSxRKdYf6JzRHjRsxN15n1i/T0NZeVYGh0okyeQdFe0AiBDxaN1h/x33Y9N0H02iKQticmU8y96WnedfBH6Hdv5x/3fkPBvr7+eWXTwBg5qMPjLjPKetsxB/P+GL4frUNt2MtJ9pUKEfrM5Jd22jnfZm01kY0Jkzh25//Lmuvvy7zZ89l9w99li32OphLT/0E22y3FbdddzPzv/IBPnHWH0jTXjQlRWkpiRk1cXUOPOkHKAzX/uxbPHbLldx/7cV87bIHgxedOIFyv3fjOJKWPaGKUQU5VHmFIut2cmttxn+43lLwRllbpGkaXBmQTWkAHA9iSlHHsoB17lwUSeAkimNBSY6ILhwklwG0gZcqyzK4mhafPmTdoW0kTUNpilI2iMIEdGTxnJeqYC4VJ1tjsJxRrtyzKh6sAu8YzKbncOs0hfGi02ItFDDnpWfawA3tZQsPQJF4l9LlU/ybgR0By/GpcbKYjTHErVFsu88R/O6iS/nmt77MouElUsuva49j5fV9acxQlofeUa0koS+NWDjQIbeGNIqItSKNNGNaCUVp6RYFPUlEK/ECKrLhGw59dVxifawVpoRSg9ZiyBYuWslRR5/Ceju8l0kzNq6ooOA1yNh6jQdPHWAttiwoiryjlI6wdrSbpID6/aMp5VPEvJ9s/eAH5GXdojdWgkmVsyNBJmV9FoU0xrTWstqMjXnu2Tc47PCUbpEJuvPCPkjloFailmbQIlyj5WCNlZKUQK3chpW0tziJHRovOeID+/H+gz7Ntgcez5gp0wgNUK0XvRdjpbUmL/IwdsZlgaAE0QW31WlleDAhHR4URvtmAQZKJd2EHVcbshfcYA4ufB1rzMMopZbPeXWj1Tba2q1lJ3ADkr5oxauwxqdCSWmwV4eTw08Ra+luURSVrnYjTcjznOHBQbJOhyjSTJy+PpNnbELsDvR9PvltrIXtDjqBZquXi799PEvnzWLrvQ/h8JO+x4VfERXE9x13Ejsc9BHSVh+l9wh9Z45VbNOOhxwf/n/zPQ5i/otPMmNokC32OpQojjny6z/j0tM/gQXmv/oiZxy5M//vkrtJW31Yq6S9UlGQxAnGWiavtQHPP3A7SkcsnfMKk9baAIUlTWMK18ZKW5mTJI5RqRZFNrd+fbBNC6XzH/lceBuja0xZpI3UVYtBked4pah6RUs3y4OxS5II5R4kjqKgViWbzJH1+ICWRRUFoJy77HaPM2gVXetLIqtg9n8yrn6RBMvp/qlqmvy9VFFzparvPUqGahH7j5O97wyuknzY1196IQduW7ZgVqEgCYPn3f/wgTb815MCagTHADqK6HYytj3oeC486RBef/UIpkxfjRXdflSq0YkOGrBgWdmRwzTWIvg81MkZ10yYPKrJvJVtEmdwjbUMZwXdoiQrSkY1YnoSca+90S9dWlbX9a42VpFot7kjRTsrOerD32DqZruy0xGfwhqp/ClCUNKGc0/VnlR+oOkMLiOJkwVK63hw+eLJfeMnO5EQp6kRUnBwQcpK9SmMYG3NCQGhar+21TxRBUN9lHnHwz/Gr048kP93yidwHacojCXGUCpLb6pFHL0w0t3XGHKf8k1Jb0PRLcTApklKnmfgGpImsWbNNaew7Y5bM+u5R9liyjQakSUrnaA3vtVMRFkW5HmJ70vnAYBx66pqFKAxRRFa+kRhYEP+o/C5viCiTpc5TyFb/IadP3/+7YAaWvTaMWy0tfIcc1HkAeDEjhoscWuhNI5eMI6H1hKld3SPdJ32HK14u0mSoFx5rq9mxCKKas6DSHv6MCiO/cGfaa9cSpyKMtf+nzoFqxOmrr9ZKIxCKWJl6FP9fHH/nZi6zgYc/pVzGDV5GkNLFzJ26tph9sdNmcaV3/8MC2e/wVZ7HwFK0TNuAsf98BLmvvgMc2Y+yeJZL2GVVPL5lk7iuceYvGDXIz/Fjoccx1lH7cSTt1/H7h/8jFeWIYpTEfgHjPPwfaaWdla3KKXjxcDS+SRJMp+3uN6uR1pe5l2iOHGHp3U96I3bsCONk1YuGmu79MRx2DA+pUzsoHAjcvorDxprCNhvWLe23PfKbWDvkIWMVxvWoKBT6ljSB5ZGbt5AH9hVqt0YaXBrb1/dS+21ZWeQoiiWAnOWL5pfvNn9e+NbHR9KdAJqNxoUuJB+S2jFHkd9jjO/9yMu+t2v6GZdBjtdIptiKSGSHl1xpOlNxYB2C0O3KFnRyWglEc1ERHUKYyitZUycsLzdxZcvgmQwRM45KLHEGpqJNHQsSxGDSRMxNt8+/Xeo5lh2OvLTrvuyfzyPVQNfE7yHeiFJtmIR1poXlCVauWju5n1O31XSqOwI99ojZvmK0BHkucXaksgVLchrasgujLyVww6f0C5fQ4veYPzE8ZSloRFr8rIg0uJdoCWTwYIzvHm4h1grslLQTZFn9LZEuzhOYgpHN/ic6rKEOG05nrgkUlIOj1bEGr77/q055LOnsv5O+wua995ahRJCCa8pC/KsCxAyduS1Tp0LqWa0RnSCceNYH7+FrzzbLsvyUUAtf/35jtpdtTw9YC2BXrBWdAuMyzgR71ITpdIXzB+CeVlSloZWqyn8u6tEBCfYo5RIuGoRqimK3B0OyIHiS+UjxYSpa4nIUhyz+gZbSzpjiLTLwAwsW8hl534JgAWvv8xl3/koy5etwJQlR3z1bDbdXfjioZXLmfvqSwDMfelpVl93E/FKVcxq62/BlHU3l4MNRaeTE0eKtNF0nrjYIVOWxEnK1y5/RBTJnEZup5PRN7qFKotAlaZJ7J5FKC2cXbTAwJIFGGNe5i2ut8peIE2SJe3BlW4j+fQg6V4wukdLaajr/KtVxTcWRUHW7VBkUvZqwxasNqGHRF7wI0nimkpQ1SMqcr3HlPalkP9mBsOPlK22nvzWbf/K1w2SbtVlHV9VCeNUC7v+Mo/CK3Q8MOs5yrK4xVpbFll3yHoRIGpZBQHweb2ECjqXZenKp32aj+cwYYNd9meogKuuvIYJo8YxsTmWcVEfk5sT6I1axDqmFUeMa6VM6m2wxugmY1spueuI25vETOhtMKG3QU8iNMPYZoMpfU0m9zVJtETpQbluuVJKqp3xjbTjT9Gcde4V3PHPB9jvs2eCVRirAir1B1gwtG7uRqqLQWfpXBYvXvzAwoUL7xtYNLuav5rBDMEIJ4ajXC5u4eIGkUPtPuhUWwIjrhEI2ULe6fC3H32ds8/6MknkMhMgpPiVBoa7TrQm8NPOCLj3GBrqOkUrRRzLsxUu+0A4dOOqAF1XEBsHLyDSmhvPOxVrDI/deHm1hqonx9NeIEjU+mpN1y0BVPAyPco1pnCKZdValVuQLJRXX3i2C8wEnn/9xWe7gYdRuPS7yCHUWiomvu0WZFnu6EGnVWAkfcyj69IU5IXoHRhjAijrtIcZ6B8g62ZgpLtuSDc1QrdEkaT/SVuoaqcteOlJFrz0JCi46pwvM3VSL3fefTEvvXILDz18Oef/9rsAjJu6dpjgRW/MZJ0N1uXYTx7Niw/cIug9SSQLhKpoRFIZXddjd2h1Ox0w0i3DGNFSiJOEVk+TUWNGEccJvkEBSiHNPXH2KAr2S0fSCmhg/qssXLDg39V8atdbc7pazRvuX7752EnSLFH4nIJup0OZQ+zIblNK0rCyXp3JknWzkP/mN4HXa1UoVORhqa90UzQaiTtZbeBOi6IErV3ZsWj0hp3g3fL63rU+D5hqT/ufOWgZXHtVbfY6UvLIoe6ujRwYOYSWvvJktmDBgusB0jR9eeWiOatNmLpW5e75OzW2em/r3cm6itbIY0CS/2Gb9x3J1VdfzMEH7MO4sePw/aVGmz7mrVjMQHeAMU0fYYehrGQ4L2nnJeOaCc1IRJ7TpuR+rtbXlLuw8pSC9HxjQ4OyigjlWCBLb6tBVpY8/MCT7HXsl+gZPTZkjIhBFERQWlc55rnU8FSEw3Vg/mvddrv9NKDai2YV1hJ7ZGvdM1c0jg2ca0A+Sgn/qaQIxyOXan5qM+yLX5QYoNlPP8I660xnl123pZNnlChCgqNTySqNoTeKaSURuRsTa6EZazrdAquk4eFgu0MjjokVJInCd421VpF1O6J34daHUhGNJOH5u67ikdtu4Ic/P40fnnFebRl54BGWnxjMOKI7nIVn8Eg0UnGogsIUjo81Ne/OB49hcOl8Iq1fsI4HWHvtGS8NLJm3/ejJ07BGPid1GgVlWaLyPHh+ga2whM7Rfj6VUkE7xH+VaPFWLOhc2sJbFA3XXslLR/rLZzlFkSv6AYo84zefPZDF82YzcdIEJq+zKS8//QR/+/PVjBs7OuyXm266n3fteyjT1t8sxGxNkTNm7Gi23mI9/vS5M2j3L+XAL/xAmlXGMXmekedFKOgqipI4lwNFKyUNLss8eOoW100j0rxw/01MXHMGU9bZ3G1m6aWn3MFVOVoCOJbPfrnb7Xaf4C2ut0S6eZa9vvDVF5yxMCSNlJ7eXkaPGUO7k5Hl0OnmlTiN59LcZBWFa8Nc40+1Vk5HNw79wpRSlEb6EhVFgY4j4iQBJadxs5GSNFLnujjdUQfvK0BjK2OnPO6qXyO/s+G/Ptglr/G8bZUe5l1e+Ve5RaO15skH7m4D9wEMDvTfu2z2S/KePh1C3gy8lmkok62MgsyXQzo+Ogg00pSNd92fMWttxvs/cBx/v+7v9C9fiS0trTRhfKsPmyvmreiwbDgjK2XzNeKIvkZCTxoLAkGi0ImOglHEQhJHTqNB0tDEZgoVYaxkBXQKqaxqDw3S7B1NlSfqD0xBDH6jVoeJM44uCo+F1wR1PQM8s+C157u+ulDUyZwGA3IP/n1COhx1Y14V3KhqIkfMq0/sF4dKMX/mY+y43RbYsiDS0IoVaQzNRNGXalqJcN/WQCvRNGIRDOpNxXhK9R50uplkHuQFWZaTaE1ZGLK8YMmS5cx87mXGTZ2BUpBoWPLSY/zj51/hnr+cz1XXX8DN/7iTFUsWVeJI7pTx+shxJGu99D2x/BrC6zj5QK4J31Nfk6rKcul/7Sm7fPmyv/lxWbZs6V+WvvREEASyFjrdLsPD7Uru0DuDNStrjAm0gj/WhJfOXZMCocdKJ93a7WYVmFDS1cOvcQ9u0jQhSVMkN1n2xZU/+DwbrDeNf/3rOv5x7Xlste5YPvihA5gwZkz4e60148f28dpTD/PcPX8P+73stom14sD9d+UvV/2Sx2673pURK1Qc0Wi16O3ro6en6bQuZAFFcUyUJC4hwNLJ5Dkj5TKarOHaX57Gxad+2nmFeuRy8ygBKynoxvD6zKdzt87/4/WWRnfJ4iUvzXzodrA2VGV4jkwEqB2KdQtghKaCEgk6H/SpbtYhSiN9mRKn6hNFwtfmhSHrZk43U0790hiyTleCFq6jZ9Uu3H9+tUZHmFc3MR5NyHhXxcKgRiANP4gBdSGb3xpX2ukOoPaKxbSHh2ZbawcAFi9efHf/nJfK0lQyltZTB8HFsbUFqBmRAO9Rt3ZdCJwYzB7H/z92PeYr/Oy8i/jcSV/hofvvo9MpmDB6LGuMmkIjb9A/VDDQLRjbTBjXTIiAlZ2c4Vzojm5ZkJWFowJcOpIVdBIpi1ZygKEqMe08lwXYbmfMee0Npqy1HpUHqoIYSP2ZvAEOMR/3nFprliycn1lrF1trFy1btKDrJ0opnEJY4Upe3Xu6L9nUFQKuu+N+VYWovZu0Oi+vtWL+qzPZfuvNibWiN4loxYq+JKIn0aSRohlJOqNfnYlStJIYa8AUJb2tBs1GSl+rQRJFJEmEjiOWD2cMdjNuvPl+jvnwKWy+16FMWXsDms0G91x6Llf/5GvsvOXqXH/9eaw5Y00eeeBx1t1m11CSXs9v1lrRaKZYBd1O2+MBtz4VQVQF/W8KfEH9SvsOwoqXH71jaGBg4Gq/DQYGBq6e9a97h33VYFkaiiyn2+5iylLkRakBAFelFkTUvY5GyN81Hu7hSu+CuylvIaXqPX29jB4zmrFjRzFmdB+jR/UwalQfSmkKY2kPLOeMgzZh5sN3882vf5yxo3pYfbVJnPW9k/jJ2V9BAREqFKp84xsf59yzv8gjV/6KG875DIMLX+P1x29nn312ItYRO2y3KRtsshGznnnEjbFbTwqiJKbV06LVako+dFSlp+auC7OXgczzkiwr2Ofjp5B12hR5N6SgRpHrbO47MwMLXnmOMw7dgsUL51lr7eCbmNNwvSW90G4Pz1lvs61lrF1FSLVYXPRYSQcI6ZJpw6AnSYJ3HyuUK8jIWK+vIByL1oo0kcKHopCE9sJlNUiHichRDYWQ75Em0gl5UQTvsvIwLSOyBpSPEssPTFjI7g9sYCnce1RFDP6XxqE6h7mwQP9Lj9r+/v4/1Ybr8XkvPd3d0tLjXTMxKH6kcD5bBc5GBPDcv5HWJImk4RR5iVKatbfcmdU32IpXH/0nJ33p63zp85/mQx/5CJPGjGN0q4dl/UvIOwU2hgm9CcuGob9TsGCgw7hWwuhmQuLKPL3KG7YAl/BiwfGGGkpL7vqENRPNTbc/zmoz1qdn9BiRDnTEuXaiH1pp137JBsMLrgDGPV935ULiOHrFP2uSJK/1L503fuyU6eG5vSHyvLByLrunqwIqDONYZUbW0YffRPWW5qPHTWDh4qUoNJHjTA2ldDDGtfp2wEIpSGNFN8/Jc0Nvq0mJDbmyppTiDK0Uc+cs4IsnfheV9rH9QR9ns3cfgNaabrfL/Tf8hdvv/BPTVp9Clpfc//izKJ2w34mnVweVM2A+jlG6yidjcdKBTnTJS4vrCOuKGJRSmCgKedGK6gAsux3mz36931r7am1cXllj2rT+Muv2xGnT7UFBIUVpRLxeVeDAOG+oKHJZF85r8HnEoWBjxAr2mUPiyfqDIWk0sNY6GsYSxa67L4qXHrwFgEMOeS9bbTaDMncgxz+PmyNtIUpkvHbbbRuuvepn/OrXl/PrLxxBksT86gcXybpBc9j738f1t13HJrvuI14m1ATk/fqiKlU3LksjkeQjrcF0JK1w4932Z+MddiNttAR++YIsx11aY7n/ygu47c+/Ztr6m6KzwTcVLq9fb4l0gTnt5YvKYNacAc26bdnAZRlKHOvRYqkU8pVKPiBWL1JQwQ31rnc3cy6Krdp2W2MoSkue5SEpvsi6mLIAJag3SeMQyENVPFmgG6iV/r7JIqlQsK/Wqi5Buh6OjcwpfeKO6wf6+/v/Ur3WLly6cF7H1ht92fpnVS6W32TyM8LPIte+qDRGWq0411MpaLR62XDn/TnyG7/gV7/9A88+/QzWGEb19pHqlKbVrFg5RCc3RFozqhGhgeGsII3k2NNauRp7g6+s8/ckaKokcmlijVjc1YsuupZt9ztaODHjs1RUKAevc9cj3X0bkP7A7Bfo7++/xf9moH/lTYteflY4wbKkNF6i0K8fVc2HXyf+rQMSq7orV4Uw3lhXaDnSinXftTc//vnv6V825N4kcl8ajSbR0rZeK0hc5ZHWWmIW1qU2Irm5Qx3RFfnnHY/xiQ//P7Z831GccPblbLbHgfhmndY9z/gJYymtYTjP+POf/84mu+5L3GjV3DKZ+zRNaKSxpGS6PF3PeUZxjIoTVBSPGHulxQj7ILFfm6YsWfriIxR5HqgFf5my/NviFx6tHfraPZvXTonDeCauI4OO4gCaJBDly7Tra1qHewYx1o1GSqvVpNlsyIFeyyTJHedrSsNjN/2FM08/mfN/+g2UcelrSCyC0gr/bCBFExlFGinSWDFx3Ci+c8onePC+i3no/ktYbeJ4Yiegtd66a7Bk1susWDAr3JuOIumxhg+SyjjHcezKjkWpTbksjDQVofQoimmNnYzgvpon722UNfSMn8K6W+7AFru8h4H+/krJ5z9cb2d0X1++YE7uXQUfmc86XcqilOqnoiDLpXeX95LllKoceGsrw1IJ4Dg1KUvYfKYsUQ41eW8lvNZU5ZLGmOD+GmPQTlhEh5357wyuRwfeyPnvA7dQM5Bh067yLv73xeByFs6ft8Ba+0b9N800fWrp3FdHfL4vlED5lLlgNwLHJa9RLhNEuX5yMqs+4mqxxGnK2NVnsPE2O/LKiy8AEkE1paLIrWud7v7OGlqpZmJfQ8RaHMcp1VxSKqxdLqZC5kBFOvRQs0Q8/uQrvPLCy6y7za7BYHsEJqkisgirObV43TCfkmeNYemrT3eWL19+qx+npUuX3rLy9WfysigDFyg8em3GahVd8gMTPkMqAsvAhYaZUVJtFseaRprQ09siShLWfdd7mLbJNlx8yT8wBRRl7uQYCfRY5O5Zu0eLlQ75z6YUrVqFZDQ88tAzfOsrZ3HwF89h2/2PdrdXpWv5rhcrl6+kLOHFV+dwzz/vY6t9jqrRMQ7Nak2kDFm3La1tlKRTOoZbVq4VCiZkhYQ1ayt6ynudSvHKQ7cML1q06DerrtwFCxZc8MYjt7YjrZ2Qt6eF5JPiJEF5iknhDnwVjGXAEt6Nc+tc14BElXkCjVYTakUe0nMuZ8Erz3HLL77Gn758GJNGpXzsqIMgB9sx2K7oI1MCpUVbRLXTgC0sJrNQEqiuCWPHMm7sKOe9CV+9685b8oEj38tFX/kQ/7rlikAmeiBmauAhdQJDvp6gLEteeOQunr7rBuIkIYpjOu12lZ5n/d52vLtSbLnnIXzwW7+iu3Red/ny5f9nozt/2aJ5xkNp5apvusODciJY69pZEwjxoJRvvaFz2gpGIsTWz1TNnlXR6prWbgADzpjrKCyC2EVF8QMYKibkL1ZFmOH+3f8Hg1t7xQg0VbtFf6J7KkUBy5+9xwwM9J+/6mAtXLjgqqWvPkNo4lhD38HQUt2q3yRaK5IkIk1iQiVVjQOuo8koihg9aSoLFi6i28lRKNJEqBZrjXN9JZm+GbukdyMITvl2REqS9SMFSmuniSvPWBhDGms6nTaf/8zp7H/it0mbPY6nLr0LIgbVeR8Vd+2fufpSwPNPPtIFHq0N1SOvPft4JzTLcQcppgzKYuFSFXfrXytkqw1ZISip0GukCWkjodlq0mimFLkoRZnSsPVBx/O7i/9Gu79AEWHLkli5mINVRDqiEUXESpPolCRKpSdZKVy+wVAoSxQrZs+az7pbbM9am20XgqO4Da2U4pbzT2OHHbdi9UkTGO50+MNFV7PlngfRGj0+TGesNaN7E5LIUBSOSqrq0V0nW1dy7g7K6qo+LxzaCkBTdIeZ9coLy6y1z666Pq21z8x+9YVleWeghnYJIEa7lLiAnKgCp96r8GtRGDehEVrNqr1TEsdgJXtpcHCQTqcTNIKfuunP/Piod3HJt45n67VHce7pJ3LVH87GlhaTG2xhMJnBZhabGyht6F0WgtrGUmYGW1h3GClsLvs0jRQtrYiV5otf+AiXX/Fjrv75aaxYMCfQHh4ERS5lzvoaApdbrJXmtSfv57pfnMrtf/oxjdT38lMBcFg3NoJBvRRCxEtPP9YBHl913Fe93tLoWmtNkWUdTy1godvpUuQ5Om6EwfBuue9G62fT1tx2OTl9VNpFSJWnLQIWDW6h5+88xwe+qaVyYiAGHXlXK5L8W1c9ozyklBsZ4e3/G3ZVNR6wvtn9s63yLkpZ7r7hioFV+FwAhoeHb53/3MOd+pt7ekLBiIWulO+AIK+KYpeTmucucOQ5c+fKa9HFLYqCOG3Qbnfodrqs7B8Kd+c7DShEjDtyfF1IqdPisqaRFE743A9roUTTyQzD7Yy8MPz0p5fSGjORjXfexxUu+AwTK6Wwjg+Tf2vzTpUXqZTGdAfodtrzba0dtbW2nWedhYMrl4MlCMCEY682/7ZmXJWqVTd6qOMMbuTiBWnaIElTslwqv0TT2TBt/c1Za/MdOOPHF0GpUURS3lmIGL1xdacyegXW5FhriLUi0aKmp5Smt5mw97678PR9/yTvdiuKzB38S954kZkP3sEZp38eYyxz5i3kjpvvZqt9PxyASOTW61CnIMsN3dx34TABMVfen89UqEqKfWlzoFdwFJpSLPzXnXZ4aOiXq65Nf3Xa7V/Ne+Iu6/NyvfeYZ3mgO8SWq4CCq7GvUYiq8mYarQatnhZpHNMMPckg72Z0hobpDizl1AM25uGrL+Lmq3/Ns49ewze+9DG232pzZ0QLTF5gXPNOCkOZl9jcHS9abIlPBVRKzl1lxTMxuaHISnDgohlJSt8mG67NB485nJ99fG+Wznl1hB0SVGscWLHVM0ead3/4JCavuS73X3MxD//9clo9TTrDw2GOFUbK2vOcLBcth/4l88GaWdba/D+Nvb/eDukSx9HS4YGVOIzG8OCACGOgQimvtU6By7uXzjVxPAM1OwrUXutOrhFoUOuKcjBuYxuDMRJR95FsydmVogbpHVbrneXd4NrCqpLWKrfeb/DQojowY+51tS9n/hmaPZPO8PCD1tqlbzJcL8x99YW2P9nrFEJIiRqB4itQWOY5RS5BlPALCG5PFGnXjlph2oOM6W3R22rQ7WYYYxlsD9PXkwg3qUUQJ41EFSl3teMKXOWMkk6t7ktrGBjucsllt/LpT32Pvfc4ntv++QiHfumcSuPAjZvSrrTWGQSZYj+OXkei8g9WvvYM7Xb76lUHqtvpXLXwpSdrm7pKRTNUGR/+d+Fz8K6gCr/TbrCTJHb1/ta1rnH6vk6nedejT+KmW25H0yDPLKYog5cICmtU+AysiJprHZMbHQ7lSEGzmeJnsFpLYrwGVy5jjTXXYNrqk8jLkksv/Qeb7LI3feMmurHxh7+q4he2QvtVWljtZyHIUUuLrIERYSDEAD16898GVqxY8ds3WZsALFu27Df/+udVg9ob0KA8ZpxXqipPC79vLeA1Iaq/85orK1espNPukBfSEy9JU9ENVopmM6XdvxyA6y79KTOmT6XsCCVpVFWVp5DPLV2GkHKVfNZYyqJSUbPOZpjcOrSrsIXFdi1lboIXJwFeOPHjhwHw4FUXBuAoiF2PoBWqeEtEs6ePE86+nL1P+DLTNt6aKEpQSjM0MMjw4ADtoSG6XQEn1gooWv7aMwz091/1n8a9fr2t0Y20fnnFwjlB8KY7PETU7PFQNSw+rEeytjqRawvJ8x8hP9OhYoNUhogupiJxNerWD4ybFaEZ6uWQuIVunWEuHT/p0jiUlUaKnkce8aWCMLS1TgXLoT/8U/l79ajUyba9eOeV7QUL5n/vzcbKWmvTOH5o0eszQ3DP34s3RpJ5Ub2fL2wWfrzGoSnXPA+njRtFFGWJVprh4UFGjRmNUlKqODQ8jE6MIFtXu565fMNWIvmXw3lB7spnu4VhoFMy2C1ZsGKIH/3iSt6z20e45OK/s/bOh/Oh03/PcT+8mNGTpoYD0s1yjULygVA5JbSSKjbfaFG7tJr5zz3cXrp06XWrjtWiRYuuWfriY1mVjO+eO9K1A1JVm7JGsfjglh9Di/CgPsgDTjzGVEUWpjT0jZvAypUrMMMdTDuXTi4lUIIy7vVGQEFhLVZF5IUIyniFu25e8Perb6PR6iFOG+Df36H9njHjWbpkKWmkWLh4OddeeTNbvu9D+KaZXoqxLEVXwYS/JXgr1gGNKlxlw34BQmoYYVxk7PpnPU+3M/yAtXbZf9rP1tqlebf9wJLXnnWVnh7tytpIGql8pvus0OHD1gyT30fWoqMYix7hocVJQhRFtHpa9Pb10mkPM236NCaNG4ftGsqhDDNcUHYLyEusa5ceDhit0LH4Yn7vl6Wh9Gg4M5h2gekYlBEDW2YlZW4QMTRFYWX0pq05lb/fdCEvPXIns555mKzbkSYLrjUSzmPwB5kfZx3H7HL4R1lj/c1BKeI0Jm02idMGKkoxPl3VzcHC5x9+R3wuvAOju2zpkgeWzXsdi7g8aatX9HNrgy8fXFsiDvl6KiFs0tricmqUIRrrNzROilAHd8ZzTMpRFR6Y1L73SMXawE+Gjga1BHKvbCZeqV9UTmzdc5DewK7yFWmFHVrCM48/PL8sy3v/03jNmTP7okXP3m+wThNYRkSeSUt6tXJpQIIeqimIXbdlb0wil8gduxJF+Z1moL+fnp4++ofaaK3odIZotRKUgk5eukNItF6V8wIU0MkNA+2cwW5JJy9YPtTlrLMu4dor/8lHvvt7Tjj7UjbdbV8mrrEWabMvGKxAu/g5rXE0yi0iWQ+6ZnBFhPqZxx9sA4+8yVA9/PJTj7b9AeeGKXyGdeMUPkMe3nkxvnOuE8txiNMHY4OHhY+4i0FutHqYvMaaPPXsTJeGGEkVno0kb9khqaK0blM57i9K6R/qctddj/HePT7GFVfex0dOvwAgvHfhkH/aM4YVy/v52GfO5KCDP8NGu+zDqEnTKAoHPJwXV7qCAxMCiebfDjPvfXnOGFvXChlJlGksz9zy5+E5c+Z88z+tTX/NmjXrmzNvu7xT5yctEnNJ0waJL0JChRJhDyJcRIQoimi0muF+vNhNt9thaHBQ9FmUpjAwfvr6zJk9h2tvulOE9ZMYVRpsJoa2tJLKSKLRqcZGSLWOU4pXFrS1qMJCVmI7BaoQPWVlBKBEqZQUZ7mMZ2Es/d2C0lo22mgdfvLzb3L1j75MNjyAKQ1dlwwQ1h2+RRfhwAmj7L0fqsB/nYrU1vDc4w8N/4d1/m/X2xrdFStWPDG04A1jrUT2RGGnSur3/bcUHrFXC8K7hIH39ei3dFVHNvh2gHCzsatSi5PItf2WDWaVqm1+z8OORK/WVpkH1nGWPp5e521M6eUmvatRbeyqQiv8pXSDtYZX77oyX758+XfsquRv7SqK4uaZD9857PdEGA9boRW/0KMowWF4gFDrrx0SlzSyaJVyWDB5h55GgkE6NDfShGYiYxe5AyLWEqzBfYKxsHQ4wwJD3ZxOXvDqq3O460YxuBOnrYO4rL4yTERXfMDKhJ54OixO7Q8Il5DvA4g+Hj4472XiKHrQ1rpF+MtaWyZx9NCi1553nkV9Tj0dJIY8iuKwqQWdRS5CXkXsoyii2WoSaSnHDUn9TvDc6/Guv91u3HjnQ5jMUmRWKAVToqxyDromVlrExhHPxCrhrn967iXM2HYvjv7en1hjo60pioK8yCV/2aHq5qgxfPrnV2MnbsYHT72APY75sjtI/FclOl6PJXhD6w/dML7IePr7V/5wk2/kZ1oxuGQeb7w883Vr7WNvt6ettY/OefWF11YumuPWlArGRVFlMfioik/51E6gJnIpYaWRve3T/AQxavIsp9Nu0+10ybKcZk8vAJ/5ypkYa4hiMZLghNwjjUriANB8vMDLCJi8Gl9JalEkzQZRnMj69J1Vc4MuDEVeoI2lpRUJoi2y267bsMb0qcx7ZeYIj9w4j0KA2iqNRN36CW2OVMWdKypKdP7L/yKO9B3231Ju3vx6W6MLzFw6++XMl2Jm3Q4+SqtdBLBKineGNhjSWt5r+JE8rO9JJAiwSucoRGNQqIYkRkcqUAThGnnI4wMPIz9nlde4UyogZTxX5TlJES3x6CrwvA4xFZ0h7r3l+uXtdvuKtxosa21/0e28vnLxQhyuDfyRtYCOqSp4nFlX2uVcylj6Fto43o+A5nEbQ1GgGB5uMzy4DB3ljBud0kw0kYbe1KuMybvEWqLhPYls1oZz4ZcuWcG4yavT6htdoapACflFXgblq7qhjXSVkYH1B4eq7tXCkuceLObNm3fRfxqrOXNm/2bB0/f5uojgwWhfWeWkQf1beyRdO+EFAERxKBEfGByi0+nW1poNyNKUhrW22p3rb75dim+siOqbwmdFyPdlWRIrWYOe341jzcqVg2y8y/vCwVTmRUCqxlapb2MmTWXXIz/JlLU3JEl8/ZGLbVDlRfsdo3HNLyOfmRO74KJ7X6uqdeQXd33jW3j59suzhQsWfO2t1mb9Wrxo0ddeuf3yvEq0EWCS58UIIypDLaJTUSSqY2makiRJUHuLXQ4sVNShtdDpdBjs7yfPcjbbdR8ATCQctIoVOo1EgwVQ2i2A0tEH3ZKyW1K2c8pOTtkpKLvixVitiXtHQRJhlNARtrSSamYlA1tZK/8iKFljGRocIm02QKnQYszHlOSqcsNRasThGEVRyJryAMOP0bwn7irmzJnzbyl6/+l6J0b39UVzXi98lBO3gJ0EAnJ/FUcJFVLx0c2wC+sPFyanSjErjUST88KQZdIqwwughziqrEWAmuvlf1aztKsY5pGX48GUrt7Dc8jh7byxFO7mjXuvLoaGhr77TqKTS5csPm/2E3fa+v1ZVGhpFNWyMOTg8bRMhXr94sWLd8sLUEox+5XnmbrGGnSGB2lEJWNHN5z+rXT1TSNP3XieFUY1YyaPapLGikaiacQRd//zEVZbb9NwuBS5jHlZFpKmZMU4yEITlJmmCY1GQqunRZI2graG5x19kEIpxSN33TxYFMU//tM4FUVx43MP/nOo3qy0In58FV9tOgOvIZZCCjRiV1QSS/+q3Okzu7HDBYj8eM7YYgcaY6dw5rm/xViDQjRvjZFKPMk590pbsavHL3jqXy+xYnk/0zfeFiB00A2enIuAJi4LxXuFUnDg0biqPAX3pLHWxC4AGLmChNJ4rteZ5HDw2vB59bEZXr6Qpx66e25RFH9/u7Xpr263e8Ozj9w7d3DpgsCno5SbeyP6CEkqhkZLQ81ms0HaEL42baQkaRwUyQSs+MIOL35uybKcoYFBDv3SWay25gz+fO0tGCdBqSKFTmNU7ISwonCKY4oyGFFJmykp2xkY5FCKW1CI1IAHBCrWzuJKWXcSSUWrijSlVay3/tosmf1K6Dzu15yuUXyS96+DCJdSMj9KiVaDpz9V+FvFMw/dNWSMueOdjv3bGl0rsoXDZVlIa2kcT5YXQfbNb7bqBFe1Gm1C9LuqIApjK66zd/Nx/bisJDkXZZXBELg7PM/i7g+PWG3tnutfVYS4SvvyN6DcWaAc3vDsq/8UOTTK7iB3XnfFspUrV76j02xoaOivL9x/87AbvxqiXvWecYjFGYhwqWBgjAe9/gmtYeWKFYwdN57IdkhTRdKwNOMUrVJaSVRxwm68fav1ojQMdnIG2hlXXHELD93/BLu8/5OACyJRy7e2cqhFcUyz2aDVatBsNrBIGpoFkjRxbr5giqDLoRRDC1/HlMUT9VSxN1lbwwrz5NLZL9Z/5sajdo7WXDo/f/XAWhLHLupeeS6ewgEV8k99+/EDv/xjrrrhVuYtXCSte4yjYMqSRpSg0JQltNuGFSu7PPDws3zuxDPY79PfdohbSbluWfGvUsoey8ZEubxyS7fTdee5q3KLpeWVR0mJq3yCSiTKGIRT9puEivOV568i7gAv3HJJtnTJki++Fe31JmNvly5ZfPKLt16S+1PJc9paRzSaTakq62mSpAlpmjh6QQoGilIOFJ8NI6jXTZgaacyKoqA93OaQL5/DWT+5iCHTQTVidDNBN1NneDVECt2MUI0I3YjQDY1qSCqoQhFZUHmBMpaiPUyZ5YQYfYDsYJWo3ulIEaeSZbGynfH0UzMZv8a6lEYCv7H2lbJhUAAbgouSlifrqN45xzrAphWsnPMSkeJR+xbteVa93gnSpdFsvLhkzut0u53KFaGOxuSGPVK3eA5SFrNP5fLtboKNdGvKo4TAeZpKlKPeY63uko0wjcpzkTVjSQWMlMse8JiyzgPXN69HE96993m6r91xRTE8PHz6Ox1Ya+2SztDAq8vnz3afa719x+cK+hupc2f4p/JG1hkJ68ZXodCxJP+v7O+nmWhG9zVI05hmo0GkC5ppNGIUYpeZIWOo6Ws1WLK0y0W//BOHfukc+sZNxJgynNohuc5KEKnp1N3iOBbuTVVegne9lJYSUkEdcv8Lnrw9nz9//n/MF/XX3Dlzfjr38TvKukGtaIXa4RgWiw06uModLFGkyPM8uPIjnB+oKABXxtrqG8OOB3+Y75/7G9qdNt0io8iNZDIYRZEbut2CgcEhhtsr+c63f877PvY1NtjhPdW6NiHLWcZW+UBoJT2ZdTOKvAg8tSCoKHiGjTR2QVLHXZrK0/GoIRwetR9X3oRmaPEcnn7onllZlv1bhsjbXVmWXffco/fOWrlotssCseHZrDGSfqcg9ExzQV1f/h3HMXGSOPEYF3jDi0pBvUdhUZT0TZjGtPU35dob7yHpiSixdIqczBgKa8mxYjAbMboRETUi4maEbkXohhhoYyTWUGZtqXDLcoeNjBOGVxRYSgQ5dwvISsNTT7+AVTET19xQDjtXUKSjqNKzhhDAj+KIPMucprC0Vxe9CbdX3RzMfvS2Yu7cub/6b8b9HRndFcuW37bw1efpDg9Lw0kqpCYMgh2xsByzE1CABGWcyxRcJMKDjshFDFHcUiqUHJqup+XUJ9cHvqwTkRa+hZBZUY+4+k1chwPW1lLcbI2NdjeXDyzhnn9cNX/FihXvmLMBWLRwwY9ef/iWcCaJ3fVYupY3bC3Wc3buqjBdhWh80Yf8f4wxBdannSno5hmRMsFOBe5JETavtZbnXpjLicd/lR0OOY7Ja62HtYiw8yqVVd517nQziqKkm2Xk3a60+nbvJ9kpKmQU+BJQreHhO24afCfublEUf3/m/luHvC5s4L49NYA7ivwUesToC2E0ZN0MBWTdrkO1bgzc/xvHV5e+1FzB9od+lIcff5anX3mFsjR02hmdbk63EJHuLC/oZAN89nPfY7UNt2fDnfcRw6Nk8xVFUZtXEWWKkxi0BD6LvJD7dOlTHjAYKxWDSSyGqygKiiwPfC9U60QOXFX9ztMAbq8p4Mmrf91ZuHDBx/8blOsva61dMH/+x5++5oJukOzEB51lvLqdLt1OxvDQMMMDQ7SHh0XoyGU0xHFMo9kQNcCyDG2f/NiENSifx25Hn8QPz/4dK4eHaDQjoliRWUsHxbAVXe7lK1Zw532P8c0zf8tO7/skm+78QY7+whlcdvPtFK0I3dtLQYltaJSR1Lsil6o9oyA31qXUiCJbNy+44/aHWWPDrQQRU9kdrwKI8lyueE5+EXn604ITb/cBbinffuyumwaLorjhvxn3d2R0ly1b+sCK2S+UpsiFw8MG2sDr4aKqgoYqKiiuUEAe1hI0AcPEEyKJwZVkZETXlG8S9XXvF9AsCmyJNXm1YT2v59GII8AV3n0wrsyyHhmuubHAM9f9trtixYrPWWsL/our0+lc+ew9Nw56hOqf3y/mEPjDPfObIe/qCBD+0f1u2jrrMWfeHKIkotWbiCiJQyIKSGIRFYm0JjcqGCtsySlf/iFbvPf97HzYR8UtDBH1MqQ+5XkeIsdFUZLnBXleyKIzdkSHD+EonaFzG6z/1afB2luttd23GydrbTdS3Dr3+cfCIURt7j26DUhfVVkSolURjaxos744hyrdzR36ZWlcpVohqlHWMnvOAnJThpbipcnolBlPPf8Cxx7/bZqT1+W9H/uGbLZIS+WgMSPmzzouxCvxWZ/I7xFqoN5wsp2Ry3wQg6EiMdayDDzf6LUO/Br344HLkbcse+0ZXpn5zJNFUdz1jhblm1xlWd75xgtP/2vRq88GBC7FL7603nmR7jAs8pJuu8Pw4CBD/SvpDA0yPDQo2hQ1AOYD0QpoNiQNLU0T1txgc1afsT4PPvKsFPEkcnCqSAzu/zv1PLbf/RhOPecyZmfjOeCLP+HTv/o7E7Z4Hz/61RW857BP04063PPYE9xyzwOYGEpbSl6vdvnGxiuoabql5Z93Psw1l9/Alu/7kAyfrg6Xyo9SwQb54h9B8UlosIny4CIiiWNmPXU/1pRXv5M4T/16ux5p/npi3kvPZOvsenirknj0hs6jEMeNOJfYX2JHaycohEXkO+XK27j/x6NoVwwhH+SQNLX30GGzUS1Ped+yDHm3vq20UjjeyQapSGNM4J4DsggPpxicM5N/PXTv8+12+/p3PKL+jqwdWmvtte+aM/PxA6dvvG11AMkv3SL2YyWb1njKRAVfoaJtlHLCIYr+5UuZvtYkkeWjBKuJtQ3vr5Wi1Yi46dbHeP6ZlznhuP3pHdXHd07/HUXUyw4HHhOi+WVpKnoHAm9YPYdxXL6toW1EBa0sg0EJPB7w4t3XtOfOnfPjdzpWs2bNOufV+64/cM3N3tXwB7oPKtowL5XRUS7QYYx4N0VRYilDkUF4BDde/qCzFjffgljTVi+33/swa621BtfdeA8PPfIUUaR46cVXQEXs/P6PSWEDQmNIG/mSLMsC7eWr6LSW6j8fPyidi+5XdRQ5kRmtg2yprc1tSHlE9kIoQAgTQUBpDqlw5x/OHpw3d+5H/4tl+abX3Llzj7/34h89esRpv+/x3kZpLGg3kAqqPn++rL9KxcR5PUJRmErPQIkaWqPZAKedUgIzH3+EhYv2REWKJ594iXN/+VeWL1uB0ooXn3+ZE8+7gZ7R48HKXo3imK3e9wE23f1Arjj9E3zis9/ltlvuYeKk8Tx2/+WoNMYqhTESOG6lMWmqMQqWrRzkjG/9jH0/daq0+LHCndssk9z7wHUiPLxDv746jciSDQ9QlIZW76gKDGKZecfV7Xnz5v3kvx3vd2R0rbWL15i+VhelW6iomghnBCOtXR4tUDNy3pj5ihZvFD1IUPjMBHfqeNffT5hfYzXnW1CqVHm5X4aNqagKLEJARlX3Eox/QOFV3yl/EPicYG0Mt/3uB4Pz58//8P/iugHMeuON7716z3Xvm77JdqnP79MQvADrTpPqzavDylh/KOFqR+WQiG3O4vnzWHvGOuQDQxhriSFUKXk6oRlrXpn5Ej8950J+es5vmThlClGzj4+c+QdR+nddOkYg7to91PMo/FiaskSrmKIoXXdYJy1IhdCzoRW88PSTi3mHieLuevj1559a0u5fvkbPmHGETRCOHh+tl++FNpADozDG6Zy6DrvUPYjqf33qmHItvctIs+dHTuby75/EjTfew9Z7HcDmB30SU+as917LtI13IE5Tt4blPfMsx2Lpdjq1g9rlbrrx8X0EfTGMMRZlS+HEFa4oogzdOZRSIX1QhrAWd6hxudXBJr9848F/2OVLF19mrX1b/da3u6y1z0+dOvXSVx/4x8dm7LSfEi/WYAtcyqINnysbuS7dKjyojqUqrT7mCjFww8NtkjTBGEORZex19Kc587sXcO119/DEI0+y7X4fYr2tDqbIu2x31PropEV7uI0XDG80JXMgafaw+4dP5g/fOB6AJYuX0TUZrWbL5ftKyVWUCGLvFpa//uVmxk9bn+mb74gP8INrAGolZ1+71EBfpGUdwhfbobjih1/ilSfu5/9dei+tvrEoaxlavpi5r724yFr7ll0i3ux6p0iXNIlndQdXjk1dorMMtnebBHUaJGewdJqyHn+OmGBqLpS3mxXpI6hAO03U2s+DdoG1WDSlQToOhHuptqdbu4638UUQhEaMcja4VBwxg4QT3PFVCx+/yS5dvOhya+1z73SM3uR66I2Z/1rSHlgxtWf02DAmSo1ElMo9lxwSIw8ouS//QJYlb7zA+hutSxQ30GlOmRvSNJZ8RYe4vK3+ypeO5r4Hn8eO25DN3n0gE6fNcKivMrgjzxO32Fa5/BrUSlcIzjXow9gKgWFZ8NitZmho8Kf/bSR93Lhx57728M3nbL7P0UoCe94rULX3B6xxGS4yRhIKKGWurU+eJ/D5HjXLmnCeg1HkecGMbXbna1c8AtbSaLVEwKTbxa+R0nVN1lqT59LJpCiEZhHNArf+ndhOoMDKEt82J4qqTAbhzktH6bggmZIiDA8+vOfj8ZeG8BnadeHIhvq5/bLzly1YsOAr73SM3+6aP3/+l+/+y28On7r5ruPjntGAV/yrB7DB+nQv473XSjdb6YhIadeOqKrAk1S+zBk62OJ9R7P2tnux+LWZfOiAzzNmynR5b/cZRV66wim5sm5HUtfiiCnrbMJJv7uNx6/7LfdcewVHfOgUrr/mJyRphO/BqCPFkqGSBUuH+P35f+agL/0kgDi/q7zHXJZSC660kwGopYR5RL/u1jvzyhP389Q/r2WHg48FLG88fIsd6O//6f8y1u+I0wXodjt3DC+bR6PRFF4juJUhEctFBX3LP7fwQx6n/NSjJ79gQ+K64xYr5Ger4Ja1oSFfEFup8X5+SQTdB+vkAX3pMD7f1tELAV1Xhrq0vvxU0Vm5lBv++MvFCxcs+NL/Mqj+stbawYH+c16+9wZnB02F7gPd542qW3ThP8FHDuOmtObJf17NWmtPZXh4mO7QILjOrF6bGOcK+yjsdttsSP/CWaw2Y0NBeEVJnhWBV65z59VJKN9XgVD3I6pS33B7bm6tc3nvveGKgZUrV/7uvx2rFStWXPj4LX8bxBjq3Z8lMObd1Qrp1XUrwjqwNtSdVBVSNTorHDJSIp5nmfDSSSzc5OAgWkc0m76dS1UGjhuaPMvwhiiKdajW8mlfogkh3ydJElpzS4q7lGd7sf762sZ6Q+CetYK44Xn9Mz12xc+y/v6Vn7fW9v+34/yfLmtt/8oVyz//+N9+keswvtU+rfR0HcgK1Y9ub9XvNYqJ4tQFVR2CN8bV+8k4jhq/Gutut5cYXGuDeppoJVdiWRhpJNnpdhkeGmZoaBgVN9npQ1/im1c9yaIl/Tz7/CsQawqtyFDMWpHz3Oxl3HDd7YydPI0pMzauwKGjg6ypAoXGGAfIBKBFvozdZeNssMNeANz0u3NYseAN2gMreeSmvw709/f/4X8Z63dsdOfPm3f7wLyXTdqQahRJFPa8jQx6EolASOXKV/SDDgn2OgSsqlbd3n1SASn4BelVzLCVIfCuoufQlPZBC3/6+gXr/gkRVDnJcPxo3L65qwAAO75JREFUldeJtzOY0vDo5T/prFi+/BPW9T/7v1wrV6688F//vHowL3JCNS+4ihjtEvx9pkE9fYyA1sOto8i7bdZYcypEBT2NCG21iDxbkV201rJw4TJee30el1x2K3/8wzXsePjHsUZyQIuixJRFOODCuNZv2lafKwUepobExRNRIXOiIn6WvPAoRZ79438xBtbalbbIb3z1yfsBL2ZTy9hw/HwInDlqywR+MQyZfz+prbc2PGblthMolm43ozPUptvN3Vpyz+YCxFGt3NgXDvg8aB+9T9IErTWmNES+klJXRRJe+tLYSjHLR8Trgd4RgdRwWLjCCpffu2jmIzz32AOPDgwMXP7fjvHbXYODg5e98ORDj89/4VFnaMOWwlNvnsP3AU1fveZ74Y1Q99MROo5JkgZpo0mSNkiaTRF519qBIw9ECIhaKeXK/2udQVDBDudFwfBwh85wmy3fezgf/dgZnPHdC/nOd85jral7cO01/+Rjh32KC395CVu894j6qnCicv7zJC6QZ7nz4BwViT/8ZOwHlsx1jXQjfv6pA/nn735AmWd3WmtX/C/j/I6NLvDo/Jee7gJS+qtVcHeUQyaSSoILGFQRW5/jF0Wuv1nQva2MtsLTFTq4XjZYHF/m65CNTytz6MCLjsiE1UpuqRaycqW22rf6DrXv3pmUq//lR5n59BP3Dg8P/9d5j292WWsHbVlc/drj94bPkxl3zx8iJj6B3+VEIvSJX4SCdBUb7LAnzz79Eu28S5xGxHEii8VaygKuvu4+Nt3kcPbd/4tc8Ke7Oeiks5i6/uaURS5914oiBM/qKXIE+1tDuNVDhDnwdfFVWpDHP/DUzZcNzp0798z/daxmz559+vO3XdH2Gxi3MXQkGTPgjKJaZUPqyuvydIwxBO/II2ZJNdOhDVK9SaMEy6Ig9uIT4I2raLPGUOZ5eC+PiOJYcpiNkSpNrVQt66OkLKpca+kAwiqHQE04Krg/uGd3e8wdzlmnzY0XfK9/3ry5H/xf4wxvdVlr7by5c4+88w9nD5gixwfCKkquQtuhEMYZXq+7EkBVbX9bVSVLBmdKR+goRmunqxHF7m+028MVEKvOoZHUV54XbLLnEez9qTN4bn7MnQ++ynrb7MSZX/shB598Np8+7x9ssNN+AEFGwFSKW+5NpRGsgHHJV/daI1iY88IT/PFbn+DrZ3yJjTbfhL6x42navDN79uw3VRp8J9c75nSttfOmTV+rq5RqScqE/Lz0fJlVFE7hyXOWyvHuBot26DNwlQ7ma+XcLmuDcfEoJrx0xGAHVgZ/ZvggXOguYZVjFmqnstucFXr2iecVnaFtwc2//9HKBfPn/58jwvVrzpzZ57x23/UfmLHN7k1JU/PBwCpNBSoeElUFMMKAIM/THRpgzPgxoDWNnoYgMWSRPD/zFZYvG2SDbXfhQ6dd6GcOUxbkWeZQbhneU7FqNc4q69H/PCBJV5hQK5v0Wce2vYy5b7w69//CgVtrn5s+fc15/UsXrDtqwhRn+CsUiMWp9EvgzKPu0mWh1A+tKkDqaQa/bqDVSOl0sorGcp8vOiJy+GmtKWwhG7BGTegocgUZEVGSkCQJ0hVb3slYFWgwi4jBeFqhYmRqVAlVSpaqjzMqABetJPg7+56rzWD/yp9Za+f8r2P8DuZg9uTJk3/+xr1XnbLee47S1iqsU8vTjpu2WJRVLrzox1WH8fXGUkPQMRCr7V0o/73T0QhEvEZpX32qXCCSmrtXedB+XIwxTF53M9bafAfK7gCvPv0Y+37qO+hC1MTCcxlRPdPKa5sQvImyNNhuJhSR33Za8fz9NzPUvxKAKy+7DtucyEe//z3+duaJC/nvAsUjrndsdAHSNH59aPnirUZNXA1CnbgYjbIonAi0oxQiHXp7CfIwVfDqP5zRwq1UxRWqRhP4PVN3hL2x9S8KrrmyLjhRQ0Oq+htbn3uHdK0xLHvsRjPQv/I31trZ/824vN1lrX1m2vQ1Fw4vX7RW7/jJyDHk7tkfWi6jQWyHdUFEHTaiPKsmyzr0tnpAxZgSsjwjbsQ8+uRzfOAjJwGwz0e/XFEDxpB3u2RZEWgbf+YFP8eO+Cd8E+7Nfw8uc0jQW914zH301nL58uX/dfrMqtfy5cvOmfXQLedtuv+xWrmsDYtaJTe24pmV1qS1dtpZLkLangv2nKQ30HGk6WYZtpba6I2fVFq5UvSypHTBRqNwUe44VGRFsRd5MZRF4Q4m935ao1wangjWEMYLh2x9kY9X5gpo0t1QJZUp79sdWMZtV/5x0ZIlS77/fx3jt7sWL1585t3XXPyJaTvsO7k5erwDPrhcb+X2iw1mtkp8lOyRgFDdWHiIFLIxaiBdKTDGwyqLUhF1EK+9fYYwjzUL7Dy8kqyb8coj93Dlj79Oq7eX9tAQn/rFtfSMnVxZhhp1Y617Y6TPo7ZWMiysBIZfePBWrvj+yeE+lvdnHHvKOcy+7xozMND/4/+Lp/Hf0Atk3ezuxbNeEDEM99VqNmm2GjSaDZqNhkNpuNbqVWVYcJH98DpEEtzn+knvFrAJZsCGTe+xcp3zCrNiq88Kp6h/gfKfW0OU/gVW2lbfcNmFyxcvXnzafz2K7+Aa6F/5s9ceu9Mp4bsqGteYsbQSYkP5lkNRIPMDh+Zc2u7wEFde/Dfuu+MB4SyjiG4nY/VJEwBo9o1mx8M+Ds7glrmUMlaBs5EH15saXPzQ+F+aasysD1jayoCjuP+mqweHh4cv+7+O09DQ0KVP3HH9kM+D9XfnN64/gGQTeSV6UYgDE4I7ldsrhrYRazAl3SwPeaSBu4PQrbrT7tAe7pB1MzdmpTO4UZA0FZpMKLYi67o162iPmlqcjKOqGQsCH+1+VQ01I5gF+TtdudfP/eNPxYrly79kra21g/r/57LWdpYvW3byM3//Q7EqYKm7ZSM8Sfdc9XY+sg3ViP0dqD4/LgHpV19+3jwF56khMeRVYE8pFSQ+8zxn7a12IU5TfnyBMFyNVl/Q3fCBdDm4PYLzc+8yF6x454ten8kNvziV/Q7bl0azxfuOO5kTzvozvaPH8OBNVw0NDAz88f8yvv+V0Z03b+59/fNet7LwXPaBta6KKXMKVVWjwtII4q1sn0xa/ZCoOV1hYP3v/aYK3GfdzwjGW/43/NQbXMIPApIwTs1sBHeJUBFLnrzVdNrtn1lrh/+bMXmnV39//19ee/SOdh0FeNRdpQ+5VDn3NGF/eqShFFkm8g9Lli7l/2vvy+OjKq/+v8+9d2ayEULYSQggIOIKasW9WhX3Wve2Wlu7/Pra1qq1rbW1alvt69ZW0VqtorjghhUQ2ZQd2VWQHWRNMjPJJDPMZDLb3c7vj2e5d7Dv7xVIAPvL6SeNJJNZnvvc85zzPd/zPdC58IrONKSzeZSUlePK2x8U60Ig0a4qJxRI/rQuWnZ9y1hsAniT68SviXR6sonCa51Ox+phmeaWjig8ElHata3PEpFdSn0OPude9KaVM3BhmhaXaZSqdIw720CAx2OW7cKWTVbwyO9y9hnAUCiYKBQKMAsFmGaBF86IICd5GDpXWTMCHNIhhzc56EbAE27XZZ1DOBNf3KD46uTVLzzHKpybzykBALmEXNsefLxodksul3vrQNf3i1oul3tr7ZI58WwqoXjtfjEkvv6ymCYDA5n5FGPsUD9Tf1bkfJWDZh7s5v/OszI/jizrIdKh8ic2gqUYcswo/OTGO9BvyAhUVvfiU2gg1t31MjflYyCyDgaYuQyWTXoGr/z2e6it7YuZk2fhB4+9hjOv+zFKKqoQb9wO17Y2HChrZJ+cLhFtjNdvtSzbgmWZKBTyyOeyyLW3I58vwCyYPryUXxwuIK+pU8XwqfrIaFNdMNGi69FjWNH9pYbhFTlZ+VsZB4tIVpNq93LciDzZvMeJDwVybMx75+W2RCLxxH6t4hdbu3BbPNaSa0/5Xp0pBwdAHFayMcLLwjwKFENJOedQ9h/QB5oG0VLq4B8vTMIRJ4zBkWPOV8/PGCtyuPw1vMOGL6WMVjwnrw4DIV9YFNlCQiDep2jduNRtaYlJEPmArSXW/M+GNYtIEvDlYaEkEYXDVIVZw1BRFg9iCUGDidQVsPlgWXhuWyTDgqWgG7qadALxeo6AMnSNV9IDho5Q0EBAOFwZkQVKStWobmhMHUQ+ABdyj8uIy+88uEi7v6jsbW4par994Ttuezr9J/o3YvCdZUTktKfb/rR13iRXOiwJ8biON4hT1VOKIlf/QcIAeIUxebBImF2TDAimFd3fmu+xkI+TayeKbVJzWPoCx3Fw+e2P4bt/fgnffehVH7Yv7nuBHfOZeVDBhOu6aGuN4MVffwu5xjW48KKzkUyb+Pqtf0SvuiMF68dG/co5blNTdJ/Ebf6d7ZPTBfBZc/12K5/LIZc1YRZMTrcQF8Gv1gMqdr66iAIAT3KQf24fNgjRnCOcr3Q6XBtBYEXiIoF5ztnbtF66o2aQiQvjp4cRip1I88fvUyGfm9CRvMd/Z/lcdkL96g8ByHZd7iC8vcU3BglyOZ/75ruBAWRTrbj6h9/HqWeeDAjs/MEnX8a67TGcdOkNRa/nqrZUV1XsXZICQk6RM3VES7AsALkkJs7KE3IvAMJP/1uzcEbm3w2f3F/LZrPvbF02JythBDVTTmc+3QcpGcqnFEMzxMHMP7ftCNx5r70Gtc+EIpo89Ejgr46nGeA5dw2G4S+IcacimTDqYHJcBAxDiO/7ipR7ZWISOgoFAygvL0MgGOByj7qmjgWZ6hYybVgx+1+JdDr9P4rBd5a1tbU9//GcKXsKmbQaACDrKgSv2Eui2uUPljyWAxMwo+csi6Jh6aR9bCe/XiCnmxrKycp9JzU/pJOGeHxF9yrUHnUCiIBCPg/bsjkHV0TGUsOEwYMnAoaBN/7wYxx/wjDceOOlmD17Ea7+7VMYdcHVyo+BCGsXz8oUCoUDZjXta6RbKGQzOVe0kDoCkyzG3+Rm9Xh7YLxbzRUEftWyqjh+Pucrl1tGw+BCI2C6wHjkKed7fv6i4k2qdyF+5qVDMnpUwzKJ4FgFzJ80PhWLxf6wPwu4L9ba2jph2/JZOfleHJWq8Tcooy2pLAXwCN22bTXufNm0N7FhxQqU6QEwcC1bIn5YlVZ095ZAFmp0uYW9KF9yoR3bVm2ptlSBc6QIkL9ICXhug+BFbQyZeASZttQOImrtqHUiopZ8JrUr1RwBxL6RWY88ZGW9wPu8UDcWIGAaYgiFAggEdAhZVJ7yC0qYY1swzYJKL3WmiYkYfAyVrnEJQO5wHTjEAwDbsmCaJgr5AqxCAbZl80GTjg3TNGHm83DFNZP7DMxzRLquo6QkhLLyMuhCNrMkFEBQ6uyqVJ1h24K33Ex7+/30BcSDOtqIqJBpT9+/Zd6bLmM+aIk8qEHS8lStRGWYTH0pWp/kP8Nztv6AS0XIPvhAK/rSPZ/hw+yl85X3dzDIa0zykPVTIL0DWPQPMCAY0HHUmHPw4YLl+NUvH8XXb/szetUM8V0vDW2xBthmfmtHBGb7GukiFArWp1ujQtfS9ck18t8r4FxyJ/nFE/KNMl30vQFtL96u3HTMP3dLguy+aFZFWr7UjPk4hAxFr8+HYhaPogERmj+aSflc9pn9JTrvixHRzmQsEssk44rz6heC92cK3jwtDxYAgJGnnoMBdYNEpsSd7j23fx8t9duxZcU878UY3+BytIrqxFG4unxtoXgmSPpyzJMmNrcSQ9f8qbnn7Zo/XeC2trb+r7q5+2qtLS3j6j+eS7K11hGArK4OWoGZynfkO7i5Hi8vdDk2l3Lk3WUeU0A1N+i6L6rlEZfGGJhgKAAilQbjAw0LJkzBv7UdF67DD0XueLmKmWk7sHxcaHE5oGkaQkGuthUqCcEFROME1MBWrwOOwUwnsWr25NYvKp7fGZZKpZ79ZM7UeL4tqYqY/ntIya36fQAD/NUaCV0peMjHqy4qsMk95vMfSpNXRs/KKXuOWcp9Mo3xpheXD3wNBINQxXlfpst1jZl6n0zTcMkt9+KXry7Brc/Pw/BTvlYUWGiahvAn892WWOwfHbGm++x0c9nMwpb6LTB0TZwiKI4uxT/9bY5cZhHqovEEjSnVebX44jTTxHBKnoYZkMwHwEvPmAIhmVpE1dklK0/wnKsrHK+HfwBmrh1zJ70Qb2lp2W+i875aui31xK5Vc0hSmlwRNTi2w4fsyQPC5Te0XC0Sq2vl2nHhpRehJFjKMURGeOPduejepwYnX3y9eh3GuLB4SWkpuldXoXtVFYKhIJ9npWnqS2N8tHhQiFEHDB3BAP9uaHzmGrkOdCYl8aBwPI0Bq+ZMTXdGgSebzb65buF7GdfhKb3tuHBcwBIHvWLDMBRFVBJSMAyD71FwDI/pupCClE6Nt+vqRoCP/NF0QOw/3bdGDOBFOMd3CJJ3kDsOp+LZdnFbtTxE1T1B/PUk68clglUo4P7LRuIPlx+NdKKVX2fmFX3Wvfe8lUol76B9lA7sSCMiqy2V/MX698bbcl/S3sELuIv1MiVPba2IKSOyLQn7qXgK8sDU1PUEfD+X3YkiUlYTug0BNWiSWcJgFUzkshnYlqWKmgp+hJTZ5YcqY6KRS9c520oPwAgGFGQhYUjbdbHq/cmZjtrn++x0m6LR+W2N29zS0hIYhgF+UngguUpIyYs+OFrJF9z/GH6yexVMyZP0KFKiO0VVQL0UREZczF8dVVQTwet05CRYcSMAILGYjIDd816z0m1tvyOi9gNeyS9oqVTq5c2Lp2cVRU4UzlyBKeJzG9VbLIcIfQYdibWfboChBWAEg9iysxGPPzUeX/3OHajo0QcQqwIwFS0TMQRLStCjVy9U9+6NHj2r0aNnNap79UTP3r1Q3asXulf3QKi0VHQH8QOPp+EcU5PayZrkzhJhz+5NcGxrRWdg4USUgmuviG5br/SO/dcU4MImgUAAhvji3FlNaNaKSEZEY4LXpTIrXdeEvoKuUlqZpuqicEYO17y1bVsxc8DgMRSYF21DHZai1dcRkyVkgQ7cQRfyBeWo25NxlFZU4pgzLkBZVbXgwXJnlYzuxLql83ZmMpkDpuEdqLW3t0/csGLBrj2RnfAiLPKgE5VJkog0aa8GJA+mUvmSwKyZqDXI21pSI6Feiansxh8seHP7/EU6UTB3eVegVeA6GZI5wjQNTP4d0xAKBsTIIV06FwU/ug7nX1uFPHZ8vBiuY83vCHYOsB9OF8Anzds3FDLZHCzLgkDW+fL4MEA/XiMjDEO0TaqJrpCnmVApgRwIyIqez+P6yQviiQ9DOXyPpSBbhKV8npKhk1EuAZmWBiyfO70xnU7vszjLgRgRxR0rt6Zph2zcIuXEZHRUPMXB97cuYfgp5+H9d6ch4ziAQVi/ZQd61w3HsBPPVM+Xz6Tx52tG44+XH41lUybAexKRQRgBRXMC45xoqfHKh/Bxx1RaVoZQSQl0oSUQMAJqg5LrYtfSaYVwY+MjnbVWDfX1/717+XRbrJtaKwnDMKZzXrPIfLjgkqEcrkzdNZ3rhGjMG5HDe+llRiWifs0bNy5n9TmO19nGGG8VNkSxLBAMqLUBoArKAPH0FR6sJgtOjuvCLPDAtVt1H/x64hJc85sn+HuErEEQlk/8S665ufmmAyHhd5QRETU3Rb+z6o2/FZjMbP1Ftc/Bh8X7WF4vl6CEN11ISIWHu1Kbl3P8fWwbFFM8/UJXstgo3Y7XngzPLSmcWF4/fn0DOu+g5RoQ/ptMfHNd5DNZZLM5bF88tdBQX99hTSn743QjieawaZqWKgTJarhHKfIKH3KzGkYATBCUlQqWPPUY9qIA6eoE4/QRnkLogtXgx4B0Sbnxpb4e5UquoAd8kNj8S195JBuLNX/zYNJwpDXU19+7Y/EUW66VPFw8HqQ3TVayQuRm6zNkJHoMGIKbv/MTfLI9jIcfHoftn65A084tqtBRyLTBKnAOfY++NSL1E0I3jhxHLifOiujRBTRdR1lZKYKhEAKhkOKdBgIGiDgBnYEP9XMKWaxbtSSxL1NQ99Vc153/2ZrlCTPLExESN6m8mQiAnLvHVDGFUFRsFVG7YejQNd4SrTEB2giEikNTjDtRndMbuVavV+xUUS3zNEQCAZ8D15iavGwI6EgV0oqKuUI1K1eAZVkCdvCyB01jCH8yD00Nu6bbtr2is9Z2X8227eUtjbunN6xeoIpTXoAj8VKP6vj5/y7GgOW9L5MQr4bhqL+TcI6z1z3gd+ge37pYUMuV+wAAj689hpMGEnARwHQDjph6zS80z0iymXbYtoNcWxL1m9fFAazsqLXcZ6dLRBQMGI2ZRAsAqXngd6A8yiASyb+vUUEpEAFeJVPz8Dh5sxgGx72CgYAY9icRBT/47nH5SJRUpYCJPIHFG4aMkMQP0PLpPGpq3D3Ftu0OW8h9Mdd15+9ctypuZtNe04frQM17EweDUluTimAA9EAIQ086G1vWrcN1F30Le+J7cPIZp2PFlBdRaOfZT/c+A/D7qetx79R1OPKUcwFy+UhqQ4MGF47NRxoxiNbYgI5AgPNQQ2IUOC9C8fS6UDD53C8les7QvGYu5bLZcSQrb51gROQW8rlxO5bPIu8m9g56P9QAeeT4Qh/D4HQgXWOAa3NZRZe4EI3jKrlF7/DhDATLtji7QTy36tUXzwnGJyLblgXHskCuAw2cvhcMBgDwhiEZkfkzQOWAFMVKEwLs/LH5dArzXh23JxIJ/7iz1nV/LRxu/NGi159MWdm0uO+oKLqV96EHDAilFdeXvcnrCM8Ry/mIEorwolu5XiL42Kt4LyPiYqzX8y/EnZAXAIIfirZw5IbhDUCVAY5tmihks0KoCIisnkeZTObxjsw49ifSRSGfn9Wyc6M4PWRapvsG8HmFL+6YPUUiXTcUmbxYCg5+6IdHWYYu8DpDnERcxQzkChDeW2R5isoNTaJAJYt3spCWT8bxwcSnWqPR6E8OYN0OyIiIspn2h7cveU/UGeUJ76oClVwOdfq7rohYCcefdzWuv/txAEAwFMKYM0/Dmnnv4uFvjUHT9g2wzEKRfCMJ3dBMewb5fAHkEixBezJNC2bBBERxJKRzic5CLodsph3ZtjZk2lIoZDOqNdN1LCyd9no6mUw+09lrlYjHn/70g0kZ1+FQlncgCYfoo7nJz+s6NuA6ouXaEjcpg2U7ME2bz0kzuRi548ox9wyO7ap/W7YDGaHy68Cr3EQE0+RRaqFgwbQc5Asm8gXeKWhZFizb5tkV8eIZCfejG4Y3DZgDzgpzB38LWPn6X622VPI2Ikp09truqxFRoi2Z/PnHbz1pe4EAU5iugsmkRgYD4JvkoYT7ye9wvaKc+iM5tgjC8crBqa4HE8oI24/XM3/RjHm0O0AW4Bls24ItnLWm8azGtW2YhTza21JoS6ZgmjwDcSwTa+a8k04m9zzXkeu4X043Go3MSNVvdHikKiNaSdfRRAODR+2C+uD+bjSZ9hkwAgaP8IS0mpxbpiTWwNQN7wh+qVkowLEsQfKX9CvXp8rvMSdIAvuuixWvPpyPt7Z+h4hSHbWI+2PJZPLZtfOmpF3bUjem67iwLROOECZ3xYBNEpCD63gV8kHHn4E7JizABTf/CrNmfYj+Q0fiazfdjm69a/kgSdMSk1wLgoNLAJNcRyb+2xBsDwbG+BTbRHseBbOAYCiI8vJyVPXojh49q+HYFhc1IkJsw1JYZv6dg0Sz2+PY5uSdnyzmAjSO92XbQsTH5V+OacIyC5xHKyhdjku8rbdQgGPZXlu0/HvXEUUvrt7g2JwO5i9n8vSXF1Z4mzBvCpJUMUkXy2bzKBSEMLrOeeUyStY0hmAwiGAoILRiRZRLctKKi/qPF2Lr6hUftrW1vdrZ67q/lk63vbJ93col9WuXeNAJADANfiqYJ7/qZWoqapWdhr771HW9wMlP7fQaeYqZIYDHD5bTTFyhqfF5Cil3wEQumGaAgalr2Z5OI51KIZNuF87WK/Pv/mQBbKvwWkfvc7Y/UTNjrGzI8JGxK+55vpz/G5Bhqi60T12Xy+B5EyYgFsTT2tUkqEZc2YmLTrtCP9UfKxdjSDLicR1HVXxlIc5f+JeYn3ytyKoZNO+NZ9+IRCLf3vel6ngbMKDmiZOv+MHPh51xsdiYUHCJ5D7KNknJ2CjqutOlNgUAkBAZ8vQqJCThCB4uExUG6RA0TVP0K03XlHMH4MMxeUdROtUG13ERCAYx/aH/at+8Ye1xRLTrYKwTY2zwkcccv+Hy3zxTJqdRK74u42I1AFTRyxVymfymFRGv+K7oZmIvci6uKLw5rtBb8MFTkgsiIifXlVtNpMG+tmoAvkjLS7IZE63EQZHdCW6wVN7TNB2ZVCtev/vGRGND/QjqwEaTzjDGWO+agXVbrvr9cz2C5VUCo/UXz5msCUoEnqfwxFlD8taW+1zSSfnPPKcq/lAxlIDiepFcWyWirvPHSn/hhzJcx4ah63CJDzJ1LEsU2P2vCc8vEWHqgz/I7Nr+2VAiau7I9duvSJeIslYhmzDzWXVae1iuR64HxOZl/huYFcEKXuVTsBgY58ypAX5CQMePhcmbB2AClyPYlgXbtGDbloqGZY81ESHZ+BnmvfHP3dFo9Acds3QHbtFo5I9rZk1sJ8cRnU/6XmRvba8NJLmQUhNBpm4MYLpyPhoDmHSekBvSEBV7A0wX2YXug3eI68kaCvYRFEDBg2Qa1zho2fox0snE3IPlcME//65cW3JeeNNHoljFQOTwVFFgzoV8QUSflsL9HEH3clw+Akg2RnjO0MOGTSnWpFbU+x/gCaOrQ9EluI4817wDUdKm/GLZ/HW4/GAul1cwj8z0iFx88MRd+daW2DcPd4cLAETUEm+JXb/wufsLpSUhgWPLoZuumsIgswXVYSk6Uv14LVSA4WmO+Os7jHFBKumHuZCWbNf2ZpnJNZc/k3g50zjjRFHaZCMQ80BQxuSh6hXiwhuWwcpn3+1ohwvsp9MVfzg3tmN9EZ6r+UZsaJL8DC/lUCR24ZBd1wU5HjdVxLBQ2J1YYL8Tdf0XlQgkuqr8YhyuGFQoi1FmNo25/7y/LRqNXEREuY5ZugM3IorbZv6lbcvfF5tVblL+eybWT97kkLxIwNts/6a6rqg5kuFhSKqeqBg7DhzbElNtXQQDsr1S4u6aYpEYOkO+kIcGIBQMYtXk5zLhcOMvDvZaNTTU37566vM5SYyXOKnj8BHxloAObIsLtks5S7l2vCgmuMYS5mKK0yKyDImka0U3vYq21CXw2Aiew/VqeP49L+E0+X55U5COUEmIv3/bwadT/uk2Nez8Ry6X++AgL+t+Wy6X+yDWsPOZte+94JaUliJUEvLVdODBgxIuUNGr71gT+1NxbcFUZudn9ahL5YuQ5X1SRE2TPkA4VcgoWGMIhkLqueQMO03wuj3utuyMBdbOfDUTDod/2xlrt99Ot7Gx4c3WrZ84usBkdTGoToo88wXVhZCHrgoIUpjaqxqTJxgiCyECRlC6DiKacBxXVItlUclVp6Yy5agEJuRYWPHSA/l4S+wWItpywCvWwRaNRO5dM+OVtJXLek5U/E7bK+r1IBd5Q8siIse0/CwQIsCxLRi6htJQAEFDg8EIAUNDKBREKBhEKBQCI0Ihn4PGCK5twjFN7pQdB5YYBmgXCtA0DU3rFiOVaJ1JRDsO9joR0fZMKjFr+0cL1aHrsQqYaqVVX64oiqkZhzyv1cTBJLWp5P5RK+tzlLKaqW54dePv5WR9Ci2eZKOuSPhSWcw/5sYyLVimiV2r5mLFzEkfNzU1/ergrGTHWVNT050fzf7X6h2r5sEIGAgIDrTsKvVkGDUBIfLr5T+w5MgvSe9TWZbmL4hp6hDzY7b8OkI8p4R6oHxJURHN0BEIBhEIBlRxXpdjgnSPe20YOupXL0R7MvFeZ2Vz++10XdddsH3N0px3Ymm8ndLQYQQD/AMK+hFfUKbwM9nJIrEbCbCrFEFWNEXl3haRbpGz9RGvZZWZgKJ0hTFg2/svO/XbNj+dTCZf66A161AjokQhl3ls88J3SFGMAJGqQjkHAEBR9CWLx14IIHFgQ2foVl6K6u4VCAYDIAA2ESyXwSEhoiP+lhgvouWzWV4gsnk1XqbrruOAmA7bsrHgjafbIpHIrQd5iZQ1Njb+dPnbz2bMfEGlmJI+JNfDO4S9dZORp6ZSVUHjkikuoNRzZHlClSEAT/pDKOAp0R3d05D1WpFlQUkempKt4GmHyKgs0fAZ3n/hkUg0Gh1Lh4AvfqBGRE4kEj5/zviHovHdW/mYdNFworpMNW9istSUkIeah8cKx+jTV/CLAynhGyayZyZb/72I2OP9+nVMxEHrO+w4TKbDCBgIlYQQCOiiaYv7JscsYMU7/2yLRMI/7ax122+nS0R5HbSjLdaoihpKyNmnn+CPHFRbqo8GJR2kvFtkBOynfvkrnlARijrjitJx8UsAQHjFDPpkwYz5zc3Nh3UU0dLS8vD6BVPjmT0SzvOf6F5UK9dYpf+6prArTWCupSUh2PkMXnvgVtx96bGY//Jfxd/yDAREXPC7UFCONZ8vwLIJpuX6RM8JjmgOcBwX9cvedbPt6WeIqOlQrRMRRc1c+9NbF00mmbZ6WD+8yFQxZxiCMvpiXjOFfCiBvJse8DkDFK2/57RlC6ohJtrqwjEYysF6+19X0Zshxc19Knnp1iZM/9uvU9FI+NyDwQLpLCOiZDTceM67f7mzLdkcEYvn8fdlc01paQnKy8tQWVmO0rISdV0MEZRxrrjB9UECXrcgbzrxZD0N0bbrqc352rFFw5VywKoW5GMwqYOWF49DIY5JSxhu05zX3Wx7+k9EFO+sNdtvpwsA8daW8Q3rlxW1YvJIlhSO5tgOx9ssm5PJbRu27fL5ab4CkeLaKZzNZ1RcuVcpH7woxt9koWsaYusW48PJE9Y0NTVdRp1I4O8II6JCMhH/3urJT9tKPQlQkYH/s/tTLwBC3EVHMMQzi+WTx+OhG87EphXz0XtAHY772lWwhSCLLSQcbcFXtSybK2X5KriSp+pv4cwmW7D0vdebY7HYvYdynQCgqanp3k8/eLulPREDwPdJUarPvAp3eXkZulVWorSs1IPAdB0heWNrfM6fp6HgrbOucRhGDqvUmA4OpQUQDAUQCgWVI3ELGT4SKBTijzF00Y7MvwcCARUB6jpDIb0HM/56RyYSbhhLRFsP3Wp2jBHR1mhjwwXTH7s9k2vbozoA+Wc3xHgvAz0rSxEMGAgFg7AyKbS3hmGI34dCPPXXNSAgxJeCQYNDYaEASktCKC8vQXl5KUpLQ9iydBY0jRAIGgIy0Lnmhg+nVc5XtGfLehEk3qyolDxwySSasGbeu02JROLxzlyvfRpMubel0+kpO1bNe3j0Rd8u8WgXe9EwfJGIh+X4sR3JLWG+B3igmsLMFHzguWMuAK68L4j4sLzWLSsxf+K4z5qaol+lQ6BDuj+WzWanD6wbtKxh/Yqz6o47lRddyKusAr5wjHmOQtMYb9MFw6xnH8CyaRxFueeF6dB7D1HFRgDqpPfwYPLWtGj9eSQgC0xr3h5XSMRbbz4c1pKI8iUlJTd98va4aWf/6E8Befp+7hAWka1pmZyHq/G6gqFzBTLbcUCmCdfdG4bga2sY3DnncnlwAX0NAUPQvnQdbS1RbFwwBStnvAXbtpFO7cFPn56GXjV8zeUbkoUZiTnm00nM/Mtt2Vg0fPWh6ojsDLNte2V5efnVMx67bfI19/2zNBAIIrl7C8b/8TZ8576nUVnVA7V9u+HEgf2wZ08Kd/zip9i2eQNOOOM8jB57NdKtMSz+1/OINzdhzGXfwpBRp6Nm2NFY+d5EaABc24RWUo72pt1ItjRh69qPkG7agX7DjkM2kwERoXftERh49GgA/iKzRwfTNeapDYI4q0Jk2+S6WPLSw4XWlth3icjuzLU6IKdLRPV1g4ZEky3RIRXVfSTIqBygF67KG6M4vJcpnng28ZyAP6rz7V8vjRT/Jx8nMV7GGJo3LMHclx/b2tQUHUMdpAp0sKyxof4aevOJz/oOPaaypKK7oB/L1Ik/xl/w0Q2hhsUY3JZdyuH+/Z8vot8JJ2BtJA2Fd7u8WqRpjFfgNUnl8V8BwXIk8LHbxBBZPZcatm+els/nZx/k5fgfLZ/Pz66tHTh9x6o53xg6Zqz3C5EhgPGo0jRtlZ4C/LMbBhe/IYJwxFD7ya/pHAoG0L4njlBFdwQMAlwHLbs3YuvyudixdjlS8RjOOusczJ03H0cMGYIf33obXvjVt3H86V/Djo1rMGDoSBx9xlgcdebFot5A2LTsfayeMj4bbdx9XSaTOWzWs6Msk8nMrqiouO7V33xnUv3ObSXV1T2RTsTxwdP3Ipvht2KsuRndKitx7LHHYcGCBF568XnMmTMZ3Xv0wpPjnsbgIYPx6sSJWDzjJUzfthXnnj8Ww4cPR1lpCcLNrTj1/GvRs7oaZeUV+Pvfn0Jy3UKESsuQN20898Q9uPCHd+HYr16K8qpeIHKg64aPS03QGQPpXIWMzy7mtmXBvyiy87M3TdOc09nrtF/NEX6rrKz82ZnX/mjc8RfewOQN/DnlHnkjy6hX0XPku4D6vcSAJVygIjGC16kmPLGkTUlu3u5V79PC15/a3BSNnkqdPHqns6yiouK6ESeeMfGcH//J0Peu5gKQ0b9MlYkxfPLeK/hozlTEG3eib9++GHXiybjmV49iW2tOnOSuKk5KXB2AyELkYSaiYF9FMpNowrRHfhaLRsLDDrcDjDHWrX9N7Y5LfjGuV7fe/QBAFUR0w/D2haiM6boOM5eF61io6NEL7YlmuC6hLR5DWfeeqOjRiz+HoSMd3YkV01/Dkmlv4MiTz0LvmsFYt2g66uoG4cKLLsZ5556Lk08Zg4qyUtEwwff9okWLsHHDevQbUIsbrr8a5112NY497yrU796FVdNeQTbZYmUzmctyudz7h2zhDoKVlpaOraurmzLtvZmltXV12NvFaMxr0dcYd4C2S3AcLjbP59JxKEZyo3WdqSCuYDm8TZugHgcAc+bOxV+eeBKrVy6FZZkgAs6+9oc45uxLUNWvTmXe/m43IqBl1xZM/9udDdFw+Eg6CNOWD9jpMsZ6DB5+VOM3H3ytzHW9oel7obI+eED+4b99CMcydW9sioddcoDcFakBExEbwFODDdNfcFfO/teyaDR6IRFlDuhDHWKrrR345omX3njtSZfewEik/UaAty+6ImoiAAZj6BECHrjpAkTDDaiq6oF1GzfjsksuwkmX3oDaMZcICh4HalWlv6i6i+IDUcS7jlnAzEdvyW7bsuncwzUNNgzjlCHDRy685Nd/LwmVlqK0tARmexK51kYkIrtxzPGjUWK3oy1vYc3KpZj/7pvY0xpDnwEDEYs0AACGjjga4YZd6F83FMFgEG2JFjBycf7Yi/CTn96Kt955Bzu3fYb7fv87jDhyhKqeO5LiyDwesO24mDt3Dq67+gpomobq6p6orRuEqupqbN24Ph2NRse6rrv8EC/bQbFQKHRaTU3tzFdefa37qBNPAuAVv+Ve82vnggh5k8NguuDWBnRdOGFRaxci9pbF6xJc0IghIAputiuYDQDqGyPYuXsXJrzwAqZM/heOP+tC6MFSVPYdiGO+einKulXBdV0Usu2YdN/3sjs/23ISEW0+GGtzwE4XAAYPOWL5RXc8Oqb3wCNhif78otrVXjAD/yZcrIdIKEBbVuU5dimVyHwXSDy2pX4bFr/1DKKb17gaOS9Ho9EffhmpN3sbYyzUr/+ATU3RyJDqfrX4+XOzwDQdO9etwsCRJ4o2aSBoaJj2t7uwfPYUAIARCGDck39HWVk5/jruKVx533iB30JFsK5qNnHUIUj+E5EBcAkrX33IXrts/q/j8fjfDskifEHr06fPncNPOuvhS299QC8zgMd/cgV2b/8MADB06DAEgkFkMhlcd931uOIb38CIESPx1qS3cOkll0M3DFR2745EIomlSxajqkcP9O3TG8OHD4eu67BsF6UlQhdEZlgiy+Lym6LzzeGqYrHWPTjuqMEAgNGjT8I9996HXDaLu379y0g43HjWoeA3H0pjjB1RWzvww0cefbT/N668Cq7LhfhlhCmLnrpQ2jMtHrQpbWPxJWs6LhEsm8SUDip6jISMeMnCx7UmFx+vXoulS5eiPZPBww/+AYOOORk3PTgeruti7tP32BtXLLw9Ho8f8JTfL7wuHeF0DcP46knnXjLnwp/92XAdB7Yjna5H8eKrDBXxcp8q+L0aQyoWhmXmUFJaDrJNjPvJFRhz8bVYMXMSBgw7GseefQl6DjwCI045FwCwY/VSvHzPD1BVVWW5rntbKpXqkPlFh4sxxmoqKys/y+VypSOPG4UtG9ejkOfNdAOPPBau4yBWvx2jxpyBXdu2IJVoRUW3bpgyZRpKyipw2imj8bt31oLpmod1AsoBqwGUgIp8+egeDRtmvOh++O7rb0aj0RuoIzZIJxpjjA2oqXn9ym9/77pF789g2fY0Vn28BsEgFwUnIpUlOY7r3ZTw4CpOi+N71hDTSwCOgwcDumquUAwP4Wxtx+WjfGwXpu0iEDDwwcwpqKqqwjnnnIN777nbmf7etKWNjY1f/zLTwg7EGGNVAwcOnHbpZZef9uCfH9IDwZBgyZDXyivrN+JvFLygec5TXiNbRLqyPsGFs5gqJ0mITPJ9iVxk8yZmzH4fE195CevXr8dVdz2BvkOOxIcvP+puXDJ7QjQS+eHB3Ocd4nQZY6x24KBtV/7+mSN69h/kYSaOWyQ+IstnmsawctpLSEbrkWqNIh7ZDXJsdKvsjnwuh/KyUjSFG6DpOvK5HI465nhsWLsaADD8hFOQz2UR3bEF3bp1S6TTbV+1LGv9AX+Iw9ACgcAxNbW1K+75/b3lJ598KkrLStGzugeWrViJTZu3oG/vXgg3NoIxhq9f8Q2Ul5Vh5szpuOe3v8FXzrsUp333bl80K7MJn9OR/wYQMhiOCOWwaOY79PbLzy+NRCJf/bJkDYwxvba2dnEwVHLaCSeMwiuvvqYcpC0kIHVd/1z2xSdMSIEUr8DrPa9H6vB3TzqOV9DlEiBiaoXBncTmTRvxs1t+lAuHG/8Ui8UeOtwPrs42xhjr3afP3bW1tfc88+z40iOPOkqIEIk5gKqYyxAI8HFLAV/jCRHEAUdiGCgHegOGJ57lN+9wdLBu01bcecdtCEejGD32Gow6/2qUVFRi+ZtP0foFU98ONzZef7CvT4c4XQAIBAKXnnbx1VPP+/H9OvOltK5LyGfbsejNf6Cie090r6pC/ZoPsWL+LNxx1+9wwqgTMWL4UIwcMQK6EHjxS7bJiFjTGGKtrZg2ZTJenjC+0NQUndTc3PxfX3b89n8zwzBOOeKIofPemDS5fOjQoYJ94CpB7q1bNuOMU09Cdc+eKOQLGHPa6bj5R7fg7PMvxO5EDttb2hFO5lGwLFWMIwCu7aBl9xZs+XAGtq5aiFh4N7pXVcHQ9dXNzc1n0GGkUfFFjDFW2r//gCX/579uGfXLX93FTIvrCfNirOdM5dBDEtGQPHhU+gWovQfyBLAhfmbbfN35JGEJefH9bpomHv/rI+7EVybUNzc3X2VZ1uqDvhCHsQUCgdH9+vWf8u0bv1N72x2/1AKBoDjI+P2uMVEYM0RDBGOqUMn55L5GKOBzzpZDDKSS6nR7Bqee9hUc87Vv4PQrvw/NCICB4ZOp4+njma/PioTDh4TD32FOlzHGamrrdt384Pi66pohQm0JmP/GM1gx/Q0kmsMAgAE1A3Hb7XfgmmuuRs+evWE5DiTkKLVwATkCXFMMhrZUCk+O+6v79luvh6PR6LWH0yiTzrZAIDB64MC6Bc+Nn1A5+sST4bhQ6lmJeCtOOGYYAODDj9Zh6LDhikbHhJ7r0//4B/7ywD2orO6Ne16ciVXzZ+CVP98JTdPwo1t+jiuvvBoz35vqTnrrtSWRSOSCw4GPuz/GGAvV1tbOu+6bN55256/uZsGgh8dqmlcA01jR3/goify7S6QiMHkj65oGw5C9RF4Dj4zYZs54Dw/+8b5MMrnnsebm5gc6m+v5ZTXGmNG3b997q3r0uPPu391fdv7Yi/nPgaJDkAmoS06AcGxXjOBhRRCl/78Vk4QIra0teOnFF/Howw/ghanzMWzkcWjP5THhif92P5w9ZVEkHL7gkF0jP1Z1oF+GYYwaNfrE/I6mFO2O52hHS5aeeHYCAaArrrqWKiu7EwA67vgTKBxro0hLG0Vb2qipNU1NrWmKJdopFm+n5niaWhLtFE9mqT7cSvf/8UF3YF1dvLKy8icAjI58z1+WLwBDBg4c2PDk35+l1mSWIrEU1Uf3UENzis48+xyqrOxOt9x6B02dOY/KyssJAE2cNJWOPuY4AkBnnn0ulZdX0PuLVnD6h/ja8Fk9XTD2QrP/gAFPA9AP9efsgHXSa2pqnrlg7EXWhs8aKBxLU3O8nRKpLMVTGYonM9SazFDLnnZqbk1TLJGm1j3t1JrMUGuynf88nqZorI3CzW1in/r2ZyJDsUSGmuPt1NTaTlOmzaaTv/KVbF1d3WQAtYf6839ZvgDU1tXVTTn5K6fk3pk6k8LNKYrG2qippY2aW9uoOZ6m1mQ7xVPe9WpJ8K9IrE09Pip8SHM8Tc3xNEViSZrxwULq1bsPnXveBXT9DTfRlt3NtGjlpzRq9Im5vn373QdAO5SfvcMiXWk1NbV/uvZbN/zuN7//Y1HsP6hPJUzTxHHHj8bNP/gxrrr2Wz7xCu+kA/jPdu3cgQkvPue8N21qWz6Xe6KlpeUR+pKlvB1tjLHyQYMGvXnUyGPGPvjQY4GamlqQyBCWLFmEFcuXYfq7k7F1yybouo6rrrke8+a+j3RbGqVlZTALefQfUIORRx+LQYOHoGfPnnj1pRfa4vHWW1Kp1GEpCLS/1qNHjxuqq3v+494/PNjtsssvB8ApRwRS2CyJo4dAij3j37SfvzP4b/O5HN6d+i9MGP9sNpVKLt+9e/dP6SDRjf7TjDF21ODBg5+u7F516s3f/1HplVddg9KyMgUrkmzuIfLqE+QJyXuPAxyHi9b/4b7fYe6c2Vi8ai00TcMrLzyHpx5/rCkSCV9sWdaaQ/l5gQ6EF9QT8qLG+opu3Y8iIow57Qyce8FYfLhwAcY/+3ecdfa5+PMjf8PgwUfwripdaiYA4cZGzJkzC9OmTM7X1+/a1dTU9Pt8Pj+FulK1IispLb2gb58+E6//1o29vnvzD1l1z95wOFccugbkcjm8/fYbmP7uZHz7xpvwlVNORSwWx+AhQ1HVvRtWLF+Kpx5/tLB16+b5DQ0NNxFRy6H+TJ1hjLHegwYNenXY8BHn3HHnr4NjTj0NtuONVff3QxaxO/iviiyXy2LZ0g8xb85se+6c99sty3y+qanpr0QUPSgf5j/cGGP9BwwYcKdhGD847/yx3c674CL99DPOQklJCVzftdCF0A0RedokDIDorjRNC+FwI8acdCx0XccpY04rNDbWT2mor/8+EWUP1efzW4c7XYB3Cw0cWPcwY+z7o078SigRb0Uul8XHH3GO/fzFK1BaUopYrBnhcCM2bVjnzvlgdi6fz9UnEokJyWTyHSLa1uFv7D/IGGMl3bp1u6lHdfUfTj/9zF5fO/9i46iRR2Ho0OFCTJ6pDdqWSmLTpo1Yt24tJr48IdvWllq+c+fOu4joo0P9OQ6GMcZOHjp06KMVFd3GfPOG75aOPPpYHDliBLp3r0KRjyWAyEW8tRWRaBjRSAQN9buwYN4H+e07tqcZ2Mzdu3e9BGAxEVmH7hP95xpjLADgrMGDB3/PdeniI4YOqzj3a+eX1NUNRr8BA1AzoBY9e/VSUpGSvZBIxLFo4QLMmzPHWb58aYEBu1tbW15OJpNTiWjTof5cfusUp6uenLHew4YPf5KBXTl06FCEw2E3m82YruvmgsFQxLbtbXv2JNYnEonVAOYTUXunvZn/UGO8hHt2//79L+teVXV2IZ8fWlpaVjZ02DAjuWePG4mETYDt0TS2JhKJLMhkMm8SUeRQv+9DYYyxmvLy8utqamrOdV33BCLqOWBAjdGzV28tEm509+zZY5umWQgGAwlN03ZmMtlNTU3RtbZtz6f/zxobDhdjjA01DOOcfv36H19RUX6047iDLcusDgSCoR7VPYyaAbXa9u3bnFwu12rZ1ju7du58E8Cqw/lQ7FSnq16EsSoAVQAiRGR2+gv+f26MMQPAEQBSAGJ0MC7yl9DEgdUHQC8AjQDautbqy2Hi2lUCqAHQQIeZNsj/yw6K0+2yLuuyLusybgckYt5lXdZlXdZl+2ZdTrfLuqzLuuwgWpfT7bIu67IuO4jW5XS7rMu6rMsOonU53S7rsi7rsoNo/xdw/77tp5fvawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "projections = [ccrs.PlateCarree(),\n",
    "               ccrs.Robinson(),\n",
    "               ccrs.Mercator(),\n",
    "               ccrs.Orthographic(),\n",
    "               ccrs.InterruptedGoodeHomolosine()\n",
    "              ]\n",
    "\n",
    "\n",
    "for proj in projections:\n",
    "    plt.figure()\n",
    "    ax = plt.axes(projection=proj)\n",
    "    ax.stock_img()\n",
    "    ax.coastlines()\n",
    "    ax.set_title(f'{type(proj)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regional Maps\n",
    "\n",
    "To create a regional map, we use the `set_extent` method of GeoAxis to limit the size of the region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_extent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextents\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Set the extent (x0, x1, y0, y1) of the map in the given\n",
       "coordinate system.\n",
       "\n",
       "If no crs is given, the extents' coordinate system will be assumed\n",
       "to be the Geodetic version of this axes' projection.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "extents\n",
       "    Tuple of floats representing the required extent (x0, x1, y0, y1).\n",
       "\u001b[0;31mFile:\u001b[0m      /srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax.set_extent?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fc448d09160>"
      ]
     },
     "execution_count": 10,
     "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": [
    "central_lon, central_lat = -10, 45\n",
    "extent = [-40, 20, 30, 60]\n",
    "ax = plt.axes(projection=ccrs.Orthographic(central_lon, central_lat))\n",
    "ax.set_extent(extent)\n",
    "ax.gridlines()\n",
    "ax.coastlines(resolution='50m')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Features to the Map\n",
    "\n",
    "To give our map more styles and details, we add `cartopy.feature` objects.\n",
    "Many useful features are built in. These \"default features\" are at coarse (110m) resolution.\n",
    "\n",
    "Name | Description\n",
    "-----|------------\n",
    "`cartopy.feature.BORDERS` | Country boundaries\n",
    "`cartopy.feature.COASTLINE` | Coastline, including major islands\n",
    "`cartopy.feature.LAKES` | Natural and artificial lakes\n",
    "`cartopy.feature.LAND` | Land polygons, including major islands\n",
    "`cartopy.feature.OCEAN` | Ocean polygons\n",
    "`cartopy.feature.RIVERS` | Single-line drainages, including lake centerlines\n",
    "`cartopy.feature.STATES` | (limited to the United States at this scale)\n",
    "\n",
    "Below we illustrate these features in a customized map of North America."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.gridliner.Gridliner at 0x7fc448994970>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_ocean.zip\n",
      "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_lakes.zip\n",
      "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cartopy.feature as cfeature\n",
    "import numpy as np\n",
    "\n",
    "central_lat = 37.5\n",
    "central_lon = -96\n",
    "extent = [-120, -70, 24, 50.5]\n",
    "central_lon = np.mean(extent[:2])\n",
    "central_lat = np.mean(extent[2:])\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = plt.axes(projection=ccrs.AlbersEqualArea(central_lon, central_lat))\n",
    "ax.set_extent(extent)\n",
    "\n",
    "ax.add_feature(cartopy.feature.OCEAN)\n",
    "ax.add_feature(cartopy.feature.LAND, edgecolor='black')\n",
    "ax.add_feature(cartopy.feature.LAKES, edgecolor='black')\n",
    "ax.add_feature(cartopy.feature.RIVERS)\n",
    "ax.gridlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want higher-resolution features, Cartopy can automatically download and create them from the [Natural Earth Data](http://www.naturalearthdata.com/) database or the [GSHHS dataset](https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html) database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.gridliner.Gridliner at 0x7fc448591880>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rivers_50m = cfeature.NaturalEarthFeature('physical', 'rivers_lake_centerlines', '50m')\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = plt.axes(projection=ccrs.AlbersEqualArea(central_lon, central_lat))\n",
    "ax.set_extent(extent)\n",
    "\n",
    "ax.add_feature(cartopy.feature.OCEAN)\n",
    "ax.add_feature(cartopy.feature.LAND, edgecolor='black')\n",
    "ax.add_feature(rivers_50m, facecolor='None', edgecolor='b')\n",
    "ax.gridlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Data to the Map\n",
    "\n",
    "Now that we know how to create a map, let's add our data to it! That's the whole point.\n",
    "\n",
    "Because our map is a matplotlib axis, we can use all the familiar maptplotlib commands to make plots.\n",
    "By default, the map extent will be adjusted to match the data. We can override this with the `.set_global` or `.set_extent` commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create some test data\n",
    "new_york = dict(lon=-74.0060, lat=40.7128)\n",
    "honolulu = dict(lon=-157.8583, lat=21.3069)\n",
    "lons = [new_york['lon'], honolulu['lon']]\n",
    "lats = [new_york['lat'], honolulu['lat']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Key point: **the data also have to be transformed to the projection space**.\n",
    "This is done via the `transform=` keyword in the plotting method. The argument is another `cartopy.crs` object.\n",
    "If you don't specify a transform, Cartopy assume that the data is using the same projection as the underlying GeoAxis.\n",
    "\n",
    "From the [Cartopy Documentation](https://scitools.org.uk/cartopy/docs/latest/tutorials/understanding_transform.html)\n",
    "\n",
    "> The core concept is that the projection of your axes is independent of the coordinate system your data is defined in. The `projection` argument is used when creating plots and determines the projection of the resulting plot (i.e. what the plot looks like). The `transform` argument to plotting functions tells Cartopy what coordinate system your data are defined in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.plot(lons, lats, label='Equirectangular straight line')\n",
    "ax.plot(lons, lats, label='Great Circle', transform=ccrs.Geodetic())\n",
    "ax.coastlines()\n",
    "ax.legend()\n",
    "ax.set_global()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting 2D (Raster) Data\n",
    "\n",
    "The same principles apply to 2D data. Below we create some example data defined in regular lat / lon coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7fc4489320d0>"
      ]
     },
     "execution_count": 15,
     "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": [
    "import numpy as np\n",
    "lon = np.linspace(-80, 80, 25)\n",
    "lat = np.linspace(30, 70, 25)\n",
    "lon2d, lat2d = np.meshgrid(lon, lat)\n",
    "data = np.cos(np.deg2rad(lat2d) * 4) + np.sin(np.deg2rad(lon2d) * 4)\n",
    "plt.contourf(lon2d, lat2d, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a `PlateCarree` projection and plot the data on it without any `transform` keyword.\n",
    "This happens to work because `PlateCarree` is the simplest projection of lat / lon data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.contour.GeoContourSet at 0x7fc4485cd3d0>"
      ]
     },
     "execution_count": 16,
     "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": [
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.set_global()\n",
    "ax.coastlines()\n",
    "ax.contourf(lon, lat, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, if we try the same thing with a different projection, we get the wrong result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.contour.GeoContourSet at 0x7fc448a8ad00>"
      ]
     },
     "execution_count": 17,
     "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": [
    "projection = ccrs.RotatedPole(pole_longitude=-177.5, pole_latitude=37.5)\n",
    "ax = plt.axes(projection=projection)\n",
    "ax.set_global()\n",
    "ax.coastlines()\n",
    "ax.contourf(lon, lat, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fix this, we need to pass the correct transform argument to `contourf`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.contour.GeoContourSet at 0x7fc44894d7f0>"
      ]
     },
     "execution_count": 18,
     "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": [
    "projection = ccrs.RotatedPole(pole_longitude=-177.5, pole_latitude=37.5)\n",
    "ax = plt.axes(projection=projection)\n",
    "ax.set_global()\n",
    "ax.coastlines()\n",
    "ax.contourf(lon, lat, data, transform=ccrs.PlateCarree())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Showing Images\n",
    "\n",
    "We can plot a satellite image easily on a map if we know its extent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-11-16 13:35:33--  https://lance-modis.eosdis.nasa.gov/imagery/gallery/2012270-0926/Miriam.A2012270.2050.2km.jpg\n",
      "Resolving lance-modis.eosdis.nasa.gov (lance-modis.eosdis.nasa.gov)... 198.118.194.28, 2001:4d0:241a:40c0::28\n",
      "Connecting to lance-modis.eosdis.nasa.gov (lance-modis.eosdis.nasa.gov)|198.118.194.28|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: https://lance.modaps.eosdis.nasa.gov/imagery/gallery/2012270-0926/Miriam.A2012270.2050.2km.jpg [following]\n",
      "--2021-11-16 13:35:33--  https://lance.modaps.eosdis.nasa.gov/imagery/gallery/2012270-0926/Miriam.A2012270.2050.2km.jpg\n",
      "Resolving lance.modaps.eosdis.nasa.gov (lance.modaps.eosdis.nasa.gov)... 198.118.194.28, 2001:4d0:241a:40c0::28\n",
      "Connecting to lance.modaps.eosdis.nasa.gov (lance.modaps.eosdis.nasa.gov)|198.118.194.28|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 256220 (250K) [image/jpeg]\n",
      "Saving to: ‘Miriam.A2012270.2050.2km.jpg’\n",
      "\n",
      "Miriam.A2012270.205 100%[===================>] 250.21K  --.-KB/s    in 0.1s    \n",
      "\n",
      "2021-11-16 13:35:34 (1.94 MB/s) - ‘Miriam.A2012270.2050.2km.jpg’ saved [256220/256220]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "! wget https://lance-modis.eosdis.nasa.gov/imagery/gallery/2012270-0926/Miriam.A2012270.2050.2km.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(-117, 33, 'San Diego')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 12))\n",
    "\n",
    "# this is from the cartopy docs\n",
    "fname = 'Miriam.A2012270.2050.2km.jpg'\n",
    "img_extent = (-120.67660000000001, -106.32104523100001, 13.2301484511245, 30.766899999999502)\n",
    "img = plt.imread(fname)\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "\n",
    "# set a margin around the data\n",
    "ax.set_xmargin(0.05)\n",
    "ax.set_ymargin(0.10)\n",
    "\n",
    "# add the image. Because this image was a tif, the \"origin\" of the image is in the\n",
    "# upper left corner\n",
    "ax.imshow(img, origin='upper', extent=img_extent, transform=ccrs.PlateCarree())\n",
    "ax.coastlines(resolution='50m', color='black', linewidth=1)\n",
    "\n",
    "# mark a known place to help us geo-locate ourselves\n",
    "ax.plot(-117.1625, 32.715, 'bo', markersize=7, transform=ccrs.Geodetic())\n",
    "ax.text(-117, 33, 'San Diego', transform=ccrs.Geodetic())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Xarray Integration\n",
    "\n",
    "Cartopy transforms can be passed to xarray! This creates a very quick path for creating professional looking maps from netCDF data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 89, lon: 180, time: 2014)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2021-10-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 ...\n",
       "Attributes: (12/38)\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science &gt; Oceans &gt; Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    ...                              ...\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2021-11-07\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</pre><div class='xr-wrap' hidden><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-c1c5c4af-b8a3-4a87-8188-7e58c55e85a3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c1c5c4af-b8a3-4a87-8188-7e58c55e85a3' 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'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 2014</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bfb7fb2d-3d5b-485d-8a8a-ce04b05c2770' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bfb7fb2d-3d5b-485d-8a8a-ce04b05c2770' 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'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-30a8e21f-05d0-45c4-b8fb-7b8cb94acc0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30a8e21f-05d0-45c4-b8fb-7b8cb94acc0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97cc8ada-eaf3-4fc5-b6dc-1f48b63e17d8' class='xr-var-data-in' type='checkbox'><label for='data-97cc8ada-eaf3-4fc5-b6dc-1f48b63e17d8' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], 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'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-f06f4e33-4a41-4df1-9ce1-3741278ff278' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f06f4e33-4a41-4df1-9ce1-3741278ff278' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e021a51e-08fb-408a-b8f3-03aee1fcb923' class='xr-var-data-in' type='checkbox'><label for='data-e021a51e-08fb-408a-b8f3-03aee1fcb923' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\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'>1854-01-01 ... 2021-10-01</div><input id='attrs-4138e83a-57d3-4c15-afdf-3bd0f35a88ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4138e83a-57d3-4c15-afdf-3bd0f35a88ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acb39145-55d0-4b55-a8b0-8f2accf466e3' class='xr-var-data-in' type='checkbox'><label for='data-acb39145-55d0-4b55-a8b0-8f2accf466e3' title='Show/Hide data repr'><svg 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>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80992.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1854-01-01T00:00:00.000000000&#x27;, &#x27;1854-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1854-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-01T00:00:00.000000000&#x27;, &#x27;2021-10-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-333cd48a-ef2c-4cdf-81d1-543c3d205091' class='xr-section-summary-in' type='checkbox'  checked><label for='section-333cd48a-ef2c-4cdf-81d1-543c3d205091' 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-2edf236e-3ae0-42b5-9989-503190079ae3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2edf236e-3ae0-42b5-9989-503190079ae3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd86c697-b674-4976-9e91-3d7f684dce85' class='xr-var-data-in' type='checkbox'><label for='data-dd86c697-b674-4976-9e91-3d7f684dce85' title='Show/Hide data repr'><svg 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 Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</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>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  89 180]</dd></dl></div><div class='xr-var-data'><pre>[32264280 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e53b61df-c670-49b1-9682-07a647a7fa03' class='xr-section-summary-in' type='checkbox'  ><label for='section-e53b61df-c670-49b1-9682-07a647a7fa03' class='xr-section-summary' >Attributes: <span>(38)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.</dd><dt><span>description :</span></dt><dd>In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature &gt;</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>source_comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>geospatial_lon_min :</span></dt><dd>-1.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.0</dd><dt><span>geospatial_laty_max :</span></dt><dd>89.0</dd><dt><span>geospatial_laty_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>project :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST)</dd><dt><span>original_publisher_url :</span></dt><dd>http://www.ncdc.noaa.gov</dd><dt><span>References :</span></dt><dd>https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 at NCEI and http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html</dd><dt><span>source :</span></dt><dd>In situ data: ICOADS R3.0 before 2015, NCEP in situ GTS from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015</dd><dt><span>title :</span></dt><dd>NOAA ERSSTv5 (in situ only)</dd><dt><span>history :</span></dt><dd>created 07/2017 by PSD data using NCEI&#x27;s ERSST V5 NetCDF values</dd><dt><span>institution :</span></dt><dd>This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Centers for Environmental Information and time aggregated. Original Full Source: NOAA/NESDIS/NCEI/CCOG</dd><dt><span>citation :</span></dt><dd>Huang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1</dd><dt><span>platform :</span></dt><dd>Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>metadata_link :</span></dt><dd>:metadata_link = https://doi.org/10.7289/V5T72FNM (original format)</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang (original)</dd><dt><span>date_created :</span></dt><dd>2017-06-30T12:18:00Z (original)</dd><dt><span>product_version :</span></dt><dd>Version 5</dd><dt><span>creator_url_original :</span></dt><dd>https://www.ncei.noaa.gov</dd><dt><span>license :</span></dt><dd>No constraints on data access or use</dd><dt><span>comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters</dd><dt><span>summary :</span></dt><dd>ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis</dd><dt><span>dataset_title :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>data_modified :</span></dt><dd>2021-11-07</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 89, lon: 180, time: 2014)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2021-10-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 ...\n",
       "Attributes: (12/38)\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science > Oceans > Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    ...                              ...\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2021-11-07\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "url = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/noaa.ersst.v5/sst.mnmean.nc'\n",
    "ds = xr.open_dataset(url, drop_variables=['time_bnds'])\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.geocollection.GeoQuadMesh at 0x7fc42d31d580>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sst = ds.sst.sel(time='2000-01-01', method='nearest')\n",
    "fig = plt.figure(figsize=(9,6))\n",
    "ax = plt.axes(projection=ccrs.Robinson())\n",
    "ax.coastlines()\n",
    "ax.gridlines()\n",
    "sst.plot(ax=ax, transform=ccrs.PlateCarree(),\n",
    "         vmin=2, vmax=30, cbar_kwargs={'shrink': 0.4})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Doing More\n",
    "\n",
    "Browse the [Cartopy Gallery](https://scitools.org.uk/cartopy/docs/latest/gallery/index.html) to learn about all the different types of data and plotting methods available!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "pygments_lexer": "ipython3",
   "version": "3.8.12"
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 },
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================================================
FILE: src/lectures/models/cmip.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with output from many different CMIP6 climate models\n",
    "\n",
    "Climate modeling has been a cornerstone of understanding our climate and the [consequences of continued emissions of greenhouse gases](https://www.gfdl.noaa.gov/awards/former-noaa-scientist-suki-manabe-shares-nobel-prize-in-physics-for-pioneering-climate-prediction/) for decades. Much of the recent efforts in the community have been focus on model intercomparison projects (MIPs), which invite submissions of many different modeling groups around the world to run their models (which are all set up slightly different) under centralized forcing scenarios. These results can then be analyzed and the spread between different models can give an idea about the certainty of these predictions. The recent [Coupled Model Intercomparison Project Phase 6](https://gmd.copernicus.org/articles/9/1937/2016/) (CMIP6) represents an international effort to represent the state-of-the-art knowledge about how the climate system might evolve in the future and informs much of the [Intergovernmental Panel on Climate Change Report]([Intergovernmental Panel on Climate Change Report](https://github.com/IPCC-WG1/Chapter-9).\n",
    "\n",
    "In this lecture we will learn how to quickly search and analyze CMIP6 data with Pangeo tools in the cloud, a process that using the 'download and analyze' workflow often becomes prohibitively slow and inefficient due to the sheer scale of the data.\n",
    "\n",
    "The basis for this workflow are the analysis-ready-cloud-optimized repositories of CMIP6 data, which are currently maintained by the pangeo community and publicly available on both [Google Cloud Storage](https://medium.com/pangeo/cmip6-in-the-cloud-five-ways-96b177abe396) and [Amazon S3](https://www.youtube.com/watch?v=C0UhiiGgbWA&t=3267s) as a collection of [zarr](https://zarr.readthedocs.io/en/stable/) stores.\n",
    "\n",
    "The cloud native approach enables scientific results to be fully reproducible, encouraging to build onto and collaborate on scientific results.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xmip.preprocessing import combined_preprocessing\n",
    "from xmip.utils import google_cmip_col\n",
    "from xmip.postprocessing import match_metrics\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing we have to do is to get an overview of all the data available. In this case we are using [intake-esm](https://intake-esm.readthedocs.io/en/stable/index.html) to load a collection of zarr stores on Google Cloud Storage, but there are [other options](https://pangeo-data.github.io/pangeo-cmip6-cloud/accessing_data.html) to access the data too.\n",
    "\n",
    "Lets create a collection object and look at it\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>pangeo-cmip6 catalog with 7674 dataset(s) from 514818 asset(s)</strong>:</p> <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>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_id</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zstore</th>\n",
       "      <td>514818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>derived_variable_id</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = google_cmip_col()\n",
    "col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This object describes all the available data (over 500k single zarr stores!). The rows describe different 'facets' that can be used to search and query subsets of the data. For detailed info on each of the facets please refer to this [document](https://docs.google.com/document/d/1h0r8RZr_f3-8egBMMh7aqLwy3snpD6_MrDz1q8n5XUk/edit).\n",
    "\n",
    "So obviously we never want to work with *all* the data. Lets check out how to get a subset. \n",
    "\n",
    "First we need to understand a bit better what all these facets mean and how to see which ones are in the full collection. Lets start with the `experiment_id`: This is the prescribed forcing for a particular MIP that is exactly the same across all different models. We can look at the values of the collection as a pandas dataframe to convieniently list all values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['highresSST-present', 'piControl', 'control-1950', 'hist-1950',\n",
       "       'historical', 'amip', 'abrupt-4xCO2', 'abrupt-2xCO2',\n",
       "       'abrupt-0p5xCO2', '1pctCO2', 'ssp585', 'esm-piControl', 'esm-hist',\n",
       "       'hist-piAer', 'histSST-1950HC', 'ssp245', 'hist-1950HC', 'histSST',\n",
       "       'piClim-2xVOC', 'piClim-2xNOx', 'piClim-2xdust', 'piClim-2xss',\n",
       "       'piClim-histall', 'hist-piNTCF', 'histSST-piNTCF',\n",
       "       'aqua-control-lwoff', 'piClim-lu', 'histSST-piO3', 'piClim-CH4',\n",
       "       'piClim-NTCF', 'piClim-NOx', 'piClim-O3', 'piClim-HC',\n",
       "       'faf-heat-NA0pct', 'ssp370SST-lowCH4', 'piClim-VOC',\n",
       "       'ssp370-lowNTCF', 'piClim-control', 'piClim-aer', 'hist-aer',\n",
       "       'faf-heat', 'faf-heat-NA50pct', 'ssp370SST-lowNTCF',\n",
       "       'ssp370SST-ssp126Lu', 'ssp370SST', 'ssp370pdSST', 'histSST-piAer',\n",
       "       'piClim-ghg', 'piClim-anthro', 'faf-all', 'hist-nat', 'hist-GHG',\n",
       "       'ssp119', 'piClim-histnat', 'piClim-4xCO2', 'ssp370',\n",
       "       'piClim-histghg', 'highresSST-future', 'esm-ssp585-ssp126Lu',\n",
       "       'ssp126-ssp370Lu', 'ssp370-ssp126Lu', 'land-noLu', 'histSST-piCH4',\n",
       "       'ssp126', 'esm-pi-CO2pulse', 'amip-hist', 'piClim-histaer',\n",
       "       'amip-4xCO2', 'faf-water', 'faf-passiveheat', '1pctCO2-rad',\n",
       "       'faf-stress', '1pctCO2-bgc', 'aqua-control', 'amip-future4K',\n",
       "       'amip-p4K', 'aqua-p4K', 'amip-lwoff', 'amip-m4K', 'aqua-4xCO2',\n",
       "       'amip-p4K-lwoff', 'hist-noLu', '1pctCO2-cdr',\n",
       "       'land-hist-altStartYear', 'land-hist', 'omip1', 'esm-pi-cdr-pulse',\n",
       "       'esm-ssp585', 'abrupt-solp4p', 'piControl-spinup', 'hist-stratO3',\n",
       "       'abrupt-solm4p', 'midHolocene', 'lig127k', 'aqua-p4K-lwoff',\n",
       "       'esm-piControl-spinup', 'ssp245-GHG', 'ssp245-nat',\n",
       "       'dcppC-amv-neg', 'dcppC-amv-ExTrop-neg', 'dcppC-atl-control',\n",
       "       'dcppC-amv-pos', 'dcppC-ipv-NexTrop-neg', 'dcppC-ipv-NexTrop-pos',\n",
       "       'dcppC-atl-pacemaker', 'dcppC-amv-ExTrop-pos',\n",
       "       'dcppC-amv-Trop-neg', 'dcppC-pac-control', 'dcppC-ipv-pos',\n",
       "       'dcppC-pac-pacemaker', 'dcppC-ipv-neg', 'dcppC-amv-Trop-pos',\n",
       "       'piClim-BC', 'piClim-2xfire', 'piClim-SO2', 'piClim-OC',\n",
       "       'piClim-N2O', 'piClim-2xDMS', 'ssp460', 'ssp434', 'ssp534-over',\n",
       "       'deforest-globe', 'historical-cmip5', 'hist-bgc',\n",
       "       'piControl-cmip5', 'rcp26-cmip5', 'rcp45-cmip5', 'rcp85-cmip5',\n",
       "       'pdSST-piArcSIC', 'pdSST-piAntSIC', 'piSST-piSIC', 'piSST-pdSIC',\n",
       "       'ssp245-stratO3', 'hist-sol', 'hist-CO2', 'hist-volc',\n",
       "       'hist-totalO3', 'hist-nat-cmip5', 'hist-aer-cmip5',\n",
       "       'hist-GHG-cmip5', 'pdSST-futAntSIC', 'futSST-pdSIC', 'pdSST-pdSIC',\n",
       "       'ssp245-aer', 'pdSST-futArcSIC', 'dcppA-hindcast', 'dcppA-assim',\n",
       "       'dcppC-hindcast-noPinatubo', 'dcppC-hindcast-noElChichon',\n",
       "       'dcppC-hindcast-noAgung', 'ssp245-cov-modgreen',\n",
       "       'ssp245-cov-fossil', 'ssp245-cov-strgreen', 'ssp245-covid', 'lgm',\n",
       "       'ssp585-bgc', '1pctCO2to4x-withism', '1pctCO2-4xext',\n",
       "       'hist-resIPO', 'past1000', 'pa-futArcSIC', 'pa-pdSIC',\n",
       "       'historical-ext', 'pdSST-futArcSICSIT', 'pdSST-futOkhotskSIC',\n",
       "       'pdSST-futBKSeasSIC', 'pa-piArcSIC', 'pa-piAntSIC', 'pa-futAntSIC',\n",
       "       'pdSST-pdSICSIT'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col.df['experiment_id'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The experiments you are probably most interested in are the following:\n",
    "- `piControl`: In most cases this is the 'spin up' phase of the model, where it is run with a constant forcing (representing pre-industrial greenhouse gas concentrations)\n",
    "- `historical`: This experiment is run with the observed forcing in the past. \n",
    "- `ssp***`: A set of [\"Shared Socioeconomic Pathways\"](https://www.sciencedirect.com/science/article/pii/S0959378016300681) which represent different combined scenarios of future greenhouse gas emissions and other socio-economic indicators ([Explainer](https://www.carbonbrief.org/explainer-how-shared-socioeconomic-pathways-explore-future-climate-change/)). These are a fairly complex topic, but to simplify this for our purposes here we can treat `ssp585` as the 'worst-case' and `ssp245` as 'middle-of-the-road'.\n",
    "\n",
    "You can explore the available models (`source_id`) in the same way as above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CMCC-CM2-HR4', 'EC-Earth3P-HR', 'HadGEM3-GC31-MM',\n",
       "       'HadGEM3-GC31-HM', 'HadGEM3-GC31-LM', 'EC-Earth3P', 'ECMWF-IFS-HR',\n",
       "       'ECMWF-IFS-LR', 'HadGEM3-GC31-LL', 'CMCC-CM2-VHR4', 'GFDL-CM4',\n",
       "       'GFDL-AM4', 'IPSL-CM6A-LR', 'E3SM-1-0', 'CNRM-CM6-1', 'GFDL-ESM4',\n",
       "       'GFDL-ESM2M', 'GFDL-CM4C192', 'GFDL-OM4p5B', 'GISS-E2-1-G',\n",
       "       'GISS-E2-1-H', 'CNRM-ESM2-1', 'BCC-CSM2-MR', 'BCC-ESM1', 'MIROC6',\n",
       "       'AWI-CM-1-1-MR', 'EC-Earth3-LR', 'IPSL-CM6A-ATM-HR', 'CESM2',\n",
       "       'CESM2-WACCM', 'CNRM-CM6-1-HR', 'MRI-ESM2-0', 'SAM0-UNICON',\n",
       "       'GISS-E2-1-G-CC', 'UKESM1-0-LL', 'EC-Earth3', 'EC-Earth3-Veg',\n",
       "       'FGOALS-f3-L', 'CanESM5', 'CanESM5-CanOE', 'INM-CM4-8',\n",
       "       'INM-CM5-0', 'NESM3', 'MPI-ESM-1-2-HAM', 'CAMS-CSM1-0',\n",
       "       'MPI-ESM1-2-LR', 'MPI-ESM1-2-HR', 'MRI-AGCM3-2-H', 'MRI-AGCM3-2-S',\n",
       "       'MCM-UA-1-0', 'INM-CM5-H', 'KACE-1-0-G', 'NorESM2-LM',\n",
       "       'FGOALS-f3-H', 'FGOALS-g3', 'MIROC-ES2L', 'FIO-ESM-2-0', 'NorCPM1',\n",
       "       'NorESM1-F', 'MPI-ESM1-2-XR', 'CESM1-1-CAM5-CMIP5', 'E3SM-1-1',\n",
       "       'KIOST-ESM', 'ACCESS-CM2', 'NorESM2-MM', 'ACCESS-ESM1-5',\n",
       "       'IITM-ESM', 'GISS-E2-2-G', 'CESM2-FV2', 'GISS-E2-2-H',\n",
       "       'CESM2-WACCM-FV2', 'CIESM', 'E3SM-1-1-ECA', 'TaiESM1',\n",
       "       'AWI-ESM-1-1-LR', 'EC-Earth3-Veg-LR', 'CMCC-ESM2', 'CMCC-CM2-SR5',\n",
       "       'EC-Earth3-AerChem', 'IPSL-CM6A-LR-INCA', 'IPSL-CM5A2-INCA',\n",
       "       'BCC-CSM2-HR', 'EC-Earth3P-VHR', 'CESM1-WACCM-SC', 'CAS-ESM2-0',\n",
       "       'EC-Earth3-CC', 'MIROC-ES2H', 'ICON-ESM-LR'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col.df['source_id'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you want to decide what variable (`variable_id`) you want to look at and in conjunction what time frequency of output (`table_id`) is available. This handy [spreadsheet](https://docs.google.com/spreadsheets/d/1UUtoz6Ofyjlpx5LdqhKcwHFz2SGoTQV2_yekHyMfL9Y/edit#gid=1221485271) can help you find the value for `variable_id` and the available time frequencies (`table_id`).  \n",
    "\n",
    "Lets look at an example for monthly (`'table_id'='Omon'`) sea surface temperature (`'variable_id'='tos'`) for the `historical` experiment: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>pangeo-cmip6 catalog with 2 dataset(s) from 6 asset(s)</strong>:</p> <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>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zstore</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>derived_variable_id</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat = col.search(\n",
    "    variable_id='tos', # ocean surface temperature\n",
    "    table_id='Omon',\n",
    "    experiment_id='historical',\n",
    "    source_id=['GFDL-ESM4'],\n",
    ")\n",
    "cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last facet we need to discuss is `member_id` and `grid_label`: \n",
    "You can see here that we have 3 `member_id` values for the given query. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['r3i1p1f1', 'r2i1p1f1', 'r1i1p1f1'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.df['member_id'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This indicates that for this particular model/experiment 3 ensemble members where run with varing realizations/initial_condiditions/physics/forcing or a combination thereof. The naming is explained in [this](https://docs.google.com/document/d/1h0r8RZr_f3-8egBMMh7aqLwy3snpD6_MrDz1q8n5XUk/edit)document:\n",
    "> Example of a variant_label:  if realization_index=2, initialization_index=1, physics_index=3, and forcing_index=233, then variant_label = “r2i1p3f233”.\n",
    "\n",
    "There can be many more members for some particular models, which provides an opportunity to investigate internal variability of the climate system as compared to forced signals.\n",
    "\n",
    "Finally there are two values of `grid_label`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['gr', 'gn'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.df['grid_label'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value `gn` always stands for the native model grid (which can be quite complex but preserves the most detail) and `gr` indicates data that has been regridded on regular lon/lat intervals.\n",
    "\n",
    "Now that you know how to query and subset the collection, it is time to actually load in some model data as xarray datasets!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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.table_id.grid_label'\n"
     ]
    },
    {
     "data": {
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      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'area'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'time_bounds'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'tos'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'tos'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with the new name 'lon_bounds' conflicts\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with the new name 'lat_bounds' conflicts\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:83: UserWarning: rename 'bnds' to 'bnds' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n",
      "  return obj.rename({kk: vv for kk, vv in rdict.items() if kk in obj.dims})\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'lat'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'lon'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'tos'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'area'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'time_bounds'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'tos'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'tos'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with the new name 'lon_bounds' conflicts\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with the new name 'lat_bounds' conflicts\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:83: UserWarning: rename 'bnds' to 'bnds' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n",
      "  return obj.rename({kk: vv for kk, vv in rdict.items() if kk in obj.dims})\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'lat'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'lon'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'tos'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'area'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'time_bounds'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'tos'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'tos'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with the new name 'lon_bounds' conflicts\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with the new name 'lat_bounds' conflicts\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:83: UserWarning: rename 'bnds' to 'bnds' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n",
      "  return obj.rename({kk: vv for kk, vv in rdict.items() if kk in obj.dims})\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'lat'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'lon'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'tos'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n"
     ]
    }
   ],
   "source": [
    "# create a smaller catalog from the full collection using faceted search\n",
    "cat = col.search(\n",
    "    variable_id='tos', # Ocean surface temperature\n",
    "    experiment_id='historical', # only runs for the historical forcing period\n",
    "    table_id='Omon', # monthly oceanic data\n",
    "    grid_label='gn', #native model grid only\n",
    "    source_id=['IPSL-CM6A-LR', 'MRI-ESM2-0', 'GFDL-ESM4'], # only choosing a few models here, there are many more!\n",
    "    member_id=['r1i1p1f1', 'r2i1p1f1', 'r3i1p1f1'], #lets restrict us here to only a few members, you can modify this later to experiment.\n",
    ")\n",
    "\n",
    "# read all datasets into a dictionary but apply the xmip preprocessing before\n",
    "ddict = cat.to_dataset_dict(\n",
    "    preprocess=combined_preprocessing,\n",
    "    xarray_open_kwargs={'use_cftime':True},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we did two things: \n",
    "- We searched the full collection based on a combination of facets like the variable, the experiment and only a test set of models.\n",
    "- We loaded the datasets into a dictionary of xarray datasets (note they are not loaded into memory, but instead these are lazyly loaded dask arrays -- more on this in the following lectures)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CMIP.IPSL.IPSL-CM6A-LR.historical.Omon.gn',\n",
       " 'CMIP.MRI.MRI-ESM2-0.historical.Omon.gn',\n",
       " 'CMIP.NOAA-GFDL.GFDL-ESM4.historical.Omon.gn']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(ddict.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that we have three different models. Lets look at them in a bit more detail to understand the layout:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-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:         (y: 332, x: 362, member_id: 3, dcpp_init_year: 1,\n",
       "                     time: 1980, vertex: 4, bnds: 2)\n",
       "Coordinates: (12/13)\n",
       "    area            (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "    lat_verticies   (y, x, vertex) float32 dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;\n",
       "    lon_verticies   (y, x, vertex) float32 dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;\n",
       "    lat             (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "    lon             (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "  * time            (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "    ...              ...\n",
       "  * y               (y) int64 0 1 2 3 4 5 6 7 ... 325 326 327 328 329 330 331\n",
       "  * x               (x) int64 0 1 2 3 4 5 6 7 ... 355 356 357 358 359 360 361\n",
       "    lon_bounds      (bnds, y, x) float32 dask.array&lt;chunksize=(1, 332, 362), meta=np.ndarray&gt;\n",
       "    lat_bounds      (bnds, y, x) float32 dask.array&lt;chunksize=(1, 332, 362), meta=np.ndarray&gt;\n",
       "  * member_id       (member_id) object &#x27;r1i1p1f1&#x27; &#x27;r2i1p1f1&#x27; &#x27;r3i1p1f1&#x27;\n",
       "  * dcpp_init_year  (dcpp_init_year) float64 nan\n",
       "Dimensions without coordinates: vertex, bnds\n",
       "Data variables:\n",
       "    tos             (member_id, dcpp_init_year, time, y, x) float32 dask.array&lt;chunksize=(1, 1, 251, 332, 362), meta=np.ndarray&gt;\n",
       "Attributes: (12/52)\n",
       "    CMIP6_CV_version:                 cv=6.2.3.5-2-g63b123e\n",
       "    Conventions:                      CF-1.7 CMIP-6.2\n",
       "    EXPID:                            historical\n",
       "    NCO:                              &quot;4.6.0&quot;\n",
       "    activity_id:                      CMIP\n",
       "    branch_method:                    standard\n",
       "    ...                               ...\n",
       "    intake_esm_attrs:variable_id:     tos\n",
       "    intake_esm_attrs:grid_label:      gn\n",
       "    intake_esm_attrs:version:         20180803\n",
       "    intake_esm_attrs:_data_format_:   zarr\n",
       "    variant_info:                     Restart from another point in piControl...\n",
       "    intake_esm_dataset_key:           CMIP.IPSL.IPSL-CM6A-LR.historical.Omon.gn</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-80c805aa-144d-4715-bc3d-74eabc504913' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-80c805aa-144d-4715-bc3d-74eabc504913' 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'>y</span>: 332</li><li><span class='xr-has-index'>x</span>: 362</li><li><span class='xr-has-index'>member_id</span>: 3</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>vertex</span>: 4</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0f16347e-bda6-4c2c-9fc4-3196a9a70ff3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0f16347e-bda6-4c2c-9fc4-3196a9a70ff3' class='xr-section-summary' >Coordinates: <span>(13)</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>area</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-a1a88ce5-1b8a-4f5c-bfe5-3f927d3921cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1a88ce5-1b8a-4f5c-bfe5-3f927d3921cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97508aed-314f-4ebe-ab36-d33ef8e9022b' class='xr-var-data-in' type='checkbox'><label for='data-97508aed-314f-4ebe-ab36-d33ef8e9022b' title='Show/Hide data repr'><svg 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>cell_area</dd><dt><span>units :</span></dt><dd>m²</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> 469.47 kiB </td>\n",
       "                        <td> 469.47 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (332, 362) </td>\n",
       "                        <td> (332, 362) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 10 Graph Layers </td>\n",
       "                        <td> 1 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",
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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_verticies</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;</div><input id='attrs-22385f19-fe90-4e32-84de-244825d0b7a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-22385f19-fe90-4e32-84de-244825d0b7a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8329d6be-9a0e-4dc5-b50f-1c28683e02e1' class='xr-var-data-in' type='checkbox'><label for='data-8329d6be-9a0e-4dc5-b50f-1c28683e02e1' title='Show/Hide data 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> 1.83 MiB </td>\n",
       "                        <td> 1.83 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (332, 362, 4) </td>\n",
       "                        <td> (332, 362, 4) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 12 Graph Layers </td>\n",
       "                        <td> 1 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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       "        </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_verticies</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;</div><input id='attrs-d911d1d9-edcd-41bb-90bb-8cc038f75313' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d911d1d9-edcd-41bb-90bb-8cc038f75313' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c93a6ac-845d-4596-baec-b7a1607e63cc' class='xr-var-data-in' type='checkbox'><label for='data-6c93a6ac-845d-4596-baec-b7a1607e63cc' title='Show/Hide data 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",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.83 MiB </td>\n",
       "                        <td> 1.83 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (332, 362, 4) </td>\n",
       "                        <td> (332, 362, 4) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 18 Graph Layers </td>\n",
       "                        <td> 1 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><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-c0d1d27b-c5c8-4cc6-bf24-7957492837b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0d1d27b-c5c8-4cc6-bf24-7957492837b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f70f784-2e83-40e9-9460-9bb40c78a868' class='xr-var-data-in' type='checkbox'><label for='data-3f70f784-2e83-40e9-9460-9bb40c78a868' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_nav_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 469.47 kiB </td>\n",
       "                        <td> 469.47 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (332, 362) </td>\n",
       "                        <td> (332, 362) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 16 Graph Layers </td>\n",
       "                        <td> 1 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=\"170\" height=\"160\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-49c09417-ce82-4e55-8e48-bc1cf45499be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49c09417-ce82-4e55-8e48-bc1cf45499be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca894d38-7689-43be-8fef-14d4c3572bb5' class='xr-var-data-in' type='checkbox'><label for='data-ca894d38-7689-43be-8fef-14d4c3572bb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_nav_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 469.47 kiB </td>\n",
       "                        <td> 469.47 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (332, 362) </td>\n",
       "                        <td> (332, 362) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 22 Graph Layers </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-70609e4e-efbc-45a5-9ae7-bca4c8314f9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70609e4e-efbc-45a5-9ae7-bca4c8314f9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6c5337c-c386-4311-9d94-75a1c34a138e' class='xr-var-data-in' type='checkbox'><label for='data-a6c5337c-c386-4311-9d94-75a1c34a138e' title='Show/Hide data repr'><svg 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_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(1850, 1, 16, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(1850, 2, 15, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(1850, 3, 16, 12, 0, 0, 0, has_year_zero=False),\n",
       "       ...,\n",
       "       cftime.DatetimeGregorian(2014, 10, 16, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(2014, 11, 16, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(2014, 12, 16, 12, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-0561191a-cc85-4bbe-a2f3-1f34242be4fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0561191a-cc85-4bbe-a2f3-1f34242be4fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6be64739-ec4f-4707-b015-501c025124f6' class='xr-var-data-in' type='checkbox'><label for='data-6be64739-ec4f-4707-b015-501c025124f6' title='Show/Hide data 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> 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> Count </th>\n",
       "                        <td> 10 Graph Layers </td>\n",
       "                        <td> 1 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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       "\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">1980</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'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 327 328 329 330 331</div><input id='attrs-afebeb86-4d4b-4177-9c88-6274928cd972' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-afebeb86-4d4b-4177-9c88-6274928cd972' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1420621-35f4-40f4-a67c-0a42a36efaf5' class='xr-var-data-in' type='checkbox'><label for='data-b1420621-35f4-40f4-a67c-0a42a36efaf5' title='Show/Hide data 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,   2, ..., 329, 330, 331])</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'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 357 358 359 360 361</div><input id='attrs-4acccba4-3582-4d8d-9b68-092b40b55ac6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4acccba4-3582-4d8d-9b68-092b40b55ac6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e5c4ca6-62bd-41fb-95c1-967381e4a6b3' class='xr-var-data-in' type='checkbox'><label for='data-4e5c4ca6-62bd-41fb-95c1-967381e4a6b3' title='Show/Hide data 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,   2, ..., 359, 360, 361])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bounds</span></div><div class='xr-var-dims'>(bnds, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 332, 362), meta=np.ndarray&gt;</div><input id='attrs-7a3267cd-722b-4cc8-b199-3372ed48350e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a3267cd-722b-4cc8-b199-3372ed48350e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce88ca86-b63a-4d3a-bb47-058be211c6df' class='xr-var-data-in' type='checkbox'><label for='data-ce88ca86-b63a-4d3a-bb47-058be211c6df' title='Show/Hide data 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> 0.92 MiB </td>\n",
       "                        <td> 469.47 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2, 332, 362) </td>\n",
       "                        <td> (1, 332, 362) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 38 Graph Layers </td>\n",
       "                        <td> 2 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bounds</span></div><div class='xr-var-dims'>(bnds, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 332, 362), meta=np.ndarray&gt;</div><input id='attrs-ea2a6bf4-cedc-4fbe-b3ac-70402268aca5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea2a6bf4-cedc-4fbe-b3ac-70402268aca5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f242f8c9-447e-47d1-9f38-87d8c096d1aa' class='xr-var-data-in' type='checkbox'><label for='data-f242f8c9-447e-47d1-9f38-87d8c096d1aa' title='Show/Hide data 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> 0.92 MiB </td>\n",
       "                        <td> 469.47 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2, 332, 362) </td>\n",
       "                        <td> (1, 332, 362) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 32 Graph Layers </td>\n",
       "                        <td> 2 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "            </table>\n",
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       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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; &#x27;r2i1p1f1&#x27; &#x27;r3i1p1f1&#x27;</div><input id='attrs-81d818a4-8345-48af-9496-07a7a85dd6b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-81d818a4-8345-48af-9496-07a7a85dd6b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c14054f-6792-4066-a5cf-4f1612e95b5a' class='xr-var-data-in' type='checkbox'><label for='data-0c14054f-6792-4066-a5cf-4f1612e95b5a' title='Show/Hide data 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;, &#x27;r2i1p1f1&#x27;, &#x27;r3i1p1f1&#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-dbc33178-bd30-4020-8e9d-0bad5d0bb694' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dbc33178-bd30-4020-8e9d-0bad5d0bb694' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60b7caad-752c-40d6-ab2a-0de05780fc71' class='xr-var-data-in' type='checkbox'><label for='data-60b7caad-752c-40d6-ab2a-0de05780fc71' title='Show/Hide data 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-5f456b02-29bd-42bd-b423-5114d9183877' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f456b02-29bd-42bd-b423-5114d9183877' 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>tos</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, 251, 332, 362), meta=np.ndarray&gt;</div><input id='attrs-19a74c3c-a74a-4063-a150-965b4248f044' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19a74c3c-a74a-4063-a150-965b4248f044' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6adea72c-d77c-4e68-8d2d-85ee2c5433ac' class='xr-var-data-in' type='checkbox'><label for='data-6adea72c-d77c-4e68-8d2d-85ee2c5433ac' title='Show/Hide data repr'><svg 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: areacello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>description :</span></dt><dd>This may differ from &quot;surface temperature&quot; in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>interval_operation :</span></dt><dd>2700 s</dd><dt><span>interval_write :</span></dt><dd>1 month</dd><dt><span>long_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>units :</span></dt><dd>°C</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    ...              ...\n",
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       "    lat_bounds      (bnds, y, x) float32 dask.array<chunksize=(1, 332, 362), meta=np.ndarray>\n",
       "  * member_id       (member_id) object 'r1i1p1f1' 'r2i1p1f1' 'r3i1p1f1'\n",
       "  * dcpp_init_year  (dcpp_init_year) float64 nan\n",
       "Dimensions without coordinates: vertex, bnds\n",
       "Data variables:\n",
       "    tos             (member_id, dcpp_init_year, time, y, x) float32 dask.array<chunksize=(1, 1, 251, 332, 362), meta=np.ndarray>\n",
       "Attributes: (12/52)\n",
       "    CMIP6_CV_version:                 cv=6.2.3.5-2-g63b123e\n",
       "    Conventions:                      CF-1.7 CMIP-6.2\n",
       "    EXPID:                            historical\n",
       "    NCO:                              \"4.6.0\"\n",
       "    activity_id:                      CMIP\n",
       "    branch_method:                    standard\n",
       "    ...                               ...\n",
       "    intake_esm_attrs:variable_id:     tos\n",
       "    intake_esm_attrs:grid_label:      gn\n",
       "    intake_esm_attrs:version:         20180803\n",
       "    intake_esm_attrs:_data_format_:   zarr\n",
       "    variant_info:                     Restart from another point in piControl...\n",
       "    intake_esm_dataset_key:           CMIP.IPSL.IPSL-CM6A-LR.historical.Omon.gn"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddict['CMIP.IPSL.IPSL-CM6A-LR.historical.Omon.gn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (member_id: 3, dcpp_init_year: 1, time: 1980, y: 576,\n",
       "                     x: 720, vertex: 4, bnds: 2)\n",
       "Coordinates:\n",
       "  * x               (x) float64 -299.8 -299.2 -298.8 ... 58.75 59.25 59.75\n",
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       "  * time            (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
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       "    lon_bounds      (bnds, y, x) float32 dask.array&lt;chunksize=(1, 576, 720), meta=np.ndarray&gt;\n",
       "    lat_bounds      (bnds, y, x) float32 dask.array&lt;chunksize=(1, 576, 720), meta=np.ndarray&gt;\n",
       "  * member_id       (member_id) object &#x27;r1i1p1f1&#x27; &#x27;r2i1p1f1&#x27; &#x27;r3i1p1f1&#x27;\n",
       "  * dcpp_init_year  (dcpp_init_year) float64 nan\n",
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       "    tos             (member_id, dcpp_init_year, time, y, x) float32 dask.array&lt;chunksize=(1, 1, 64, 576, 720), meta=np.ndarray&gt;\n",
       "Attributes: (12/48)\n",
       "    Conventions:                      CF-1.7 CMIP-6.0 UGRID-1.0\n",
       "    activity_id:                      CMIP\n",
       "    branch_method:                    standard\n",
       "    branch_time_in_child:             0.0\n",
       "    comment:                          &lt;null ref&gt;\n",
       "    contact:                          gfdl.climate.model.info@noaa.gov\n",
       "    ...                               ...\n",
       "    intake_esm_attrs:experiment_id:   historical\n",
       "    intake_esm_attrs:table_id:        Omon\n",
       "    intake_esm_attrs:variable_id:     tos\n",
       "    intake_esm_attrs:grid_label:      gn\n",
       "    intake_esm_attrs:_data_format_:   zarr\n",
       "    intake_esm_dataset_key:           CMIP.NOAA-GFDL.GFDL-ESM4.historical.Omo...</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-ad775c27-4c29-4c6e-b787-d517b5b2906f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ad775c27-4c29-4c6e-b787-d517b5b2906f' 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>: 3</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>: 576</li><li><span class='xr-has-index'>x</span>: 720</li><li><span>vertex</span>: 4</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-46f5cbd8-a634-4771-a417-5f7c3af6bfb3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46f5cbd8-a634-4771-a417-5f7c3af6bfb3' 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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-299.8 -299.2 ... 59.25 59.75</div><input id='attrs-767c4bb8-476b-42c1-aca1-eb3c180a25e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-767c4bb8-476b-42c1-aca1-eb3c180a25e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56b094fe-d115-409d-aa65-3d6272238bd7' class='xr-var-data-in' type='checkbox'><label for='data-56b094fe-d115-409d-aa65-3d6272238bd7' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><pre>array([-299.75, -299.25, -298.75, ...,   58.75,   59.25,   59.75])</pre></div></li><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'>-77.91 -77.72 ... 89.68 89.89</div><input id='attrs-70765904-3f0c-45d8-b485-519189dc5546' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70765904-3f0c-45d8-b485-519189dc5546' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3efa19c6-122a-440c-8ad2-811781ee6890' class='xr-var-data-in' type='checkbox'><label for='data-3efa19c6-122a-440c-8ad2-811781ee6890' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><pre>array([-77.907938, -77.723813, -77.539688, ...,  89.472   ,  89.6832  ,\n",
       "        89.8944  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;</div><input id='attrs-20a35c45-0249-412b-88f9-55e6c03f35ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20a35c45-0249-412b-88f9-55e6c03f35ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2170303d-1742-4058-acf1-7c1294098e4f' class='xr-var-data-in' type='checkbox'><label for='data-2170303d-1742-4058-acf1-7c1294098e4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>cell_methods :</span></dt><dd>y: point x: point</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.58 MiB </td>\n",
       "                        <td> 1.58 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (576, 720) </td>\n",
       "                        <td> (576, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 16 Graph Layers </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
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       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_verticies</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(576, 720, 4), meta=np.ndarray&gt;</div><input id='attrs-a7589bb4-804b-4d61-a820-da32d34bd1eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7589bb4-804b-4d61-a820-da32d34bd1eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23674632-320b-4a33-a62f-e3c296a3a2f2' class='xr-var-data-in' type='checkbox'><label for='data-23674632-320b-4a33-a62f-e3c296a3a2f2' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>latitude bounds</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                        <td> 6.33 MiB </td>\n",
       "                        <td> 6.33 MiB </td>\n",
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       "                    <tr>\n",
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       "                        <td> (576, 720, 4) </td>\n",
       "                        <td> (576, 720, 4) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
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       "                        <td> 1 Chunks </td>\n",
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       "                    <th> Type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;</div><input id='attrs-7162d618-8f23-464c-8b04-c3cef547f960' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7162d618-8f23-464c-8b04-c3cef547f960' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5421acac-96a8-4fae-8143-84350592e74c' class='xr-var-data-in' type='checkbox'><label for='data-5421acac-96a8-4fae-8143-84350592e74c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>cell_methods :</span></dt><dd>y: point x: point</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 1.58 MiB </td>\n",
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       "                        <td> (576, 720) </td>\n",
       "                        <td> (576, 720) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 22 Graph Layers </td>\n",
       "                        <td> 1 Chunks </td>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_verticies</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(576, 720, 4), meta=np.ndarray&gt;</div><input id='attrs-cc732350-847f-4c32-9d37-98f9926674f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc732350-847f-4c32-9d37-98f9926674f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9defc25b-c252-44b8-980c-e6555533dd38' class='xr-var-data-in' type='checkbox'><label for='data-9defc25b-c252-44b8-980c-e6555533dd38' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>longitude bounds</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 6.33 MiB </td>\n",
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       "                        <td> (576, 720, 4) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
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       "  <text x=\"28.235294\" y=\"168.235294\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,28.235294,168.235294)\">576</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-2fb2c024-6140-477a-b431-6f30d7788360' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fb2c024-6140-477a-b431-6f30d7788360' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac51ceaa-969f-4aa8-90a2-87ee38e43ca9' class='xr-var-data-in' type='checkbox'><label for='data-ac51ceaa-969f-4aa8-90a2-87ee38e43ca9' title='Show/Hide data repr'><svg 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>calendar_type :</span></dt><dd>noleap</dd><dt><span>description :</span></dt><dd>Temporal mean</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-1e0b0e1f-127a-4f77-a820-52a56571f4c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e0b0e1f-127a-4f77-a820-52a56571f4c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-120cd0d0-1d61-4b76-9fb1-8bbcc2b17970' class='xr-var-data-in' type='checkbox'><label for='data-120cd0d0-1d61-4b76-9fb1-8bbcc2b17970' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time axis boundaries</dd></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> 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> Count </th>\n",
       "                        <td> 10 Graph Layers </td>\n",
       "                        <td> 1 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",
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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_bounds</span></div><div class='xr-var-dims'>(bnds, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 576, 720), meta=np.ndarray&gt;</div><input id='attrs-fc7dee9e-867f-4294-95e3-4b53b9f66005' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc7dee9e-867f-4294-95e3-4b53b9f66005' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9800aa4e-7955-4032-88cf-e00ab92e6b1b' class='xr-var-data-in' type='checkbox'><label for='data-9800aa4e-7955-4032-88cf-e00ab92e6b1b' title='Show/Hide data 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> 3.16 MiB </td>\n",
       "                        <td> 1.58 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2, 576, 720) </td>\n",
       "                        <td> (1, 576, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 38 Graph Layers </td>\n",
       "                        <td> 2 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "    </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'>(bnds, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 576, 720), meta=np.ndarray&gt;</div><input id='attrs-2ec2435c-e276-4621-b297-4f4d7ddbcb27' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ec2435c-e276-4621-b297-4f4d7ddbcb27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26f8f49e-bed2-4964-b6ab-9a89de40e4d3' class='xr-var-data-in' type='checkbox'><label for='data-26f8f49e-bed2-4964-b6ab-9a89de40e4d3' title='Show/Hide data 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> 3.16 MiB </td>\n",
       "                        <td> 1.58 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2, 576, 720) </td>\n",
       "                        <td> (1, 576, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 32 Graph Layers </td>\n",
       "                        <td> 2 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",
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       "</table></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; &#x27;r2i1p1f1&#x27; &#x27;r3i1p1f1&#x27;</div><input id='attrs-23ecfee3-00f8-4fd7-9215-b7c14607381e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23ecfee3-00f8-4fd7-9215-b7c14607381e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96726079-87fe-4c24-969c-70c51eafb2a7' class='xr-var-data-in' type='checkbox'><label for='data-96726079-87fe-4c24-969c-70c51eafb2a7' title='Show/Hide data 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;, &#x27;r2i1p1f1&#x27;, &#x27;r3i1p1f1&#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-2d7b85a8-df13-45a6-b385-313632772e2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d7b85a8-df13-45a6-b385-313632772e2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a7eda48-de5c-46a1-980a-97cafe3c3b01' class='xr-var-data-in' type='checkbox'><label for='data-7a7eda48-de5c-46a1-980a-97cafe3c3b01' title='Show/Hide data 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-8a32664d-d9e3-4f95-9782-1c6f9256e707' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8a32664d-d9e3-4f95-9782-1c6f9256e707' 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>tos</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, 64, 576, 720), meta=np.ndarray&gt;</div><input id='attrs-1bdcb94a-9d0a-4346-811c-3d434ec4d39a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bdcb94a-9d0a-4346-811c-3d434ec4d39a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c0849f2-fe2f-40f9-a77a-5d01129ecfb4' class='xr-var-data-in' type='checkbox'><label for='data-4c0849f2-fe2f-40f9-a77a-5d01129ecfb4' title='Show/Hide data repr'><svg 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: areacello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>long_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>original_name :</span></dt><dd>tos</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>units :</span></dt><dd>°C</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
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       "    tos             (member_id, dcpp_init_year, time, y, x) float32 dask.array<chunksize=(1, 1, 64, 576, 720), meta=np.ndarray>\n",
       "Attributes: (12/48)\n",
       "    Conventions:                      CF-1.7 CMIP-6.0 UGRID-1.0\n",
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       "    branch_method:                    standard\n",
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       "    comment:                          <null ref>\n",
       "    contact:                          gfdl.climate.model.info@noaa.gov\n",
       "    ...                               ...\n",
       "    intake_esm_attrs:experiment_id:   historical\n",
       "    intake_esm_attrs:table_id:        Omon\n",
       "    intake_esm_attrs:variable_id:     tos\n",
       "    intake_esm_attrs:grid_label:      gn\n",
       "    intake_esm_attrs:_data_format_:   zarr\n",
       "    intake_esm_dataset_key:           CMIP.NOAA-GFDL.GFDL-ESM4.historical.Omo..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddict['CMIP.NOAA-GFDL.GFDL-ESM4.historical.Omon.gn']"
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    "You can see that both datasets have the same names for many of the coordinates (e.g. 'x' and 'y' for the logical indices in zonal and meridional direction). This is actually not always the case for the raw CMIP6 data, which is why [xMIP](https://github.com/jbusecke/xMIP) was developed in an effort to crowdsource these common data-cleaning tasks. For this example we only use the `combined_preprocessing` function which fixes some of the naming, but check out the [docs](https://cmip6-preprocessing.readthedocs.io/en/latest/?badge=latest) to see more helpful code for CMIP analysis.\n",
    "\n",
    "Ok but now lets analyze the data! Using what we know about xarray we can get a timeseries of the global sea surface temperature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, ds in ddict.items():\n",
    "    # construct yearly global mean timeseries\n",
    "    mean_temp = ds.tos.mean(['x', 'y']).coarsen(time=12).mean()\n",
    "    plt.figure()\n",
    "    mean_temp.plot(hue='member_id')\n",
    "    # lets also plot the average over all members\n",
    "    mean_temp.mean('member_id').plot(color='k', linewidth=2)\n",
    "    plt.title(ds.attrs['source_id']) #Extract the model name right from the dataset metadata\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But wait! This is not exactly right. We need to weight the spatial mean by the area of each grid cell, since the area varies based on the position on the globe (and the particularities of the curvilinear grid used in each model). But we can easily query the cell area (by modifying the query from above slightly) and then match them to the data using xmip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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.table_id.grid_label'\n"
     ]
    },
    {
     "data": {
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       "        /* 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",
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       "</style>\n"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
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       "      100.00% [3/3 00:05&lt;00:00]\n",
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       "    "
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'area'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'areacello'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'area'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'areacello'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'area'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'areacello'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'areacello'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'lat'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'lon'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'areacello'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with the new name 'lon_bounds' conflicts\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with the new name 'lat_bounds' conflicts\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'areacello'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with the new name 'lon_bounds' conflicts\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with the new name 'lat_bounds' conflicts\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with 'areacello'\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with the new name 'lon_bounds' conflicts\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/preprocessing.py:106: UserWarning: Renaming failed with the new name 'lat_bounds' conflicts\n",
      "  warnings.warn(f\"Renaming failed with {e}\")\n"
     ]
    }
   ],
   "source": [
    "# Repeat the above steps to get the cell area matching the temperature we loaded earlier\n",
    "cat_area = col.search(\n",
    "    variable_id='areacello', # the cell area is a different variable in the collection\n",
    "    table_id='Ofx', # since area is not varying in time we need to specify a different table_id\n",
    "    experiment_id='historical', # Same as before\n",
    "    grid_label='gn', # Same as before\n",
    "    source_id=['IPSL-CM6A-LR', 'MRI-ESM2-0', 'GFDL-ESM4'], # Same as before\n",
    "    member_id=['r1i1p1f1', 'r2i1p1f1', 'r3i1p1f1'], # Same as before\n",
    ")\n",
    "# read all datasets into a dictionary (make sure to apply the same preprocessing as before!)\n",
    "ddict_area = cat_area.to_dataset_dict(\n",
    "    preprocess=combined_preprocessing,\n",
    "    xarray_open_kwargs={'use_cftime':True}\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CMIP.NOAA-GFDL.GFDL-ESM4.historical.Ofx.gn',\n",
       " 'CMIP.IPSL.IPSL-CM6A-LR.historical.Ofx.gn',\n",
       " 'CMIP.MRI.MRI-ESM2-0.historical.Ofx.gn']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(ddict_area.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can inspect the new dictionary `ddict_area` and will find that the structure is very similar to the one for surface temperature above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/xmip/postprocessing.py:593: UserWarning: CMIP.NOAA-GFDL.GFDL-ESM4.historical.none.Omon.gn.none.tos:`metric` dimensions ['member_id:1'] do not match `ds` ['member_id:3']. Aligning the data on `inner`\n",
      "  warnings.warn(msg + \" Aligning the data on `inner`\")\n"
     ]
    }
   ],
   "source": [
    "ddict_w_area = match_metrics(ddict, ddict_area, 'areacello', dim_length_conflict='align')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (x: 720, y: 576, time: 1980, member_id: 1,\n",
       "                     dcpp_init_year: 1, vertex: 4, bnds: 2)\n",
       "Coordinates: (12/13)\n",
       "  * x               (x) float64 -299.8 -299.2 -298.8 ... 58.75 59.25 59.75\n",
       "  * y               (y) float64 -77.91 -77.72 -77.54 ... 89.47 89.68 89.89\n",
       "  * time            (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * member_id       (member_id) object &#x27;r1i1p1f1&#x27;\n",
       "  * dcpp_init_year  (dcpp_init_year) float64 nan\n",
       "    lat             (y, x) float32 dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;\n",
       "    ...              ...\n",
       "    lon             (y, x) float32 dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;\n",
       "    lon_verticies   (y, x, vertex) float32 dask.array&lt;chunksize=(576, 720, 4), meta=np.ndarray&gt;\n",
       "    time_bounds     (time, bnds) object dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "    lon_bounds      (bnds, y, x) float32 dask.array&lt;chunksize=(1, 576, 720), meta=np.ndarray&gt;\n",
       "    lat_bounds      (bnds, y, x) float32 dask.array&lt;chunksize=(1, 576, 720), meta=np.ndarray&gt;\n",
       "    areacello       (member_id, dcpp_init_year, y, x) float32 dask.array&lt;chunksize=(1, 1, 576, 720), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: vertex, bnds\n",
       "Data variables:\n",
       "    tos             (member_id, dcpp_init_year, time, y, x) float32 dask.array&lt;chunksize=(1, 1, 64, 576, 720), meta=np.ndarray&gt;\n",
       "Attributes: (12/48)\n",
       "    Conventions:                      CF-1.7 CMIP-6.0 UGRID-1.0\n",
       "    activity_id:                      CMIP\n",
       "    branch_method:                    standard\n",
       "    branch_time_in_child:             0.0\n",
       "    comment:                          &lt;null ref&gt;\n",
       "    contact:                          gfdl.climate.model.info@noaa.gov\n",
       "    ...                               ...\n",
       "    intake_esm_attrs:experiment_id:   historical\n",
       "    intake_esm_attrs:table_id:        Omon\n",
       "    intake_esm_attrs:variable_id:     tos\n",
       "    intake_esm_attrs:grid_label:      gn\n",
       "    intake_esm_attrs:_data_format_:   zarr\n",
       "    intake_esm_dataset_key:           CMIP.NOAA-GFDL.GFDL-ESM4.historical.Omo...</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-30c374a6-aa6a-4720-9086-9095c1f3336b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-30c374a6-aa6a-4720-9086-9095c1f3336b' 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'>x</span>: 720</li><li><span class='xr-has-index'>y</span>: 576</li><li><span class='xr-has-index'>time</span>: 1980</li><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>vertex</span>: 4</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-eb1321ca-37cb-4205-a06d-e048d9ecb805' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eb1321ca-37cb-4205-a06d-e048d9ecb805' class='xr-section-summary' >Coordinates: <span>(13)</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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-299.8 -299.2 ... 59.25 59.75</div><input id='attrs-ef7ebe6b-b083-4434-8496-07b2a915d68b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef7ebe6b-b083-4434-8496-07b2a915d68b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e0d5544-0b96-4e04-9d2f-a2d463c58144' class='xr-var-data-in' type='checkbox'><label for='data-7e0d5544-0b96-4e04-9d2f-a2d463c58144' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cartesian_axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><pre>array([-299.75, -299.25, -298.75, ...,   58.75,   59.25,   59.75])</pre></div></li><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'>-77.91 -77.72 ... 89.68 89.89</div><input id='attrs-3fd2f932-4eb3-47b5-928e-8c95519976c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fd2f932-4eb3-47b5-928e-8c95519976c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61947dc8-8269-4674-822f-681364585bd4' class='xr-var-data-in' type='checkbox'><label for='data-61947dc8-8269-4674-822f-681364585bd4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cartesian_axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><pre>array([-77.907938, -77.723813, -77.539688, ...,  89.472   ,  89.6832  ,\n",
       "        89.8944  ])</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'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-30d9d0b9-9c3d-47c0-8128-9957a64753bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30d9d0b9-9c3d-47c0-8128-9957a64753bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4718fe4-5e0a-4eea-83aa-5ee9476ffba6' class='xr-var-data-in' type='checkbox'><label for='data-c4718fe4-5e0a-4eea-83aa-5ee9476ffba6' title='Show/Hide data repr'><svg 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>calendar_type :</span></dt><dd>noleap</dd><dt><span>description :</span></dt><dd>Temporal mean</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 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 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-b044c994-e159-4c74-a1c1-6c627ad5d2f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b044c994-e159-4c74-a1c1-6c627ad5d2f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-280fc921-e201-42d9-9372-fc32fb353cee' class='xr-var-data-in' type='checkbox'><label for='data-280fc921-e201-42d9-9372-fc32fb353cee' title='Show/Hide data 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-41ad83d0-b8e1-43ee-b50a-1c3357ba9f71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41ad83d0-b8e1-43ee-b50a-1c3357ba9f71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd1e586c-7b3a-4874-a397-99c9f03e83dd' class='xr-var-data-in' type='checkbox'><label for='data-bd1e586c-7b3a-4874-a397-99c9f03e83dd' title='Show/Hide data 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>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;</div><input id='attrs-55eea287-0503-4fa8-990c-d005d07beadd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55eea287-0503-4fa8-990c-d005d07beadd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95092f9a-2270-44d1-bc85-4816331cf902' class='xr-var-data-in' type='checkbox'><label for='data-95092f9a-2270-44d1-bc85-4816331cf902' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>cell_methods :</span></dt><dd>y: point x: point</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "            <table>\n",
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       "                    <tr>\n",
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       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.58 MiB </td>\n",
       "                        <td> 1.58 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (576, 720) </td>\n",
       "                        <td> (576, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 16 Graph Layers </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </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>lat_verticies</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(576, 720, 4), meta=np.ndarray&gt;</div><input id='attrs-ffd950df-81d0-4e72-ab3e-33e103d20681' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffd950df-81d0-4e72-ab3e-33e103d20681' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-167360df-8d17-4599-a4c5-d900d35c615f' class='xr-var-data-in' type='checkbox'><label for='data-167360df-8d17-4599-a4c5-d900d35c615f' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>latitude bounds</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 6.33 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (576, 720, 4) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 12 Graph Layers </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;</div><input id='attrs-faf88058-ec82-480b-b20a-7d3f8a03d2c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-faf88058-ec82-480b-b20a-7d3f8a03d2c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-918c094c-7824-4015-96d5-4ebea7dc7f33' class='xr-var-data-in' type='checkbox'><label for='data-918c094c-7824-4015-96d5-4ebea7dc7f33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>cell_methods :</span></dt><dd>y: point x: point</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 1.58 MiB </td>\n",
       "                        <td> 1.58 MiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (576, 720) </td>\n",
       "                        <td> (576, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 22 Graph Layers </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
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       "                        <td> (1980, 2) </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d881876f-c384-4fac-845c-5596dec1ffa0' class='xr-section-summary-in' type='checkbox'  ><label for='section-d881876f-c384-4fac-845c-5596dec1ffa0' 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>Conventions :</span></dt><dd>CF-1.7 CMIP-6.0 UGRID-1.0</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>0.0</dd><dt><span>comment :</span></dt><dd>&lt;null ref&gt;</dd><dt><span>contact :</span></dt><dd>gfdl.climate.model.info@noaa.gov</dd><dt><span>data_specs_version :</span></dt><dd>01.00.27</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>areacello</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>grid :</span></dt><dd>ocean data on native grid tripolar - nominal 0.5 deg latitude/longitude</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 Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA</dd><dt><span>institution_id :</span></dt><dd>NOAA-GFDL</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by NOAA-GFDL 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>nominal_resolution :</span></dt><dd>50 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>GFDL-ESM4</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-1-1</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>realm :</span></dt><dd>ocean</dd><dt><span>references :</span></dt><dd>see further_info_url attribute</dd><dt><span>source_id :</span></dt><dd>GFDL-ESM4</dd><dt><span>source_type :</span></dt><dd>AOGCM AER CHEM BGC</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>Omon</dd><dt><span>title :</span></dt><dd>NOAA GFDL GFDL-ESM4 model output prepared for CMIP6 all-forcing simulation of the recent past</dd><dt><span>variable_id :</span></dt><dd>tos</dd><dt><span>variant_info :</span></dt><dd>N/A</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;tos&#x27;]</dd><dt><span>intake_esm_attrs:activity_id :</span></dt><dd>CMIP</dd><dt><span>intake_esm_attrs:institution_id :</span></dt><dd>NOAA-GFDL</dd><dt><span>intake_esm_attrs:source_id :</span></dt><dd>GFDL-ESM4</dd><dt><span>intake_esm_attrs:experiment_id :</span></dt><dd>historical</dd><dt><span>intake_esm_attrs:table_id :</span></dt><dd>Omon</dd><dt><span>intake_esm_attrs:variable_id :</span></dt><dd>tos</dd><dt><span>intake_esm_attrs:grid_label :</span></dt><dd>gn</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>zarr</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.NOAA-GFDL.GFDL-ESM4.historical.Omon.gn</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (x: 720, y: 576, time: 1980, member_id: 1,\n",
       "                     dcpp_init_year: 1, vertex: 4, bnds: 2)\n",
       "Coordinates: (12/13)\n",
       "  * x               (x) float64 -299.8 -299.2 -298.8 ... 58.75 59.25 59.75\n",
       "  * y               (y) float64 -77.91 -77.72 -77.54 ... 89.47 89.68 89.89\n",
       "  * time            (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * member_id       (member_id) object 'r1i1p1f1'\n",
       "  * dcpp_init_year  (dcpp_init_year) float64 nan\n",
       "    lat             (y, x) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>\n",
       "    ...              ...\n",
       "    lon             (y, x) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>\n",
       "    lon_verticies   (y, x, vertex) float32 dask.array<chunksize=(576, 720, 4), meta=np.ndarray>\n",
       "    time_bounds     (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "    lon_bounds      (bnds, y, x) float32 dask.array<chunksize=(1, 576, 720), meta=np.ndarray>\n",
       "    lat_bounds      (bnds, y, x) float32 dask.array<chunksize=(1, 576, 720), meta=np.ndarray>\n",
       "    areacello       (member_id, dcpp_init_year, y, x) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>\n",
       "Dimensions without coordinates: vertex, bnds\n",
       "Data variables:\n",
       "    tos             (member_id, dcpp_init_year, time, y, x) float32 dask.array<chunksize=(1, 1, 64, 576, 720), meta=np.ndarray>\n",
       "Attributes: (12/48)\n",
       "    Conventions:                      CF-1.7 CMIP-6.0 UGRID-1.0\n",
       "    activity_id:                      CMIP\n",
       "    branch_method:                    standard\n",
       "    branch_time_in_child:             0.0\n",
       "    comment:                          <null ref>\n",
       "    contact:                          gfdl.climate.model.info@noaa.gov\n",
       "    ...                               ...\n",
       "    intake_esm_attrs:experiment_id:   historical\n",
       "    intake_esm_attrs:table_id:        Omon\n",
       "    intake_esm_attrs:variable_id:     tos\n",
       "    intake_esm_attrs:grid_label:      gn\n",
       "    intake_esm_attrs:_data_format_:   zarr\n",
       "    intake_esm_dataset_key:           CMIP.NOAA-GFDL.GFDL-ESM4.historical.Omo..."
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddict_w_area['CMIP.NOAA-GFDL.GFDL-ESM4.historical.Omon.gn']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok the dataset now has a matching `areacello` coordinate that we can use to weight our results. To see the effect of area weighting we will plot both timeseries (only the member average)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10, 4])\n",
    "for i, (name, ds) in enumerate(ddict_w_area.items()):\n",
    "    plt.subplot(1,3,i+1)\n",
    "    # construct yearly global mean timeseries\n",
    "    mean_temp = ds['tos'].mean(['x', 'y']).coarsen(time=12).mean()\n",
    "    weights = ds.areacello.fillna(0) # ocean area can contain nans, which are not allowed for weighting\n",
    "    mean_temp_weighted = ds['tos'].weighted(weights).mean(['x', 'y']).coarsen(time=12).mean()\n",
    "    # lets look at the difference\n",
    "    mean_temp.mean('member_id').plot(label = 'unweighted', color='r')\n",
    "    mean_temp_weighted.mean('member_id').plot(label = 'weighted', color='b')\n",
    "    plt.title(ds.attrs['source_id'])\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quite the difference! This makes sense when we think about the fact that generally cells near the poles become smaller, and if we do a naive mean, these will count too strongly into the global value and reduce the resulting temperature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok lets finish by making a single nice plot of all models. In order to compare the models better we will look at the anomaly of temperature with regard to the period of 1850-1900."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Global average sea surface temperature')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "timeseries_dict = {}\n",
    "for name, ds in ddict_w_area.items():\n",
    "    # construct yearly global mean timeseries\n",
    "    weights = ds.areacello.fillna(0)\n",
    "    mean_temp_weighted = ds['tos'].weighted(weights).mean(['x', 'y']).coarsen(time=12).mean()\n",
    "    anomaly = mean_temp_weighted - mean_temp_weighted.sel(time=slice('1850', '1900')).mean('time')\n",
    "    anomaly.mean('member_id').plot(label = ds.attrs['source_id'])\n",
    "plt.legend()\n",
    "plt.ylabel('Global temperature anomaly \\n with respect to preindustrial period')\n",
    "plt.title('Global average sea surface temperature')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feel free to add new models to this or maybe change the experiment, to see how each model predicts future climates."
   ]
  }
 ],
 "metadata": {
  "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": "3.10.8"
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  "vscode": {
   "interpreter": {
    "hash": "5057447e008afe6adaa3e32ed42dedd411098e643f05a1f12413525ec9f69cd7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: src/lectures/models/intro_models.ipynb
================================================
{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working With Earth Science Model Output\n",
    "\n",
    "In this lecture, we will discuss some fundamental aspects and techniques to work with the output of general circulation models (GCMs). These types of models are used in a variety of contexts in the Earth Science: From idealized experiments to understand meachanisms in the climate systems, to reanalyses and state estimates to synthesize past and current observations, to fully coupled representations of the earth system enabling forecasts of climate conditions under future scenarios.\n",
    "\n",
    "## What is a General Circulation Model?\n",
    "\n",
    "A GCM is a computer model that simulates the circulation of a fluid (e.g., the ocean or atmosphere). It is based on a set of partial differential equations, which describe the motion of a fluid in 3D space, and integrates these forward in time.  Most fundamentally, these models use a discrete representation of the [Navier-Stokes Equations](https://en.wikipedia.org/wiki/Navier%E2%80%93Stokes_equations) but can include more equations to represent e.g., the thermodynamics, chemistry or biology of the coupled earth system.\n",
    "Depending on the goal these equations are forced with predetermined boundary conditions (e.g. an ocean only simulation is forced by observed atmospheric conditions) or the components of the earth system are coupled together and allowed to influence each other. A 'climate model' is often the latter case which is forced by e.g. different scenarios of greenhouse gas emissions. This enables predictions of how the coupled system will evolve in the future.\n",
    "\n",
    "### The globe divided into boxes\n",
    "Since there is no analytical solution to the full Navier-Stokes equation, modern GCMs solve them using numerical methods. They use a discretized version of the equations, which approximates them within a finite volume, or grid-cell. Each GCM splits the ocean or atmosphere into many cells, both in the horizontal and vertical.\n",
    "\n",
    "![gcm-schematic](https://www.ipcc-data.org/img/3d_gcm_structure.jpg)\n",
    "> Source: www.ipcc-data.org\n",
    "\n",
    "It is numerically favorable to shift (or 'stagger') the grid points where the model calculates the velocity with regard to the grid point where tracer values (temperature, salinity, or others) are calculated. There are several different ways to shift these points, commonly referred to as [Arakawa grids](https://en.wikipedia.org/wiki/Arakawa_grids). Most modern ocean models use a C-Grid. Thus, this lecture will focus on this particular grid configuration.\n",
    "In the C-grid, the zonal velocity $u$ is located on the right side (or east face) of the tracer location and the meridional velocity $v$ is located on the upper side (or north face). Similarly, the vertical velocity $w$ is shifted with depth, but horizontally (when looking at it from straight above), it is on the tracer location.\n",
    "\n",
    "![c-grid](https://xgcm.readthedocs.io/en/latest/_images/grid2d_hv.svg)\n",
    "> Source: xgcm.readthedocs.io\n",
    "\n",
    "Most GCMs use a [curvilinear grid](https://en.wikipedia.org/wiki/Curvilinear_coordinates) to avoid infinitely small grid cells at the North Pole. Some examples of curvilinear grids are a tripolar grid (the Arctic region is defined by two poles, placed over landmasses)\n",
    "\n",
    "<img src=\"https://www.geomar.de/fileadmin/_migrated/pics/globe_grid2_10.png\" width=\"500\"/>\n",
    "\n",
    "> Source: [GEOMAR](https://www.geomar.de)\n",
    "\n",
    "or a cubed-sphere grid or a lat-lon cap (different versions of connected patches of curvilinear grids).\n",
    "\n",
    "<img src=\"https://xgcm.readthedocs.io/en/latest/_images/cubed_sphere.jpeg\" width=\"600\"/>\n",
    "\n",
    "> Credit: [Gael Forget](http://www.gaelforget.net). More information about the simulation and grid available at [https://doi.org/10.5194/gmd-8-3071-2015](https://doi.org/10.5194/gmd-8-3071-2015).\n",
    "\n",
    "In most ocean models, due to these 'warped' coordinate systems, the boxes described are not perfectly rectangular [cuboids](https://en.wikipedia.org/wiki/Cuboid). To accurately represent the volume of the cell, we require so-called grid metrics - the distances, cell areas, and volume to calculate operators like e.g., divergence.\n",
    "\n",
    "If you want to compare models with different grids it can sometimes be necessary to regrid your output. However, some calculations should be performed on the native model grid for maximum accuracy. We will discuss these different cases below. \n",
    "\n",
    "### Grid resolution\n",
    "Discretizing the equations has consequences: \n",
    "\n",
    "- In order to get a realistic representation of the global circulation, the size of grid cells needs to be chosen so that all relevant processes are resolved.  In reality, this usually requires too much computing power for global models and so processes that are too small to be explicitly resolved, like [mesoscale eddies](https://www.gfdl.noaa.gov/ocean-mesoscale-eddies/) or [vertical mixing](https://www.gfdl.noaa.gov/ocean-mixing/), need to be carefully parameterized since they influence the large scale circulation. The following video illustrates the representation of mesoscale eddies in global models of different grid resolution. It shows the surface nutrient fields of three coupled climate models (produced by NOAA/GFDL) around the Antarctic Peninsula with increasing ocean resolution from left to right.\n",
    "\n",
    "<div style=\"text-align: center\"><iframe width=\"640\", height=\"360\" src=\"https://player.vimeo.com/video/259423826\" frameborder=\"4\"></iframe></div>\n",
    "\n",
    "> Nominal model resolution from left to right: 1 degree (CM2.1deg), 0.25 degree (CM2.5) and 0.1 degree (CM2.6). The left ocean model employs a parametrization for both the advective and diffusive effects of mesoscale eddies, while the middle and right model do not."
   ]
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    "\n",
    "\n",
    "\n",
    "## Goals of this section\n",
    "The aim of this book section is to teach you the basic techniques to work with output of GCM data of various 'flavors'.\n",
    "\n",
    "> We will learn how to compare different climate model simulations from the CMIP6 project with each other.\n",
    "\n",
    "> We will learn how to `regrid` high-resolution model fields in order to compare them to observations.\n",
    "\n",
    "> We will learn how to use [xgcm](https://xgcm.readthedocs.io/en/latest/) in order to deal with computations on the raw model grid for maximum accuracy."
   ]
  }
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================================================
FILE: src/lectures/models/regridding.ipynb
================================================
{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model validation: Comparing a state estimate to observations\n",
    "\n",
    "We start by importing xarray, numpy and matplotlib as usual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model we will be using in this lecture is the [Southen Ocean State Estimate (SOSE)](http://sose.ucsd.edu), which is available in the pangeo cloud as an [intake catalogue](https://pangeo-data.github.io/pangeo-datastore/master/ocean.html). We will just follow the [instructions](https://pangeo-data.github.io/pangeo-datastore/master/ocean.html#collapse1-sose) how to load the data into our notebook and inspect the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (XC: 2160, XG: 2160, YC: 320, YG: 320, Z: 42, Zl: 42, Zp1: 43, Zu: 42, time: 438)\n",
       "Coordinates:\n",
       "    Depth     (YC, XC) float32 dask.array<shape=(320, 2160), chunksize=(320, 2160)>\n",
       "    PHrefC    (Z) float32 dask.array<shape=(42,), chunksize=(42,)>\n",
       "    PHrefF    (Zp1) float32 dask.array<shape=(43,), chunksize=(43,)>\n",
       "  * XC        (XC) float32 0.083333336 0.25 0.4166667 ... 359.75 359.9167\n",
       "  * XG        (XG) float32 5.551115e-17 0.16666667 ... 359.6667 359.83334\n",
       "  * YC        (YC) float32 -77.87497 -77.7083 -77.54163 ... -24.874966 -24.7083\n",
       "  * YG        (YG) float32 -77.9583 -77.79163 -77.62497 ... -24.9583 -24.791632\n",
       "  * Z         (Z) float32 -5.0 -15.5 -27.0 -39.5 ... -5075.0 -5325.0 -5575.0\n",
       "  * Zl        (Zl) float32 0.0 -10.0 -21.0 -33.0 ... -4950.0 -5200.0 -5450.0\n",
       "  * Zp1       (Zp1) float32 0.0 -10.0 -21.0 -33.0 ... -5200.0 -5450.0 -5700.0\n",
       "  * Zu        (Zu) float32 -10.0 -21.0 -33.0 -46.0 ... -5200.0 -5450.0 -5700.0\n",
       "    drC       (Zp1) float32 dask.array<shape=(43,), chunksize=(43,)>\n",
       "    drF       (Z) float32 dask.array<shape=(42,), chunksize=(42,)>\n",
       "    dxC       (YC, XG) float32 dask.array<shape=(320, 2160), chunksize=(320, 2160)>\n",
       "    dxG       (YG, XC) float32 dask.array<shape=(320, 2160), chunksize=(320, 2160)>\n",
       "    dyC       (YG, XC) float32 dask.array<shape=(320, 2160), chunksize=(320, 2160)>\n",
       "    dyG       (YC, XG) float32 dask.array<shape=(320, 2160), chunksize=(320, 2160)>\n",
       "    hFacC     (Z, YC, XC) float32 dask.array<shape=(42, 320, 2160), chunksize=(42, 320, 2160)>\n",
       "    hFacS     (Z, YG, XC) float32 dask.array<shape=(42, 320, 2160), chunksize=(42, 320, 2160)>\n",
       "    hFacW     (Z, YC, XG) float32 dask.array<shape=(42, 320, 2160), chunksize=(42, 320, 2160)>\n",
       "    iter      (time) int64 dask.array<shape=(438,), chunksize=(438,)>\n",
       "    rA        (YC, XC) float32 dask.array<shape=(320, 2160), chunksize=(320, 2160)>\n",
       "    rAs       (YG, XC) float32 dask.array<shape=(320, 2160), chunksize=(320, 2160)>\n",
       "    rAw       (YC, XG) float32 dask.array<shape=(320, 2160), chunksize=(320, 2160)>\n",
       "    rAz       (YG, XG) float32 dask.array<shape=(320, 2160), chunksize=(320, 2160)>\n",
       "  * time      (time) datetime64[ns] 2005-01-06 2005-01-11 ... 2010-12-31\n",
       "Data variables:\n",
       "    ADVr_SLT  (time, Zl, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    ADVr_TH   (time, Zl, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    ADVx_SLT  (time, Z, YC, XG) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    ADVx_TH   (time, Z, YC, XG) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    ADVy_SLT  (time, Z, YG, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    ADVy_TH   (time, Z, YG, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    DFrE_SLT  (time, Zl, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    DFrE_TH   (time, Zl, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    DFrI_SLT  (time, Zl, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    DFrI_TH   (time, Zl, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    DFxE_SLT  (time, Z, YC, XG) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    DFxE_TH   (time, Z, YC, XG) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    DFyE_SLT  (time, Z, YG, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    DFyE_TH   (time, Z, YG, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    DRHODR    (time, Zl, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    ETAN      (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    EXFswnet  (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    KPPg_SLT  (time, Zl, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    KPPg_TH   (time, Zl, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    PHIHYD    (time, Z, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    SALT      (time, Z, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    SFLUX     (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIarea    (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIatmFW   (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIatmQnt  (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIdHbATC  (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIdHbATO  (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIdHbOCN  (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIdSbATC  (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIdSbOCN  (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIempmr   (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIfu      (time, YC, XG) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIfv      (time, YG, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIheff    (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIhsnow   (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIsnPrcp  (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SItflux   (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIuheff   (time, YC, XG) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIuice    (time, YC, XG) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIvheff   (time, YG, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    SIvice    (time, YG, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    TFLUX     (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    THETA     (time, Z, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    TOTSTEND  (time, Z, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    TOTTTEND  (time, Z, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    UVEL      (time, Z, YC, XG) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    VVEL      (time, Z, YG, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    WSLTMASS  (time, Zl, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    WTHMASS   (time, Zl, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    WVEL      (time, Zl, YC, XC) float32 dask.array<shape=(438, 42, 320, 2160), chunksize=(1, 42, 320, 2160)>\n",
       "    oceFreez  (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    oceQsw    (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    oceTAUX   (time, YC, XG) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    oceTAUY   (time, YG, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    surForcS  (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>\n",
       "    surForcT  (time, YC, XC) float32 dask.array<shape=(438, 320, 2160), chunksize=(1, 320, 2160)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import intake\n",
    "cat = intake.Catalog(\"https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean.yaml\")\n",
    "ds_model = cat[\"SOSE\"].to_dask()\n",
    "ds_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset has a lot of output variables. For this lecture we are going to focus on sea surface temperature. In the model this is represented by the surfac layer in the vertical (dimension `Z`) of the temperature dataarray `THETA`. \n",
    "\n",
    "Lets create a new `sst_model` dataarray and plot the first time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f466c068710>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sst_model = ds_model.THETA.isel(Z=0)\n",
    "sst_model.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the name suggests, this model output is not global, but instead focusses on the Southern Ocean.\n",
    "A common analysis step is model validation - comparing model output to an observation of the same variable. We will use the previously used sea surface temperature dataset. \n",
    "\n",
    "We load the dataset as before, but limit the latitude dimension to exclude values in the tropics and the northern hemisphere.\n",
    "> Note that the `lon` dimension in the dataset is given in descending order, e.g. the first value is the northernmost. You can change the order by applying `sortby('lat')`, which will reshuffle the full dataset so that `lat` values are monotonically increasing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 32, lon: 180, nbnds: 2, time: 1990)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -88.0 -86.0 -84.0 -82.0 ... -32.0 -30.0 -28.0 -26.0\n",
       "  * lon        (lon) float64 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0\n",
       "  * time       (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2019-10-01\n",
       "Dimensions without coordinates: nbnds\n",
       "Data variables:\n",
       "    time_bnds  (time, nbnds) float64 dask.array<shape=(1990, 2), chunksize=(12, 2)>\n",
       "    sst        (time, lat, lon) float32 dask.array<shape=(1990, 32, 180), chunksize=(12, 32, 180)>\n",
       "Attributes:\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science > Oceans > Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    geospatial_lon_min:              -1.0\n",
       "    geospatial_lon_max:              359.0\n",
       "    geospatial_laty_max:             89.0\n",
       "    geospatial_laty_min:             -89.0\n",
       "    geospatial_lat_max:              89.0\n",
       "    geospatial_lat_min:              -89.0\n",
       "    geospatial_lat_units:            degrees_north\n",
       "    geospatial_lon_units:            degrees_east\n",
       "    cdm_data_type:                   Grid\n",
       "    project:                         NOAA Extended Reconstructed Sea Surface ...\n",
       "    original_publisher_url:          http://www.ncdc.noaa.gov\n",
       "    References:                      https://www.ncdc.noaa.gov/data-access/ma...\n",
       "    source:                          In situ data: ICOADS R3.0 before 2015, N...\n",
       "    title:                           NOAA ERSSTv5 (in situ only)\n",
       "    history:                         created 07/2017 by PSD data using NCEI's...\n",
       "    institution:                     This version written at NOAA/ESRL PSD: o...\n",
       "    citation:                        Huang et al, 2017: Extended Reconstructe...\n",
       "    platform:                        Ship and Buoy SSTs from ICOADS R3.0 and ...\n",
       "    standard_name_vocabulary:        CF Standard Name Table (v40, 25 January ...\n",
       "    processing_level:                NOAA Level 4\n",
       "    Conventions:                     CF-1.6, ACDD-1.3\n",
       "    metadata_link:                   :metadata_link = https://doi.org/10.7289...\n",
       "    creator_name:                    Boyin Huang (original)\n",
       "    date_created:                    2017-06-30T12:18:00Z (original)\n",
       "    product_version:                 Version 5\n",
       "    creator_url_original:            https://www.ncei.noaa.gov\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2019-11-04\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# xr.set_options(display_style='html') # !!! need to update xarray for this?\n",
    "url = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/noaa.ersst.v5/sst.mnmean.nc'\n",
    "ds_obs = xr.open_dataset(url).chunk({'time': 12}).sortby('lat')\n",
    "# restrict the obs to the southern ocean\n",
    "ds_obs = ds_obs.sel(lat=slice(-90, -25))\n",
    "ds_obs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets now define a new dataarray so that we have a consistent naming between the model output and observations. As before we also plot the first time step of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f465431c8d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sst_obs = ds_obs.sst\n",
    "sst_obs.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Considerations for model validation\n",
    "Now that we have both datasets loaded, how do we approach the model validation?\n",
    "\n",
    "\n",
    "It is always crucial to put some thought into which time periods of a model and observations are actually comparable. In this case both should be directly comparable since SOSE is a state estimate, which incorporates observations to estimate the state of the ocean at the time observations were taken (and in between). If however the model data would be from a fully coupled climate model, we would have to take e.g. long averages due to the fact that internal variability - like for instance ENSO - can not be expected to happen in the same year as observations. But since we chose a state estimate, we can actually compare the data in a single year or even month (this will save us some computing time, but if you are interested, go ahead and choose a longer averaging period below and compare the results).\n",
    "\n",
    "Now select a time period that overlaps between model and observations, e.g. the year 2009 and average over the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/dask/array/numpy_compat.py:38: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    }
   ],
   "source": [
    "sst_model = sst_model.sel(time='2009').mean('time').load()\n",
    "sst_obs = sst_obs.sel(time='2009').mean('time').load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For later we will also rename the model dimensions `XC` and `YC` into `lon` and `lat`, so that both model and observations are named consistently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sst_model = sst_model.rename({'XC':'lon', 'YC':'lat'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot both fields next to each other. By specifying `vmax=30` in the plot command, we ensure that both plots have the exact same colorbar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f4654183c50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[16,4])\n",
    "plt.subplot(1,2,1) # !!! is this taught before?\n",
    "sst_obs.plot(vmax=30) # make sure to have the same colorlimit\n",
    "plt.ylim(-80, -24)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sst_model.plot(vmax=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks nice and the large scale structure (colder waters near Antarctica) agree. But since temperature differs over such a wide range, it is hard to quantify the differences with the naked eye. Naturally we would want to plot the difference between both datasets. \n",
    "\n",
    "### Regridding via interpolation\n",
    "\n",
    "There is however a major problem: Both datasets are on a different grid - the model has a much higher resolution (~ 1/5 deg) than the observations (~ 2 degree). In the model we can see signs of mesoscale eddies, even in the yearly average.\n",
    "\n",
    "To compare the dataset directly they have to be on the same grid, e.g. each point of the array has to correspond to the same position.\n",
    "\n",
    "*We have 3 options to achieve this*:\n",
    "\n",
    "1. Regrid the lower resolution dataset (observations) onto the higher resolution dataset (model).\n",
    "2. Regrid the higher resolution dataset (model) onto the lower resolution dataset (observations).\n",
    "3. Define a completely new grid and regrid both datasets onto that.\n",
    "\n",
    "Both 1. and 2. have unique advantages and disadvantages, whereas 3. can share some or all of these, depending on whether the new grid is defined on a higher, lower or intermediate resolution compared to the two datasets.\n",
    "\n",
    "Lets try both approaches and see how they compare. For this step we will use [xesmf](https://xesmf.readthedocs.io/en/latest/) a very powerful geospatial regridding tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xesmf as xe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To regrid a dataarray with xesmf you need to execute the following steps:\n",
    "\n",
    "1. Create a target grid dataset (this can also be an existing dataset)\n",
    "    \n",
    "    > Note: Rename the dataset appropriately. xesmf expects longitude and latitude to be labelled as `lon` and `lat` (this is the reason we renamed the model dataset previously).\n",
    "2. Create a `regridder` object (using the target grid and the source dataset - e.g. the dataset that should be regridded)\n",
    "    \n",
    "    > Note: You might have to clean existing files that the regridder might have written out using `regridder.clean_weight_file()` (this is only needed if the regridder has been previously applied, but it does not hurt to apply it just in case)\n",
    "3. Apply the `regridder` to a dataset, e.g. `regridder(ds)`\n",
    "\n",
    "We will start by regridding the low resolution observations onto the high resolution model grid, by using linear interpolation in two dimensions (`bilinear`) and specifying that our domain is periodic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create weight file: bilinear_32x180_320x2160_peri.nc\n",
      "Remove file bilinear_32x180_320x2160_peri.nc\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "xESMF Regridder \n",
       "Regridding algorithm:       bilinear \n",
       "Weight filename:            bilinear_32x180_320x2160_peri.nc \n",
       "Reuse pre-computed weights? False \n",
       "Input grid shape:           (32, 180) \n",
       "Output grid shape:          (320, 2160) \n",
       "Output grid dimension name: ('lat', 'lon') \n",
       "Periodic in longitude?      True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regridder = xe.Regridder(sst_obs, sst_model, 'bilinear', periodic=True) #since this is global we need to pass periodic\n",
    "regridder.clean_weight_file()\n",
    "regridder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now apply the regridder to the observed sea surface temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'sst' (lat: 320, lon: 2160)>\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]])\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 0.083333336 0.25 0.4166667 ... 359.75 359.9167\n",
       "  * lat      (lat) float32 -77.87497 -77.7083 -77.54163 ... -24.874966 -24.7083\n",
       "Attributes:\n",
       "    regrid_method:  bilinear"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst_obs_regridded = regridder(sst_obs)\n",
    "sst_obs_regridded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You see that the size of the dimensions has increased. It is now the same as the model data. Lets see how the regridded data compares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f4648031320>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[16,4])\n",
    "plt.subplot(1,2,1)\n",
    "sst_obs_regridded.plot(vmax=30) # make sure to have the same colorlimit\n",
    "# plt.ylim(-80, -24)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sst_model.plot(vmax=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The observations now have more datapoints and look a lot smoother, but since the values are just linearly interpolated, there is no new information on smaller scales. \n",
    "\n",
    "Now lets compare both estimates by computing the difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f4643f08320>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = sst_model - sst_obs_regridded \n",
    "diff.plot(vmax=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the model sea surface temperature is quite a bit different (up to 2 deg C in some places). But the comparison is hindered by a lot of smaller scale structure that stems from eddies in the model data, which are not resolved in the observations. \n",
    "\n",
    "This is the major disadvantage of Method 1. from above: The comparison becomes 'unfair' because we are usually only interested on larger scale differences. Even if the observations would have a higher resolution, from our physical understanding we would not expect that turbulent features like ocean eddies would happen at the exact same space and time in the model as the observations.\n",
    "\n",
    "So how do we solve this issue? Lets look at method 2. to compare the model and observations on a lower resolution.\n",
    "We first try the exact same procedure as befor (linear interpolation), but switch the source and target datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create weight file: bilinear_320x2160_32x180_peri.nc\n",
      "Remove file bilinear_320x2160_32x180_peri.nc\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'THETA' (lat: 32, lon: 180)>\n",
       "array([[ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,  0.      ],\n",
       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,  0.      ],\n",
       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,  0.      ],\n",
       "       ...,\n",
       "       [20.185935, 19.964374, 19.690668, ..., 20.646542, 20.335984, 20.317114],\n",
       "       [20.801154, 20.477957, 20.144566, ..., 21.67011 , 21.22205 , 21.067159],\n",
       "       [20.903206, 20.605169, 20.364102, ..., 21.828444, 21.537927, 21.231561]])\n",
       "Coordinates:\n",
       "    PHrefC   float32 49.05\n",
       "    Z        float32 -5.0\n",
       "    drF      float32 10.0\n",
       "  * lon      (lon) float64 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * lat      (lat) float64 -88.0 -86.0 -84.0 -82.0 ... -32.0 -30.0 -28.0 -26.0\n",
       "Attributes:\n",
       "    regrid_method:  bilinear"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regridder = xe.Regridder(sst_model,sst_obs, 'bilinear', periodic=True) #since this is global we need to pass periodic\n",
    "regridder.clean_weight_file()\n",
    "sst_model_regridded = regridder(sst_model)\n",
    "sst_model_regridded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets compare the original observations against the regridded model data as before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f4643ebf4a8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y87tBimzhpbmXJwHbRhbp4BzRUz2RYy4akbqn55dfPwzROByvLDp7U3T2HrwbquMJT8Af5jXM9wKgih3gp8ALxpir91HsNuBCq+45Hug0xmw/kPjHZMQvIv+O3rAIgJ3AB40x2+x7XwQ+DBSAy4wxdw23PyN+0bTMRBMEMeue6YehTlUAfvcOKmatBqDv9rdTcu63RxVzYYD9ouStUqiQK7p/5hrmwWOqggg3z6KwTZUSZvoSRGCrvf6TkRJiIb0i1vv1dIXUJLC5JEJZabUW8nxkoMOoPXZrtJYJWVX7BJWT8HvVNTHXfAySt66KxrCxUxVEW7rSHFWjCqKwJ2yzhmURX2ix6p0JJRG6s6qO2N7jkbYGb+++4MsAZJ85fCZtQbIWgESslcq4VbuEPbJWpZPJGyqs0VqZZOnKRYrLheKNNkMiomV29OYotQqnXak8mzu1jZrKo0yt0G1FoMfO18bLy+ixypKUV0HEjtjKIh6FhJ6nrB8lmlNlVRBLIjk9sSYcJ55TVVZjIsH6jiwRe553p3LMrFbVzW9X7OC4SRUANJdHsc1NedQr/nY34RK83j3ZJnP1RwGwpltIWjVSXShLr2id86EEfv/1aYLi9ZUPpGjYFmBIBWrqFonXUeZZ87r2TQQ1aijo1c8GawKYzOVo2LTHvO1AaF22lorzZuqxa2fTldU6JMIeNlQymYCJCT3nm3ty1JToGwUvim8/b6VAaLdOdUumd4+KDopmh/ma6cXPWs3sGUTT7Vr/eGVR+XMw8FDzvaEYoYj/NcAFwHM21ShoPun+nM0/QnNcn4PmXe4DPrQfIb+EsZrq+bYx5isAInIZ8FXgoyIyF03OPQ+YCNwtIrOM6ferdTgcjrEn7AmRve1P98ILZNiZeGPMQwzzHWHUQ/wTowxxSMZkqmevmxel7Gme84BbjDEZY8x69BvuuMMdn8PhcAyFjviHmeoZ6yCHYMxu7orIlcCFQCdwql3dCDw6oNg+ZUtWFnUJQPOkiYcuUIfD4dgLT2T4qZ5x3PMfshG/iNwtIssHeZ0HYIz5sjGmCfglcGn/ZoPsatAfS8aY64wxS4wxSyZUVR2aSjgcDscg+CKEh3mNZ+XMIRvxG2NOH2HRm4A7gMs5BLIlh8PhONj0T/UMV2a8MlaqnpnGmBftn28GVtrl24CbRORq9ObuTODxMQgRgNRt147VoccN/XLXwdQ9A6Wwuy6diB9TlUXVdzbscxvHkUHkmIvY9dAPxjqMQ4bKOYeb6hm/cz1jNcd/lYjMRuWcG4GPAhhjVojIrcDzqHnRJ0as6LEJzL2+9uKqTLKenkC/dztDDUw/+Z0A9N31S56845oRB3vf4o8wzRp8TSmPkArUKKs0vYn2hP5AiYkQXXyWbtC2gdwCXc4WArr69kgJa0ojhK0aIOIL5VaeuLU7y3Z7odSXelT1J9suqcRLaZ0qSqooRFRW2FcQesK63NMTUFOiksQkAfUJbYva0gQbOvS4YV+YXqkdc0mhj1CVmsj95vkdNJfrcizk0dqnMr87b/0PznnX50fcRgeDp3btkZg2lan08N4NHXRmtC3eMnsCYTvMWtvlMXPz3QAEs19bnE/NFAxhX2cHJ0ZybEqrRHDd7hTHNtg2ivpkrI4yTo5Ev4FeRyf1VlJr/DChTvtjsztbzAEbLuSKeV8ll8Z0qDmglNeSr9H8saUUmF8ekAtpu4Y9KRrknTSlir6cXkvbe7I023omoz6eleSmiBC3eZl7TZjyNh0jVVfMKCZ1l3yaeIlKGL18hpQJ27g9Euh+ElEhhTVjE6Ezo8dNhqArr+ursymw0uPQhInkWzZom3a1sfI3y4rnZup3BjtjQ7P5kncAMOUnv2FnX38a2kLROG5dR5ZOaxDYWBYp5lCOhzzW7M7Y9WEKgca6O50nbSYBMLE6UswnXTCQSWnDNPg+IU/XC7DbLwegoyvH5PKK0VdiH/Tr+Idi/Hb7Y9TxG2PePsR7VwKH3xrS4XA4RkhYRiDnHMc9v7NscDgcjlEyIlXPOB7zu47f4XA4RslIpnrciN/hcDheQYSE4ad6DlMs+4Pr+B0Oh2OUjOjmrhvxj28Gc+hM/+rDYxDJ+KX35isAaFu2lmRz3Yi2Gdiuw0k7f/zYRo6uGzq5hWP8c6TksvYYyRz/+OUV0fFnJcxuTyV2laVx/LWawCbq+fREVJ6XygVsiqq1Q+M7P0MirT/E5pzxD7mNi9w5VeVotdEQT2xVZ79pZVX0qOqQnaGJ5KxMra40RErUgXFXyRwarFwwUzAYA4vq9b2YybI5pZdEby5gW7fK1l5s62VujXZ87akcnqhTZazgURbT9Y2Boc3K1gomoM0ee0tXmo39eXYjIRrLVLYZD3nUlqoUriruE7I/TbtNCTH7O/T106qZaZ0qvVQ7VOglIS8+MkSLKz33fYsgovVauTvDUeW2XjPupfSjtw27/d5YdR5hT4rzo9OrSui2MsRSr0DOXrJ1pSG2TXs9AO3deWY9pPLc2qNPoLNJ7Z1a0uGiRLC5PE6XzVFbFoalO9TCcfaEOP0fg2wkRk1YlyO7XqSwRd1c/WQFEtfrK9+ygaBTnTOD7g78GnUUCUXjYB1VQ20b2JqcTqX9dE2njSBpc+VKDN9WLp03WGUjqVxQlJX2BiFCUT0na1vTlMWnAlDjCam8nv812QQ1IT2eL2HKUuqk2VtSS6hDHSw7KqbTZduuMuZTj1pk5aUKX3TbfOUkQgU9btCxCy+m53PH40sJl+5xvxwtz931XaYZbadHW/poT+sxnt3aycnTqv+hfC4wvLhbXTibyqIkop6tm9CT1zrUlITI2PobY9jRq/usjoeZZqXKXi7NzpxuWwgMkwKNoba7hYLU73d99saN+B0Oh+MIIzQCOedIsnSNFa7jdzgcjlEysid3D1Mw+4Hr+B0Oh2OUeCJ4L+M5/vGsOHI4HI5xiRcS/Ig35EtGIOQXketFZKeILN/H+6eISKeIPGtfwyZuHwluxO9wOByjRDwP8YcZN4/sCa4bgO8DNw5R5kFjzLkjjW0kuBG/47BwpMj8jmSeffuZYx3C4cMTxB/6NZK5HmPMA8DuQx7vXrwiRvwRKRTdD1f1Qk/5sQDkugzd2R4AlkxM0J1RCdtfN6c5qVkTsm+893vUrH+wuK/CzBMAWNENlZ7qNvtyBVa2aCLt+xNRVuzU5ckVcebVqtRy+c6+oovk3JrSootkNjCsbO0jZxOGv7YpScTXOHqyBeoSKkObUx3HVoFNnRnq7fpcYCiPqoPnzr58sZ6FgKJUsyERJhHR7/Dd6T3uh+mCKToy7uzNEbPruzN5JpdrUu3pFVHksd8C4E+oZ3ez1v93wUI+9w1NkP2V103imVaVzi14SwktVkZXEqKYFDxXKOVHS/X6nVf7GlL/m6TQtBCAxCmf28eZeyn9kr85ExJMSLcAsDFfycxqjVVyvcTs8ULPPUjlrMXaFmWzqbhKHSy7H7qU8vUqRU3WTCNX0gBAXYnBt26bHfkGjmvU87a9J0dNiX4MOtI5Omwy86rKZnyx46JchqCrVdto0mzMgskaQ5AntGMVAJmVTxGq1vVBNEG99BDavknjS9aytlf3lcplWRDtAKCFaqb3rCqW6U8Y/sNHNxGx8twPLG4kZaXBf9/SzYaOPgAuWtRAYK06I31tdMRU/tudLhCvmqLtXkgTiqtDaDzVxmNdujwvHJAIdD+seAAzaZbGPW063gbN9133xnOoe+M5RE7Yp5/ioPztt98GwBiYc/nTAHzn0hN5brtKSefUJdnapbLNsliYnNXw1iUibOhQiW2uYJhWqee8O1vYk4S9O0uzlSpv7MxSEdPzVhHzaenRa+exrb2UR3X95Io4W0IqHfUqJ1AePXjjXC/k4Yf9oct4HsDbROR/Bqy+zhhz3SgPd4KILEVzk3zWGLNilNv/A6+Ijt/hcDgOJ54H3sh8mX9njDmQefmngcnGmB4ROQf4PZqn5IBwUz0Oh8MxWkYw1XMw5JzGmC5jTI9dvhMIi8iEA92vG/E7HA7HKBFPhr+5exB6fhGpB3YYY4yIHIcO1tsOdL+u43c4HI5R4vkefnjojn+Ecs6bgVOACSKyBc09HgYwxvwIeAfwMRHJAyngfGP687DtP67jdzgcjlHieYI3zIh/JDl3jTHvGeb976Nyz4OK6/gdh429JZ2P3341R3c+a/+aevgDchw0IsdcxOPTxjqKw0i/ZHMInGXDIUdoyGrS66qKidz+oko4N3ek+MOj6lR49pJGPnacJmo+YVKyKEmcxm6oVRme8SP4qx4CYOZRp3BUoPK/bfEmplVqUvUAwxQrNUuEffp/dU2tC+P1qsyzLZKk08oCe/MBDcloMcH2rr4C0ZBeEYmIT8xePLvThaKs0Pcoyj/VvVMTclfGfHKB7ifkw1br7Bn2PJJW8llTEiLhFWysXtGdMuL7vLCrF4A3NoXxNz8KwMrqV7G0/GQAjqsr56/P77Sxhbj0xGZt3VQ71XFNWn3/xk6m2UTtrVmfzrzKYhdUC8emt2udGxt41y0JalduBaD7gatJvu7TQ5w/eG5LJzdv0qTy11+wGK9V235JMkuPb89PEKJQpvLM8KTpYJOTN298gJYHdFDUG0DV/NMA6OtLkbVSyHTeMKFEHTL7cgExew7aUwWas3qsqtJJxVFarwlTUq09md/VQmATqYc2PYO3c6O2S+Ns8lUam3/iZCSj53+V10i152Oq5wFQHXSSsDqKutIQvdZ5tckzFIzKDftKali2QyWW7140kVkJPf+S6UB/5UPVxBp29mqdN3VlmZlap+0SjlJhHTa35iuZ5KtcMhMtJxZYK1k/xJwJet6SvduRgq4vLH4jtOp+vHQ3UqkOll57y5DnazAqZtQzt0qvvcSp/0J581EAPLd9PjffqbLV9587h7l16nT6xKZ2zp6jbVEIYIFdbwysatO2mF9Twu6U1j/meyy1bbSgtqSYbD0uBUqttPmtM8roCfSzsKsvT51db37/HcJTjhp1nfaFF/LwI0PLOYf7YhhLXiEdv8PhcBw+xBt+xD+eh/yu43c4HI5RIqLz/MOVGa+4jt/hcDhGiT+SqZ5xnG3ddfwOh8MxWtxUj8MxvtjdrTcAw+P45torgdxjv+eRy75l/5ozprEcbsQf3p1zHPf7Y9vxi8hngW8DNcaYVrvui8CHgQJwmTHmrmF3ZAyBVWx0ZwPeMqsKgKd39HHXc6pO+K/v384DZ7wagLcsbuQdc1VNkAnV0W1zsVbEfPwXnwBgXVeB0pgqeRpW3M6HN84A4Mfx+1j1v38B4Pkrfs55EVVEtNz6C5b+z98BeO3zjxJd9gcAvJIkUtvM9qTKFWuDDtZkVQkzPSnkRH8u1uVbMUbVQtHqMkqs6mRKeQQ/px1ZIRyhIqbl+3JBMfXbxGgBjCofUhLCeHpay6NCVUFzBUsug9SqgmRLTqiddiIAs7q2UT5Zk6en8qaolNjVm2H5Tj3u6/0WJlfZ/KtVJdSUaAw92YD+R0ke3p4GT82+GntyXHXu3KJxnJduo+3hHwAQ69rGjogeb9ppl/HJr38GgBWb2/jFB9Rcr2AgO+VVAPipDnq+rXmRI3OnEZpoZZ9T5lFoWa/LkxewvUfrf8w5nywqfHIFUzRIrJFejBctxr2hQ5UvUytjSK8qXOJhj2hG26snVIb19CPZ24aX0vXZaccTWfOwxhkvR7JqLOb1tRfjmR3biJk0l793qena4oZKym07lXRvKypqJJcpLneHa5hRFbPnzQd7zp/PJNjUqbGub9/Bm2bXAHDrcy28aY6qjmau+zM9C9S1tzRVYBc2P3CqwMScmsutlRruXqfKt4vnVdJPaPcGAqvYkmwvuefV4C4yYwGjoeNLc6j4xkr4yxeK6zo3vQDAFZ97objuzxMvYFmN5vVtrCwpGs11Z/M0len5uWttG4mIXsOtqULxOurJBpTa6ZUntvUww6rL4qECYWvet7pQxVHdzwCQrJsNKVX4eXOPI5i8aFR1GgoZgVfPSHT8Y8WYdfwi0gScAWwasG4ucD4wD5gI3C0is4wxhbGJ0uFwOP4RfXL35TvHP5YmbdcAnwMGPn58HnCLMSZjjFkPrAGOG4vgHOyHw3QAACAASURBVA6HY1/ISPz4x7EF5piEJiJvBrYaY5bu9VYjsHnA31vsusH2cYmIPCkiT7a2HbBnkcPhcIyYfpO2oV7jeZL/kE31iMjdQP0gb30Z+BLwhsE2G2TdoIZENpnBdQDHLlp4wKZFDofDMVK8kI8fHrr7tIlYxiWHrOM3xpw+2HoRORo1Zllqb35MAp62lqNbgKYBxSehWWccDodj3DCinLvjd8B/+G/uGmOeA2r7/xaRDcASY0yriNwG3CQiV6M3d2cCjx/uGB0Oh9JvrJd95vriuuyDtwAgkdiYxDQuOEx+/IeKcaXjN8asEJFbgeeBPPCJkSh6pJCjtaBSsN3pPPdvVAlXa1+WTErNq1Jt28hbw65d3RkaT74UgKfuvIbEf+qy/+Vvk7jsL3avf2HeOe8E4KvvOZl3VutJXP7pO2lYopLCo6u6yMd0uXL+LKJlTwHwo9qFrLUSQYBvXX8hvW/8LACLv/Us/3mx3q/+0u3r+P7b5gNQZwzBY7cBUBorJb9dpYGxha+lpUGljVX3/BC/Rm95PFJ1Mm+o0brlQxMId6ohWqx8Il1Wnlr22C0EJ71X2yiSpclXSWa2EBQlrFukhlrrK54LCjSXq0Qu7Hvstm23fMJMauiXiMKGTq1bLOTRnVUZ5ZSKGA02N6oYQ1/eFHX0feEaeqw2cptXw9GnXVZsm9NmqTzxYyc0Fw3lEn++Fqm05l3pXho++DGt59ql+NU6e9hdOZ3YCpUell3ye2rnvgaAji/MxH/sV3o+axohr3XYPeMU2vv0UppeEWZyucZaMLDTU1lkiYF7d2pbzKsJijLCOzNNNCT0+jo61cGupuMBqPRyGNumxg8T1KsJ2PO7M8QCj/m12maRIIvXpwZ0hbJ61QKiOZHjRtuywveJWCmpt3oZhWl6jfieELUdzKSyGOm8zmpevKSRhFGZZ8+Cc9lljcx6MgFNST2u5DM826vS5vm1Ed47X2W0Uugpxh1Ek0hWzfto28rOB3WcVbpuHQ0/HfreWdDbxQnXaj7l0o/eNmTZL16l5brTeT50nP6ov2ddG81Wwlke8/n7ZjW5a0zGiqaDlTGfkFXH7E7lSVg55/3r2tjQoXLZGx5cz10Xqtx6zs4n6JiseaPLNz6CxFXaWsimkRcfHTLG0SAiyDBTOU7OOQTGmCl7/X0lcOXYRONwOBzDI6EQXiQ8dJlxPMc/fiNzOByOcYrnCZ7nDfkayVSPiFwvIjtFZPk+3hcRuVZE1ojIMhFZfFDiPxg7cTgcjiMKb2gpp/jeSKf4bwDOGuL9s9F7nTOBS4AfHmjoMA6mehwOh+Plhud7eMPIORnBVI8x5gERmTJEkfOAG22e3UdFpEJEGowx20ce7SChHcjGDofDcSQiIxrxC8Db+h80ta9LRnmoET/UOhrciN/hcDhGiz8SOScAvzPGfPUAjjTih1pHwyui4w96O6nfqlKt6m0bmDtLXR43NU6nPKp33nef+HEuWKBSwMWf+iOTT3wTAN/862pWl74fgF2fuYdZp70NgKmzqvnc6eo2+fDG3SxsUEfN2PX/x5u+r+6Mf6+czOMnnQrA6276Du2/fDcAf7hzFf99gd6DmVfYxPrYFL57v7p4vnjvH7hg7WpdPnc3sWd1+e+Tz6FpyXsA2HjG6Zx0x40ArP7yvzD7X/8VgNzrP4DXrQ6LZ7Gb3KP3AOAnKth29HkANK5+kDXlWv/k0e9g5oM3aSN5HrFpR2t71c+jMqLXTkUswpYuK3nNB0wqU212QyLMjl6VCGbyAdu6tUxnJke9lTZ2pPO0W8nntIoons37KqlOkkEeE1G5XT5aRk1B5YylJVX0/vkrAKwMqnhoo67PFAI8Oyl61qxjCCarO2QQKyffv9+KiQSr9Tyb//06KaDyY1dx+ob53Jj9rZYpncZXUiqFbO4rYeU2lQjOy+3izBnqTvr0jj6e3t4FwJnTJzB18wO6bW0zJzapLDDeuQVpUYlsXWIhpVbaudtL8shm3TYZDbGwTp0mf/pcG+9dqHLRoyp8JNVJ4KsLptfVWnSP3dBdwJpwEg97+O36iz3cu5vcRJX2iu8j1p1zZkUlpVZuO7E0xBPbdf208jA5o9Lbzr488ZCWyRZUSguQyKdp7dM2TeWiJDK7AQjt3sg9otf2wrpaytf+CQC/firlM3QwWfN9fW5yoH6/H5mp8mIB7mpPAFC/sJvOTS+QGiRXb/WMxXSn9Vr65ElTqItoOzUtrMe3Us1Nnbli+ddX9mFCKjfdlItQZtt+VlWMVitb3dmVYVatHrtjVy+XP9IBwOe3/o2eX/4CgDXPbGTqmQsBqHjfP2NWHTw5p+cP/+TuQVL1HJKHWt1Uj8PhcIySkXj1HCQd/23AhVbdczzQeaDz+/AKGfE7HA7H4eRgWTaIyM3AKcAEEdkCXA6EAYwxPwLuBM5BnYr7gA/tf9R7cB2/w+FwjBbv4Dy5a4x5zzDvG+ATo4ptBLiO3+FwOEaJFwrhD/fk7nC/CMYQ1/E7HA7HKJGXuUnb+P1KcjhGQL97pOPwkN+yYqxDGBf0m7QN9RrPvsyviBG/lNdwG3MBaD7qWKri6uA3qdDKu2ZoIulb10CLlSfe8e9nF7+MN3akeP1bZwLwof97kfOXqHJqQkmYo2tVjlgV3+PmmC0YnrpYZaE3NC7iiXZ1SJx07X8x3zpV3nPV9/nrwpMAKH3bXJ67+BruvlflnMmJ05m8SGV77y8kWZipAODGf/8TeZsY+qG/3s3jPeraeOMNy+CGdwHwgxU3sPxr/wHAUf/8ITj9wwAEf7mOxi2a6F3CEWZUql7wJ09u5bXTVeb5gW/+jS9erE6Y3/3eX7n5s68D4Ojci0wp0RhuaQnxHv95AHqrT6Iuq9JREy+nx9O2SKa7yCXU8dAvGHKeSgp7ckHR5dHv3kmhqplVfdpmR/WuJ33frwGIN06n8GqVzCZ680StDLEyFqY9rZK+YPICWKESS7rbCc1Vt8Xs8ofp27AB4CXOke9YMon8zK8D8MiuPk61LqzLd3Rz1lx1pNzQ0cc961TO+Mi63Uyq1LhDnjp3grpl1ue0DtK2iUKnHmNhfYqNeVtnEd7srQKgrfZV9GRVmvjZ2QGrreOph0d9the/RctRXkvwpEqAZ9Y2kp2hTqKRlpX01c7W5fKJGE8/jqsmvIrNu/S6Cvs9zLJJxTuyAcfU63nY0VegP9d3adijwmjcNZVlRLep7Utm4nwakrqffGDYGOhnYVq8jMUVKkMtJUv3/LP1fEiO2PSjbKvuWzHYn3i+0LKeZO3JACx+7Uw+dtKZfOFGdahdceevmXjsmQDEk3H++vBGAJ5Zv5uLT1Y31HdOzHH7Lr1WN3WmWLFVZbLhxY28rlTlmVNMN7mH7wSg+9RLitLOz506jT+9qOfn3953DM/vUNnuB703c+5bGwB475Jfk9mpyeZNpIT8krfss06jRUI+XmQYOecwydjHkldEx+9wOByHEzlIN3fHCtfxOxwOxygR8RHPH67UYYllf3Adv8PhcIwWz4PhOn434nc4HI5XEL6PhIeWc47EnXOscB2/w+FwjBZxI36Hw+E4shjRVM/4HfGLPhH88ubYxceYv9/3NwAkyCNplXblyhsJ9+zU9Zue474ydW3szuSLroAL6xIEtg3Wd6SZmFTnyZnp9XxiikohP/fJE5n8yc8D8IvWKqZUqLzuNV1PsmOKyjarYx5eSp0m8/EqQimVDiIeFPJ0RzXp9e5Ugd6cSgB9T5gd1lif7i1hSasmD++adSqf/uNKAP7n5Dibr/kGAGVT6qk8Rx1AN/34e4Ri6mBY++lv4q17EoD0849T8qrTANhRu6joZtidyRO2Pz3vW9/Gpa+eBEBbX56UdXPMBYbZOZXdrY9NKTo+tvTkWBjaBYDZtKL4E9arnVxcDlrWExyl0r4XOg1z433s9lU+KD/+AvEadacsPf2drItOAaAk7JEpqASyKb8T2bFW22/7BiSiMj+TTROar/LH3sppJHav0VgnzEDyGS0TirLOujt+6nfPcfvbVG7rd7Ww4Do9J9F4CM/WJxT2uekSvRae29nL3BqVNr7Y1sdpddoWkkvTHdeE7/nAULAfk4gvxaTwrX15GkMp+pGMSip7S1VCGn9Uk757819X7CS8TDf5tct0uTSJV9Os2wZ58lW6nIkkCezx+nIBSSth7MsFTGh7Qds7Xk6hVN1GxQR0iso846E9o8xMwRC1sRpgxS6NNRby6M7oNXh8SQdBsrbYXvml9wJQ9kV1stzbnfP7j6zn4gUTNP7lf0Oa5wHQ/Mm7aV39BI/ffjUA0yujrG7T8/Pub97DdJvo/dp3HM1Nz6hU9PSZNfxmqS7PrE/w6kaVFS+oDhWT0+cf+yOhOpVYF9p3FePwF51WlL96W5/HS1YV2yWI6vnsCVfQ8gn9vMz8zvfwu9Q51J95wlPGmCXsJyJy+a8v//jXzj1+4ZDlPnrNjfz8Lw8fY4x5dn+PdahwI36Hw+EYLW6qx+FwOI4s+p/cHbrQ4Yllf3Adv8PhcIwWPwQ2Wcw+keF0/mOH6/gdDodjtHge4g/dsY/nJ3fH721nh8MxbrjpmS3c9MyWsQ5j/OB5w79G0PGLyFkiskpE1ojIFwZ5/xQR6RSRZ+3rQPL3FhmTEb+IfA24GOi/Tf8lY8yd9r0vAh8GCsBlxpi7RrBHggduBmDjqy4gXdCfYI8sbeFNs1SBkJzzek4yanwmzz3AysmnA+ALRVXLlIoYD25Uc6iSqdP5t//QvLwVH/037t+pKogLKjeS26BGZi33P0jDO/UhjiDVS/7oNwAQaVuP16uqnqC0isILjxJ/jeZbmJZ6EVJqRmUyKYJmzS27uCyLsceo2LmcK85S07nghTupOUZN5OLHnkrLTT8FoOnjn2ZFeAoAO7oMTdNUXdRSd0JRjdP09xupnaKmW4XG+bQZbZf3L2wgsutFAGoTNQhq5BWsfBiZqPuMlQoPb1Yzrrc25PG6VGVxX/VrmFCi+1nZ2stKa451+szX8OpNzwAwe9rxBEGYvj5V7DTMmcmnzlO1x7uO/jOrrrsVgIujK5EqNdTaVTGDqj4VP4TqmiHQtjD5HEFCzeVi5Akiif6TThBW5Y+X7WN2qx771g+cyOfvVnXQ1vYwbz9Dc+g+uraNyRNU7TGzLsEVd6s66IyjaulM67EaklHW57TtpkY8KrbrPgNrYgcQJGrIhzT/8sRoAX+3ZsEz4Rg9Zao+CQwk8l3wKr1+SHfj9el1JYUsoQn1xf1hFWgmKOD1qulYLN1Na0zLVEY9Qm1q8BeumESuTk3d/PZNA9ohTnmfXm/GSxZlhMYPFfMY+0GOaRWqWCv380he67wlX4uxwqTJ6W7Cs/vFLoPnp71oUQM8oufPnzSdrT+4CoAX3tBA5Of/zVX3byi2970/0Wu1Zs7xbHhBY/rxI5uY36jt9/4r7uKub54LqKLsqMDm+f3zHVCt10W4aWbxGgl6u3nwE98FYNabHyrmB+5cs7W4HCqJEWqYCsDueW9m1+U/A8AzEa5/oXfQOu0XB+Hmroj4wA+AM9Dcuk+IyG3GmOf3KvqgMebc/Q/2HxnLqZ5rjDHfGbhCROYC5wPzgInA3SIyyxhTGIsAHQ6HYzDE95HQME/uDq/jPw5YY4xZByAitwDnAXt3/Aed8TbVcx5wizEmY4xZj+aZPG6MY3I4HI6X0G/SNtTLjvjfJiJPDnhdMmA3jcDmAX9vsev25gQRWSoifxKReQcj/rEc8V8qIhcCTwKfMca0o5Ue+BtzXw2BbcBLAJqamg5xqA6HwzGAkZu0/c4Ys695+cHmgvZ+ovZpYLIxpkdEzgF+D8wcXbD/yCEb8YvI3SKyfJDXecAPgenAImA78J/9mw2yq0EfLTbGXGeMWWKMWVIzYcIhqYPD4XAMhliTtiFfw5u0bQEGjlonsVcGHGNMlzGmxy7fCYRF5IA7vEM24jfGnD6SciLyE+B2++ewDeFwOBxjzsF5cvcJYKaITAW2ovc33/vSXUg9sMMYY0TkOHSw3vYPexolYzLHLyINA/58K7DcLt8GnC8iUdsYM4HHD3d8DodjDy6v8SD0T/UM9Rrm0V1jTB64FLgLeAG41RizQkQ+KiIftcXeASwXkaXAtcD55iAYrI3VHP+3RGQROo2zAfgIgK30rehd7TzwiZEoevIG/tCk+TRfH/OpWvMgAHNnLMTfqZK8lrrFdBv9hs7OfAOSU6lhLjBUxHR9VdDN2feq0CixrhGm6o+PVT0+JyVUzph99mmy21XPXD69EZNRKWShrQXuVvlaNpMiMk3z6lJahSx5I6GMSjiDWBlB9RQA/M5teBusf5PnYQKNCRNQi5YP1TTS/cwTAKwvX0D+yX8HoPaiGM1lqipo6c3TY+sz12zjb+1q3lVywoVYLzoqoj51W/RYJt2LKWizbi+ZyvaUyjOnL34rKZuvNh8Yjp+ksrtne3JUJNVkLehMUxLW8cKrG8t4e0Slk4Xty8nPV3O46NZltN12M5Mu+jIASxddwBdbtfOYuOZuyieqPDK3dC2RqEoyq8q6CRaepe2yYxVrSlWGubkzQ65FY1rdtotPTM0Vz3neGryVta4j16Lyxi+vquHqU/WXcCY2nYJ1O3tkciVbu/RcHdNQztvnqWlYIYDJISupDMUoWIloaOvzbKhSqW1bX575tWrMJ/f8DHq1vNc8i+ebT6Wf2x7T6+KsWbUcveE+8jv1bwlHiteJxEsJuvVa8pKVxemAcPMsJFBDvaC0muq8lgm1biFfoYZ6BAUksMZ0kVLw9fz7vW0EUZW5eqlOjK8f63C8kpztI0K5FJVWOqp1tdLOaII+e+2sjM1g1qZ7GIzahJYPpXZT6NP9pJ95gOl36P53fizAT3fxr4u0HMc00nXB9wH4xbIWGpLaridOKqMmqtu8/z9fj9+z0dYtIGhZD0DsmFPY/r8/0fOTztL4Ab12QlPn89prbH+Yz2Ky2qbeph307dT2ynT0UG+loE3RHJRpPJODVr5wiso8vz1oDUeJeCOwbBhex2+nb+7ca92PBix/H/j+fsU4BGPS8RtjLhjivSuBKw9jOA6HwzE6PL/4xbtPxrEts7NscDgcjtEiMnzHPo4tG1zH73A4HKNFPMywI3rX8TscDscrB+fH73A4HEcYIsMnU3cdv8PheDnipJyDY0Yw1TOek9q+Ijr+EAXOmGZzuoaEzZNfB0BPLiBSpXkx48YQs/lH42GP6rhWPRZkkLy69hXiFVSddjaguV79CRMBmBPrJRPT5fymDWS7tHz76s2YB1Qi2XDCfGLT1QkzAPLbVZoWrqxHVv19T57a+qlIizpjUt1IoVOfxZBoDL9Sc58W2ncSsvlE00sfZtlPHwDgdac8yZWf+B4A/3VfL7VJPcbrZkygPaUyR2mexCnWTLI9D90ZleolIh5+jUokH2mFyrgqEibHfBp8lRFKagdVaZWR5lY9Ca89X9srFCUZFrufEnqyuk9fYPfEYwHoqCqwsUXldQ2JOUy65BuYx34NwMJwBI5+vcY05wwef0FNWf1j3sdR5brfR3fkqE+qxHRK7Uy2btd9feXXy1hkHVavnbyR3j/pYx3RxmbSq1cBkJk2rSjnrS50c8c23efRdXnsKWf5jm4eXattvbC+jKUtPQB0ZfJMaVTXVr9lNVI/C4AXv/Utpn7jWgAmlUXw2jRf69ol78OqSJnTt5o5W+4HIL9jM59NaMP7rdXIlHl0LnyzbtOeZmGd5sRt/86n6KfquAbCRw2wourW+EKZXgpl6s6Zr55CENPcxX5XCwWbHzcfSRLp0/L9uXcBcuGSonTY72oBW74vlKAUlWGq/FOdShOeR2lYY2vo3Ux+nspT7+KbnMkeW5hz3vV5ss9cTzqVKjqnAnRd+SoAUhs3su2b/8Kk89WFNujrorJG3VbOnHE001arYjG3Yg25ftkysPluzRU9YcF0qt/8HtuWG4lWqjw115um52E16I027Pm8gEo9AdbcsZyKySo9znRlWfV/PwBg8ceWM/lEzQPtlVeTPJgjcM8Hb5ju06l6HA6H45XECFQ97uauw+FwvHIwIsOresZvv+86fofD4Rg14rkRv8PhcBxRODmnw+FwHFmMaKpnHI/4x+9tZ4fDMa544UNvHusQxg9ycJKtjxVyEBw+x5xjFy82Dz/8MKAStny5uvMZA7FtywAI4uXF8kFJJV5KE4kH0VKkoHLGQqKGlbvV/dATIRrSExfxhIkxlaCFtizD9KpcLr9jE751Amz54x8xVuc3YdEsvHKV2IUnTUfiSYIOlTAWOtswaZWDmiDAt+WCVG/RtTE8aUbReTBI7UkQvXnRu7h7ncrZzphezdrdmiV7fm0pnRmV2JVFPOLWPXN3qkB9Qn/URTOdPNmpEs4ZlTGssSeB+Pi5Pv3D8+kx+kZrKk/GJqHvyxWotPLXtr4cnWltr7DvMaFEy8fDHjW2TCLXgdfXTmtyCgCVT/2Wzqef0vgWLCA093gA2sum0vZptR9/4ZM/5JypKuGTZX9h1xx16iyLesVxU7iQIX2ruqd2vvULXHSTSmknTyjluKlVAJw3ZwL3rNN2rE9GyRW0DiVhn2RUf5o3lUVI5jT5ud/bBlZeWKho5KFdWv6ksl56f3+dnqdCQPJElaMWpi7hufPfqcc9YwFLz/2SLpfHeHr+qwG4d5e259W3fASAHaddim9tUmtKQqz50NsAmP3xC/FmqBy2sOrxogQ4fuyppJt0fbT1RfLlKouUQpYi4pGNJAGIpNsJ4pXFMpK111eklOXtWp9YyGOWp9eOGWAu5nfvRIytf7IWyWeK8XQ+qdLZlb9+gpq5KqltOn0Jb+9UF9ZPnzaTidb9cmp5hPiGx8hv0zr0vvrdlK1RGXJq9snE2zfofl94lPCUo4rHLyRqAOi89b+LbpeRslJKjz8DgFzjgmJM5NJ6voBczQyMdcPt3bCR7k07Adjwt9UkG/Q6Cicixc/k5NOPIfmWD+v6ibOeMsb0Z5UfNSJy+W9++fOvvemcs4Ys95F/+iQ3/OLmY4wxz+7vsQ4VbqrH4XA4RsvL/ObuiKZ6ROQ/RrLO4XA4jgjsHP+Qr5d7xw+cMci6sw9mIA6Hw/GyoV/VM9RrBHP8InKWiKwSkTUi8oVB3hcRuda+v0xEFh+M8Iec6hGRjwEfB6aJyLIBbyWBhw9GAA6Hw/GyYyRTPcN0/CLiAz9AB9ZbgCdE5DZjzPMDip2NpqCdCbwa+KH9/4AYbo7/JuBPwDeBgd9G3caY3Qd6cIfD4Xg5cpDknMcBa4wx6wBE5BbgPDT1bD/nATfaPLuPikiFiDQYY7bvZ+jAMFM9xphOY8wGY8x7jDEbgRRqOpcQkeYDObDD4XC8fJE9o/59vZS3iciTA16XDNhJI7B5wN9b7DpGWWbUjEjVIyJvAq4GJgI7gcloVvh5Q213+DB051S2lgnXkLQSvqgHuVp1W5QgX/yGliBPvlzdNr1MN30lKikzBVOU3WXzpihbBNjRq+uT5QuZNkWTk4cnbsaz8rLaL34Xv10TfuNHMJtW6HJpJdkXn8FbfCYAoe6dkFG5nUTipB77MwBty9ZS0qCSxL4nV5BqU7lp85tPI3PKhwCY0rOdi5s1pvUGrv6bunyeelRtMal4c2UJc2tUzlZXGsKuJogmWVSncsZQajfYBNu90SoiIU0kHu1rI2kTdSdKS9mRsfI6X/Dtz9ZCAHMmqJvj9p4sc0tUdtoTKqPDSkpjpVXEWtczQbQ9ulc9T2mjtvHG39zBlPdq4u3KWSGqLtfk8VVlZfirH9Rgm+aQu+rj2nwf+SeuWKuyxQ8sbqTpFNWSb88F3HyhTnd2ZwvFsdUz23uK52xqRYy17RpfdUmYGXGVQ2b8KBS0nltKppKIaD0r2lbz2hpNbL78A5ew9XEdVDUcW0/Fyg16Dj5Vy4KbfwVAp5RwkqeuqLu+/WlOukydIN/xtndDsprsM/dqnc96A76V2HZO2SMr7njsEXbNOAeAC/9WzdtOUafLE7xKMhv1/C+qn8ZTW62TaDrP/Dpti75cgYqYHru+tIKfPL4VgGw+4J9PaNKY+vLkAi0zIRKiI1xXPJ9R0fOfL51AwUq6o8/8sWglHJo6n/5IXzVrOnLGxQDcu7GLa6v0epn64p/JL9dz/Nup59NYNo+SmZqgvjpVoKXhRABmZLvx+q/5GceQW6ezxpmNa0icpPWvOued5DatBiDo6SC3Tds7DKyt0H12ZULUJNUBtVY8QieoI2tZzRNku+7W9rrkVEoXaTsOdBFl7usIhnPTHAUGIRhmRG9v7v7OGPPVfRQZbAd76+tHUmbUjPTm7hXA8cBqY8xU4DTcHL/D4ThCCTAEZuiXGb5/3gI0Dfh7ErBtP8qMmpF2/DljTBvgiYhnjLkXWHSgB3c4HI6XK2aY1wh4ApgpIlNFJAKcD9y2V5nbgAutuud4oPNA5/dh5A9wdYhIAngA+KWI7ATyw2zjcDgcr0gKAcXp1X0xnCmCMSYvIpcCdwE+cL0xZoWIfNS+/yPgTuAcYA3QB3zogINn5B3/eUAa+BTwPqAc+PrBCMDhcDhebhhgmH5/RKN+Y8ydaOc+cN2PBiwb4BOjj3BoRtTxG2N6B/z584MdhMPhcLycGMV0zrhkSJM2Eelm8PoJ+mVUtt8HFvkn4FJ0yugOY8zn7PovAh8GCsBlxpi7htvX4mOPNfc+8BCgSp6iqZMJisZWJlK6ZwMTkPVUmRMpZEh7ajSVygekrNqlL2fw7R0QX4QSq8rwRMsBJMIeKWtkFvKkWL4QQHVM/2hNFUhGfaL2vVDHlmI+1cAP46c69sQUU6OtSKYTyWvcQTSB5FWZYkKxokzMhKJITk3arnmqjddMVkVQTzbPpHJVzdSXhouxhj0p/jRN5Q3TMhu0ntUzis3SmS7QZfPppnIBrX0aQdjqIwAAHjlJREFUQ3ksRE9WFRLbuzMcO7GsuM9+RUxJ2COeVSUKXgivtw2z+YXivnuXquHXxrufKRpnzTr/NEKnXwjAylScucEWAPLLH8Y/Vg2wcolaousf1XZta8GvrLENGaGjUY3M4iGP9R0aa3c2z4JaVR1Fdq8nsPloJdtH9m+/ACC28LVFxUdu2wbCzar8IiiQ37nVHms7navVcGzrgyuZ+9s7tB65oJh/uOsHX2Dtneq/1bGug7hVO736a+8ne+pFZK26rGBM8Wd/tmDY1KnX54aOFImIKq3CvscMq5bZ2JHmQWvG11RVQldalTmFYI/qLBENUR7VcVtLT4Y3ztZ2mRbs4oVADdVueGIzHzpO7wvOSsJ6q0yrjoco87X+rdk9t/l29eWojOk+60r84ueo7xdX4cf085Lt7sV8UJVYiQd+VmzHyLR5FOpns8VqgXqyAVUxrVtN1BQVb5LP0ffA73Wbt1xGuEWvkXzVZAoJjdvP9OD1qdFeobSadqOfz85MgalhNcDrC5cVR9wG/fyB5oH2rAItMKa4XDB7DBSSpSUHbNJ24823fu2sc944ZLlPfuJj3PSLG19+Jm3GmOShOKiInIpOHy0wxmREpNaun4ve4JiHSkfvFpFZxpjCvvfmcDgch5fAmKIMdl+M518EY+XH/zHgKmNMBsAYs9OuPw+4xRiTMcasR29oHDdGMTocDsegGDOC11gHOQRj1fHPAk4SkcdE5H4RsU9cjPwpNRG5pP9puNZduw5xuA6Hw7GH/pu7Q73Gc6qTQ+bHLyJ3A/WDvPVle9xK9KGwVwG3isg0RvGUmvn/7Z1/kFzVld8/573+Nb8YSdZvgS0Zy8bAmh9WhFmwwRhjo3IiQwLIIbVswkbJLtR6U9lyIDiUnSoS/9pUbcVeO1qvvY6XGLMxiqlCBgOJwzqxLSAgkCyBBAiQRkhCEprR/Oru907+uLdft6TpXzOa7p6Z86l6Nbfvu+++07dfn7l93/edo7oJ2ARujf9M2GwYhtEIjS31dK5bmjbHr6rXVtvno34+5KVKW0UkBhYyTU+pGYZhnEm0w2f09WjXUs//AK4BEJH3AxngbdxTahtEJCsiq3ChSLe2yUbDMIwJcUs9dUI2dPA/hnalXvwe8D0R2Q7kgdv87H+HiDyIC0taBO5oRNHjZFv+hcZJTtFYQgqhD0BWIfOMUjkKXuYYSSZZX+pKBeRC92p+LoljRl9KKfj/kWNFpd/nbo1ixTd3kj1vaSYUfMw45uVCwkAoeu1ZPO+c5CdipjieyDY1laH7LRfYLc71gc+Dmho5hvrAaZrpIRxy98G1ME60zOUuveV3lpL3/a/tTnE47+w7MlpMZG4BMOblhUdGC7ww6GSO3cOD9GZc/9sPDSXllfO66PfSvlwqYJ4vX7i4OxmjQkwi5zyRj8mlnIw0HDxANO9sgrQb+2DkGH0+t/DyDV9k/vB+/8EVGcs6+d8CjXj0oJPzfWr+Yl5VF4xr2+6jLOhysQCvWZKmuOhcAL7/8jiFYwcB2P3WCdb/jltVXNqbpeDHovDo/Qy94dpIENC92Mllu3IuiB1AcNl61EsHdd/OsjzxfR/iXYtdwLbsnV8nlXeB0vo1JjzknphfeMOt9K5yP1Bzl1ydyBGRgPTgPrpyThQnxTxxtzs3aQAnT+xOh6T9WB4bLeBVrqRD4aPvdeM1HsWJnHNhT4ZeL+HMhgFHRso5eLe95ew71DuPcS9h/sLVqzg66mWrQZqulJfknigw4q/hZeEw4ZAbo2WFcYpdLnihbH+G8F2u3HvpR3j9h//Nvf9zV9Dz2DcBSH30HzJ2lmuTVxcUrnRt9GYC+r2cczRWunPucxaNya77A2d0lKew1OffDULEj72mshTmu3FVhV7/fQlFOM5ZyTCWpK2ZsLxCPF6Mk9cjhRiRsszzTPrhWCE6Aw9wtYu2OH5VzQP/pMq++4D7WmuRYRhG4zS01NPBnt+SrRuGYTSJosR1PPucvLlrGIYxW1EaCMLWEksmhzl+wzCMJoljknsy1bCbu4ZhGLOIuIGlnk7GHL9hGMYksKWeNiOQREJMhxDkfX7PdBeBz7MZ5IeJsk7GN1aIE3llGAiR/82Wr8i56x65dm1O5JW8l5oVo7J8rCsliWQtE5eldUiQyDFVAqRYJKjI/5ked9I7zXSX8wAX8xTnO/lgMHIsOR5AIpfzRkbeQUMXJVEk4ETgokEqEYu63Pt8u6CknEksSqcSqWouVX5kIxcKy/tcP+NFZVG3j6LYk2bLyy78xcLudCLnXNKTxqviyEdlfbIIiVxwtBjz20FnZ29mGUvGhWzWR1vMLeStYbdvfqwM9rooHJlQkvHuz4VctsLJH3+mH+FK/36uOKc/kb++qRcx6qOHXnduD2l/bNeFS3jpyKi3L+bACSd/fM+tX0S8fV0p4awjLqdrlOtD/PhG2V5eHHTnWrBqKV0+CmtvOiDtP9MeiWB8zH9O42jKfwZRgewFlwEQH38bGTrq6s/yss4uJ2HUVIbU0b3ufF3zWeIjhi7qTjHmZcVn96U5OuZs7cukODjszr2iL8cHF/YkY1yKEvv2SCEZu7Eo5oX9LjLqrlTAeUle3phzu924//pAnqy/Bi5ZmGYwcuXDUQ/PnVgMwO+e3VeWRl7wSboPvOhsPnaIVf/6Llce2ENqybtdWQKOfPlfunMdOMq5v38T4XzXV7T6cmJvX3bsGKSd1FejfHI9U8yX5cyZ7uTaVkiknSIBpYuvq8JbBSLJdTFaiBmv0FaOFt2xoZC0CUQIJ4oLMEmKkVKoo+esF6+/ncwKx28YhtFKSg9w1WwzxUV+EVkA/BhYCewFblbVYxO02wsM4ULZFxsJOd2uJ3cNwzBmLKVYPbW2MzDhvwt4UlVXA0/619X4uKpe3GieAXP8hmEYTVIvXEO9XwMNsp5yxsMfAJ89E52COX7DMIymiWIo+HX+alvsFvlvLIWP99vGJk6zRFUPAPi/i6u0U+DnIvJso/3bGr9hGEaTxNQPy+zlGQ+p6r3V2tQJX98oV6jqgM9k+LiI7FLVp2odYI7fMAyjSUrJVqZKnfD1B0VkmaoeEJFlwKGJ2qnqgP97SEQ247IWzn7HX4g0kRUOBpD2Msd5ElL0kqujhSyFcS8d05Mj+pWewBOBog/JKQKZoJSoudymLxvQ4yV12VCSCH0nNJ3IxfYPFhnKF5P+P+IToQPsODBIV9pFapQC5FIugmVvJiBbcDLU4vx3J5FEg5FjaMa1ORr2c8LLGVPfuJPcv/0L9z6zYfJ+eoKI40VnXyiw71/clJx7xEseAdY++j8BGH/i++gVGwBY1pvm9kuX09/jzhdtf9LZs+ASRgMnx+v/u78m/N0bXP9D5cxnB3/4HVYDy7/47aRu96EhAIYLEUdHncRyYIiyhPGsLCv7nYQvDIRcyr2Hj6/sT+SxuVw2SRifj5TQy18LsSbRRgtxxLyci8gaqTIvV46eujDtPofwnX3E3S7i58HUwuTY9HCR5X3u2EwodHnJYxiNI14WjATgJZyazqE++mXUt4RgbNA1yQ8T7XE5tYOxEbSYJxW7c8fd8xOpYlAYRQbfcufQmFTKReoknaPbj/uCXJbFPc6m4+NR8v5FyrLl/YNjLOtzxwYiLOx2/a9e0JNE/FzRBZH/Lizty7My56/JqEA6cGPUHw3xyRVenlocREbcNRKeOIwW3DWYXvlBooE9AAw99zQvP/QtZ9vrg1z2p59y/axcho4NU/zAR91nHgm9/hrWVC6Rz4KTboKTOlOKpBumyxJOjYmCdPI5lxTxqpp83yrX0IuxJtfUaFHJVzxSmwnd55lLCcOFOo/aNkEUK4U6j+7GU//P8DBwG/AV//enpzYQkR4gUNUhX74O+Pf1OrY1fsMwjCYphWWutZ2BHwRfAT4pIruBT/rXiMhyEdni2ywBfiki23C5Sx5R1UfrdTwrZvyGYRitpBHlzlQdv6oeAT4xQf0AsM6XXwUuarZvc/yGYRhN0tBSTwdHaTPHbxiG0SQxDWTg6ly/b47fMAyjWRpb6ulcz2+O3zAMo0lUta5qx2b8LaAkc4uBEZ/p/I3B8SR5eCgV8k3VRF4YxSTyt1AkSSoeVuid0oEkcslcKIwVXf/ZUJJATKHAvF4vIy3n8j6NC5adVeNdODnfyN9+jf1X/SEAR8bnc2nWyQcXDu9jIZBa/gH46g+qdXLS433z7n+kxvkge+0/Tcq5U/aFF7r7SiGl9ODAZ+4oN1iwPClWyjhLrF7cV/PcAEMjoxPW5wMnMUQ1GftMKMnnGKsy6j+HfKRkfETSys9tKB8j4qOz9q1M6jOQROFMBeWojSkBicaT81KKnFoYQ32UV8JUuT4uoj7qJBIQfmCtK44NoZkuNO/em4wPJxEm3XFRckzsI6PK8JFEYtfb1U+vuPXjpf19jKqXp6qzF2Bl/4JEwgjgqwkDKevLo1Hy/tiVuWIiqdRUlu7iiGs/eBDx0WIrKbzxMvmB1wFILz0Hybj3+dy3f8HrB5xMNxThwK92ALDi2w8yDskdzVCUMfGfYUgSDZegO3kP+UhdWDGAKE6irY4WA0o7RMpSatWyhDcUOOHlmfliefYdiCTfZ3eM+zta0JN8wFSJYvVS0+pYdE7DMIxZhJNz2lKPYRjGnCG2pR7DMIy5RaS21GMYhjGnsKUewzCMOUasSmRLPc0hIj8GPuBfzgPeUdWL/b67gdtxt/X/WFUfq9dfLh1y8FMuyN3ytcsJvAxg8Kk3OTrqAlMV4pjXhp2Sp3R3//ODLwHwxt1O2bLg3/0Fh+8th7Ne9Y0fTuVtTprum77Aal9eXblj/kTRW2c2fd1dddtUKn9KP69HCnFZyVWR7SiMyyqdGJIvZ6UyK5sKTkqLVyoVFfBqohQx+EBmmu6CigBiJ+EVPpoO0NB/ndJdTvGTckoYKY4l7aLeRagPzBZLSBgXfJtxipne5P2U8ulGsZZzxcZ60vspkY+Vca9wWtIljHt9UCHVRbcP/EeUJ+6a78dFCLyqSd+1kvD4gKt/dRs7/7NTiw2+OXhSUL/XD48k5Y3HdiXl48MTq7Lg5NSDlfluS/XpKpHCulInq2/ioPQ+oct/d4uxJsESe9IkuaUDkUQ11JsJyKlTxElhlBduvbWqrc0SxY04/s71/G1x/Kp6S6ksIn8GHPfl84ENwAXAcuAJEXm/qkYTdmQYhtEGipGSL9YL2dAiYyZBW5d6RESAm4FrfNV64AFVHQdeE5E9uNjSv2qTiYZhGKfRyFJPJ8fqaXdY5o8CB1V1t3+9AnizYv8+X3caIrKxlM7s8OHDEzUxDMOYFmItL/dU2zrY70/fjL9WSjFVLSUU+Bzwo8rDJmg/4fCp6iZgE8CaNWuU/BSMNQzDaIJiFDew1NO5nn/aHH+tlGIA4p6lvxH4cEX1PuCcitdnAwNn3jrDMIzJ0wpVj4jcBHwJ+CCwVlWfqdLu08Cf46KrfFdVv1Kv73Yu9VwL7FLVfRV1DwMbRCQrIqtwopatbbHOMAyjCnHcyFLPlGf823GT46r5c0UkBL4FXA+cD3zOi2Rq0s6buxs4eZkHVd0hIg8CvwWKwB2NKnqueeH0/w8fbNCQd//H7yfl3jZJOI3qTCT57Ad+vNhd3xdv/zVFf5XExIlcNwzK0t1AIUm5WowTuaSoon6BUUSSYGdFAkqqQomjRNoZB1nUl4Pi+MnTuqD8dVIJEJ9zV+lDfW7ZQpApS08r3o+GaVJj77hz5PqRyEuPC6NQCq6W6SFfcY4ecW0eX305b42VvyZ/9PhXnTlLVjopKTg56duvuX7SXWjOBdDTVDbpP//h9Zz33etcn+ddxbAf1KP5mHyTEhVpMCBapXOsekwSCFHcB4kLRqf+gxOBnVc7fcirrx8H4PajJclp6drpZ82WJ0kOmCKFKGa8zlJPvXj99VDVnVB3LNcCe3wmLkTkAZxI5re1Dmqb41fV369Sfx9wX2utMQzDaJzSzd1a+H9qN4rIdyuqN/n7k2eKiQQxl9U7yJ7cNQzDaJJYtX7IBrf7IVW9t1qbBkUwtWhYEFOJOX7DMIwmaUjH30CsnnoimAaYlCDGHL9hGEaTFBp5crf27jPF08BqL4bZj7t3+o/rHdTuB7gMwzBmHLHGRHHtbaqqHhG5QUT2AZcDj4jIY75+uYhsAVDVInAn8BiwE3hQVXfU69tm/MaMYfPSC1j38t8BcEJOTRRpGK2jJOesxVTDMqvqZmDzBPUDwLqK11uALc30bY7fmLFctftpAE7kYyLxOVdVEoVlFJPI/wKFsFSvkkTQSlVEuVRV4op7ZZH/QRyEScZhR5I/NlPVNgECH50zjMaRosvl233kJQqvO6nh3/yj/5DITQ+PR2S8Lf3pICmflQ658kt/3x17wcX86DPuPuEfPPs3FPY6xd7Ki5bwxq/2J+dOX/bZqnbVwsUGdfLHzwy82NAxpeGL9fRolI1IOkttglOalpxqKa8yuKid/dtcDuknb3XCv+v3bgNg6XO/AeDjDVk9dQpxA3LO1iz1TApz/IZhGE3S0Ix/LoZsMAzDmK00FLKhRbZMBnP8hmEYTdJQPP4ODshvjt8wDKNJGovH3yJjJoE5fsMwjCbRWNF6nr2D1/hNx28YxqTYNP+8dpvQNlTdUk6trXPdvs34jRnEta8+QzDonkbv6l9B1s+oFkTHiXtdIvGRYvkLN1qIGfbrsIVYTwpqImFZRpgJ3fynUn1YrJjNVR4XiCTtnr7qGna8dDTZ94fv7GJiKp45mLeY7LlrAbj96O/Vecens/HYzUk5+941AFx2ze/Vj8o1TfzknEsBl/D9ln3PTbqfUyfPpciWJbntorO6XcUV7v1fv/dm2onGMXEdvaapegzDMGYRpVl9TTrX75vjNwzDaBbVJI1BzTadijl+wzCMJoljJbKlHsMwjDlEQ6qe1pgyGczxG4ZhNIlb6rGQDYZhGHMGVSXuYMdeD3P8xoyhr7sLus8FIF25oyIZeyuDNX/sN/+Xj7XwfJ3GxmPV5KuznzhW4johG+ouBbURc/yGYRhNog3IOTvX7duTu4ZhGE2jKKq1t6nqOUXkJhHZISKxiKyp0W6viLwoIs+LyDON9G0zfsMwjCaJI4iK0x6kbTtwI/BfGmj7cVV9u9GOzfEbhmE0SWNB2qZ4DtWd0Fgms2axpR7DMIxmUU2cf7XNL/XcKCLPVGwbp8Ma4Oci8myj/bdlxi8iFwPfwYkwisAfqepWv+9u4HYgAv5YVR9rh42GYRjVaOLJ3YdU9d5qbUTkCWDpBLvuUdWfNmjOFao6ICKLgcdFZJeqPlXrgHYt9XwN+LKq/kxE1vnXV4vI+cAG4AJgOfCEiLxfVaM22WkYhnEaqvWXehq5t6uq154BWwb830MishlYC9R0/O1a6lHgLF/uBwZ8eT3wgKqOq+prwB7cmzAMw+gYNK4diz+OtSVB2kSkR0T6SmXgOtxN4Zq0y/H/CfB1EXkT+AZwt69fAbxZ0W6frzsNEdlYWjc7fPjwtBprGIZRiVNr1pFzTvHurojcICL7gMuBR0TkMV+/XES2+GZLgF+KyDZgK/CIqj5ar+9pW+qptXYFfAL4V6r6ExG5Gfgr4FpOznlRYsLRU9VNwCaANWvWdPKzEoZhzDLiOCaa5id3VXUzsHmC+gFgnS+/ClzUbN/T5vhrrV2JyH8FPu9f/i3wXV/eB5xT0fRsystAhmEYHYHGMRrXvvXYyckX27XUMwBc5cvXALt9+WFgg4hkRWQVsBr388UwDKNjUHWOv9bWyZlY2qXq+efAn4tIChgDNgKo6g4ReRD4LU7meYcpegzD6DS0WCQu5Gu3ieuk6GojbXH8qvpL4MNV9t0H3NdaiwzDMBqnNOOv06g1xkwCC9lgGIbRLOb4DcMw5haN3dy1pR7DMIxZQxwViYu2xm8YhjFnUI2IbanHMAxj7tDIzd1O1vGb4zcMw2iSxuScnatEN8dvGIbRJCbnNAzDmGs0oOoxx28YhjGLiOOIuFio2UbVVD2GYRizh1I8nlrYjN8wDGP20JCqxxy/YRjG7CGO47o6/k52/O0Ky2wYhjFj0cjJOWttTFHOKSJfF5FdIvKCiGwWkXlV2n1aRF4SkT0iclcjfZvjNwzDaJJG4vGfgQe4HgcuVNUPAS9TTlGbICIh8C3geuB84HMicn69js3xG4ZhNEs9px9HMMVYPar6c1Ut+pe/xmUkPJW1wB5VfVVV88ADwPp6fZvjNwzDaI6nomOv1nT6cWGMeORtgCtF5JmKbeMkz/nPgJ9NUL8CeLPi9T5fVxO7uWsYhtEcv6Awgo69g+QmXHYnPrKLYMFqov2/+SbwzWodicgTwNIJdt2jqj/1be7BZSS8f6IuJqiru8Zkjt8wDKMJVFVT77ue6OA2Uu+56vT9UYH46Cswdqyngb6urbVfRG4DPgN8QieWCe0Dzql4fTYup3lNbKnHMAyjSaJXHg1Ks/5TKc32VXVkKucQkU8D/wb4BzX6ehpYLSKrRCQDbAAerte3OX7DMIwmUVUNllxMdHDbyfV+th8PbK0722+AbwJ9wOMi8ryIfAdARJaLyBZvRxG4E3gM2Ak8qKo76nVsSz2GYRiTIHrl0UB6l8aVa/0Va/tTmu0DqOr7qtQPAOsqXm8BtjTTt834DcMwJsGps/4zPNufVszxG4ZhTJLKtf4ztbbfCmypxzAMY5IkCp8Dz6LjQw0peToBm/EbhmFMgeiVRwOi/IyZ7QNIJ0eQaxQROQwMA2+325ZTWIjZ1CidaJfZ1Bgzzab3qOqiVhrTacwKxw8gIs+o6pp221GJ2dQ4nWiX2dQYZtPMw5Z6DMMw5hjm+A3DMOYYs8nxb2q3ARNgNjVOJ9plNjWG2TTDmDVr/IZhGEZjzKYZv2EYhtEA5vgNwzDmGLPC8U8m2fA02bFXRF70kfSe8XULRORxEdnt/86fZhu+JyKHRGR7RV1VG0Tkbj9uL4nIp1po05dEZL8fq+dFZF3FvlbYdI6I/C8R2SkiO0Tk876+bWNVw6a2jZWI5ERkq4hs8zZ92de3+5qqZldbr6sZg6rO6A0IgVeA9wIZYBtwfpts2QssPKXua8BdvnwX8NVptuFjwKXA9no24JIzbwOywCo/jmGLbPoS8KcTtG2VTcuAS325D5fM+vx2jlUNm9o2VrgMT72+nAZ+A3ykA66pana19bqaKdtsmPFPKtlwC1kP/MCXfwB8djpPpqpPAUcbtGE98ICqjqvqa8Ae3Hi2wqZqtMqmA6r6/3x5CBfLfAVtHKsaNlWjFTapqp7wL9N+U9p/TVWzqxotsWumMBsc/6SSDU8TCvxcRJ6tSKq8RFUPgPtiA4vbYFc1G9o9dneKyAt+Kai0VNBym0RkJXAJbtbYEWN1ik3QxrESkVBEngcOAY+rakeMUxW7oEOuq05mNjj+SSUbniauUNVLgeuBO0TkY22yo1HaOXbfBs4FLgYOAH/WDptEpBf4CfAnqjpYq+kEddNi1wQ2tXWsVDVS1Ytx+VzXisiFNZq3bJyq2NUR11WnMxsc/6SSDU8H6jLjoKqHgM24n5IHRWQZgP97qA2mVbOhbWOnqgf9FzcG/pLyz+6W2SQiaZyDvV9VH/LVbR2riWzqhLHydrwD/AL4NB10TVXa1Slj1enMBsc/qWTDZxoR6RGRvlIZuA7Y7m25zTe7Dfhpq22rYcPDwAYRyYrIKmA1sLUVBpWchucG3Fi1zCYREeCvgJ2q+p8qdrVtrKrZ1M6xEpFFIjLPl7uAa4FdtPmaqmZXu6+rGUO77y6fiQ2Xf/Jl3J36e9pkw3txqoFtwI6SHcC7gCeB3f7vgmm240e4n7gF3Czn9lo2APf4cXsJuL6FNv0QeBF4AfelXNZim67E/dR/AXjeb+vaOVY1bGrbWAEfAp7z594O3Fvvum7R51fNrrZeVzNls5ANhmEYc4zZsNRjGIZhNIE5fsMwjDmGOX7DMIw5hjl+wzCMOYY5fsMwjDmGOX5jxiMiJ+q3MgyjhDl+wzCMOYY5fmPWII6vi8h2cXkRbvH1V4vIL0Tkv4vILhG53z8laxhzklS7DTCMM8iNuOBcFwELgadF5Cm/7xLgAlx8lv8DXAH8sh1GGka7sRm/MZu4EviRuiBdB4H/Dfw9v2+rqu5TF7zreWBlm2w0jLZjjt+YTdRavhmvKEfYr11jDmOO35hNPAXc4hN0LMKlfJy7ERgNowo26zFmE5uBy3ERUhX4gqq+JSLntdcsw+gsLDqnYRjGHMOWegzDMOYY5vgNwzDmGOb4DcMw5hjm+A3DMOYY5vgNwzDmGOb4DcMw5hjm+A3DMOYY/x/LRvG9krc2dwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = sst_model_regridded - sst_obs\n",
    "diff.plot(vmax=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was easy enough, but there is a problem with this approach. Using linear interpolation works very well when we 'upsample' (going from lower to higher resolution), since most of the interpolated points are between the wider spaced original data. If we 'downsample' (going from higher resolution to lower resolution), this method will only use the two nearest points to infer a new value. This means most of the high resolution source data is not taken into consideration, and small noisy features can have a disproportionate influence on the regridded data. It also implies that properties like tracer concentrations over the full domain are not necessarily conserved.\n",
    "\n",
    "We can plot the original and the downsampled modeldata along a constant latitude to illustrate the problem "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4638207898>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_lat = -46\n",
    "sst_model.sel(lat=sample_lat, method='nearest').sel(lon=slice(0,40)).plot(marker='*')\n",
    "sst_model_regridded.sel(lat=sample_lat, method='nearest').sel(lon=slice(0,40)).plot(marker='*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the upsampled datapoints depend highly on the position of the target grid. What we should rather do in this case is average values around the wider target grid. This can be achieved by using a different method in xesmf."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regridding using conservative interpolation\n",
    "\n",
    "The `conservative` option will take an average over several grid cells to reduce the resolution of the model. This ensures that no information is discarded and that for instance adjacent positive and negative anomalies are not increasing the noise in the regridded data.\n",
    "\n",
    "This method is always preferable, when moving to lower resolutions, especially when the data is not very smooth. Unfortunately this also requires some more input parameter, namely the bounding coordinates of each gridpoint (in addition to the center or tracer coordinate; see schematic in the introduction). xesmf expects those coordinates to be named `lon_b` and `lat_b`.\n",
    "\n",
    "When we look carefully at the model data, these bounding coordinates are already given (as the coodinates for the $u$ and $v$ velocities on a staggered grid - `XC` and `YC` respectively), but xesmf requires a bounding value at each side (This model does only provides the same amount of coordinate values for both tracer and velocity points).\n",
    "\n",
    "We have two options: a) Remove one of the tracer points and leave the velocity points as is, or b) add an additional coordinate value. \n",
    "\n",
    "Since the model has a significantly higher resolution, we will chose the simpler way a) and just remove one tracer point, omitting one out of 10 points for the new coarse grid (which is acceptable in this case)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (Z: 42, Zl: 42, Zp1: 43, Zu: 42, lat: 319, lat_b: 320, lon: 2159, lon_b: 2160, time: 438)\n",
       "Coordinates:\n",
       "    Depth     (lat, lon) float32 dask.array<shape=(319, 2159), chunksize=(319, 2159)>\n",
       "    PHrefC    (Z) float32 dask.array<shape=(42,), chunksize=(42,)>\n",
       "    PHrefF    (Zp1) float32 dask.array<shape=(43,), chunksize=(43,)>\n",
       "  * lon       (lon) float32 0.083333336 0.25 0.4166667 ... 359.58334 359.75\n",
       "  * lon_b     (lon_b) float32 5.551115e-17 0.16666667 ... 359.6667 359.83334\n",
       "  * lat       (lat) float32 -77.87497 -77.7083 ... -25.041632 -24.874966\n",
       "  * lat_b     (lat_b) float32 -77.9583 -77.79163 ... -24.9583 -24.791632\n",
       "  * Z         (Z) float32 -5.0 -15.5 -27.0 -39.5 ... -5075.0 -5325.0 -5575.0\n",
       "  * Zl        (Zl) float32 0.0 -10.0 -21.0 -33.0 ... -4950.0 -5200.0 -5450.0\n",
       "  * Zp1       (Zp1) float32 0.0 -10.0 -21.0 -33.0 ... -5200.0 -5450.0 -5700.0\n",
       "  * Zu        (Zu) float32 -10.0 -21.0 -33.0 -46.0 ... -5200.0 -5450.0 -5700.0\n",
       "    drC       (Zp1) float32 dask.array<shape=(43,), chunksize=(43,)>\n",
       "    drF       (Z) float32 dask.array<shape=(42,), chunksize=(42,)>\n",
       "    dxC       (lat, lon_b) float32 dask.array<shape=(319, 2160), chunksize=(319, 2160)>\n",
       "    dxG       (lat_b, lon) float32 dask.array<shape=(320, 2159), chunksize=(320, 2159)>\n",
       "    dyC       (lat_b, lon) float32 dask.array<shape=(320, 2159), chunksize=(320, 2159)>\n",
       "    dyG       (lat, lon_b) float32 dask.array<shape=(319, 2160), chunksize=(319, 2160)>\n",
       "    hFacC     (Z, lat, lon) float32 dask.array<shape=(42, 319, 2159), chunksize=(42, 319, 2159)>\n",
       "    hFacS     (Z, lat_b, lon) float32 dask.array<shape=(42, 320, 2159), chunksize=(42, 320, 2159)>\n",
       "    hFacW     (Z, lat, lon_b) float32 dask.array<shape=(42, 319, 2160), chunksize=(42, 319, 2160)>\n",
       "    iter      (time) int64 dask.array<shape=(438,), chunksize=(438,)>\n",
       "    rA        (lat, lon) float32 dask.array<shape=(319, 2159), chunksize=(319, 2159)>\n",
       "    rAs       (lat_b, lon) float32 dask.array<shape=(320, 2159), chunksize=(320, 2159)>\n",
       "    rAw       (lat, lon_b) float32 dask.array<shape=(319, 2160), chunksize=(319, 2160)>\n",
       "    rAz       (lat_b, lon_b) float32 dask.array<shape=(320, 2160), chunksize=(320, 2160)>\n",
       "  * time      (time) datetime64[ns] 2005-01-06 2005-01-11 ... 2010-12-31\n",
       "Data variables:\n",
       "    ADVr_SLT  (time, Zl, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    ADVr_TH   (time, Zl, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    ADVx_SLT  (time, Z, lat, lon_b) float32 dask.array<shape=(438, 42, 319, 2160), chunksize=(1, 42, 319, 2160)>\n",
       "    ADVx_TH   (time, Z, lat, lon_b) float32 dask.array<shape=(438, 42, 319, 2160), chunksize=(1, 42, 319, 2160)>\n",
       "    ADVy_SLT  (time, Z, lat_b, lon) float32 dask.array<shape=(438, 42, 320, 2159), chunksize=(1, 42, 320, 2159)>\n",
       "    ADVy_TH   (time, Z, lat_b, lon) float32 dask.array<shape=(438, 42, 320, 2159), chunksize=(1, 42, 320, 2159)>\n",
       "    DFrE_SLT  (time, Zl, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    DFrE_TH   (time, Zl, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    DFrI_SLT  (time, Zl, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    DFrI_TH   (time, Zl, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    DFxE_SLT  (time, Z, lat, lon_b) float32 dask.array<shape=(438, 42, 319, 2160), chunksize=(1, 42, 319, 2160)>\n",
       "    DFxE_TH   (time, Z, lat, lon_b) float32 dask.array<shape=(438, 42, 319, 2160), chunksize=(1, 42, 319, 2160)>\n",
       "    DFyE_SLT  (time, Z, lat_b, lon) float32 dask.array<shape=(438, 42, 320, 2159), chunksize=(1, 42, 320, 2159)>\n",
       "    DFyE_TH   (time, Z, lat_b, lon) float32 dask.array<shape=(438, 42, 320, 2159), chunksize=(1, 42, 320, 2159)>\n",
       "    DRHODR    (time, Zl, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    ETAN      (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    EXFswnet  (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    KPPg_SLT  (time, Zl, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    KPPg_TH   (time, Zl, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    PHIHYD    (time, Z, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    SALT      (time, Z, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    SFLUX     (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SIarea    (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SIatmFW   (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SIatmQnt  (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SIdHbATC  (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SIdHbATO  (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SIdHbOCN  (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SIdSbATC  (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SIdSbOCN  (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SIempmr   (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SIfu      (time, lat, lon_b) float32 dask.array<shape=(438, 319, 2160), chunksize=(1, 319, 2160)>\n",
       "    SIfv      (time, lat_b, lon) float32 dask.array<shape=(438, 320, 2159), chunksize=(1, 320, 2159)>\n",
       "    SIheff    (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SIhsnow   (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SIsnPrcp  (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SItflux   (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    SIuheff   (time, lat, lon_b) float32 dask.array<shape=(438, 319, 2160), chunksize=(1, 319, 2160)>\n",
       "    SIuice    (time, lat, lon_b) float32 dask.array<shape=(438, 319, 2160), chunksize=(1, 319, 2160)>\n",
       "    SIvheff   (time, lat_b, lon) float32 dask.array<shape=(438, 320, 2159), chunksize=(1, 320, 2159)>\n",
       "    SIvice    (time, lat_b, lon) float32 dask.array<shape=(438, 320, 2159), chunksize=(1, 320, 2159)>\n",
       "    TFLUX     (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    THETA     (time, Z, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    TOTSTEND  (time, Z, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    TOTTTEND  (time, Z, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    UVEL      (time, Z, lat, lon_b) float32 dask.array<shape=(438, 42, 319, 2160), chunksize=(1, 42, 319, 2160)>\n",
       "    VVEL      (time, Z, lat_b, lon) float32 dask.array<shape=(438, 42, 320, 2159), chunksize=(1, 42, 320, 2159)>\n",
       "    WSLTMASS  (time, Zl, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    WTHMASS   (time, Zl, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    WVEL      (time, Zl, lat, lon) float32 dask.array<shape=(438, 42, 319, 2159), chunksize=(1, 42, 319, 2159)>\n",
       "    oceFreez  (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    oceQsw    (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    oceTAUX   (time, lat, lon_b) float32 dask.array<shape=(438, 319, 2160), chunksize=(1, 319, 2160)>\n",
       "    oceTAUY   (time, lat_b, lon) float32 dask.array<shape=(438, 320, 2159), chunksize=(1, 320, 2159)>\n",
       "    surForcS  (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>\n",
       "    surForcT  (time, lat, lon) float32 dask.array<shape=(438, 319, 2159), chunksize=(1, 319, 2159)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modified_grid_model = ds_model.isel(XC=slice(0,-1),\n",
    "                                    YC=slice(0,-1)).rename({'XC':'lon',\n",
    "                                                            'XG':'lon_b',\n",
    "                                                            'YC':'lat',\n",
    "                                                            'YG':'lat_b'})\n",
    "\n",
    "modified_grid_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the observations we choose method b. \n",
    "\n",
    "Since the data are given on a regular 2 by 2 degree grid, we construct the bounding coordinates by subtracting 1 deg of the center point and adding another point at the end. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 32, lat_b: 33, lon: 180, lon_b: 181, nbnds: 2, time: 1990)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -88.0 -86.0 -84.0 -82.0 ... -32.0 -30.0 -28.0 -26.0\n",
       "  * lon        (lon) float64 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0\n",
       "  * time       (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2019-10-01\n",
       "  * lon_b      (lon_b) float64 -1.0 1.0 3.0 5.0 7.0 ... 353.0 355.0 357.0 359.0\n",
       "  * lat_b      (lat_b) float64 -89.0 -87.0 -85.0 -83.0 ... -29.0 -27.0 -25.0\n",
       "Dimensions without coordinates: nbnds\n",
       "Data variables:\n",
       "    time_bnds  (time, nbnds) float64 dask.array<shape=(1990, 2), chunksize=(12, 2)>\n",
       "    sst        (time, lat, lon) float32 dask.array<shape=(1990, 32, 180), chunksize=(12, 32, 180)>\n",
       "Attributes:\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science > Oceans > Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    geospatial_lon_min:              -1.0\n",
       "    geospatial_lon_max:              359.0\n",
       "    geospatial_laty_max:             89.0\n",
       "    geospatial_laty_min:             -89.0\n",
       "    geospatial_lat_max:              89.0\n",
       "    geospatial_lat_min:              -89.0\n",
       "    geospatial_lat_units:            degrees_north\n",
       "    geospatial_lon_units:            degrees_east\n",
       "    cdm_data_type:                   Grid\n",
       "    project:                         NOAA Extended Reconstructed Sea Surface ...\n",
       "    original_publisher_url:          http://www.ncdc.noaa.gov\n",
       "    References:                      https://www.ncdc.noaa.gov/data-access/ma...\n",
       "    source:                          In situ data: ICOADS R3.0 before 2015, N...\n",
       "    title:                           NOAA ERSSTv5 (in situ only)\n",
       "    history:                         created 07/2017 by PSD data using NCEI's...\n",
       "    institution:                     This version written at NOAA/ESRL PSD: o...\n",
       "    citation:                        Huang et al, 2017: Extended Reconstructe...\n",
       "    platform:                        Ship and Buoy SSTs from ICOADS R3.0 and ...\n",
       "    standard_name_vocabulary:        CF Standard Name Table (v40, 25 January ...\n",
       "    processing_level:                NOAA Level 4\n",
       "    Conventions:                     CF-1.6, ACDD-1.3\n",
       "    metadata_link:                   :metadata_link = https://doi.org/10.7289...\n",
       "    creator_name:                    Boyin Huang (original)\n",
       "    date_created:                    2017-06-30T12:18:00Z (original)\n",
       "    product_version:                 Version 5\n",
       "    creator_url_original:            https://www.ncei.noaa.gov\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2019-11-04\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lon_boundary = np.hstack([ds_obs.coords['lon'].data - 1.0, ds_obs.coords['lon'][-1].data + 1])\n",
    "lat_boundary = np.hstack([ds_obs.coords['lat'].data - 1.0, ds_obs.coords['lat'][-1].data + 1])\n",
    "\n",
    "modified_grid_obs = ds_obs\n",
    "modified_grid_obs.coords['lon_b'] = xr.DataArray(lon_boundary, dims=['lon_b'])\n",
    "modified_grid_obs.coords['lat_b'] = xr.DataArray(lat_boundary, dims=['lat_b'])\n",
    "modified_grid_obs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create a conservative `regridder` by passing the modified grids and the method `conservative`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create weight file: conservative_319x2159_32x180.nc\n",
      "Remove file conservative_319x2159_32x180.nc\n"
     ]
    }
   ],
   "source": [
    "regridder = xe.Regridder(modified_grid_model, modified_grid_obs, 'conservative') #since this is global we need to pass periodic\n",
    "regridder.clean_weight_file()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then apply this new regridder to the model again. Note that we have to cut one data point of again, as done above for the full dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/xesmf/smm.py:70: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'THETA' (lat: 32, lon: 180)>\n",
       "array([[ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,  0.      ],\n",
       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,  0.      ],\n",
       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,  0.      ],\n",
       "       ...,\n",
       "       [18.509935, 19.955008, 19.740049, ..., 20.661682, 20.400264, 20.323238],\n",
       "       [19.04155 , 20.459897, 20.165475, ..., 21.616568, 21.226791, 21.009231],\n",
       "       [19.162298, 20.607543, 20.363888, ..., 21.796622, 21.500974, 21.201145]])\n",
       "Coordinates:\n",
       "    PHrefC   float32 49.05\n",
       "    Z        float32 -5.0\n",
       "    drF      float32 10.0\n",
       "  * lon      (lon) float64 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * lat      (lat) float64 -88.0 -86.0 -84.0 -82.0 ... -32.0 -30.0 -28.0 -26.0\n",
       "Attributes:\n",
       "    regrid_method:  conservative"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst_model_regridded_cons = regridder(sst_model.isel(lon=slice(0,-1), lat=slice(0,-1)))\n",
    "sst_model_regridded_cons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compare the difference between the datasets using both the interpolation and conservative method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f464348bb38>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[16,4])\n",
    "plt.subplot(1,2,1)\n",
    "diff = sst_model_regridded - sst_obs\n",
    "diff.plot(vmax=1)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "diff = sst_model_regridded_cons - sst_obs\n",
    "diff.plot(vmax=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected the downsampling (regridding to lower resolution) via interpolation shows more noise. But even in the conservative regridding case there are still many alternating positive and negative anomalies, indicative of the fact that the observations so not resolve features down to the scale of the grid. This is a common issue with observational datasets, which have to use smoothing techniques to aquire a gridded dataset from point measurements.\n",
    "\n",
    "So lastly we will conservatively regrid both datasets to an even coarser resolution (Method 3.) to alleviate some of this problematic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do this we define a coarse global grid using `xesmf.util.grid_global` and cut it appropriately (remember to leave the bounding coordinates `lon_b` and `lat_b` one element longer)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (x: 72, x_b: 73, y: 16, y_b: 17)\n",
       "Coordinates:\n",
       "    lon      (y, x) float64 -177.5 -172.5 -167.5 -162.5 ... 167.5 172.5 177.5\n",
       "    lat      (y, x) float64 -87.5 -87.5 -87.5 -87.5 ... -12.5 -12.5 -12.5 -12.5\n",
       "    lon_b    (y_b, x_b) int64 -180 -175 -170 -165 -160 ... 160 165 170 175 180\n",
       "    lat_b    (y_b, x_b) int64 -90 -90 -90 -90 -90 -90 ... -10 -10 -10 -10 -10\n",
       "Dimensions without coordinates: x, x_b, y, y_b\n",
       "Data variables:\n",
       "    *empty*"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coarse_grid = xe.util.grid_global(5,5).isel(y=slice(0,16), y_b=slice(0,17))\n",
    "coarse_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we define two new regridders and create dataarrays like before.\n",
    "First with the modified model grid and the new coarse grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create weight file: conservative_319x2159_16x72.nc\n",
      "Remove file conservative_319x2159_16x72.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/xesmf/smm.py:70: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    }
   ],
   "source": [
    "regridder = xe.Regridder(modified_grid_model, coarse_grid, 'conservative') #since this is global we need to pass periodic\n",
    "regridder.clean_weight_file()\n",
    "sst_coarse_model = regridder(sst_model.isel(lon=slice(0,-1), lat=slice(0,-1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then with the modified model grid and the new coarse grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create weight file: conservative_32x180_16x72.nc\n",
      "Remove file conservative_32x180_16x72.nc\n"
     ]
    }
   ],
   "source": [
    "regridder = xe.Regridder(modified_grid_obs, coarse_grid, 'conservative') #since this is global we need to pass periodic\n",
    "regridder.clean_weight_file()\n",
    "sst_coarse_obs = regridder(sst_obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a final step we visualize all four comparisons next to each other usign a Stereographic projection of the South Pole:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cartopy.crs as ccrs\n",
    "\n",
    "fig, axarr = plt.subplots(ncols=2, nrows=2, subplot_kw={'projection':ccrs.SouthPolarStereo()}, figsize=[15,15])\n",
    "\n",
    "for ds, ax in zip([sst_model - sst_obs_regridded, sst_model_regridded - sst_obs, sst_model_regridded_cons - sst_obs, sst_coarse_model - sst_coarse_obs],\n",
    "                  axarr.flat):\n",
    "    ds.plot.contourf(ax=ax, levels=21, vmax=1, x='lon', y='lat', transform=ccrs.PlateCarree())\n",
    "    ax.set_extent([-180, 180, -90, -30], ccrs.PlateCarree())\n",
    "    ax.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ultimately which method you use will depend on the particular use case. Before regridding, carefully inspect your datasets and think of what you want to achieve:\n",
    "    \n",
    "  * Are both datasets of a similar resolution and relatively smooth? Then using linear interpolation should be acceptable and is by far the easiest way to compare the datasets.\n",
    "  * Is the goal of the comparison to evaluate differences on larger scales than the grid scale? Then create a coarser grid and interpolate conservatively to gain a larger scale perspective without getting distracted by smaller scale features."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.9 (default, May 17 2022, 12:55:41) \n[Clang 13.1.6 (clang-1316.0.21.2.5)]"
  },
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: src/lectures/models/xgcm.ipynb
================================================
{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating properties in the native model grid using xgcm\n",
    "\n",
    "For maximum accuracy it is always preferrable to calculate properties in the closest way possible to the model code, which at the least means executing calculations on the native grid. For comparison you can then regrid properties afterwards. To demonstrate how to do this we now calculate the meridional sea surface temperature gradient for the SOSE output.\n",
    "\n",
    "For these sort of calculations we use [xgcm](https://xgcm.readthedocs.io/en/latest/index.html), which helps us to deal with the staggered grids that models use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgcm import Grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in order to work with xgcm we have to create a `Grid` object which 'understands' which coordinates of a dataset are representing the various staggered grid positions.\n",
    "\n",
    "But first we need to check quickly how the model grid is designed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray ()>\n",
      "array(False)\n",
      "Coordinates:\n",
      "    XC       float32 0.083333336\n",
      "    XG       float32 5.551115e-17\n",
      "<xarray.DataArray ()>\n",
      "array(False)\n",
      "Coordinates:\n",
      "    YC       float32 -77.87497\n",
      "    YG       float32 -77.9583\n"
     ]
    }
   ],
   "source": [
    "print(ds_model.XC[0] < ds_model.XG[0])\n",
    "print(ds_model.YC[0] < ds_model.YG[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that for the first datapoint of the dataset, the 'grid coordinates' (`XG` and `YG`) have smaller values than the 'tracer coordinates' (`XC` and `YC`). This means that for both `axes` (each axis decribes a logical direction and both tracer and grid coordinates), the tracer coordinate is at the center and the grid coordinate is on the left (this can be on the right for some model setups; thus, carefull checking beforehand is always recommended).\n",
    "\n",
    "With this information, we can now construct the `Grid` object and configure it properly (this step is only required once for a single grid)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = Grid(ds_model,\n",
    "            coords={'X': {'center': 'XC', 'left': 'XG'},\n",
    "                    'Y': {'center': 'YC', 'left': 'YG'}})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grid` object can now be used to apply various operations on the data variables of the dataset. Most GCMs calculate all properties by using simple differences and interpolation between neighboring cells.\n",
    "\n",
    "We first select the surface layer of temperature and average over year 2009."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sst = ds_model.THETA.isel(Z=0).sel(time='2009').mean('time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to compute the meridional gradient of sea surface temperature, we have to simply compute the finite difference along axis `Y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'sub-9563c8ac51aadebd24c532c2db11db98' (YG: 320, XC: 2160)>\n",
       "dask.array<shape=(320, 2160), dtype=float32, chunksize=(1, 2160)>\n",
       "Coordinates:\n",
       "  * YG       (YG) float32 -77.9583 -77.79163 -77.62497 ... -24.9583 -24.791632\n",
       "  * XC       (XC) float32 0.083333336 0.25 0.4166667 ... 359.75 359.9167"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltay_sst = grid.diff(sst, 'Y')\n",
    "deltay_sst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you look carefully, you see that the `Y` dimension has been shifted, from `YC` to `YG`. The gradient is defined on the cell face or $v$ velocity point. This is however just a difference between cells. To derive the gradient in the y direction in physical units `degC/m`, we need to divide the array by the distance along the `Y` axes between adjacent tracer points `dyC` , which itself is located at the $v$ velocity point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (YG: 320, XC: 2160)>\n",
       "dask.array<shape=(320, 2160), dtype=float32, chunksize=(1, 2160)>\n",
       "Coordinates:\n",
       "  * YG       (YG) float32 -77.9583 -77.79163 -77.62497 ... -24.9583 -24.791632\n",
       "  * XC       (XC) float32 0.083333336 0.25 0.4166667 ... 359.75 359.9167\n",
       "    dxG      (YG, XC) float32 dask.array<shape=(320, 2160), chunksize=(320, 2160)>\n",
       "    dyC      (YG, XC) float32 dask.array<shape=(320, 2160), chunksize=(320, 2160)>\n",
       "    rAs      (YG, XC) float32 dask.array<shape=(320, 2160), chunksize=(320, 2160)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsst_dy = deltay_sst / ds_model.dyC\n",
    "dsst_dy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets select the surface layer and the first timestep and plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f46382c17f0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes(projection=ccrs.SouthPolarStereo())\n",
    "dsst_dy.plot(ax=ax, robust=True, transform=ccrs.PlateCarree())\n",
    "ax.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The meridional gradient is positive over the whole domain (temperature is increasing towards the equator), as might be expected. The stronges gradient is associated with the boundary between the retroflection of the warm [Agulhas current](https://en.wikipedia.org/wiki/Agulhas_Current) and the cold waters of the [Antarctic Circumpolar Current](https://en.wikipedia.org/wiki/Antarctic_Circumpolar_Current) in the Indian Ocean sector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.9 (default, May 17 2022, 12:55:41) \n[Clang 13.1.6 (clang-1316.0.21.2.5)]"
  },
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: src/lectures/pandas/basic_pandas.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Fundamentals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Pandas Data Structures: Series\n",
    "\n",
    "A Series represents a one-dimensional array of data. The main difference between a Series and numpy array is that a Series has an _index_. The index contains the labels that we use to access the data.\n",
    "\n",
    "There are many ways to [create a Series](https://pandas.pydata.org/pandas-docs/stable/dsintro.html#series). We will just show a few.\n",
    "\n",
    "(Data are from the NASA [Planetary Fact Sheet](https://nssdc.gsfc.nasa.gov/planetary/factsheet/).)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mercury    3.000000e+23\n",
       "Venus      4.870000e+24\n",
       "Earth      5.970000e+24\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['Mercury', 'Venus', 'Earth']\n",
    "values = [0.3e24, 4.87e24, 5.97e24]\n",
    "masses = pd.Series(values, index=names)\n",
    "masses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series have built in plotting methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "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": [
    "masses.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arithmetic operations and most numpy function can be applied to Series.\n",
    "An important point is that the Series keep their index during such operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mercury    6.006452e-46\n",
       "Venus      2.396820e-48\n",
       "Earth      1.600655e-48\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(masses) / masses**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access the underlying index object if we need to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Mercury', 'Venus', 'Earth'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masses.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing\n",
    "\n",
    "We can get values back out using the index via the `.loc` attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.97e+24"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masses.loc['Earth']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or by raw position using `.iloc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.97e+24"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masses.iloc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pass a list or array to loc to get multiple rows back:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Venus    4.870000e+24\n",
       "Earth    5.970000e+24\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masses.loc[['Venus', 'Earth']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can even use slice notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mercury    3.000000e+23\n",
       "Venus      4.870000e+24\n",
       "Earth      5.970000e+24\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masses.loc['Mercury':'Earth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mercury    3.000000e+23\n",
       "Venus      4.870000e+24\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masses.iloc[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we need to, we can always get the raw data back out as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.00e+23, 4.87e+24, 5.97e+24])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masses.values # a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Mercury', 'Venus', 'Earth'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masses.index # a pandas Index object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Data Structures: DataFrame\n",
    "\n",
    "There is a lot more to Series, but they are limit to a single \"column\". A more useful Pandas data structure is the DataFrame. A DataFrame is basically a bunch of series that share the same index. It's a lot like a table in a spreadsheet.\n",
    "\n",
    "Below we create a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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>mass</th>\n",
       "      <th>diameter</th>\n",
       "      <th>rotation_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mercury</th>\n",
       "      <td>3.000000e+23</td>\n",
       "      <td>4879000.0</td>\n",
       "      <td>1407.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venus</th>\n",
       "      <td>4.870000e+24</td>\n",
       "      <td>12104000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>5.970000e+24</td>\n",
       "      <td>12756000.0</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mass    diameter  rotation_period\n",
       "Mercury  3.000000e+23   4879000.0           1407.6\n",
       "Venus    4.870000e+24  12104000.0              NaN\n",
       "Earth    5.970000e+24  12756000.0             23.9"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first we create a dictionary\n",
    "data = {'mass': [0.3e24, 4.87e24, 5.97e24],       # kg\n",
    "        'diameter': [4879e3, 12_104e3, 12_756e3], # m\n",
    "        'rotation_period': [1407.6, np.nan, 23.9] # h\n",
    "       }\n",
    "df = pd.DataFrame(data, index=['Mercury', 'Venus', 'Earth'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas handles missing data very elegantly, keeping track of it through all calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3 entries, Mercury to Earth\n",
      "Data columns (total 3 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   mass             3 non-null      float64\n",
      " 1   diameter         3 non-null      float64\n",
      " 2   rotation_period  2 non-null      float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 96.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A wide range of statistical functions are available on both Series and DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mass               3.000000e+23\n",
       "diameter           4.879000e+06\n",
       "rotation_period    2.390000e+01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mass               3.713333e+24\n",
       "diameter           9.913000e+06\n",
       "rotation_period    7.157500e+02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mass               3.006765e+24\n",
       "diameter           4.371744e+06\n",
       "rotation_period    9.784237e+02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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>mass</th>\n",
       "      <th>diameter</th>\n",
       "      <th>rotation_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.713333e+24</td>\n",
       "      <td>9.913000e+06</td>\n",
       "      <td>715.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.006765e+24</td>\n",
       "      <td>4.371744e+06</td>\n",
       "      <td>978.423653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000e+23</td>\n",
       "      <td>4.879000e+06</td>\n",
       "      <td>23.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.585000e+24</td>\n",
       "      <td>8.491500e+06</td>\n",
       "      <td>369.825000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.870000e+24</td>\n",
       "      <td>1.210400e+07</td>\n",
       "      <td>715.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.420000e+24</td>\n",
       "      <td>1.243000e+07</td>\n",
       "      <td>1061.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.970000e+24</td>\n",
       "      <td>1.275600e+07</td>\n",
       "      <td>1407.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mass      diameter  rotation_period\n",
       "count  3.000000e+00  3.000000e+00         2.000000\n",
       "mean   3.713333e+24  9.913000e+06       715.750000\n",
       "std    3.006765e+24  4.371744e+06       978.423653\n",
       "min    3.000000e+23  4.879000e+06        23.900000\n",
       "25%    2.585000e+24  8.491500e+06       369.825000\n",
       "50%    4.870000e+24  1.210400e+07       715.750000\n",
       "75%    5.420000e+24  1.243000e+07      1061.675000\n",
       "max    5.970000e+24  1.275600e+07      1407.600000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get a single column as a Series using python's getitem syntax on the DataFrame object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mercury    3.000000e+23\n",
       "Venus      4.870000e+24\n",
       "Earth      5.970000e+24\n",
       "Name: mass, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mass']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...or using attribute syntax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mercury    3.000000e+23\n",
       "Venus      4.870000e+24\n",
       "Earth      5.970000e+24\n",
       "Name: mass, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing works very similar to series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mass               5.970000e+24\n",
       "diameter           1.275600e+07\n",
       "rotation_period    2.390000e+01\n",
       "Name: Earth, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Earth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mass               5.970000e+24\n",
       "diameter           1.275600e+07\n",
       "rotation_period    2.390000e+01\n",
       "Name: Earth, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we can also specify the column we want to access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.97e+24"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Earth', 'mass']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mercury    3.000000e+23\n",
       "Venus      4.870000e+24\n",
       "Name: mass, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:2, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we make a calculation using columns from the DataFrame, it will keep the same index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mercury    4933.216530\n",
       "Venus      5244.977070\n",
       "Earth      5493.285577\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume =  4/3 * np.pi * (df.diameter/2)**3\n",
    "df.mass / volume"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which we can easily add as another column to the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>mass</th>\n",
       "      <th>diameter</th>\n",
       "      <th>rotation_period</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mercury</th>\n",
       "      <td>3.000000e+23</td>\n",
       "      <td>4879000.0</td>\n",
       "      <td>1407.6</td>\n",
       "      <td>4933.216530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venus</th>\n",
       "      <td>4.870000e+24</td>\n",
       "      <td>12104000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5244.977070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>5.970000e+24</td>\n",
       "      <td>12756000.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>5493.285577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mass    diameter  rotation_period      density\n",
       "Mercury  3.000000e+23   4879000.0           1407.6  4933.216530\n",
       "Venus    4.870000e+24  12104000.0              NaN  5244.977070\n",
       "Earth    5.970000e+24  12756000.0             23.9  5493.285577"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['density'] = df.mass / volume\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging Data\n",
    "\n",
    "Pandas supports a wide range of methods for merging different datasets. These are described extensively in the [documentation](https://pandas.pydata.org/pandas-docs/stable/merging.html). Here we just give a few examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mercury    167\n",
       "Venus      464\n",
       "Earth       15\n",
       "Mars       -65\n",
       "Name: temperature, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature = pd.Series([167, 464, 15, -65],\n",
    "                     index=['Mercury', 'Venus', 'Earth', 'Mars'],\n",
    "                     name='temperature')\n",
    "temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>mass</th>\n",
       "      <th>diameter</th>\n",
       "      <th>rotation_period</th>\n",
       "      <th>density</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mercury</th>\n",
       "      <td>3.000000e+23</td>\n",
       "      <td>4879000.0</td>\n",
       "      <td>1407.6</td>\n",
       "      <td>4933.216530</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venus</th>\n",
       "      <td>4.870000e+24</td>\n",
       "      <td>12104000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5244.977070</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>5.970000e+24</td>\n",
       "      <td>12756000.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>5493.285577</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mass    diameter  rotation_period      density  temperature\n",
       "Mercury  3.000000e+23   4879000.0           1407.6  4933.216530          167\n",
       "Venus    4.870000e+24  12104000.0              NaN  5244.977070          464\n",
       "Earth    5.970000e+24  12756000.0             23.9  5493.285577           15"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns a new DataFrame\n",
    "df.join(temperature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>mass</th>\n",
       "      <th>diameter</th>\n",
       "      <th>rotation_period</th>\n",
       "      <th>density</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mercury</th>\n",
       "      <td>3.000000e+23</td>\n",
       "      <td>4879000.0</td>\n",
       "      <td>1407.6</td>\n",
       "      <td>4933.216530</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venus</th>\n",
       "      <td>4.870000e+24</td>\n",
       "      <td>12104000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5244.977070</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>5.970000e+24</td>\n",
       "      <td>12756000.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>5493.285577</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mars</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mass    diameter  rotation_period      density  temperature\n",
       "Mercury  3.000000e+23   4879000.0           1407.6  4933.216530          167\n",
       "Venus    4.870000e+24  12104000.0              NaN  5244.977070          464\n",
       "Earth    5.970000e+24  12756000.0             23.9  5493.285577           15\n",
       "Mars              NaN         NaN              NaN          NaN          -65"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns a new DataFrame\n",
    "df.join(temperature, how='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>mass</th>\n",
       "      <th>diameter</th>\n",
       "      <th>rotation_period</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mercury</th>\n",
       "      <td>3.000000e+23</td>\n",
       "      <td>4879000.0</td>\n",
       "      <td>1407.6</td>\n",
       "      <td>4933.216530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venus</th>\n",
       "      <td>4.870000e+24</td>\n",
       "      <td>12104000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5244.977070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>5.970000e+24</td>\n",
       "      <td>12756000.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>5493.285577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mars</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mass    diameter  rotation_period      density\n",
       "Mercury  3.000000e+23   4879000.0           1407.6  4933.216530\n",
       "Venus    4.870000e+24  12104000.0              NaN  5244.977070\n",
       "Earth    5.970000e+24  12756000.0             23.9  5493.285577\n",
       "Mars              NaN         NaN              NaN          NaN"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns a new DataFrame\n",
    "everyone = df.reindex(['Mercury', 'Venus', 'Earth', 'Mars'])\n",
    "everyone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also index using a boolean series. This is very useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mass</th>\n",
       "      <th>diameter</th>\n",
       "      <th>rotation_period</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Venus</th>\n",
       "      <td>4.870000e+24</td>\n",
       "      <td>12104000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5244.977070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>5.970000e+24</td>\n",
       "      <td>12756000.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>5493.285577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mass    diameter  rotation_period      density\n",
       "Venus  4.870000e+24  12104000.0              NaN  5244.977070\n",
       "Earth  5.970000e+24  12756000.0             23.9  5493.285577"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults = df[df.mass > 4e24]\n",
    "adults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "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>mass</th>\n",
       "      <th>diameter</th>\n",
       "      <th>rotation_period</th>\n",
       "      <th>density</th>\n",
       "      <th>is_big</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mercury</th>\n",
       "      <td>3.000000e+23</td>\n",
       "      <td>4879000.0</td>\n",
       "      <td>1407.6</td>\n",
       "      <td>4933.216530</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venus</th>\n",
       "      <td>4.870000e+24</td>\n",
       "      <td>12104000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5244.977070</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>5.970000e+24</td>\n",
       "      <td>12756000.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>5493.285577</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mass    diameter  rotation_period      density  is_big\n",
       "Mercury  3.000000e+23   4879000.0           1407.6  4933.216530   False\n",
       "Venus    4.870000e+24  12104000.0              NaN  5244.977070    True\n",
       "Earth    5.970000e+24  12756000.0             23.9  5493.285577    True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['is_big'] = df.mass > 4e24\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modifying Values\n",
    "\n",
    "We often want to modify values in a dataframe based on some rule. To modify values, we need to use `.loc` or `.iloc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "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>mass</th>\n",
       "      <th>diameter</th>\n",
       "      <th>rotation_period</th>\n",
       "      <th>density</th>\n",
       "      <th>is_big</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mercury</th>\n",
       "      <td>3.000000e+23</td>\n",
       "      <td>4879000.0</td>\n",
       "      <td>1407.6</td>\n",
       "      <td>4933.216530</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venus</th>\n",
       "      <td>4.870000e+24</td>\n",
       "      <td>12104001.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5244.977070</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>2.998000e+01</td>\n",
       "      <td>12756000.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>5493.285577</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mass    diameter  rotation_period      density  is_big\n",
       "Mercury  3.000000e+23   4879000.0           1407.6  4933.216530   False\n",
       "Venus    4.870000e+24  12104001.0              NaN  5244.977070    True\n",
       "Earth    2.998000e+01  12756000.0             23.9  5493.285577    True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Earth', 'mass'] = 5.98+24\n",
    "df.loc['Venus', 'diameter'] += 1\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "DataFrames have all kinds of [useful plotting](https://pandas.pydata.org/pandas-docs/stable/visualization.html) built in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mass', ylabel='diameter'>"
      ]
     },
     "execution_count": 34,
     "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": [
    "df.plot(kind='scatter', x='mass', y='diameter', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "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": [
    "df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Indexes\n",
    "\n",
    "Indexes are very powerful. They are a big part of why Pandas is so useful. There are different indices for different types of data. Time Indexes are especially great!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "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": [
    "two_years = pd.date_range(start='2014-01-01', end='2016-01-01', freq='D')\n",
    "timeseries = pd.Series(np.sin(2 *np.pi *two_years.dayofyear / 365),\n",
    "                       index=two_years)\n",
    "timeseries.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use python's slicing notation inside `.loc` to select a date range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "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": [
    "timeseries.loc['2015-01-01':'2015-07-01'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The TimeIndex object has lots of useful attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "            ...\n",
       "            12, 12, 12, 12, 12, 12, 12, 12, 12,  1],\n",
       "           dtype='int64', length=731)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeseries.index.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "            ...\n",
       "            23, 24, 25, 26, 27, 28, 29, 30, 31,  1],\n",
       "           dtype='int64', length=731)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeseries.index.day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Data Files: Weather Station Data\n",
    "\n",
    "In this example, we will use NOAA weather station data from https://www.ncdc.noaa.gov/data-access/land-based-station-data.\n",
    "\n",
    "The details of files we are going to read are described in this [README file](ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/README.txt)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/jovyan/.cache/noaa-data/data.txt'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pooch\n",
    "POOCH = pooch.create(\n",
    "    path=pooch.os_cache(\"noaa-data\"),\n",
    "    base_url=\"doi:10.5281/zenodo.5564850/\",\n",
    "    registry={\n",
    "        \"data.txt\": \"md5:5129dcfd19300eb8d4d8d1673fcfbcb4\",\n",
    "    },\n",
    ")\n",
    "datafile = POOCH.fetch(\"data.txt\")\n",
    "datafile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WBANNO LST_DATE CRX_VN LONGITUDE LATITUDE T_DAILY_MAX T_DAILY_MIN T_DAILY_MEAN T_DAILY_AVG P_DAILY_CALC SOLARAD_DAILY SUR_TEMP_DAILY_TYPE SUR_TEMP_DAILY_MAX SUR_TEMP_DAILY_MIN SUR_TEMP_DAILY_AVG RH_DAILY_MAX RH_DAILY_MIN RH_DAILY_AVG SOIL_MOISTURE_5_DAILY SOIL_MOISTURE_10_DAILY SOIL_MOISTURE_20_DAILY SOIL_MOISTURE_50_DAILY SOIL_MOISTURE_100_DAILY SOIL_TEMP_5_DAILY SOIL_TEMP_10_DAILY SOIL_TEMP_20_DAILY SOIL_TEMP_50_DAILY SOIL_TEMP_100_DAILY \n",
      "64756 20170101  2.422  -73.74   41.79     6.6    -5.4     0.6     2.2     0.0     8.68 C     7.9    -6.6    -0.5    84.8    30.7    53.7 -99.000 -99.000   0.207   0.152   0.175    -0.1     0.0     0.6     1.5     3.4\n",
      "64756 20170102  2.422  -73.74   41.79     4.0    -6.8    -1.4    -1.2     0.0     2.08 C     4.1    -7.1    -1.6    91.1    49.1    77.4 -99.000 -99.000   0.205   0.151   0.173    -0.2     0.0     0.6     1.5     3.3\n",
      "64756 20170103  2.422  -73.74   41.79     4.9     0.7     2.8     2.7    13.1     0.68 C     3.9     0.1     1.6    96.5    80.1    91.5 -99.000 -99.000   0.205   0.150   0.173    -0.1     0.0     0.5     1.5     3.3\n",
      "64756 20170104  2.422  -73.74   41.79     8.7    -1.6     3.6     3.5     1.3     2.85 C     9.4    -4.5     1.3    97.4    34.0    73.1 -99.000 -99.000   0.215   0.153   0.174    -0.1     0.0     0.5     1.5     3.2\n",
      "64756 20170105  2.422  -73.74   41.79    -0.5    -4.6    -2.5    -2.8     0.0     4.90 C     5.0    -7.6    -3.3    51.0    34.4    42.5 -99.000 -99.000   0.215   0.154   0.177    -0.1     0.0     0.5     1.4     3.1\n",
      "64756 20170106  2.422  -73.74   41.79    -2.5   -10.1    -6.3    -4.7     1.3     5.17 C     1.8   -12.9    -5.2    89.8    40.0    60.8 -99.000 -99.000   0.210   0.153   0.177    -0.2     0.0     0.5     1.4     3.1\n",
      "64756 20170107  2.422  -73.74   41.79    -7.3   -11.7    -9.5    -8.7     3.1     1.19 C    -5.0   -19.0    -8.5    84.4    50.9    71.2 -99.000 -99.000   0.204   0.152   0.175    -0.4    -0.1     0.5     1.4     3.0\n",
      "64756 20170108  2.422  -73.74   41.79    -5.9   -14.5   -10.2    -9.4     0.0     6.15 C    -5.5   -23.1   -14.0    76.9    40.3    59.8 -99.000 -99.000   0.206   0.150   0.175    -0.4    -0.2     0.4     1.4     3.0\n",
      "64756 20170109  2.422  -73.74   41.79    -6.5   -20.2   -13.3   -12.5     0.0     5.86 C    -6.4   -23.4   -16.2    82.5    45.1    65.8 -99.000 -99.000   0.223   0.148   0.175    -0.7    -0.4     0.4     1.3     3.0\n"
     ]
    }
   ],
   "source": [
    "! head '/home/jovyan/.cache/noaa-data/data.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a text file on our hard drive called `data.txt`. Examine it.\n",
    "\n",
    "To read it into pandas, we will use the [read_csv](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) function. This function is incredibly complex and powerful. You can use it to extract data from almost any text file. However, you need to understand how to use its various options.\n",
    "\n",
    "With no options, this is what we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WBANNO LST_DATE CRX_VN LONGITUDE LATITUDE T_DAILY_MAX T_DAILY_MIN T_DAILY_MEAN T_DAILY_AVG P_DAILY_CALC SOLARAD_DAILY SUR_TEMP_DAILY_TYPE SUR_TEMP_DAILY_MAX SUR_TEMP_DAILY_MIN SUR_TEMP_DAILY_AVG RH_DAILY_MAX RH_DAILY_MIN RH_DAILY_AVG SOIL_MOISTURE_5_DAILY SOIL_MOISTURE_10_DAILY SOIL_MOISTURE_20_DAILY SOIL_MOISTURE_50_DAILY SOIL_MOISTURE_100_DAILY SOIL_TEMP_5_DAILY SOIL_TEMP_10_DAILY SOIL_TEMP_20_DAILY SOIL_TEMP_50_DAILY SOIL_TEMP_100_DAILY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64756 20170101  2.422  -73.74   41.79     6.6 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64756 20170102  2.422  -73.74   41.79     4.0 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64756 20170103  2.422  -73.74   41.79     4.9 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64756 20170104  2.422  -73.74   41.79     8.7 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64756 20170105  2.422  -73.74   41.79    -0.5 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  WBANNO LST_DATE CRX_VN LONGITUDE LATITUDE T_DAILY_MAX T_DAILY_MIN T_DAILY_MEAN T_DAILY_AVG P_DAILY_CALC SOLARAD_DAILY SUR_TEMP_DAILY_TYPE SUR_TEMP_DAILY_MAX SUR_TEMP_DAILY_MIN SUR_TEMP_DAILY_AVG RH_DAILY_MAX RH_DAILY_MIN RH_DAILY_AVG SOIL_MOISTURE_5_DAILY SOIL_MOISTURE_10_DAILY SOIL_MOISTURE_20_DAILY SOIL_MOISTURE_50_DAILY SOIL_MOISTURE_100_DAILY SOIL_TEMP_5_DAILY SOIL_TEMP_10_DAILY SOIL_TEMP_20_DAILY SOIL_TEMP_50_DAILY SOIL_TEMP_100_DAILY \n",
       "0  64756 20170101  2.422  -73.74   41.79     6.6 ...                                                                                                                                                                                                                                                                                                                                                                                                          \n",
       "1  64756 20170102  2.422  -73.74   41.79     4.0 ...                                                                                                                                                                                                                                                                                                                                                                                                          \n",
       "2  64756 20170103  2.422  -73.74   41.79     4.9 ...                                                                                                                                                                                                                                                                                                                                                                                                          \n",
       "3  64756 20170104  2.422  -73.74   41.79     8.7 ...                                                                                                                                                                                                                                                                                                                                                                                                          \n",
       "4  64756 20170105  2.422  -73.74   41.79    -0.5 ...                                                                                                                                                                                                                                                                                                                                                                                                          "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(datafile)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas failed to identify the different columns. This is because it was expecting standard CSV (comma-separated values) file. In our file, instead, the values are separated by whitespace. And not a single whilespace--the amount of whitespace between values varies. We can tell pandas this using the `sep` keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>WBANNO</th>\n",
       "      <th>LST_DATE</th>\n",
       "      <th>CRX_VN</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_5_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170101</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.175</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170102</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170103</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>13.1</td>\n",
       "      <td>...</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170104</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.174</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170105</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.177</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   WBANNO  LST_DATE  CRX_VN  LONGITUDE  LATITUDE  T_DAILY_MAX  T_DAILY_MIN  \\\n",
       "0   64756  20170101   2.422     -73.74     41.79          6.6         -5.4   \n",
       "1   64756  20170102   2.422     -73.74     41.79          4.0         -6.8   \n",
       "2   64756  20170103   2.422     -73.74     41.79          4.9          0.7   \n",
       "3   64756  20170104   2.422     -73.74     41.79          8.7         -1.6   \n",
       "4   64756  20170105   2.422     -73.74     41.79         -0.5         -4.6   \n",
       "\n",
       "   T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  ...  SOIL_MOISTURE_5_DAILY  \\\n",
       "0           0.6          2.2           0.0  ...                  -99.0   \n",
       "1          -1.4         -1.2           0.0  ...                  -99.0   \n",
       "2           2.8          2.7          13.1  ...                  -99.0   \n",
       "3           3.6          3.5           1.3  ...                  -99.0   \n",
       "4          -2.5         -2.8           0.0  ...                  -99.0   \n",
       "\n",
       "  SOIL_MOISTURE_10_DAILY  SOIL_MOISTURE_20_DAILY  SOIL_MOISTURE_50_DAILY  \\\n",
       "0                  -99.0                   0.207                   0.152   \n",
       "1                  -99.0                   0.205                   0.151   \n",
       "2                  -99.0                   0.205                   0.150   \n",
       "3                  -99.0                   0.215                   0.153   \n",
       "4                  -99.0                   0.215                   0.154   \n",
       "\n",
       "   SOIL_MOISTURE_100_DAILY  SOIL_TEMP_5_DAILY  SOIL_TEMP_10_DAILY  \\\n",
       "0                    0.175               -0.1                 0.0   \n",
       "1                    0.173               -0.2                 0.0   \n",
       "2                    0.173               -0.1                 0.0   \n",
       "3                    0.174               -0.1                 0.0   \n",
       "4                    0.177               -0.1                 0.0   \n",
       "\n",
       "   SOIL_TEMP_20_DAILY  SOIL_TEMP_50_DAILY  SOIL_TEMP_100_DAILY  \n",
       "0                 0.6                 1.5                  3.4  \n",
       "1                 0.6                 1.5                  3.3  \n",
       "2                 0.5                 1.5                  3.3  \n",
       "3                 0.5                 1.5                  3.2  \n",
       "4                 0.5                 1.4                  3.1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(datafile, sep='\\s+')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! It worked. \n",
    "\n",
    "If we look closely, we will see there are lots of -99 and -9999 values in the file. The [README file](ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/README.txt) tells us that these are values used to represent missing data. Let's tell this to pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>WBANNO</th>\n",
       "      <th>LST_DATE</th>\n",
       "      <th>CRX_VN</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_5_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170101</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.175</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170102</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170103</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>13.1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170104</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.174</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170105</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.177</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   WBANNO  LST_DATE  CRX_VN  LONGITUDE  LATITUDE  T_DAILY_MAX  T_DAILY_MIN  \\\n",
       "0   64756  20170101   2.422     -73.74     41.79          6.6         -5.4   \n",
       "1   64756  20170102   2.422     -73.74     41.79          4.0         -6.8   \n",
       "2   64756  20170103   2.422     -73.74     41.79          4.9          0.7   \n",
       "3   64756  20170104   2.422     -73.74     41.79          8.7         -1.6   \n",
       "4   64756  20170105   2.422     -73.74     41.79         -0.5         -4.6   \n",
       "\n",
       "   T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  ...  SOIL_MOISTURE_5_DAILY  \\\n",
       "0           0.6          2.2           0.0  ...                    NaN   \n",
       "1          -1.4         -1.2           0.0  ...                    NaN   \n",
       "2           2.8          2.7          13.1  ...                    NaN   \n",
       "3           3.6          3.5           1.3  ...                    NaN   \n",
       "4          -2.5         -2.8           0.0  ...                    NaN   \n",
       "\n",
       "  SOIL_MOISTURE_10_DAILY  SOIL_MOISTURE_20_DAILY  SOIL_MOISTURE_50_DAILY  \\\n",
       "0                    NaN                   0.207                   0.152   \n",
       "1                    NaN                   0.205                   0.151   \n",
       "2                    NaN                   0.205                   0.150   \n",
       "3                    NaN                   0.215                   0.153   \n",
       "4                    NaN                   0.215                   0.154   \n",
       "\n",
       "   SOIL_MOISTURE_100_DAILY  SOIL_TEMP_5_DAILY  SOIL_TEMP_10_DAILY  \\\n",
       "0                    0.175               -0.1                 0.0   \n",
       "1                    0.173               -0.2                 0.0   \n",
       "2                    0.173               -0.1                 0.0   \n",
       "3                    0.174               -0.1                 0.0   \n",
       "4                    0.177               -0.1                 0.0   \n",
       "\n",
       "   SOIL_TEMP_20_DAILY  SOIL_TEMP_50_DAILY  SOIL_TEMP_100_DAILY  \n",
       "0                 0.6                 1.5                  3.4  \n",
       "1                 0.6                 1.5                  3.3  \n",
       "2                 0.5                 1.5                  3.3  \n",
       "3                 0.5                 1.5                  3.2  \n",
       "4                 0.5                 1.4                  3.1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(datafile, sep='\\s+', na_values=[-9999.0, -99.0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great. The missing data is now represented by `NaN`.\n",
    "\n",
    "What data types did pandas infer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 365 entries, 0 to 364\n",
      "Data columns (total 28 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   WBANNO                   365 non-null    int64  \n",
      " 1   LST_DATE                 365 non-null    int64  \n",
      " 2   CRX_VN                   365 non-null    float64\n",
      " 3   LONGITUDE                365 non-null    float64\n",
      " 4   LATITUDE                 365 non-null    float64\n",
      " 5   T_DAILY_MAX              364 non-null    float64\n",
      " 6   T_DAILY_MIN              364 non-null    float64\n",
      " 7   T_DAILY_MEAN             364 non-null    float64\n",
      " 8   T_DAILY_AVG              364 non-null    float64\n",
      " 9   P_DAILY_CALC             364 non-null    float64\n",
      " 10  SOLARAD_DAILY            364 non-null    float64\n",
      " 11  SUR_TEMP_DAILY_TYPE      365 non-null    object \n",
      " 12  SUR_TEMP_DAILY_MAX       364 non-null    float64\n",
      " 13  SUR_TEMP_DAILY_MIN       364 non-null    float64\n",
      " 14  SUR_TEMP_DAILY_AVG       364 non-null    float64\n",
      " 15  RH_DAILY_MAX             364 non-null    float64\n",
      " 16  RH_DAILY_MIN             364 non-null    float64\n",
      " 17  RH_DAILY_AVG             364 non-null    float64\n",
      " 18  SOIL_MOISTURE_5_DAILY    317 non-null    float64\n",
      " 19  SOIL_MOISTURE_10_DAILY   317 non-null    float64\n",
      " 20  SOIL_MOISTURE_20_DAILY   336 non-null    float64\n",
      " 21  SOIL_MOISTURE_50_DAILY   364 non-null    float64\n",
      " 22  SOIL_MOISTURE_100_DAILY  359 non-null    float64\n",
      " 23  SOIL_TEMP_5_DAILY        364 non-null    float64\n",
      " 24  SOIL_TEMP_10_DAILY       364 non-null    float64\n",
      " 25  SOIL_TEMP_20_DAILY       364 non-null    float64\n",
      " 26  SOIL_TEMP_50_DAILY       364 non-null    float64\n",
      " 27  SOIL_TEMP_100_DAILY      364 non-null    float64\n",
      "dtypes: float64(25), int64(2), object(1)\n",
      "memory usage: 80.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One problem here is that pandas did not recognize the `LDT_DATE` column as a date. Let's help it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 365 entries, 0 to 364\n",
      "Data columns (total 28 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   WBANNO                   365 non-null    int64         \n",
      " 1   LST_DATE                 365 non-null    datetime64[ns]\n",
      " 2   CRX_VN                   365 non-null    float64       \n",
      " 3   LONGITUDE                365 non-null    float64       \n",
      " 4   LATITUDE                 365 non-null    float64       \n",
      " 5   T_DAILY_MAX              364 non-null    float64       \n",
      " 6   T_DAILY_MIN              364 non-null    float64       \n",
      " 7   T_DAILY_MEAN             364 non-null    float64       \n",
      " 8   T_DAILY_AVG              364 non-null    float64       \n",
      " 9   P_DAILY_CALC             364 non-null    float64       \n",
      " 10  SOLARAD_DAILY            364 non-null    float64       \n",
      " 11  SUR_TEMP_DAILY_TYPE      365 non-null    object        \n",
      " 12  SUR_TEMP_DAILY_MAX       364 non-null    float64       \n",
      " 13  SUR_TEMP_DAILY_MIN       364 non-null    float64       \n",
      " 14  SUR_TEMP_DAILY_AVG       364 non-null    float64       \n",
      " 15  RH_DAILY_MAX             364 non-null    float64       \n",
      " 16  RH_DAILY_MIN             364 non-null    float64       \n",
      " 17  RH_DAILY_AVG             364 non-null    float64       \n",
      " 18  SOIL_MOISTURE_5_DAILY    317 non-null    float64       \n",
      " 19  SOIL_MOISTURE_10_DAILY   317 non-null    float64       \n",
      " 20  SOIL_MOISTURE_20_DAILY   336 non-null    float64       \n",
      " 21  SOIL_MOISTURE_50_DAILY   364 non-null    float64       \n",
      " 22  SOIL_MOISTURE_100_DAILY  359 non-null    float64       \n",
      " 23  SOIL_TEMP_5_DAILY        364 non-null    float64       \n",
      " 24  SOIL_TEMP_10_DAILY       364 non-null    float64       \n",
      " 25  SOIL_TEMP_20_DAILY       364 non-null    float64       \n",
      " 26  SOIL_TEMP_50_DAILY       364 non-null    float64       \n",
      " 27  SOIL_TEMP_100_DAILY      364 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(25), int64(1), object(1)\n",
      "memory usage: 80.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(datafile, sep='\\s+',\n",
    "                 na_values=[-9999.0, -99.0],\n",
    "                 parse_dates=[1])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It worked! Finally, let's tell pandas to use the date column as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WBANNO</th>\n",
       "      <th>CRX_VN</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>SOLARAD_DAILY</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_5_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LST_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.68</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.175</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.08</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>13.1</td>\n",
       "      <td>0.68</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.174</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.90</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.177</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            WBANNO  CRX_VN  LONGITUDE  LATITUDE  T_DAILY_MAX  T_DAILY_MIN  \\\n",
       "LST_DATE                                                                    \n",
       "2017-01-01   64756   2.422     -73.74     41.79          6.6         -5.4   \n",
       "2017-01-02   64756   2.422     -73.74     41.79          4.0         -6.8   \n",
       "2017-01-03   64756   2.422     -73.74     41.79          4.9          0.7   \n",
       "2017-01-04   64756   2.422     -73.74     41.79          8.7         -1.6   \n",
       "2017-01-05   64756   2.422     -73.74     41.79         -0.5         -4.6   \n",
       "\n",
       "            T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  SOLARAD_DAILY  ...  \\\n",
       "LST_DATE                                                            ...   \n",
       "2017-01-01           0.6          2.2           0.0           8.68  ...   \n",
       "2017-01-02          -1.4         -1.2           0.0           2.08  ...   \n",
       "2017-01-03           2.8          2.7          13.1           0.68  ...   \n",
       "2017-01-04           3.6          3.5           1.3           2.85  ...   \n",
       "2017-01-05          -2.5         -2.8           0.0           4.90  ...   \n",
       "\n",
       "           SOIL_MOISTURE_5_DAILY  SOIL_MOISTURE_10_DAILY  \\\n",
       "LST_DATE                                                   \n",
       "2017-01-01                   NaN                     NaN   \n",
       "2017-01-02                   NaN                     NaN   \n",
       "2017-01-03                   NaN                     NaN   \n",
       "2017-01-04                   NaN                     NaN   \n",
       "2017-01-05                   NaN                     NaN   \n",
       "\n",
       "            SOIL_MOISTURE_20_DAILY  SOIL_MOISTURE_50_DAILY  \\\n",
       "LST_DATE                                                     \n",
       "2017-01-01                   0.207                   0.152   \n",
       "2017-01-02                   0.205                   0.151   \n",
       "2017-01-03                   0.205                   0.150   \n",
       "2017-01-04                   0.215                   0.153   \n",
       "2017-01-05                   0.215                   0.154   \n",
       "\n",
       "            SOIL_MOISTURE_100_DAILY  SOIL_TEMP_5_DAILY  SOIL_TEMP_10_DAILY  \\\n",
       "LST_DATE                                                                     \n",
       "2017-01-01                    0.175               -0.1                 0.0   \n",
       "2017-01-02                    0.173               -0.2                 0.0   \n",
       "2017-01-03                    0.173               -0.1                 0.0   \n",
       "2017-01-04                    0.174               -0.1                 0.0   \n",
       "2017-01-05                    0.177               -0.1                 0.0   \n",
       "\n",
       "            SOIL_TEMP_20_DAILY  SOIL_TEMP_50_DAILY  SOIL_TEMP_100_DAILY  \n",
       "LST_DATE                                                                 \n",
       "2017-01-01                 0.6                 1.5                  3.4  \n",
       "2017-01-02                 0.6                 1.5                  3.3  \n",
       "2017-01-03                 0.5                 1.5                  3.3  \n",
       "2017-01-04                 0.5                 1.5                  3.2  \n",
       "2017-01-05                 0.5                 1.4                  3.1  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('LST_DATE')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now access values by time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WBANNO                     64756\n",
       "CRX_VN                     2.422\n",
       "LONGITUDE                 -73.74\n",
       "LATITUDE                   41.79\n",
       "T_DAILY_MAX                 19.3\n",
       "T_DAILY_MIN                 12.3\n",
       "T_DAILY_MEAN                15.8\n",
       "T_DAILY_AVG                 16.3\n",
       "P_DAILY_CALC                 4.9\n",
       "SOLARAD_DAILY               3.93\n",
       "SUR_TEMP_DAILY_TYPE            C\n",
       "SUR_TEMP_DAILY_MAX          22.3\n",
       "SUR_TEMP_DAILY_MIN          11.9\n",
       "SUR_TEMP_DAILY_AVG          17.7\n",
       "RH_DAILY_MAX                94.7\n",
       "RH_DAILY_MIN                76.4\n",
       "RH_DAILY_AVG                89.5\n",
       "SOIL_MOISTURE_5_DAILY      0.148\n",
       "SOIL_MOISTURE_10_DAILY     0.113\n",
       "SOIL_MOISTURE_20_DAILY     0.094\n",
       "SOIL_MOISTURE_50_DAILY     0.114\n",
       "SOIL_MOISTURE_100_DAILY    0.151\n",
       "SOIL_TEMP_5_DAILY           21.4\n",
       "SOIL_TEMP_10_DAILY          21.7\n",
       "SOIL_TEMP_20_DAILY          22.1\n",
       "SOIL_TEMP_50_DAILY          22.2\n",
       "SOIL_TEMP_100_DAILY         21.5\n",
       "Name: 2017-08-07 00:00:00, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2017-08-07']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or use slicing to get a range:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "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>WBANNO</th>\n",
       "      <th>CRX_VN</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>SOLARAD_DAILY</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_5_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LST_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-01</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>23.9</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>19.28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.163</td>\n",
       "      <td>25.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>23.7</td>\n",
       "      <td>21.9</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-02</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>29.8</td>\n",
       "      <td>18.4</td>\n",
       "      <td>24.1</td>\n",
       "      <td>23.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.162</td>\n",
       "      <td>26.8</td>\n",
       "      <td>26.4</td>\n",
       "      <td>24.5</td>\n",
       "      <td>22.3</td>\n",
       "      <td>20.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-03</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>28.3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.162</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.3</td>\n",
       "      <td>24.8</td>\n",
       "      <td>22.8</td>\n",
       "      <td>20.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-04</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>26.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>19.7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.161</td>\n",
       "      <td>25.9</td>\n",
       "      <td>25.8</td>\n",
       "      <td>24.6</td>\n",
       "      <td>22.9</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-05</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.90</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.161</td>\n",
       "      <td>25.3</td>\n",
       "      <td>25.3</td>\n",
       "      <td>24.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>20.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-06</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>25.7</td>\n",
       "      <td>14.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.160</td>\n",
       "      <td>24.7</td>\n",
       "      <td>24.7</td>\n",
       "      <td>23.9</td>\n",
       "      <td>22.7</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-07</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>25.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>21.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>13.88</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.160</td>\n",
       "      <td>24.2</td>\n",
       "      <td>24.2</td>\n",
       "      <td>23.4</td>\n",
       "      <td>22.4</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-08</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>29.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>22.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.92</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.159</td>\n",
       "      <td>25.5</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>22.4</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-09</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>26.3</td>\n",
       "      <td>10.9</td>\n",
       "      <td>18.6</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.158</td>\n",
       "      <td>24.8</td>\n",
       "      <td>24.8</td>\n",
       "      <td>23.8</td>\n",
       "      <td>22.5</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-10</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>27.6</td>\n",
       "      <td>11.8</td>\n",
       "      <td>19.7</td>\n",
       "      <td>21.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.158</td>\n",
       "      <td>24.7</td>\n",
       "      <td>24.7</td>\n",
       "      <td>23.6</td>\n",
       "      <td>22.5</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-11</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>27.4</td>\n",
       "      <td>19.2</td>\n",
       "      <td>23.3</td>\n",
       "      <td>22.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>17.79</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.157</td>\n",
       "      <td>25.6</td>\n",
       "      <td>25.4</td>\n",
       "      <td>24.1</td>\n",
       "      <td>22.6</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-12</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>29.4</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.9</td>\n",
       "      <td>23.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>16.27</td>\n",
       "      <td>...</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.157</td>\n",
       "      <td>25.8</td>\n",
       "      <td>25.6</td>\n",
       "      <td>24.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>29.5</td>\n",
       "      <td>18.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>23.4</td>\n",
       "      <td>23.3</td>\n",
       "      <td>13.61</td>\n",
       "      <td>...</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.156</td>\n",
       "      <td>25.7</td>\n",
       "      <td>25.7</td>\n",
       "      <td>24.4</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-14</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>18.5</td>\n",
       "      <td>15.9</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.155</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>23.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-15</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>26.6</td>\n",
       "      <td>16.5</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>21.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.155</td>\n",
       "      <td>24.6</td>\n",
       "      <td>24.4</td>\n",
       "      <td>23.2</td>\n",
       "      <td>22.2</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-16</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>27.9</td>\n",
       "      <td>13.3</td>\n",
       "      <td>20.6</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.155</td>\n",
       "      <td>25.4</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>22.6</td>\n",
       "      <td>21.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-17</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>29.2</td>\n",
       "      <td>16.1</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.47</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.156</td>\n",
       "      <td>25.7</td>\n",
       "      <td>25.6</td>\n",
       "      <td>24.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-18</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>30.3</td>\n",
       "      <td>19.3</td>\n",
       "      <td>24.8</td>\n",
       "      <td>24.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.99</td>\n",
       "      <td>...</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.156</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.7</td>\n",
       "      <td>24.9</td>\n",
       "      <td>23.2</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-19</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>31.2</td>\n",
       "      <td>19.1</td>\n",
       "      <td>25.1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.69</td>\n",
       "      <td>...</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.156</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.4</td>\n",
       "      <td>25.6</td>\n",
       "      <td>23.7</td>\n",
       "      <td>21.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>31.8</td>\n",
       "      <td>16.6</td>\n",
       "      <td>24.2</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>21.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.156</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-21</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>30.6</td>\n",
       "      <td>16.6</td>\n",
       "      <td>23.6</td>\n",
       "      <td>23.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.155</td>\n",
       "      <td>27.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-22</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>27.7</td>\n",
       "      <td>15.6</td>\n",
       "      <td>21.7</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>16.04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.156</td>\n",
       "      <td>25.9</td>\n",
       "      <td>26.1</td>\n",
       "      <td>25.3</td>\n",
       "      <td>24.1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-23</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>26.4</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.155</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.9</td>\n",
       "      <td>23.8</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-24</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>19.4</td>\n",
       "      <td>14.8</td>\n",
       "      <td>17.1</td>\n",
       "      <td>16.7</td>\n",
       "      <td>29.2</td>\n",
       "      <td>9.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.154</td>\n",
       "      <td>23.1</td>\n",
       "      <td>23.6</td>\n",
       "      <td>23.9</td>\n",
       "      <td>23.5</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-25</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>18.6</td>\n",
       "      <td>13.7</td>\n",
       "      <td>16.2</td>\n",
       "      <td>16.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.153</td>\n",
       "      <td>21.9</td>\n",
       "      <td>22.2</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.5</td>\n",
       "      <td>21.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-26</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>24.7</td>\n",
       "      <td>11.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.22</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.152</td>\n",
       "      <td>22.9</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-27</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>24.2</td>\n",
       "      <td>15.2</td>\n",
       "      <td>19.7</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.154</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.7</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-28</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>26.5</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.7</td>\n",
       "      <td>20.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.154</td>\n",
       "      <td>24.1</td>\n",
       "      <td>24.1</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-29</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>24.2</td>\n",
       "      <td>10.4</td>\n",
       "      <td>17.3</td>\n",
       "      <td>18.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.154</td>\n",
       "      <td>23.3</td>\n",
       "      <td>23.6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-30</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>25.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>16.8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.68</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.154</td>\n",
       "      <td>22.8</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-31</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>29.4</td>\n",
       "      <td>10.1</td>\n",
       "      <td>19.7</td>\n",
       "      <td>20.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.49</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.153</td>\n",
       "      <td>23.8</td>\n",
       "      <td>23.8</td>\n",
       "      <td>22.7</td>\n",
       "      <td>21.9</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            WBANNO  CRX_VN  LONGITUDE  LATITUDE  T_DAILY_MAX  T_DAILY_MIN  \\\n",
       "LST_DATE                                                                    \n",
       "2017-07-01   64756   2.422     -73.74     41.79         28.0         19.7   \n",
       "2017-07-02   64756   2.422     -73.74     41.79         29.8         18.4   \n",
       "2017-07-03   64756   2.422     -73.74     41.79         28.3         15.0   \n",
       "2017-07-04   64756   2.422     -73.74     41.79         26.8         12.6   \n",
       "2017-07-05   64756   2.422     -73.74     41.79         28.0         11.9   \n",
       "2017-07-06   64756   2.422     -73.74     41.79         25.7         14.3   \n",
       "2017-07-07   64756   2.422     -73.74     41.79         25.8         16.8   \n",
       "2017-07-08   64756   2.422     -73.74     41.79         29.0         15.3   \n",
       "2017-07-09   64756   2.422     -73.74     41.79         26.3         10.9   \n",
       "2017-07-10   64756   2.422     -73.74     41.79         27.6         11.8   \n",
       "2017-07-11   64756   2.422     -73.74     41.79         27.4         19.2   \n",
       "2017-07-12   64756   2.422     -73.74     41.79         29.4         18.5   \n",
       "2017-07-13   64756   2.422     -73.74     41.79         29.5         18.3   \n",
       "2017-07-14   64756   2.422     -73.74     41.79         18.5         15.9   \n",
       "2017-07-15   64756   2.422     -73.74     41.79         26.6         16.5   \n",
       "2017-07-16   64756   2.422     -73.74     41.79         27.9         13.3   \n",
       "2017-07-17   64756   2.422     -73.74     41.79         29.2         16.1   \n",
       "2017-07-18   64756   2.422     -73.74     41.79         30.3         19.3   \n",
       "2017-07-19   64756   2.422     -73.74     41.79         31.2         19.1   \n",
       "2017-07-20   64756   2.422     -73.74     41.79         31.8         16.6   \n",
       "2017-07-21   64756   2.422     -73.74     41.79         30.6         16.6   \n",
       "2017-07-22   64756   2.422     -73.74     41.79         27.7         15.6   \n",
       "2017-07-23   64756   2.422     -73.74     41.79         26.4         18.5   \n",
       "2017-07-24   64756   2.422     -73.74     41.79         19.4         14.8   \n",
       "2017-07-25   64756   2.422     -73.74     41.79         18.6         13.7   \n",
       "2017-07-26   64756   2.422     -73.74     41.79         24.7         11.2   \n",
       "2017-07-27   64756   2.422     -73.74     41.79         24.2         15.2   \n",
       "2017-07-28   64756   2.422     -73.74     41.79         26.5         16.9   \n",
       "2017-07-29   64756   2.422     -73.74     41.79         24.2         10.4   \n",
       "2017-07-30   64756   2.422     -73.74     41.79         25.5          8.2   \n",
       "2017-07-31   64756   2.422     -73.74     41.79         29.4         10.1   \n",
       "\n",
       "            T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  SOLARAD_DAILY  ...  \\\n",
       "LST_DATE                                                            ...   \n",
       "2017-07-01          23.9         23.8           0.2          19.28  ...   \n",
       "2017-07-02          24.1         23.7           4.0          27.67  ...   \n",
       "2017-07-03          21.7         21.4           0.0          27.08  ...   \n",
       "2017-07-04          19.7         20.0           0.0          29.45  ...   \n",
       "2017-07-05          20.0         20.7           0.0          26.90  ...   \n",
       "2017-07-06          20.0         20.3           0.0          19.03  ...   \n",
       "2017-07-07          21.3         20.0          11.5          13.88  ...   \n",
       "2017-07-08          22.1         21.5           0.0          21.92  ...   \n",
       "2017-07-09          18.6         19.4           0.0          29.72  ...   \n",
       "2017-07-10          19.7         21.3           0.0          23.67  ...   \n",
       "2017-07-11          23.3         22.6           8.5          17.79  ...   \n",
       "2017-07-12          23.9         23.1           1.9          16.27  ...   \n",
       "2017-07-13          23.9         23.4          23.3          13.61  ...   \n",
       "2017-07-14          17.2         17.5           4.1           5.36  ...   \n",
       "2017-07-15          21.5         21.0           0.8          21.13  ...   \n",
       "2017-07-16          20.6         21.0           0.0          27.03  ...   \n",
       "2017-07-17          22.6         22.9           0.0          20.47  ...   \n",
       "2017-07-18          24.8         24.7           0.0          24.99  ...   \n",
       "2017-07-19          25.1         25.0           0.0          27.69  ...   \n",
       "2017-07-20          24.2         23.4           0.7          21.53  ...   \n",
       "2017-07-21          23.6         23.6           0.0          25.55  ...   \n",
       "2017-07-22          21.7         21.2           0.5          16.04  ...   \n",
       "2017-07-23          22.5         22.2           0.0          19.03  ...   \n",
       "2017-07-24          17.1         16.7          29.2           9.10  ...   \n",
       "2017-07-25          16.2         16.2           0.0           7.35  ...   \n",
       "2017-07-26          18.0         18.3           0.0          22.22  ...   \n",
       "2017-07-27          19.7         19.5           0.0           8.28  ...   \n",
       "2017-07-28          21.7         20.9           0.0          21.06  ...   \n",
       "2017-07-29          17.3         18.1           0.0          21.28  ...   \n",
       "2017-07-30          16.8         17.3           0.0          27.68  ...   \n",
       "2017-07-31          19.7         20.1           0.0          25.49  ...   \n",
       "\n",
       "           SOIL_MOISTURE_5_DAILY  SOIL_MOISTURE_10_DAILY  \\\n",
       "LST_DATE                                                   \n",
       "2017-07-01                 0.157                   0.136   \n",
       "2017-07-02                 0.146                   0.135   \n",
       "2017-07-03                 0.141                   0.132   \n",
       "2017-07-04                 0.131                   0.126   \n",
       "2017-07-05                 0.116                   0.114   \n",
       "2017-07-06                 0.105                   0.104   \n",
       "2017-07-07                 0.114                   0.100   \n",
       "2017-07-08                 0.130                   0.106   \n",
       "2017-07-09                 0.119                   0.103   \n",
       "2017-07-10                 0.105                   0.096   \n",
       "2017-07-11                 0.106                   0.093   \n",
       "2017-07-12                 0.108                   0.094   \n",
       "2017-07-13                 0.134                   0.110   \n",
       "2017-07-14                 0.194                   0.151   \n",
       "2017-07-15                 0.190                   0.163   \n",
       "2017-07-16                 0.171                   0.154   \n",
       "2017-07-17                 0.155                   0.143   \n",
       "2017-07-18                 0.142                   0.132   \n",
       "2017-07-19                 0.126                   0.118   \n",
       "2017-07-20                 0.111                   0.103   \n",
       "2017-07-21                 0.100                   0.093   \n",
       "2017-07-22                 0.092                   0.086   \n",
       "2017-07-23                 0.087                   0.082   \n",
       "2017-07-24                 0.145                   0.118   \n",
       "2017-07-25                 0.167                   0.133   \n",
       "2017-07-26                 0.155                   0.128   \n",
       "2017-07-27                 0.144                   0.122   \n",
       "2017-07-28                 0.137                   0.117   \n",
       "2017-07-29                 0.126                   0.108   \n",
       "2017-07-30                 0.113                   0.099   \n",
       "2017-07-31                 0.101                   0.090   \n",
       "\n",
       "            SOIL_MOISTURE_20_DAILY  SOIL_MOISTURE_50_DAILY  \\\n",
       "LST_DATE                                                     \n",
       "2017-07-01                   0.144                   0.129   \n",
       "2017-07-02                   0.143                   0.129   \n",
       "2017-07-03                   0.139                   0.128   \n",
       "2017-07-04                   0.136                   0.126   \n",
       "2017-07-05                   0.131                   0.125   \n",
       "2017-07-06                   0.126                   0.124   \n",
       "2017-07-07                   0.123                   0.123   \n",
       "2017-07-08                   0.122                   0.123   \n",
       "2017-07-09                   0.119                   0.121   \n",
       "2017-07-10                   0.113                   0.120   \n",
       "2017-07-11                   0.110                   0.120   \n",
       "2017-07-12                   0.108                   0.118   \n",
       "2017-07-13                   0.108                   0.118   \n",
       "2017-07-14                   0.114                   0.120   \n",
       "2017-07-15                   0.119                   0.122   \n",
       "2017-07-16                   0.123                   0.123   \n",
       "2017-07-17                   0.124                   0.122   \n",
       "2017-07-18                   0.122                   0.122   \n",
       "2017-07-19                   0.118                   0.122   \n",
       "2017-07-20                   0.114                   0.121   \n",
       "2017-07-21                   0.108                   0.120   \n",
       "2017-07-22                   0.104                   0.119   \n",
       "2017-07-23                   0.100                   0.118   \n",
       "2017-07-24                   0.102                   0.117   \n",
       "2017-07-25                   0.107                   0.116   \n",
       "2017-07-26                   0.108                   0.118   \n",
       "2017-07-27                   0.109                   0.118   \n",
       "2017-07-28                   0.110                   0.119   \n",
       "2017-07-29                   0.108                   0.118   \n",
       "2017-07-30                   0.104                   0.117   \n",
       "2017-07-31                   0.099                   0.116   \n",
       "\n",
       "            SOIL_MOISTURE_100_DAILY  SOIL_TEMP_5_DAILY  SOIL_TEMP_10_DAILY  \\\n",
       "LST_DATE                                                                     \n",
       "2017-07-01                    0.163               25.7                25.4   \n",
       "2017-07-02                    0.162               26.8                26.4   \n",
       "2017-07-03                    0.162               26.4                26.3   \n",
       "2017-07-04                    0.161               25.9                25.8   \n",
       "2017-07-05                    0.161               25.3                25.3   \n",
       "2017-07-06                    0.160               24.7                24.7   \n",
       "2017-07-07                    0.160               24.2                24.2   \n",
       "2017-07-08                    0.159               25.5                25.3   \n",
       "2017-07-09                    0.158               24.8                24.8   \n",
       "2017-07-10                    0.158               24.7                24.7   \n",
       "2017-07-11                    0.157               25.6                25.4   \n",
       "2017-07-12                    0.157               25.8                25.6   \n",
       "2017-07-13                    0.156               25.7                25.7   \n",
       "2017-07-14                    0.155               23.0                23.3   \n",
       "2017-07-15                    0.155               24.6                24.4   \n",
       "2017-07-16                    0.155               25.4                25.3   \n",
       "2017-07-17                    0.156               25.7                25.6   \n",
       "2017-07-18                    0.156               27.0                26.7   \n",
       "2017-07-19                    0.156               27.6                27.4   \n",
       "2017-07-20                    0.156               27.0                27.0   \n",
       "2017-07-21                    0.155               27.1                27.0   \n",
       "2017-07-22                    0.156               25.9                26.1   \n",
       "2017-07-23                    0.155               26.0                26.0   \n",
       "2017-07-24                    0.154               23.1                23.6   \n",
       "2017-07-25                    0.153               21.9                22.2   \n",
       "2017-07-26                    0.152               22.9                23.0   \n",
       "2017-07-27                    0.154               22.5                22.7   \n",
       "2017-07-28                    0.154               24.1                24.1   \n",
       "2017-07-29                    0.154               23.3                23.6   \n",
       "2017-07-30                    0.154               22.8                23.0   \n",
       "2017-07-31                    0.153               23.8                23.8   \n",
       "\n",
       "            SOIL_TEMP_20_DAILY  SOIL_TEMP_50_DAILY  SOIL_TEMP_100_DAILY  \n",
       "LST_DATE                                                                 \n",
       "2017-07-01                23.7                21.9                 19.9  \n",
       "2017-07-02                24.5                22.3                 20.1  \n",
       "2017-07-03                24.8                22.8                 20.3  \n",
       "2017-07-04                24.6                22.9                 20.6  \n",
       "2017-07-05                24.2                22.8                 20.7  \n",
       "2017-07-06                23.9                22.7                 20.9  \n",
       "2017-07-07                23.4                22.4                 20.8  \n",
       "2017-07-08                23.9                22.4                 20.8  \n",
       "2017-07-09                23.8                22.5                 20.8  \n",
       "2017-07-10                23.6                22.5                 20.9  \n",
       "2017-07-11                24.1                22.6                 20.9  \n",
       "2017-07-12                24.2                22.8                 21.0  \n",
       "2017-07-13                24.4                23.0                 21.0  \n",
       "2017-07-14                23.4                22.9                 21.2  \n",
       "2017-07-15                23.2                22.2                 21.2  \n",
       "2017-07-16                23.9                22.6                 21.1  \n",
       "2017-07-17                24.4                22.9                 21.2  \n",
       "2017-07-18                24.9                23.2                 21.3  \n",
       "2017-07-19                25.6                23.7                 21.5  \n",
       "2017-07-20                25.6                24.0                 21.7  \n",
       "2017-07-21                25.5                24.0                 21.9  \n",
       "2017-07-22                25.3                24.1                 22.0  \n",
       "2017-07-23                24.9                23.8                 22.1  \n",
       "2017-07-24                23.9                23.5                 22.1  \n",
       "2017-07-25                22.4                22.5                 21.9  \n",
       "2017-07-26                22.3                22.0                 21.7  \n",
       "2017-07-27                22.4                22.0                 21.4  \n",
       "2017-07-28                22.8                22.0                 21.3  \n",
       "2017-07-29                23.0                22.2                 21.3  \n",
       "2017-07-30                22.4                22.0                 21.3  \n",
       "2017-07-31                22.7                21.9                 21.2  \n",
       "\n",
       "[31 rows x 27 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2017-07-01':'2017-07-31']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "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>WBANNO</th>\n",
       "      <th>CRX_VN</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>SOLARAD_DAILY</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_5_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.0</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>3.650000e+02</td>\n",
       "      <td>3.650000e+02</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.470767</td>\n",
       "      <td>-7.374000e+01</td>\n",
       "      <td>4.179000e+01</td>\n",
       "      <td>15.720055</td>\n",
       "      <td>4.037912</td>\n",
       "      <td>9.876374</td>\n",
       "      <td>9.990110</td>\n",
       "      <td>2.797802</td>\n",
       "      <td>13.068187</td>\n",
       "      <td>...</td>\n",
       "      <td>0.189498</td>\n",
       "      <td>0.183991</td>\n",
       "      <td>0.165470</td>\n",
       "      <td>0.140192</td>\n",
       "      <td>0.160630</td>\n",
       "      <td>12.312637</td>\n",
       "      <td>12.320604</td>\n",
       "      <td>12.060165</td>\n",
       "      <td>11.978022</td>\n",
       "      <td>11.915659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.085997</td>\n",
       "      <td>5.265234e-13</td>\n",
       "      <td>3.842198e-13</td>\n",
       "      <td>10.502087</td>\n",
       "      <td>9.460676</td>\n",
       "      <td>9.727451</td>\n",
       "      <td>9.619168</td>\n",
       "      <td>7.238628</td>\n",
       "      <td>7.953074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052031</td>\n",
       "      <td>0.054113</td>\n",
       "      <td>0.043989</td>\n",
       "      <td>0.020495</td>\n",
       "      <td>0.016011</td>\n",
       "      <td>9.390034</td>\n",
       "      <td>9.338176</td>\n",
       "      <td>8.767752</td>\n",
       "      <td>8.078346</td>\n",
       "      <td>7.187317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.422000</td>\n",
       "      <td>-7.374000e+01</td>\n",
       "      <td>4.179000e+01</td>\n",
       "      <td>-12.300000</td>\n",
       "      <td>-21.800000</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>-16.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>0.087000</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>0.117000</td>\n",
       "      <td>-0.700000</td>\n",
       "      <td>-0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.422000</td>\n",
       "      <td>-7.374000e+01</td>\n",
       "      <td>4.179000e+01</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>-2.775000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>2.275000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.225000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.152000</td>\n",
       "      <td>0.139000</td>\n",
       "      <td>0.118750</td>\n",
       "      <td>0.118000</td>\n",
       "      <td>0.154000</td>\n",
       "      <td>2.225000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.475000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>4.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.422000</td>\n",
       "      <td>-7.374000e+01</td>\n",
       "      <td>4.179000e+01</td>\n",
       "      <td>17.450000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>10.850000</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.865000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192000</td>\n",
       "      <td>0.198000</td>\n",
       "      <td>0.183000</td>\n",
       "      <td>0.147500</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>12.850000</td>\n",
       "      <td>11.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.422000</td>\n",
       "      <td>-7.374000e+01</td>\n",
       "      <td>4.179000e+01</td>\n",
       "      <td>24.850000</td>\n",
       "      <td>11.900000</td>\n",
       "      <td>18.150000</td>\n",
       "      <td>18.450000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>19.740000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.234000</td>\n",
       "      <td>0.227000</td>\n",
       "      <td>0.203000</td>\n",
       "      <td>0.157000</td>\n",
       "      <td>0.173000</td>\n",
       "      <td>21.025000</td>\n",
       "      <td>21.125000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>19.800000</td>\n",
       "      <td>19.325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.622000</td>\n",
       "      <td>-7.374000e+01</td>\n",
       "      <td>4.179000e+01</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>20.700000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>26.700000</td>\n",
       "      <td>65.700000</td>\n",
       "      <td>29.910000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.296000</td>\n",
       "      <td>0.321000</td>\n",
       "      <td>0.235000</td>\n",
       "      <td>0.182000</td>\n",
       "      <td>0.192000</td>\n",
       "      <td>27.600000</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>24.100000</td>\n",
       "      <td>22.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        WBANNO      CRX_VN     LONGITUDE      LATITUDE  T_DAILY_MAX  \\\n",
       "count    365.0  365.000000  3.650000e+02  3.650000e+02   364.000000   \n",
       "mean   64756.0    2.470767 -7.374000e+01  4.179000e+01    15.720055   \n",
       "std        0.0    0.085997  5.265234e-13  3.842198e-13    10.502087   \n",
       "min    64756.0    2.422000 -7.374000e+01  4.179000e+01   -12.300000   \n",
       "25%    64756.0    2.422000 -7.374000e+01  4.179000e+01     6.900000   \n",
       "50%    64756.0    2.422000 -7.374000e+01  4.179000e+01    17.450000   \n",
       "75%    64756.0    2.422000 -7.374000e+01  4.179000e+01    24.850000   \n",
       "max    64756.0    2.622000 -7.374000e+01  4.179000e+01    33.400000   \n",
       "\n",
       "       T_DAILY_MIN  T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  SOLARAD_DAILY  \\\n",
       "count   364.000000    364.000000   364.000000    364.000000     364.000000   \n",
       "mean      4.037912      9.876374     9.990110      2.797802      13.068187   \n",
       "std       9.460676      9.727451     9.619168      7.238628       7.953074   \n",
       "min     -21.800000    -17.000000   -16.700000      0.000000       0.100000   \n",
       "25%      -2.775000      2.100000     2.275000      0.000000       6.225000   \n",
       "50%       4.350000     10.850000    11.050000      0.000000      12.865000   \n",
       "75%      11.900000     18.150000    18.450000      1.400000      19.740000   \n",
       "max      20.700000     25.700000    26.700000     65.700000      29.910000   \n",
       "\n",
       "       ...  SOIL_MOISTURE_5_DAILY  SOIL_MOISTURE_10_DAILY  \\\n",
       "count  ...             317.000000              317.000000   \n",
       "mean   ...               0.189498                0.183991   \n",
       "std    ...               0.052031                0.054113   \n",
       "min    ...               0.075000                0.078000   \n",
       "25%    ...               0.152000                0.139000   \n",
       "50%    ...               0.192000                0.198000   \n",
       "75%    ...               0.234000                0.227000   \n",
       "max    ...               0.296000                0.321000   \n",
       "\n",
       "       SOIL_MOISTURE_20_DAILY  SOIL_MOISTURE_50_DAILY  \\\n",
       "count              336.000000              364.000000   \n",
       "mean                 0.165470                0.140192   \n",
       "std                  0.043989                0.020495   \n",
       "min                  0.087000                0.101000   \n",
       "25%                  0.118750                0.118000   \n",
       "50%                  0.183000                0.147500   \n",
       "75%                  0.203000                0.157000   \n",
       "max                  0.235000                0.182000   \n",
       "\n",
       "       SOIL_MOISTURE_100_DAILY  SOIL_TEMP_5_DAILY  SOIL_TEMP_10_DAILY  \\\n",
       "count               359.000000         364.000000          364.000000   \n",
       "mean                  0.160630          12.312637           12.320604   \n",
       "std                   0.016011           9.390034            9.338176   \n",
       "min                   0.117000          -0.700000           -0.400000   \n",
       "25%                   0.154000           2.225000            2.000000   \n",
       "50%                   0.165000          13.300000           13.350000   \n",
       "75%                   0.173000          21.025000           21.125000   \n",
       "max                   0.192000          27.600000           27.400000   \n",
       "\n",
       "       SOIL_TEMP_20_DAILY  SOIL_TEMP_50_DAILY  SOIL_TEMP_100_DAILY  \n",
       "count          364.000000          364.000000           364.000000  \n",
       "mean            12.060165           11.978022            11.915659  \n",
       "std              8.767752            8.078346             7.187317  \n",
       "min              0.200000            0.900000             1.900000  \n",
       "25%              2.475000            3.300000             4.100000  \n",
       "50%             13.100000           12.850000            11.600000  \n",
       "75%             20.400000           19.800000            19.325000  \n",
       "max             25.600000           24.100000            22.100000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Values\n",
    "\n",
    "We can now quickly make plots of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, nrows=2, figsize=(14,14))\n",
    "\n",
    "df.iloc[:, 4:8].boxplot(ax=ax[0,0])\n",
    "df.iloc[:, 10:14].boxplot(ax=ax[0,1])\n",
    "df.iloc[:, 14:17].boxplot(ax=ax[1,0])\n",
    "df.iloc[:, 18:22].boxplot(ax=ax[1,1])\n",
    "\n",
    "\n",
    "ax[1, 1].set_xticklabels(ax[1, 1].get_xticklabels(), rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is very \"time aware\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LST_DATE'>"
      ]
     },
     "execution_count": 52,
     "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": [
    "df.T_DAILY_MEAN.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: we could also manually create an axis and plot into it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pandas Made This!')"
      ]
     },
     "execution_count": 53,
     "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": [
    "fig, ax = plt.subplots()\n",
    "df.T_DAILY_MEAN.plot(ax=ax)\n",
    "ax.set_title('Pandas Made This!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LST_DATE'>"
      ]
     },
     "execution_count": 54,
     "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": [
    "df[['T_DAILY_MIN', 'T_DAILY_MEAN', 'T_DAILY_MAX']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling\n",
    "\n",
    "Since pandas understands time, we can use it to do resampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.resample.DatetimeIndexResampler object at 0x7f1f14a1d5e0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# monthly reampler object\n",
    "rs_obj = df.resample('MS')\n",
    "rs_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "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>WBANNO</th>\n",
       "      <th>CRX_VN</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>SOLARAD_DAILY</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_5_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LST_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.422000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>3.945161</td>\n",
       "      <td>-3.993548</td>\n",
       "      <td>-0.025806</td>\n",
       "      <td>0.038710</td>\n",
       "      <td>3.090323</td>\n",
       "      <td>4.690000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.236900</td>\n",
       "      <td>0.248300</td>\n",
       "      <td>0.204550</td>\n",
       "      <td>0.152806</td>\n",
       "      <td>0.175194</td>\n",
       "      <td>0.209677</td>\n",
       "      <td>0.267742</td>\n",
       "      <td>0.696774</td>\n",
       "      <td>1.438710</td>\n",
       "      <td>2.877419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.422000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>7.246429</td>\n",
       "      <td>-4.360714</td>\n",
       "      <td>1.442857</td>\n",
       "      <td>1.839286</td>\n",
       "      <td>2.414286</td>\n",
       "      <td>10.364286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.226333</td>\n",
       "      <td>0.243000</td>\n",
       "      <td>0.207545</td>\n",
       "      <td>0.152857</td>\n",
       "      <td>0.175786</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.192857</td>\n",
       "      <td>1.492857</td>\n",
       "      <td>2.367857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.422000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>5.164516</td>\n",
       "      <td>-5.335484</td>\n",
       "      <td>-0.090323</td>\n",
       "      <td>0.167742</td>\n",
       "      <td>3.970968</td>\n",
       "      <td>13.113548</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218033</td>\n",
       "      <td>0.229267</td>\n",
       "      <td>0.196258</td>\n",
       "      <td>0.153484</td>\n",
       "      <td>0.174548</td>\n",
       "      <td>2.122581</td>\n",
       "      <td>2.161290</td>\n",
       "      <td>2.345161</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>3.387097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.422000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>17.813333</td>\n",
       "      <td>5.170000</td>\n",
       "      <td>11.493333</td>\n",
       "      <td>11.540000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>14.645000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.199733</td>\n",
       "      <td>0.210300</td>\n",
       "      <td>0.190667</td>\n",
       "      <td>0.151000</td>\n",
       "      <td>0.172400</td>\n",
       "      <td>11.066667</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>9.636667</td>\n",
       "      <td>8.426667</td>\n",
       "      <td>6.903333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.422000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>19.151613</td>\n",
       "      <td>7.338710</td>\n",
       "      <td>13.229032</td>\n",
       "      <td>13.638710</td>\n",
       "      <td>4.141935</td>\n",
       "      <td>16.519677</td>\n",
       "      <td>...</td>\n",
       "      <td>0.206613</td>\n",
       "      <td>0.210935</td>\n",
       "      <td>0.185613</td>\n",
       "      <td>0.147710</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>16.454839</td>\n",
       "      <td>16.290323</td>\n",
       "      <td>15.361290</td>\n",
       "      <td>14.270968</td>\n",
       "      <td>12.696774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.422000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>25.423333</td>\n",
       "      <td>12.176667</td>\n",
       "      <td>18.796667</td>\n",
       "      <td>18.986667</td>\n",
       "      <td>3.743333</td>\n",
       "      <td>21.655000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.185167</td>\n",
       "      <td>0.184300</td>\n",
       "      <td>0.173167</td>\n",
       "      <td>0.142533</td>\n",
       "      <td>0.167000</td>\n",
       "      <td>22.350000</td>\n",
       "      <td>22.166667</td>\n",
       "      <td>20.880000</td>\n",
       "      <td>19.370000</td>\n",
       "      <td>17.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-01</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.422000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>26.912903</td>\n",
       "      <td>15.183871</td>\n",
       "      <td>21.048387</td>\n",
       "      <td>20.993548</td>\n",
       "      <td>2.732258</td>\n",
       "      <td>20.566129</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131226</td>\n",
       "      <td>0.115774</td>\n",
       "      <td>0.116613</td>\n",
       "      <td>0.121032</td>\n",
       "      <td>0.156677</td>\n",
       "      <td>24.993548</td>\n",
       "      <td>24.980645</td>\n",
       "      <td>23.925806</td>\n",
       "      <td>22.745161</td>\n",
       "      <td>21.164516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.422000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>25.741935</td>\n",
       "      <td>12.954839</td>\n",
       "      <td>19.351613</td>\n",
       "      <td>19.477419</td>\n",
       "      <td>2.758065</td>\n",
       "      <td>18.360000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.143871</td>\n",
       "      <td>0.122258</td>\n",
       "      <td>0.105452</td>\n",
       "      <td>0.115290</td>\n",
       "      <td>0.151034</td>\n",
       "      <td>23.374194</td>\n",
       "      <td>23.519355</td>\n",
       "      <td>22.848387</td>\n",
       "      <td>22.193548</td>\n",
       "      <td>21.377419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.422000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>24.186667</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>17.746667</td>\n",
       "      <td>17.463333</td>\n",
       "      <td>1.893333</td>\n",
       "      <td>15.154667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.145167</td>\n",
       "      <td>0.139633</td>\n",
       "      <td>0.117267</td>\n",
       "      <td>0.112167</td>\n",
       "      <td>0.141926</td>\n",
       "      <td>20.256667</td>\n",
       "      <td>20.386667</td>\n",
       "      <td>19.966667</td>\n",
       "      <td>19.766667</td>\n",
       "      <td>19.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-01</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.602645</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>21.043333</td>\n",
       "      <td>7.150000</td>\n",
       "      <td>14.100000</td>\n",
       "      <td>13.976667</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>10.395000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.151767</td>\n",
       "      <td>0.137767</td>\n",
       "      <td>0.111900</td>\n",
       "      <td>0.108900</td>\n",
       "      <td>0.122067</td>\n",
       "      <td>16.086667</td>\n",
       "      <td>16.193333</td>\n",
       "      <td>16.370000</td>\n",
       "      <td>16.893333</td>\n",
       "      <td>17.386667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.622000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>10.346667</td>\n",
       "      <td>-2.093333</td>\n",
       "      <td>4.120000</td>\n",
       "      <td>4.336667</td>\n",
       "      <td>0.826667</td>\n",
       "      <td>6.723333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241633</td>\n",
       "      <td>0.224467</td>\n",
       "      <td>0.203367</td>\n",
       "      <td>0.159500</td>\n",
       "      <td>0.155233</td>\n",
       "      <td>7.056667</td>\n",
       "      <td>7.273333</td>\n",
       "      <td>8.043333</td>\n",
       "      <td>9.633333</td>\n",
       "      <td>11.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>64756.0</td>\n",
       "      <td>2.622000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>1.496774</td>\n",
       "      <td>-7.412903</td>\n",
       "      <td>-2.967742</td>\n",
       "      <td>-2.838710</td>\n",
       "      <td>2.109677</td>\n",
       "      <td>4.474194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.255929</td>\n",
       "      <td>0.239071</td>\n",
       "      <td>0.213258</td>\n",
       "      <td>0.165387</td>\n",
       "      <td>0.163290</td>\n",
       "      <td>2.064516</td>\n",
       "      <td>2.241935</td>\n",
       "      <td>2.874194</td>\n",
       "      <td>4.248387</td>\n",
       "      <td>6.019355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             WBANNO    CRX_VN  LONGITUDE  LATITUDE  T_DAILY_MAX  T_DAILY_MIN  \\\n",
       "LST_DATE                                                                       \n",
       "2017-01-01  64756.0  2.422000     -73.74     41.79     3.945161    -3.993548   \n",
       "2017-02-01  64756.0  2.422000     -73.74     41.79     7.246429    -4.360714   \n",
       "2017-03-01  64756.0  2.422000     -73.74     41.79     5.164516    -5.335484   \n",
       "2017-04-01  64756.0  2.422000     -73.74     41.79    17.813333     5.170000   \n",
       "2017-05-01  64756.0  2.422000     -73.74     41.79    19.151613     7.338710   \n",
       "2017-06-01  64756.0  2.422000     -73.74     41.79    25.423333    12.176667   \n",
       "2017-07-01  64756.0  2.422000     -73.74     41.79    26.912903    15.183871   \n",
       "2017-08-01  64756.0  2.422000     -73.74     41.79    25.741935    12.954839   \n",
       "2017-09-01  64756.0  2.422000     -73.74     41.79    24.186667    11.300000   \n",
       "2017-10-01  64756.0  2.602645     -73.74     41.79    21.043333     7.150000   \n",
       "2017-11-01  64756.0  2.622000     -73.74     41.79    10.346667    -2.093333   \n",
       "2017-12-01  64756.0  2.622000     -73.74     41.79     1.496774    -7.412903   \n",
       "\n",
       "            T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  SOLARAD_DAILY  ...  \\\n",
       "LST_DATE                                                            ...   \n",
       "2017-01-01     -0.025806     0.038710      3.090323       4.690000  ...   \n",
       "2017-02-01      1.442857     1.839286      2.414286      10.364286  ...   \n",
       "2017-03-01     -0.090323     0.167742      3.970968      13.113548  ...   \n",
       "2017-04-01     11.493333    11.540000      2.300000      14.645000  ...   \n",
       "2017-05-01     13.229032    13.638710      4.141935      16.519677  ...   \n",
       "2017-06-01     18.796667    18.986667      3.743333      21.655000  ...   \n",
       "2017-07-01     21.048387    20.993548      2.732258      20.566129  ...   \n",
       "2017-08-01     19.351613    19.477419      2.758065      18.360000  ...   \n",
       "2017-09-01     17.746667    17.463333      1.893333      15.154667  ...   \n",
       "2017-10-01     14.100000    13.976667      3.500000      10.395000  ...   \n",
       "2017-11-01      4.120000     4.336667      0.826667       6.723333  ...   \n",
       "2017-12-01     -2.967742    -2.838710      2.109677       4.474194  ...   \n",
       "\n",
       "            SOIL_MOISTURE_5_DAILY  SOIL_MOISTURE_10_DAILY  \\\n",
       "LST_DATE                                                    \n",
       "2017-01-01               0.236900                0.248300   \n",
       "2017-02-01               0.226333                0.243000   \n",
       "2017-03-01               0.218033                0.229267   \n",
       "2017-04-01               0.199733                0.210300   \n",
       "2017-05-01               0.206613                0.210935   \n",
       "2017-06-01               0.185167                0.184300   \n",
       "2017-07-01               0.131226                0.115774   \n",
       "2017-08-01               0.143871                0.122258   \n",
       "2017-09-01               0.145167                0.139633   \n",
       "2017-10-01               0.151767                0.137767   \n",
       "2017-11-01               0.241633                0.224467   \n",
       "2017-12-01               0.255929                0.239071   \n",
       "\n",
       "            SOIL_MOISTURE_20_DAILY  SOIL_MOISTURE_50_DAILY  \\\n",
       "LST_DATE                                                     \n",
       "2017-01-01                0.204550                0.152806   \n",
       "2017-02-01                0.207545                0.152857   \n",
       "2017-03-01                0.196258                0.153484   \n",
       "2017-04-01                0.190667                0.151000   \n",
       "2017-05-01                0.185613                0.147710   \n",
       "2017-06-01                0.173167                0.142533   \n",
       "2017-07-01                0.116613                0.121032   \n",
       "2017-08-01                0.105452                0.115290   \n",
       "2017-09-01                0.117267                0.112167   \n",
       "2017-10-01                0.111900                0.108900   \n",
       "2017-11-01                0.203367                0.159500   \n",
       "2017-12-01                0.213258                0.165387   \n",
       "\n",
       "            SOIL_MOISTURE_100_DAILY  SOIL_TEMP_5_DAILY  SOIL_TEMP_10_DAILY  \\\n",
       "LST_DATE                                                                     \n",
       "2017-01-01                 0.175194           0.209677            0.267742   \n",
       "2017-02-01                 0.175786           1.125000            1.100000   \n",
       "2017-03-01                 0.174548           2.122581            2.161290   \n",
       "2017-04-01                 0.172400          11.066667           10.666667   \n",
       "2017-05-01                 0.170000          16.454839           16.290323   \n",
       "2017-06-01                 0.167000          22.350000           22.166667   \n",
       "2017-07-01                 0.156677          24.993548           24.980645   \n",
       "2017-08-01                 0.151034          23.374194           23.519355   \n",
       "2017-09-01                 0.141926          20.256667           20.386667   \n",
       "2017-10-01                 0.122067          16.086667           16.193333   \n",
       "2017-11-01                 0.155233           7.056667            7.273333   \n",
       "2017-12-01                 0.163290           2.064516            2.241935   \n",
       "\n",
       "            SOIL_TEMP_20_DAILY  SOIL_TEMP_50_DAILY  SOIL_TEMP_100_DAILY  \n",
       "LST_DATE                                                                 \n",
       "2017-01-01            0.696774            1.438710             2.877419  \n",
       "2017-02-01            1.192857            1.492857             2.367857  \n",
       "2017-03-01            2.345161            2.700000             3.387097  \n",
       "2017-04-01            9.636667            8.426667             6.903333  \n",
       "2017-05-01           15.361290           14.270968            12.696774  \n",
       "2017-06-01           20.880000           19.370000            17.333333  \n",
       "2017-07-01           23.925806           22.745161            21.164516  \n",
       "2017-08-01           22.848387           22.193548            21.377419  \n",
       "2017-09-01           19.966667           19.766667            19.530000  \n",
       "2017-10-01           16.370000           16.893333            17.386667  \n",
       "2017-11-01            8.043333            9.633333            11.440000  \n",
       "2017-12-01            2.874194            4.248387             6.019355  \n",
       "\n",
       "[12 rows x 26 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_obj.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can chain all of that together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LST_DATE'>"
      ]
     },
     "execution_count": 57,
     "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": [
    "df_mm = df.resample('MS').mean()\n",
    "df_mm[['T_DAILY_MIN', 'T_DAILY_MEAN', 'T_DAILY_MAX']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next time we will dig deeper into resampling, rolling means, and grouping operations (groupby)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: src/lectures/pandas/pandas_groupby.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas: Groupby\n",
    "\n",
    "`groupby` is an amazingly powerful function in pandas. But it is also complicated to use and understand.\n",
    "The point of this lesson is to make you feel confident in using `groupby` and its cousins, `resample` and `rolling`. \n",
    "\n",
    "These notes are loosely based on the [Pandas GroupBy Documentation](http://pandas.pydata.org/pandas-docs/stable/groupby.html).\n",
    "\n",
    "The \"split/apply/combine\" concept was first introduced in a paper by Hadley Wickham: <https://www.jstatsoft.org/article/view/v040i01>.\n",
    "\n",
    "\n",
    "Imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (12,7)\n",
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we read the Earthquake data from our previous assignment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>depth</th>\n",
       "      <th>mag</th>\n",
       "      <th>magType</th>\n",
       "      <th>nst</th>\n",
       "      <th>gap</th>\n",
       "      <th>dmin</th>\n",
       "      <th>rms</th>\n",
       "      <th>net</th>\n",
       "      <th>updated</th>\n",
       "      <th>place</th>\n",
       "      <th>type</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>usc000mqlp</th>\n",
       "      <td>2014-01-31 23:08:03.660</td>\n",
       "      <td>-4.9758</td>\n",
       "      <td>153.9466</td>\n",
       "      <td>110.18</td>\n",
       "      <td>4.2</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.940</td>\n",
       "      <td>0.61</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:19.000Z</td>\n",
       "      <td>115km ESE of Taron, Papua New Guinea</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mqln</th>\n",
       "      <td>2014-01-31 22:54:32.970</td>\n",
       "      <td>-28.1775</td>\n",
       "      <td>-177.9058</td>\n",
       "      <td>95.84</td>\n",
       "      <td>4.3</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.063</td>\n",
       "      <td>1.14</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:19.000Z</td>\n",
       "      <td>120km N of Raoul Island, New Zealand</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>New Zealand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mqls</th>\n",
       "      <td>2014-01-31 22:49:49.740</td>\n",
       "      <td>-23.1192</td>\n",
       "      <td>179.1174</td>\n",
       "      <td>528.34</td>\n",
       "      <td>4.4</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.439</td>\n",
       "      <td>0.95</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:19.000Z</td>\n",
       "      <td>South of the Fiji Islands</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>South of the Fiji Islands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mf1x</th>\n",
       "      <td>2014-01-31 22:19:44.330</td>\n",
       "      <td>51.1569</td>\n",
       "      <td>-178.0910</td>\n",
       "      <td>37.50</td>\n",
       "      <td>4.2</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.83</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:19.000Z</td>\n",
       "      <td>72km E of Amatignak Island, Alaska</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mqlm</th>\n",
       "      <td>2014-01-31 21:56:44.320</td>\n",
       "      <td>-4.8800</td>\n",
       "      <td>153.8434</td>\n",
       "      <td>112.66</td>\n",
       "      <td>4.3</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199.0</td>\n",
       "      <td>1.808</td>\n",
       "      <td>0.79</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:19.000Z</td>\n",
       "      <td>100km ESE of Taron, Papua New Guinea</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              time  latitude  longitude   depth  mag magType  \\\n",
       "id                                                                             \n",
       "usc000mqlp 2014-01-31 23:08:03.660   -4.9758   153.9466  110.18  4.2      mb   \n",
       "usc000mqln 2014-01-31 22:54:32.970  -28.1775  -177.9058   95.84  4.3      mb   \n",
       "usc000mqls 2014-01-31 22:49:49.740  -23.1192   179.1174  528.34  4.4      mb   \n",
       "usc000mf1x 2014-01-31 22:19:44.330   51.1569  -178.0910   37.50  4.2      mb   \n",
       "usc000mqlm 2014-01-31 21:56:44.320   -4.8800   153.8434  112.66  4.3      mb   \n",
       "\n",
       "            nst    gap   dmin   rms net                   updated  \\\n",
       "id                                                                  \n",
       "usc000mqlp  NaN   98.0  1.940  0.61  us  2014-04-08T01:43:19.000Z   \n",
       "usc000mqln  NaN  104.0  1.063  1.14  us  2014-04-08T01:43:19.000Z   \n",
       "usc000mqls  NaN   80.0  5.439  0.95  us  2014-04-08T01:43:19.000Z   \n",
       "usc000mf1x  NaN    NaN    NaN  0.83  us  2014-04-08T01:43:19.000Z   \n",
       "usc000mqlm  NaN  199.0  1.808  0.79  us  2014-04-08T01:43:19.000Z   \n",
       "\n",
       "                                           place        type  \\\n",
       "id                                                             \n",
       "usc000mqlp  115km ESE of Taron, Papua New Guinea  earthquake   \n",
       "usc000mqln  120km N of Raoul Island, New Zealand  earthquake   \n",
       "usc000mqls             South of the Fiji Islands  earthquake   \n",
       "usc000mf1x    72km E of Amatignak Island, Alaska  earthquake   \n",
       "usc000mqlm  100km ESE of Taron, Papua New Guinea  earthquake   \n",
       "\n",
       "                              country  \n",
       "id                                     \n",
       "usc000mqlp           Papua New Guinea  \n",
       "usc000mqln                New Zealand  \n",
       "usc000mqls  South of the Fiji Islands  \n",
       "usc000mf1x                     Alaska  \n",
       "usc000mqlm           Papua New Guinea  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('http://www.ldeo.columbia.edu/~rpa/usgs_earthquakes_2014.csv', parse_dates=['time'], index_col='id')\n",
    "df['country'] = df.place.str.split(', ').str[-1]\n",
    "df_small = df[df.mag<4]\n",
    "df = df[df.mag>4]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An Example\n",
    "\n",
    "This is an example of a \"one-liner\" that you can accomplish with groupby."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112e20780>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9YlV9C9gFIMnGwA+Bo4FnAP9aVa+ffnySnYG9gLsBtwc+n2Snqrp2fWOQJEmShrRQZRuP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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104d17cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('country').mag.count().nlargest(20).plot(kind='bar', figsize=(12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112b42d68>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C/S7M+0lEPD4zL2087kTbDM0S7xcRn2J+H9pWjouItSgdJi6kJHJfaRzD3Zn5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\ntXmr4h0l6T4R8T0WLv+4hTKD/aXMvL1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111793320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_small.groupby('country').mag.count().nlargest(20).plot(kind='bar', figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What Happened?\n",
    "\n",
    "Let's break apart this operation a bit. The workflow with `groubpy` can be divided into three general steps:\n",
    "\n",
    "1. **Split**: Partition the data into different groups based on some criterion.\n",
    "1. **Apply**: Do some caclulation within each group. Different types of \"apply\" steps might be\n",
    "  1. *Aggregation*: Get the mean or max within the group.\n",
    "  1. *Transformation*: Normalize all the values within a group\n",
    "  1. *Filtration*: Eliminate some groups based on a criterion.\n",
    "1. **Combine**: Put the results back together into a single object.\n",
    "\n",
    "![split-apply-combine](https://miro.medium.com/max/1840/1*JbF6nhrQsn4f-TaSF6IR9g.png)\n",
    "\n",
    "### The `groupby` method\n",
    "\n",
    "Both `Series` and `DataFrame` objects have a groupby method. It accepts a variety of arguments, but the simplest way to think about it is that you pass another series, whose unique values are used to split the original object into different groups.\n",
    "\n",
    "via <https://medium.com/analytics-vidhya/split-apply-combine-strategy-for-data-mining-4fd6e2a0cc99>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x10626f358>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df.country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a shortcut for doing this with dataframes: you just pass the column name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x10626f208>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('country')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The `GroubBy` object\n",
    "\n",
    "When we call, `groupby` we get back a `GroupBy` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x1131bcf60>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = df.groupby('country')\n",
    "gb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The length tells us how many groups were found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the groups are available as a dictionary via the `.groups` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups = gb.groups\n",
    "len(groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['',\n",
       " 'Afghanistan',\n",
       " 'Alaska',\n",
       " 'Albania',\n",
       " 'Algeria',\n",
       " 'American Samoa',\n",
       " 'Angola',\n",
       " 'Anguilla',\n",
       " 'Antarctica',\n",
       " 'Argentina',\n",
       " 'Arizona',\n",
       " 'Aruba',\n",
       " 'Ascension Island region',\n",
       " 'Australia',\n",
       " 'Azerbaijan',\n",
       " 'Azores Islands region',\n",
       " 'Azores-Cape St. Vincent Ridge',\n",
       " 'Balleny Islands region',\n",
       " 'Banda Sea',\n",
       " 'Bangladesh',\n",
       " 'Barbados',\n",
       " 'Barbuda',\n",
       " 'Bay of Bengal',\n",
       " 'Bermuda',\n",
       " 'Bhutan',\n",
       " 'Bolivia',\n",
       " 'Bosnia and Herzegovina',\n",
       " 'Bouvet Island',\n",
       " 'Bouvet Island region',\n",
       " 'Brazil',\n",
       " 'British Indian Ocean Territory',\n",
       " 'British Virgin Islands',\n",
       " 'Burma',\n",
       " 'Burundi',\n",
       " 'California',\n",
       " 'Canada',\n",
       " 'Cape Verde',\n",
       " 'Carlsberg Ridge',\n",
       " 'Cayman Islands',\n",
       " 'Celebes Sea',\n",
       " 'Central East Pacific Rise',\n",
       " 'Central Mid-Atlantic Ridge',\n",
       " 'Chagos Archipelago region',\n",
       " 'Chile',\n",
       " 'China',\n",
       " 'Christmas Island',\n",
       " 'Colombia',\n",
       " 'Comoros',\n",
       " 'Cook Islands',\n",
       " 'Costa Rica',\n",
       " 'Crozet Islands region',\n",
       " 'Cuba',\n",
       " 'Cyprus',\n",
       " 'Davis Strait',\n",
       " 'Democratic Republic of the Congo',\n",
       " 'Djibouti',\n",
       " 'Dominica',\n",
       " 'Dominican Republic',\n",
       " 'Drake Passage',\n",
       " 'East Timor',\n",
       " 'East of Severnaya Zemlya',\n",
       " 'East of the Kuril Islands',\n",
       " 'East of the North Island of New Zealand',\n",
       " 'East of the Philippine Islands',\n",
       " 'East of the South Sandwich Islands',\n",
       " 'Easter Island region',\n",
       " 'Eastern Greenland',\n",
       " 'Ecuador',\n",
       " 'Ecuador region',\n",
       " 'Egypt',\n",
       " 'El Salvador',\n",
       " 'Eritrea',\n",
       " 'Ethiopia',\n",
       " 'Falkland Islands region',\n",
       " 'Federated States of Micronesia region',\n",
       " 'Fiji',\n",
       " 'Fiji region',\n",
       " 'France',\n",
       " 'French Polynesia',\n",
       " 'French Southern Territories',\n",
       " 'Galapagos Triple Junction region',\n",
       " 'Georgia',\n",
       " 'Greece',\n",
       " 'Greenland',\n",
       " 'Greenland Sea',\n",
       " 'Guadeloupe',\n",
       " 'Guam',\n",
       " 'Guatemala',\n",
       " 'Gulf of Alaska',\n",
       " 'Haiti',\n",
       " 'Hawaii',\n",
       " 'Honduras',\n",
       " 'Iceland',\n",
       " 'Idaho',\n",
       " 'India',\n",
       " 'India region',\n",
       " 'Indian Ocean Triple Junction',\n",
       " 'Indonesia',\n",
       " 'Iran',\n",
       " 'Iraq',\n",
       " 'Italy',\n",
       " 'Japan',\n",
       " 'Japan region',\n",
       " 'Jordan',\n",
       " 'Kansas',\n",
       " 'Kazakhstan',\n",
       " 'Kermadec Islands region',\n",
       " 'Kosovo',\n",
       " 'Kuril Islands',\n",
       " 'Kyrgyzstan',\n",
       " 'Labrador Sea',\n",
       " 'Laptev Sea',\n",
       " 'Macedonia',\n",
       " 'Macquarie Island region',\n",
       " 'Malawi',\n",
       " 'Malaysia',\n",
       " 'Mariana Islands region',\n",
       " 'Martinique',\n",
       " 'Mauritania',\n",
       " 'Mauritius',\n",
       " 'Mauritius - Reunion region',\n",
       " 'Mexico',\n",
       " 'Micronesia',\n",
       " 'Mid-Indian Ridge',\n",
       " 'Molucca Sea',\n",
       " 'Mongolia',\n",
       " 'Montana',\n",
       " 'Montenegro',\n",
       " 'Morocco',\n",
       " 'Mozambique',\n",
       " 'Mozambique Channel',\n",
       " 'Nepal',\n",
       " 'New Caledonia',\n",
       " 'New Mexico',\n",
       " 'New Zealand',\n",
       " 'Nicaragua',\n",
       " 'Niue',\n",
       " 'North Atlantic Ocean',\n",
       " 'North Indian Ocean',\n",
       " 'North Korea',\n",
       " 'North of Ascension Island',\n",
       " 'North of Franz Josef Land',\n",
       " 'North of New Zealand',\n",
       " 'North of Severnaya Zemlya',\n",
       " 'North of Svalbard',\n",
       " 'Northern East Pacific Rise',\n",
       " 'Northern Mariana Islands',\n",
       " 'Northern Mid-Atlantic Ridge',\n",
       " 'Northwest of Australia',\n",
       " 'Norway',\n",
       " 'Norwegian Sea',\n",
       " 'Off the coast of Central America',\n",
       " 'Off the coast of Ecuador',\n",
       " 'Off the coast of Oregon',\n",
       " 'Off the east coast of the North Island of New Zealand',\n",
       " 'Off the south coast of Australia',\n",
       " 'Off the west coast of northern Sumatra',\n",
       " 'Oklahoma',\n",
       " 'Oman',\n",
       " 'Oregon',\n",
       " 'Owen Fracture Zone region',\n",
       " 'Pacific-Antarctic Ridge',\n",
       " 'Pakistan',\n",
       " 'Palau',\n",
       " 'Palau region',\n",
       " 'Panama',\n",
       " 'Papua New Guinea',\n",
       " 'Peru',\n",
       " 'Peru-Ecuador border region',\n",
       " 'Philippine Islands region',\n",
       " 'Philippines',\n",
       " 'Poland',\n",
       " 'Portugal',\n",
       " 'Portugal region',\n",
       " 'Prince Edward Islands',\n",
       " 'Prince Edward Islands region',\n",
       " 'Puerto Rico',\n",
       " 'Republic of the Congo',\n",
       " 'Reykjanes Ridge',\n",
       " 'Romania',\n",
       " 'Russia',\n",
       " 'Russia region',\n",
       " 'Saint Helena',\n",
       " 'Saint Lucia',\n",
       " 'Saint Vincent and the Grenadines',\n",
       " 'Samoa',\n",
       " 'Santa Cruz Islands region',\n",
       " 'Saudi Arabia',\n",
       " 'Scotia Sea',\n",
       " 'Sea of Okhotsk',\n",
       " 'Serbia',\n",
       " 'Slovenia',\n",
       " 'Socotra region',\n",
       " 'Solomon Islands',\n",
       " 'Somalia',\n",
       " 'South Africa',\n",
       " 'South Atlantic Ocean',\n",
       " 'South Carolina',\n",
       " 'South Georgia Island region',\n",
       " 'South Georgia and the South Sandwich Islands',\n",
       " 'South Indian Ocean',\n",
       " 'South Napa Earthquake',\n",
       " 'South Sandwich Islands',\n",
       " 'South Sandwich Islands region',\n",
       " 'South Shetland Islands',\n",
       " 'South Sudan',\n",
       " 'South of Africa',\n",
       " 'South of Australia',\n",
       " 'South of Panama',\n",
       " 'South of Tasmania',\n",
       " 'South of Tonga',\n",
       " 'South of the Fiji Islands',\n",
       " 'South of the Kermadec Islands',\n",
       " 'South of the Mariana Islands',\n",
       " 'Southeast Indian Ridge',\n",
       " 'Southeast central Pacific Ocean',\n",
       " 'Southeast of Easter Island',\n",
       " 'Southern East Pacific Rise',\n",
       " 'Southern Mid-Atlantic Ridge',\n",
       " 'Southern Pacific Ocean',\n",
       " 'Southwest Indian Ridge',\n",
       " 'Southwest of Africa',\n",
       " 'Southwest of Australia',\n",
       " 'Southwestern Atlantic Ocean',\n",
       " 'Spain',\n",
       " 'Sudan',\n",
       " 'Svalbard and Jan Mayen',\n",
       " 'Sweden',\n",
       " 'Syria',\n",
       " 'Taiwan',\n",
       " 'Tajikistan',\n",
       " 'Tanzania',\n",
       " 'Thailand',\n",
       " 'Tonga',\n",
       " 'Tonga region',\n",
       " 'Trinidad and Tobago',\n",
       " 'Tristan da Cunha region',\n",
       " 'Turkey',\n",
       " 'Turkmenistan',\n",
       " 'Uganda',\n",
       " 'Ukraine',\n",
       " 'United Kingdom',\n",
       " 'Utah',\n",
       " 'Uzbekistan',\n",
       " 'Vanuatu',\n",
       " 'Vanuatu region',\n",
       " 'Venezuela',\n",
       " 'Vietnam',\n",
       " 'Wallis and Futuna',\n",
       " 'West Chile Rise',\n",
       " 'West of Australia',\n",
       " 'West of Macquarie Island',\n",
       " 'West of Vancouver Island',\n",
       " 'West of the Galapagos Islands',\n",
       " 'Western Australia',\n",
       " 'Western Indian-Antarctic Ridge',\n",
       " 'Yemen',\n",
       " 'Zambia',\n",
       " 'north of Ascension Island',\n",
       " 'northern Mid-Atlantic Ridge',\n",
       " 'south of Panama',\n",
       " 'western Xizang']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(groups.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterating and selecting groups\n",
    "\n",
    "You can loop through the groups if you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>depth</th>\n",
       "      <th>mag</th>\n",
       "      <th>magType</th>\n",
       "      <th>nst</th>\n",
       "      <th>gap</th>\n",
       "      <th>dmin</th>\n",
       "      <th>rms</th>\n",
       "      <th>net</th>\n",
       "      <th>updated</th>\n",
       "      <th>place</th>\n",
       "      <th>type</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>usc000mkc8</th>\n",
       "      <td>2014-01-25 16:10:38.760</td>\n",
       "      <td>-55.2925</td>\n",
       "      <td>-27.0527</td>\n",
       "      <td>10.00</td>\n",
       "      <td>4.1</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.0</td>\n",
       "      <td>5.553</td>\n",
       "      <td>0.35</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-03-27T18:15:40.000Z</td>\n",
       "      <td>156km N of Visokoi Island,</td>\n",
       "      <td>earthquake</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mkc7</th>\n",
       "      <td>2014-01-25 09:43:23.230</td>\n",
       "      <td>-55.9434</td>\n",
       "      <td>-27.6772</td>\n",
       "      <td>103.48</td>\n",
       "      <td>4.3</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.0</td>\n",
       "      <td>5.324</td>\n",
       "      <td>0.62</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-03-27T18:15:40.000Z</td>\n",
       "      <td>89km NNW of Visokoi Island,</td>\n",
       "      <td>earthquake</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mh0c</th>\n",
       "      <td>2014-01-19 15:42:45.510</td>\n",
       "      <td>-56.9656</td>\n",
       "      <td>-26.7803</td>\n",
       "      <td>120.25</td>\n",
       "      <td>4.6</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151.0</td>\n",
       "      <td>6.120</td>\n",
       "      <td>0.39</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-03-22T00:05:23.000Z</td>\n",
       "      <td>39km SE of Visokoi Island,</td>\n",
       "      <td>earthquake</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usb000m7f2</th>\n",
       "      <td>2014-01-10 13:50:38.730</td>\n",
       "      <td>-56.0110</td>\n",
       "      <td>-26.1186</td>\n",
       "      <td>10.00</td>\n",
       "      <td>4.2</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106.0</td>\n",
       "      <td>6.192</td>\n",
       "      <td>0.71</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-03-15T03:38:58.000Z</td>\n",
       "      <td>101km NE of Visokoi Island,</td>\n",
       "      <td>earthquake</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usb000m78v</th>\n",
       "      <td>2014-01-07 06:51:09.160</td>\n",
       "      <td>-56.9651</td>\n",
       "      <td>-26.6696</td>\n",
       "      <td>124.37</td>\n",
       "      <td>4.3</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.0</td>\n",
       "      <td>6.176</td>\n",
       "      <td>0.59</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-03-07T00:26:01.000Z</td>\n",
       "      <td>43km SE of Visokoi Island,</td>\n",
       "      <td>earthquake</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              time  latitude  longitude   depth  mag magType  \\\n",
       "id                                                                             \n",
       "usc000mkc8 2014-01-25 16:10:38.760  -55.2925   -27.0527   10.00  4.1      mb   \n",
       "usc000mkc7 2014-01-25 09:43:23.230  -55.9434   -27.6772  103.48  4.3      mb   \n",
       "usc000mh0c 2014-01-19 15:42:45.510  -56.9656   -26.7803  120.25  4.6      mb   \n",
       "usb000m7f2 2014-01-10 13:50:38.730  -56.0110   -26.1186   10.00  4.2      mb   \n",
       "usb000m78v 2014-01-07 06:51:09.160  -56.9651   -26.6696  124.37  4.3      mb   \n",
       "\n",
       "            nst    gap   dmin   rms net                   updated  \\\n",
       "id                                                                  \n",
       "usc000mkc8  NaN   97.0  5.553  0.35  us  2014-03-27T18:15:40.000Z   \n",
       "usc000mkc7  NaN   87.0  5.324  0.62  us  2014-03-27T18:15:40.000Z   \n",
       "usc000mh0c  NaN  151.0  6.120  0.39  us  2014-03-22T00:05:23.000Z   \n",
       "usb000m7f2  NaN  106.0  6.192  0.71  us  2014-03-15T03:38:58.000Z   \n",
       "usb000m78v  NaN  109.0  6.176  0.59  us  2014-03-07T00:26:01.000Z   \n",
       "\n",
       "                                   place        type country  \n",
       "id                                                            \n",
       "usc000mkc8   156km N of Visokoi Island,   earthquake          \n",
       "usc000mkc7  89km NNW of Visokoi Island,   earthquake          \n",
       "usc000mh0c   39km SE of Visokoi Island,   earthquake          \n",
       "usb000m7f2  101km NE of Visokoi Island,   earthquake          \n",
       "usb000m78v   43km SE of Visokoi Island,   earthquake          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The key is \"\"\n"
     ]
    }
   ],
   "source": [
    "for key, group in gb:\n",
    "    display(group.head())\n",
    "    print(f'The key is \"{key}\"')\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you can get a specific group by key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>depth</th>\n",
       "      <th>mag</th>\n",
       "      <th>magType</th>\n",
       "      <th>nst</th>\n",
       "      <th>gap</th>\n",
       "      <th>dmin</th>\n",
       "      <th>rms</th>\n",
       "      <th>net</th>\n",
       "      <th>updated</th>\n",
       "      <th>place</th>\n",
       "      <th>type</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>usc000mqlq</th>\n",
       "      <td>2014-01-31 20:00:16.000</td>\n",
       "      <td>-33.6550</td>\n",
       "      <td>-71.9810</td>\n",
       "      <td>25.10</td>\n",
       "      <td>4.5</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.63</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:19.000Z</td>\n",
       "      <td>34km WSW of San Antonio, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mql6</th>\n",
       "      <td>2014-01-31 13:48:23.000</td>\n",
       "      <td>-18.0690</td>\n",
       "      <td>-69.6630</td>\n",
       "      <td>149.10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.77</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:18.000Z</td>\n",
       "      <td>17km NW of Putre, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mqk8</th>\n",
       "      <td>2014-01-30 14:20:56.560</td>\n",
       "      <td>-19.6118</td>\n",
       "      <td>-70.9487</td>\n",
       "      <td>15.16</td>\n",
       "      <td>4.1</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159.0</td>\n",
       "      <td>1.227</td>\n",
       "      <td>1.34</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:17.000Z</td>\n",
       "      <td>107km NW of Iquique, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mdi2</th>\n",
       "      <td>2014-01-30 10:02:14.000</td>\n",
       "      <td>-32.1180</td>\n",
       "      <td>-71.7860</td>\n",
       "      <td>25.70</td>\n",
       "      <td>4.5</td>\n",
       "      <td>mwr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.10</td>\n",
       "      <td>us</td>\n",
       "      <td>2015-01-30T21:28:21.955Z</td>\n",
       "      <td>64km NW of La Ligua, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mqeh</th>\n",
       "      <td>2014-01-29 18:58:23.000</td>\n",
       "      <td>-18.6610</td>\n",
       "      <td>-69.6440</td>\n",
       "      <td>123.10</td>\n",
       "      <td>4.8</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.52</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:16.000Z</td>\n",
       "      <td>51km S of Putre, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              time  latitude  longitude   depth  mag magType  \\\n",
       "id                                                                             \n",
       "usc000mqlq 2014-01-31 20:00:16.000  -33.6550   -71.9810   25.10  4.5      mb   \n",
       "usc000mql6 2014-01-31 13:48:23.000  -18.0690   -69.6630  149.10  4.3      mb   \n",
       "usc000mqk8 2014-01-30 14:20:56.560  -19.6118   -70.9487   15.16  4.1      mb   \n",
       "usc000mdi2 2014-01-30 10:02:14.000  -32.1180   -71.7860   25.70  4.5     mwr   \n",
       "usc000mqeh 2014-01-29 18:58:23.000  -18.6610   -69.6440  123.10  4.8      mb   \n",
       "\n",
       "            nst    gap   dmin   rms net                   updated  \\\n",
       "id                                                                  \n",
       "usc000mqlq  NaN    NaN    NaN  1.63  us  2014-04-08T01:43:19.000Z   \n",
       "usc000mql6  NaN    NaN    NaN  1.77  us  2014-04-08T01:43:18.000Z   \n",
       "usc000mqk8  NaN  159.0  1.227  1.34  us  2014-04-08T01:43:17.000Z   \n",
       "usc000mdi2  NaN    NaN    NaN  1.10  us  2015-01-30T21:28:21.955Z   \n",
       "usc000mqeh  NaN    NaN    NaN  1.52  us  2014-04-08T01:43:16.000Z   \n",
       "\n",
       "                                     place        type country  \n",
       "id                                                              \n",
       "usc000mqlq  34km WSW of San Antonio, Chile  earthquake   Chile  \n",
       "usc000mql6         17km NW of Putre, Chile  earthquake   Chile  \n",
       "usc000mqk8      107km NW of Iquique, Chile  earthquake   Chile  \n",
       "usc000mdi2      64km NW of La Ligua, Chile  earthquake   Chile  \n",
       "usc000mqeh          51km S of Putre, Chile  earthquake   Chile  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.get_group('Chile').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregation\n",
    "\n",
    "Now that we know how to create a `GroupBy` object, let's learn how to do aggregation on it.\n",
    "\n",
    "One way us to use the `.aggregate` method, which accepts another function as its argument. The result is automatically combined into a new dataframe with the group key as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>depth</th>\n",
       "      <th>mag</th>\n",
       "      <th>magType</th>\n",
       "      <th>nst</th>\n",
       "      <th>gap</th>\n",
       "      <th>dmin</th>\n",
       "      <th>rms</th>\n",
       "      <th>net</th>\n",
       "      <th>updated</th>\n",
       "      <th>place</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>2014-12-31 14:49:19.200</td>\n",
       "      <td>-37.5219</td>\n",
       "      <td>78.9418</td>\n",
       "      <td>248.18</td>\n",
       "      <td>6.9</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>195.0</td>\n",
       "      <td>28.762</td>\n",
       "      <td>1.47</td>\n",
       "      <td>us</td>\n",
       "      <td>2015-03-17T02:38:27.040Z</td>\n",
       "      <td>99km NW of Visokoi Island,</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>2014-12-27 06:37:50.010</td>\n",
       "      <td>37.0112</td>\n",
       "      <td>71.6062</td>\n",
       "      <td>248.39</td>\n",
       "      <td>5.6</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.0</td>\n",
       "      <td>3.505</td>\n",
       "      <td>1.55</td>\n",
       "      <td>us</td>\n",
       "      <td>2015-06-22T20:12:10.712Z</td>\n",
       "      <td>8km SE of Ashkasham, Afghanistan</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>2014-12-30 21:22:21.580</td>\n",
       "      <td>67.9858</td>\n",
       "      <td>179.9288</td>\n",
       "      <td>266.61</td>\n",
       "      <td>7.9</td>\n",
       "      <td>mww</td>\n",
       "      <td>152.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>7.712</td>\n",
       "      <td>2.15</td>\n",
       "      <td>us</td>\n",
       "      <td>2015-05-30T05:34:08.822Z</td>\n",
       "      <td>9km WSW of Little Sitkin Island, Alaska</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>2014-05-20 04:43:25.500</td>\n",
       "      <td>41.5297</td>\n",
       "      <td>20.2804</td>\n",
       "      <td>28.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>mwr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.299</td>\n",
       "      <td>1.34</td>\n",
       "      <td>us</td>\n",
       "      <td>2015-01-30T15:28:03.533Z</td>\n",
       "      <td>6km NE of Durres, Albania</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>2014-12-26 17:55:18.140</td>\n",
       "      <td>36.9391</td>\n",
       "      <td>5.6063</td>\n",
       "      <td>21.40</td>\n",
       "      <td>5.5</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.0</td>\n",
       "      <td>3.250</td>\n",
       "      <td>1.45</td>\n",
       "      <td>us</td>\n",
       "      <td>2015-03-17T02:37:18.040Z</td>\n",
       "      <td>5km SSW of Bougara, Algeria</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               time  latitude  longitude   depth  mag magType  \\\n",
       "country                                                                         \n",
       "            2014-12-31 14:49:19.200  -37.5219    78.9418  248.18  6.9     mww   \n",
       "Afghanistan 2014-12-27 06:37:50.010   37.0112    71.6062  248.39  5.6     mww   \n",
       "Alaska      2014-12-30 21:22:21.580   67.9858   179.9288  266.61  7.9     mww   \n",
       "Albania     2014-05-20 04:43:25.500   41.5297    20.2804   28.26  5.0     mwr   \n",
       "Algeria     2014-12-26 17:55:18.140   36.9391     5.6063   21.40  5.5     mww   \n",
       "\n",
       "               nst    gap    dmin   rms net                   updated  \\\n",
       "country                                                                 \n",
       "               NaN  195.0  28.762  1.47  us  2015-03-17T02:38:27.040Z   \n",
       "Afghanistan    NaN  172.0   3.505  1.55  us  2015-06-22T20:12:10.712Z   \n",
       "Alaska       152.0  338.0   7.712  2.15  us  2015-05-30T05:34:08.822Z   \n",
       "Albania        NaN   69.0   1.299  1.34  us  2015-01-30T15:28:03.533Z   \n",
       "Algeria        NaN  174.0   3.250  1.45  us  2015-03-17T02:37:18.040Z   \n",
       "\n",
       "                                               place        type  \n",
       "country                                                           \n",
       "                         99km NW of Visokoi Island,   earthquake  \n",
       "Afghanistan         8km SE of Ashkasham, Afghanistan  earthquake  \n",
       "Alaska       9km WSW of Little Sitkin Island, Alaska  earthquake  \n",
       "Albania                    6km NE of Durres, Albania  earthquake  \n",
       "Algeria                  5km SSW of Bougara, Algeria  earthquake  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.aggregate(np.max).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the operation is applied to every column. That's usually not what we want. We can use both `.` or `[]` syntax to select a specific column to operate on. Then we get back a series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "               6.9\n",
       "Afghanistan    5.6\n",
       "Alaska         7.9\n",
       "Albania        5.0\n",
       "Algeria        5.5\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.aggregate(np.max).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Chile                         8.2\n",
       "Alaska                        7.9\n",
       "Solomon Islands               7.6\n",
       "Papua New Guinea              7.5\n",
       "El Salvador                   7.3\n",
       "Mexico                        7.2\n",
       "Fiji                          7.1\n",
       "Indonesia                     7.1\n",
       "Southern East Pacific Rise    7.0\n",
       "                              6.9\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.aggregate(np.max).nlargest(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are shortcuts for common aggregation functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Chile                         8.2\n",
       "Alaska                        7.9\n",
       "Solomon Islands               7.6\n",
       "Papua New Guinea              7.5\n",
       "El Salvador                   7.3\n",
       "Mexico                        7.2\n",
       "Fiji                          7.1\n",
       "Indonesia                     7.1\n",
       "Southern East Pacific Rise    7.0\n",
       "                              6.9\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.max().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Mexico         4.01\n",
       "Oregon         4.02\n",
       "California     4.04\n",
       "               4.10\n",
       "Afghanistan    4.10\n",
       "Alaska         4.10\n",
       "Albania        4.10\n",
       "Algeria        4.10\n",
       "Angola         4.10\n",
       "Antarctica     4.10\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.min().nsmallest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "South Napa Earthquake          6.020000\n",
       "Bouvet Island region           5.750000\n",
       "South Georgia Island region    5.450000\n",
       "Barbados                       5.400000\n",
       "New Mexico                     5.300000\n",
       "Easter Island region           5.162500\n",
       "Malawi                         5.100000\n",
       "Drake Passage                  5.033333\n",
       "North Korea                    5.000000\n",
       "Saint Lucia                    5.000000\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.mean().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Barbados                              1.555635\n",
       "Bouvet Island region                  1.484924\n",
       "Puerto Rico                           0.957601\n",
       "Off the coast of Ecuador              0.848528\n",
       "Palau region                          0.777817\n",
       "East of the South Sandwich Islands    0.606495\n",
       "Southern East Pacific Rise            0.604508\n",
       "South Indian Ocean                    0.602194\n",
       "Prince Edward Islands region          0.595259\n",
       "Panama                                0.591322\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.std().nlargest(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also apply multiple functions at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>4.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4.582544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>4.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.410656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>4.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4.515025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>4.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.391667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>4.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.583333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             amin  amax      mean\n",
       "country                          \n",
       "              4.1   6.9  4.582544\n",
       "Afghanistan   4.1   5.6  4.410656\n",
       "Alaska        4.1   7.9  4.515025\n",
       "Albania       4.1   5.0  4.391667\n",
       "Algeria       4.1   5.5  4.583333"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.aggregate([np.min, np.max, np.mean]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112a09e80>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YlHn1RAn9n1WRXsNFLLMiYtcB982MiAkdPm6rLeZ9Iv+ADSRtTbpAKeCXEVHG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\n/wEz2s0FAPu8XwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a097f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb.mag.aggregate([np.min, np.max, np.mean]).nlargest(10, 'mean').plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformation\n",
    "\n",
    "The key difference between aggregation and transformation is that aggregation returns a *smaller* object than the original, indexed by the group keys, while *transformation* returns an object with the same index (and same size) as the original object. Groupby + transformation is used when applying an operation that requires information about the whole group.\n",
    "\n",
    "In this example, we standardize the earthquakes in each country so that the distribution has zero mean and unit variance. We do this by first defining a function called `standardize` and then passing it to the `transform` method.\n",
    "\n",
    "I admit that I don't know why you would want to do this. `transform` makes more sense to me in the context of time grouping operation. See below for another example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "usc000mqlp   -0.915774\n",
       "usc000mqln   -0.675696\n",
       "usc000mqls   -0.282385\n",
       "usc000mf1x   -0.684915\n",
       "usc000mqlm   -0.666807\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def standardize(x):\n",
    "    return (x - x.mean())/x.std()\n",
    "\n",
    "mag_standardized_by_country = gb.mag.transform(standardize)\n",
    "mag_standardized_by_country.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Grouping\n",
    "\n",
    "We already saw how pandas has a strong built-in understanding of time. This capability is even more powerful in the context of `groupby`. With datasets indexed by a pandas `DateTimeIndex`, we can easily group and resample the data using common time units.\n",
    "\n",
    "To get started, let's load the timeseries data we already explored in past lessons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib\n",
    "import pandas as pd\n",
    "\n",
    "header_url = 'ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/HEADERS.txt'\n",
    "with urllib.request.urlopen(header_url) as response:\n",
    "    data = response.read().decode('utf-8')\n",
    "lines = data.split('\\n')\n",
    "headers = lines[1].split(' ')\n",
    "\n",
    "ftp_base = 'ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/'\n",
    "dframes = []\n",
    "for year in range(2016, 2019):\n",
    "    data_url = f'{year}/CRND0103-{year}-NY_Millbrook_3_W.txt'               \n",
    "    df = pd.read_csv(ftp_base + data_url, parse_dates=[1],\n",
    "                     names=headers, header=None, sep='\\s+',\n",
    "                     na_values=[-9999.0, -99.0])\n",
    "    dframes.append(df)\n",
    "\n",
    "df = pd.concat(dframes)\n",
    "df = df.set_index('LST_DATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WBANNO</th>\n",
       "      <th>CRX_VN</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>SOLARAD_DAILY</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LST_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>3.4</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>...</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.147</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.144</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>5.1</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.69</td>\n",
       "      <td>...</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.141</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-14.4</td>\n",
       "      <td>-6.9</td>\n",
       "      <td>-7.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.139</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>-5.2</td>\n",
       "      <td>-15.5</td>\n",
       "      <td>-10.3</td>\n",
       "      <td>-11.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            WBANNO  CRX_VN  LONGITUDE  LATITUDE  T_DAILY_MAX  T_DAILY_MIN  \\\n",
       "LST_DATE                                                                    \n",
       "2016-01-01   64756   2.422     -73.74     41.79          3.4         -0.5   \n",
       "2016-01-02   64756   2.422     -73.74     41.79          2.9         -3.6   \n",
       "2016-01-03   64756   2.422     -73.74     41.79          5.1         -1.8   \n",
       "2016-01-04   64756   2.422     -73.74     41.79          0.5        -14.4   \n",
       "2016-01-05   64756   2.422     -73.74     41.79         -5.2        -15.5   \n",
       "\n",
       "            T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  SOLARAD_DAILY ...  \\\n",
       "LST_DATE                                                           ...   \n",
       "2016-01-01           1.5          1.3           0.0           1.69 ...   \n",
       "2016-01-02          -0.4         -0.3           0.0           6.25 ...   \n",
       "2016-01-03           1.6          1.1           0.0           5.69 ...   \n",
       "2016-01-04          -6.9         -7.5           0.0           9.17 ...   \n",
       "2016-01-05         -10.3        -11.7           0.0           9.34 ...   \n",
       "\n",
       "           SOIL_MOISTURE_10_DAILY  SOIL_MOISTURE_20_DAILY  \\\n",
       "LST_DATE                                                    \n",
       "2016-01-01                  0.233                   0.204   \n",
       "2016-01-02                  0.227                   0.199   \n",
       "2016-01-03                  0.223                   0.196   \n",
       "2016-01-04                  0.220                   0.194   \n",
       "2016-01-05                  0.213                   0.191   \n",
       "\n",
       "            SOIL_MOISTURE_50_DAILY  SOIL_MOISTURE_100_DAILY  \\\n",
       "LST_DATE                                                      \n",
       "2016-01-01                   0.155                    0.147   \n",
       "2016-01-02                   0.152                    0.144   \n",
       "2016-01-03                   0.151                    0.141   \n",
       "2016-01-04                   0.148                    0.139   \n",
       "2016-01-05                   0.148                    0.138   \n",
       "\n",
       "            SOIL_TEMP_5_DAILY  SOIL_TEMP_10_DAILY  SOIL_TEMP_20_DAILY  \\\n",
       "LST_DATE                                                                \n",
       "2016-01-01                4.2                 4.4                 5.1   \n",
       "2016-01-02                2.8                 3.1                 4.2   \n",
       "2016-01-03                2.6                 2.8                 3.8   \n",
       "2016-01-04                1.7                 2.1                 3.4   \n",
       "2016-01-05                0.4                 0.9                 2.4   \n",
       "\n",
       "            SOIL_TEMP_50_DAILY  SOIL_TEMP_100_DAILY      \n",
       "LST_DATE                                                 \n",
       "2016-01-01                 6.0                  7.6 NaN  \n",
       "2016-01-02                 5.7                  7.4 NaN  \n",
       "2016-01-03                 5.2                  7.2 NaN  \n",
       "2016-01-04                 4.9                  6.9 NaN  \n",
       "2016-01-05                 4.3                  6.6 NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This timeseries has daily resolution, and the daily plots are somewhat noisy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112f05b00>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jBmKUM+m8vHy7K+2rqOG/8H7tZiwNQx7kmSxz5ZX/+aNLJFuv9b9KNmRMU1m5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\nEwIfoIkHP+HnHw53EhMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a09518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.T_DAILY_MEAN.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common way to analyze such data in climate science is to create a \"climatology,\" which contains the average values in each month or day of the year. We can do this easily with groupby. Recall that `df.index` is a pandas `DateTimeIndex` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WBANNO</th>\n",
       "      <th>CRX_VN</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>SOLARAD_DAILY</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LST_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>2.924731</td>\n",
       "      <td>-7.120430</td>\n",
       "      <td>-2.100000</td>\n",
       "      <td>-1.905376</td>\n",
       "      <td>2.480645</td>\n",
       "      <td>5.811613</td>\n",
       "      <td>...</td>\n",
       "      <td>0.240292</td>\n",
       "      <td>0.199547</td>\n",
       "      <td>0.150376</td>\n",
       "      <td>0.162533</td>\n",
       "      <td>0.168817</td>\n",
       "      <td>0.232258</td>\n",
       "      <td>0.788172</td>\n",
       "      <td>1.749462</td>\n",
       "      <td>3.394624</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.487882</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>6.431765</td>\n",
       "      <td>-5.015294</td>\n",
       "      <td>0.712941</td>\n",
       "      <td>1.022353</td>\n",
       "      <td>4.080000</td>\n",
       "      <td>8.495176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.244286</td>\n",
       "      <td>0.203089</td>\n",
       "      <td>0.154306</td>\n",
       "      <td>0.165506</td>\n",
       "      <td>1.217647</td>\n",
       "      <td>1.183529</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>1.602353</td>\n",
       "      <td>2.460000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>7.953763</td>\n",
       "      <td>-3.035484</td>\n",
       "      <td>2.455914</td>\n",
       "      <td>2.643011</td>\n",
       "      <td>2.786022</td>\n",
       "      <td>13.210645</td>\n",
       "      <td>...</td>\n",
       "      <td>0.224202</td>\n",
       "      <td>0.190742</td>\n",
       "      <td>0.153140</td>\n",
       "      <td>0.161763</td>\n",
       "      <td>3.469892</td>\n",
       "      <td>3.407527</td>\n",
       "      <td>3.379570</td>\n",
       "      <td>3.472043</td>\n",
       "      <td>3.792473</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>14.793333</td>\n",
       "      <td>1.816667</td>\n",
       "      <td>8.302222</td>\n",
       "      <td>8.574444</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>15.295222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.208600</td>\n",
       "      <td>0.183911</td>\n",
       "      <td>0.150022</td>\n",
       "      <td>0.159989</td>\n",
       "      <td>9.402222</td>\n",
       "      <td>9.138889</td>\n",
       "      <td>8.438889</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>6.633333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>21.235484</td>\n",
       "      <td>8.460215</td>\n",
       "      <td>14.850538</td>\n",
       "      <td>15.121505</td>\n",
       "      <td>3.016129</td>\n",
       "      <td>17.287849</td>\n",
       "      <td>...</td>\n",
       "      <td>0.198925</td>\n",
       "      <td>0.175452</td>\n",
       "      <td>0.146032</td>\n",
       "      <td>0.157710</td>\n",
       "      <td>16.889247</td>\n",
       "      <td>16.691398</td>\n",
       "      <td>15.569892</td>\n",
       "      <td>14.193548</td>\n",
       "      <td>12.344086</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>25.627778</td>\n",
       "      <td>11.837778</td>\n",
       "      <td>18.733333</td>\n",
       "      <td>19.026667</td>\n",
       "      <td>3.053333</td>\n",
       "      <td>21.912111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132856</td>\n",
       "      <td>0.127044</td>\n",
       "      <td>0.128311</td>\n",
       "      <td>0.154978</td>\n",
       "      <td>22.372222</td>\n",
       "      <td>22.198889</td>\n",
       "      <td>20.916667</td>\n",
       "      <td>19.417778</td>\n",
       "      <td>17.452222</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>28.568817</td>\n",
       "      <td>15.536559</td>\n",
       "      <td>22.054839</td>\n",
       "      <td>22.012903</td>\n",
       "      <td>3.867742</td>\n",
       "      <td>21.569140</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100871</td>\n",
       "      <td>0.084957</td>\n",
       "      <td>0.112538</td>\n",
       "      <td>0.153278</td>\n",
       "      <td>25.453763</td>\n",
       "      <td>25.408602</td>\n",
       "      <td>24.141935</td>\n",
       "      <td>22.725806</td>\n",
       "      <td>20.994624</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>27.473118</td>\n",
       "      <td>15.351613</td>\n",
       "      <td>21.410753</td>\n",
       "      <td>21.378495</td>\n",
       "      <td>4.476344</td>\n",
       "      <td>18.492688</td>\n",
       "      <td>...</td>\n",
       "      <td>0.150946</td>\n",
       "      <td>0.121290</td>\n",
       "      <td>0.125409</td>\n",
       "      <td>0.162650</td>\n",
       "      <td>24.784946</td>\n",
       "      <td>24.897849</td>\n",
       "      <td>24.117204</td>\n",
       "      <td>23.311828</td>\n",
       "      <td>22.278495</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>24.084444</td>\n",
       "      <td>12.032222</td>\n",
       "      <td>18.057778</td>\n",
       "      <td>17.866667</td>\n",
       "      <td>3.728889</td>\n",
       "      <td>13.625000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131144</td>\n",
       "      <td>0.113678</td>\n",
       "      <td>0.117722</td>\n",
       "      <td>0.156965</td>\n",
       "      <td>21.060000</td>\n",
       "      <td>21.218889</td>\n",
       "      <td>20.947778</td>\n",
       "      <td>20.823333</td>\n",
       "      <td>20.717778</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.533688</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>19.548000</td>\n",
       "      <td>6.704000</td>\n",
       "      <td>13.124000</td>\n",
       "      <td>13.149333</td>\n",
       "      <td>3.408000</td>\n",
       "      <td>9.659733</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127653</td>\n",
       "      <td>0.087333</td>\n",
       "      <td>0.103053</td>\n",
       "      <td>0.141000</td>\n",
       "      <td>15.598667</td>\n",
       "      <td>15.749333</td>\n",
       "      <td>15.986667</td>\n",
       "      <td>16.608000</td>\n",
       "      <td>17.434667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.522000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>10.986667</td>\n",
       "      <td>-1.536667</td>\n",
       "      <td>4.721667</td>\n",
       "      <td>4.858333</td>\n",
       "      <td>2.481667</td>\n",
       "      <td>6.991500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197867</td>\n",
       "      <td>0.159883</td>\n",
       "      <td>0.140783</td>\n",
       "      <td>0.150367</td>\n",
       "      <td>7.018333</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.860000</td>\n",
       "      <td>9.181667</td>\n",
       "      <td>11.268333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.522000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>2.845161</td>\n",
       "      <td>-6.472581</td>\n",
       "      <td>-1.819355</td>\n",
       "      <td>-1.677419</td>\n",
       "      <td>2.454839</td>\n",
       "      <td>4.752903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.225093</td>\n",
       "      <td>0.186726</td>\n",
       "      <td>0.154403</td>\n",
       "      <td>0.171903</td>\n",
       "      <td>1.732258</td>\n",
       "      <td>1.845161</td>\n",
       "      <td>2.622581</td>\n",
       "      <td>3.914516</td>\n",
       "      <td>6.038710</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          WBANNO    CRX_VN  LONGITUDE  LATITUDE  T_DAILY_MAX  T_DAILY_MIN  \\\n",
       "LST_DATE                                                                    \n",
       "1          64756  2.488667     -73.74     41.79     2.924731    -7.120430   \n",
       "2          64756  2.487882     -73.74     41.79     6.431765    -5.015294   \n",
       "3          64756  2.488667     -73.74     41.79     7.953763    -3.035484   \n",
       "4          64756  2.488667     -73.74     41.79    14.793333     1.816667   \n",
       "5          64756  2.488667     -73.74     41.79    21.235484     8.460215   \n",
       "6          64756  2.488667     -73.74     41.79    25.627778    11.837778   \n",
       "7          64756  2.488667     -73.74     41.79    28.568817    15.536559   \n",
       "8          64756  2.488667     -73.74     41.79    27.473118    15.351613   \n",
       "9          64756  2.488667     -73.74     41.79    24.084444    12.032222   \n",
       "10         64756  2.533688     -73.74     41.79    19.548000     6.704000   \n",
       "11         64756  2.522000     -73.74     41.79    10.986667    -1.536667   \n",
       "12         64756  2.522000     -73.74     41.79     2.845161    -6.472581   \n",
       "\n",
       "          T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  SOLARAD_DAILY ...  \\\n",
       "LST_DATE                                                         ...   \n",
       "1            -2.100000    -1.905376      2.480645       5.811613 ...   \n",
       "2             0.712941     1.022353      4.080000       8.495176 ...   \n",
       "3             2.455914     2.643011      2.786022      13.210645 ...   \n",
       "4             8.302222     8.574444      2.400000      15.295222 ...   \n",
       "5            14.850538    15.121505      3.016129      17.287849 ...   \n",
       "6            18.733333    19.026667      3.053333      21.912111 ...   \n",
       "7            22.054839    22.012903      3.867742      21.569140 ...   \n",
       "8            21.410753    21.378495      4.476344      18.492688 ...   \n",
       "9            18.057778    17.866667      3.728889      13.625000 ...   \n",
       "10           13.124000    13.149333      3.408000       9.659733 ...   \n",
       "11            4.721667     4.858333      2.481667       6.991500 ...   \n",
       "12           -1.819355    -1.677419      2.454839       4.752903 ...   \n",
       "\n",
       "          SOIL_MOISTURE_10_DAILY  SOIL_MOISTURE_20_DAILY  \\\n",
       "LST_DATE                                                   \n",
       "1                       0.240292                0.199547   \n",
       "2                       0.244286                0.203089   \n",
       "3                       0.224202                0.190742   \n",
       "4                       0.208600                0.183911   \n",
       "5                       0.198925                0.175452   \n",
       "6                       0.132856                0.127044   \n",
       "7                       0.100871                0.084957   \n",
       "8                       0.150946                0.121290   \n",
       "9                       0.131144                0.113678   \n",
       "10                      0.127653                0.087333   \n",
       "11                      0.197867                0.159883   \n",
       "12                      0.225093                0.186726   \n",
       "\n",
       "          SOIL_MOISTURE_50_DAILY  SOIL_MOISTURE_100_DAILY  SOIL_TEMP_5_DAILY  \\\n",
       "LST_DATE                                                                       \n",
       "1                       0.150376                 0.162533           0.168817   \n",
       "2                       0.154306                 0.165506           1.217647   \n",
       "3                       0.153140                 0.161763           3.469892   \n",
       "4                       0.150022                 0.159989           9.402222   \n",
       "5                       0.146032                 0.157710          16.889247   \n",
       "6                       0.128311                 0.154978          22.372222   \n",
       "7                       0.112538                 0.153278          25.453763   \n",
       "8                       0.125409                 0.162650          24.784946   \n",
       "9                       0.117722                 0.156965          21.060000   \n",
       "10                      0.103053                 0.141000          15.598667   \n",
       "11                      0.140783                 0.150367           7.018333   \n",
       "12                      0.154403                 0.171903           1.732258   \n",
       "\n",
       "          SOIL_TEMP_10_DAILY  SOIL_TEMP_20_DAILY  SOIL_TEMP_50_DAILY  \\\n",
       "LST_DATE                                                               \n",
       "1                   0.232258            0.788172            1.749462   \n",
       "2                   1.183529            1.280000            1.602353   \n",
       "3                   3.407527            3.379570            3.472043   \n",
       "4                   9.138889            8.438889            7.600000   \n",
       "5                  16.691398           15.569892           14.193548   \n",
       "6                  22.198889           20.916667           19.417778   \n",
       "7                  25.408602           24.141935           22.725806   \n",
       "8                  24.897849           24.117204           23.311828   \n",
       "9                  21.218889           20.947778           20.823333   \n",
       "10                 15.749333           15.986667           16.608000   \n",
       "11                  7.100000            7.860000            9.181667   \n",
       "12                  1.845161            2.622581            3.914516   \n",
       "\n",
       "          SOIL_TEMP_100_DAILY      \n",
       "LST_DATE                           \n",
       "1                    3.394624 NaN  \n",
       "2                    2.460000 NaN  \n",
       "3                    3.792473 NaN  \n",
       "4                    6.633333 NaN  \n",
       "5                   12.344086 NaN  \n",
       "6                   17.452222 NaN  \n",
       "7                   20.994624 NaN  \n",
       "8                   22.278495 NaN  \n",
       "9                   20.717778 NaN  \n",
       "10                  17.434667 NaN  \n",
       "11                  11.268333 NaN  \n",
       "12                   6.038710 NaN  \n",
       "\n",
       "[12 rows x 27 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_climatology = df.groupby(df.index.month).mean()\n",
    "monthly_climatology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row in this new dataframe respresents the average values for the months (1=January, 2=February, etc.)\n",
    "\n",
    "We can apply more customized aggregations, as with any groupby operation. Below we keep the mean of the mean, max of the max, and min of the min for the temperature measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LST_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.100000</td>\n",
       "      <td>16.9</td>\n",
       "      <td>-26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.712941</td>\n",
       "      <td>24.9</td>\n",
       "      <td>-24.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.455914</td>\n",
       "      <td>26.8</td>\n",
       "      <td>-16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.302222</td>\n",
       "      <td>30.6</td>\n",
       "      <td>-11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.850538</td>\n",
       "      <td>33.4</td>\n",
       "      <td>-1.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          T_DAILY_MEAN  T_DAILY_MAX  T_DAILY_MIN\n",
       "LST_DATE                                        \n",
       "1            -2.100000         16.9        -26.0\n",
       "2             0.712941         24.9        -24.7\n",
       "3             2.455914         26.8        -16.5\n",
       "4             8.302222         30.6        -11.3\n",
       "5            14.850538         33.4         -1.6"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_T_climatology = df.groupby(df.index.month).aggregate({'T_DAILY_MEAN': 'mean',\n",
    "                                                              'T_DAILY_MAX': 'max',\n",
    "                                                              'T_DAILY_MIN': 'min'})\n",
    "monthly_T_climatology.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f0577b8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nnxnsjtrNqfhTALg6uRJUJYg2/m2yDwiVPSpf9xoyxLTkZFg0r684wPwtp+nT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\niB46AYpR6MA7jBNdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129e5f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_T_climatology.plot(marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to do it on a finer scale, we can group by day of year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1129e5c88>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OGpISadA8+H+7eCa+cx6pTBit88LnRyArKhysYjOAHY3deGVLPa5/ahNWbKHs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\nUCwDpsPBO4feQVCOfqzB8CC8Li98Tk4J/Kz4Z7r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28Vr7zOBHa4AeDdESQYMnn9I5YUiS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o9tHzlx2c8lu3gmc+MZN44ZrJ10cT5M6d0JfHwhNnZ/f/7vbYNYmqGlHN7L8pge0US39TFVQ4Vb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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11260d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_T_climatology = df.groupby(df.index.dayofyear).aggregate({'T_DAILY_MEAN': 'mean',\n",
    "                                                            'T_DAILY_MAX': 'max',\n",
    "                                                            'T_DAILY_MIN': 'min'})\n",
    "daily_T_climatology.plot(marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating anomalies\n",
    "\n",
    "A common mode of analysis in climate science is to remove the climatology from a signal to focus only on the \"anomaly\" values. This can be accomplished with transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rpa/miniconda3/envs/geo_scipy/lib/python3.6/site-packages/pandas/core/ops.py:692: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  lambda x: op(x, rvalues))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ef63828>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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09lBBTQtEkZ1QO1WgXBq7+jvVf5g3vU+L1DWvvRHLVAZxiqfa3xnfugWv/NIf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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f72ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def standardize(x):\n",
    "    return (x - x.mean())/x.std()\n",
    "\n",
    "anomaly = df.groupby(df.index.month).transform(standardize)\n",
    "anomaly.plot(y='T_DAILY_MEAN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling\n",
    "\n",
    "Another common operation is to change the resolution of a dataset by resampling in time. Pandas exposes this through the [resample](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling) function. The resample periods are specified using pandas [offset index](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases) syntax.\n",
    "\n",
    "Below we resample the dataset by taking the mean over each month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1126a5278>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KHbcCO4GFSqkmpdRngM3A1Uqpd4CrfV/HvHpbd9x1dJzM2spC9hzvZMjtifZQ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10549d748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.resample('M').mean().plot(y='T_DAILY_MEAN', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like with `groupby`, we can apply any aggregation function to our `resample` operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10efc6160>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fWICBcTdnlKqcU0MT+NHzh3DRklp8Ksb8p0rPSGQJo9cvcNHiWtSU6VFdUoyaUj1+8NcD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef636d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.resample('M').max().plot(y='T_DAILY_MAX', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rolling Operations\n",
    "\n",
    "The final category of operations applies to \"rolling windows\". (See [rolling](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rolling.html) documentation.) We specify a function to apply over a moving window along the index. We specify the size of the window and, optionally, the weights. We also use the keyword `centered` to tell pandas whether to center the operation around the midpoint of the window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112cd4b38>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9ODI26WjFF7NE1FDb1mm0lPAzoGe1bZdp5KdM84v3JFCGP0HiBgJ55TNmGytk\nIthzoPhibrW+xcbqLuQkCyNPW3b8EQ4+p5cq7G/gwJa13PfEbkoz4vneVaXkmnqYv2Ap8dGRUTYi\nuvxSrMKPpWWz0VLCz4C+Z6aD1BmdHkMZ/gRJd9WiISB9Gho+wHmfIlNzMK/7xZk5ytf8yM2/pjNp\nMUeuegpMFkar/oVPk/zhjmUkuhoxIZlVuchopUEjrmw1bmklpXOT0VLCz0ArGibi0/Ij5gI+EZTh\nTwC310+xv4GB2AKImqbzgRVX4slezhcsz7C9ptVoNeGncQNioIUfOC7k35+qgtxlZPZuJj0hmqK0\nOOgKlILMnGusziAirLHsNc0mr3+L0VLCz0ArPSKFgvSZueHqXZThT4DOATeVopmRpAqjpUwcIYi6\n4gdkiX5SD/zRaDVhR+5/GhcxrNOW0Nznwlt0MXmjVVTYA2GqjkNgskJqqbFCg0xVzEKyPQ1HF6ln\nCnKghVYthYKUaTpACxLK8CeAo7eXQuHAnz7HaCmTQhSuZFv0Si7segw8Myhax+/Dve95XvEvozw3\nHb8maUtZiQnJJdbD+jFdByGtHMyRlSytN65Mf9JdZayQMOPvb6FVSyVfGb7iXHG3HcAkJNbchUZL\nmTSbsm7HJl2w/xmjpYSN4cadxPqHcRVexg8+qBcF//ImM4PSxlL/LvC6oWkT5EdcRm9cSeX6E8ch\nY4WEE03DNNRGu0xTI3yjBUxH/B16pufEoum/oOfLXsZBrRD59k/BPWi0nLDQf+gNAEqWXk5xWhwA\nO1qGeFNbxLy+12HTr8A7ArOvMVJmSDAlFzAio5FdM8jwXT2Y/GO0STXCV4Y/AaI6tuPETlzGNIzB\nP4GKbDv/6b0bMdgGu/5stJzw0LKNei2LouISEmOtfPWKCkwC/p/3FkRsMqz/ISTkQNFFRisNOmkJ\nsdTIPPwzyfADMfh95oyjF/iZysyNT5ogLX0uCgZ305K0mKQIKGS9IC+RXbKc/oQKkqvXwgWfNVpS\nyIl3HmGLKOGKQMbET19Syr9fPAuPT8MydBFsewjm3xSRtWvTEqKo0vKZ79hvtJTwEYjBt6TkYZ7O\n6TGCgBrhnwO9wx5u/elT5Ju6kYWrjJYTFApSbNhjLGyVs6F1h777NJJxD5A81kFvfBniuAu2EIIY\nq1nfOX3Ff0PuEgNFho60+GjqZTbm0d6ZE6kzqKcBlwnTJ415qFCGf5asPdDB0h+u43yTfitccf5V\nBisKDkIIrpyXzd/7CsE3Ch17jZYUWhx6FI43dZpumJskafHRtMh0/YWzxVgx4WKonTEsxNjTjFZi\nOMrwz4KBUS+ff3IPWfYYPpcbMHLuAAAgAElEQVR9GOy5RGdHzoacaxfmsFsLxJt37DFWTIjxtutT\nGabs+QYrMYaMhGhajxp+s7FiwkRLUx1dWjKpCTOz6MnxKMM/Cw62DeD2avzPNbkU9G2GeR8CU+T8\n183OTqCTFNyWROiKuFrz78HTupcBaSM2tcBoKYaQbIuiQ2ToL5xNxooJA16/RmtTPZ0kM2sG59B5\nl8hxrRByqEMPV1zY+SxoXlj8MYMVBZfU+Ggy7THs8+Ux1rbPaDmhxXGQI7KAdPvMHO2ZTAJrfBoe\nETsjRvjNfS4yRR9dMoU1szONlmM4yvDPhJQcae3hvrg3sG39BVRcBenTOKXCKfjM+0o56MtDdB/S\ni3dHIppGTN8RDmmFpCdEG63GMNITY+k2ZxwNV4xkGhzDZIl+li+YS6ItsnZNT4SgGL4Q4o9CCIcQ\n4sBx76UIIV4XQtQEHpOD0VdYGRuBP1/Pz6rW8CX/w1B8IVz3K6NVhYSPnV9Iu7UQq9+tF++ORJyN\nWHwujsgCsmdoEWuAzIRoHCTDUJfRUkKOo8eBTXiIS8s3WsqUIFgj/EeAK05472vAG1LKMuCNwOvp\nxbaHoOEt/uC/mhcqfwoffQbiInOlXwhx7M6lp9pYMaHCcQSABlFASlzkxdifLZn2GNp8iTAU+YVQ\nRrr1uxhbqjJ8CJLhSynfBvpOePt64NHA80eBDwajr7By4Bn6UxbyQ+9HyVx+I0TARqvTkVSgJ4Pz\ndh0xWEmICMxZe+0F74nBn2nkJMXS4ktEDnVAhBe/cffrd6umxByDlUwNQjmHnyml7AAIPGaMd5AQ\n4l4hxA4hxI7u7u4QyjlHhjqhcz87bauJsZpYUZxitKKQU1JQRL+MZ6AlQrfdD7TgIZrYpJm9eDcr\nPQ6HTEJoXnCdOE6LMAbb9ceEbGN1TBEMX7SVUv5eSrlMSrksPT3daDnH6NCjVTa5i6nITJgRW7Ln\n5iZRJ3PQHJE7wu8QaeTM4CLWAMVpcXTJwJLaUIexYkKM1RVYp1CGD4TW8LuEENkAgUdHCPsKPoEd\np6/2pjMnx26wmPCQlxxLI7nYBuuNlhIS/M5mmvyp5M9ww89MjDnO8CN3Ht/j8xM31s2oJRGsM3eR\n/nhCafj/AO4MPL8T+HsI+wo+XfvxJRXTNmplTvbMMHyTSeBNKSPe1482Enm3+lp/M21a2oy5gJ+K\nhGgLA5ZU/UUEj/DbnW6y6McTO7On8I4nWGGZTwCbgQohRKsQ4h7gx8AaIUQNsCbwevrQV48jSl/Z\nnz1DDB+gsELP8X9w33aDlQSZsRGs7j5aZRqzsxOMVmMoQgjM9sAUx3DkjvBb+/VNV2o65xhBSY8s\npbz1FB+9PxjtG4Kzmf2mIpJtVublzpzCx0uWnA9bYdOWzcxf+QGj5QSPQIrcPmsWuUmxBosxnmR7\nPIMuO/YIntJp6RulQvRjTV5ptJQpg+GLtlOSUSe4B6j1pnBRebqeNneGEJNehE9EIXqrGXR7jZYT\nPAKZIaNSC2d0SOa7ZNpj6CEpoufw23sHSWOA2NQ8o6VMGZThj0cgXvvIaDI5M200aDLjTixhlmjn\nD29HzuKtFvhOk3JmGaxkapCVGEOrL5ExZ5vRUkLGUG8bJiEx2VUM/rsowx+PgDk0+NNmnuEDcTmz\nmR/dye/eqsPnj4y8OgMddXilmfyC6V+WMhjMy02kSybT3xm5CdS0gUAMvjL8oyjDH4+A4bfKdHKT\nZl44l8iaR4avA5t/iJb+UaPlBIXRnmY6ZQrlWUlGS5kSvL8ygy6SScEZscnyzMOBCCS1aHsUZfjj\n4WzCZ7HhJH5GjvAp0Be5lpqqqe8eNlhMkBjupJNkcpNn4Pc5DnHRFspmlWHFj3+kx2g5ISHaHdh0\npUb4R1GGPx79TTijsgFBQcoM3KSTuwRpsrLcVEV994jRaoJClKuLblJIncFJ004kPlWv8drV2mCw\nkuDj9vpJ9vXiFxawpRotZ8qgDP8ENE3S1XiQg+5UshNjsEUFJXJ1emGNReQsZqWlmroIGeHHjfXg\nikpTETrHkZpTCEBXW+QZfveQh0zRhzsmI+KTHp4LyvBPYFt9N0medg6PZVCSHme0HOMoOJ+51NHe\n02+0ksnjGSZWG2HMpnZcHk9uXhEAzu7Ii9RxDOm7bH1xWUZLmVIowz+OjbU9fPnhl4gWPhplFoWp\nM9nwV2LFR3zvfqOVTJ5hfS5XxqvFu+NJSNXntl39kReL7xjUR/jY1Xd+PDPL8D3D7H38Gzzxq2/R\nP/Te6BO3189H/7CVYqH/8jfKLBblzeCIjoLzAShy7cPj8xssZnIMdetRV/HpuQYrmWJYYxkVNvyD\nkVf5qnvITZbox5qkNl0dz8wxfM0Pj32IhTW/4dbeX3HgyW+/5+O3qvVc/Fdk64uU37rjOj68bAb/\nsthSGEyYxTJRRUvf9A7NbGnSN5AVFKpNVyfiikrFMupARlghlMG+bmzCQ0xagdFSphQzx/B3PwYt\nW/nS2KdY61/OgpbHaGo/lrF5V3M/VrPgI7PGwBrHvIryGb/A5889n2WmanY19hotZVL0BTYXlc4q\nM1jJ1MNvSyNZG6DNOb0v6ifi7de/c1OSKm14PDPH8Hf8kdHUuTyrXciL8TeRKFz0bnns6MfbG/qY\nm5OIpb8BUkrUyj6QVHkhduGi+cgOo6VMCldfK6PEkGBPNlrKlMNizyRdOKnuGjJaSlAxDerJ8kic\nwXfp4zAzDL+3Djr20JR7LSD4t1tupkrLJ6f+aQAGRr3sbR3gwrI0/djUEmP1ThFEySVoCHK73jRa\nyqQQQ50MWdPURXwc4lJzSRMDVHVGRvjtu0SNBNIqJKoR/vHMDMNveAuAfXH6QmRJejyvWNeQNXwI\nOg+ws6kPvya5oDgJnE2QouZ6AbBn02Sbz/KRt6ZtsWtNk9h9PXhixy2pPOOJTswiSYzQ7Iisgjc2\ndxdeEQW2NKOlTClmhuE3bsAXl8XPd/pJiLaQGGtlV/LleLHCrj/T2OMCoDKmDzQfpCrDf5fW/Oso\npYXBmk1GS5kQQ24fGfQzpgx/fOL1/xfv4PSqQHo6/Jok09vGUHQWmGaGxZ0tM+N/o2U7G71ltA96\nMJsFQghsiRlssJwP+56kvbcfW5SZpFE9Z7oa4R/DP/dGhmQs3q0PGS1lQvSNeMgUTrR4tQFnXOJ0\nw5fDkWP4vSMeykULQ3a1SH8ikW/4YyMw0MwRv75443TpRT0KUm087FoNbifprevIT7Yhug/r56RX\nGKV2yjErL4vn/KtJangJXNPvtt/p7MUmPIgEZfjjEhjhm12RY/jdfU6KRBdjaXOMljLliHzD760F\noFHouwp/detiAK6en81GbS4tMoPl3c+RnxILXQchIQdsKYbJnWrkJsXynOlyzNqYHto6zRjt1aM1\nopJVxsRxCRi+xdWDpk3PdZoT6ardjUlILNlzjZYy5Yh8w++pAWDXSDpf+UAF1y7U//AX5ifx+hcv\n4Sn/+1jCYebEDUHnAciaZ6T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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127c8f98>"
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   "source": [
    "df.rolling(30, center=True).T_DAILY_MEAN.mean().plot()\n",
    "df.rolling(30, center=True, win_type='triang').T_DAILY_MEAN.mean().plot()"
   ]
  }
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   "display_name": "Python 3",
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================================================
FILE: src/lectures/pandas/pandas_intro.md
================================================
# Pandas

[Pandas](http://pandas.pydata.org/) is a an open source library providing high-performance, easy-to-use data structures and data analysis tools. Pandas is particularly suited to the analysis of _tabular_ data, i.e. data that can can go into a table. In other words, if you can imagine the data in an Excel spreadsheet, then Pandas is the tool for the job.

A [2017 recent analysis](https://stackoverflow.blog/2017/09/06/incredible-growth-python/) of questions from Stack Overflow showed that python was the fastest growing and most widely used programming language in the world (in developed countries).
As of 2021, the growth has now leveled off, but Python remains at the top.

![python growth](SO_language_growth_2021.svg)

[Link to generate your own version of this figure](https://insights.stackoverflow.com/trends?tags=java%2Cc%2Cc%2B%2B%2Cpython%2Cc%23%2Cvb.net%2Cjavascript%2Cassembly%2Cphp%2Cperl%2Cruby%2Cvb%2Cswift%2Cr%2Cobjective-c)

A [follow-up analysis](https://149351115.v2.pressablecdn.com/wp-content/uploads/2017/09/related_tags_over_time-1-2000x2000.png) showed that this growth is driven by the data science packages such as numpy, matplotlib, and especially pandas.

![pandas growth](https://149351115.v2.pressablecdn.com/wp-content/uploads/2017/09/related_tags_over_time-1-2000x2000.png)

The exponential growth of pandas is due to the fact that it _just works_. It saves you time and helps you do science more efficiently and effictively.

## Pandas capabilities (from the Pandas website):

* A fast and efficient DataFrame object for data manipulation with integrated indexing;
* Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;
* Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;
* Flexible reshaping and pivoting of data sets;
* Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;
* Columns can be inserted and deleted from data structures for size mutability;
* Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;
* High performance merging and joining of data sets;
* Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;
* Time series-functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;
* Highly optimized for performance, with critical code paths written in Cython or C.
* Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.

In this lecture, we will go over the basic capabilities of Pandas. It is a very deep library, and you will need to dig into the [documentation](http://pandas.pydata.org/pandas-docs/stable/) for more advanced usage.

Pandas was created by [Wes McKinney](http://wesmckinney.com/). Many of the examples here are drawn from Wes McKinney's book [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do), which includes a github repo of [code samples](https://github.com/wesm/pydata-book).


================================================
FILE: src/lectures/xarray/xarray-part2.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Xarray Interpolation, Groupby, Resample, Rolling, and Coarsen\n",
    "\n",
    "In this lesson, we cover some more advanced aspects of xarray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exception reporting mode: Minimal\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "from matplotlib import pyplot as plt\n",
    "%xmode Minimal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpolation\n",
    "\n",
    "In the previous lesson on Xarray, we learned how to select data based on its dimension coordinates and align data with dimension different coordinates.\n",
    "But what if we want to estimate the value of the data variables at _different coordinates_.\n",
    "This is where interpolation comes in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
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       "  --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",
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       "  --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.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;\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",
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       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
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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",
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       "\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",
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       "\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",
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       "\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",
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       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
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       "\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",
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       "\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.DataArray (x: 11)&gt;\n",
       "array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100])\n",
       "Coordinates:\n",
       "  * x        (x) int64 0 1 2 3 4 5 6 7 8 9 10</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 11</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-12fd21f4-4bbb-4d4e-b6c1-236898262286' class='xr-array-in' type='checkbox' checked><label for='section-12fd21f4-4bbb-4d4e-b6c1-236898262286' 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>0 1 4 9 16 25 36 49 64 81 100</span></div><div class='xr-array-data'><pre>array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100])</pre></div></div></li><li class='xr-section-item'><input id='section-697bfead-2fe0-436e-9c38-217a98c50783' class='xr-section-summary-in' type='checkbox'  checked><label for='section-697bfead-2fe0-436e-9c38-217a98c50783' 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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10</div><input id='attrs-b6a3c000-ec0d-4bb6-8a17-e712db153f32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6a3c000-ec0d-4bb6-8a17-e712db153f32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bda2efa-18f9-4b61-be3d-316d085ba57a' class='xr-var-data-in' type='checkbox'><label for='data-6bda2efa-18f9-4b61-be3d-316d085ba57a' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-312688fd-7f1c-41ca-bd50-65d44cf54897' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-312688fd-7f1c-41ca-bd50-65d44cf54897' 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 (x: 11)>\n",
       "array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100])\n",
       "Coordinates:\n",
       "  * x        (x) int64 0 1 2 3 4 5 6 7 8 9 10"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we write it out explicitly so we can see each point.\n",
    "x_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
    "f = xr.DataArray(x_data**2, dims=['x'], coords={'x': x_data})\n",
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8e565f4c70>]"
      ]
     },
     "execution_count": 3,
     "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": [
    "f.plot(marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only have data on the integer points in x.\n",
    "But what if we wanted to estimate the value at, say, 4.5?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "4.5",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31mKeyError\u001b[0m\u001b[0;31m:\u001b[0m 4.5\n",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m\u001b[0;31m:\u001b[0m 4.5\n"
     ]
    }
   ],
   "source": [
    "f.sel(x=4.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpolation to the rescue!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       ".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-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.DataArray ()&gt;\n",
       "array(20.5)\n",
       "Coordinates:\n",
       "    x        float64 4.5</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-293dfd91-a0fc-4afd-bbe2-b02af687c22b' class='xr-array-in' type='checkbox' checked><label for='section-293dfd91-a0fc-4afd-bbe2-b02af687c22b' 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>20.5</span></div><div class='xr-array-data'><pre>array(20.5)</pre></div></div></li><li class='xr-section-item'><input id='section-ccd38393-8eb7-4440-ba12-a12a1fa17e0b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ccd38393-8eb7-4440-ba12-a12a1fa17e0b' 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>x</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.5</div><input id='attrs-814504f6-7651-4081-953f-c68e9f897071' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-814504f6-7651-4081-953f-c68e9f897071' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f9cc28a-64db-4d0e-8a8c-d834412e0040' class='xr-var-data-in' type='checkbox'><label for='data-5f9cc28a-64db-4d0e-8a8c-d834412e0040' title='Show/Hide data 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(4.5)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d14f7485-591c-48ca-b454-f51097ad5a89' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d14f7485-591c-48ca-b454-f51097ad5a89' 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 ()>\n",
       "array(20.5)\n",
       "Coordinates:\n",
       "    x        float64 4.5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.interp(x=4.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpolation uses [scipy.interpolate](https://docs.scipy.org/doc/scipy/reference/interpolate.html) under the hood.\n",
    "There are different modes of interpolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(20.5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# default\n",
    "f.interp(x=4.5, method='linear').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(16.)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.interp(x=4.5, method='nearest').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(20.25)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.interp(x=4.5, method='cubic').values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can interpolate to a whole new coordinate at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8e55efd940>"
      ]
     },
     "execution_count": 9,
     "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": [
    "x_new = x_data + 0.5\n",
    "f_interp_linear = f.interp(x=x_new, method='linear')\n",
    "f_interp_cubic = f.interp(x=x_new, method='cubic')\n",
    "f.plot(marker='o', label='original')\n",
    "f_interp_linear.plot(marker='o', label='linear')\n",
    "f_interp_cubic.plot(marker='o', label='cubic')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that values outside of the original range are not supported:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.5,  2.5,  6.5, 12.5, 20.5, 30.5, 42.5, 56.5, 72.5, 90.5,  nan])"
      ]
     },
     "execution_count": 10,
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     "output_type": "execute_result"
    }
   ],
   "source": [
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   "source": [
    "```{note}\n",
    "You can apply interpolation to any dimension, and even to multiple dimensions at a time.\n",
    "(Multidimensional interpolation only supports `mode='nearest'` and `mode='linear'`.)\n",
    "But keep in mind that _Xarray has no built-in understanding of geography_.\n",
    "If you use `interp` on lat / lon coordinates, it will just perform naive interpolation of the lat / lon values.\n",
    "More sophisticated treatment of spherical geometry requires another package such as [xesmf](https://xesmf.readthedocs.io/).\n",
    "```"
   ]
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   },
   "source": [
    "## Groupby\n",
    "\n",
    "Xarray copies Pandas' very useful groupby functionality, enabling the \"split / apply / combine\" workflow on xarray DataArrays and Datasets. In the first part of the lesson, we will learn to use groupby by analyzing sea-surface temperature data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we load a dataset. We will use the [NOAA Extended Reconstructed Sea Surface Temperature (ERSST) v5](https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5) product, a widely used and trusted gridded compilation of of historical data going back to 1854.\n",
    "\n",
    "Since the data is provided via an [OPeNDAP](https://en.wikipedia.org/wiki/OPeNDAP) server, we can load it directly without downloading anything:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 89, lon: 180, time: 708)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan\n",
       "Attributes: (12/38)\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science &gt; Oceans &gt; Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    ...                              ...\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2021-11-07\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</pre><div class='xr-wrap' hidden><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-bd3ec339-c3bc-485a-af81-60da595da804' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd3ec339-c3bc-485a-af81-60da595da804' 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'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 708</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9bbe6cb5-0ebb-4413-9233-4a3d5d990f01' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9bbe6cb5-0ebb-4413-9233-4a3d5d990f01' 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'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-ba1f393e-58de-4289-98e9-c9223c443861' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba1f393e-58de-4289-98e9-c9223c443861' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd053710-3994-4072-8c62-0749ff299bae' class='xr-var-data-in' type='checkbox'><label for='data-bd053710-3994-4072-8c62-0749ff299bae' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], 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'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-a0c9c574-b594-4eba-a38c-a3cd78ad6df9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0c9c574-b594-4eba-a38c-a3cd78ad6df9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52309da0-f391-478d-9988-3d4ce545d97e' class='xr-var-data-in' type='checkbox'><label for='data-52309da0-f391-478d-9988-3d4ce545d97e' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\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'>1960-01-01 ... 2018-12-01</div><input id='attrs-7c2d3f34-3b00-4e3a-b044-6502833a4f53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c2d3f34-3b00-4e3a-b044-6502833a4f53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15e2beca-5720-48b8-9a14-a3292d74df95' class='xr-var-data-in' type='checkbox'><label for='data-15e2beca-5720-48b8-9a14-a3292d74df95' title='Show/Hide data repr'><svg 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>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80992.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f595e2dd-bb41-49e9-a3e3-f0a07e0584a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f595e2dd-bb41-49e9-a3e3-f0a07e0584a8' 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'>-1.8 -1.8 -1.8 -1.8 ... nan nan nan</div><input id='attrs-6f133636-85e2-4772-8e41-6849c0fcec20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f133636-85e2-4772-8e41-6849c0fcec20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2643fdc4-3fa2-4f5a-acc7-bb9ed93e9f7f' class='xr-var-data-in' type='checkbox'><label for='data-2643fdc4-3fa2-4f5a-acc7-bb9ed93e9f7f' title='Show/Hide data repr'><svg 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 Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</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>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  89 180]</dd></dl></div><div class='xr-var-data'><pre>array([[[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9fd75980-2d2a-4e71-8cb5-da0d4e97ca20' class='xr-section-summary-in' type='checkbox'  ><label for='section-9fd75980-2d2a-4e71-8cb5-da0d4e97ca20' class='xr-section-summary' >Attributes: <span>(38)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.</dd><dt><span>description :</span></dt><dd>In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature &gt;</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>source_comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>geospatial_lon_min :</span></dt><dd>-1.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.0</dd><dt><span>geospatial_laty_max :</span></dt><dd>89.0</dd><dt><span>geospatial_laty_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>project :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST)</dd><dt><span>original_publisher_url :</span></dt><dd>http://www.ncdc.noaa.gov</dd><dt><span>References :</span></dt><dd>https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 at NCEI and http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html</dd><dt><span>source :</span></dt><dd>In situ data: ICOADS R3.0 before 2015, NCEP in situ GTS from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015</dd><dt><span>title :</span></dt><dd>NOAA ERSSTv5 (in situ only)</dd><dt><span>history :</span></dt><dd>created 07/2017 by PSD data using NCEI&#x27;s ERSST V5 NetCDF values</dd><dt><span>institution :</span></dt><dd>This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Centers for Environmental Information and time aggregated. Original Full Source: NOAA/NESDIS/NCEI/CCOG</dd><dt><span>citation :</span></dt><dd>Huang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1</dd><dt><span>platform :</span></dt><dd>Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>metadata_link :</span></dt><dd>:metadata_link = https://doi.org/10.7289/V5T72FNM (original format)</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang (original)</dd><dt><span>date_created :</span></dt><dd>2017-06-30T12:18:00Z (original)</dd><dt><span>product_version :</span></dt><dd>Version 5</dd><dt><span>creator_url_original :</span></dt><dd>https://www.ncei.noaa.gov</dd><dt><span>license :</span></dt><dd>No constraints on data access or use</dd><dt><span>comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters</dd><dt><span>summary :</span></dt><dd>ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis</dd><dt><span>dataset_title :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>data_modified :</span></dt><dd>2021-11-07</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 89, lon: 180, time: 708)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan\n",
       "Attributes: (12/38)\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science > Oceans > Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    ...                              ...\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2021-11-07\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/noaa.ersst.v5/sst.mnmean.nc'\n",
    "ds = xr.open_dataset(url, drop_variables=['time_bnds'])\n",
    "ds = ds.sel(time=slice('1960', '2018')).load()\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do some basic visualizations of the data, just to make sure it looks reasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f8e4e41c3a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bS4q0oIj23WZdkdQuveba4vNhcRQnp58vUmMfuedzy1TXYVGd7bct+qTbKeMGzEjWrQMgGQywq69xu9O08PfPNm1uFatzjwe9szkRJqEKRPzkK82OF0vJJNLND4sYB0i2366aMDBXH3WE/A+mJRSFJx5w2OvJZnwSwbXdokyBUz1RHJvf38r+ROTOSiYa1VMIMpVvyt+CoN2E8kFlrQzhUT01afYF/gx8XNKvJH1E0jpgFzO7AsD/e7OmgyUdJek0SacNNl2/dKOORCI3ejKD1DTytVQ2DUlrJb1G0nH+/f6SHrwY51puQ3gXOBR4jpn9QtL7mIcqysyOBY4FWHfTvSxJXX4ppXnBJLj7Q98FQO+6AckmZxU7+WevLvrI1vTKoi2Aeu7YE//vg1tyXZEaTavU9LpyolciOr5Q1ZG7PaswYJ94xX9WD8olh27HFdYBt7oNV7sdv8KVGpdFSqfQmhm/XZbrTaAs2nPDJo7c9Zluu9dj9ha7kuaRxImK71iFJDifwd0e+e5ifLm99GefH+2YMWlGSQkL7SvkAWueWLjcqj+Abli9LC9olhT3xYLccDIjFxdMpYHaOhSR35ZQlUZ8m6wD9XIH5XmD3Xl+qqAcgsaV22tJu4jwJVuXfxw4HbiLf38Z8EXgm5M+0XJLGpcBl5nZL/z7L+EmkT9J2g3A/3vlMo0vEolEGhlYh751R74GY4L/Jsh+ZvZOoA9gZjcwR3k6GZZ10jCz/wMulXRLv+s+wLnA14En+X1PAr62DMOLRCKRoYxTTeWvJWJW0hq8zCVpP2DzYpxoudVTAM8BTpA0BVwEPAU3mX1B0lOBS4B/btORvGoqFz+VGYmvx5hsTkl8+uT73+VNRRI0egn4Ql/04MRfvWEyVxUpaKMa0fR0+SaIJg45cv0LIK8F3emUaqiOSuP3nI7LP9rCx3+KwjDrztfQfsdtCmNsNtVxTha5CmSIg4XTtsz9kbCEwoB7+OPfMzZ2aDHYkjTxozjphk9x/6nHAdDZaQeUOWcDTfWKmA3rdch8inLrCgvVh0WEd6liNAVZHTKQf2NJEBEeqKqGaKbAIPF/28ooYjZOP+4F6CMv3KLrdt1rrPrJWiz2Je0FfBLYFffNPNbM3ifp9cDTcHZfgFea2beHdPM64DvAXpJOAO6GdzCaNMs+aZjZmcBhDR/dZ4mHEolEIq1pF9zXSpkzAF5kZmdI2hY4XdLJ/rNjzOzd4zows5MlnQEcjps7n2dmV7U5+XxZ9kljUtx6/S6cevwLOfwJRxeutsog8VGp3auu48Tz376cQ4w00NnGrUzpdctVugS9XtGmKHLV6zkDOGDdpHC1HSlpBFgoBeT5jJKkstptKuCVG8eL71XQV8VQ2625iiZ5/qRyG8GdnuTqZZ96/JavdreUunvzQiLivzv7mWL7Ads4rXJy050QPq9Ur0M25V2rZzrlPeqqvEcdAgN5UCa1JkYU0kVS3SZoXhi6Mzjz/S8A4NBnHDOxSPAcV4RpTMLCFhYA7yGae4teK+k8YI/5jEXSw4H/MbNv+fc7SPpHM/vv+fTThuU2hEcikchWSe49NfolgIPy0AD/OmpYn5LWA4cAuXPQsyWdJeljYzJjvM7Mrs7fmNnfcSqribNqJI3z/vAn7nDUMXQD/fFPPr/8wVWR5rKkOTbbB3ym1DxIL0kKe8WR+764dINNEicZAHQ6TtrASwud0bpjq9saQukiX/mKWqDa8L5K91BhRc6kqqRR5EzqKCgeVAar3eFpx3D6cS8YOe6lYFJuueHx+tvVKPEXPd0rnmE6kxSuy+lULVgvILy/+X3MusE97ZZ/5xUJwkrbhQS3e6ErOaup0k5y8HMWXoY2pE09DXOfn21mzxvXn6RtgC8DzzezayR9CHgTToB6E/Ae4F+HHN4kACzK73uUNCKRSGQBZGispNE2TkNSDzdhnGBmXwEwsz+ZWWpmGXAccKcRXZwm6WhJ+0naV9IxuLiNiRMnjUgkElkALk5j9KtNESZJAj4KnGdmRwf7w7TbDwfOHtHNc4BZ4PO4oL5NwLMWcFljWTXqqVvffBdOO3b5Rf1Ie5QIrXPGUqZ6ZS6ppGZcLCKLy4jrOeqm3HIqlZ8N0xwMM3gHLqD1aPKKSiopVVLOnTZQT+WqlE6ownLv8/alaswZaN0HcMaHl/f729YIXldjKWm40b0uzM4CkGzqkWxyngfJmg79bVz7/jZi4B+/icBVnvI51AzeuXqq4nIbahSN8rmlkOZB/EH+KzV7TM8bM431jmrpPXU34InAbySd6fe9EnispINx6qkNwNOHj8WuZ4kSu66aSSMSiUSWkhS1KMI0HjP7Cc1LnGExGXOQtDPwUuA2wEzQ973b9tGWkZOGpEe06GPTiICTSASAI/d4DgCd7bcj8zmnknVr0Yz/fk9NFe60c8iDwXodrDCW05xdNQmCxOptPCYKw2woUVhF0gjcZMEZsCtG7vJ8Femik28HrridqtG2sjLOqv8uJg+4g3emyag4Dpx02uvn1U/dTVfdXIqYQVPelzm875v7JJudw4PSXiEt9NdCf1u3bZ3gXgzKQ0PHAYLMtpUstyFZYBgP+rQs6D+dx8WOwEjGSxpLZwE4AaeaejDwDFwmjT+PPGKBjJM0jsOl8Bg1nd6decyIkUgkshpoE6cxLvhvgtzEzD4q6XlmdgpwiqRTFuNE4yaNE81smIsXAJI+PcHxRCKRyFZBXmhpXJslou//vULSg4DLgT0X40QjJw0ze8K4Dtq0WWnc8k3HcP5r2hsdD3/C0YXI+4tPvpA7/qtzcLAEThuSw+Z+d3szyayTg5PLrsR2uQkA3znzjVsw8q2TI2/9CvCR35qephOqMHxabaZ7Qe6hwAidJFiet6jXmWsAB6fCyGM2KrESqvr/V/JQlceWqipq6o/q/kqEd2H8rqqeCoN3uD+Idq4XD1JT5PMicdLpbyi273+4+x5qkPGAw17vx2CVNm1Qp0Oyw3buzbq1zqHB90Xq9EHW65Ktcc95dvsum3ZyN2DzjpCuyfNKBfEVqYbel6FGbL+/bhQvCqxllIbwCakCzcbXALcleK6eN0vaHngR8H5gO2BRPCtaG8Il3RVYHx5jZp9chDFFIpHIiiePxRjFYts0JL3DzF4GrPER4VcD91rMc7aaNCR9CtgPOBPIzUiGy8y44tj/bd6NMchVg4wLX+KkgqwHt3ina3PhS8dPxmmv7OcORx2D9fLcOXD75x/Dr987t490pkt/W1/Wctf1nPKNl2zBFW2dHHE7V+zKtpkhm85DeY2Tf+ryrx154CsDCaFTFPCxblJkIa5sh4bpYAlXcYENoq/rhuz5ELrGWqI5BYAskCgqBYO65XYRBZ6E30OK5W9l9bt0K9IKJ532eh5wSJltItw+Kcj4fJ97va24553NKckhtwac9Ld5rfuep2s7xTUnKSSz7uKyrth0E3djrttNbLqZ+fZZKVFmTsJwnRrKa7ZaIBkY5b0L71e42g/2K2wfGMJbuTS1ILVkbL2MNrmntpAHSno18ApcfMai01bSOAw40GwJha1IJBJZwWSMr5exBOVevwNcBayTdE2wX4CZ2XaTPmHbafBsXK73SCQSiVDmnhr1skWeNMzsJWa2PfAtM9sueG27GBMGjI/T+AZOwNsWOFfSqQTVoMzsoZMYhKQOcBrwRzN7sKSdcD7H63GRkI8ys7+17a9QBSRWUQvse4wzYGsKfv9Cp6ra971HuwLCwEXPbS6Ok3XL6N4zPvwCDn62U21Z16kgbvti9/437y7VVP/z/Zdzpye78w1mxO2f79o0qbJWC/e599uQT0WvgWE7rgWgv65LuqZcn9zjIa5ue7Ln9sW+rKtC7Zf2RDYVRFk3qHZC//2KKqkWazGsjnRoaFWDAF3pJ6lFJs8xfjfsD4zflVgDqvECSxmnEfLdn782GE958u/8+k1z2t7jIe+CbbrFdabT08zmUd3biv66fD/FPdMAOi4gHEtg4NvM7pjCOheIocQwH7JtqTCvklJGsU2qyr3Jn1v1xziweNefsT/29y+uOqzo9RMowtQqInzRJY3893Pdop/IM049Nbb4x4R4HnAezuIPLhz++2b2dkkv9+9ftkRjiUQikbG0iwhf/EnDzFJJGyVtH6ZHXyzGudyeAhULfYGkdwBbHDwiaU/gQcBbgHz6fxhwT799PPBDxkwaZ//fn7jFu452K7qiwA6VlaZ5icKAfd7/HrczXBEC+3zA7TfBhmc5yaOevvrMD7STFm7wroVZrzSQbu084JDXka3pMfBG/nQm4Udfc0b+63ebqri1FivTKZH5Z/KrD76AQ57p01Vn5co8q6W6Dl1iGxdrSfW5NRo8aT5WdYNqIbLMPUfehyXB+06zkbviWtuxqltvMM7c4JsMlk/SCGmSLu531zez6WauBO/G3boM1nhJAhfFPbuDd6fdvk9n2kkOnU5WSABZmrB54J0FTCQdXwytmxVfkSxT+TfZKSUNTOC3LVVpuDYF/g/BwwqeZ/jAZXDxsxevtG5qHQbZaEP4EsZpbMLlrjoZuD7faWbPnfSJ2v6U3Y+5P9pHNuxbCO/F5UzZNti3i69mhZldIelmTQf6YiZHAXR32HECQ4lEIpF2OO3X8ksanm/516Izzqbx78Azgf0knRV8tC3w0y09uaQHA1ea2emS7jnf483sWOBYgJk994qeXZFIZMlIbUV4TwFgZscvyYkYL2l8BjgReBvVtLvXmtlfJ3D+uwEPlfRAXGbG7Xxakj9J2s1LGbsBV47ryFSqB6zTMH+oVE+Fic9cRLDbv/64d4EPULYE1n/iHQBsePLCBKrBNu7frAtLt+BYXLK1Pfrb9hisc2J5Ol1e2DX7JITSeuIjeiyB897oVHr7HX007Ov2ywJ1TiDFhwbMOTSorcL2ylRGFo+KHm7y96fcX1Fr5hHhofqsU36Xyv1WNcI3Dd8oao1bXyR9b/xN4YC3OLXdBa9afmeJa/dZw3W7u4eycQ8j3W5AMuVubG9mwI5rnT/M2qlZEn8T0ywhzXxVvkyFaiYzkXl1U5olle0iqaOB+WMtCwzdWejxQCWavsACw3lN3bj+OOd0seFpk4+TMmuRsHCJJg1JFzNXwYqZ7Ru0Oav+eQN/NrP7jGowzqZxtaRrgdua2R9anHBemNkrcEEpeEnjxWb2BEnvwmVpfLv/92uTPnckEolsCQPT2CJLS2jTOCzYngH+Gdip1qYDPHBEHwK+Pu5EY20aZpZJ+rWkvc3sknHtJ8TbgS9IeipwCe4GjOS2u+zCaS8c7UZ384+4VYdb7vnNcCFQ26/ELV/3/exbC0Ne0rFiZfXbR7yWUeSr69XEyT99NXd+4tH017obNQgc/TbunlYjf/zqf8MzX8w+n34bANrVilWkVG5XVouZnAEUmJNMqMmF1oJcRVlgOM2qq7ymfEajIrGreasCSTWhkE6dpGH+eob0E0g2ZkLeQKzAiJ4M6idcXv50F7DtnTSx002uY5vpwtOejjK6SSkK9lMvddb66HjrviQSf3OSxApjdpYlxUrczL2HmhCRlbEOdc/oQjKpSRoVg7o/Zv1x74JCqpvMfbY2NcKXSMVgZn+p7XqvpJ8A4Y/U08ct/iU9c9y52hrCdwPO8XEaoWV+InEavq8f4ryk8hswUkSKRCKR5SRrU09j6dRThwZvE5zksW2t2fmSDjSzc2vH3gZnW/6zLwg1kraTxhtatlvRqOtXR4mhpFy2hIFC8ktPdco2kpH47WS5kgStIDbvUAZ0bd4J1n/8nQB0bzpLEtiT8nt5m6+9nu12mFv5xoKI2TRTsdLMslIHbqaiH9dnfmzw3EJXzGClacFK030U6r3LzapxJD8Rte3g+xDYLiSaxZVhY8pUFpXqqNTpd8sgtn3e/x6s5w7e8IwXz+17EXnQj5yH5voDe0x13DOb6QzIAlVMFqyw6z+K+bPqBL+l3SH+xOFK3azqaWRB/xZIaU370ywp7m+WqZBAsiBgcMOTqzYNPXfL72u7LLdLJj2+J9ge4IOia23eD3yo4dg9gVcBj2tzolaThpmdImkX4I5+16lmNtY4HYlEIqsVl7BwZdg0zOxeLZrdNo+9qx17kqT3NB3QRKsrkvQo4FScbeFRwC8kPbLtSSKRSGS10Sb31GLHbEp6iKSbB+9f623QX5e0T615b0RXoz6r0FY99Srgjrl04YuYfw/4UtsTrQRC19n9PvfWYjtP3nvRY19Z7Nv/i2+uqLBytZRknPfwMn30jZHBmrK2c3+nAWt3uAGAHdbdQMcbSEeJ5aVKolRPpDXVwzhC9VRdzRHurxxTOb75HBVVmP838dfUCdSValBJDVW3WakmydKELM2joJMixTqDILmdrDCurz/+HYX6a8O/hF7vk+ffTnsye6wtr+v61Pmfb0p7DLKEbuHXXB6TmYrCU70sbXx2iUrnkURWadOk5goT/Q0sCe5p1XCer+IHacIgTco2hSY04fePf8W870NbQjXdMJZAPfUW4HAo4t6eADwWOAT4L+ABQdvfSXqgmVXKc0s6Erio7QnbThpJTR31F9pnyI1EIpFVhzF+gbMEhnAzs41++xHAR83sdOD0Bk+oFwDf9Jqj0/2+w4C7AA9ue8K2k8Z3JJ0EfNa/fzTw7RHtVzy/f8wrR37+u39+9Raf424nO8nmp/d7xxb3tZKY3QFmvWF7esdN7LzddQDsOH1D8UcysIRBsNIOCd+3WfEPa5MbTrPKCr95LdMkGTSRVIzu5eo4obo/HFM+jmHBbZWVcCchS3NDrZXlYbs+gtC9Kwz+SoyL/Wp538+8lYseN/p7W+c2X3u969Gg1/XPrJsWEmEiK76f6zqbWdNxpaY3Z93iuhLMefg3EBqvsyFZX5MhhvDMSskho2pcH/Y9yu/vwBIG3tW3/mzLwMDFdVppZ9NY9ElDkrYBNuI8Tj8YfDYTNjSzCyTdFmfwPsjvPgXnirup7QnbGsJfIumfcBHcAo41s6+2PUkkEomsNlqpp1rEaUjaC1cFdVdclNGxZva+liUi3ourqHoNcJ6Zneb7PAS4Ys54zDYDHx87qBG0zr1qZl8GvrwlJ4tEIpHVQpvgvpbqqQHwIjM7Q9K2ONXSycCTGVMiwsw+5rVANwN+HXz0f8BTmk7ms3zUxbCrcTWNXmRmI+0bbWuEPwJ4hx9YLkcvSinBlc5zznh8IW6/75DPjmzb88mXHvLj5/CNf3j/oo9tqZjdfZaZ7VyE8G47XM1NZ5xKtZukRaro2axT8c/vBuqQnASrqDdyFZAznPr60oH4n5ka42TqMQQhbeJq6mMad6zziilVKYNAZTKbR0dnCX1/L/pJh0EnUFUlpaoqjCkJI54L9ZSMA77sUpd3p+FWX3kjMD4bAcA9v/9ibrJNqWJa03Oqp226s0WbgSU86dSnArD3mk2s7bjnujnr0fNJvPrdDp3avQjVLvlnaSV+o1ltk1JtU6j2TMV3J6Pcrqun8uc8m3bK5zPokvp7qgwu+KfXjL03kyBjMjYNn9E7z+p9raTzgD1oWSLCzP4I/LGhz2EcDVyOyy0o4DE4Ked84GPBORtpa8x+J/BQM9t+sUsJRiKRyNZA7i036jU3heBoJK3HeT79glqJCNyifRIcYWYfNrNrzewany38gWb2eWDHcQe3VU/9yczO26JhrhLef+gJvOY3D2/V9of3cYUPH/vzo/i3054MOGNjL191k/HO238RgJefVYa9vP12K9eT+dZffQM32WWWPbe9GoCdpq+nl0sFqFhdzwRSQaKsWI12lRbtE2WV1Xx9NQtzDYnD2uQrur41W2w7wV/vuGpr9WPSQKpw/yb0cyN31ilXv1m3kK4GltBJc1fdrMjP1FFCkrhts7TRbTg/BlyuplxihVICufv3X8KP7vOukePfZe11TCWuQFI3SQsj947djUWfG9Op4hls29nE2sRJGjMasE1nvG20LlHk9z+tSBGlwTslNH4n9AspIqGf5BJbh9SPe5B1CslskHWYDdIoh04PDRV7F51WhnC3Lj9I0mnB7mP9D3UFb9D+MvB8M7tGw5KZbTmZ96DKf2jCmLuxd7LtpHGapM8D/021RvhXWh4fiUQiq4owLmdUG+BsM3veqHaSergJ44Tgd3XeJSJ8wbrCa2pIktnHA+/DeVoZ8HPgCZLWAM8ed462k8Z2OJeu+wf7DIiTRiQSuVHSJvdUG5uGnEjxUZz309HBR1+nZYkISQ/F5Z/aHTe53Bw4D7jN3HHbRcBDhnQ1mYSFZtZohc+R9Aoze1ubvlYD23ed4fcDv703z77V/4xt/9nD50iigFNJveqsRwDODT5XE7zmNw/nTbddWR7Nd/++S/i2+w4pu669lptNXwtATylZg2ksoVQ9dcgKdUhPaWFgTZQVKqBRvvzDKNVGSWW7+HzIscNKcNbbh3/wKQmDQvVSGmo3q1uoqhJZoZ6azTqFUb2bZEWa8LSTMOVDlrMRK9VcPTXT7TPTGRT95HSTjMf/4mkAnHDn4xr72HXm6kIV2g3u+07d69m2c8Oc6+4Ez2CGfjH+zogC5nU1lFfH0LdO8Sz61qk+q2B/V53iXnT9/R1kWXFPOzKSQCWVn2s2VAumnSL6/pyHvX7oWCeNMV7SaGnTuBvwRFyN7zP9vlcyvxIRb8JFhn/PzA6RdC9cZPgcJB2AS1y4i5kdJOl2OJv1m9sMtrXL7Rj+GVfdLxKJRG4UpJlIs9GTRpvgPp+OfFjDtiUi+mb2F0mJpMTMfiBpWFTxccBLgA/7858l6TPAkk4aC7LYbGFQy7Lx4lufBMCnfnf4FvUzk/SL7QQrVnSJMt593gMq52rLu897QGGM7GC89MATWx/7gFOeX2x3lRWpsaeSlH22dWPdobeRHXsbmfZj72fdyrjz1WwHo6eB329M5dvB/o6MDuWxTVJFSig5hFHY5Yo1lBzC1W5dUmgilFLy93X61iG1pLivfStdUBNlJFm3uJ6+vxfdJGUqCdxGc5fbLBkq6eQkWJCWvM867yI7lQwqBv1uYCBvYpvO5oqEl0tza5PN7NRxUfwzST+4j0nlHufPsolcukxN9M1df0r1HuX9hvuz4D52LKNjbkz9rHShddJFkp+INC/gpNKIPsjKiPD+oMNvHvqGkfdiMTA09lkuVREm4O/ekP5j4ARJV+LiP5pYa2an1gztw9rOYVL5oxbqu5AHtdwaJ1o9S9KBuCCW75vZ/sD3qdYnj0QikWUnT4451u12aXgYzu78fOA7wO8Zbre4StJ++N9tn7F8VFxHhWWVNLY0qCUSiUSWi3YR4Us1Frvep0jf38yOl7SWoRnDeBZwLHArSX8ELsZlx23FpCaNL25pB6OCWrwb2Ypju84mvnHR7XjIvmfN67i3netqu08Hcl5oIO4pLdQ5H7vg//GvB4x1aOBjF/w/ALZNOoUqpkPG8b+7KwBP2v9nY/vYYWpTofLoKmNNx6lF1nT6bN91htMdu9czo36p7ulQqJjcNTSrnqZy4zelUTj/fBRpoIaCqlqkVEMltfZqaB/20azOcn3NVWn1LHUqKa9K6VhGP8vjKEq1SmYDsiQ38palQDNUjClM0hcSRsF3ZEV8xdpOnzWJew4zSb9sg41UH4HLSJCrEXtKWev7efotT+F/NtzS7SdtXPKFxm9n7K6q8Qpjtjr0zEeOW4eO/0npKa2q8/yzTpWQ+PuYWFZuY2z2ar6MoKa4rDE2JyTNVETNL1U0OPjMAGNsGkslaUh6GnAUsBOwH27x/V802ES899R9Ja3DZTC/dj7naptG5J04I8kNONHn9rgAlE/7Qbx1xOFt+l9QUIuko3A3ir333ntLhhCJRCLzYh5xGkvBs4A74RbdmNnv6ottSS9sOjD/va25+w6lraRxfzN7qaSHA5fhvKV+AHy65fFD2ZKgFh9VeSzAYYcdtuQxoQ/b90y+dtHBfOX3hwDwiP1+NfaYd5/3AHrB9yg3bPY0KAzja5NZZuRWheNWWQBf/P0dWJc0m6fylX8bdp25mulihTtbrGSnkz7bJi46eNvOJh53i1/wxd/fIbiGcoVcSBqBFBEa+TsYSQsJo5AQUFUSyN07FUggqkog+YLYRR+XbdKwTd6fapKMSvfR3OU0CyQmcCvkIi9TYPAFKpHP9X3hdeUkRd+lZDadDOj55zCjQXFPZ5J+RRrNvyP/s+GW7JA4SfDQvctYrtcf1OzWf8qGA1inwAkjcI3uNJgnZ9VxackD19rS4SGjTx7tntHxodmzQWR+QkY//6kxGiWbTEkZ+Z6V98l9X7zTAaVL81SS0s0dNXqjJa7Foo16agknjc1mNptPAJK6zLU1+9Jp3BJXuvvr/v1DgB+1PVHbSSMvBfhA4LNm9tdJhLhPIqglEolElgNnCB/fZok4RdIrgTWS7gc8E/hGdSz2BgBJ3wUOzdVSkl7PPEwMbSeNb0j6LU499Uxf7rV10Y4RTCKoZVnZlPUqq6omPvDbe5f68YqUUQbATWnAtF/9zWiWdUEOoF/8YR8A7nzziyv9fu2ig/2xkAQrx6J/ZU5njVtd5ivWu968OfPx+w75LO/77X39ectV7UzSD8Yz61e25Yp5mBQR6sXDFeyo7LHgg/Z8k4QgiyqCPJCOtKp3J98fjAsDfw2pqfisp9IOktbGnBX9lCv/lKRSprSnNDheNZtK+8VUh9KOEdq0pjSoSBT5c5sKtnukzPhnvi6ZZZ3ff9Flu7HJj+fv2XQhBf3D+guL895j/QWVcZxxyd7l/QoIryWRFc+ko7T8LJAcErMhLjFdKh6dvp8MFfe7Yp9JgrxStT+tsGxsuG+2OynzbHucTWO0A+oSVO7LeTnwVOA3wNNxRfI+MqTt3sBs8H4WF97QirYR4S/3gSLXmFkqaSPOw2mLmFBQSyQSiSw5xsJjDSaNmWXAcZKOx6UO+aPZUDnnU8Cpkr6Ku4SH47xUW9EqTsO7bz0LF3oOLr/JYW1PEolEIquOFjEai23TkPRfkm7jt7fHVfH7JPArSY1pRMzsLbgCTX8D/g48ZT5poNrKdB/HFSK/q39/GU4H9s22J1qtPPoWvyy2P3LBP1SK+OQuh4kSEkrxuzQcZ0X70BC+LtnMjFc3rE1mmaLZ0BcauXM1VF0lVKgzRuQPCtmh4/JqzagfpMnuF6qQjjJSSwqVUU9llHJozJ0vuQoI8CoPrxqyJFAfJXRydVOgqnKU9zR0Oc7VM4mCVOemMoqdpFAZOjWT71+l626mqtuuc6Eto53HqSBSkqpjQGh4DtRT+T3uBeqpUMXYURa0yYrvRU8ZSWH8V0WtlLs6n3vpHsz4c22yhAP3qtTsKY4Nx1xRF9bbBvc+H58rzUa5XSF3p83GR8QHfxcuP5V3yLABm1PXz5rOLDM+1fvabn9sivJFoY2osfiiyD+Y2TP89lOAC8zsHyXtCpwINFaKM7MzgDMWcsK2d3o/M3sn0PcnvIEFBvRFIpHIaiBPWDjytfg/k6Ft4n648hWY2f/VG0oaO0m0adNW0pj1udbzsPP9COpqRByhlJGhShBb+Fm5yi2D3maSfuFCOaNBsaKcIi1WpudeukdlhVgEz2FFP8NW/qGR89eX7FVs337vSyvXkJ93bbKZ7byb7Yz6QQCbSCldRcMVrztftXBRE9UAOlWuBQAljUbXiuRA1UAe2kvzlW/oGloJ6FN1rdTkuuv2h4WDkoorbyGFVNq3W4M1Pf8kkCJCQ3joXDCltCKlFOetBA+K2eBu5O37lrApCLYLnSvCZxbm7crv+11vfhGnbDig8ZmG3yvneJAVxzMPd+9ERs/K9n3l+axUBglmXbJO6fYbujcvVMLdErKsRXDf4g/r75IejCv3ejecMTx3uV1Ta3trSaMikQVsP+6EbSeN1+GC+vaSdIIf3JNbHhuJRCKrDwPGpkZfdEnj6cB/4JK+Pj+QMO4DfKvW9lYt+hsb9NLWe+pkL7YcjpuNnmdmV7U5NhKJRFYlKyBOw8wuAI5o2H8ScFJt3x8mcc62aUQEHAnsa2ZvlLS3pDuZ2amTGMRqYViOqE/97vBCldIhq+RnKqJ91S/UKlOVQkXNta1d3IWPGyAN1BxVlVSpzrDK8XmRm5/9Yd8ibuNTvzucdUkwnsKIHiw+lFQih931hOfDj8mGJmtLg8DQPGo0HFsn8PdPUdHGHdsUfT1kNadRhZgCtdKQQNWK4XtEDqyifUsTYRhBnwQG/NDgHW6H6sbwXP383MF9TgMjfcgm67LJekWf67wq9NxL9yjyPIXXksi4895lXFA9tiPklA0HzNnXUxoYxatqqlKtmg21jOb3N4y47yel2q2ntNGJ4NsXH8QD9zl76FgnzkrxuV1C2hrCPwjchbIS1LXAfy7KiCKRSGQrwDK1eq022to07mxmh0r6FYCZ/U3S1CKOa1WQ52eaSaqZYKeCFXyTdDFMQshM/PKS9f7YpFiBhxlPw2Pz9/Xt0OC5yXpF7qwpdYOo5LTx2I6PxM7f337vSzn/0t2BqltrAsUqsm4L7DUszyqFkEZ8VrjEUuaYGuXyOlSKaFgv1ftJgzxUELjeBqv5rNZ/2O8w42zScP3hPc3fl33OlQSapJ6wbU6/KFFbFn+awgqX7p6s6LdvnSK7QRhBPo5cCskz5+YU0rIFBcboFs9tZkgervCa6tmM8wj6MDp+nWYDV+SU31y6JwC33euy1tewEFZCwkJJz/OF6+5mZj+dx3F5GvXveSenbttst20ljb6kDqX31M7M/duORCKRGw/W4rX4PMX/+/62B/g06l/Cl3sF9sS76rahraTxH8BXgZtJegvwSODVbU8SiUQiqw8xPlxt0dVT50naAOxcc6cVYGZ2u4ZjxqZRH8XYSUNSgqvs9FKcG5eAfzSz89qeZCnYNHsW51+6O4lK9UdHsNcerasYLhrOWFwmoytVA4PCiBzGYzSlpw77grmR303HhobpSh9m3HbvDQCcfPGta+m2y6jkUM2Vx3Oce+kezATnyO95cb7wPMV+6Iz540nDaxakRVK78nmGqpdQxRWKvHX1TNJw3sxEJ1RzBeOtqK0KQ242JzKdwvhfJVmgu4wrBpWPI2M2m15QP6MoElAGcTd9E5t8je/rbapQZy2Ee68/v/L+xxtuUZy3MTFhwLBkiXWjfpgcc6ZI8DlodMa46LLd2HfPRfz7N8brWxZZH2Nmj/XR3ycBD215WJs06kMZO2mYWSbpPWZ2F+C3bTuORCKRVY2pRZzGEgzDxWbc3tuZc1e2881sbuprx9g06qNoq576rqR/Ar4yInPisiKqhlhwq9WLLtuteL+oq44an7/wjpVVfmkILN0s6/mDmiQMF+3s96tqRG0yVNcli9Awvd5f/+8u2734fErVFOO9YDw5h+59SWHsnlHzirBOR1XpoiqBzD2mh0j9VysDKCSnUupIsOaFm9SqFnNTJHo9d1RTSdOwIFS+f1j+pHFut/WVc1MEer2frMifNXzZGhrdw8JdoUtvfp3XM8VsWkoUuStu/u+kyI3pLvp8fKGkULItnonCrAHVdOhNjgbh9z9Fxfc2f1/vZ0toVU9jImcaj6R74BIVbsD9HO4l6Ulm1lRc6WXAv9Eujfoc2k4aLwTWAQNJmyj1Zdu1PVEkEomsKlZGwsKco3EVVs8HkHQALlnhHcJG3txwlpkdBBy3kBO18p4ys23NLDGzKTPbzr+PE0YkErnRIgNlGvsa24/0MUlXSjo72Pd6SX+UdKZ/PXBMN718woAiUnyO6Ojrbvxa0t7tr7RK24jwQxt2Xw38wczaZyWbB5KOAN6H01B8xMzePqq9AZm56N1QRG2jtpgkn7/wjnP2dQJRekpl9HZYua8qepfjblJB1amowcJ+KI3RAJf+0anq9t/zCs69dA8A1ia9QhWQyLijN5D/+pK9KskMb7nX5QBsCNR9jmbjd2V8lc/H/xG59rn6yIo+Uyv7GmVfDONamva7aOoy3Xld/VTub1YXhce493OvqUm9lO+v1CgnHEep/goTB4YMq3qYE6Zbh1Kd6dLwl5Ho1VTtZWLNNnXu58udb35xkSSznn49HHdI8cwJarWH981U1h2Hojb5sCSd+WcTZXJutZ8APoBTL4UcY2bvbtnHaZI+iiuwBPB4XDmLJnYDzpF0KnB9vtPMWhnS26qnPggcitOBAdwW+DVwE0nPMLPvtuynFT4m5D9xqX4vA34p6etmdu4kzxOJRCILppUhfPwiycx+JGn9Fo7m33GutM/Frbh+hPvdbuINW3KitpPGBuCpZnYOgKQDgZcAbwK+Akx00sD5EF9oZhf5830OV1526KSRIq6xKbfSarBOzad280L5yu8PKQr6hHmFqjWfS1fWuvE7lBjGSUvDpAsYr3O89I+7ceBezij+60v2Kms8ywrD4e33vrxyzOV/3N1fiyr9D1vx18dQtBtiOexIVSkkXzmi8lhZYBRvPncYQR/W9Z6T9jswcg8zcKeBFJC3LT4bY7QelT49lCiKiO08/TrVuuP1GuQhoVG9Il00GMzDeuTV71xW1H+fbqgzPyl6hSOICikvpO5A0Oi0EEh4s9YZ6ozQVIagnmJ+YrSzaRwk6bRg77FmdmyL3p8t6V+A04AXmdnfhp7GbDPOrnH0uE7N7JQW5x5K24jwW+UThj/pucAh+Y/6IrAHEBZ6uMzvqyDpKEmnSTrt73+d4BchEolExpG1fMHZZnZY8GozYXwI2A84GLgCeM+khi3pWknX+NcmSamka9oe31bSOF/Sh4DP+fePBi6QNI2v5jdhmpYQc+Z0f/OPBTjgtmvs7+kaV6imIQCubYGchXDyxbcGYF2iig45X9GFhZF6QU6nDjbW/W+YTSazUtqouOXWyFfmnYpkUr65/d6XFvaNYcGAIc5OoiJoLpsj5YT6evfZzrv/sbCnhFN7JeBvhO9i+eQU6LqtCKzrYUW/GaWbaWZUsuWm+X5UKyoVuN825HEqJJSGQk6pJYWbspNayjKyncJWUuZ3SpTR98F0BKVpsTLjaxoE2FULGw0v+JSO+AwgU1Y8m37QV2oJmxNnL83L+y4GYfGwMy5xNtgOWaPtZq5tqZS6QsmsIi02SH6hnSTMituZVGWkNuqpBWo4zOxPRQ/ScUywtLaZbRu+l/SPOO1OK9r+kj4ZuBB4PvAC4CK/rw/cq+3J5sFlwF7B+z2By4e0jUQikSVH1u61oL6l0OPk4UDrfO+SEkmtvVvN7L+Be7dt37YI0w2SPgh8M3Tr8lzX9mTz4JfA/pL2wZUxfAzwuEU4TyQSiSycCcRpSPoscE/gppIuw1VKvaekg30PG3BBeKP6+AzwDJwwfzqwvaSjzexdDW0fEbxNgMPajdTR1uX2ocC7gClgH38xb2zrojVfzGwg6dm4fCod4GOhTaWJDLHRpulT1teuujqKky4+EIAH7DM5J6wzLtmb7YLiME01nEe5/oVRqk2qqJEG/MLNsNZPfp4hh6VYoS7aa48rCtVBGD2/4bLdgtxRocrJPZC6WsqdT5W24XZTDrBL/7jb2JxUTcbx/FyVMVUigcsI+rQ4pLwvqcSMpcV2k4tu3TW0osYK05Kr+h2rF27K21dTfbvvZ9+6TFu5PczlNkwN3jS+NtS/g33KeuF/HawD4KW3O3FefS6UQ/e+pHH/z/6wLzA37XvacF+GOxGUx4Z6875lFdfilYSZPbZh90fn2c2BZnaNpMfjIrxfhps85kwawEOC7QFuUnpY2xPNp0b4nYAfApjZmRNwERuJmX0bd/GRSCSy4mgTvDfp0JAR9CT1gH8EPmBmfWno2T9Sr70h6W7AlW1O1HbSGJjZ1WoRmLVcZIjrs2l66hRGyhn1C+NXuEqZJDNKmRnjwlcpMGSq5NUJCyNV8mYVRtjqPW8yeA8zijuj8JAx+W5yiQNcbq7wfTmW2jmDILvQKD6May7fm+12n7u63GuPKwpX3ryvJvL+E6lRwgnzsqZmlfHkLtBzCjsVf0+lG2+m5hV8ZnMlkmFBahUX36B4U97eFTpyf3ZTSouiR1NWbmcKjLwq+2kqdTsfQgk38cbgFHFdOgPAUac9iWMPO37e/U6KvOzwjzfcYo6bMlQdEZwEUkpgxb2rleVtojMih9e8WFlpRD6Mkxh+DfzIF1ka5hH1flzc3bh9jbSdNM6W9DigI2l/XADJz1oeG4lEIquPFTRpmNl/4Ooe5fxBUsVJSdJdgLviam+8MPhoO+Zm+R9K20njOcCrgM24JFgn4QL7IpFI5EbLOPXTUqmnfPjDPwHrqf6uvzHYngK28Z+HbrfX4ArrtaKt99RG3KTxqrYdLzUDb9CbSfps8obwdclmZpLSHLYYsRrDckZB1TgdRik3HRu2r2+3oWJEr6h5rPJPndRKI/Llf9y9MEzXI67rdy5XJfVICnVQqF5Ka3EX11zufPO32/0S/n55nofI2H0P50n958vL2M26uqtTqOqMpjiQpnHN2R9s11VYTfmy8vMB3qAepGUP1Fj1553XFc+s3O5bp2jfo6wF37eOq5mNU0nlKqPMEmYp1S0FNjz1ehvVaxGzQ1YYg2c0KCLBE02N7WMp6Cgrvq/VuJlS9ZRaGSm/yXrFfnevRzsRTIwyeG84S6ee+houH+DpuMX93KG4SPBTJH3CzP6w0BONnDQkfYMRl71Y3lORSCSy0mkVh7F0k8aeZnZEy7YbJb0LuA0wk+80s1axGuMkjTzD4iOAXYFP+/ePxRldVgwDS7hqsC09pUx76WLbZFMR5RpKHJ+58M487ha/mMh5b7nX5UW+pv6QrKpQNXiPY2hhoxEZb6tRtOX+irRQuNBWM+EW7WUVF9iecqlDgQTiV8zqlJ95A60riOrO2B9SdOf6K25eWeX/5fI957TJahLFHEM8zVJG0b4hunxUdt1ha9C6Qb1w2cXKsq7B/l4gUWY0S5p9OpUsrHkGgT6dIpK9H8hFs+buqztXVo38DlbRVWeL8avqsOzq2s4sANel0zzxF/8GwKfu3Lomz8TIy8MOk/yglDz61i2M36FzQd86hStu3zqVyPfQpXliTCBh4YT4maTbmtlvxjflBODzwINxsR1PAv7c9kQjJ408sZWkN5nZ3YOPviGpqSJUJBKJ3DiYXGr0SfD/gCdLuhinnhKuUN7tGtrexMw+Kul5gcqqdRLDtobwnSXtG2Sd3QfYue1JIpFIZLWhrEgLNrLNEnHkPNrmapcrJD0Il6Jprsg/hLaTxguAH0rKs9quB45qe5KlILWEv/XXksgK0fu6zgxrEyd6r002B+nJJ5sRd7ZIkDY3zmLY+6Z9wyKSwxrh444dpdoqkinW1FxFkkIrlbQV1QxJRR3V0/CvTa5KWbPbheXY/u+AcpuMzUVEdPUvqqJWGmacDtrM5+8xs7JYVP24hSgrmgpDOfJnpTKCPUgW6M6XHzGoHlbElJSJDzuByi+zUrWVEahegkR+mSW1iOq5KhlXkMkn0EzKMfSSrGj/oB89l2/dPfTgXFxy1RTUki+iyjXkKqlZ67A5r22e9YokkJusSz9z25uzbqHCG1inTFU/IZVRG5vGUnlP5YZtSTcjsFMM4c2StgdehIvP2A73G9+Ktt5T3/HxGbfyu37r87dHIpHIjZcVYgj3qZ7eA+yOi+y+OXAeztgdtusA+5vZN3HeVvdinozznjrUzM6AosjHr0e1WU5SS7hm4CbYjp/erxnMFMVfuknp6vjO239x0cfTwYqcTnmpyzrjoopzclkyGfENbIpEHjq2IG17ImPKcgkso1cY7K1YrmShm7Dm9p3seoFr938HFNvDSEhYt5vz9tsYuNlWXFpxkkETTUbxOW3GNKkbWnO5c1werPDcTeeYI8EEElxoFKeQFsp07R0peIZZMZaMrHTXJml0L6inTG8q8pSZKu6rudG9l3WDY8vU/t1k5eRnKlPTlzmmnCG8W2xvsly66LEp6/ntLoMsz0+lYntY8aZ5s7JsGm8CDge+Z2aH+MC+OTmtzCz1E8wxCz3ROEnj45Luyeik8B8FDlnoACKRSGSrZAWpp4C+mf3Fp0VPzOwHkt4xpO3PJH0A50EV1ghvtfgfN2lsjwsWGTVptHbVWkwMMZt156zS89XFwBIG2eQDfH55yXqmiuA2qxSbyRklUTRmTiVptk2M+AI25ecJg6HyfnPCUrT56nJKaZEheG3SJyvWtWmZ/wmRBNJGKFkMkzKSXS+o2DXKMQeZZ8McUMMvs5FhdpxhBazC+zisbO6ob0o4vrm2pfbjqxMW8CoLD2VFMaeM0uaQegdnd5yKwk0uMC4/tsS5n+Y6/fLqbqAsD5tZwnTH2TjW2iz/9LNn+v3iq3f7z1bXsFCy4Hry91AvLqVGiTxFhR2jn3UY+HsxyDpFadmBdehnncpxW8wKSiMC/F3SNsCPgRMkXUnFaFbhrv7fMFrcaFlTY5zL7fo2nUQikciNjRUW3Pcw3Brg+cDjcQv+NzY1NLN52zFC2npPRSKRSKTOCrFpmNn1PrPt/mZ2vKS1DElCKGkX4K3A7mZ2pKQDgbuYWasaHss2afgw9ocAs8DvgaeY2d/9Z68AnoqTsJ9rZieN689w4qgz+Dnxc5AlbErdJV7fn2Jzf7KX+7M/7Fuk3m7il5es91tJc8rsWoGZUPUwTK2U01QTGeqFesrayWGRn5AEoycnxU4pLSLot7NNZMkNrk0yKBwKckP5OIP3nPM0tN91j8uLVOx9K9U4/SGux6MYpnIYd3wnLOw0Ii/YqAj8YfuaXKjT2ndhvpRu06WqKiOBPJ8VpUqno6wwhHdkFaNwk6oq7H9dd5Zu6p75wBL++Wf/zhfv+qF5j7cts9ZpjNrPWjh2jKKMCC+veTCpiPAVpJ6S9DRcGMROwH7AHsB/AfdpaP4J4OOUuQQvwNk3Wk0ai5DFqzUnAwf5iMULgFcA+FnvMThXsSOAD3o3sUgkElkx5MF9415LxLOAu+FraJjZ74CbDWl7UzP7At48Z2YDqiawkbQt9yqcnmxfM3ujpL2BXc3s1LYnqmNm3w3e/pwyNe/DgM95F9+LJV2Iqxr4v236zVBh8B5kpfF7kHbYOOuyeB7wpTdxwSNfs9Chc8oGZ9RNSNwKdMiCcaHSRTVTZ1jisuo22RTEFe5PSRrz8OT7cnJX5OmkzzovafQ7ZZsZXceMD8Rbs/vFI+/NfMnLwP7ust0LCaMfZnmtFTzKqQdSNq1GqxlSq8GSodE1L8rTqbn5JqpLG+N/AYa5TqcNz2fod2HISjghK/rvqNxOKI3fGUaW/1JZUjzbzEQ38SVus27wXUgq0kZRElVWGMWTrDNHIpkUX/z9HQCYUa/x/lZK5dbWuEVpZbPiGSayIltwoqy45ixT4To+ynV9PrQK7pvImVqx2cxm80J5kroMl3Oul3ST/HNJh+NiNlrR9pvwQeAulH6/1wKTdKf4VyAvULwHcGnw2WV+XyQSiawsrMVraThF0iuBNZLuB3wR+MaQti8Evg7sJ+mnwCdxNZNa0VbJf2czO1TSrwDM7G/S+OT7kr6Hy45b51Vm9jXf5lU417AT8sMa2jfeeklH4dOZrN1lm7EXEYlEIhNjBdk0gJfj7MC/AZ4OfBtoTFVsZmdIugdwS9zv7flm1m9q20TbSaPv7Qq5OLMzLdzpzey+oz6X9CRcet77mBW6gcuAMIR6T1xCrab+jwWOBdjp1jtbEviow/DIzzTdMlF7tlDvVNU8aU0tuMnnxqmqpxSomwLRu1YwJqx5nKuTwjrn2Ryf9TIPUSUnUTY3ZXSYhwfKCPpp9dmmu8lfY5dN5tYF91g/P6P3QsgCQ/gsHTbl46YzR53WRNM9rXxeu95cPRUW/AnVI6EKo1BfNfwCJCP+DBrHMeS7kI8d5qonh/VX5qFSsdRKqNZsz/8GwnxrmUSWG52tNMxnqPj7SbBC/TMxw3EDec6ozJJCNTYMl1crVDMGzyt31FBaXFuWJMWvVKayKFYyqdxTbRIWLl3uqQw4zr9GImkGeCYuM64BP5b0X2a2qc252k4a/wF8FbiZpLfg7A+vbnlsI5KOAF4G3MNXBsz5OvAZSUfj8qjsDyzYdhKJRCKLxjJLGpLOGnn65tTon8SZGN7v3z8W+BTwz23O2TZh4QmSTse5bwn4RzM7r82xI/gAMA2c7I03PzezZ5jZOZK+AJyLU1s9y8zGWvaFN4INeUqdJJtj2FwoG2262N5kPaa8y2p47q9ddDBJkGwylChy6pJCaPDuB0VlhpWvLCSQQKIIXRQzU8UQvtm3CSNlQ6aTARszJ11s7Ezzt2TdfG7LFnHLvUph8scbblFIaZusV2yH92gYo5wFcjoKJYrQIB663FZXsZ0h7RKySn9tqBjCG74Pw5wf6sbypnvRwcoCVcrolQMl9U4hiUopoiMrwoazoIRq3YV5UvmaPnbB/6v0lfgRhhLfKJqktERZ4cSQKaNXGPyTwmqbWVY6CExKcJpQcJ+kj+E0Llea2UF+3044N9j1uIJ3jzKzvzUcnvmzfAZnw7ihxchvaWa3D97/QNKcvILDGHn7JO2Uv3CZEz/rB/cnv2/BmNktzGwvMzvYv54RfPYWM9vPzG5pZieO6icSiUSWhTZG8Hbr1E/gwgtCXg5838z2B77v388dgtnBOElhG9xv81tw4Qp/HFEH/FfeYwoASXcGftpqpIyfc08HTvP//hkXT/E7v31625NEIpHIaqNVnEaLScPMfgT8tbb7YcDxfvt44B9HHP9bM3udmR2KkzY+yej6GHfGJS3cIGkDLpzhHpJ+M07dBeNzT+0DIOm/gK+b2bf9+yOBkUbupUYYXWVkSVoY7kJ1kWR0Oj5Nem/hRZg+9bvDyWuc5GqD0MjoPod6HZQmoygMSTRYM3jnqqq6kTuMu+gX0b5JkbCtn5UqrEEWqKcsKdNEB2qITUnKDalTT93Q6RVR4EvNRpsqjPDXpDONqjpojgIfFh3fhtAQW1VPWU09VRYxqqu6ctVVk5G2Pq6qEbyqhiraBurGMJo8PL6aBaBZjZRQXkNnxC9Z/rczyJLG+7tQ8r+LjhKywLEjL2CWZdVzhfcmH3cia7z+YY4SYdR8N0nL+u3Z5K6rpU3jIEmnBXuP9U48o9jFzK4AMLMrfHGlRiTtgQuIfjjwN9yE8dURfdelmnnR1hB+x5r66ERJb9qSE0cikcjWjBgfvOc/P9vMnrcoY3C1vbcFvgA8mVJimZK0k5nVJRjM7A+SdsR5qXaD/RNJjZ5zlaRXA5/GzZ1PAP7S8tgVQSJjquMNZN2EW33FJYD87SNeO69+NmbTQ91eQ5wxc0zeoyHlYesrxzJ/TvMKtG7wzqWIviUV4/dsRdIIVpRF2vPyHm0cTBXRtEvN9dk012fO2WBjNs1Gvz1M0goNxCGV+2WqOEKMW0VX8lDl0cZN0kWloFW53dOgNDYPMZbXjdllBoGkUXoY9p2qS1TDyr2GxvUmsuB7MZt1iu3MxPfvdXTjMW0JpcX8u7rJeo1lauv5psL7P+w+hteZ38e+dQrpbZJSU8Hixmn8SdJuXsrYDWdTbuLm/ixPp1qCW37/vvUD/IL/ybicf/kIJ5MaPeCxwOsoRZ4f0VAVKhKJRG4sLHKcxteBJwFv9/9+ranRAstXPArYz8xmFzKwti63fwUWRbyKRCKRrZIJSRqSPgvcE7ippMtwC/S3A1+Q9FTgElrGULTkbGAHhksvI2mbsPAHNFy+mbUSZ5aCDDHrVTCVCFdPgtHzqhfrlcfd6itvbKWiess5D3b9qIxdyEXqJtE4NEiHpKbCEJnUjK1lv+NF6axmRA3TXs8GVcxms9IQXm4nlW0L7ldnkNdUz5atTvT1gUrKqaecUXyUwb/J6F1PAR4apLNAFTKMesxPornPq4MV+xNZ4RTRUxrEQjQbzitjranY6qq1/JqHfTeGOVqU/amitim+L2EpgZrasp+WVS+3hPf99r7kjiH1Gt6b/R9j+AyHPTfnkFCqAofRpNqrVPHLJpM0W0wmTsPMhmltmtKaT4K34dxuzwY2B+N4aJuD26qnXhxszwD/xPBSgpFIJLL6WVm5p+bD8cA7cHmq5r0ybKueqsdk/NRb7VcMmYmNgykyU7Eymk07RRRshpBfFkx102J1pRZKxxf/+tFAKWGkxSpIlVWbM+CVK6RQ4mlaIXZVRqnXV06j3CLrpLVI3uL6s26jYbOfBiv2wLUyzAzeSYyul8z+3/deyk/u+87Gcx/67Vc3rp4zxOlHvqX1NYRsrEgaU2xMS0kjN+xvzrqFpLE5rX6Ni+JEgQRWlyiy4BnOxyje9Fn+uXueWbGdOxJ0gkwF9efcRsLMKgbiZoeJ4eMr63+H/WfBCjz8e8m3+2mnuK+DBeZqK6XzKTZ76WJT1uOG1G3fkE4NdQMPn2H1esJofCv2NUmB4fH1v8eJsIJSo0t6N/BxMzunRfOrzOw/FnqutuqpMPo7Ae5Ac/baSCQSuVHQyhC+dBre3wLH+joaHwc+a2bDamScLultOGN7qJ6aqMvt6ThBSzi11MW4NLwrhtQSrt48U1nVW20Vma9AJGOm60ucdlLu/v2XALCuN8s2XedQMNPpBy6naysr+XAVNJt1mU3nrpDy9zA3UKqULqywG4RZRUOGrXLrq6lw5RwGaIUSRa6jTk2lBJaV23VJIw+GDM91q6+8EckKCa2TdBioeSV6+2+6QlcSnPmg8WE9x5x3fzc+ZtiYOknjunS6CDjcnHWLlels6EKclecPA8AGWVK5F+H9yu04AwtsOi3dMi1op0DSkFyAKVRznYXSSGgnCffn75tokoqGrZbrrsVF30P0JPWiZbktZXZQfq/TBQTDvfysR9KhlBCLoNFA0phNu4VtLZSEw3FUJeHh971iP2q4/vDZWsvn3IoVop4ys48AH5F0S+ApwFm+VsZxZvaDWvND/L+Hh10wYZfbW9fT5kqaHtY4EolEVj3tc0stCb58xa386yrg18ALJT3dzB6TtzOze23JedoqK3/WsK9V+dVIJBJZjeTlXse9lmQsrpTE+cADgbea2R3M7B1m9hBKySJvu4ukj0o60b8/0Lv2tmKkpCFpV1yp1TWSDqG062wHrG19RUvAIE34y/XOWN2kZkhk9LpO3TTVSQsj73SSFq64CVa6ooaGs5paIFR/9LPS2J7OUYG47dSa1VNQumBqiMScBKqgUYQqltxYOkgTsqJGek3Mt/K4IhdPKP4nGUmSp9iG/b/4Zt8+qYy3Xu6rGKsCNVyStYrAv6rvqi+G6ozr0qlCDRWqM0K34fq9LYoHZUlgzO2Urqs19Vx470IVXVs1hgo1SVUFOmx/NSda2U/YJqRpfBlqHF94bP0849RfQOW70x+4+3vuP76+8bgmHv+LpwGwrrOuME4PrMO1fedyu3HQK1VSaYdZ/3zSLKEf/B3lhdLSUH1aV5OpvOb8kyTJhv4t5UyqRIIMlI3ua9znE+Rs4NW12kQ5d6q9/wTO7vEq//4CXBr2j7Y50Tj11ANw4eZ7AmEegWuBV7Y5QSQSiaxKVoDLraRD/eaZwK1UmzHN7IzcIC6pa2YD4KZm9gVJr/BtBpJa5wwal+X2eOB4Sf9kZl9ufylLT5YmXPv3tZT2+ipKMjpTXrqYHrB2yhm8rVveq37gcphmydgVXpqJrOayasFqvljBtQrwqY45XLG3okFysEzF6sxSlZ2FHh211XV5fiCZ5xiwcvWXGErKlXYup+/3ubfy+8e49cbtvvFa1k2753DTNdezw9ROfnhikzeWbhqU0kX4fML7W793P3/A24rtO5zoFlODNGGQG3ZDqSurSlrFvbARkkZ4v9T8rMJrdu/LezFUclS17Zxrq3y/5hqGK+fO91XGZMVp8u16H/n+LEvmnQ32jie+kunejgCs6fZZ23VyaGbi2llnAt006FUM7Lk0k2YJWVpKf9nAP+dUpQScVZ9POfBAYqtdc+Pf3lIWYVp83jPis7px+1TgUOB6STehLN99ODDM02oO49RTTzCzTwPrJb1wzojMtiyLmTvHi4F3ATub2VV+3ytw3lkp8FwzO2lLzxOJRCITZQVIGvM0aufT6Qtx7rb7eQ+rnXElvFsxTj2VR7Rt0/DZFt8OSXsB98PlVsn3HYjLDX8bXI3w70k6oE3J10gkElkqnE1jTJslzMQj6a648rBhuvNPBk12Dhb/XwW+jZtINuPqI40twATj1VMf9pvfM7NKOUBJd2tzgjEcA7yUagbHhwGfM7PNwMWSLsQZckZ7aw1E56oepmaR0TowmPKGubUpm6adobU7NSim38xElnqVVJpUpsVCzWOBuJyvNEJxOFAHFV8YgeWqms4Q8bmmJhpnzHNjae6nOG9WGrzJVN6X8Dy1e9WokdGQ/XkHKtvlKq0sKbeVWKkSkLHf594KQHeqWxjb/6q1hQojQ2VMSVbmQ0qzqvE335asojLMje5O3dIr+km9yiNLE6+u8881VH/k987UWvUQ3PryhtZVVcWbFp0OU6/UVYlNXQ1T1Sg4YMizVGIknUA11uIH7+BvvaZQMW3atK64l72pAWtnnOpRMm7Y7GMzZrukfdfe0gTy52AUfzvKgEG+HXyfTYVqVfVVfujLkV9fqD4Kr2VC6qk23lETjAgZfR7pU8B+ONtGvsA2XBW/nA5OAKgPa15OTW3jNN6P04WN29caSQ/F1bH9dc14swfw8+D9ZX5fUx9H4XPId3fccaFDiUQikfmzAtRTAYcBB5o1WSgLrjCzN27picbZNO4C3JWqWAPO5XZsqkhJ36M53circN5X9286rGFf443wJROPBViz2142/Zeq4TlcBVgHsp5315zpkE27D2eDlZUykfsQqLaiS4JVkMJ5PBx4IK6GvgiWgE+9QzptRZbdXPrIz11ZDan2b36+hmurjMV8X/l2k+Gw4frq561IFqGkUX86sspnRTqkTrnfOlZ8W6xjpH51mfYTzvcuuLl0UFxmYKjO/wzmRAQH9y930cwliGKs+bGZsNwlOlXRTmlw3zORVFayLdeJoeF63CH1z4dIC8N+bIYJLcMlweDzUAoKVuMEz2nQCaRDz/qPv5MNT3lppdsDvuwj/G2K2Y3+C31Dtxh3Ot1ls99vJmzWu2pvTki8FJH0q1JxMbyMUqIIU3XUvs/DpIjwXjQdO6mA8FaSxtJNGmfjfmuvGNFmIlc+TtKYwokzXVxJwZxraGE4MbPGOuKSbgvsA+RSxp7AGZLuhJMs9gqa7wlcPu5ckUgkspTIbHwcxiJPGpK+4c+yLXCupFMZnu58IqnWx9k0TgFOkfQJM/vDJE7o+/0NUBRKl7QBOMzMrpL0deAzPsJxd2B/nKtYJBKJrBxWhnrq3W0bNtULXwhtbRobJb0L59E0Ewxi4kWYzOwcSV8AzsUlR3xWG88pDWDmL049UlHJ5NsJ+MBisi5YUjizV1U+w9QCgSic1NRT1c+8eJ+W+7MOpNPufIMZka7JxxGoW0K1lx8vuOvJxemKt0aoVgjUDRXRPrye0GA/zLAdqi1gjoqqfi63X1X1VL6diCxQSRX1qDpG1vEX1zVu/lGXcl2d6eJgG/XHmF9noD7Z8OSXsf7DQ/52Gr4LClUhocNCVqr2lDH3+Ybd1u7ByM+DcQ+ljX28zY9U7VxNakVLas4YhXrKqxLx//rHtOHpYTkdx+z1XvXUT0g2uoebbFZxTNYXyO9PhXzqgM6sSPx20g++8xkV9VSo5q185/MhV54nFS+linqqST07Kev0Cshy6xf2SHqHmb2scm7pHcApkz5nWz+CE3Cpd/cB3gBsAH45qUGY2fo8RsO/f4uZ7WdmtzSzEyd1nkgkEpkoNua1dNyvYd+Ri3GitpLGTczso5KeF6isJj6DbQlJCmv+kjWvMvAr9mD1Hq4OC71kbSVvxUq52laDuf27hkYy8JJGsHIyQTrljb8zCYPphn5Tq7no5mNVaVyuGcXD6wnr8VRWbKFU1CARuHtRjrOQcAKJonKZajimUdIIxtdRZTuX+JwEkpTX1iQVhQMQZZQ6FO6at3jX0XTzh1W3DofHhp+Fjg2B0bW4zEDSqEf+NklgjeMeRmiEbsM46XfYYXOkQr8/qQ4yfzZZFyx/Nl1VHDVC1n/o3UXDzuaE7vXesD2oPnMavofJAJLZcjuUKHIJXinF31EycH8bxfWG0kZoLK+0mdve/Y3kYmrjZc2blWAIl/TvwDOBfSWFcRbb0pxodotpO2nkeemukPQgnGF6z8UYUCQSiWwNKBtvCF+ChIWfAU7E1f1+ebD/2knZMOq0nTTeLGl74EW4+IztgOcvxoAikUhkq2AFGMJ9MsKrgcf6ehq74H7Xt5G0jZldMrKDBdC2Rvg3/ebVwL0AJD1/0oPZEjQwpq4eNHxQbg7zzy7VOVbx+c7VR6F6xamwShVU5Rzm3PCgFJdzOl0fjXxDxpQ3gIfR63WDWTX2wbcPfdClwLDfrMKqGhStaizMVW+iUNVZRS1W9mWqivbWCdoFx1DfDs5VnC+hNJZ2VFWHUR47DAvViuG9C4zcjXE6SfA86378TQbVIUbXeTEf43dbQ/i4fkZ93xtUksV7IOmWzhlZt3xmdTobk+I+JZtFd1O+XRtj8ByK7+GA0hCeWqm2Ssu/mWQASd/KNoHKNwyJr8Rs5OqpYPWvQVbem0SVv+dJUNd4LieSng28HvgT5a+YAbeb9LnaShpNvBB474TGEYlEIlsXLtXw+DZLw/OBW5rZXxb7RFsyaUzKcW0iJGnG1F9vwLmABkPLV74tXN+UZdWHnPgo425SddHNVzL5l6bR5VLVVf/A147erIq0UB7Q/OUamhsqEVnPSy9dkfVyaSQ4byAVJSmQNpwjNEDX9lu37LNwn02EBS7LWSh11IyNFXfPYJVbuuLWDOdNK+HarW10dKgZsysSRv5vMM6Kz2DN+WFopHwoeTB/htzixnE0OTUs6ERD7t2caP/8+XVB/hdBvbkG81u+8RgAupRuykm/NGx3ZgMpYhB89walIbwzWzqLdGZLKQKzqlSc+uJkmc2RwoHibxCCv0N/LJk/dpAV224g5d/zJKi7+g5r06ovF6t2LS5v1MDMDpvncC5lHunNt4QtmTRWiGAWiUQiy0Ab76n59XivMPRgnlwE/FDSt6hGhG9x+Yo643JPXcvwRdKaSQ9mi0gzkmucpEElcC90syxXNQXhaj+UNCTo+myc3aRslwSrFGvoL+jX8radYLu+yBmWzras+lTYSciyUmJKwDp+fFOdQuqwRNVzFCvwYDVWWUEHK7a6XSXQAWd+dZbbd3IpJKu501pQuCkMSiz6VNlvFujQR9lJimNruviK7aIpVxFBn53y2LpNY6ikUbkZVR1606/BsAzLlXMOuS/h9cyhyf243mQe7rdDt9NSUsj6VYnnwFcdQ24K0MBJD+Dvn9/u9ANbRB86+fas0d3stzdnJLO+vPJsVkjgoZSv1CANIl1zCSH42wuli4qaKPN9AQzScn+agnz+q2TI39x8MSoSTyNLV+71Ev+a8q9FY1wakW1HfR6JRCI3WibrPWXAd+VKJ37YJ2Ntf7DZGwAkbeve2nXzOX4+bIl6KhKJRG60tIrTcB8fJOm0YPexDZPC3czsckk3A06W9Fsz+1HrsUgHAZ8CdvLvrwL+xczOadtHW1bPpJEZ3LCpqu4JxdDw4Q7zaAiN2p0EcmPcIKnuz7fNXL+hmFwWaEY+2tkk1Cn3h2J2RZXUZBiviNtZuU0prlu/S9Itz9WYHCZUcw3z+gjPr9BFMSnUCE79VX7m1FP5vanmoSoHGhqtrXguWVeli2fPu+CSG9TDcVGeK49YTijVU3NUTOW1he7KlUsN1HZNEeF5X2H7wpWzvsKsqHrmuvWG569cWwstSd2honls1kotFvZZuR+hWtA7KST98pjbvdCppn7znhcAcPvnHlOop0KDdxjVnQyMZNbt7242Opu8SmpzSuK31U9RroZKrapW6ud6MnN/c4C63crfV6N62ALVllnxN1wxiLepcNaCVi637vOzzex5I5uZXe7/vVLSV3GF51pPGrgSES80sx8ASLoncByutMVEmVANq0gkErmRYS1fY5C0zquVkLQOV2fo7HmOZl0+YQCY2Q8py3VPlNUjaZjBYFA1VNdz4+arjWTIXFlfgeT1LlNKqSWUJvIVe2FgzwpjG4lK1z+pTMTSSVCxGg2kghEG8SJQMK26EMovjWXmSmcyZvHaYFyfM4Z8bEmCijKopcukk5RKSUNJ6XjQTsoJxqFSSrGK1KHK/SiG0VWRLTjrBhcbuKvOlQLCVajfFUoXQTZi5ywQ9uNX0D63URF8FmxXixiplJY64X0ppYusq4ojQCiZNGfTbacUL430tfbhfSwkCmuU5KoG8kAa8e7Qhz79GAB6AyskjU6/DNBTakH+KCuM4p3NKcmsu+HJ5gHq+0b9FA0CqaCQNAZO2gAnNfjvNoM0cEipfdNDJ5dQAsnq4uPkkFlFqm1s086osQvwVf+70AU+Y2bfmedwLpL0GpyKCuAJwMXz7KMVq2fSiEQikSVEmc3J/DCnzZjPAczsIuD2Wzicf8VlIP8Kbur/EfCULeyzkThpRCKRyEKYrPfUFmFmfwOeuxTnWtZJQ9JzgGfjii19y8xe6ve/AngqTjH0XDM7qVWHkxBHm9RElpWqLiWl2io/Z4VApaUGXU1FvRSoYcL4krrRvvA1r11b3n5AEUE7R2wvztvsCKBaXEseTa8kg7ymdpgiO3HHVGNQgmvIT5GExunaPcpVQIMwWpciTXrWS2p9lWqrbCoptxtyCLk8R4HqaMj+UL0UqqdKo265iswjlIvxDrLSgBuqK5ME83Xos16niGUJnQqyXlKqsLo1g3SDSs7tn3OZ7bDif+5tmG4/zCvVaMi3OZ/n96MzCx2vblLfgjTmWXnPBhnq521Kg7cGWal6yrLSaB2qXuvG7Lx96IQSqpizIX+Pw1RHEzKEt0ojssizhq90Ovzs1XKvE2HZJg1J9wIeBtzOzDZ7VzMkHQg8BlclcHfge5IOaFO9LxKJRJaM0CtwCEuQ0PAuuBQinwV+wcKXGK1ZTknj34G3m9lmcK5mfv/DgM/5/RdLuhDnfva/rXodYQgsaMhHM/d4Na8iwrmryUc77C5tyLoLVWmgkDSSZhdhGyE5FaUyLZBS6sb8FqutQO+q8PNO4NNaWxErNJg3GNLdWHJ3x+DYpHyvumToj02SBEIX5fxc3QTLI9/DKPUg59UwicJFEOeXExqyA1/70OkgywIHhNRth0bbpqwCSYJmvUNCR5BH7HdURO8n3fI5W0VKI8jCW3WzbcrOPLQoVDieIX8L1m12jQ4ljcph3nEglxw6s1lh2NYgLaO6B6XkoNSg77//aQZZLl0EzzxNg+9IIBFZ1py9IS2lYjVJ8fmxlUsO3dsn+3saFn8a3mbRZ41dcVX7Hgs8DvgW8NnFiM/IWU6X2wOAf5D0C0mnSLqj378HbubMuczvm4OkoySdJum02eyGRR5uJBKJBOSLh5GvxR6CpWb2HTN7EnA4cCEuB9VzFuuciyppSPoebias8yp/7h1xF3pH4AuS9qVZvGq89T6q8liA7Xs3WyKTUyQSibBiDOGSpoEH4aSN9cB/4LyoFoVFnTTM7L7DPvO1bb9iZgacKhd0cFOcZLFX0HRPXHnZhQ4iP2G5b1icRtg+nas2Gdo2JBRXh0aeN6inyKqG8DaidCH6huq2If7rdTXQMH/3UF2W5qqAhjaFwTyI7agUSApUOOH11KPeG9QQdeN80Vwq1VZJUhbTSZLy3J16/w3jSa1iyA5VVY3+/rl6pVEdqeo9884DGog8gEGdBCuy+qlMwFffDu9joT6qGsuHOhcUDYaoJ42izzy55Zz2Kp+hgnuRq/Mqhu2Bux7NpoExO61GYKeBwdv3VVE9pSnm+xmV2E+9PF97GfAyNH6l4piwuOp9MT5OY7HraUg6HjgIV/L1DWY236DAebOc6qn/Bu4NIOkAXGbGq4CvA4+RNC1pH2B/4NTlGmQkEok00kY9tfiixhNxqv7nAT+TdI1/XSvpmsU44XIawj8GfEzS2cAs8CQvdZwj6QvAuThX3Ge19pyqp0Kvr7pGSRgwQjKYx2oidBsMpZwmaaU+ntyIbTbXoF0/V5Y1GhErK18oVn42O0t2g6vLqakemnLZk9XpVFf13mBb7SMtx5rf4yaDqVTkCao8izSrrv4ac2wNMS6H/ddW9QrHHbouBzm/imPnSA6BMXbYGIZt1x0pQjfQ8LkNG2tSpui2Yrt2zeH1hAwbU9Ox9Tb5kMNU/6O+2zWHhTKSewCzPsVBv184CNhggG3a7Lf7RTe2hcZgBX01N0jc9xhcxoVerxi/JV466nSG39OFjiudTHDflmBWz6q2+CzbpGFms7hQ96bP3gK8ZWlHFIlEIvNkkdVPK5EYER6JRCILoU1w3yqcVFbPpGGG9ftVMbT+wMIo05xRRu6mfhpE7YpveGhI9cd/56/HVdofufMz3EaSjT5/0xjzRIM3bML6QRxIMAbz1xmqBZKZab47+xkAHrDmiZUx3a/z6PJUXo3y3f7nGj8fx8np5+fsO3KXfy+voROEIgcqNusPyueT1Ay1oW9+k0oqPCY00ocGdag6BYRqvpxRf+DDPhuWcjtQQ1WoqdLU5CBQj9kZRn49TXE/I1DYbqjhvP63k5Xpyvt9bLMrDG6zs2SbNrOYjFdvpeX3P00wrzpTUqpbXXmCXIXVaepk/mRUY5CGtVllrJ5JIxKJRJYSq9a3aUKjgnO3UlbPpGEuAtXq7qpNkaUh9ZxPTSvQ4HirrFKNMB36STd8ijac+Of/GtvmiJseFQzRG3YHA2zWrfBOuv6Trc7VRH2cTdLBfD4fx4l/+tDYNm2kGSWqGvBD6aVJGnE7yu3gudUjhxvPF0QTN0qT4xhWBGzY+dqsgMdJFW2MvOHfQngfoZD2bPNssZ1t3FhZ7XfWrS2kC0tXRnafcnxpIb1ZlkDmXYMTYXlRtd6EVEat1FOTOdVKYvVMGpFIJLKUGNGmsTWz/yHr+c5pH+MB6/4lCDxLnGSQb6dB/hu/GglX7Eds95TKymlLVvNbyneumldd+a2eNtLM/TqPLlw6lZQ6ak1NQdd9lS0JMhLXyQPMvLQGI1b3ZmT9IbnDavajYTr30l4R2DZGSDiVsYRSTmGLyxrPpZr9Y0tdXJs4Of18IQ1+55qPVz6bj81rSQm0AJaZy9zMBKUjY7zNYvXNGatn0ohEIpGlRFkLm8YiVg5cLuKkEYlEIgshs/G2qihprHy2RKVUF7sjK4thKqwjtv/XYrueMrswYGeGbfbG2/5gSQy4oXG2Xfsh6rDW51kckl535aqgRhC6jdeR2jmtjCXaNCKRSCTSihjcF4msfI7Y7imAkxaK7Kdh/qyAei6kxV6RrxS21EV6ayHx7tdtXd0nTpbNLcHc1GaVESeNSCQSWQhmI73hijarjDhpRCKRyEKI6qlIZOWT3eDL+gYGb4X5nDqUBu8sW9KI5RuLWijk/lOPI/FqwqFxLRNixd3fVt5Tq2/SWM4iTJFIJLIV06IIU8s5Q9IRks6XdKGkly/uuLeMKGlEtipGuVFGlp48c/KNkszGG7pbGMIldYD/BO6HK3f9S0lfN7NzJzDKibNskoakgyX9XNKZkk6TdKfgs1f4Gfd8SQ9YrjFGIpHIUPLU/qNe7dRTdwIuNLOLfHG6zwEPW9SxbwHLqZ56J64Q+sHAa/17JB0IPAa4DXAE8EE/E0cikcgKoo16ygAO8gvj/HVUraM9gEuD95f5fSuS5VRPGbCd394euNxvPwz4nJltBi6WdCFuJv7fpR9iJBKJDKG999TZZva8Ea2a8tmvWAv6ck4azwdOkvRunMRzV79/D+DnQbuhs66fsY8C2HvvvRdtoJFIJFLH0gxLRnvnWbvgvsuAvYL3e1IuolccizppSPoesGvDR68C7gO8wMy+LOlRwEeB+zKPWdfMjgWOBTjssMNW7MwciURWITYxl9tfAvtL2gf4I049/7gtHd5isaiThpndd9hnkj4J5CLbF4GP+O2tataNRCI3UiZUhMnMBpKeDZwEdICPmdk5ExjhorCchvDLgXv47XsDv/PbXwceI2naz7z7A6cuw/gikUhkONbCe6plvjMz+7aZHWBm+5nZWxZ55FvEcto0nga8T1IX2IS3TZjZOZK+AJwLDIBnmdnKKEQciUQiOWmGjUl7b9nq++latknDzH4C3GHIZ28BVvRsG4lEbuS08p5amqEsJTEiPBKJRBbC5AzhWxVx0ohEIpGFYFmL1OixnkYkEolE8FmUx5hbLU4akUgkEgFXm90U1VORSCQSGY/90X7PVTY6hGwTG2GVmcNlq2QmlPRn4HrgquUeS42bEsfUlpU4rjimdmxtY7q5me280I4l7Q4c0rL56Wb2fws910pj1UwaAJJOM7PDlnscIXFM7VmJ44pjakcc042HWLkvEolEIq2Jk0YkEolEWrPaJo1jl3sADcQxtWcljiuOqR1xTDcSVpVNIxKJRCKLy2qTNCKRSCSyiMRJIxKJRCKtWRWThqQjJJ0v6UJJL1/msWyQ9BtJZ0o6ze/bSdLJkn7n/91xkcfwMUlXSjo72Dd0DJJe4e/d+ZIesIRjer2kP/p7daakBy7xmPaS9ANJ50k6R9Lz/P5lu1cjxrRs90rSjKRTJf3aj+kNfv9yf6eGjWtZv1erHjPbql+4Sle/B/YFpoBfAwcu43g2ADet7Xsn8HK//XLgHYs8hrsDh+IK2o8cA3Cgv2fTwD7+XnaWaEyvB17c0HapxrQbcKjf3ha4wJ972e7ViDEt273ClWDexm/3gF8Ah6+A79SwcS3r92q1v1aDpHEn4EIzu8jMZoHPAQ9b5jHVeRhwvN8+HvjHxTyZmf0I+GvLMTwM+JyZbTazi4ELcfd0KcY0jKUa0xVmdobfvhY4D9iDZbxXI8Y0jKUYk5nZdf5tz7+M5f9ODRvXMJZkXKud1TBp7AFcGry/jNF/ZIuNAd+VdLqko/y+XczsCnA/CsDNlmFcw8aw3Pfv2ZLO8uqrXL2x5GOStB6XFuIXrJB7VRsTLOO9ktSRdCZwJXCyma2I+zRkXLBCvlerkdUwaahh33L6Ed/NzA4FjgSeJenuyziWNizn/fsQsB9wMHAF8J7lGJOkbYAvA883s2tGNW3YtyjjahjTst4rM0vN7GBgT+BOkg4a0XzJ7tOQca2I79VqZTVMGpcBewXv9wRGp55cRMxc2kszuxL4Kk78/ZOk3QD8v1cuw9CGjWHZ7p+Z/cn/0WfAcZSqgiUbk6Qe7sf5BDP7it+9rPeqaUwr4V75cfwd+CFwBCvoOxWOa6Xcq9XKapg0fgnsL2kfSVPAY4CvL8dAJK2TtG2+DdwfONuP50m+2ZOAry3D8IaN4evAYyRNS9oH2B84dSkGlP/geB6Ou1dLNiZJAj4KnGdmRwcfLdu9Gjam5bxXknaWtIPfXgPcF/gty/ydGjau5f5erXqW2xI/iRfwQJyXye+BVy3jOPbFeWf8GjgnHwtwE+D7wO/8vzst8jg+ixPL+7jV1VNHjQF4lb935wNHLuGYPgX8BjgL9we92xKP6f/h1BNnAWf61wOX816NGNOy3SvgdsCv/LnPBl477nu9RM9v2LiW9Xu12l8xjUgkEolEWrMa1FORSCQSWSLipBGJRCKR1sRJIxKJRCKtiZNGJBKJRFoTJ41IJBKJtCZOGpFIJBJpTZw0bgRIum58qy3q/9uSdvCvZy7g+HtK+uY8218t6dtDPv+EpEfOdxxbI/5e3DV4/wJJl0j6wHKOK7J6iZNGZIsxsweaS+OwAzDvSWOB/NjMHji+2cKR1F3M/ifEPYFi0jCzY4DXLttoIqueOGncSJF0sKSf+0ygX80zgUr6oaR3+OI2F0j6B79/raQv+Pafl/QLSYf5zzZIuinwdmA/X/jmXXUJQtIHJD3Zbx8h6beSfgI8Imizzmcm/aWkX0kam+Zejg9IOlfStwiyCEu6g6RTfNbhk4JcSXf01/K/fqxn+/1PlvRFSd/AZStuHI9cdtV3+f1nSXq637+bpB/5e3B2fv+GjPv+/vxn+HNu4/e/1vd7tqRjfWoRJD3XX+NZkj4nlwX3GcAL/PmGnisSmRjLHZIeX4v/Aq5r2HcWcA+//UbgvX77h8B7/PYDge/57RcDH/bbBwED4DD/fgNwU2A91SJL9wS+Gbz/APBkYAaXonp/XObRL+TtgLcCT/DbO+DSw6yrjb3e7yOAk3EFuXYH/g48Eldf4WfAzr7do4GP+e2zgbv67bfn4/bjuwyfEmPYeICjgFf7/dPAabjCPi+iTB/TAbYd8kxuCvwovzbgZZRpMMJ0HJ8CHuK3Lwem87H4f19PreCQv4YPLPf3Lr5W52trEL8jE0bS9rgfnVP8ruOBLwZN8myvp+MmAnA5kd4HYGZnSzprC4ZwK+BiM/udH8+ncT/C4JI8PlTSi/37GWBvXDGiYdwd+KyZpcDlkv7H778lboI72S/WO8AVPsndtmb2M9/uM8CDg/5ONrO8YNSw8dwfuF1gO9keNwn+EviYXKba/zazM4eM+XBcJbmf+rFNAf/rP7uXpJcCa4GdcHnMvoGb6E+Q9N/Af4+4H5HIohEnjUgTm/2/KeV3pKkWwTgGVFWgM8H2sKRnAv7JzM6f57ma+hNwjpndpbJzfI3268eNx6uMnmNmJ805qauh8iDgU5LeZWafHDK2k83ssbVjZ4AP4qS4SyW9nvK+PQg3QT4UeI2k24y5jkhk4kSbxo0QM7sa+FugA38icMqIQwB+AjwKQNKBwG0b2lyLq2ud8wfgQLlU1NsD9/H7fwvsI2k//z784TwJeE6gxz+kxSX9CJfyuuNtFvfy+88HdpZ0F99XT9JtzOxvwLWSDvftHjOi72HjOQn4dy9RIOkAb/+4OXClmR2HS3F+6JB+fw7cTdIt/PFrJR1AOUFc5W0cj/SfJ8BeZvYD4KU4Vdk2zL3nkciiEiWNGwdrJV0WvD8aV//gvyStBS4CnjKmjw8Cx3u1VJ6O+uqwgZn9RdJPvVH5RDN7iaQv+La/88dhZpvkSuF+S9JVuAkprwT3JuC9wFn+h3oDVdVRE18F7o1Lh30BfgI0s1mvPvoPP2l1fd/n4FKzHyfpepwd5+q53Y4cz0dwqrsz/P4/42pk3xN4iaQ+cB3wL02dmtmf5ZwCPitp2u9+tZldIOk4fy0bcOoucKq1T/vrEHCMmf3dG+y/5A30zzGzH4+5V5HIFhFTo0daIakD9PwP/n64+gkHmNnsMozlnjjj77jJZFQf25jZdX775biaC8+bzAiXFz8ZHWZmz17usURWH1HSiLRlLfADr44R8O/LMWF4ZoGDJH3bFh6r8SBJr8D9DfwB53G01SPpBTg33C8v91giq5MoaUQii4ykX+DcckOeaGa/WY7xRCJbQpw0IpFIJNKa6D0ViUQikdbESSMSiUQirYmTRiQSiURaEyeNSCQSibTm/wM217bBOb2bJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.sst[0].plot(vmin=-2, vmax=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that xarray correctly parsed the time index, resulting in a Pandas datetime index on the time dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 708)&gt;\n",
       "array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "Attributes:\n",
       "    long_name:        Time\n",
       "    delta_t:          0000-01-00 00:00:00\n",
       "    avg_period:       0000-01-00 00:00:00\n",
       "    prev_avg_period:  0000-00-07 00:00:00\n",
       "    standard_name:    time\n",
       "    axis:             T\n",
       "    actual_range:     [19723. 80992.]\n",
       "    _ChunkSizes:      1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 708</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e261b61f-04ae-4966-a0ce-44474a719b8a' class='xr-array-in' type='checkbox' checked><label for='section-e261b61f-04ae-4966-a0ce-44474a719b8a' 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>1960-01-01 1960-02-01 1960-03-01 ... 2018-10-01 2018-11-01 2018-12-01</span></div><div class='xr-array-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-a5dafc63-b060-458d-83e8-5b48e1ce9fc5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a5dafc63-b060-458d-83e8-5b48e1ce9fc5' 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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2018-12-01</div><input id='attrs-6cd95343-8f65-4510-b864-0b47068ddf10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6cd95343-8f65-4510-b864-0b47068ddf10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5bb86bd-474b-4af0-83fd-120698bad9f4' class='xr-var-data-in' type='checkbox'><label for='data-a5bb86bd-474b-4af0-83fd-120698bad9f4' title='Show/Hide data repr'><svg 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>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80992.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e6a6ba67-8e1e-4029-95a0-8a91246a6bc7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6a6ba67-8e1e-4029-95a0-8a91246a6bc7' 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>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>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80992.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 708)>\n",
       "array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',\n",
       "       '1960-03-01T00:00:00.000000000', ..., '2018-10-01T00:00:00.000000000',\n",
       "       '2018-11-01T00:00:00.000000000', '2018-12-01T00:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "Attributes:\n",
       "    long_name:        Time\n",
       "    delta_t:          0000-01-00 00:00:00\n",
       "    avg_period:       0000-01-00 00:00:00\n",
       "    prev_avg_period:  0000-00-07 00:00:00\n",
       "    standard_name:    time\n",
       "    axis:             T\n",
       "    actual_range:     [19723. 80992.]\n",
       "    _ChunkSizes:      1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "[<matplotlib.lines.Line2D at 0x7f8e4e3cbeb0>]"
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    },
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.sst.sel(lon=300, lat=50).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the plot, the timeseries at any one point is totally dominated by the seasonal cycle. We would like to remove this seasonal cycle (called the \"climatology\") in order to better see the long-term variaitions in temperature. We will accomplish this using **groupby**.\n",
    "\n",
    "The syntax of Xarray's groupby is almost identical to Pandas.\n",
    "We will first apply groupby to a single DataArray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msqueeze\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrestore_coord_dims\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Returns a GroupBy object for performing grouped operations.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "group : str, DataArray or IndexVariable\n",
       "    Array whose unique values should be used to group this array. If a\n",
       "    string, must be the name of a variable contained in this dataset.\n",
       "squeeze : bool, optional\n",
       "    If \"group\" is a dimension of any arrays in this dataset, `squeeze`\n",
       "    controls whether the subarrays have a dimension of length 1 along\n",
       "    that dimension or if the dimension is squeezed out.\n",
       "restore_coord_dims : bool, optional\n",
       "    If True, also restore the dimension order of multi-dimensional\n",
       "    coordinates.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "grouped\n",
       "    A `GroupBy` object patterned after `pandas.GroupBy` that can be\n",
       "    iterated over in the form of `(unique_value, grouped_array)` pairs.\n",
       "\n",
       "Examples\n",
       "--------\n",
       "Calculate daily anomalies for daily data:\n",
       "\n",
       ">>> da = xr.DataArray(\n",
       "...     np.linspace(0, 1826, num=1827),\n",
       "...     coords=[pd.date_range(\"1/1/2000\", \"31/12/2004\", freq=\"D\")],\n",
       "...     dims=\"time\",\n",
       "... )\n",
       ">>> da\n",
       "<xarray.DataArray (time: 1827)>\n",
       "array([0.000e+00, 1.000e+00, 2.000e+00, ..., 1.824e+03, 1.825e+03,\n",
       "       1.826e+03])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31\n",
       ">>> da.groupby(\"time.dayofyear\") - da.groupby(\"time.dayofyear\").mean(\"time\")\n",
       "<xarray.DataArray (time: 1827)>\n",
       "array([-730.8, -730.8, -730.8, ...,  730.2,  730.2,  730.5])\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31\n",
       "    dayofyear  (time) int64 1 2 3 4 5 6 7 8 ... 359 360 361 362 363 364 365 366\n",
       "\n",
       "See Also\n",
       "--------\n",
       "core.groupby.DataArrayGroupBy\n",
       "core.groupby.DatasetGroupBy\n",
       "\u001b[0;31mFile:\u001b[0m      /srv/conda/envs/notebook/lib/python3.8/site-packages/xarray/core/common.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.sst.groupby?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Step\n",
    "\n",
    "The most important argument is `group`: this defines the unique values we will us to \"split\" the data for grouped analysis. We can pass either a DataArray or a name of a variable in the dataset. Lets first use a DataArray. Just like with Pandas, we can use the time indexe to extract specific components of dates and times. Xarray uses a special syntax for this `.dt`, called the `DatetimeAccessor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.core.accessor_dt.DatetimeAccessor at 0x7f8e4e343b20>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.time.dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;month&#x27; (time: 708)&gt;\n",
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "...\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'month'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 708</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e1a55174-72c4-4600-bc9d-2ba8737f3d6d' class='xr-array-in' type='checkbox' checked><label for='section-e1a55174-72c4-4600-bc9d-2ba8737f3d6d' 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>1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 ... 11 12 1 2 3 4 5 6 7 8 9 10 11 12</span></div><div class='xr-array-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "...\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></div></li><li class='xr-section-item'><input id='section-2b661581-d9c1-40c5-84e0-36eee2036c09' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b661581-d9c1-40c5-84e0-36eee2036c09' 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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2018-12-01</div><input id='attrs-bdf32ff5-b7c6-4ef7-bd85-407c0b55e5ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdf32ff5-b7c6-4ef7-bd85-407c0b55e5ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fe32de1-2ff5-48a1-a795-f39c4d62d61b' class='xr-var-data-in' type='checkbox'><label for='data-2fe32de1-2ff5-48a1-a795-f39c4d62d61b' title='Show/Hide data repr'><svg 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>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80992.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7a3d74c-eed5-4a3d-8efc-b809b7b11020' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f7a3d74c-eed5-4a3d-8efc-b809b7b11020' 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 'month' (time: 708)>\n",
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "...\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.time.dt.month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ds.time.dt.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use these arrays in a groupby operation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataArrayGroupBy, grouped over 'month'\n",
       "12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = ds.sst.groupby(ds.time.dt.month)\n",
    "gb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Xarray also offers a more concise syntax when the variable you're grouping on is already present in the dataset. This is identical to the previous line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataArrayGroupBy, grouped over 'month'\n",
       "12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12."
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = ds.sst.groupby('time.month')\n",
    "gb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the data are split, we can manually iterate over the group. The iterator returns the key (group name) and the value (the actual dataset corresponding to that group) for each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
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       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.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;\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-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.DataArray &#x27;sst&#x27; (time: 59, lat: 89, lon: 180)&gt;\n",
       "array([[[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1961-01-01 ... 2018-01-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 59</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a8642f0c-9b7f-4ea7-9f3d-c52acf35d6be' class='xr-array-in' type='checkbox' checked><label for='section-a8642f0c-9b7f-4ea7-9f3d-c52acf35d6be' 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>-1.8 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-88e51543-90d8-4bf6-9ddd-c806ce8494fa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-88e51543-90d8-4bf6-9ddd-c806ce8494fa' 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'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-3da351dc-2a9c-4999-aee7-602a52bb6d25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3da351dc-2a9c-4999-aee7-602a52bb6d25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b15900f6-9ea7-47e5-b8a2-ec836219dfff' class='xr-var-data-in' type='checkbox'><label for='data-b15900f6-9ea7-47e5-b8a2-ec836219dfff' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], 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'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-2b4fba19-2f77-4389-b59a-82dfaa0b804a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b4fba19-2f77-4389-b59a-82dfaa0b804a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5af056df-de46-43fc-a20b-e870d79e897d' class='xr-var-data-in' type='checkbox'><label for='data-5af056df-de46-43fc-a20b-e870d79e897d' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\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'>1960-01-01 ... 2018-01-01</div><input id='attrs-b0be7a3f-bd5e-4411-ab50-96d1f687053c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0be7a3f-bd5e-4411-ab50-96d1f687053c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0994a8f7-d50e-4b7e-b2b5-7a93e303f5b7' class='xr-var-data-in' type='checkbox'><label for='data-0994a8f7-d50e-4b7e-b2b5-7a93e303f5b7' title='Show/Hide data repr'><svg 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>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80992.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1961-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1962-01-01T00:00:00.000000000&#x27;, &#x27;1963-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1964-01-01T00:00:00.000000000&#x27;, &#x27;1965-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1966-01-01T00:00:00.000000000&#x27;, &#x27;1967-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1968-01-01T00:00:00.000000000&#x27;, &#x27;1969-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1970-01-01T00:00:00.000000000&#x27;, &#x27;1971-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1972-01-01T00:00:00.000000000&#x27;, &#x27;1973-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1974-01-01T00:00:00.000000000&#x27;, &#x27;1975-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1976-01-01T00:00:00.000000000&#x27;, &#x27;1977-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1978-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1981-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-01-01T00:00:00.000000000&#x27;, &#x27;1983-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1984-01-01T00:00:00.000000000&#x27;, &#x27;1985-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-01-01T00:00:00.000000000&#x27;, &#x27;1987-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1988-01-01T00:00:00.000000000&#x27;, &#x27;1989-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1992-01-01T00:00:00.000000000&#x27;, &#x27;1993-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-01-01T00:00:00.000000000&#x27;, &#x27;1995-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-01-01T00:00:00.000000000&#x27;, &#x27;1997-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-01-01T00:00:00.000000000&#x27;, &#x27;1999-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2001-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2003-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-01-01T00:00:00.000000000&#x27;, &#x27;2005-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-01-01T00:00:00.000000000&#x27;, &#x27;2007-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-877579db-690c-448e-a26f-44be3948ac90' class='xr-section-summary-in' type='checkbox'  ><label for='section-877579db-690c-448e-a26f-44be3948ac90' class='xr-section-summary' >Attributes: <span>(10)</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>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</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>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  89 180]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (time: 59, lat: 89, lon: 180)>\n",
       "array([[[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1961-01-01 ... 2018-01-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for group_name, group_da in gb:\n",
    "    # stop iterating after the first loop\n",
    "    break \n",
    "print(group_name)\n",
    "group_da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map & Combine\n",
    "\n",
    "Now that we have groups defined, it's time to \"apply\" a calculation to the group. Like in Pandas, these calculations can either be:\n",
    "- _aggregation_: reduces the size of the group\n",
    "- _transformation_: preserves the group's full size\n",
    "\n",
    "At then end of the apply step, xarray will automatically combine the aggregated / transformed groups back into a single object.\n",
    "\n",
    "```{warning}\n",
    "Xarray calls the \"apply\" step `map`. This is different from Pandas!\n",
    "```\n",
    "\n",
    "The most fundamental way to apply is with the `.map` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshortcut\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Apply a function to each array in the group and concatenate them\n",
       "together into a new array.\n",
       "\n",
       "`func` is called like `func(ar, *args, **kwargs)` for each array `ar`\n",
       "in this group.\n",
       "\n",
       "Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how\n",
       "to stack together the array. The rule is:\n",
       "\n",
       "1. If the dimension along which the group coordinate is defined is\n",
       "   still in the first grouped array after applying `func`, then stack\n",
       "   over this dimension.\n",
       "2. Otherwise, stack over the new dimension given by name of this\n",
       "   grouping (the argument to the `groupby` function).\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "func : callable\n",
       "    Callable to apply to each array.\n",
       "shortcut : bool, optional\n",
       "    Whether or not to shortcut evaluation under the assumptions that:\n",
       "\n",
       "    (1) The action of `func` does not depend on any of the array\n",
       "        metadata (attributes or coordinates) but only on the data and\n",
       "        dimensions.\n",
       "    (2) The action of `func` creates arrays with homogeneous metadata,\n",
       "        that is, with the same dimensions and attributes.\n",
       "\n",
       "    If these conditions are satisfied `shortcut` provides significant\n",
       "    speedup. This should be the case for many common groupby operations\n",
       "    (e.g., applying numpy ufuncs).\n",
       "*args : tuple, optional\n",
       "    Positional arguments passed to `func`.\n",
       "**kwargs\n",
       "    Used to call `func(ar, **kwargs)` for each array `ar`.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "applied : DataArray or DataArray\n",
       "    The result of splitting, applying and combining this array.\n",
       "\u001b[0;31mFile:\u001b[0m      /srv/conda/envs/notebook/lib/python3.8/site-packages/xarray/core/groupby.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb.map?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aggregations\n",
    "\n",
    "`.apply` accepts as its argument a function. We can pass an existing function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (month: 12)&gt;\n",
       "array([13.659641, 13.768647, 13.76488 , 13.684034, 13.642146, 13.713043,\n",
       "       13.921847, 14.093956, 13.982147, 13.691116, 13.506494, 13.529454],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-460513a4-8b62-4937-9e2b-2bd682f09c4d' class='xr-array-in' type='checkbox' checked><label for='section-460513a4-8b62-4937-9e2b-2bd682f09c4d' 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>13.66 13.77 13.76 13.68 13.64 13.71 ... 14.09 13.98 13.69 13.51 13.53</span></div><div class='xr-array-data'><pre>array([13.659641, 13.768647, 13.76488 , 13.684034, 13.642146, 13.713043,\n",
       "       13.921847, 14.093956, 13.982147, 13.691116, 13.506494, 13.529454],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-000e8f23-5df1-4beb-89ff-5a74076e8e7c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-000e8f23-5df1-4beb-89ff-5a74076e8e7c' 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'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-dad76825-678e-41bf-8f0c-9f1f90f2cb43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dad76825-678e-41bf-8f0c-9f1f90f2cb43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fdc9fed-a0cb-4515-a824-d51b08cde221' class='xr-var-data-in' type='checkbox'><label for='data-4fdc9fed-a0cb-4515-a824-d51b08cde221' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5d3f10bb-c8a1-48ca-bb17-75181caeaaf3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5d3f10bb-c8a1-48ca-bb17-75181caeaaf3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'sst' (month: 12)>\n",
       "array([13.659641, 13.768647, 13.76488 , 13.684034, 13.642146, 13.713043,\n",
       "       13.921847, 14.093956, 13.982147, 13.691116, 13.506494, 13.529454],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.map(np.mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we specified no extra arguments (like `axis`) the function was applied over all space and time dimensions. This is not what we wanted. Instead, we could define a custom function. This function takes a single argument--the group dataset--and returns a new dataset to be combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "  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-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.DataArray &#x27;sst&#x27; (month: 12, lat: 89, lon: 180)&gt;\n",
       "array([[[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995025, -1.7995973, -1.7998415, ..., -1.7997988,\n",
       "         -1.7996519, -1.7995045],\n",
       "        [-1.7995876, -1.7997634, -1.8000009, ..., -1.8000009,\n",
       "         -1.7998358, -1.7996247],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5b73ea6d-c951-426b-9bf7-0b052d07fa0d' class='xr-array-in' type='checkbox' checked><label for='section-5b73ea6d-c951-426b-9bf7-0b052d07fa0d' 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>-1.8 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995025, -1.7995973, -1.7998415, ..., -1.7997988,\n",
       "         -1.7996519, -1.7995045],\n",
       "        [-1.7995876, -1.7997634, -1.8000009, ..., -1.8000009,\n",
       "         -1.7998358, -1.7996247],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-bf86498f-6ebb-4e89-8ff9-3b8d7454b5d7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bf86498f-6ebb-4e89-8ff9-3b8d7454b5d7' 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'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-ac14b9f6-dbd1-4e89-aa3c-22d0159156bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac14b9f6-dbd1-4e89-aa3c-22d0159156bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-447af607-2f1e-45e2-8721-45a4398308dd' class='xr-var-data-in' type='checkbox'><label for='data-447af607-2f1e-45e2-8721-45a4398308dd' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], 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'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-ebf05e40-96fe-42ef-8235-611572a5ee9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebf05e40-96fe-42ef-8235-611572a5ee9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5fa170c-571f-4b49-a062-28a29fba3122' class='xr-var-data-in' type='checkbox'><label for='data-c5fa170c-571f-4b49-a062-28a29fba3122' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-37e60765-a4ce-4a8a-9ff7-b047372bce34' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37e60765-a4ce-4a8a-9ff7-b047372bce34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69ba59cb-96f8-41f6-9154-f82c6a62ed00' class='xr-var-data-in' type='checkbox'><label for='data-69ba59cb-96f8-41f6-9154-f82c6a62ed00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9ad87f3f-07c5-4a67-b84f-e4a725e72e84' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ad87f3f-07c5-4a67-b84f-e4a725e72e84' 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 'sst' (month: 12, lat: 89, lon: 180)>\n",
       "array([[[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995025, -1.7995973, -1.7998415, ..., -1.7997988,\n",
       "         -1.7996519, -1.7995045],\n",
       "        [-1.7995876, -1.7997634, -1.8000009, ..., -1.8000009,\n",
       "         -1.7998358, -1.7996247],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def time_mean(a):\n",
    "    return a.mean(dim='time')\n",
    "\n",
    "gb.apply(time_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like Pandas, xarray's groupby object has many built-in aggregation operations (e.g. `mean`, `min`, `max`, `std`, etc):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (month: 12, lat: 89, lon: 180)&gt;\n",
       "array([[[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995025, -1.7995973, -1.7998415, ..., -1.7997988,\n",
       "         -1.7996519, -1.7995045],\n",
       "        [-1.7995876, -1.7997634, -1.8000009, ..., -1.8000009,\n",
       "         -1.7998358, -1.7996247],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f7e964c7-5a7d-4eb5-9591-45527cd0b1a9' class='xr-array-in' type='checkbox' checked><label for='section-f7e964c7-5a7d-4eb5-9591-45527cd0b1a9' 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>-1.8 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995025, -1.7995973, -1.7998415, ..., -1.7997988,\n",
       "         -1.7996519, -1.7995045],\n",
       "        [-1.7995876, -1.7997634, -1.8000009, ..., -1.8000009,\n",
       "         -1.7998358, -1.7996247],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-7cd609c8-5a4c-4ef0-8958-33fa7b216342' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7cd609c8-5a4c-4ef0-8958-33fa7b216342' 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'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-2ebd74bb-bfab-4c23-bff1-077c5ca0eba6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ebd74bb-bfab-4c23-bff1-077c5ca0eba6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5332cfaa-0d2e-47d2-ae78-94f7648120ed' class='xr-var-data-in' type='checkbox'><label for='data-5332cfaa-0d2e-47d2-ae78-94f7648120ed' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], 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'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-ae88707b-b7de-4ec5-b03a-6199d4010d53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae88707b-b7de-4ec5-b03a-6199d4010d53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04c7a2fe-20cf-433d-a01f-320d182b5305' class='xr-var-data-in' type='checkbox'><label for='data-04c7a2fe-20cf-433d-a01f-320d182b5305' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-b4a00eea-d4d2-4f60-a9ff-39bc81a015cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4a00eea-d4d2-4f60-a9ff-39bc81a015cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-194513e6-2039-43f9-b9bc-c45f25f6d485' class='xr-var-data-in' type='checkbox'><label for='data-194513e6-2039-43f9-b9bc-c45f25f6d485' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70fb5e60-c4fb-49be-8856-6f7c713e9be4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-70fb5e60-c4fb-49be-8856-6f7c713e9be4' 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 'sst' (month: 12, lat: 89, lon: 180)>\n",
       "array([[[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995025, -1.7995973, -1.7998415, ..., -1.7997988,\n",
       "         -1.7996519, -1.7995045],\n",
       "        [-1.7995876, -1.7997634, -1.8000009, ..., -1.8000009,\n",
       "         -1.7998358, -1.7996247],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this does the same thing as the previous cell\n",
    "sst_mm = gb.mean(dim='time')\n",
    "sst_mm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we did what we wanted to do: calculate the climatology at every point in the dataset. Let's look at the data a bit.\n",
    "\n",
    "_Climatlogy at a specific point in the North Atlantic_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8e4e27b220>]"
      ]
     },
     "execution_count": 25,
     "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": [
    "sst_mm.sel(lon=300, lat=50).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Zonal Mean Climatolgoy_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f8e4e1db100>"
      ]
     },
     "execution_count": 26,
     "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": [
    "sst_mm.mean(dim='lon').transpose().plot.contourf(levels=12, vmin=-2, vmax=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Difference between January and July Climatology_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f8e4e1b0790>"
      ]
     },
     "execution_count": 27,
     "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": [
    "(sst_mm.sel(month=1) - sst_mm.sel(month=7)).plot(vmax=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transformations\n",
    "\n",
    "Now we want to _remove_ this climatology from the dataset, to examine the residual, called the _anomaly_, which is the interesting part from a climate perspective.\n",
    "Removing the seasonal climatology is a perfect example of a transformation: it operates over a group, but doesn't change the size of the dataset. Here is one way to code it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 89, lon: 180, time: 708)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 9.537e-07 9.537e-07 9.537e-07 ... nan nan</pre><div class='xr-wrap' hidden><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-4eca906e-ea0c-4e15-b84e-a1803c85dffb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4eca906e-ea0c-4e15-b84e-a1803c85dffb' 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'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 708</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-48c5fa6e-0f1b-40e8-aabf-72edf85522c6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48c5fa6e-0f1b-40e8-aabf-72edf85522c6' 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'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-49ac2248-7de4-4fca-b095-dfaf2ae43c12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49ac2248-7de4-4fca-b095-dfaf2ae43c12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4e36fea-6b33-4e62-a31e-10bb1feb76fc' class='xr-var-data-in' type='checkbox'><label for='data-c4e36fea-6b33-4e62-a31e-10bb1feb76fc' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], 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'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-393d961c-ed18-456e-b604-16fd33d600c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-393d961c-ed18-456e-b604-16fd33d600c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e211fce-fa6e-4be4-a168-df5b4662fab8' class='xr-var-data-in' type='checkbox'><label for='data-1e211fce-fa6e-4be4-a168-df5b4662fab8' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\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'>1960-01-01 ... 2018-12-01</div><input id='attrs-1863df95-f259-4671-b7e9-b327be4d7499' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1863df95-f259-4671-b7e9-b327be4d7499' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5721547c-a324-4529-88e9-34fb462b443e' class='xr-var-data-in' type='checkbox'><label for='data-5721547c-a324-4529-88e9-34fb462b443e' title='Show/Hide data repr'><svg 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>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80992.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a7e76f90-f7a0-4750-8845-9cac987dd5be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a7e76f90-f7a0-4750-8845-9cac987dd5be' 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'>9.537e-07 9.537e-07 ... nan nan</div><input id='attrs-8ce55906-67c5-4315-9891-05924747aab6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ce55906-67c5-4315-9891-05924747aab6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59faada3-c681-4801-a29e-37cbc1730ae5' class='xr-var-data-in' type='checkbox'><label for='data-59faada3-c681-4801-a29e-37cbc1730ae5' title='Show/Hide data 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([[[ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "        [ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "        [ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "        ...,\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "        [ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "        [ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "...\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[-4.9746037e-04, -4.0268898e-04, -1.5842915e-04, ...,\n",
       "         -2.0110607e-04, -3.4809113e-04, -4.9543381e-04],\n",
       "        [-4.1234493e-04, -2.3651123e-04,  9.5367432e-07, ...,\n",
       "          9.5367432e-07, -1.6415119e-04, -3.7527084e-04],\n",
       "        [ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "        ...,\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c74f01dd-8e94-43fd-945a-269ab52c019b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c74f01dd-8e94-43fd-945a-269ab52c019b' 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.Dataset>\n",
       "Dimensions:  (lat: 89, lon: 180, time: 708)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 9.537e-07 9.537e-07 9.537e-07 ... nan nan"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_time_mean(x):\n",
    "    return x - x.mean(dim='time')\n",
    "\n",
    "ds_anom = ds.groupby('time.month').apply(remove_time_mean)\n",
    "ds_anom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "In the above example, we applied `groupby` to a `Dataset` instead of a `DataArray`.\n",
    "```\n",
    "\n",
    "Xarray makes these sorts of transformations easy by supporting _groupby arithmetic_.\n",
    "This concept is easiest explained with an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 89, lon: 180, time: 708)\n",
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       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 9.537e-07 9.537e-07 9.537e-07 ... nan nan</pre><div class='xr-wrap' hidden><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-9cdd1479-6dd4-4038-acb3-32b0bde0c2a8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9cdd1479-6dd4-4038-acb3-32b0bde0c2a8' 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'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 708</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-872ecec6-1d22-4d78-ac66-4d401c2d1edb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-872ecec6-1d22-4d78-ac66-4d401c2d1edb' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-c881fc00-a029-4909-b51e-a31aa91267cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c881fc00-a029-4909-b51e-a31aa91267cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b320e675-0995-4455-94fd-891e4f094321' class='xr-var-data-in' type='checkbox'><label for='data-b320e675-0995-4455-94fd-891e4f094321' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], 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'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-cb4cb973-af8c-47ce-a162-26356dbe9b76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb4cb973-af8c-47ce-a162-26356dbe9b76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed5bc498-5ed1-4844-86fd-1e2185cefa2c' class='xr-var-data-in' type='checkbox'><label for='data-ed5bc498-5ed1-4844-86fd-1e2185cefa2c' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\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'>1960-01-01 ... 2018-12-01</div><input id='attrs-8415b66f-7753-46c4-98e6-86979eef14b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8415b66f-7753-46c4-98e6-86979eef14b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccc9c78c-af9f-4d77-8d88-615e12dff2ec' class='xr-var-data-in' type='checkbox'><label for='data-ccc9c78c-af9f-4d77-8d88-615e12dff2ec' title='Show/Hide data repr'><svg 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>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80992.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12</div><input id='attrs-1de784c2-6282-4747-94b1-f7c2b58b6f35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1de784c2-6282-4747-94b1-f7c2b58b6f35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1d56e82-645b-4c41-8a40-d754d96cabac' class='xr-var-data-in' type='checkbox'><label for='data-f1d56e82-645b-4c41-8a40-d754d96cabac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "...\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-585cbcfe-6fb6-4ce0-a8ca-7f27abf1660a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-585cbcfe-6fb6-4ce0-a8ca-7f27abf1660a' 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'>9.537e-07 9.537e-07 ... nan nan</div><input id='attrs-b345c07e-7b0d-4a35-832b-6a30290bb26b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b345c07e-7b0d-4a35-832b-6a30290bb26b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a7997be-ed61-46a9-b730-b9608b6a498d' class='xr-var-data-in' type='checkbox'><label for='data-9a7997be-ed61-46a9-b730-b9608b6a498d' title='Show/Hide data 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([[[ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "        [ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "        [ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "        ...,\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "        [ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "        [ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "...\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[-4.9746037e-04, -4.0268898e-04, -1.5842915e-04, ...,\n",
       "         -2.0110607e-04, -3.4809113e-04, -4.9543381e-04],\n",
       "        [-4.1234493e-04, -2.3651123e-04,  9.5367432e-07, ...,\n",
       "          9.5367432e-07, -1.6415119e-04, -3.7527084e-04],\n",
       "        [ 9.5367432e-07,  9.5367432e-07,  9.5367432e-07, ...,\n",
       "          9.5367432e-07,  9.5367432e-07,  9.5367432e-07],\n",
       "        ...,\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-93e9b892-7326-4645-9521-181d12759c76' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-93e9b892-7326-4645-9521-181d12759c76' 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.Dataset>\n",
       "Dimensions:  (lat: 89, lon: 180, time: 708)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 9.537e-07 9.537e-07 9.537e-07 ... nan nan"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = ds.groupby('time.month')\n",
    "ds_anom = gb - gb.mean(dim='time')\n",
    "ds_anom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can view the climate signal without the overwhelming influence of the seasonal cycle.\n",
    "\n",
    "_Timeseries at a single point in the North Atlantic_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8e4e0bc2e0>]"
      ]
     },
     "execution_count": 30,
     "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": [
    "ds_anom.sst.sel(lon=300, lat=50).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Difference between Jan. 1 2018 and Jan. 1 1960_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f8e4dfca340>"
      ]
     },
     "execution_count": 31,
     "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": [
    "(ds_anom.sel(time='2018-01-01') - ds_anom.sel(time='1960-01-01')).sst.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouby-Related: Resample, Rolling, Coarsen\n",
    "\n",
    "\n",
    "### Resample\n",
    "\n",
    "Resample in xarray is nearly identical to Pandas.\n",
    "**It can be applied only to time-index dimensions.** Here we compute the five-year mean.\n",
    "It is effectively a group-by operation, and uses the same basic syntax.\n",
    "Note that resampling changes the length of the the output arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 13, lat: 89, lon: 180)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-12-31 1965-12-31 ... 2020-12-31\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 -0.0005707 -0.0005493 ... nan nan</pre><div class='xr-wrap' hidden><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-ecaee694-dd3f-46c0-afe5-71570f5c57e6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ecaee694-dd3f-46c0-afe5-71570f5c57e6' 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>: 13</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-39a1e44c-8db4-4d5e-a644-398230e5849c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39a1e44c-8db4-4d5e-a644-398230e5849c' 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'>1960-12-31 ... 2020-12-31</div><input id='attrs-59a3bc20-14c1-4650-b974-5e0e8278db34' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59a3bc20-14c1-4650-b974-5e0e8278db34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08a527a7-8ab3-4b95-97b7-1684e5b1eaa4' class='xr-var-data-in' type='checkbox'><label for='data-08a527a7-8ab3-4b95-97b7-1684e5b1eaa4' title='Show/Hide data 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;1960-12-31T00:00:00.000000000&#x27;, &#x27;1965-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1970-12-31T00:00:00.000000000&#x27;, &#x27;1975-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-12-31T00:00:00.000000000&#x27;, &#x27;1985-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-12-31T00:00:00.000000000&#x27;, &#x27;1995-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-12-31T00:00:00.000000000&#x27;, &#x27;2005-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-31T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-3320d00e-429b-42e8-a0ac-0b47f2c3c0f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3320d00e-429b-42e8-a0ac-0b47f2c3c0f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bfe30b6-4691-496f-8636-75ea8db034d9' class='xr-var-data-in' type='checkbox'><label for='data-3bfe30b6-4691-496f-8636-75ea8db034d9' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], 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'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-2aff64d1-6df3-4f61-b4aa-76161675d382' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2aff64d1-6df3-4f61-b4aa-76161675d382' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc6cdc34-cd8f-4db7-8215-b2834bae207d' class='xr-var-data-in' type='checkbox'><label for='data-fc6cdc34-cd8f-4db7-8215-b2834bae207d' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9781967b-a8f1-43f7-949f-351e6b9b494f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9781967b-a8f1-43f7-949f-351e6b9b494f' 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'>-0.0005707 -0.0005493 ... nan nan</div><input id='attrs-a36341bb-c818-43ed-b776-d05aff3a3934' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a36341bb-c818-43ed-b776-d05aff3a3934' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-676bbe32-8e43-47cb-94f7-f9c9767bffee' class='xr-var-data-in' type='checkbox'><label for='data-676bbe32-8e43-47cb-94f7-f9c9767bffee' title='Show/Hide data 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([[[-5.70684671e-04, -5.49256802e-04, -5.38418710e-04, ...,\n",
       "         -4.15881485e-04, -5.20139933e-04, -5.83072484e-04],\n",
       "        [-5.15530526e-04, -5.89827716e-04, -6.42200292e-04, ...,\n",
       "         -1.39196709e-04, -3.24239343e-04, -4.26093728e-04],\n",
       "        [-1.40964985e-05, -1.79181501e-04, -2.83718109e-04, ...,\n",
       "         -1.24106809e-04, -7.90158883e-05, -3.50972005e-05],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[-5.70684671e-04, -5.49256802e-04, -5.38418710e-04, ...,\n",
       "         -4.15881485e-04, -5.20139933e-04, -5.83072484e-04],\n",
       "        [-5.15530526e-04, -5.89827716e-04, -6.42200292e-04, ...,\n",
       "         -1.39196709e-04, -3.24239343e-04, -4.26093728e-04],\n",
       "        [-1.40964985e-05, -1.79181501e-04, -2.83718109e-04, ...,\n",
       "         -1.24106809e-04, -7.90158883e-05, -3.50972005e-05],\n",
       "...\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[ 1.06710363e-02,  1.02709997e-02,  1.00688171e-02, ...,\n",
       "          7.78120104e-03,  9.72754415e-03,  1.09023089e-02],\n",
       "        [ 9.64192208e-03,  1.10286539e-02,  1.20063825e-02, ...,\n",
       "          2.61675986e-03,  6.07070001e-03,  7.97217432e-03],\n",
       "        [ 2.81731278e-04,  3.36324517e-03,  5.31469798e-03, ...,\n",
       "          2.31114682e-03,  1.49351358e-03,  6.73671544e-04],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan]]],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-58fb3342-ad60-49e6-9099-3b4403982822' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-58fb3342-ad60-49e6-9099-3b4403982822' 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.Dataset>\n",
       "Dimensions:  (time: 13, lat: 89, lon: 180)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-12-31 1965-12-31 ... 2020-12-31\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 -0.0005707 -0.0005493 ... nan nan"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_anom_resample = ds_anom.resample(time='5Y').mean(dim='time')\n",
    "ds_anom_resample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8e4ded45e0>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+lwz0Fkhlcq6cpwPVOwEaauM0p/KzemwpffdKdwelQtw+d7w8hzUt/gVi+RZA5QTgwSgAg6HqqCQ3RFdkASVj+aZNOiuRUoamb44d0hBhAUjf9igXUHDxl07WWAAGg6GSqCwLoPPXAQQtAHDcNBlbhgr6LYf0Dj2OEwPIfeBf7y3UFpdpCqCA9WIHLIBKysACowAMhqqjkiah7XUBldKdK2aFKICM7bltztprDK9cvJ/3XlQwOWgBADz+/iKklL4soELWS1gQeOm6Vk65/XWvHWY5MQrAYKgy2usCklLywYKuaWjeXhdQWHevIGEZPamM7aVrbtK3juF967z3lHsm/zj59+oH97/LHS/PzYsB/PnFWfzx+S/yjqFXAwXH0vn7a/P53+fLufuVuUWvpasxCsBgqDLa6wJ65J2vOPKm6TwTEkDdUJQgLVUpldLOMsyd5CgAJwU02IA+zGUEjvAOu1fvfbk6Lwbw66c/DT9GiAWwSd9aAL5a3Rr6me7EKABDtzB3eRNfLF1X7mEYaH9N+pnu9zZneVOnjyVXr6e0MZXiKtKVicr1T2WzXiG4oMCPVACBdQCKXjUxnwKIGlPajTtArhZQxrbpV+90I1u0pqXotXQ1RgEYuoV9fvMiB/xuWsn7Syl5bfaKkny+hvah5JUosQi/6tbVFV+F17axxHUA6RJcRboCqHMXaKUy0svXV8JYEY9YEJHO2qEWQH0y7lNYUdZLcyrrKQeliPR9l60zMQCDIZSnP1zMN259jfvemF/uoWx0KAsga0t+8e+POu2478xfxaiLn2D+iugc/SBqhlxqFlApLiA9LVOt0E1lbc8CCLqAohRhJhvuAuqVjHnK5ChrOuP/sTuza05mevI8jrJyC9Ra01lPsalxZLS+A4XSR7sLowAMFclXqx3zePayznc7VDu6ULvj5blF91fyUVJYYD345pcAvDxreclj0Ru3v79gNYvXFPaLl+YCyimJnAWQiwFEuXzyzmXboRlTvWripDM2R1nTuTZxGzVNX2EJGGEt59rEbZ4SaE5lNReQawFktdTS8st/owAMlYmalVXSoqWNha6aeXakxITeEeyom15mr+tfKLh/VBlm3zE1JVGjKQAVcA5aADq7bz7Ae52OsABiliCVtbkw/iD1wl92ol6kuDD+IACfLV7rXZ9yO53997e9DmOVgFEAhopELeaspEVLGwvtDQKXKs/VYa12NPgNyvNURLN1RSkWgP6bqdODwEoBJGKhnwP/moB01vbdqzP2GO0dP521GS7CLZ1NxHJ2FDM59+9vebGCXjW5YnFvzl0JVIQBYKqBGiqTmBVeX92w4XTUqCr2Vajvqn0KoH2z4famgdZqFoAiKu8f/ArAiQHk3rvwkHH87eU5ZG3YcfV/CyrGR2quYJnsw9r3DuAAazR9Ezt476lDVsJv2ygAQ0WScwGVeSA9nDnLm/jJg+9xx+mTPeGmu9UaajtPBCjBGw+pxVPsM6Xiy7+3pTdR0NFn7fVJ5/r0mv01idIUQNr29wOIWQKBzaTZN7Pzstv5XA5nBMup09xAzTLJL9Kn0EIdB8Te5pDF/+G25Hrs+27mr4mt+Y+9E3WpI4CuyapqL0YBGCoSzwIwMYAN4qbnv+Cd+at55sPFnLjzSMBfTqF3TXERUOqEvmMWgCRuiZIVgb5fKmNTl/S7c96Zv4rZ2nqF+mTOAlCXXSgG0EdXABm/BRDLtHBT4kZ2nv8GL/U6hHNXf4v95Sv8uu+/qGlaxEI5gOsyJzLVngLAVHt3rjhwS559+hH+usNSJrzzLw6MvY39yV85NLklb6Z3hWWbwKCxJV17V2AUgKGiePmL5Uwe3d8LJFaCmdyT6VvvCLTVLblZalY6M+fjdxzBSzOXddq5bLtjCqAmbpFJZYvvjL+5S1ABtGWyfO1Pr/j29xRANhcE7pWMFnsj+uVKRDhZQM41XTKlL9Zdh3Oo9R4vbXYet2YOQ6xfy9S2KRx66Hmcc+87vuMcMXEYHy1cS4oY0+1tyR5yMHu8uh8TxDy+O/QzRq94iXPSd8PNd8OALWDcoTDucBg5GazoGEVnYxRAD+LteSvZenij59fc2Hh3/iq+edvrnL3XGLYY7FRorIRc6Z5MX3dGq9epz9oQEwLLKk3B5haCFd5XHauAiz2PrC3pXROnqQQF8Lfpc7jy8Y+9v520ztyM/e15q/I+o1xA6YztlYLuVcDq0WsEpbOSrJRsLeZy+sc3Qnod52R/zObDTiQzbxU1cYv1beFurGTcIpXJrQR2FpsJPpajeKDXTry86GB2aGzi0f3XwGdPwmt/gVf+CPUDYOwhMO4w2HxfSPaC9x+E566ENQugcQTs/zOYeGLR+1UKZVUAQoi/AUcAS6WU25RzLJXO3OVNHPfnV/nGziO59riJ5R5Ol7CyyZmlfr50PeOGNgCV4SftyTQqC6A5pwCklFiWE2dpj4et2L7KPV+qBSClJONaAKXwxAeLfH+3ZWwef38hL3+xgmu+ti1L1+avrNUtgPVtWZIxq+A6AD1AnLVtBnz5Hx5K/gLEADjzGV66eQGjbOlZLuD8XsOOk87aXtaSvtq4qc1RdkvFQJh8Ikz+P2hdA1885yiDTx+H9+6FeC0MHAvLPoGs+/2t+RL+fZ7zuhOUQLnTQO8EDinzGHoEagb38aK1ZR5J1yE0t4+KAZh1ABuGEjurfRaAxBICS5RWXlnN7IuVayg1BvD67BVMm7nMUyhBi1ZKZ4XyW266pGJoY63v77aMzffve5f735hPJmuzqtmfkw9QX5OLATS1Zby/g7z00334z4/2IuEpAMmOX97FttO/z0w5gllHT4Wh2xITTrwiY0svnfTG5z7PO14iZjn7ufcs5lMAGe86PWobYZuvwXG3wU9nwalTYafTYMlHOeGvSLc4FkEnUFYFIKWcBqwsuqOhKlCZP7bUsoBCBJSUMjJf/K/TZnt51ga93r6/emVMCCwhSnKxKTdGqkgOfs4FVFgBfP3W1zj1b294i6SCM/KsLbnj5bkc/5dX/R8MnF6t7AVYuLqVVc0BQQnUxv0KIMr/v9mAXowd0kAyLkiS5jeJWzh08V94o34vvp66nGyvwd61ZV0LoFA6aTwmSGds0rYkERO+chOeAoj6cCwBY/aGQ38NMkLprlkQee72UG4LwFAi1TAPVo+IlNILKIbNUH/y0AzG/r+nQo/xqyc/4YSg4KhilAWlG1JSgmU5CiAsy+ofb8znhc+Wen+X2ri9vdaaWgIQXJgVpZSCq4DbMjYDe9cAMHdFE6tDLIBETJCMW7Rlbda3ZYpmPdWkVnFP8hqOj03jhszX+PrKs2gj6Vk18ZhF1nYWghVKJ03GLFJZ25eq+tQP9wRgvWcBFByKQ+OI9m1vJxWvAIQQZwkh3hJCvLVsWedlLPRU2rHKvsdx67TZ3ms1mwwTKo+88xUAS9eWv556paOqZ+qK1HEBOa6aMCF08SMfcPodb3p/Kz92sUVY6jsrNXMrVybBL4ai1oalszbbbtLI38/cBXBm9crH35zK+OIcinjMoibmBGSbUhlvRW4oSz9l2ENHsL2YxXmp73ND5njUE6eajMXclNVsgdjFmEG9PBdQOmuTcD+81bA+NNTGvTUJxWorAU7AN1Hn35aoc7Z3AhWvAKSUt0opJ0kpJw0aNKjcwzF0Ia/MWgE4AkQJ/jCZo1L1NuZ4SGeh+tXqelSlgVoiX1iHzaKVoE4XmeGHWRul7B+MAURaABmbmrjlpX62ZWzf7yQsBhCzhJeR09SWjc4A+uJZuP1ArHQT30j9P6bau/veVhZATAiytnPe4Lj/8I3tmfGzg3j6h3sRjzmuolTG9i2MS7iWAZRoAUw8EY68ERpHAsL5/8gbN44sIEPp9OS6+KWMXd9HypxgCvtsY12CBatafKmNhnDSdr4lJaUbBLbys4BmhzR9UXGEdJE6PepYpf5Wg5UyFVGupHTWJhGzvP3b0lmf1RFmASgXUCpjk87a4X77N/4KT10Eg8az/ti/8+4fPsnbxVMAliBrO2MPHquhNu5lXalgcks6S1zbL27lrK6Sn+iJJ3aawA9SVgtACHE/8CowTgixQAhxZjnH0yNoxyKbSqGUVZ5+AZUTJmGzQfVAtQRyx82q4XyU3z5YQsESAhFiAaxxhajuK1fHKPY92h20APJdQFExAEkybnnNVXQLwJaSVc2pvGPFLMsXuPUFqLMZePKn8OQFsOWBcOYzxAdsGnpu9TFnZu+ct1gMAGD658t9CjGhKYNKmNOV1QKQUp5UzvMbuoeSarhrT4PfBRSiANynsSXtVwDtrSuj8/DbC+hdG+fgrYd2+BiVSK7loq4AcF1AIk8BNKWcAKWemaOsiGKlmLMBq01KySPvfMWBWw+hT20if/92uoDSGWUB5DJ7ghbAoIYaFqzKtVpMWMJRAO5vynPHtK6Bh06HWc/Bbt+HA68EK0Yi4hp1F5CTBmrndRbTYxfqPEsDXb/8dZLKrwEqPgZg6PmU0sZP38XvAsrfN1oBdLxy3E8emsHZ97zd4c9XKsp9o6dwqoVgsZCFYM2uVZXQBJWyANqK1LFXx1L/P/PRYn7y0AxueWlWxNiigsD5X/qa5jQfL1rrpGnG8y2AtrST5TOoocb3uZirAFTg1hICVs6B2w6EOS85/vSDf+WVX4hqD+l3AYUHge2Imb6OfvxKsACqSgE0tWX4ImTVXqVx6aMf8GRg1WMF/FY6TDYgfMLQHx6J9LUtDKK8YEEX0IZYAF3FSzOX8aKWUtndqHLL+rqJrMwtBAtaAC2eAsiJBmXB6Xn3Ycz4cjWQO6aqM6RX2NRRyiZoAYR9jz9+8D0Alqxt88aWzuYUgFoDoNJCFTFLOIrOXbw1tvUD+Ot+sH4JnPIo7PRt3/5R7SHVZqUAMrb0WUm7jO7PHlsM9P6OWiPgcwGF7tG9VFUQ+Iw73+T1OSuZe+3h5R5KQe57fT73vT4/dJw9LwLgtwBsmWv2oqOb/VLmGpeHuQOUQMpTACW4mrqbb//tDYCy/ebSoS4gZyGYcNNApZSe4PNcQLF8F1AhC+Czxeu810oBrGstXHun2T1XKUHgxW7Kb1Nbhpi3YDBXrVNZg8Hy1kLkhPZB6ec4d/6fYcAoOPlBGLB55PUEUbEDld2TzUrilsVtp05i4ohGBvfxr1KOKomtb6+ExI6qUgCvz3FWiNq2xGpP3zrDBqE/0LaUxELUmB3Yp1DfVCXMOtMFtLGSDcnh1xeCqb+DVpUSeKubU7S4grqYBaAfH3Juk6isnigLIGwdgcr3z9gS4eqLrJ1r2ajGVq9VBz3Kms5uUy/gyeZFtK6po14283mvHdnyO49AXb+SrkWRHwNw4gkHTBgSur8ebD5vvy2813HLWABlJ2NLkj1MAVTAZKHD6DPzrC0JK2bqywLS/g5bLKP82nkKoAItgHKTDnMBeQvBnL9tKbFcpayEsnLDbH/lf73PFeplqysYJZSVUon6Xk653bGONhtQ79sepjD0zl664lL7qutTTeBVw/Y6d21AvWwmg8W7/Q5ly3YKf8jdq5jlBM4ztl2w5IX+3rihfbzXiZiJAZSdnlhjvhLMxY4SJhyC+LOACpeB9iyAgAvIFI7LJ2wVrxcDUAX3tHut3DJhq34LWQB6xy31NShRp38vf3g2v3BasDyDLx7kvlY1fFrS2YALSFkArgJw9wtr2B7H5sAlt0VeQyGUiyxuWcxZ3kQ6KyMDxs5+uff0cIDPAqiAZ7oqFUAlBguL4T1UEb+55z5ZUrELo8KEQxCf90bK3GKZdriASukXW20o4bt8fYovljp+eumuBFa/Jf0eexZAyKy9kAWgWxhKsHkuIC0t9PfPzsz7rGUJX6BY/xrV70W5dlpSWW/cKrUTcvEJtV9Uw/a+6Y4F5HULQKWZFpLfekVU/bUvBtChkXQuVakAeuJMsZDVsqYlzZl3vcWZd74ZuU85aQu4H8LQr0+l7EH4Q5KOCgL3wO+1q9GV4s+nfgTkFoLpM2mAe1+fx2PvLQTC4ymFLIBUIMYAel9n/yw977jpLIO19M1gzAig1hXsu47p77lX9AWDegzgYOuNyHGuSYb77Iuhp4EqVFG3MGI+C8BfCsKjAn6uVakAKnnFaGSaZIExt7kz4bdCOiJVAm3aTF2v8vnMR4s9l4P+0Gd1BRByP5RQazUxgKLo93Vwg5Opkg0EgdUulz36obdvOivzfnOlWgBRMYAoBZCMW778fX0ysKYlzaiLn+C+1+cD8MeTdgwNLjvHlmw/705uSd7AfDmYFpn0nadFJnlpxHcjr6EQyl2mC/O1BSxufT894cS3DqBDI+lcqlIBVPJMMdJFEvCr6hR6MCuBthDhMHXGQs6+523+/to833Zwvh8l+IP3Y/rny73Vlc15FkBl34dyoBdwG97XUQBSSmLC34AnSCZr58Vh2jLZyAmKP87jf0+tRWhL51sQD5y1K/uOG8xJk3MlGHTX3or1OT/+Jn3rqEvGQvtFZ9NtXBe/lYmf/p5/Z3fl4NR1XJT+Dq31w5EIllqDuTT7f3w2+NDQ8evcdcbkvG1eKQhdAbSWpgDiERaAiQGUiUoOApfiIglSanpeuWgLLEIC+O/HS4Bc1kaeBRBRWljvvtSZpSA2VjJZm0mb9XMar2uL6yyhpYG6X4+eoZK2ZZ5FZcvoexxmAXglvd3/n/s03/++y5gBCCE4crvh/P7r2wHwqbamQBekauGVUHWM3LH0ZR0XLLmIE+MvMWfr73Ne+vu0kWSqPYU1332XMzb7D9/pfwf/yu5RtFkNwN5j86sOq3tl+SyAAi4gze8fMzGAyqKSYwBRgj5s+5tzV3LNU5/0AAtAdwHB7tc8x+PvOyudRcAPnYgJ5ixv4uYXnPIBwe+qjxYs7EoXkN6URudf737FqIufqNiAe5BMVnoVNFWgNOuugwnOpPUMlUzWDrWomiL83mFBYPWTVUrjkkc+KDjW7Uc66ZkfLFidG4c2Bl1BWULQlrUZIxbyaPJnbJn6hB+mzmXRDj9CYtErGWPutYczpE8tMUuQzjqJBaUogDC8hjDa57ca1hC5f7QLSO85XH45ZBRAhdEeC+CEv7zKLS/NrngLIDg7XLgm18glWPc/uIQ+XwHkUgbzVwJ3niI8/c43GXPpk3nb//TiFwAsXlMZzWhszV0WRiprk4hb1CRi3u8kazspjME0UH12akv/9zZ2SG8Ang+ZxQO0aff+3zMWsWxdW+67LVExD3N7/uoF1HSlrpdeiAnB8JVv8GjyZzSIFi5uuIbH7Ck01ifYdUx/bv7mjrl9LUHKvfZCqZuF0EtBKC49fKvI/aODwPo9Lr8cMgqgwojKf1cTobBaJU2pylYAhbKAlO9YbQ/rD6ujV5VsTvt90p3pAnrxs/Duc8raCo6zK/hs8bqCwr01nWXMpU/yh+c+5+15q1i+vi1vH6cGvqA2kbMAWjNZ6hIxLQisrC//NbVq39tuYwYAMH9lc+hYdGXxxtyVnHHnm7lm8iV+LzVxC0v4g6sPv5PrfauP7wTreb71+Y9YIvtxTOpKZohx7jFi/OOs3dhn3GBv35glvN9gRysABC2Akf3r8qqB+va3/NaKIqhky011KoAK0LwA02YuY/rn/nzlqB7QhWYLhbIRKgFfFlDgOpRwUNvzFEDEdZ+99xik9CuXUoLAti257/X5kU3lgwQFsHI7Ba/jgTfnc/JfXyvpmKXwyqzlHHzDNO51s1/CUBbQHS/P5bg/v8LRN72ct086axO3nBLK6l61pLLUJnMKYPKvnuPOl+f4BBX4XWyJmCOcoyZPwfv51eoWzwX04mdLQxXZ9cdP9P0thKBXTdyrIaSuTR8DdhaeuYxfxW7l8147cVzqFyyQgz3rJhFSg8cSwhtfRy0ANYtXgl135YQRjwgCV5oLqKRSEEKI0VLKOcW29RQq4cYDnBpSKCzSAigwZP2BqUQKWQDKbROpAAL7t6SyDOlTw1C3+FZrOuuVCSglBjB1xkIuffQDlqxt5UcHjgWC3cikz8pqTvnbCKprCZ7rooc/CP18R5njdub6aGHxtpdKUH+1uiXvvXRWOi6guOXt15q2XQsgt98V//6YfvX+qp26AojFhFdULYygAhDkvtO5K5rzXEen7T6KEyaNzDtO75p4ZHZNg2iDB74Fnz3JffJgXhv+U9atcI6rrJt4SBXOuCW8dQqxIoI7CnWrlDAvFkvQlWmUCwjKX5es1LvxcMi2f3bmQLqT7lQA02YuY+dfPZvnr46i1BjAHK11X6F0tEqg0EpgtajLcwEViQG0ZhyBr7KH9EygUlwNquet3j82mIGkE1SuaqYZtep4nRYk7Zz1JtHHUJOFqPx6cARzIiacILCyANJ+F5AiqNT0a09onbVCz5PN/33r+wYbo0QJ0PpkLDS7Zigr+NmyH8PMp+HQ67hGnElLNncMJeBDLQBrwywAPV6iFEix48QiXUDRPQTKQUEFIIQYL4Q4DmgUQnxN+3caUFvos5VMdyqAq5/8hGXr2pi9vLQ+BMWygNRPad/fvOi9t64bFYDUlt+Xuv/v/ptb/p/vAgpaAMEuS/kWQF0i5s36dcVaSikIdTT98c0WiCMElatnAQT2U8/76qbc/qU0wgnj/jfm+xZlRVGKglF9cGu1IHBLKktdMpZXVmRdW4aG2rjnmlnVlFOS8ZggblncMm02/3f3W3nnCVoAK5pS/MdN9QV/KiRAlPzsXRPPS+/dVszmsZrLGZxZ5JRx3uVsN7CbO6eyABIhM/x4B2IAJ04a4b3Wq5Uq+V3MAvDXAtIsgMDnyu2OLmYBjAOOAPoCR2r/dgT+r0tH1oWECbCH317Aq7NW+LZJKfntfz7j8yXr8vYvFb1yYfDYxcZ22/9mM+riJ2hqyxR0ARXKR+5sbvvfHDa/9ElvJl2MtYEZdL4LKJAFVCQG0JqxqUnEvIdqv9++xFMfLOL12Su4ddrsouMJlikIjimoRILK1UttDOzX4AanV0ZYFu1Z9KMrzEKUIjwytpYGmrGRUtLius2CFgDA7psP8Grb69eSiFnePf+vJtj180Q1QQkjShAHewccYr3Bg8krSRPnd5ve5PTuxU0D1bLfvBhASHBet1xKtQCuO347r7dArdb7N16iBWD5FEBue9ACKHc6ccFvTEr5mJTydOAIKeXp2r/zpJSvdNMYOwX9AQx7cH7y0AxOCgTx1rZk+OPzX+Rtbw9R7uCozB1daKgA2MqmVMHZXnstgHfnr2LWso51RvvHm05Qctm6/IyTMBatcfzSkzZzcryjgsBRDcLzXECpLHUJyzereuTdr/j6ra+V5C8PQz+HUkiqqJjuBvlkUe746YC7RFW01F1L+j7tMTqTJTYOL8US0/votqazuaqZmhLVqYnH6OMKPp8F4LZWLDSWMPeLN46ANRSmfEBXAJJzY4/xl+QNfCI35Zi2q1hRv7nv82GuxTDBHJWSWQw1Rj3bJ+a5goq4gHwxAE2BBO7RX0uYtHQlparsY4UQfYQQCSHEc0KI5UKIb3XpyDoZ/Vkp1TerhFXwYe8IwQc5SvOHuYCEKOwr1JVJsZnmglXNHPunVzj/H+8V3C+KsBl0IRa5+fI7j+4PBKp+kptJq3EXVQAZNXvNbdtqaPSCnCBhd0cfkxJU6vBRfuxgxpFSGHpGlm4lLF1X+rqBqG5SQUrxMDnrAJw+uqmM7bnM6hJW6OQkGbdy1ozmzorHrIJCz2mQEi1Ogt9j0CWkqE/GSJLmN4lbuDDxAI9ld+ek1P9jOY2+NFBLwLvzV+d9PqwXb9Sq3GKoy/VbAKVlAfmUjnbOoIuqo5OWzqJUBXCQlHItjjtoATAW+GmXjaoLCNaa6S48F1BA9ETN2qPcBoXKQet+8GKzQlXK9sOFawruF4W6j6VOpJatdYTm8EZViCywDkBZABG56FnbvyK3LW1TE7d8CqiQ4Ami7ql+H3VhHgyE6spfv8/B/dRfvvo02rgPueF/JY9Rn8UWkleluIBUDCBmCbKu+wdwa+qEWQCWt9hulc8FJHzj+jKwHiDrrjiOIni/on4/jfZa7klew/Gxafw+fRw/TH+PNpyibol4eGBVodo/Bik0Ay+EOoceAwgrCheGfyVwbrt+/m/usikzl6wva02gUp8clR92GHC/lHJlF42ny9AfzPZaABuS1ZcruOXfrj8QxRqmCCFCm2QoVEVNKK7c1LW3Zybk+3yEBfD2vJVeYTcdJUQGuM2681xAwYVgASGytjXDmEuf5Am3dEQ6a5OM+4VXWE5/sYdK/7wuSFXqo7o+vcxxa4GMI3U+3/eq7dMeX2+iRBfQ1U9+UvA4TstElcMvsO1c1lRUDCAZt+j7xb+YnjyP332yD9OT53GUNd07hmLP617wn0sWdgEFM5VCYwDLZnLe3HPYXszivNT3+UP2OPRwvX5fwgRwXVirOfw++Pa4gESIAvAsgCKKJMrtpE9WLjx4PK9esl+npA13lFJbQk4VQnwKtADnCiEGAZWxFr5E9AepVAtACaUN+XrUZ4OCT/+zNZ31ftx6bNGbVWqz4DfnrmL0Jf4SBXpVzGJpZerSO5p9oCyZ4HmO+/OrAHxr181821c1p0nGLM9H/tFXfstDCcyodQCKpz5cxOEThzkuDa2ODfiFtDdOGa64PReWtk13pQQzUM67/11a01m2GtrHn3IaOGeoBdBB12HYTHqHK//DlC0HUZewuOTQrejXKxkajNVJe6mRFjHL+T0rJVYTj4Uqye1X/5fk+9cywnIsxRHWcq5N3MZHizcjHhsXea6sLQsKRX2SAiEz+NkvwoOnUmtbnJS6jHfk2Lxj6JODsO822FtYoVsAG+oC8haEFTlOZCkI7XVjYN1FOShVAbwD/Bn4CrgE2Av4cVcNqivwPZglCr9OSdGKaIytj6clnfX8rv6Zfm7fQjpLF0zFXEC57kzFhx6GEpalWlGrm1P0rU94D8Hlj33ke99zAbnHDcYAFGrmlc7aebPRMAsgK3N9bnWUAotyAXkWgPaZC//5PgA/O2JC3ri1A7vH0i2AjqWBhgnSVc1p/j3DadaSiFn86thtix5Hz41XLiClcBMxQUh1ZvZd8GdI+xeU1YsU231wNXvHzuFjkWQFjayQDWQyWeLxXLP2Qn7xpjb/ycYvexp+fyysWQB1faFlNQwaz62DruKdd8IzzIpZACubwj/XUQsgNAjsbiumR6KrgVZW8YVSFcDlUsqJQogpwMHAb4DfAbt02cg6mY4EgdXDsiEmmvpkML1QF/SvzV7JUdsNB4LN0XMZMoVm9s3tiAFs6OIkz9URcRy1ErY5laE+GWdVc4p+9cnIGVOeCyhCAahZXCYrScSFz4XQlrGJW8I3pqj7FRbE1uX09M9XsO0mfUM/+8QHi/LG7R3XO1Z4DKA9JKzCLqDmVLakwnfpTK5ukeMCkt49cpqb53+moS3cqkim13Bl+lrI9W1BXn0u1A+EXgP53tokC9O9mRevY7lsZCUNrJR9WC77sJI+ZJv74twlwVHWdPad+TewXSdCyyoQFux6DusWDgfmho4hqr5OMXwWQLsUgPO/zwIoMYZgRVkA7YhBdAelKgAlYQ4H/iylfEwIcUXXDKlrKDUIrC/l74wFY17/UteN88snPuGbu27qG89597/L4dsOcx/KcAugkE+7PUHgUlcetmWy3D59Dt+ZMsYnlD0XUsR50lnJi58t4ax73uax7+3B6uY0jfWJvKDfwVsPYd6K5qK1gBTKAkh5FkDuvVTGJhm3yOiuME0+zlyyjumfL+eMKaNDs4B0S+/3z85kfVs61O/3ttZxLS8IHKIYO6wA4oWFRFNbpqQCgGlvtm9pFkDOLZS1/ccYwBpsESMm89eVpOqG8KPYJaxduZgBrGWAWMv5u/WjIbsamleQXD2H8fYido6tpkHkl6TgE7isJsZq+tCXtSQC50baMO16EuMeirwe/a60Z06mz8DHDOpd8ue8GEA8PwZQ7PR6wDyqHHQlUKoC+EoIcQtwAPBrIUQNPayQnF5kLTgL1v9OZW3P5FOCYUN0tpqpZLKSL5at528vz+GVWcu56phtfPs1pTL0qU0ELACHrF1YmOjByWJuq1KF0m3/m8P1z3xGTTzGmVNGe9u9Jh8Rx2nNZJn2uVNJc8aC1bRmbPrWJfJmXruNGcDC1a35tYAiTGTlFgl1AWWdzKCoWMgRf5xOKmNz+h6jQmMA2YCrZuaS4mskgu6dMMXYUQUQFBLB43y1uoXtfvGf4mPUhL0lnMVQSkHFLeG7h0NZwb3Jq50fXawGsrmU12aZZNnkS5n34eZ8aOeqbH5j571oGOKk4F5915ssXN3Kx4vWUkOKfqxjgHAUxQDWsutQycqlXzEkvp5jpT+A7LFmQYfr9RdC/XYG9q5h9MBeJX9OfQ01vpXApYm9qDTQ9mQhdQelKoATgUOA30gpVwshhtGD00DzUxFzD3NzWzanAFQQeEOygNQ5sra3XF2Z4zpNbY4CCLMAVDZHFO2ZdZZqASirItgApFgQWVdGAke5xiyRl/WRcYOGwYVgxSwAp8GJ8H0narGTjj4+FSPI2jKXjqt9PuhNKZTO6I0/Lw00XzG2J86Syti8MWclU7YcmOcmCMY4vlhaWEH9+IH3mPb5ch44e1cgFwOwbekphXjMQgjnu9pULOHexNU0ivW8uNvt7D8sxZrHL6ehbQkL5QCuy5zI9ycch/hwhu88uhWivk+ANpIsZgCL5QBvFrOsfiDTM8sZUJNkcvoDRgh/FVwAGkcUXGGrf+ftcWX2ctdo9KqJLt8chlKQyZguzNv3WahsF1BJ6kxK2SylfERK+bn79yIpZfEpSAURlZ8N/odZzyjJ7bfhX1rGll7BrGTcyhPoStD6hVHOPVKq4C4aBC4xLqkEdv55C1sAbYHuZMH2g4qMLUlYlieQilkAMcvyfNhhFkBQaIeV1VZdoQCE9p0GryUZF5HfeIObzZSfBuoey7d2Q+Z9Lorrnv6Ub93+Ou/OX5VnAQQ7nxUq/gbOyujl69sCWUCOCygbsAC2FAt4KPkLeokWTk5dxppBk2Diidw+aSpj2u5lSupGptpTqEvE8n4L+uQg6yr6IP17OTn8KlEhEbO4LnMiGStQSixRB/v/rKAFoH8r7VnL099NQW6vRaZ+Y77gcyzXlrIQUbWAKs0FVNbRCCEOEUJ8JoT4QghxcVeeS//u8xRAYKFRcL8NsQA8F5AtvQc3GbfyfPrr23LdmhR+C6C0H2+xxJNSj6N+s3nusiIxAKdxuPuHu34hZuWn32WytmMBuMo3nXH+j07lg5tecLpx6XVpwJkhBwVH2HXqlp6+e3BRXtyyIh9wVaogLwis7os2mdCVwbYjGgF4f8FqHngzv8a/Ks2xqjnlq2cjkUUFfhTqnurrAFRcIB4TNKz8kAeSVyKAr6d+xodyTG7WG7DEahOxvEnL+oAC0IWe6u61/ci+QE6JJeMWU+0pvDLhZ9A4EhDO/0feCBNPLNkCCPv9nX/AlqGfG+AqofYmQKjz6d9HqTEAayNzAXU6QogYcDNwIM7q4jeFEFOllB93xflkQQsg94DpZW07pWqoyJ1DmfI1cSvPhdLsPkxhgitbJA00uG8hSlUA6kcbPG9UDMBJK5S0pm1fxU01MwxOfNJZp3SAciMsc7tZDesbXmQ2ZllekbRETOQFgYMPVth90C09XZh8//53fft9uHBN5MKt+ppc2qOO+n3p5w3rVnaU27Tl6ztvGnp88OeKZ2063PJTKTzHBeSMTaW8Nix9i+HPncpiavlm6lLmyaFA7r7kK4D8SYtuAWRcS08xeXR/HntvofPdC90CcPaZN+IIOP57eWNWPnZVukLHH7cJUwD5awcABvR2FEB707o9CyBEmLdrRbHPBWQsAMVk4Asp5WwpZQr4B3B0V52sVAtAb7Cutm+IzlafzbjCEZy84qjZVFgQ2G6HBRAMaC5d2xp4v0QLwP3RBh8aNYvKVwDOTykorLJuVlXQBZS1JQlLeMp3ydpW+tTGfS0fdeKBh0ifobdl7bwaK8H7dZQ1nca/7MC5L01ievI8Jw/dJdirYfayJqJQNX+Cab3ZEMVY6DcXNRuV0i98M7bt+022By8NNGYRU0HgrGSK9QEjHj+ZdN0gTmj7uSf8IdztAY4FEPwJ6uOyAwvBlKC0hBNvaE3lViCr7WGo7zlqPYhC3c/tXAujEH3rku6x2yfuwlplLnGfp1EDSg8m63S0I1lXUU4FsAnwpfb3Andbl6ALhGDvVF0BhMcAOo6SUxlb0pJ2hHxN3Mr3p7orJcN8yFlblhxQ1OXSq7NWMPnq57wyCs4xSztOsF+sQndL6Sih1Zq2fWO1bUlM5FeSTNu2r0zvkrWtDOlTGzlD0oVL2EKwoMdG14NHWdO5NnEbsXULEEhGWMs5ZPY18P6DgDNbHV9iQbnauFOILhgEzpW1zrc0k3Erz2XUlIou4a3fg0xWdtgCUK6jRNzyFPrAr/7L7YnryfTdnM8OfZBFDPB9JsoFlIjl/2ZTGX8QWM+QUeezhCBhCc8CGNRQ4342XKnFCigA/+I9Zyxn7DEq9Dg6Q/rUcOaU0dx+2qSi++ooJa27gFR/iK2G9WnXsRTGAsgRpgrzxJMQ4iwhxFtCiLeWLQtv1F0K+oGf/mix7z394ezsGIAKXGVs20tT1GMA395tMyC3UlI3s73sFVl6ExZ9v4/cgm/vzF/lbSt1oVJYDOD9Bau9jldBF4gK3vqygIQz9piVbwGkM04wV82kv1rdwtDG2uiFYIGsCl2ftGWyecJJ//vC+IPUC/8q0YTdCs9d6Y25d5Egrfc5t7lKMDAbbG6vj6EmZuXdr/Vt4QogWPk1ne24BXDDs8pl5lgAx1jT2fXNH/GxHMWK4x9G9hqU9xl1X9X3OW5IA9Mv2td3PYrgZEn3ingWgCWIxyxPAQxpcHsNRKzaVS6iYr0F1O+ylB4EQgguP2IC44e2T2jrKbOK7+27BRceMo4j3YWb7aXSYgDlVAALAL0p6AhgYXAnKeWtUspJUspJgwbl/2BLRRdkQZM/XSQLKDonpDhKbqWz0jtvMmZ5M9T9thoC6Ctic5/1GpDbpfvuSy0F4YwpWrBYWgzg/QWreXveKs9/HXaeoW7Q79PF/uY5to2bBeQ//mm7j/LSQNe3Zfhk0Tq2G9E38oHWT5fJ2j6F0pa28wRs1pbOyef8j03CUg4B1nwJ6RZfX+FiJOMW9cl43kKsYDor5KyQRNzytvcK6TMQJOg6ao8FoE8g3nHLJSdigm0XP8zvEn/mDXs830pdQqxXP59VduAE53coAhZAn7o4I/rVO8cOnEt/bgpZAHFLeEpsSB/HAli+Pqpsg3OM8MYuumss3z3T2YSlJvetT3LuPluUtF7hh/tvyd/P9BdLqLQsoLIFgYE3gS2FEKNxagx9Azi5q04mAw+Vjv53WzsWVZWC5wLSgsBJLQisBF6oAFHulg4GgcOGrx8/lbUjBZ+eBnrMzS/nnT94D1WmxetzVrKJFsh1gsD+VLjTdh/FpgPqiVsWmaxk5pJ1ZG3J9iP7RloAfsXlDzi2pLO+/OoRYikNr/0WZj4Eq+chEYio3rrXbc757MhH/fbhbUbTUqTTaSJm0bsmlrc+Qn1/YeUonAC5893X1zjKI0oBSOkX4mlbRloAQuR/xx9rTWsUg9+/ha0/v4bn7B04N/1D2kh6PX4Vu4zuz38/XsJmAxxhr36XhSqT+hqyBLKA1MecGEBu+yb96pzPhhUi0ggK9q/tuAnf3n0z72+vfHiRWMGGkN1AJfOjA/OD0pW2DqBsCkBKmRFCfB94BogBf5NSflTkYx1GL+2ctxDMlwWkWwC29xmdpeta6V+fLKmwU84FJH0FxNRDnozn3tfH6Ru7LUtOYQuzAPTh+4RLgfRC3QUUduq84LD757SZy7wfuUCEuoCUf1cJxmbX/dVYn4hUAPr1Z2zbl1XUksrSP5HmOGsax8emsVvsY+QbAsbsDfv9P376jze5KnGnzw2UEjUk9/getK5mm7cfZq+l/+O0miQv2NvzVHYyz9k70hyiDGpcCyBY3TK4oll/ndQsgN41cZata4vsB5EJBPwzWTvUAohZAimDXSbg8Buna39JfhT/J4Nee5RZgw/i7PnfIuM+8rGYPy5z5pTRHLz1UEb2dxSAEnr69xH8benPTca2fXVyckFg4SvTfPDWQ5m7opkz9hgdev1ZL3PJ/zu4+thtfZOVXEmLrhOo7W0jWQo18fYtRutqymkBIKV8Eniy6I6dgDcbs6y8XPmodQDBQB84QaDJv3qO0/cYxc+P3LroeXMWgPR+3PqMPhnzrzoOTQO1ZekVTH1ZROHHUhTqdBbzLICo80QXt0t7RfQchWMFgsDq4VYuIBUQrUvEfA90v/oEq5odQRluAUgmi085nmkc3vo6vZKtzLWH8Jv0CZxw5k/ZbIxTvvjh+3qRTse5vt9jJJsW8pU9gFdHfY8TDnAK2h79wWGcNPQrDuRVJs19msNib9AqE7xkb8eTrjJYjxKMgt41cZ8PX2rf56uzVvDcJ0tYuq7Nc3ckY5avFSP4C/jppLM2toRN+tYxsn+dEwQOsQB+fdxELn74/QKuQcnl8b9zZvwpmrY+iZeGXkBm/me578CyAo1nhCf8IVchVRfEawNKSw/kZt1gv0JZkELAiH71zF3hNJCpice46JDxEWPO/XaCE4GgEM49P11nASi5EDUp6QgqJbVSKKsC6E7UDyYRE3nt/HRhplsAntWg7bvEbXE4bWZpAWl1jLZMNpcqmJW+2SHodfHzj5GxiweBVR5+qAWgL6DR3i4UA/AK4kUImOBHQy0XN3gdtADUNcfdlcAqNtKrJu572O45cxeO+ON093y549euX8CAt+7jpeQ9bGYtZb2s5Rl25762KbwlxwGCY3r7g3RT7SmcfNwFPPbeQu5/Yz4n99uUE9z3mjPwVd+d2PLY0xhz8b/ZSczksNjrHBp7g4Njb9Em40yzJ/JUdjK9xZGsrkn6gpj6Lf986XrOvOstAG47dZJ7vTFP4CtFGLWSNeP+NoRwhO/6TIbWEAugNqKlI4CFzdXx2/hG/EX+ljmEIw7+HdYH/iqf8Vh+YF4npQSxJmBXNxdQADLgAnKPHROCTQfUwxe58xbC6w0dEOxRPvf6ZJy/njqJrtADXhZQJx680rKAqkYBKNdHPGblCS99JtwW0vVJzzlXC4T61JXWzEH9oFvTtudWyWhulZjl1LVRSims6qctZag1olMTj5HOZkJrCfmOFZHyGkQ9wFFVSPMsgJBDZV3FZQn/QrCcAnDSQJUFUJ+M+QTOyH65GamVbubExHSO5iX2eNvxFH4qt+b3qeN5xp5Em6gNBE/zx5PJ5qph6vdBDwLbWLwpx/NmZjxXZk5hB/GFpwwOjL1D5tPb+LR+J57MTobmCVDfP1I558pbCO83pn4Dr89ewaTN+jG8r+MTV7+xdNZJo1XB0ygLoDYec92L/nPHyfD7xJ84MvYaN2aO4XeZE/haPJ4nQOOWKBiQVO7Bgo3edXdpNlcKYovBvX2NUzbVLIti7hS1eE2fCJy995jIldkNtXEvgN3ZhGUBbWxUjQLQLYCg8CpeCyiHUgCN7VUAmawn3GyZ8+lbAq+W/dzlTXkZSuoYxbKAknEL2sJnlr7a9z7/ciEXUO7cyZiVpyxKsQCc2ayj5HyNsT0XkBME9hqVJ2O+h62hJsYk8SknxKZxzIw3qIk1szQ+DPa8jC9HHs03b/0idzIJJ00eyS6jB3D+A+9FloJQwVf9u23N2L6a77lDWrwjxzJi63341YwFbCdmc+GmnzJ+5fNcmH4dfvNnGL03YtyR9KOWPa33uTD+IMPFchbKgayYdxGwiRsDcAsButd37+vzeeitBcz81aH+e+Z+12oBVTprh1oANQkrL5G6hhQ3J/7AAbF3uSZ9Erdkj/Tud7AYnxD5q7N1VOmKw7YdFrmPbgFkXEtvxs8PoiZu8YfnPnevFwY35JoIFKuho0pp6IrnkkO3ity/V4npux3BCwJ3cqB5YO8aX7/lclJFCiBnzqUywQyOwusAdJQZHLViNUjOAsh6QlAP9DkzPYvWdJZ9fvNi5NiDbitwFjgpgbPUHsTV1gm0pHby3g8T7x1JA03GwxRAeC0c/z6FXUCJmCBt24xY8DjTk3+g4doV0LgJ37b2pUG0Yt10Gf+smUMzNXzU9wDuTe3J+sGTuGXvnZErmvH8Ci69knHqkv6Yik46Y7O6JeW7D+msTdaWkf1kwREyEov35Ba8tNlB/Gf493j7tRe4YMQn7L3yZRJPns9bNQCCmHCOO0IsZ+jbl3OUdQYr4kd7ijlYxC5vjG4MQFkAWTvcAgjWVqqnlb8mfstu1sdclj6De7MHeO/FYyK0FWIhC2DskAZmXX2Yz3K4+eQd+d5973h/twXXAVjCmxjlOmcJBvbWusgUob3pne2t8Nkegpl6nYVaV1EJVJZDqguxPRdQfiekTCCfOfhaf3aU5u5TV5ruVIJIz1XP2rbPBRS3BGtbovPClSDVUatbR1jLsQQMlcu4NnEb/Wb9i5ZUNnKhUakuIE8B2DI0CFaoxaUi4wavHReQpgDc2V3csjhSTGe/z3/FCGs5AolYs4ArEvdwQeIhaBwBx/yFg6zbeHjkJcywJhB3g+ZhE8mYJujCFFLGlp4CV/dhvWsR1CWjv0/dKknGLRJxiw/kGL795eFw3nusP+15mqjzhL/3uWwrv0zcwaS2N2jMruKe1+b5msqEkdZiAHF3AVlrJpv3HQiRW53Sh/X8PXk1u1if8OP0OT7hD07AN8zfXywlPeg2OnziMHbfPLdyOF0gBpBbB5Bb/VsKapylWthdmVXTFVlA4JTDKHXdSVdTNRaAEggJy8qbTet/+xfy5CsAtWK30IzRf+ycBaBmKxktCCyEI7iCGRY6qoaLTtjq1nqRYuyHv2OrV531dSrbQv/56nK7UBpobn8ZOgMKupqigsB2yDoALwYQE1wYf5Ck7a9XJAQslv0YetrjALQ98ax2rNwK0yBxreicbumofPl01vZceGr8c1Y4dX820/zUQX5y0FjueW0eQH5vAyHIDp5IAyFdsIAGWvjRssv5UQy+emoAAxNjmGFvzgy5OR/a+amQmaztZU4lLOH1kagJFEcTwvk3gDXck7yWLcQCvpf+Ic/YO+cd07JEaEZYRxYl6V9zKi8GoJVN1h6a9lgA39lzNGtb0/z4wLE89PaCdo+vK+jKtQblpmoUgCrilNBW4ap8fl2YLVzT4pYqzi3fX7auzdumlrQv1bbp5xjYu8Yn7Ba5WUOtmax3POccfhfQrAJNPlQ2jc7wiNWtjaml3muvWbybjrlkbVte3aPFa1oZ3FDjCTUpJYvXtuYylmT4bDtrSxavaWVInxqEcKyq3cYM4O15qzzBkMlqFoB2DGXe19EauUp3MKu9114hM10BhIxp7opmdh7V3x2f7Y1P0Zaxve9DKSx13zcfnN8qcOKIRiaP6k/f+iTf33cLbnrhCyelVbshS9a2kohZLJQDQ5ucLJQDeGDTn9M0+w22s2YxUczm0MSbuR3++GvYZEcOWDuQVWIwS1cO9VZPx2OCtS1p7nxlbt5xN+lbx1BWcnvyKoaLFXwnfQHT7O1C76VzP/K3hbmF2kMqYzNr2XpmLV3vfjfasd3XUuYWCZZCr5o4lx8xAYDjdhxB/16lWQJdSVemmpabqlAAS9a2eql5cTcNdMnaVna5+jn23HIgx+04wtv3kXe+YsX6FHedMdkTEq1pm3PvfYdbT51Ei5ux8th7C2lqy3Lbt51Uv/krmtnr+hc4ZOuh/OUUxw8/Z3mTly7Yksp6QvzDr9Zy6aMfAM6Dvnx9W16BOp2sHZxxS9ZRTyPNefuupQ4LGxuLG91AHMD7C9Zw9M0v+/Z9Y85KTrn9Dc6cMtp76F6fs5Jv3PqaN2tz4g/5s8eXZ63gmqc+5Zx9NueiQ8ZjS0lNwuLACUO85ukZO5fR4osBxCyY/zrHvXFm5DUvlANQ34pTNC7nZ4Zw4dWWzpWI+PeMRdz5ylyuOnprL1fmkXdyM0r1Xaxwvx9dUSjuPmMyfesd4dW71nlUetXEfbGTXa5+jiuOnMA7mRO5NnGbf7GZVcuvU1+nrnEHHsgORHXWbmQ9E63ZTBSzOa12BYNmv8TJ6xdzcg2k34sxk5HMqxlH6+rteb+tH01sQhbHejzKms5v+z9G4saFPGsJUsQ4JXUJb8nw3Pq9xjrlU8IstFIbnOvsMqY/r85ewdA+taSzNif+5VXvHup+e29C4b7ulYyV1MdY57cnRiu0h767mzep6ypGDXDWL6i40sZIVSgAPWc7HrM4Qkyn360/ZXbNVyycN5AFAy4AcvXZX3Jz/HW3ywufOTPrFi1N9NlPcrnVqp69XmhOF+qtIfVqILosrk5WOjPpzQf14tJDxzP7vh/RKJrJSIu40MxwBH1FMw8mr+TC9FnMlk4uvECEKpiFq50HSPdLL1jV4hu77cYfttmkDx9+lSszsHiNs98bc1Y6+7kzfb3wnHJZxKzcQrAkacZ9+Fv49Das2qH8MXMMZyWeohbtXlHD21v+wFMAlpULhHsNOUIUwDVf25aZS5xaREo4zNJKOy92Z/99auPejFjl59cGfMm68AenfMWIfnXsNXYQt/9vjm/fL5atZ6o9BdK4QfkVLJQDeHnkuUz9fDxPTxnFA2/lCt+uoTf/syfyPyYybNtt+Naum3HeLU/QOu9NJlqz2U7MYs/0dBoWPMPXaqBFJvlQjmK9rGUP62MS651JSFxIMjLGcLGcSZv2Y11rhs+W+GsxqRRJPZ13hFuOoSO+7fP225Jjtt+En0/9iCVrWz3hD7lS2ZCvoF+5eP+Cbs72oiy9ruT+s3ZlxpdrGNOOPsI9japQALr7ZN+2FzgrcRvJ9SkQbrbG+1dwlHWG8xBr6D5OFWwqtIIziFIgA3snac1kfd2iFGG+7CC2LclmJUkh2f/zX7J//EnuyBzMe/YYfhp/iOFiBdmG4fyq5QRWtaT4ReIunkxewm8yJ/K37KHuOoP8c6tic/qzujbQCMVZg2AzabP+PgWg6tOomjjKbaELhDZNAVhCMEHM5beJP7PZJ1/Cjqfy1IBz+d2/Z7O612jOydzLIHsZNI6gdv+fcfTEE73jxC3LXQznDy4GGdRQw+dLHQGo8u6l248AKb3vbnCfWm9G3JLKUJeI5X0PwXK/tYkYR0wc7l2PjkotnWpPYWoq9xs6utdwYCENtQn2GjsodPFg1pb8bfocps4B2Jn/uD78bQY1cOjwFj575yW2s2Yz0ZrF3tb7edddK9JcGH+Qy2uPZfuRffMUgCo+p77+TfvX89B3d3PvYfsVgGUJRg3sRSJm5ZW01gPpMc+l6PzdWJ+gsb787pz2MKyxjmGNdeUeRpey8Tq3NHTh/M2mu/KCp3G7lQvjD+Z9Ts/VVfVrgmWAw86h8CpA1sRDK1ZCaQ9h1paQbePSluvh3Xv4Q+ZYfpE5lcfsPZmSupExbfey6PS3eKX3fjxmT+HAtuuZZk/k/yXu5Z/JK+jfMje0lpCyZvQRBGdpWRsvl19Hze6VUFW563pbPqVAYzKL9b/f8K/k5QwQ65i5/+1w1B+hxvG7P5rZnYs2vQ+uWA0/+hA04e/cI2WJ5CyAqPumZp7q3PpVq+utTeTiO82prG/mqijk9w3ei1XN4TNbdW8sEZ3Vks7aXPl4fhM8y7JYW78pU+09uCpzCp8d9s/I8QwXK2ioTYSuIlfJCuq3uO+4QQzp49Q42pDslmRc0JLy/+b1+6juf+8uTNM0bDhVoQB0C2BgNryEw3CxIm/b6qbcg60UQJQFENbgQmUX9a6Js6o55TWE0SnlGbQyzXxv8eXsmX4ZDvol+5x9A8FVQDUJy+t8tIy+nJX+Meelvs9osZjT3z+FzT67DQv/GHMWQO5YwSqVMuB6UWNWBcpUUTTl6z93ny28/dIZm9FiEce+dwbihV/yH3tnDmr7NS2jnDRFtdhnbWumoJ9VNY7xB4HDb5yayasMJylz379acFYTj3nrGFpS2dBzJ+LRX0zw3GsiFvW0eApA+NoK6kSVhBBuEBhg/NAGvrXrZiyUA0P3XSgHMHZI71A/v8q4ymWd5adqdgS1dkVHVwCrXEuwf6/SM4AM3U9VKAD9IVsRC+8psJTGvG26BaAepJYOWAD7jx9McyrLy1/kK5liKyP70MTB757L1q1vc3PDebD7D9huZF/2Gz/Yt19N3AoUmhJMtXfnoLbrmN13NyZ8+BseTl7B5uIrbw91LbocCHMBqeDrLafsxFVHb03MEp57Z73Wy1hlAT7+gykIbHZZ9k+eTF5CY/N8OO52vp8+j9U05FYCux/I2pL6Amm1lnCqiqruYhCdw66E86uznXutp4Nm3FXNcStXmqEplQm1AAotRAq+pSyAwwOrZpWLJFgMT+fapz718uRfu2R/7Tpy96fGvTfXZU6kWfozamSijuW7XMQ5+2wRqgDUdai3SqljXwrxmMh7FvTcduUKbE8GkKH7qQ4FoPne/9X/zPyHCOjHOk6IvYhyGkgpfcWvVAwgrFQD5Ipn+c7rKoBdtcUzQQo9jwNYw/3JXzJ47Yfc1P8SXuyVKxsQrNFTm4iFVhpcRl/+NfbXvLHT9WwmFvNk8lLOjv2bGFnvWvSGN3kuIOlcR9wSHLz1UE7ZbRSWyCkAFQuwla8d2KbXWh6s+zXHL72R1+2t+Ndu/4Rtj/eOqZSpXlO/0JL+mCWwlQUQK2IBBDY3BxbExWPOzLqpLUPWduICYYvACrlHgude7U4UDt5mqG97i+YCKlQ6fNm6Nk7bfZTXcF6dI9cdy/l/qj2Fi9PfQTaOAAQ0jkQceSPbH36Wc48KKAD1XmcpgIRl5aUm64p0pWcBGAVQyVSHAtAWer3b7yAuTn+H5rph2FKwwB7IS2N+ytv2OK5P3MqNiZtooJmWdNZnASiXQKQFoLmAgu0B+/dKRja51h/ITfrmAk7HbwGP1f+SMWIR/xx7Hc9ZexR8eJMxiwER5rawBHOHHcpBbdfzvL09lyTu5+HkFQxpm+vukNs3WPEx66Zy+ro9CeF7+Pe87nnHBQTw3n3w593ZWn7OXQPO57T0haTq/cW6lH99kpbJUdQF5FoixWIAwe3BFdGqCNpHC9dy3v3v0pLKhlofhSyz4Pew1nWbBTNfmjUXULFU8v69kv5yyprS0K2RqfYU5A8/DI2XhOX6q34TWc8FVHgcpRLmItMVwEETHGW49SYd651r6B6qQgHowiphCabaU3hi//8ypu1epqRu5N1hJ/Kt9KVclz6Rw6zXeTJ5CW1zXvf5+9XkKqqZte4CUjNbtS1uWQwOyTMHv8BSQnC0WMR1ay9kRGIdp2Uu4aIZQ5mxYE1BBWBZgoEFao3btmQ5jZyTPp8LOJ+RYil/bfkR58SmEpO56wzmVq9vU2WMc9uC4/hyZQsN9ir+b+H/g3+dA0O24dvJ3/Ns/WFAftlhZQFsMbi3l5JYzAW0tiXtWABejZnwfaMydPRzq5n1Ex8sigwCFyLqe7AE/OKorbn7jMlALj5iFam8CW69nkB9fqXsgtZD1LXrVuGAXkn2GjuIbTZpdN9TY+wkF1DI9eguoJMmj+SzXx6y0WfR9HSqQgHo5Z7VbEpP8XQ6TMX4U/YYTkz9DCEkff9xJN9I/ROBfzYfFbTTFYCyEnIt5YTXDDuI/jwmYhZbiXk8mPwFpFvg2//mg9gE7/1YESES5UYRCG3cgpcSe3JQ23X8N7sjFyX+wS9XnA9LP/FWAeuomIDfAvAf/2DrDe5qPo8JTW/AgVfBaY+zJD7MuydBgalXelTuuUK1UWKW8Prbqq5qUYIsuDmoAOKBVoit6fAgcCEKBaC/vfso9txyIEL4s4CKZdwEF8tZQuvKFViwFWWd6BOdcUMbuPuMyZ7rcjfXDbnP2I731dYJKxNdr7nShBAV1/3KkE9VKAD9wVCzqWDnL5Vx8Y4cy2Ft17B+zKH8wL6XB+p+zSBWeceIqv2uxwCCyiJmCc8C2H5kXw7YKucS0R/6bbKf8I/kVaSJ0/Ktx2H49r4iYMWESKHApe4fjluCFTTyvfT5nJs6j4HZpXDLXrQ9fz3ptN8FpPzbYYW++tDEbxN/4pbkDSwWA/nd6Ntgj/PAimGJnHAPukbCissVahSif14pJP1WDG6oYX83KJ5nAbT5ryd4nraM3e6OT1EWgF4BsyZu+VxAqtduFJbwH1eVgoDSa/boP83gRGXHTfsx++rD2GVMdDyqPYTFNPrUVsWyoo2KqlAAegxACTJlAQjhWAj6Q7aWXnx1wJ+43D6LHcRMnu91KZPSTv2WTJijlUBzjICyiFsWg10LYHBDDZsPyq0sVApgivUBV639f6yUDZzQ9nMY6DSU1vPRiwXwdAUQnKHpgXBd6Txp78pFQ26F8YdT+79f8WjyZ4wT8733V3sWQO4zMSGYYn3A0zUXcbT1Cn/IfI3/S1zLsrrRuXNYuUbowXRDfaaoipQVUm66vzlsPG9cdgC3n7Zz3rVBrtqnIhmzfAXNMrZNop1F0aJq6Ohj0i2amCX45i6bFTxmsF6SJQQLVzurrUf2L82NoruAwtZ9bEjaZ5CwtFYT8O15VIcC0ISfenaVL1/gBDqDM8OMDf/I7std297F2vhArmm5ihX//DGW7c/5Vg9dOtAcQ/8/Zglv8U1wxmkJONh6k9sT17M4PpwTUz/nKwZ5gkwXaLqQDLNDlNCvTVhMHp0LsArhtwCCimR9rB+ccCdfHvAXhosV/Dt5Gd+PPUqcjCcsvfuTauJieRt/T15Ds6zla6lf8PvM8aSJB1wY+V2wos4PhYWTrjDWuEHqKDdIUAHkuYBiwnfvslpmUalEjVXfrpeWEMJ5b68C7hchhPsv95lRA5yJwtHbb1LSuC46dDxD3d9ZlKuyswizNoulNBsqj6qw2XyN0t2XaiGTJQRpW+aZ2emsTTorWdt7C/4w+s9M+PA3nPbh7dwrnuP74gfMkcPc/STJuPBZAGr2lfWCwMLrirSyKeXLCBIz7udPiRuYITfnpgHXsGy9EnD+cULpFoAdyNoRgXsQPI4QMHd5Ez+csSnz267jF4m7uCDxEAfH3uSJ7C58K/YcmzyzAqYPAiQn2Mu4PXMotye/xfJsDLK2U/VTu4WWyFlepaQeFrIAemk+etXQJYrgYYKCMG75LYB0VkYu0ooiytOmWwZ6h7GcMi9wTJE7Rsatq3TcjiPYd/zgksspD+lTy5++tSNf+9MrRTvIbSiF0loNPYeq+BbTmgtIPRieBSAct05QAKlAbjImsGO1XJE5je+kfsJwlvN48lKOj70ESE/wp0KayngWQEwwrNGZmS1b1+ZZAKfFnoZ/nUPriD2Qp/yLtkQuZU7JktZ0vvtK58aTduDdyw8E9EU/kuCkVheEQfdQ1pZc+M/3mfHlalbRh/PSP+Ds1PlsJhZzUfwBr1kLTUuhaTl3WMdxVeYUZKKOH7qlH9rSWf8qUyF8Ja+jyC1Qiv4p1mvB7Z8cNC5yP+c4RZRk3PK5SrK2bLcwK2UNgm61xEKsubzPqvRWrdaRk9nVvpW0ymVYrIf0hqL/hm47dRLv/ezALj2foWuoCgUQFrjNuYCcDJmgC0gt4knELE9pPGvvxCFt1/KBHMNvErfwh8TNpJrWAOExAK+9nWV5zb+XrGslaQl+EHuEKxJ3w/gj6HXaw+y05cjArN0Zj94PNsz10FATp5/re1U532G1e2yfAvB/7baUvqyouCV4xp7MelkfknIoOVROc/aLCa1Gkp3nw04HLICHvrsbL/10n7xrUOeMQlkA/Xsl2Xfc4Mj91HnDUKmeCcvvAkoHlP/Fh47nu3tvXvAckWmg2vaGWj0jRo0t+phKeZaiLAqRa+XZxRaAdjFDG2t9lVMNPYeqcAHpsyH1Sg8CZ7IyTyg2awpAf5aW0J+TU5dxbuwxzo8/jLhrXzjhDtLZ3EwtEwgCxyzBUNcCkFKyyxe/Z6vEP3k4uyfHnXAXxJyvIVhvx9k/d+5iWUDKjSWl9AspIQIWQEAB2P7MnNpEjPVtGYaKlaHnGSydxicJy/LKFKSytj8GYJFnAYSV8FUfKTRzV7PpUkRilH++b12C5lQ2T9FnA8q/mPAH8hZs6e09FQ1uz2ghcsK9kI/cCtyHjvrT1bWUGgMYP7SBCcPbv1hL75JVKIPLUNlUhQWgZwHlYgCaArDtPAGkLIBk3MqbTdlY3JQ9lq+nLkfaGfjbQey26J78NQPZXIZLbSKGhc3dA+5hq7l3c0fmYC5In+0Jf/ALkDABUGwdgD8GoB0L/4wwWOky65Z8Vij/dVTxsaVioHe+mni+r1u91stBR+EFmQvs09t1ARXqYZw7b/h2NUN1FLrfXVfsvuadQztJMuL6VUpkUFlEj9sfJ+howo6611HpykGePn8vfnfi9u0+j5451ZHWkobKoCq+OTUbOmTroV4RNSWcLCFIZewQC8DJHknGrNAyuwBvy3F8/rWnYNxhHLr4L9yduDawZsD2MkDItDFz+wfZq+lpPhn7XX6RORUZuP1hFkDU+2EWfjLuF8Bh9wDyl/FnbenLllELeMKKj5Go4/bkKc54NBdQ8JxCCK8ezBYh7RaDFLQA3Bo5+tqNKKJcJ6occyLk+2x3EFg7RyIiTVe5gIJKsdgxi1U7LUa8nQqgo+iz/rBFYYaeQXUoAHcmftPJO3j10VOaApi9rIlNA7nWzW4QOBEPL7KlWCN7w4l3c/+QC5hkzeSpmkvo/eXzznlV7ZpUE9x/EvFPp8JBv+KLrX9ImEOjuAUQ8qBpm+K+WVn7YgC6wFAWgCo+tsAeiHSLj3HkjbxYs49zjpjly3f3xwCc//v3SjKqyCIo51iFYgClWwBRi+GUyyIRSAOF9rdG9DW419sgaodRLiB89yT6POotVVn0vS9Xt2tMCrWqudS1Ax1FD5ybjKCeS1V8c3o+ftKr6+/MeFvSWeasaMrrAOULAheYTa1vzYAQvNT7MI5M/ZJlspEJz5+JvOd4zn7nKD6LnwS/HgWznnOaoOz+/cgZU67dYfi5dHdD6DoAfTYeUAAFYwBuy0mFLtSn2lOYkrqR6d/83Cs+pgRgQrufwXOqGW2vmlhBf7Y6ayE3jJ5SWYx+EV2n1Cw/HnABOe91/DHw9cHVrlNZAHrtKP02nDlltO84QeUQLMlRKoMbarn1lJ24+eQdO/T5UslqbtX2WlCGyqEqFIDqJCWEoK8rIFasT7nvSaSE4Y11nLxLri9wc0gWUBiq1EA6a/OFHMExqauYlt0GMeu/NKaWOLPCbApiSYirTkzht91rpB1xutqQkgX6o6crFr+7yD/Dz4sB2AQsgPwaLnpdFyWsahMx37FEyGy3VOFacCVwxAzzkkPH88BZu/q2CSF4/id75+2rUklH9su3RjYkiKkrQN0yUEF/HXVPDtl6KJcfMcH/Xgm3qdBCMp2Dth7a5Vk5qp80GAugJ1OWb04IcYIQ4iMhhC2EmNTV58tkc1kxSgEEm6THY4Krj92Wly/eD8gpgEIxAMitNHWyYKCNJGPE4vwdsyl47krAP1PXeeSdr/K2PXnenl7FzJoCBdPAL2x1YWTLQEXUgMBTjd8VYTNun6/ffdlYl8irX6NQLwvVJ9IpWOo64n6dvffmobVtxgzKjzmoGXljXSJPwW5Ya8Twe755yBi8AG/I5ZTi87/r9J2Ze+3h7R9kF7Cb1uPCGAA9l3Kp7g+BrwHTuuNkGTuX5qnaJi5f719Rqrs1wGkWDvlZI0GUAkhnbS++MFwsD995zQLfOYL0CqlKOWF4H6+2elRPAUUiIiAbdPEEZ2zZYAwgpIpjjaYUlHunT108MnDtWQAFWitCaVlAhfrzFuOArQZz9PbDOX33UQzpU8MRE4flWXQbMoMNW/ELMEar9xR8P8wlVkraZyWVWthx0348c/5enDlldGS/Y0PlUxYFIKX8REr5WXedT7U0BGfG1jukbLLXpjCmYgTKBSQiMyqSMcvroJXOSq+zVFT6JI0jfOcIogqa5Y8/V1KiEJEuIPxB4OBh8i2AIi4g9wB9aqMtADXLLTVFsJAFEGUxlcJt396ZP3xjB7Yc0sDrlx7AyP71eRbAhnTJ0st868epT8ZpCPzO1H1Te+l++mCBuXu/s0uHx9RdjBvawOVHTKgoxWRoH1XhvItZwif0+/XKn7Goh1f5g1UpiHiIC+iGr2/P7KsPo6E27lWbTGdtbzYYlT7J/j9zXkb4nFW9oCC5khJFLAC9Zr/ltwD0IHCwTEDQQqgp4gJSq5771CV8Aj4s5bHU2XshRdERC+D2b0/i0XN3D31vSKA5T3vTGKUWgh/cJ6cAgnrktUv3Z9pP9817X92bwycOi1wlHExKMBi6gi5bCSyEeBYYGvLWZVLKx9pxnLOAswA23XTTInuHc/kRE3xBtz61CaDFt0/ccwE5wkZfxJTvMhBYlqChNp6LAWRyLqCp9hRIw4XxBxkuVmD1HeEIf7d9X5RfPGommisrXVhQ6UI/uGZAtwCaA32N81xAoRZAbsxNbpewPgViAOp18QBrLkMrivbW6wfYf6shke9ddcw2LFnb5jWO35CFTMMadQXgv4ZeNXFfk56wonAC5w4EZ9Emt97QHXSZApBSHtBJx7kVuBVg0qRJnbK6JaxzVtxzATkPXptrAcSEiAwa9q6Ns641zXtfrmbuiibGDmnw9plqT2Fqagrn7bcFPw4UMIsSilGBQM8CELpQD78VgxpqOGvPMSxvygW5VUN1RbCvsR3IAgqbcesBaNVnt09t3HctYesASg0CF1IUGxIDCKOhNsGpu22mKYCOC9tttJ63xVxJYSUhhBAgZZ4FUOp9Mxg2hKr8lQV9s5ATAur/QhaAyllvqEmwrjXDMTe/TGvaDp05D++bvyCnoxZAKb7qNy87gP/ba4xfWYDPxaMrgKF9askGYgBhAlG3AFRue10i5rcAQqyBUmeyXRUDiEKvMNrRIHB9MsZ+43OWRrHvR70tAhaA817QAqjKR9PQzZQrDfRYIcQCYDfgCSHEM915/lALQCvCFbf8dWyCQWC1r+4CgnDXSVhxsqjc+KIuIE2Y/vzICey55UB2jWjxpwtxW0qymt+/NZ3l4XN248JDxrHv+EFkpSzYMCZ4PFU3KBG3fNvD2kYWE2RdnQUUhZ5x1VELYBet6Q4UT+UMa/KjCN7zDQlMGwylUq4soEellCOklDVSyiFSyoO78/y9Q3qX+mubWLQqF5CVvzAr5imABOtacz1n60KCp2HtA6NSI6PkR5gFsMXgBu45c5fIZuq64pHSsQDUppZUlp0268+5+2yBEAIZCBKHCR/dbaH2rYn5G6zrM2m1udTZdSHh2ZEYQDH0ev2dVc2y2IrlsEJvegcwg6G7qUo7MyzfXg8E1iYsmlw/tyUEPzpwbGDfcAugLkQYh8+mIyyAyBhAaWmgUcdSK4E37e+sgj1p8qa+/TLuauhz99mcGT8/qOjsUymkZNzyXUvCFw9onwuoO2MA4JSoULR3tr3FICfWc+g2w3zbw9KLdbwYgLZ+W73uaPE3g2FDqEoFsGydEyAd3hiew12XiNGUynrbd9t8AD/TsojUvjUJizZfGeXSFECUUIwSRIds4yRTjR9aemqgXuBMrQRurEsw99rDOUOrQxOzhNfPuC4Ro7EuUdRtk9EUgM8CCEkJLVV4d3YWUDH0dQ3t9bdvOqCez355CCfuPNK3vVg+vOcC0k8XSA01GLqTqlQAaVeA6Y0wdCFQm4x5s1z1YB60dS7Yp2aryZgVqKNfqgIoXAsoyLE7jOCzXx7CqIH5q0uj8KpRAve8No+mtkx4M3ahNW/XFsuVQrJADEDJs1LdKwVLJXeBPzwWMe5SqQlZLV2MXBBYtwD87xkM3UlVKoCfHzGBK46cwI6b9fO2BS2A4PawfPe45V8kVhfiWioU8MsL/BUQgu0VOIN6+xeivTVvVaggjVm5MstKEJY6a0/GLJ+loQt7r7RGsSBwSWfqfPyrprvnMQiuBNYxq2kN5aAqFcDgPrWctsdon8CNF1EAuiD3hFsgmBtWQydM6NbELQ7fdhh3nT656L4dJayZeJQFEHy/1LTLRCxgAYSURi6mANTq50LKryvwB6+759y5Vb/5lpLJ+jGUg6roCRyF/iDqQkCfyYel7kWVOg7LAgmTf0IIbv5mfr32zvQDl6wAQiybmhItgJpADCAR4gIqFgS+8/TJTPt8mdfYvhDjhzYU3adUdMW0IQvBAC49bHzo/Q4SvhI4f5vB0F1UtwKICGCGWwDkbQsKt7Bqne0R6p0pBAaF1BUKa7oSC1GC7YsBhJdDVoKtmHtlaGMtJ04aWXAfgDcu279olk17iEpf7Qhn7VW8kTzk7rUIsQCMC8hQDqpbAWjPXJQFEOzVqr8OCo4w10l7/Mud6QboVRPngysOYtsr/pM7fsjhwyyA9igAfcj6rFotLOusDJ7BDfkNVjaEqOB1V1J4JbDz/xPnTfG60RkMXU2VK4DiMQAlv/2C0vk/GCwN83e3J77Y2bNAPRMIilsAXsnsdgSBRYQbrdQCduUiatzdcU7fOoCAi3Hr4Y3dMhaDAao0CKywItwAtUWCwFGVLsNcQN0d3CxEmFwPzdwpcdYetIB0a0cpgJ5Q06bbsoA8F1D+eyYIbCgHlf90diE+F5D2h64A1EMbNlMOCrcwYVdJD3aYoNOvW11jR1feJkIsgJ5Q1ri7XUB6aRF15gqaJxiqiKpWAGFCHQJC0cqftUWVOQh3AVXOkx02Fn0W394gcBD9Hqrqoz3CAugmJRX6WwhJDTUYuovKfzq7kKgYgM8tUiAInG8B5D/EFeUCChlKPCQIXKz38CWHjvfVwVf4gsDGBZRH2E8hFwSunN+JoXqo6iCwLxsjQhlYITGAqCygMNdJJbmAwoLAYTGAYhbA2Xtvztl756c+6sfKhJSwrlS6zQJwf0N6S8lcELhbhmAw+Kj86VkXEiWcwwRlWAnfPBdQWBC4gp7ssMl4IiSPPzhrP27HESUdX59Je2mgPcIC6N4YgI5ZB2AoJ1VtASiBVx+o4RPmytEf0JgIF5SVHgQOTQMNCwJrimzGzw8KLZ8dhn7fVOP5DV1k1R10l/Dt7oJ3BkMxqloBqB6+wSbpxR7G9sQAKsm3W2oaaB+tYU5jXSLvM9HHDwsCV871lxulaPQsoLDyEAZDd1H507MuRCmAIyb6G3sUcwmouEBwv2TM4rHv7cH39s35xytpZhcWkA4r5RBcQFYqPTUI3F0oS6pvfe7+miCwoZxUtQUQswTvXn4g9TWxwPbCQivMVQKOsNtuZAOjBvTi5hdm+fatBMJKL4dZAO0lEXN6CuhCLGMUQB7H7TSCYX3r2HHTvt420xLSUE6qWgEAoVUoi2WF5PoBhAeBa7SqoLEyu0B2HdOf12avBMhrbg/+a9BjIU/9cE/WtqTz9g/j1lMnccN/Z/pmtioI3BOygLqLRMxi77GDAlvzs8wMhu6i6hVAGMVdQM7/wQYw6nN6Hn25LYB/nLUbd7w8h1/8+2NPKOvoQdphjXXe662Gld5+ct9xg9l33GDfNm8lcDfl2Pd0KslVaKgezNMZQtEgsCvU6xN+/RmW0lcJ8k+5YcIsAL2Gf1gJ6Y6izmUEW2HMxN9QTipAPFUexfzWSqgFLYCwto3ltgAgl4uvtS/2CFvh3BkYBVAa6u6EGGcGQ5djFEAIxYSWCAkC33n6zqEpk5UgANUYirmAOhOVBloJ11/JqPmBLFt3ZEM1YxRACB1ZGZof3HOoBAGoxlAsCNyZGAugNFRvAGMBGMqBUQAhdERoRa0mrQQBqNYtZEMtgC5WABXgAotil9H9vab05aImpI+0wdBdmCygEDojd33fcYN44bNl3VZpshBKCNuhFoAzvs6W0/17JVmwqqXTWkJ2BQ+cvVu5h8Adp+3MI+98xbDGzm15aTCUglEAIXTGrP3P39qJBauaK0IAKn0W6gJyLYDakAD2hvDXUyfx3CdLGWoEW0HGDOrNBQePK/cwDFVK+aVTBdIZfvHaRIwtBjd0wmg2HLXIKCwIrPL0O9sVMaRPLSfvsmmnHtNgMHQuxgIIoT2ZMYdPHMbK9alOO3dN3Or08gmFgsDKQ9XZFoDBYKh8yqIAhBDXA0cCKWAWcLqUcnU5xhJGeyyAm0/esVPP/f4VB3mZIZ1FLgic/17a3WiCkQZD9VGup/6/wDZSyonATOCSMo0jlFhISYfuoiYe6/S4QaEg8JA+ThbMT40f2mCoOspiAUgp/6P9+RpwfDnGEYWyAPq0oxZ+JTNmUC8ADt5maN579ck4c689vLuHZDAYKoBKiAGcATxQ7kHoqImy3hilJzOiXz2fXHkItcbNYzAYNLpMwgkhngXyp5xwmZTyMXefy4AMcG+B45wFnAWw6abdk1WSdovmjB7Yq1vO1x0E6xYZDAZDlykAKeUBhd4XQnwbOALYX8rohfBSyluBWwEmTZrULQvmtx7ehyuOnMCxO5TWDN1gMBh6IuXKAjoEuAjYW0rZXI4xFEIIwWl7jC73MAwGg6FLKZdT+CagAfivEOI9IcRfyjQOg8FgqFrKlQW0RTnOazAYDIYcJi3EYDAYqhSjAAwGg6FKMQrAYDAYqhSjAAwGg6FK2TiWunYTM35+UGg9HYPBYOiJGAXQDsKavhsMBkNPxbiADAaDoUoxCsBgMBiqFKMADAaDoUoxCsBgMBiqFKMADAaDoUoxCsBgMBiqFKMADAaDoUoRBXqxVBxCiGXAvA5+fCCwvBOHUy7MdVQW5joqC3Md4WwmpRwU3NijFMCGIIR4S0o5qdzj2FDMdVQW5joqC3Md7cO4gAwGg6FKMQrAYDAYqpRqUgC3lnsAnYS5jsrCXEdlYa6jHVRNDMBgMBgMfqrJAjAYDAaDhlEABkMVIIQQ5R5DZ2Cuo3PZ6BRApdzYDcVcR2WxEVzHxtLMwlxHJ7JRKAAhxNZCiH0AZA8OapjrqCw2husQQuwmhHgI+I0QYoIQIlbuMXUEcx1dNJ4e+rsGQAhhATcB+wHzgdeBx6SUbwkhLCmlXdYBloi5jspiI7qOwcBTONcyEtgEeEtK+VchhOgpSs1cR9fR0y2AfkADsBXwTWAF8BMhRO+e8pC6NLJxXMfG8n30BXrT869jO2CmlPIO4LfAI8DRQoixUkrZg9xa27BxXEfFfR89TgEIIY4XQpzr/tkH2A2ol1IuAx4GVgLfc/et2B+GEOJrQojfu38OoOdex45CiLHun43A7vTM6xgthKh1/+xPD7wOIcRJQohfCCGOcje9C0wSQmwupWwC3gTeAs6GynVrCSH2FkLsom2agXMdY3rYdRwjhLhUCHG4u+k9Kuz76DEKQAjRWwjxMHABsEoIEZdSzgFeBs53d1uEo1V3EEIMr8Qfhuv3uw+4HPihO84vgFfpWdcxWgjxBHAzcI8Q4kAp5WzgFXrWdYwSQjwF3AbcK4SY4H4f04Afu7tV9HUIh+8CFwJzgeuFEN8B1gN3Az90d10NPAvUCyGGlWGoBRFCNAghHgEeBc4WQvQDkFKuAB4AznN3XU1lX8cgIcS/cH4/K4E7hBDHa5OJH7i7rqbM11HRCiAw0xoJLJFS7iqlvB/IutvvBPZwZwcZYAnQCtR162ALoK5DCLEX8FfgNSnlDsAfADXTuR3nOkZX+nW4XAC8J6XcDXgMOMPd/jd63nW8LqXcH3gB+IUQYgLO72rXSv5dKVyFtBtwrete+B6wD7A/8CSwhRDiANd9tQLH97ymTMMtRAp4HvgWsBA4QXvvYWC8EGL/HnAdmwMvSyn3klL+BfgJ8CP3vfupoOuoaAUA1GqvJwIjAFwX0M+FEFOAj3Fmnb8BkFJ+CGwGtHXvUAuihMbHwEFSyhuFEElgC0D5lGcA7wDXQcVeRy14ArQJSLvb+wCfCCG2wLHI3qSyvw91HXH3748ApJQ3AZOBb+AIoDep0O9DCHGq6yrp7276BNjEtYyfBT4EdgWWAfcBN7jfz/6AAJLlGHcQ7Tr6SinbcCyxZ4GZOO6Sce6uM4B/UNnXsY8Qoh54G8fyQjhZPh+7/wA+wLmOP1TCdVSkAhBCHCiE+C9wnRDiJHfzO8AiIcTfcGY7q4HLgKOB3wGDhRA3CSE+xOkZsKbcvtrAdXxDSrlcStkkhKiVUqZwfgzfBJBSrgKuBEYIIf5YoddxvRDiRHfGOR3YUgjxLnAIEMMRNHsD1wDDKvj7UNeRwTHRdxBCbCeE2A5HcI7GuZ5fUkHfh+vqGSaEeAH4Ns5v549CiD7Al8BgnEkFOEJmG2CAlPLvwL3AxTjK7UIp5eruHr8i4jpuFkIMlFK2us/Gq8BS4EQAKaUtpbwTuAe4hMq8jpNxLPxGKeUSIURMSpnFSSZoBN913E0lfB9Syor6h/MDfh1HsO+A88P9CRDHiZy/DSTcfU8BbnVfD8YJ3B1V7muIuI6/A5e676nx7+1uH6R9blCFX8d9wAXue+OAR7R9Lwducl8PqfDruB84Fydr6XLgcRylNsm9xvMr6TqAmPv/WODv7us48CfgLpyFRX9zn4lG9/07gV9px0hWwPcQdR1/BB4O7Huse31bAL0Aq4dcxyOBfe4GTnRfD62k70OZwGVFOHnXSMcntgvwtpTyMfe953EE/+04vuYdcHyD9+GYhccJJzd7Kc6MoWyUcB2/E0Lc5o4VnIe2EViljiGdQNGybh14gCLX8SzOddyDM3v+UgixlZTyExwf+vlCCCGlXILjNy8bJVzHb4GHpJRXub7+2e57L5Nz9Sx1r6UsuG6qK4GYEOJJHHdbFkBKmRFCfB8nSD0B55k4BsdVeg2Oe/EVdSzpzKzLQgnXcR6wUAixt5TyJXf7o0KIrYCncdJy9wU+6WnXgROMnyOEuBL4mhDiECnlgnJeh6LsLiAhxOnAAuAqd9MHwElCiFHu33FgDnCdlHIacANOTvZFOGbudPc45XYvFLuOBDAL1zcOIB1f7SScGWZFUOJ1zHbfX4eTMnmeEOKHwC04/tuyU+LvahagUnHnuJ87CzgTx+WIdKdq5UAIsTeOxdsP+ALnWtLAvkKIye74bByB9Gv393QrMEUI8br7uRfLMHQfJV6HxLmOK7TPnYDj5n0BmOhOMspGR67DjQGcAfwTR1nsK6Vc0O2Dj6LMZlRv4F84aWrvAOPd7TfgmOgv47hItsXJZhjqvr8zTu7sbuU2oTpwHU9o15EAzgJGlfsaOnAdT+GY5VvhpLXdBexa7mvo4PcxxH3/fJzA787lvgZ3PHsCp2h//wk4BzgNx5oBZxI3FHhI/Y5wFrJtUu7xd/A6HgRGa5/bs9zj34Dr2AwnI+gGYMdyjz/0mso+ANjU/f9a4AH3dQxnZjnF/Xskjj+zttzj7YTruAOoKfd4O+E67qICfJid9Luqcf+uL/e4A9dQD9SQ8yV/E7jGff0e8AP39STg/nKP11yH7zr+Ue7xlvKv7C4gKeV89+UNwGghxMHSiZyvkVJOd9/7LtBMLu2w4mjHdbQAmTIMsSTacR1N5NZiVBzt/F1l3M80d/tACyClbJZStrnjBjiQXHzodGArIcTjOFbNO+UYYylU6XW8DeV3TRel3BoooGHPBl7S/p6ME/j13D894Z+5jsr619OvA8dysXDcblu427bAcfVMoYLcPeY6es51SCkrpxqom8ljCyH+iZPV0IYTUPxcSjmrvKMrHXMdlcXGcB3uLDKJs0jqUZyg4gocl8Paco6tPZjrqDwqIg0UnGwG4ayiG4yzjP1KKeXT5R1V+zHXUVlsDNchpZRCiB1wfM6jgTuklLeXeVjtxlxH5VExCsDlXBwf4IHSWRbeUzHXUVlsDNexACcl8nc9+BrAXEdFUTEuIMiZ6+Uex4ZirqOy2Fiuw2DobCpKARgMBoOh+yh7GqjBYDAYyoNRAAaDwVClGAVgMBgMVYpRAAaDwVClGAVgMEQghOgrnO5zCCGGu4vJDIaNBpMFZDBE4JaOflxKuU25x2IwdAWVthDMYKgkrgU2F0K8B3wObCWl3EYIcRpO45UYTtvF3+KUBjgFp9TEYVLKlUKIzYGbcbq8NQP/J6X8tLsvwmCIwriADIZoLgZmSSm3B34aeG8bnB6wk4FfAc1Syh1wetme6u5zK059mJ2AC3DqxxsMFYOxAAyGjvGClHIdsE4IsQb4t7v9A2CiEKI3Tqe3h7SKwDXdP0yDIRqjAAyGjqHXf7G1v22c58oCVrvWg8FQkRgXkMEQzTqgoSMfdMsCz3H72iIctuvMwRkMG4pRAAZDBFLKFcDLQogPges7cIhvAmcKIWYAHwFHd+b4DIYNxaSBGgwGQ5ViLACDwWCoUowCMBgMhirFKACDwWCoUowCMBgMhirFKACDwWCoUowCMBgMhirFKACDwWCoUowCMBgMhirl/wNbYB0gE8DvBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_anom.sst.sel(lon=300, lat=50).plot()\n",
    "ds_anom_resample.sst.sel(lon=300, lat=50).plot(marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f8e4dea2730>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ds_anom_resample.sel(time='2015-01-01', method='nearest') -\n",
    " ds_anom_resample.sel(time='1965-01-01', method='nearest')).sst.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rolling\n",
    "\n",
    "Rolling is also similar to pandas.\n",
    "It does not change the length of the arrays.\n",
    "Instead, it allows a moving window to be applied to the data at each point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "}\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-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:  (lat: 89, lon: 180, time: 708)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan</pre><div class='xr-wrap' hidden><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-403b389f-74c8-4d3a-9ea8-024057de50f9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-403b389f-74c8-4d3a-9ea8-024057de50f9' 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'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 708</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-93fbeb3c-56ed-492b-832f-9253787bafb6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-93fbeb3c-56ed-492b-832f-9253787bafb6' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-b876a705-5289-453c-b0a2-29336dab8a1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b876a705-5289-453c-b0a2-29336dab8a1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d81c7067-9c84-45f4-abaf-bdb2871119dc' class='xr-var-data-in' type='checkbox'><label for='data-d81c7067-9c84-45f4-abaf-bdb2871119dc' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], 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'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-45ddfccf-4562-4d71-ae94-6bf7f016e12d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45ddfccf-4562-4d71-ae94-6bf7f016e12d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5136aa21-f70a-4e5f-8b40-1c4b1ee8f82d' class='xr-var-data-in' type='checkbox'><label for='data-5136aa21-f70a-4e5f-8b40-1c4b1ee8f82d' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\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'>1960-01-01 ... 2018-12-01</div><input id='attrs-63cdea09-c959-4da1-a682-acbf8c9000fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63cdea09-c959-4da1-a682-acbf8c9000fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4955554a-9dd0-4eec-b9c5-035a2ad54f22' class='xr-var-data-in' type='checkbox'><label for='data-4955554a-9dd0-4eec-b9c5-035a2ad54f22' title='Show/Hide data repr'><svg 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>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80992.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12</div><input id='attrs-1d60d4ce-b207-49ab-864b-56bcc40bde13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d60d4ce-b207-49ab-864b-56bcc40bde13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d12bdf2-6d5e-4a9a-9bef-7a9b407614bc' class='xr-var-data-in' type='checkbox'><label for='data-0d12bdf2-6d5e-4a9a-9bef-7a9b407614bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "...\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e2557b5a-3b56-40a7-a20e-7a7216754be0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e2557b5a-3b56-40a7-a20e-7a7216754be0' 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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-5f20e14d-0867-4af2-b023-363150c9fd8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f20e14d-0867-4af2-b023-363150c9fd8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6987c3a6-3515-4311-8e70-21b0bd769b6d' class='xr-var-data-in' type='checkbox'><label for='data-6987c3a6-3515-4311-8e70-21b0bd769b6d' title='Show/Hide data 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, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d124ed42-8dc6-4a18-aebb-76e60e443770' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d124ed42-8dc6-4a18-aebb-76e60e443770' 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.Dataset>\n",
       "Dimensions:  (lat: 89, lon: 180, time: 708)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_anom_rolling = ds_anom.rolling(time=12, center=True).mean()\n",
    "ds_anom_rolling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8e4de40820>"
      ]
     },
     "execution_count": 36,
     "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": [
    "ds_anom.sst.sel(lon=300, lat=50).plot(label='monthly anom')\n",
    "ds_anom_resample.sst.sel(lon=300, lat=50).plot(marker='o', label='5 year resample')\n",
    "ds_anom_rolling.sst.sel(lon=300, lat=50).plot(label='12 month rolling mean', color='k')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coarsen\n",
    "\n",
    "`coarsen` is a simple way to reduce the size of your data along one or more axes.\n",
    "It is very similar to `resample` when operating on time dimensions; the key difference is that `coarsen` only operates on fixed blocks of data, irrespective of the coordinate values, while `resample` actually looks at the coordinates to figure out, e.g. what month a particular data point is in. \n",
    "\n",
    "For regularly-spaced monthly data beginning in January, the following should be equivalent to annual resampling.\n",
    "However, results would different for irregularly-spaced data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 59, lat: 89, lon: 180)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-06-16T08:00:00 ... 2018-06-16T12:00:00\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan\n",
       "Attributes: (12/38)\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science &gt; Oceans &gt; Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    ...                              ...\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2021-11-07\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</pre><div class='xr-wrap' hidden><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-dac44cbd-388c-4fdc-8424-66391b8018a4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dac44cbd-388c-4fdc-8424-66391b8018a4' 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>: 59</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bc88f2ad-bd73-408f-a73f-cfa9b0c34b47' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bc88f2ad-bd73-408f-a73f-cfa9b0c34b47' 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'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-253d8a86-5979-4abc-9d49-d3537b3f647d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-253d8a86-5979-4abc-9d49-d3537b3f647d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53d58440-318d-4182-b29c-3bdc60ee10b0' class='xr-var-data-in' type='checkbox'><label for='data-53d58440-318d-4182-b29c-3bdc60ee10b0' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], 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'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-3edb53f7-7c8c-4ce6-8bdb-cba1656da64d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3edb53f7-7c8c-4ce6-8bdb-cba1656da64d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec0273d3-0fbc-4bc4-b262-131f8564ed65' class='xr-var-data-in' type='checkbox'><label for='data-ec0273d3-0fbc-4bc4-b262-131f8564ed65' title='Show/Hide data repr'><svg 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>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\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'>1960-06-16T08:00:00 ... 2018-06-...</div><input id='attrs-0d1fffe6-db57-46f6-99df-fb97fa9aabfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d1fffe6-db57-46f6-99df-fb97fa9aabfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-370a73b9-ab4f-4276-9bad-67a351be4144' class='xr-var-data-in' type='checkbox'><label for='data-370a73b9-ab4f-4276-9bad-67a351be4144' title='Show/Hide data repr'><svg 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>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80992.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-06-16T08:00:00.000000000&#x27;, &#x27;1961-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1962-06-16T12:00:00.000000000&#x27;, &#x27;1963-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1964-06-16T08:00:00.000000000&#x27;, &#x27;1965-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1966-06-16T12:00:00.000000000&#x27;, &#x27;1967-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1968-06-16T08:00:00.000000000&#x27;, &#x27;1969-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1970-06-16T12:00:00.000000000&#x27;, &#x27;1971-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1972-06-16T08:00:00.000000000&#x27;, &#x27;1973-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1974-06-16T12:00:00.000000000&#x27;, &#x27;1975-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1976-06-16T08:00:00.000000000&#x27;, &#x27;1977-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1978-06-16T12:00:00.000000000&#x27;, &#x27;1979-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1980-06-16T08:00:00.000000000&#x27;, &#x27;1981-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1982-06-16T12:00:00.000000000&#x27;, &#x27;1983-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1984-06-16T08:00:00.000000000&#x27;, &#x27;1985-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1986-06-16T12:00:00.000000000&#x27;, &#x27;1987-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1988-06-16T08:00:00.000000000&#x27;, &#x27;1989-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1990-06-16T12:00:00.000000000&#x27;, &#x27;1991-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1992-06-16T08:00:00.000000000&#x27;, &#x27;1993-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1994-06-16T12:00:00.000000000&#x27;, &#x27;1995-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1996-06-16T08:00:00.000000000&#x27;, &#x27;1997-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1998-06-16T12:00:00.000000000&#x27;, &#x27;1999-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2000-06-16T08:00:00.000000000&#x27;, &#x27;2001-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2002-06-16T12:00:00.000000000&#x27;, &#x27;2003-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2004-06-16T08:00:00.000000000&#x27;, &#x27;2005-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2006-06-16T12:00:00.000000000&#x27;, &#x27;2007-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2008-06-16T08:00:00.000000000&#x27;, &#x27;2009-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2010-06-16T12:00:00.000000000&#x27;, &#x27;2011-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2012-06-16T08:00:00.000000000&#x27;, &#x27;2013-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2014-06-16T12:00:00.000000000&#x27;, &#x27;2015-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-16T08:00:00.000000000&#x27;, &#x27;2017-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2018-06-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c82ab2a6-8176-4365-acfa-d70de1ae1566' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c82ab2a6-8176-4365-acfa-d70de1ae1566' 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'>-1.8 -1.8 -1.8 -1.8 ... nan nan nan</div><input id='attrs-510e3fe4-84e0-4d92-a44a-3c4822bee69a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-510e3fe4-84e0-4d92-a44a-3c4822bee69a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27d2b8e0-9523-48e8-bf39-9bc9eb6f014e' class='xr-var-data-in' type='checkbox'><label for='data-27d2b8e0-9523-48e8-bf39-9bc9eb6f014e' title='Show/Hide data repr'><svg 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 Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</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>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  89 180]</dd></dl></div><div class='xr-var-data'><pre>array([[[-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87a6e8ca-a090-4143-b1b6-0f22e326047c' class='xr-section-summary-in' type='checkbox'  ><label for='section-87a6e8ca-a090-4143-b1b6-0f22e326047c' class='xr-section-summary' >Attributes: <span>(38)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.</dd><dt><span>description :</span></dt><dd>In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature &gt;</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>source_comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>geospatial_lon_min :</span></dt><dd>-1.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.0</dd><dt><span>geospatial_laty_max :</span></dt><dd>89.0</dd><dt><span>geospatial_laty_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>project :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST)</dd><dt><span>original_publisher_url :</span></dt><dd>http://www.ncdc.noaa.gov</dd><dt><span>References :</span></dt><dd>https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 at NCEI and http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html</dd><dt><span>source :</span></dt><dd>In situ data: ICOADS R3.0 before 2015, NCEP in situ GTS from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015</dd><dt><span>title :</span></dt><dd>NOAA ERSSTv5 (in situ only)</dd><dt><span>history :</span></dt><dd>created 07/2017 by PSD data using NCEI&#x27;s ERSST V5 NetCDF values</dd><dt><span>institution :</span></dt><dd>This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Centers for Environmental Information and time aggregated. Original Full Source: NOAA/NESDIS/NCEI/CCOG</dd><dt><span>citation :</span></dt><dd>Huang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1</dd><dt><span>platform :</span></dt><dd>Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>metadata_link :</span></dt><dd>:metadata_link = https://doi.org/10.7289/V5T72FNM (original format)</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang (original)</dd><dt><span>date_created :</span></dt><dd>2017-06-30T12:18:00Z (original)</dd><dt><span>product_version :</span></dt><dd>Version 5</dd><dt><span>creator_url_original :</span></dt><dd>https://www.ncei.noaa.gov</dd><dt><span>license :</span></dt><dd>No constraints on data access or use</dd><dt><span>comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters</dd><dt><span>summary :</span></dt><dd>ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis</dd><dt><span>dataset_title :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>data_modified :</span></dt><dd>2021-11-07</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 59, lat: 89, lon: 180)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-06-16T08:00:00 ... 2018-06-16T12:00:00\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan\n",
       "Attributes: (12/38)\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science > Oceans > Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    ...                              ...\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2021-11-07\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.coarsen(time=12).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coarsen also works on spatial coordinates (or any coordiantes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f8e4ddc6880>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qIj4A/gz0BDYg64FNny+syjwkwMzMzKzkioxhzRPWWyKi2QnqJS1ElqzeGBG3A0TEGxWvXwHc09b2tjcnrGZmZmYlFhRcOKCV1yUJuBKYEBEXV+zvnI9vBdgNGFuwqVXjhNXMzMys1MScQosBtBq7BfAz4DlJo/N9JwJ7S9qALOedCqQv8VllKrJUXZlJqo83YmZmZmUyMiL61qpySZ06r/212QfcvFXyMbcf+zTjh7zaOyImV69lHauueljXPubi1oNy4y4cQJ/j0+PHnzeANU9Oj5941gC23rbAWsvDi6/T/a2fpo+FfvLGgfTvdWxy/JDJFySvu13GNcDnt/j+3zg0OX7IG3+m3xL7JscP/fi6QmuSb//1/5dc9v3v/oX+3Y5Kjh8y7RL69z4uPX7S+Wy//inp7Xn2TPpt9Jvk+KGjzqBf39PS40ecxrabn5kcP/yxU/ju9uclxz98//Fstmf69ImPDz46eQ32EdcMZON9C6zvft3A0v2e9FvkJ8nxQ2feRL/Ff5Ye/8n1bLfQ3snxwz67mf6rH5McP+SFC9l+6Z8nx9//wdX073l0Wtkv/rbwv+0av0n/e/j8GQPoc1yBv7fnDyj893ydgenxYy8awPpHpMc/+8cBybHVVGSlq4Qe1rkmaXFgILBqRBwoqRewRkRUZdyrZwkwMzMzK7FsloAiK111iKuBmcBm+fPpQLM3ec0tJ6xmZmZmZRZZD2vq1trCAe2kZ0RcAHwGEBH/pYpdu3U1JMDMzMys3gQwp0AfYwf1sH4qabGG6iT1JOtxrQonrGZmZmYlFojP5hRIWAuNd22zU4EhQFdJN5LNPLB/tSpzwmpmZmZWcm1YOKCqImKYpFHApmRDAY6MiLerVZ/HsJqZmZmVWEDBpVmrT9JuwKyIuDefGWCWpF2rVZ8TVjMzM7OSK3TTVccMYj01It5veBIR75ENE6iKUiSskhaQ9Iyke/Lny0oaJmly/vPrtW6jmZmZWS1EiDnRKXnriHlYaTqHrNpQ01IkrMCRwISK58cDD0REL+CB/LmZmZnZfGlOvjxrytZBswSMkHSxpJ6SVpP0f8DIalVW84RVUhdgR+AvFbt3Aa7NH18L7NrBzTIzMzMrhQBmh5K3DpqH9ZfAp8AtwN+A/wGHV6syFVnarioNkG4FzgWWAo6OiJ0kvRcRX6uIeTciWhwWIKm2b8TMzMzq0ciI6FuryiV1Wm6t5WfveM3Oycc8cvKDTB02pXdETK5i0zpUTae1krQT8GZEjJS0VRuOPwg4qOH5mqekrw088cwBrH5OevwLJw5gtd+mr7X80tED6f779DW9p/7qaLpdnb5m+LSfH194He0ND0p/v88MGpBcfkesAT6/xW+7RfrqdsMfPZk+xxZYp/uCAaxxaoF1wE8fQO8z0uIn/WYAvU9PL3vSqdWPX+uk9PgJZw+gz/EFzuV5Awqf+7VOLNCec9LPPWTnv+d56fEvHp/+OfjCiQNY64QCbT93AL3OSo+ffHL6Zw6U8/e26LXT7U8XJsdPO/wYVh98ZnL8C3uewmo3n50U+9LeJ9HjxrRYgCk/Panw36tul1+QHn/wsYXPTY9L0v8+TzlqID0vSI9/8diBybHVEmQ3XaXHV7+HVdIKwLHA2sCin9cd8b1q1Ndiwirphwll/C8i/tHG+rcAdpa0A9mbXVrSDcAbkjpHxAxJnYE3mzo4IgYBg/K2uofVzMzM6tKcjvmav4gbyYYD7AQcAuwHvFWtylrrYb0C+Dst3262JdCmhDUiTgBOAMh7WI+OiH0kXUj2xs/Lf/69LeWbmZmZ1YOy9bACy0XElZKOjIiHgYclPVytylpLWO+LiF+0FJD3iLa384DBkg4AXgZ+VIU6zMzMzEqvYVqr9PgqNuYLn+U/Z0jaEXgN6FKtylpMWCNin9YKSIlJEREPAQ/lj98BtmmPcs3MzMzmdUV6WDtoHtazJC0DDAT+ACwN/LpalSXfdCVpc6B75TERcV0V2mRmZmZmuaDYGNZqdrBKOj8ijgMWy1e6eh/YuopVAonzsEq6Hvgt8G1g43yr2RQPZmZmZvOTEi3NuoOkhcjvQeooqT2sfYE+UetJW83MzMzmOyrTkIAhwNvAEpI+aFRpRMTS1ag0dQTvWGClajTAzMzMzJrXMA9rcg9rNdsScUxELAPcGxFLV2xLVStZhdbnYb2b7DwtBYyX9BQws6LR6csumJmZmVlxUXBaqyp/Hy5pAWCJ6tbyZa0NCUhfqsnMzMzM2l3xm66qO0tARMyW9ImkZfIbr6pOKcNSK+4Ia3FfLXmlKzMzM6uCkRFRsxvNJXVaeo0VZ296+d7Jx4w54z5e/+ek3hExuYrtGgxsCgwDPm7YHxG/qkZ9qTddbQc0Tk6/38S+mqr2OtGF10K+6vz0+F8cxxbDjkmOf3S7Cwu3f/OhxybHP9bvAvZ/av+k2Gs2uYYDn943uewrNr6udGuAly2+26UF1tE+7Bg2vT/9V/GJ7c8vHJ967TzW74Kqt2Xj+9JvTH36++ey4b0nJcc/s+PZVY8v2v6+/0iPH7HDuax1x6nJ8RN2O53uN5yTFDt1nxOLfwYOKhB/0LGF17Mv+nu10T9OTI4ftcM59HvoyOT4oVv9rvDn4J6PHZwcP3jzy9nj0UOS42/d4jJ2/tfhSbF3fedP7PTIEcll37PlH/neP9On2/zn9/6v8O95nzvTr+Pxu55Ot2vPTY6ftt8Jha+d2it201UH9eDdm28dorUxrIcChwE9JY2peGkp4NFqNszMzMzMvrjpKjm+0IwCbRMR11a9kgqt9bDeBNwHnAscX7H/w4j4T9VaZWZmZmafKzatVfVJmkITnbkRsVqjuDGNY5rwVkS0uMJpa0uzvi/pQ2DdiJiWUKGZmZmZtaco1mvaQUMCKsf1Lgr8CFi2ibgFgB1aKEfAXa1V1uoY1oiYI+lZSatGxMutxZuZmZlZ+yk6S0CVFw4AICLeabTrEkn/Bn7TaP/BrXV6SjqstfpSb7rqDIzL52GtvBPM87CamZmZVVXBm646oItV0kYVTzuR9bgu1UTo85L6RMT4RsevDbwZEW9FxL9bqy81YT09Mc7MzMzM2lHQMTdSFXRRxeNZwFRgzybi/gD8uYn9XYCTgJ+kVJaUsEbEw5K+AWyc73oqIt5MOdbMzMzM5k6xaa06ZEjA1omh60bEw00cf7+ki5o6oCmdUoIk7Qk8RTagdk/gSUl7pFZiZmZmZm0TAbPndEreqjkkQNIPJHWreP6b/F6nuyT1aOKQhVoorqXXviR1SMBJwMYNvaqSVgCGA7emVmRmZmZmbdMR41ITnU22whWSdgL2AfYGNgQuA7ZvFD9Z0g4R8Y/KnZK+D7yUWmlqwtqp0RCAd0jsnTUzMzOzuaFCswRUeUhARMQn+eMfAldGxEhgZDN3+/8auCf/tn5kvq8vsBmwU2qlSlmeTNKFwHrAzfmuHwNjIqI0S7NKKs//PczMzKxejIyIvq2HVYekTov36jx7jUt+kXzMlPPv4L1HxveOiMnNlNkVuA5YCZgDDIqI30laFrgF6E5+E1VEvNvo2DHA5sAnwBRg94gYkb82PiL6NFHfImQ3V62T7xoH3BQR/0t9T6k3XR0jaXdgC7LJvQZFxB2plXSUsq0HXzT+4vHbJccP6DOM6yZ9Kzl+395PFm7PMaPThilfuMGtnPpc+gxnp697F1c8/+3k+APX+DeXTdwyOf6QNR/hwvH9kuOP6TO08LkpusZ40TXDB45u6kbLpl20wWDOGrtjcvzJ69zLJRNaXFDkS45a6wGumbRZUuz+vR9Pjm2IL/pvW7Tt54/rnxx/3NpDCp/Lotd+6u8VZL9bBzy9X3L8lRtfyw8fPTQ5/vYt/sy2Dx6VFDt860vY6oEByWU/tM3FyWvZQ7ae/cEj9kmOv7zvDTwwpXdy/DY9JvGrUXslx/9+o79y4+RNkuN/2uspbnthg+T43VcfzeAXNmo9MLfn6qMKf+an/k0Z0GdY4ev40JE/TY7/8zdvLHxdrnvXKcnxz+18Jt2uOzc5ftq+JxT+zK+1okuzJpgFDIyIUZKWIusdHQbsDzwQEedJOp5sldPGnZOXAKOBD4AJFcnqhsCMJtsfMRO4em4anDokgIi4DbhtbiozMzMzs4Ki2BjW1kIjYgZ5chkRH0qaAKwC7AJslYddCzxEo4Q1Iq6SdD+wIvBsxUuvAz9vrs585dTGTXsfGEGWPLc4njUpYZX0Q+D8vHHKt4iIpVOONzMzM7O2KzQPa4FYSd3Jbph6EvhGnswSETMkrdh0W+JV4NVG+5rsXa1wMfAacBNZHrkX2ZCE54Gr+CJRblLqjVMXADtHxDIRsXRELNUeyaqkrpIelDRB0jhJR+b7l5U0TNLk/OfX57YuMzMzs3mTiEjfcj+WNKJiO+grpUpLkn17flREfFDlN9E/Ii6PiA8j4oOIGATsEBG3AK3meakJ6xsRMWGumtm0hjEUa5FNkXC4pD5kYyYeiIhewAP5czMzM7P5TsMY1tQtnyXglojoW7ENqixT0kJkyeqNEXF7vvsNSZ3z1zsD7blI1BxJe0rqlG+VN2u0OuAhdQzrCEm3AHcCMz8v/Ys32CZzM4bCzMzMbH5RaB7WVmKV3Ul2JdlNUxdXvHQXsB9wXv7z761VlQ8bWPSLdsbLzYT+FPgdcGnewieAfSQtBhzRWj2pCevSZNMXVN56HcBcJayV2jKGwszMzKzuRbExrAm57RbAz4DnJI3O951IlqgOlnQA8DLZCqdNkrQzcBGwMllPbDdgArB2k23Kbqr6QTPF/bu1BqdOa9XsXV8Akk6IiPQ5Jb56/JfGUKROIZGPx/jKmAwzMzOzelLspqvWyop/Q7OrC6TOHXgm2XDO4RGxoaStyVa8apKk3sCfyTol15G0Htn9UWelVNZeq1U1m4G3Zm7GUETEoIaxGW2t38zMzKzMouDWQT6LiHeATpI6RcSDwAYtxF8BnAB8BhARY8hmCkjSXglrm2azTRhDAYljKMzMzMzqVZFZAqq8NGuD9/JvyP8F3Cjpd2Q30zdn8Yh4qtG+luK/pL0S1rYm9A1jKL4naXS+7UA2hmI7SZOB7fLnZmZmZvOn8nWx7kJ2f9NRwBDgRZofowrwtqSe5C2UtAfNrIzVFBVZnqzZQqRnImLDuS5o7trQgb3gZmZmNp8YWcuhh5I6LdJzldldzz0k+ZjXLxnMR4891zsiJlexaUjqBvSKiOGSFgcWiIgPm4ldDRgEbA68C0wB9omIqSl1JS/N2oq/tVM5c6Xo2sDzenzRNdUvHN+v9cDcMX2GJpd/1FoPFF5D+4mp3ZPjN+0+lTtfXD85fteezzL0pTWT4/utNpFHp/ZIjt+i+xSueP7byfEHrvHvwuen2u0vev5T4zftPpWHpvRKLnurHpMLv9e7Xlw3OX7nns8VPvd/nZz+d2mvXiMKr+9+2cQtk+MPWfORwr/n54/rnxx/3NpDkuOPW3sIf5q4VXLZh6/5EINf2Cg5fs/VR/HAlN7J8dv0mMRT07olx2/SbVrha7Po79UjU3omx2/Z48XC73fIS2slx/dfbULytbxXrxH8YcLWyWX/cq0HOWvsjsnxJ69zL0c98+Pk+Es2vIV9n/xFcvx137qKA57er/XA3JUbX1v4723NFVyatSNIOpDsxvdlgZ5k05JeRjM3beWzBGwraQmgU3OJbXNSl2a9ADgL+C9Zt+/6ZHf035A34pwilZqZmZlZunae1qo9HA5sQjYdKRExualpSCUNaOrghv8INLqHqVmpY1j75Ut27QRMB3oDxyQea2ZmZmZzI1Rsq76ZEfFpwxNJC9J0rrxUvvUFDiXriV0FOATok1pZ6pCAhfKfOwA3R8R/StFFbmZmZlbngvINCQAelnQisJik7YDDgLsbB0XE6QCShgIbNQwFkHQaBYaUpvaw3i1pIll2/ICkFYD/pVZiZmZmZnOhfLMEHA+8BTwHHAz8Azi5hfhVgU8rnn8KdE+tLHWlq+MlnQ98EBGzJX1CNp2BmZmZmVVTwaVZOyJpjYg5wBWSriVbjvXVaPlutuuBpyTdkbdwN+Da1PqSeljzqQoOJ1tSC7J1Y726lJmZmVlHKEkPq6TLJK2dP14GGA1cBzwjqdmlWSPibODnZFNavQf8PCLOTa03dQzr1cBIsrmzILvx6m/APakVmZmZmVnblGiWgO9ERMOksD8HJkXErpJWAu4Dbm62XRGjgFFtqTR1DGvPiLiAL9Z//S9tXI7VzMzMzAoq0sNa3Yy1chzqdsCdABHxelPBklpNUFNiUntYP5W0GF8sp9UTmJl4rJmZmZm1mSjWT1jVPsX3JO0EvApsARwAn09rtVgT8WtJGtNCeQKWaa3S1IT1VLIFA7pKujFv4P6Jx5qZmZnZ3CjPtFYHA78HViJbRKqhZ3Ub4N4m4lOWM5zdWoBSlyeTtBywKVkm/EREvJ10YAeRVJ5/SjMzM6sXIyOiZjeaS+q0cPcuszufekTyMW9ddjOfPPls74iYXMWmdajUpVkFfB9YLSLOkLSqpE0i4qnqNq+YcS+vnBy79qqvFY4vuvZwteOLrmdfdM3z1DXe+602kZHTuiaX/c1urxSOL7pm+JiXV0mOX2/VVwvHF13Pvuga40XbM+mVzsnxvbvOYMorKyXH9+j6Oi9PT4tftcvrvFig7J5dXy/8ezh6Wpfk+A26TS987RRdP77oevD3vrR2cvyOq43jthc2SI7fffXRDH5ho+T4PVcfVWi9+Rsnb5Jc9k97PVU4vuhnVNH41PcK2fsd8tJayfH9V5vAnS+unxy/a89nC3/mF/3cSW1//9UmFH6vReOLXvepf3+gY/4GlUHMKde0Vh0t9aarS4HNgIbpCj4E/lSVFpmZmZnZl5XjhquaSU1YvxURh5OvbhUR7wILV61VZmZmZpYJIFRgq15TJB2Z/9yiDcd2k7Rt/ngxSUulHpuasH4maQG+mCVgBWBO0YaamZmZWXGK9K3Kfp7//EORgyQdCNwKXJ7v6kI+JVaK1FkCfg/cAawo6WxgD1peL9bMzMzM2kt5vuqfIGkqsEKj6aoERESs18xxhwObAE+SBU6WtGJqpa0mrJI6AVOAY8mmLBCwa0RMSK3EzMzMzOZCgZWuCsUWbUbE3vmqVvcDOxc4dGZEfJrdx//5vK3JaXirCWtEzJF0UURsBkws0DAzMzMzaw/l6WFtWNVqfUkLAw1TpTwfEZ+1cNjDkk4EFpO0HXAYcHdqnaljWIdK2l0NabGZmZmZdYzyLMv6OUnfBSaTzRp1KTBJ0pYtHHIc8BbwHNniA/+gwPDS1DGsA4AlgFmS/scX4xSWTq3IzMzMzNqoSCLaMUnrxUC/iHgeQFJv4Gbgm40D8+GlYyJiHeCKtlSW1MMaEUtFRKeIWDgils6fVzVZldRf0vOSXpB0fDXrMjMzMyu1ItNadYyFGpJVgIiYBCzUZNMj5gDPSlq1rZWlrnTV1NIp7wPTImJWWytvob4FyLqYtwOmA09Luisixrd3XWZmZmZlV2S6qg5arH6EpCuB6/PnPwVGthDfGRgn6Sng44adEZF045ZSloaT9ASwEdm4A4B1gWeB5YBDImJoSmWpJG0GnBYR2+fPTwCIiHNbOKZEw5HNzMysToyMiPR1fduZpE4Lr9pl9spHH5V8zFvX3MDHo0b3jojJVWzXImRTVX2bbKjoI8ClETGzmfjvNrU/Ih5OqS91DOtU4ICIGJdX2gc4BjgTuB1o14QVWAWoXLx3OtDqotFF1youuhZy0XWfqx1fdA3zovGpa7Bv0m0aY15eJbns9VZ9tfB68EXLn/RK5+T43l1n8OIrKyXH9+z6OuNeXjk5fu1VX2NKgfJ7dH2dV6ent3+VLjOYUSC+c5cZhct/eXpa+1ftUrztqWU3lF/032riy+ntWXPVGYXXGH9kSs/k+C17vFh4jfSi68cPfqGpL8Satufqo7jy+bTFag5Y41EuHr9dctkD+gzj9Od+kBx/6rp3c+KzuyXHn7P+HRz37O7J8eevf1vVyy8aX5a/KZJ4Ymr35LI37T6Vh6b0So7fqsfkwn9v/zo5PT/cq9eI5OsYsmv5ukmtphSf27f3k8mx85M8Mb0431LikxLT5qQmrGs2JKt5peMlbRgRL1Vp4oCmCv3Kb56kg4CDqtEAMzMzs1LomBWsqkrSh3yRyy1MNt7149R7olIT1ucl/Rn4a/78x2TTFywCtDTnVltNByq7OboArzUOiohBwCDwkAAzMzOrVwVvpiphRhQRS1U+l7Qr2cpXSVLnYd0feAE4Cvg18FK+7zNg69TKCnga6CWpRz4p7V7AXVWox8zMzKz8SjYPayVJnSQVmj0qIu4Evpcan9TDGhH/lXQpcE/lFAa5j9KblyYiZkk6gmzZrwWAqyqHJJiZmZnNV0rWayrpJuAQYDbZ7ADLSLo4Ii5sJv6HFU87AX1pz6VZ80p2Bi4kG3PQQ9IGwBmpUxG0RUT8g2wVBDMzM7P5lig4hrVjkts+EfGBpJ+S5WvHkSWuTSasQOVdmLPIbujfJbWy1DGsp5KNM3gIICJGS+qeWomZmZmZtVGNvupvxUKSFgJ2Bf4YEZ+1cj/RXyLi0codkrYA3kypLHUM66yIeD8x1szMzMzaUzuPYZV0laQ3JY2t2HeapFcljc63HVoo4nKyXtIlgEckdQM+aCH+D4n7mpTawzpW0k+ABST1An4FPJZaiZmZmZm1XRVWuroG+CNwXaP9/xcRv23t4Ij4PfD7il3TJH3lRvx8MajNgRUkDah4aWmy+5SSpCasvwROAmYCN5PdDHVmaiVmZmZmNheKTGvV5HT2jYqLeGRuhnfmU5vuDnTny/nkGY1CFwaWzGMqp7b6ANgjtb7UWQI+IUtYT0ot2MzMzMzaSceNYT1C0r7ACGBgRLzbTNzfgffJbrRqcjlW+HyFq4clXRMR09raKLW0dJuku2nhFFVzloCivHCAmZmZVcHIiEhfK7adSeq0yCpdZ3c94tfJx7x+8/V8NOaZU8huiGowKF9wqbLs7mRTlq6TP/8G8DZZ7ncm0DkiftFMu8Y2HJf4PlYAjgXWBhZt2B8RSXOxttbD2jCG4YfASsAN+fO9yQbalsqvRu2VHPv7jf7K+eP6J8cft/aQ0qz73BA/elqX5PgNuk0vvEb6U9O6JcVu0m1acmxDfNF1q4u2vdrlF12ffsb09PjOXWbwyWvdk+MXX3kqn81IX89+oc4vFo5/89WVk2JXXOW1wu/11QLxq3SZwcvTV0qOX7XL66WLn/JKenyPrq8z6ZX089O76wzGvLxKcvx6q77Ko1N7JMVu0X0Kd764fnLZu/Z8liue/3Zy/IFr/Jtzxn0/Of7Ete/jxGd3S44/Z/07GDh6z+T4izYYXDh+538dnhx/13f+VJq/KW0pe+hLaybH91ttYuFr508Tt0qOP3zNhwpfCweP2Cc5/vK+N7Qe1BGKd8vdEhFnFaoi4o2Gx5KuAO5pIfwxSetGxHOJxd8I3ALsRDZ/637AW6ltazFhzbtxkXRmRGxZ8dLdkh5JrcTMzMzM2q4jvkeW1DkiZuRPdwPGthD+bWB/SVPIhgQIiIhYr5n45SLiSklHVgwTeDi1bak3Xa0gabWIeAlAUg9ghdRKzMzMzGwutPPCAZJuBrYClpc0nWzO/a3yxaGC7Jv0g1soIv0rkcxn+c8ZknYEXgOSvypOTVh/DTwk6aX8eXfgoNRKzMzMzGwutHMPa0Ts3cTuKwscPw1A0opUjEltwVmSlgEGks2/ujRZfpkkdZaAIfn8qw2DViZGRLN3hJmZmZlZO4mC87BWryVf1CHtDFwErEy2WlU3YALZTVWNYxcAekXEPWQzC3xlvtbWtLjSlaSNGh5HxMyIeDbfZjYVY2ZmZmZVUGSlq46ZN+lMYFNgUkT0ALYBHm0qMCJmA3M1s1RrPaxXS9qKlpP1K4EN56YRZmZmZtY00TE3XRX0WUS8I6mTpE4R8aCk81uIf0zSH8lmCvi4YWdEjEqprLWEdRmyCWFbSliTpyQwMzMzszYoX8L6nqQlgX8BN0p6E5jVQvzm+c/KlbACmPt5WCOie0ohZmZmZlZF7TxLQDvYBfgvcBTwU7JOzsbLsn7RpIjC41Yrpc4SYGZmZma1UPCmq44QER9L6kZ2M9W1khYHFmguPl9F6xxg5Yj4vqQ+wGYRkTQzQYs3XZmZmZlZCZTspitJBwK3Apfnu1YB7mzhkGuA+8lmFQCYRNY7m8QJq5mZmVnJKQpsHdOkw4EtgA8AImIysGIL8ctHxGBgTh4/C5idWplS1g+WJLLxCatFxBmSVgVWioinUiuqNqlsneVmZmZWB0ZGRN9aVS6p06Kdu87u8fPkOfZ59c7r+WD8M73zJLJa7XoyIr4l6ZmI2FDSgsCo5pZmlfQQsDswLCI2krQpcH5EfDelvtQxrJeSZcTfIxtQ+yFwG7Bx4vEd4rvDBybHPrztRax5+2nJ8RN/eBopyX0DSYXj733pK3PtNmvH1cbx6NQeyfFbdJ/CA1N6J8dv02NScnt2XG0cg19In453z9VHcc2kzZLj9+/9OJdN3DI5/pA1H+GSCdskxx+11gOcNXbH5PiT17mXC8f3S44/ps9QnpjaPTl+0+5T+WxGz+T4hTq/WPhamz2jV3L8Ap0nM+mVzkmxvbvO4Klp3ZLL3qTbNB6Zkv5et+zxYuH4otd90fiiv4dFr4Vqxz80Je1a2KrHZO56cd3ksnfu+Vzh3/Oiv1cnPrtbcvw569/BwSP2SY6/vO8N7PHoIcnxt25xGTs9ckRy/D1b/pFtHzwqOX741pdU7W9QW/5eFf3M/+vk9Hxvr14jCl8LRf9td/7X4cnxd33nT8mxVVP0a/6O6cJ7WNKJwGKStgMOA+5uIX4AcBfQU9KjwArAHqmVpSas38qz4WcAIuJdSQunVmJmZmZmbVfC75GPBw4AngMOBv4B/KW54IgYJem7wBpkoxaej4jPUitLTVg/y5fVCgBJK5CPQWgrSRcCPwA+BV4Efh4R7+WvnUB2EmYDv4qI++emLjMzM7N5WskS1oiYA1yRb62StChZL+y3yd7NvyRdFhH/Szk+NWH9PXAHsKKks8m6cE9OPLY5w4ATImJWvjLCCcBx+TQHe5GtRbsyMFxS73xZLzMzM7P5T0mGBEga02LVzYxhBa4jG1L6h/z53sD1wI9S6k1KWCPiRkkjydaJFbBrRExIObaFModWPH2CL8Yx7AL8NSJmAlMkvQBsAjw+N/WZmZmZzatKNCRgDllKfBPZmNX/Jh63RkSsX/H8QUnPplbaYsIqadmKp28CN1e+FhH/Sa2oFb8gW1sWsnm8nqh4bXq+z8zMzGz+00Fzq6aIiA0krUnWQ3oTMD7/OTSfqqo5z0jaNCKeAJD0LeDR1Hpb62EdSXaKBKwKvJs//hrwMtDi7bGShgMrNfHSSRHx9zzmJLK1Z29sOKyJ+Cb/mSQdBBzUynswMzMzm2eJYj2s1e6NjYiJwKnAqZJ+TPZ1//nAhS0c9i1gX0kv589XBSZIei4rstmhBEArCWtE9ACQdBlwV0T8I3/+fWDbhDfUYoyk/YCdgG3iizk1pgNdK8K6AK81U/4gYFBeVkn+72FmZmbWzkqU5Uhahex+o93IOjN/TXavU0v6z02dqStdbdyQrAJExH1A0kSvzZHUHzgO2DkiPql46S5gL0mLSOoB9AJKs0CBmZmZWYcrybKskh4mG7u6ELA/sB9wL7Bwo6GkXxIR08hWxVoGWK5hi4hp+WstSp0l4G1JJwM3kJ2KfYB3Eo9tzh+BRYBh2UJaPBERh0TEOEmDycZEzAIO9wwBZmZmNj/roOVWU3QjywUP5svDMpXvX62pgySdSZbgvsgXaXWQLUrVqtSEdW+ysQoN3b2P5PvaLCJWb+G1s4Gz56Z8MzMzs7pRkmmtIqJ7Gw/dE+gZEZ+25WAVWY6tzDyG1czMzKpgZESkry3bziR1WmzFrrN77f3r5GOm3Xc97096pndETK5i0wqRdBtwaES82Zbjk3pYJT1IE/l6RCR143aU7w4fmBz78LYX0fu205PjJ+1+Kt2uOzc5ftq+JxRem/n8cenjkY9be0jh+HPGfT85/sS17+OssTsmxZ68zr2c+tzOyWWfvu5dHDM6eflgLtzgVo4Y+ZPk+D9+8yZ+8sT/S46/adO/0O+hI5Pjh271u8LXWtF1q6u1Zni14yVx5fNbJJd9wBqPcvpzP0iOP3XduwuvHz9w9J7J8RdtMLjq8cc9u3ty/Pnr31b4d6vo58IlE7ZJij1qrQcKr++e+hkC2edItf9tDx350+T4P3/zRvZ49JDk+Fu3uIwthh2THP/odhfyrSHHJ8c/2f+8wr+3qb9bp657d+G/D0Wv46Kf+UXji/7b7vnYwcnxgze/PDm2qub9brlzyaa2GgvMbNgZEUkfcqlDAo6ueLwosDvZ+FIzMzMzq7aSDAmYC9eSTX31HNniA4WkrnQ1stGuR/O7xMzMzMysygrNw1q9ZnxRh/Rb4OqIGJd4yNsR8fu21pc6JKBymoJOwDdpekEAMzMzM2tv5es1nQgMkrQgcDVwc0S830L8SEnnkk1fWjkkYFRKZalDAipXvJoFTAEOSDzWzMzMzNoqCq5e1QHJbUT8BfiLpDWAnwNjJD0KXBERDzZxyIb5z00ri6Gdp7VaKyL+V7lD0iKJx5qZmZnZ3ChfDyuSFgDWzLe3gWeBAZIOjoi9KmMjYuu5qSt1pavHmtj3+NxUbGZmZmatE6A56VtHJLeSLgaeB3YAzomIb0bE+RHxA77oTa2M/4akKyXdlz/vIyn52/oWe1glrQSsAiwmaUO+GMe7NLB4aiVmZmZmNhfK18M6Fjg5Ij5p4rVNmth3DdlY15Py55OAW4ArUyprbUjA9mTLaHUBLq7Y/yFwYkoFZmZmZjYXCo5hreYsAZI2yh+OBtaUvlxbRIyqvPlK0oIRMQtYPiIGSzohj5slaXZqvS0mrBFxLXCtpN0j4rbUQs3MzMysHZXnpquLWqm58U1UTwEbAR9LWi6PQdKmQEuzCnxJa0MC9omIG4DukgZ8pVURFzdxmJmZmZm1IxVY6ayaGWsbbp5q6IIdQDalVc98NoEVgOQlzdTSUm/5XV6XSzq1iZcjIs4o0OCqkgpN+GBmZmaWYmRE9K1V5ZI6Lb5819lr7XxU8jEvPXg9704Z3TsiJlevZSBpc6A7FR2gEXFdo5jpfDGstBOwCFkSOxOYndr52dqQgIYFdIdHxKONGpC+YHgHWe2vZyXHvrTXyfS85czk+Bd/fArr/P03yfFjdzmDzYcemxz/WL8LCq9DveG9J7UemHtmx7Ppc2dT/+9o2vhdT2etO9LiJ+x2OuvedUpy2c/tfCbr3X1ycvyYH5xVOH6N205Pjn9+91PpfsM5yfFT9zmRblednxw/7RfH0e2a89Lj9z+eVf+SXv7L/+84ul1boPz9ji/cniLXwv5P7Z9c9jWbXMO2Dx6VHD9860sKr7/e9x8nJMeP2OFc1rz9tOT4iT88jdUHp3+OvLDnKVX/nOpd4NqftPupyZ9rY3c5o/Dv4fr3pMc/u9NZhT9ji36mFf2cKtqeXn9L78OZ/KPfFP637XVrgfL3+A27/vuwpNg7v30p3x0+MLnsh7e9qPC5L3rd97jp7OT4KT85qfDvYdH4MijLGNbP65CuB3qSjWVtGIsawHWNQhcAlmyiWYVu3k+dh/UPZOMPWttnZmZmZu0pKOMsAX2BPtHSV/WZGe3xjXxrY1g3AzYHVmg0hnVpsozZzMzMzKqsvVe6knQVsBPwZkSsk+9blmyqqe7AVGDPiHi3mSLGAisBM1qrKqXJrWlt4YCFybpxFwSWqtg+oMBAWTMzMzObC1FgS3MN0L/RvuOBByKiF/BA/vxLJN0t6S5geWC8pPsl3dWwNVHPNsktakFrY1gfBh6WdE1ETGuPCs3MzMwsnSjYw5ogIh6R1L3R7l2ArfLH1wIPAcc1ivltwXr+04bmfUXqGNZPJF0IrA0sWtGIxnNtmZmZmVl7KjqGte3J7TciYgZARMyQtOJXis46M5F0fkR8KZmVdD7wcJtrb0FrQwIa3AhMBHoAp5ONa3i6Gg0yMzMzsy9TFNiyQ34saUTFdlA7N2m7JvZ9v53r+FxqD+tyEXGlpCMrhglUJYM2MzMzs0oBhRYOAOCWiEifTyzzhqTOee9qZ+DNxgGSDgUOA1aTNKbipaWAx4o2MlVqwvpZ/nOGpB2B14Au1WmSmZmZmVVq71kCmnEXsB9wXv7z703E3ATcB5zLl2/K+rC9xqs2JXVIwFmSlgEGAkcDfwGOao8GSDpaUkhavmLfCZJekPS8pO3box4zMzOzeVY7zxIg6WbgcWANSdMlHUCWqG4naTLZV/5fWWUmIt6PiKkRsTcwnaxTM4AlJa06N2+xJUk9rBFxT/7wfWBrAElHzW3lkrqSnZCXK/b1AfYiu8FrZWC4pN4RMbvpUszMzMzqWIDmFItvNSRLOJuSNA2VpCOA04A3gIbWBbBeyvFFqfUFCpo5UHo5IuYqk5Z0K3AmWZdz34h4W9IJABFxbh5zP3BaRDzeSlnlWwPCzMzM5nUjI6JvrSqX1GmJr3eZvd62RyUfM+mJG3jnldG9I2JyFdv1AvCtiHinWnVUSh3D2pS5WrlA0s7AqxHxrPSlolYBnqh4Pj3f11QZBwGf3/X2rZ9elFz/kzcOpO/+6fEjrhnIekdenBw/5ncDWP/w9Phn/zSADQ9Oj3/m8gF88xfp7R951UA2+Vl6/FPXD2Tdo9La89wlAwqfm3V+nR4/9v8GsG6B+Of+bwDr/apAe34/gA0OS48ffWnxf9tqn58+x6bHj79gAGuclh7//GkD6HFJ2rUz5aiBdLv0wuSypx12DN3/kD6l39RfHk333xeI/9XR9Lg4/bqfMmAgq5+bfm5eOGEAa/ymwLk8YwBrnZAeP+HcAax1YoH4cwaw1kkF4s8ewJqnpMVPPDM9tq3xRdteNL7ov1Xv09PjJ506gJ7np8e/eNwAel6Qfm2+eOxAVvttevxLRw+k53lp7Xnx+AGseXKBf6uzBrDeLwt8pv2hDX+v9inw9+qGgWy6d/rnwhM3H82mexWI/+vRybHVVKRbrl2WlmrdK2TfvHeIuUlYWz11koaTLdvV2EnAiUC/pg5LrSsiBgGD8rrcw2pmZmb1qcg34h2TEb0EPCTpXmDm51VHpP9vpoAWE1ZJH9L02xawWGuFR8S2zZS7Ltmcrg29q12AUZI2IetR7VoR3oVsVgIzMzOz+U7D/Kol83K+LZxvVdXa0qxLVaPSiHgO+Hz1BElT+WIM613ATZIuJrvpqhfwVDXaYWZmZjZPKFnCGhGnA0haKnsaH1WzvrkZElAVETFO0mBgPDALONwzBJiZmdn8rIPmYU0maR3gemDZ/PnbwL4RMa4a9ZUiYY2I7o2enw2cXZvWmJmZmZVMoVmdOqQ7dhAwICIeBJC0FXAFsHk1KitFwmpmZmZmzSvhLAFLNCSrABHxkKQlqlWZE1YzMzOzMiuwgtXn8dX3kqRTyIYFAOwDTKlWZalLs5qZmZlZjTTMFJCydZBfACsAtwN35I9/Xq3K3MNqZmZmVnZzyjUPa0S8C/yq+jVlnLCamZmZlZgCNKfAAVVMWPPpR5uvOmLnqtQbhe46Ky+vdGVmZmZVMDIi+taqckmdllq6y+yNNjsi+Zjxz97MW68/2zsiJlehPW+RLct6M/Akje7xioiH27tOqLMe1u0W2js5dthnN5cufvul04d+3P/B1fRbYt/k+KEfX1e4/P7fODQpdsgbf6b/Socllz3k9Uvpv+Ih6fFvXkb/bkelx0+7hP5rnZAeP+Fc+vcYmB4/5SL690xfW3rIi78t3J7tNj49OX7Y06fyva3PSY7/54MnslW/85LjHxp6PN/e9cKk2H/feQzf+kn6Gt1P3nQ0G++bvmb409e1YY3xgmuGb/KzAuVfX7z8zXdPO5cAj912DFvslh7/6B3HsMUPC8Tffgyb75HW/sduPZrNfpT+Xh//29HJZTeUXzS+Wu+1ofyi69Nv9P/SV6Qc9ZcBbHRggfgrBhS+9r+7fdrv+cP3H8/2652cXPb9Y86i/yq/TI4f8uofCv/92X6p/dPjP7yG7Rb8cXL8sFm3sK32SI4fHrcmx1ZTiWYJWAnYDtgb+AlwL3BzteZfbeCbrszMzMzKLgpu1WpGxOyIGBIR+wGbAi8AD0lK/19MG9RVD6uZmZlZ/QlUooUDJC0C7EjWy9od+D3ZbAFV44TVzMzMrMwCKM9NV9cC6wD3AadHxNjq1fYFJ6xmZmZmJVesh7WqfgZ8DPQGfiV9PmJWQETE0tWo1AmrmZmZWdmVJF+NiJrc/+SE1czMzKzsivSwliS5bU9OWM3MzMzKrOCSq1We1qomnLCamZmZlV2JZgmoBSesZmZmZiUmyrM0a604YTUzMzMrs6BgD2v9UdTJCZCKjO4wMzMzSzIyIvrWqnJJnZZeYpXZm6yXvqT5c5MG88Y7z/WOiMlVbFqHqqse1qJrAzu+feLL1JZ6ie+3yE+S44fOvKnq63T3W2LftLZ8fB3bL3dgetnvXEH/bkclxw+Zdgnbr3Nievljz6H/msenlz/xPPr3GJgeP+Ui+n/j0PT4N/6cfC4hO5+Fr53Ff5Ze/ifX03/lI5Jih7z2R/r3Oja57CGTL2D7PgX+rcafU/jftnD5BeP7bfSb5Piho85gu41PT44f9vSp9NugQPmjz0j+t4Ls3yv1c2TozJtK9xlYtvjaK7rSVf2pq4TVzMzMrC75piszMzMzK60SLc1aKzVZraCBpF9Kel7SOEkXVOw/QdIL+Wvb17KNZmZmZrWmiPSt1o2tgpr1sEraGtgFWC8iZkpaMd/fB9gLWBtYGRguqXdEzK5VW83MzMxqaj5f6aqWPayHAudFxEyAiHgz378L8NeImBkRU4AXgE1q1EYzMzOz2otI3+owY61lwtob+I6kJyU9LGnjfP8qwCsVcdPzfWZmZmbzn4YxrKlb/eWr1R0SIGk4sFITL52U1/11YFNgY2CwpNVoegncJk+9pIOAg9qntWZmZmZl5GmtqpqwRsS2zb0m6VDg9shWLnhK0hxgebIe1a4VoV2A15opfxAwKC9v/v6XNDMzs/pVaAxr/aVEtRwScCfwPQBJvYGFgbeBu4C9JC0iqQfQC3iqVo00MzMzq7lCY1jrTy3nYb0KuErSWOBTYL+8t3WcpMHAeGAWcLhnCDAzM7P5WhUSUUlTgQ+B2cCsWi5B2xpFnWTiHhJgZmZmVTCylomcpE5LL9p59marpS/B/ez0O3n9g/G9I2JyK2VPBfpGxNtz2cyqq6uVrsq29vD8El+mtji+tvFlaovj2ze+TG1xfPvGl6ktZY2vvaI3XdVfH15NV7oyMzMzswRFxrCm56sBDJU0Mp95qbTqqofVzMzMrO4EMHtOgfgA+LGkXSv2DspnV6q0RUS8lq82OkzSxIh4ZC5bWxVOWM3MzMzKrvg9R7dExFktFxmv5T/flHQH2cqipUxYPSTAzMzMrNSKDAdIS2wlLSFpqYbHQD9gbBXfxFxxD6uZmZlZmQUwp90XDvgGcIckyPLBmyJiSBta1yGcsJqZmZmVXRQYw5pSXMRLwPrtWmgVOWE1MzMzK7v5fGlWJ6xmZmZmZRZRbEhAHXLCamZmZlZ2hXpYq9eMWnHCamZmZlZ28/lKV4o6GecgqT7eiJmZmZXJyIjoW6vKJXVaeqEVZ2++wp7Jx4x+935e/+/k3hExuYpN61B11cNaJPmW5Ph2ii9TWxxf2/i2lF22NcMdX/62OL5948vUlo6KL/o5VXMRMKfwSld1pa4SVjMzM7O65DGsZmZmZlZqddhrWoQTVjMzM7Oya/+VruYpTljNzMzMyiyCaOeVruY1TljNzMzMys49rGZmZmZWanWYhBbhhNXMzMyszAJPa1XrBpiZmZlZS6Iuk9AiOtWqYkkbSHpC0mhJIyRtUvHaCZJekPS8pO1r1UYzMzOzMog5c5K3ekxua9nDegFwekTcJ2mH/PlWkvoAewFrAysDwyX1jojZNWyrmZmZWe3UYRJahIosT9auFUv3A1dFxC2S9gZ+EBE/kXQCQEScWxF3WkQ83kp58/e/pJmZmVXDyIjoW6vKJXVaWsvO3nSRHZKPGfPpv3h9zrTeETG5ik3rULXsYT0KuF/Sb8mGJmye718FeKIibnq+r1VlWU99fosvU1scX9v4MrXF8e0bX6a2OL5948vUlrLGl4LnYa0eScOBlZp46SRgG+DXEXGbpD2BK4FtgaaujCavLEkHAQe1U3PNzMzMSikKzMNaq2/Pq6mqCWtEbNvca5KuA47Mn/4N+Ev+eDrQtSK0C/BaM+UPAgbl5dXfv46ZmZlZxHzfw1qzWQLIktDv5o+/BzSMs7gL2EvSIpJ6AL2Ap2rQPjMzM7NSiDmRvNXjDVq1HMN6IPA7SQsC/yP/aj8ixkkaDIwHZgGHe4YAMzMzm1/NZhYxZ3byeNrPmAlZblU3apawRsS/gW8289rZwNkd2yIzMzOzcomIOV21Om/xGism3IP+YbyH6EREvNIBzeswtRwSYGZmZmat6M4aTGVi0s1ULzGe1VirA1rVsZywmpmZmZXYv+JelmZZ3mr6HvTPfRjvMYc5PBX/7KCWdRwnrGZmZmYll9LLWq+9q1DDla7am6e1MjMzsyqo6UpXuQDoqtVZlhVZUV8dy/phvMcLjOXtmNGwqyQrHrSPWs4S0O7KtjLG/BJfprY4vrbxZWqL49s3vkxtcXz7xpepLWWNL4vurMEYHmeFWPkr7arn3lXwkAAzMzOzeUJzY1nreexqAyesZmZmZvOIx1956CtjWeu9dxWcsJqZmZnNM7p06fKlXtb5oXcVnLCamZmZzVMqe1nnh95VcMJqZmZmNk9p6GV9ifHzRe8qOGE1MzMzm+dkvazPzxe9q1Bf87C+BUwDlgfernFz5gU+T+l8rtL4PKXxeUrnc5XG5yldW85Vt4hYoRqNmVuSlomI92vdjo5QNwlrA0kjSjDBb+n5PKXzuUrj85TG5ymdz1Uan6d0PlfzLg8JMDMzM7NSc8JqZmZmZqVWjwnroFo3YB7h85TO5yqNz1Man6d0PldpfJ7S+VzNo+puDKuZmZmZ1Zd67GE1MzMzszpSNwmrpP6Snpf0gqTja92espE0VdJzkkZLGpHvW1bSMEmT859fr3U7O5qkqyS9KWlsxb5mz4ukE/Jr7HlJ29em1R2vmfN0mqRX82tqtKQdKl6bL88TgKSukh6UNEHSOElH5vt9XVVo4Tz5uqogaVFJT0l6Nj9Pp+f7fT010sK58jVVB+piSICkBYBJwHbAdOBpYO+IGF/ThpWIpKlA34h4u2LfBcB/IuK8PMn/ekQcV6s21oKkLYGPgOsiYp18X5PnRVIf4GZgE2BlYDjQOyJm16j5HaaZ83Qa8FFE/LZR7Hx7ngAkdQY6R8QoSUsBI4Fdgf3xdfW5Fs7Tnvi6+pwkAUtExEeSFgL+DRwJ/BBfT1/Swrnqj6+peV699LBuArwQES9FxKfAX4FdatymecEuwLX542vJ/ljMVyLiEeA/jXY3d152Af4aETMjYgrwAtm1V/eaOU/NmW/PE0BEzIiIUfnjD4EJwCr4uvqSFs5Tc+bX8xQR8VH+dKF8C3w9fUUL56o58+25mhfVS8K6CvBKxfPptPzBNz8KYKikkZIOyvd9IyJmQPbHA1ixZq0rl+bOi6+zrzpC0ph8yEDDV5I+TzlJ3YENgSfxddWsRucJfF19iaQFJI0G3gSGRYSvp2Y0c67A19Q8r14SVjWxb94f69C+toiIjYDvA4fnX/FaMb7OvuzPQE9gA2AGcFG+3+cJkLQkcBtwVER80FJoE/vmm/PVxHnyddVIRMyOiA2ALsAmktZpIXy+PU/Q7LnyNVUH6iVhnQ50rXjeBXitRm0ppYh4Lf/5JnAH2dc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      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_coarse = ds.coarsen(lon=4, lat=4, boundary='pad').mean()\n",
    "ds_coarse.sst.isel(time=0).plot(vmin=2, vmax=30, figsize=(12, 5), edgecolor='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An Advanced Example\n",
    "\n",
    "In this example we will show a realistic workflow with Xarray.\n",
    "We will\n",
    "- Load a \"basin mask\" dataset\n",
    "- Interpolate the basins to our SST dataset coordinates\n",
    "- Group the SST by basin\n",
    "- Convert to Pandas Dataframe and plot mean SST by basin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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       "\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-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:  (Z: 33, X: 360, Y: 180)\n",
       "Coordinates:\n",
       "  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03\n",
       "  * X        (X) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "  * Y        (Y) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "Data variables:\n",
       "    basin    (Z, Y, X) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL</pre><div class='xr-wrap' hidden><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-21c3ea99-ad48-4db9-859b-2cbef4300ad9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-21c3ea99-ad48-4db9-859b-2cbef4300ad9' 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'>Z</span>: 33</li><li><span class='xr-has-index'>X</span>: 360</li><li><span class='xr-has-index'>Y</span>: 180</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-38b23a95-a28b-45b6-85ff-f3e68891a45c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-38b23a95-a28b-45b6-85ff-f3e68891a45c' 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'>Z</span></div><div class='xr-var-dims'>(Z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 10.0 20.0 ... 5e+03 5.5e+03</div><input id='attrs-95b8c9b7-e495-42bc-bb65-23fdc0fb5642' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95b8c9b7-e495-42bc-bb65-23fdc0fb5642' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6c66046-5c56-409c-871f-2cb271182325' class='xr-var-data-in' type='checkbox'><label for='data-a6c66046-5c56-409c-871f-2cb271182325' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([   0.,   10.,   20.,   30.,   50.,   75.,  100.,  125.,  150.,  200.,\n",
       "        250.,  300.,  400.,  500.,  600.,  700.,  800.,  900., 1000., 1100.,\n",
       "       1200., 1300., 1400., 1500., 1750., 2000., 2500., 3000., 3500., 4000.,\n",
       "       4500., 5000., 5500.], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-655f6ff8-ae2e-42ac-bf0d-b44624a7e4bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-655f6ff8-ae2e-42ac-bf0d-b44624a7e4bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7f1ba46-807f-4a26-9512-a8aa0b02523e' class='xr-var-data-in' type='checkbox'><label for='data-b7f1ba46-807f-4a26-9512-a8aa0b02523e' title='Show/Hide data repr'><svg 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>pointwidth :</span></dt><dd>1.0</dd><dt><span>gridtype :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>degree_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], dtype=float32)</pre></div></li><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'>float32</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-a8b26474-09d8-416e-8d6a-0aba9e5fc9d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8b26474-09d8-416e-8d6a-0aba9e5fc9d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac1528f9-efc6-47a2-8760-6d9be6261774' class='xr-var-data-in' type='checkbox'><label for='data-ac1528f9-efc6-47a2-8760-6d9be6261774' title='Show/Hide data repr'><svg 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>pointwidth :</span></dt><dd>1.0</dd><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>degree_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8f267c6c-91be-4651-8991-90ad28b08203' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f267c6c-91be-4651-8991-90ad28b08203' 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>basin</span></div><div class='xr-var-dims'>(Z, Y, X)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e87c5a85-2406-483d-8848-c0d3da2e8ae0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e87c5a85-2406-483d-8848-c0d3da2e8ae0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35cf4089-9f53-4153-90c8-fae4843df7b3' class='xr-var-data-in' type='checkbox'><label for='data-35cf4089-9f53-4153-90c8-fae4843df7b3' title='Show/Hide data repr'><svg 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>basin code</dd><dt><span>CLIST :</span></dt><dd>Atlantic Ocean\n",
       "Pacific Ocean \n",
       "Indian Ocean\n",
       "Mediterranean Sea\n",
       "Baltic Sea\n",
       "Black Sea\n",
       "Red Sea\n",
       "Persian Gulf\n",
       "Hudson Bay\n",
       "Southern Ocean\n",
       "Arctic Ocean\n",
       "Sea of Japan\n",
       "Kara Sea\n",
       "Sulu Sea\n",
       "Baffin Bay\n",
       "East Mediterranean\n",
       "West Mediterranean\n",
       "Sea of Okhotsk\n",
       "Banda Sea\n",
       "Caribbean Sea\n",
       "Andaman Basin\n",
       "North Caribbean\n",
       "Gulf of Mexico\n",
       "Beaufort Sea\n",
       "South China Sea\n",
       "Barents Sea\n",
       "Celebes Sea\n",
       "Aleutian Basin\n",
       "Fiji Basin\n",
       "North American Basin\n",
       "West European Basin\n",
       "Southeast Indian Basin\n",
       "Coral Sea\n",
       "East Indian Basin\n",
       "Central Indian Basin\n",
       "Southwest Atlantic Basin\n",
       "Southeast Atlantic Basin\n",
       "Southeast Pacific Basin\n",
       "Guatemala Basin\n",
       "East Caroline Basin\n",
       "Marianas Basin\n",
       "Philippine Sea\n",
       "Arabian Sea\n",
       "Chile Basin\n",
       "Somali Basin\n",
       "Mascarene Basin\n",
       "Crozet Basin\n",
       "Guinea Basin\n",
       "Brazil Basin\n",
       "Argentine Basin\n",
       "Tasman Sea\n",
       "Atlantic Indian Basin\n",
       "Caspian Sea\n",
       "Sulu Sea II\n",
       "Venezuela Basin\n",
       "Bay of Bengal\n",
       "Java Sea\n",
       "East Indian Atlantic Basin</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>58</dd><dt><span>scale_min :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>ids</dd><dt><span>scale_max :</span></dt><dd>58</dd></dl></div><div class='xr-var-data'><pre>[2138400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-31bcfac3-c5a7-4c7c-b9af-510dc5c2738d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-31bcfac3-c5a7-4c7c-b9af-510dc5c2738d' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>IRIDL</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (Z: 33, X: 360, Y: 180)\n",
       "Coordinates:\n",
       "  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03\n",
       "  * X        (X) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "  * Y        (Y) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "Data variables:\n",
       "    basin    (Z, Y, X) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basin = xr.open_dataset('http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/.Masks/.basin/dods')\n",
    "basin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "\n",
       ".xr-dim-list {\n",
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       ".xr-var-item > .xr-var-name span {\n",
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       "\n",
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       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "\n",
       ".xr-var-data > table {\n",
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       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 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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       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\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:  (Z: 33, lon: 360, lat: 180)\n",
       "Coordinates:\n",
       "  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03\n",
       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "Data variables:\n",
       "    basin    (Z, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL</pre><div class='xr-wrap' hidden><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-f1e4389b-538e-46ac-b6d9-2db5b43be013' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f1e4389b-538e-46ac-b6d9-2db5b43be013' 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'>Z</span>: 33</li><li><span class='xr-has-index'>lon</span>: 360</li><li><span class='xr-has-index'>lat</span>: 180</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0c42081f-f13d-43c5-8b36-556c05e35607' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c42081f-f13d-43c5-8b36-556c05e35607' 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'>Z</span></div><div class='xr-var-dims'>(Z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 10.0 20.0 ... 5e+03 5.5e+03</div><input id='attrs-011ca849-b235-4335-a4d2-1bda6d9b52e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-011ca849-b235-4335-a4d2-1bda6d9b52e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb91d536-53a6-4fd7-ac21-4a949452a2a5' class='xr-var-data-in' type='checkbox'><label for='data-cb91d536-53a6-4fd7-ac21-4a949452a2a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([   0.,   10.,   20.,   30.,   50.,   75.,  100.,  125.,  150.,  200.,\n",
       "        250.,  300.,  400.,  500.,  600.,  700.,  800.,  900., 1000., 1100.,\n",
       "       1200., 1300., 1400., 1500., 1750., 2000., 2500., 3000., 3500., 4000.,\n",
       "       4500., 5000., 5500.], 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'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-ba05ab13-aed7-4c2e-bf4a-7dfc37584014' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba05ab13-aed7-4c2e-bf4a-7dfc37584014' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fe2b3d4-a93b-4e6f-8227-1e16ca14e25c' class='xr-var-data-in' type='checkbox'><label for='data-3fe2b3d4-a93b-4e6f-8227-1e16ca14e25c' title='Show/Hide data repr'><svg 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>pointwidth :</span></dt><dd>1.0</dd><dt><span>gridtype :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>degree_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], dtype=float32)</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'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-cca34d2d-997c-4be6-af45-95b9dd17a324' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cca34d2d-997c-4be6-af45-95b9dd17a324' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f8ab971-3a95-44d8-abb3-00dc8303f685' class='xr-var-data-in' type='checkbox'><label for='data-5f8ab971-3a95-44d8-abb3-00dc8303f685' title='Show/Hide data repr'><svg 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>pointwidth :</span></dt><dd>1.0</dd><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>degree_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-05696577-67d8-4e52-b9a8-b7e61bb62a3e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-05696577-67d8-4e52-b9a8-b7e61bb62a3e' 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>basin</span></div><div class='xr-var-dims'>(Z, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5e76b97a-2366-4d54-9444-1c6bd4269f60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e76b97a-2366-4d54-9444-1c6bd4269f60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-660d3d5f-79af-4515-b547-ff93b79f37c3' class='xr-var-data-in' type='checkbox'><label for='data-660d3d5f-79af-4515-b547-ff93b79f37c3' title='Show/Hide data repr'><svg 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>basin code</dd><dt><span>CLIST :</span></dt><dd>Atlantic Ocean\n",
       "Pacific Ocean \n",
       "Indian Ocean\n",
       "Mediterranean Sea\n",
       "Baltic Sea\n",
       "Black Sea\n",
       "Red Sea\n",
       "Persian Gulf\n",
       "Hudson Bay\n",
       "Southern Ocean\n",
       "Arctic Ocean\n",
       "Sea of Japan\n",
       "Kara Sea\n",
       "Sulu Sea\n",
       "Baffin Bay\n",
       "East Mediterranean\n",
       "West Mediterranean\n",
       "Sea of Okhotsk\n",
       "Banda Sea\n",
       "Caribbean Sea\n",
       "Andaman Basin\n",
       "North Caribbean\n",
       "Gulf of Mexico\n",
       "Beaufort Sea\n",
       "South China Sea\n",
       "Barents Sea\n",
       "Celebes Sea\n",
       "Aleutian Basin\n",
       "Fiji Basin\n",
       "North American Basin\n",
       "West European Basin\n",
       "Southeast Indian Basin\n",
       "Coral Sea\n",
       "East Indian Basin\n",
       "Central Indian Basin\n",
       "Southwest Atlantic Basin\n",
       "Southeast Atlantic Basin\n",
       "Southeast Pacific Basin\n",
       "Guatemala Basin\n",
       "East Caroline Basin\n",
       "Marianas Basin\n",
       "Philippine Sea\n",
       "Arabian Sea\n",
       "Chile Basin\n",
       "Somali Basin\n",
       "Mascarene Basin\n",
       "Crozet Basin\n",
       "Guinea Basin\n",
       "Brazil Basin\n",
       "Argentine Basin\n",
       "Tasman Sea\n",
       "Atlantic Indian Basin\n",
       "Caspian Sea\n",
       "Sulu Sea II\n",
       "Venezuela Basin\n",
       "Bay of Bengal\n",
       "Java Sea\n",
       "East Indian Atlantic Basin</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>58</dd><dt><span>scale_min :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>ids</dd><dt><span>scale_max :</span></dt><dd>58</dd></dl></div><div class='xr-var-data'><pre>[2138400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f56f8d1a-5283-42ed-8702-43aef9bd2418' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f56f8d1a-5283-42ed-8702-43aef9bd2418' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>IRIDL</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (Z: 33, lon: 360, lat: 180)\n",
       "Coordinates:\n",
       "  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03\n",
       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "Data variables:\n",
       "    basin    (Z, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basin = basin.rename({'X': 'lon', 'Y': 'lat'})\n",
    "basin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;basin&#x27; (lat: 180, lon: 360)&gt;\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [11., 11., 11., ..., 11., 11., 11.],\n",
       "       [11., 11., 11., ..., 11., 11., 11.],\n",
       "       [11., 11., 11., ..., 11., 11., 11.]], dtype=float32)\n",
       "Coordinates:\n",
       "    Z        float32 0.0\n",
       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "Attributes:\n",
       "    long_name:  basin code\n",
       "    CLIST:      Atlantic Ocean\\nPacific Ocean \\nIndian Ocean\\nMediterranean S...\n",
       "    valid_min:  1\n",
       "    valid_max:  58\n",
       "    scale_min:  1\n",
       "    units:      ids\n",
       "    scale_max:  58</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'basin'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ea9eddcd-c908-4f31-bbc7-09cdc57fdd07' class='xr-array-in' type='checkbox' checked><label for='section-ea9eddcd-c908-4f31-bbc7-09cdc57fdd07' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... 11.0 11.0 11.0 11.0 11.0 11.0 11.0</span></div><div class='xr-array-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
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       "       ...,\n",
       "       [11., 11., 11., ..., 11., 11., 11.],\n",
       "       [11., 11., 11., ..., 11., 11., 11.],\n",
       "       [11., 11., 11., ..., 11., 11., 11.]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-beeaa389-8f34-4613-b8d0-7407df8b8142' class='xr-section-summary-in' type='checkbox'  checked><label for='section-beeaa389-8f34-4613-b8d0-7407df8b8142' 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>Z</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-8ebb5a13-5f94-4e65-8e5a-2e7002a5396f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ebb5a13-5f94-4e65-8e5a-2e7002a5396f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa5bde53-a6c7-458e-9e92-83f999493e14' class='xr-var-data-in' type='checkbox'><label for='data-aa5bde53-a6c7-458e-9e92-83f999493e14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(0., 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'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-dce5baaa-a4d5-4170-bf09-49b308cb001c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dce5baaa-a4d5-4170-bf09-49b308cb001c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff5d699a-2f28-4b60-87af-1d895b6edf79' class='xr-var-data-in' type='checkbox'><label for='data-ff5d699a-2f28-4b60-87af-1d895b6edf79' title='Show/Hide data repr'><svg 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>pointwidth :</span></dt><dd>1.0</dd><dt><span>gridtype :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>degree_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], dtype=float32)</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'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-3c38a38e-9de6-458a-92fb-a29ccbad7ec3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c38a38e-9de6-458a-92fb-a29ccbad7ec3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a98441ab-01b1-40f5-846e-a36b1a502d40' class='xr-var-data-in' type='checkbox'><label for='data-a98441ab-01b1-40f5-846e-a36b1a502d40' title='Show/Hide data repr'><svg 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>pointwidth :</span></dt><dd>1.0</dd><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>degree_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1be7af81-a830-437e-ba33-73b7e10ef458' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1be7af81-a830-437e-ba33-73b7e10ef458' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>basin code</dd><dt><span>CLIST :</span></dt><dd>Atlantic Ocean\n",
       "Pacific Ocean \n",
       "Indian Ocean\n",
       "Mediterranean Sea\n",
       "Baltic Sea\n",
       "Black Sea\n",
       "Red Sea\n",
       "Persian Gulf\n",
       "Hudson Bay\n",
       "Southern Ocean\n",
       "Arctic Ocean\n",
       "Sea of Japan\n",
       "Kara Sea\n",
       "Sulu Sea\n",
       "Baffin Bay\n",
       "East Mediterranean\n",
       "West Mediterranean\n",
       "Sea of Okhotsk\n",
       "Banda Sea\n",
       "Caribbean Sea\n",
       "Andaman Basin\n",
       "North Caribbean\n",
       "Gulf of Mexico\n",
       "Beaufort Sea\n",
       "South China Sea\n",
       "Barents Sea\n",
       "Celebes Sea\n",
       "Aleutian Basin\n",
       "Fiji Basin\n",
       "North American Basin\n",
       "West European Basin\n",
       "Southeast Indian Basin\n",
       "Coral Sea\n",
       "East Indian Basin\n",
       "Central Indian Basin\n",
       "Southwest Atlantic Basin\n",
       "Southeast Atlantic Basin\n",
       "Southeast Pacific Basin\n",
       "Guatemala Basin\n",
       "East Caroline Basin\n",
       "Marianas Basin\n",
       "Philippine Sea\n",
       "Arabian Sea\n",
       "Chile Basin\n",
       "Somali Basin\n",
       "Mascarene Basin\n",
       "Crozet Basin\n",
       "Guinea Basin\n",
       "Brazil Basin\n",
       "Argentine Basin\n",
       "Tasman Sea\n",
       "Atlantic Indian Basin\n",
       "Caspian Sea\n",
       "Sulu Sea II\n",
       "Venezuela Basin\n",
       "Bay of Bengal\n",
       "Java Sea\n",
       "East Indian Atlantic Basin</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>58</dd><dt><span>scale_min :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>ids</dd><dt><span>scale_max :</span></dt><dd>58</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'basin' (lat: 180, lon: 360)>\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [11., 11., 11., ..., 11., 11., 11.],\n",
       "       [11., 11., 11., ..., 11., 11., 11.],\n",
       "       [11., 11., 11., ..., 11., 11., 11.]], dtype=float32)\n",
       "Coordinates:\n",
       "    Z        float32 0.0\n",
       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "Attributes:\n",
       "    long_name:  basin code\n",
       "    CLIST:      Atlantic Ocean\\nPacific Ocean \\nIndian Ocean\\nMediterranean S...\n",
       "    valid_min:  1\n",
       "    valid_max:  58\n",
       "    scale_min:  1\n",
       "    units:      ids\n",
       "    scale_max:  58"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basin_surf = basin.basin[0]\n",
    "basin_surf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f8e4dcb73d0>"
      ]
     },
     "execution_count": 42,
     "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": [
    "basin_surf.plot(vmax=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f8e4db88d00>"
      ]
     },
     "execution_count": 43,
     "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": [
    "basin_surf_interp = basin_surf.interp_like(ds.sst, method='nearest')\n",
    "basin_surf_interp.plot(vmax=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 708, basin: 14)&gt;\n",
       "array([[-1.8       , -1.8       , 23.455315  , ..., -1.8       ,\n",
       "         3.3971915 , 24.182198  ],\n",
       "       [-1.8       , -1.8       , 23.722523  , ..., -1.8       ,\n",
       "         0.03573781, 24.59657   ],\n",
       "       [-1.8       , -1.8       , 24.601315  , ..., -1.8       ,\n",
       "        -0.26487017, 26.234186  ],\n",
       "       ...,\n",
       "       [ 0.6758132 ,  6.504184  , 29.279463  , ..., 10.920228  ,\n",
       "        15.955025  , 29.41976   ],\n",
       "       [-0.7937442 ,  3.0715032 , 27.608435  , ...,  5.4078875 ,\n",
       "        10.673693  , 27.7558    ],\n",
       "       [-1.8       , -0.06063586, 25.881481  , ...,  0.5253569 ,\n",
       "         7.267694  , 26.163145  ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "    Z        float32 0.0\n",
       "  * basin    (basin) float64 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 708</li><li><span class='xr-has-index'>basin</span>: 14</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-42f626bb-4f47-460b-8f8e-931224e45627' class='xr-array-in' type='checkbox' checked><label for='section-42f626bb-4f47-460b-8f8e-931224e45627' 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>-1.8 -1.8 23.46 13.2 1.804 7.341 ... 3.645 -1.8 0.5254 7.268 26.16</span></div><div class='xr-array-data'><pre>array([[-1.8       , -1.8       , 23.455315  , ..., -1.8       ,\n",
       "         3.3971915 , 24.182198  ],\n",
       "       [-1.8       , -1.8       , 23.722523  , ..., -1.8       ,\n",
       "         0.03573781, 24.59657   ],\n",
       "       [-1.8       , -1.8       , 24.601315  , ..., -1.8       ,\n",
       "        -0.26487017, 26.234186  ],\n",
       "       ...,\n",
       "       [ 0.6758132 ,  6.504184  , 29.279463  , ..., 10.920228  ,\n",
       "        15.955025  , 29.41976   ],\n",
       "       [-0.7937442 ,  3.0715032 , 27.608435  , ...,  5.4078875 ,\n",
       "        10.673693  , 27.7558    ],\n",
       "       [-1.8       , -0.06063586, 25.881481  , ...,  0.5253569 ,\n",
       "         7.267694  , 26.163145  ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-ebe62679-9c09-40a0-8cc2-69499e749303' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ebe62679-9c09-40a0-8cc2-69499e749303' 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'>1960-01-01 ... 2018-12-01</div><input id='attrs-93ed3afe-6d3e-42c6-9e90-d9b8e6e0d563' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93ed3afe-6d3e-42c6-9e90-d9b8e6e0d563' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac114159-a095-47c9-a5db-7df6d5e290fd' class='xr-var-data-in' type='checkbox'><label for='data-ac114159-a095-47c9-a5db-7df6d5e290fd' title='Show/Hide data repr'><svg 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>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80992.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-7f69f3ad-a844-4165-b076-18e08c2f5cf3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f69f3ad-a844-4165-b076-18e08c2f5cf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03cd6844-b438-47d8-8223-f348f38b8a53' class='xr-var-data-in' type='checkbox'><label for='data-03cd6844-b438-47d8-8223-f348f38b8a53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(0., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>basin</span></div><div class='xr-var-dims'>(basin)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 4.0 ... 12.0 53.0 56.0</div><input id='attrs-be768e6a-ec8f-4143-92ca-56e2dac52206' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be768e6a-ec8f-4143-92ca-56e2dac52206' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9c592ff-6c28-4ecc-8c53-e32005bd5817' class='xr-var-data-in' type='checkbox'><label for='data-b9c592ff-6c28-4ecc-8c53-e32005bd5817' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 53., 56.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c7b73c1-2f07-4a23-ba09-9447563f2460' class='xr-section-summary-in' type='checkbox'  ><label for='section-0c7b73c1-2f07-4a23-ba09-9447563f2460' class='xr-section-summary' >Attributes: <span>(10)</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>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</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>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  89 180]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (time: 708, basin: 14)>\n",
       "array([[-1.8       , -1.8       , 23.455315  , ..., -1.8       ,\n",
       "         3.3971915 , 24.182198  ],\n",
       "       [-1.8       , -1.8       , 23.722523  , ..., -1.8       ,\n",
       "         0.03573781, 24.59657   ],\n",
       "       [-1.8       , -1.8       , 24.601315  , ..., -1.8       ,\n",
       "        -0.26487017, 26.234186  ],\n",
       "       ...,\n",
       "       [ 0.6758132 ,  6.504184  , 29.279463  , ..., 10.920228  ,\n",
       "        15.955025  , 29.41976   ],\n",
       "       [-0.7937442 ,  3.0715032 , 27.608435  , ...,  5.4078875 ,\n",
       "        10.673693  , 27.7558    ],\n",
       "       [-1.8       , -0.06063586, 25.881481  , ...,  0.5253569 ,\n",
       "         7.267694  , 26.163145  ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "    Z        float32 0.0\n",
       "  * basin    (basin) float64 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.sst.groupby(basin_surf_interp).first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
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       "\n",
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       "\n",
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       "\n",
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       ".xr-var-data,\n",
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       "\n",
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       "\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",
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       "  padding-right: 10px;\n",
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       "\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.DataArray &#x27;sst&#x27; (time: 708, basin: 14)&gt;\n",
       "array([[18.585493 , 20.757555 , 21.572067 , ...,  6.238062 ,  6.889794 ,\n",
       "        26.49982  ],\n",
       "       [18.705065 , 20.81674  , 21.902279 , ...,  4.8877654,  5.44638  ,\n",
       "        26.577093 ],\n",
       "       [18.845842 , 20.865038 , 22.031416 , ...,  4.686406 ,  5.5322194,\n",
       "        27.908558 ],\n",
       "       ...,\n",
       "       [19.84992  , 21.960493 , 20.389412 , ..., 17.571943 , 18.184528 ,\n",
       "        29.336565 ],\n",
       "       [19.424026 , 21.722925 , 21.061403 , ..., 13.461868 , 13.863244 ,\n",
       "        28.755905 ],\n",
       "       [19.265354 , 21.512274 , 21.814356 , ...,  9.417906 , 10.607256 ,\n",
       "        27.905243 ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "    Z        float32 0.0\n",
       "  * basin    (basin) float64 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 708</li><li><span class='xr-has-index'>basin</span>: 14</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-987bdde7-e4b2-4b14-9408-b427bca5f00c' class='xr-array-in' type='checkbox' checked><label for='section-987bdde7-e4b2-4b14-9408-b427bca5f00c' 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>18.59 20.76 21.57 15.46 2.553 ... 1.762 -0.8986 9.418 10.61 27.91</span></div><div class='xr-array-data'><pre>array([[18.585493 , 20.757555 , 21.572067 , ...,  6.238062 ,  6.889794 ,\n",
       "        26.49982  ],\n",
       "       [18.705065 , 20.81674  , 21.902279 , ...,  4.8877654,  5.44638  ,\n",
       "        26.577093 ],\n",
       "       [18.845842 , 20.865038 , 22.031416 , ...,  4.686406 ,  5.5322194,\n",
       "        27.908558 ],\n",
       "       ...,\n",
       "       [19.84992  , 21.960493 , 20.389412 , ..., 17.571943 , 18.184528 ,\n",
       "        29.336565 ],\n",
       "       [19.424026 , 21.722925 , 21.061403 , ..., 13.461868 , 13.863244 ,\n",
       "        28.755905 ],\n",
       "       [19.265354 , 21.512274 , 21.814356 , ...,  9.417906 , 10.607256 ,\n",
       "        27.905243 ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-791e86c3-a342-47db-967e-bd38c09943a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-791e86c3-a342-47db-967e-bd38c09943a5' 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'>1960-01-01 ... 2018-12-01</div><input id='attrs-bd81b635-f842-4fa1-b913-2d803af49177' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd81b635-f842-4fa1-b913-2d803af49177' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2351769-78f3-471f-9d51-ebfeff93af39' class='xr-var-data-in' type='checkbox'><label for='data-f2351769-78f3-471f-9d51-ebfeff93af39' title='Show/Hide data repr'><svg 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>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80992.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-4584b9f2-c330-467a-8c13-9e7f4947ace5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4584b9f2-c330-467a-8c13-9e7f4947ace5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6282cfc7-5b02-42c7-b6e7-afb77fbc61df' class='xr-var-data-in' type='checkbox'><label for='data-6282cfc7-5b02-42c7-b6e7-afb77fbc61df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(0., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>basin</span></div><div class='xr-var-dims'>(basin)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 4.0 ... 12.0 53.0 56.0</div><input id='attrs-8e37ac50-64cc-4e0b-b935-94f9df265a85' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e37ac50-64cc-4e0b-b935-94f9df265a85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-378711e0-2237-48ff-ac4e-3030d35148f9' class='xr-var-data-in' type='checkbox'><label for='data-378711e0-2237-48ff-ac4e-3030d35148f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 53., 56.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-36670b27-e23b-4913-870a-cc52b5828bdd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-36670b27-e23b-4913-870a-cc52b5828bdd' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'sst' (time: 708, basin: 14)>\n",
       "array([[18.585493 , 20.757555 , 21.572067 , ...,  6.238062 ,  6.889794 ,\n",
       "        26.49982  ],\n",
       "       [18.705065 , 20.81674  , 21.902279 , ...,  4.8877654,  5.44638  ,\n",
       "        26.577093 ],\n",
       "       [18.845842 , 20.865038 , 22.031416 , ...,  4.686406 ,  5.5322194,\n",
       "        27.908558 ],\n",
       "       ...,\n",
       "       [19.84992  , 21.960493 , 20.389412 , ..., 17.571943 , 18.184528 ,\n",
       "        29.336565 ],\n",
       "       [19.424026 , 21.722925 , 21.061403 , ..., 13.461868 , 13.863244 ,\n",
       "        28.755905 ],\n",
       "       [19.265354 , 21.512274 , 21.814356 , ...,  9.417906 , 10.607256 ,\n",
       "        27.905243 ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "    Z        float32 0.0\n",
       "  * basin    (basin) float64 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basin_mean_sst = ds.sst.groupby(basin_surf_interp).mean()\n",
    "basin_mean_sst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "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>Z</th>\n",
       "      <th>sst</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.284992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>21.178225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>21.127054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.845881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.131749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15.084384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>28.494108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26.619698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.310854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.547191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.816617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.085889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14.338935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>28.465738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Z        sst\n",
       "basin                \n",
       "1.0    0.0  19.284992\n",
       "2.0    0.0  21.178225\n",
       "3.0    0.0  21.127054\n",
       "4.0    0.0  19.845881\n",
       "5.0    0.0   8.131749\n",
       "6.0    0.0  15.084384\n",
       "7.0    0.0  28.494108\n",
       "8.0    0.0  26.619698\n",
       "9.0    0.0   0.310854\n",
       "10.0   0.0   1.547191\n",
       "11.0   0.0  -0.816617\n",
       "12.0   0.0  12.085889\n",
       "53.0   0.0  14.338935\n",
       "56.0   0.0  28.465738"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = basin_mean_sst.mean('time').to_dataframe()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1                 Atlantic Ocean\n",
       "2                 Pacific Ocean \n",
       "3                   Indian Ocean\n",
       "4              Mediterranean Sea\n",
       "5                     Baltic Sea\n",
       "6                      Black Sea\n",
       "7                        Red Sea\n",
       "8                   Persian Gulf\n",
       "9                     Hudson Bay\n",
       "10                Southern Ocean\n",
       "11                  Arctic Ocean\n",
       "12                  Sea of Japan\n",
       "13                      Kara Sea\n",
       "14                      Sulu Sea\n",
       "15                    Baffin Bay\n",
       "16            East Mediterranean\n",
       "17            West Mediterranean\n",
       "18                Sea of Okhotsk\n",
       "19                     Banda Sea\n",
       "20                 Caribbean Sea\n",
       "21                 Andaman Basin\n",
       "22               North Caribbean\n",
       "23                Gulf of Mexico\n",
       "24                  Beaufort Sea\n",
       "25               South China Sea\n",
       "26                   Barents Sea\n",
       "27                   Celebes Sea\n",
       "28                Aleutian Basin\n",
       "29                    Fiji Basin\n",
       "30          North American Basin\n",
       "31           West European Basin\n",
       "32        Southeast Indian Basin\n",
       "33                     Coral Sea\n",
       "34             East Indian Basin\n",
       "35          Central Indian Basin\n",
       "36      Southwest Atlantic Basin\n",
       "37      Southeast Atlantic Basin\n",
       "38       Southeast Pacific Basin\n",
       "39               Guatemala Basin\n",
       "40           East Caroline Basin\n",
       "41                Marianas Basin\n",
       "42                Philippine Sea\n",
       "43                   Arabian Sea\n",
       "44                   Chile Basin\n",
       "45                  Somali Basin\n",
       "46               Mascarene Basin\n",
       "47                  Crozet Basin\n",
       "48                  Guinea Basin\n",
       "49                  Brazil Basin\n",
       "50               Argentine Basin\n",
       "51                    Tasman Sea\n",
       "52         Atlantic Indian Basin\n",
       "53                   Caspian Sea\n",
       "54                   Sulu Sea II\n",
       "55               Venezuela Basin\n",
       "56                 Bay of Bengal\n",
       "57                      Java Sea\n",
       "58    East Indian Atlantic Basin\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "basin_names = basin_surf.attrs['CLIST'].split('\\n')\n",
    "basin_df = pd.Series(basin_names, index=np.arange(1, len(basin_names)+1))\n",
    "basin_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(basin_df.rename('basin_name'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='basin_name'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(y='sst', x='basin_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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================================================
FILE: src/lectures/xarray/xarray.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Xarray Fundamentals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Xarray data structures\n",
    "\n",
    "Like Pandas, xarray has two fundamental data structures:\n",
    "* a `DataArray`, which holds a single multi-dimensional variable and its coordinates\n",
    "* a `Dataset`, which holds multiple variables that potentially share the same coordinates\n",
    "\n",
    "### DataArray\n",
    "\n",
    "A `DataArray` has four essential attributes:\n",
    "* `values`: a `numpy.ndarray` holding the array’s values\n",
    "* `dims`: dimension names for each axis (e.g., `('x', 'y', 'z')`)\n",
    "* `coords`: a dict-like container of arrays (coordinates) that label each point (e.g., 1-dimensional arrays of numbers, datetime objects or strings)\n",
    "* `attrs`: an `OrderedDict` to hold arbitrary metadata (attributes)\n",
    "\n",
    "Let's start by constructing some DataArrays manually "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (8,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple DataArray without dimensions or coordinates isn't much use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "\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",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
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       "}\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",
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       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
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       "  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",
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       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
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       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
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       "\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",
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       "  display: inline-block;\n",
       "}\n",
       "\n",
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       "  display: inline-block !important;\n",
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       "\n",
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       "  display: inline-block;\n",
       "  padding: 0;\n",
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       "\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
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       "\n",
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       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
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       "\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",
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       "\n",
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       "\n",
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       "  display: block;\n",
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       "\n",
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       "}\n",
       "\n",
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       "\n",
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       ".xr-var-attrs,\n",
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       "\n",
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       "  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 (dim_0: 5)&gt;\n",
       "array([9, 0, 2, 1, 0])\n",
       "Dimensions without coordinates: dim_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'></div><ul class='xr-dim-list'><li><span>dim_0</span>: 5</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b332476e-12e8-4fd2-b62b-c5c93c584628' class='xr-array-in' type='checkbox' checked><label for='section-b332476e-12e8-4fd2-b62b-c5c93c584628' 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>9 0 2 1 0</span></div><div class='xr-array-data'><pre>array([9, 0, 2, 1, 0])</pre></div></div></li><li class='xr-section-item'><input id='section-2bc1e804-2bf2-47a2-861e-bdca30c1c5fd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2bc1e804-2bf2-47a2-861e-bdca30c1c5fd' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-7de1087c-7b6a-463a-a268-4cdc9a7e6f87' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7de1087c-7b6a-463a-a268-4cdc9a7e6f87' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e3f8dded-a60f-4f72-b168-43ad13122fe4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e3f8dded-a60f-4f72-b168-43ad13122fe4' 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 (dim_0: 5)>\n",
       "array([9, 0, 2, 1, 0])\n",
       "Dimensions without coordinates: dim_0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da = xr.DataArray([9, 0, 2, 1, 0])\n",
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add a dimension name..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (x: 5)&gt;\n",
       "array([9, 0, 2, 1, 0])\n",
       "Dimensions without coordinates: x</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'></div><ul class='xr-dim-list'><li><span>x</span>: 5</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ed38a24a-60b0-46e3-8068-57484478ee73' class='xr-array-in' type='checkbox' checked><label for='section-ed38a24a-60b0-46e3-8068-57484478ee73' 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>9 0 2 1 0</span></div><div class='xr-array-data'><pre>array([9, 0, 2, 1, 0])</pre></div></div></li><li class='xr-section-item'><input id='section-5e316f08-eacb-49cc-bb10-6633980069e2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5e316f08-eacb-49cc-bb10-6633980069e2' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-5837e6fa-506a-4e59-8ba0-38d7ac416ff0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5837e6fa-506a-4e59-8ba0-38d7ac416ff0' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-b5e65d8d-7a08-4de0-b375-b7f89eeeb193' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b5e65d8d-7a08-4de0-b375-b7f89eeeb193' 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 (x: 5)>\n",
       "array([9, 0, 2, 1, 0])\n",
       "Dimensions without coordinates: x"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da = xr.DataArray([9, 0, 2, 1, 0], dims=['x'])\n",
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But things get most interesting when we add a coordinate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "}\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 (x: 5)&gt;\n",
       "array([9, 0, 2, 1, 0])\n",
       "Coordinates:\n",
       "  * x        (x) int64 10 20 30 40 50</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'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 5</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2cddaace-103b-43f7-bea5-fd716d07ca5b' class='xr-array-in' type='checkbox' checked><label for='section-2cddaace-103b-43f7-bea5-fd716d07ca5b' 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>9 0 2 1 0</span></div><div class='xr-array-data'><pre>array([9, 0, 2, 1, 0])</pre></div></div></li><li class='xr-section-item'><input id='section-4ee5c8b7-ac28-4880-bf70-e778622cb5a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4ee5c8b7-ac28-4880-bf70-e778622cb5a1' 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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>10 20 30 40 50</div><input id='attrs-b9523129-4ec6-4487-aec9-b92455044245' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9523129-4ec6-4487-aec9-b92455044245' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2d7af84-79cb-490a-b3a5-39fda137ea2b' class='xr-var-data-in' type='checkbox'><label for='data-a2d7af84-79cb-490a-b3a5-39fda137ea2b' title='Show/Hide data 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([10, 20, 30, 40, 50])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77fb9f3c-8ee5-4066-a335-3b7eb258dcfc' class='xr-section-summary-in' type='checkbox'  ><label for='section-77fb9f3c-8ee5-4066-a335-3b7eb258dcfc' 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>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d97dbd8a-8fc1-4f1d-82b1-de9991fe9979' class='xr-index-data-in' type='checkbox'/><label for='index-d97dbd8a-8fc1-4f1d-82b1-de9991fe9979' 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(Int64Index([10, 20, 30, 40, 50], dtype=&#x27;int64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ede78e71-561b-46d0-8437-1bc57cbfa2fd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ede78e71-561b-46d0-8437-1bc57cbfa2fd' 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 (x: 5)>\n",
       "array([9, 0, 2, 1, 0])\n",
       "Coordinates:\n",
       "  * x        (x) int64 10 20 30 40 50"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da = xr.DataArray([9, 0, 2, 1, 0],\n",
    "                  dims=['x'],\n",
    "                  coords={'x': [10, 20, 30, 40, 50]})\n",
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This coordinate has been used to create an _index_, which works very similar to a Pandas index.\n",
    "In fact, under the hood, Xarray just reuses Pandas indexes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Indexes:\n",
       "    x        Int64Index([10, 20, 30, 40, 50], dtype='int64', name='x')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Xarray has built-in plotting, like pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9aae456f80>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "da.plot(marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multidimensional DataArray\n",
    "\n",
    "If we are just dealing with 1D data, Pandas and Xarray have very similar capabilities. Xarray's real potential comes with multidimensional data.\n",
    "\n",
    "Let's go back to the multidimensional ARGO data we loaded in the numpy lesson."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/jovyan/.cache/pooch/7e6685dbe2a3c0b0870f770f3ef413d9-float_data_4901412.zip.unzip/float_data/P.npy',\n",
       " '/home/jovyan/.cache/pooch/7e6685dbe2a3c0b0870f770f3ef413d9-float_data_4901412.zip.unzip/float_data/S.npy',\n",
       " '/home/jovyan/.cache/pooch/7e6685dbe2a3c0b0870f770f3ef413d9-float_data_4901412.zip.unzip/float_data/T.npy',\n",
       " '/home/jovyan/.cache/pooch/7e6685dbe2a3c0b0870f770f3ef413d9-float_data_4901412.zip.unzip/float_data/date.npy',\n",
       " '/home/jovyan/.cache/pooch/7e6685dbe2a3c0b0870f770f3ef413d9-float_data_4901412.zip.unzip/float_data/lat.npy',\n",
       " '/home/jovyan/.cache/pooch/7e6685dbe2a3c0b0870f770f3ef413d9-float_data_4901412.zip.unzip/float_data/levels.npy',\n",
       " '/home/jovyan/.cache/pooch/7e6685dbe2a3c0b0870f770f3ef413d9-float_data_4901412.zip.unzip/float_data/lon.npy']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pooch\n",
    "url = \"https://www.ldeo.columbia.edu/~rpa/float_data_4901412.zip\"\n",
    "files = pooch.retrieve(url, processor=pooch.Unzip(), known_hash=\"2a703c720302c682f1662181d329c9f22f9f10e1539dc2d6082160a469165009\")\n",
    "files.sort()\n",
    "files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will manually load each of these variables into a numpy array.\n",
    "If this seems repetitive and inefficient, that's the point!\n",
    "NumPy itself is not meant for managing groups of inter-related arrays.\n",
    "That's what Xarray is for!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "P = np.load(files[0])\n",
    "S = np.load(files[1])\n",
    "T = np.load(files[2])\n",
    "date = np.load(files[3])\n",
    "lat = np.load(files[4])\n",
    "levels = np.load(files[5])\n",
    "lon = np.load(files[6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's organize the data and coordinates of the salinity variable into a DataArray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " */\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 (level: 78, date: 75)&gt;\n",
       "array([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [34.91585922, 34.92390442, 34.92390442, ..., 34.93481064,\n",
       "        34.94081116, 34.94680786],\n",
       "       [34.91585922, 34.92390442, 34.92190552, ..., 34.93280792,\n",
       "        34.93680954, 34.94380951],\n",
       "       [34.91785812, 34.92390442, 34.92390442, ...,         nan,\n",
       "        34.93680954,         nan]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...</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'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 78</li><li><span class='xr-has-index'>date</span>: 75</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-74d85073-abd3-40b6-8f96-30d69b5eaec8' class='xr-array-in' type='checkbox' checked><label for='section-74d85073-abd3-40b6-8f96-30d69b5eaec8' 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>35.64 35.51 35.57 35.4 35.45 35.5 ... 34.94 nan 34.94 nan 34.94 nan</span></div><div class='xr-array-data'><pre>array([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [34.91585922, 34.92390442, 34.92390442, ..., 34.93481064,\n",
       "        34.94081116, 34.94680786],\n",
       "       [34.91585922, 34.92390442, 34.92190552, ..., 34.93280792,\n",
       "        34.93680954, 34.94380951],\n",
       "       [34.91785812, 34.92390442, 34.92390442, ...,         nan,\n",
       "        34.93680954,         nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-bbafcb3c-2b24-4a9e-96a8-a329672de8da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bbafcb3c-2b24-4a9e-96a8-a329672de8da' 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'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-023d7250-9ef8-4258-8487-53e2cc948bf5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-023d7250-9ef8-4258-8487-53e2cc948bf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5c070e8-9a78-43d3-8f84-168ad71a6cdc' class='xr-var-data-in' type='checkbox'><label for='data-d5c070e8-9a78-43d3-8f84-168ad71a6cdc' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-34c30a98-9bab-484a-b897-abb76cd890fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34c30a98-9bab-484a-b897-abb76cd890fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-702a10b2-a71b-46ba-8eaf-bc7d3262065a' class='xr-var-data-in' type='checkbox'><label for='data-702a10b2-a71b-46ba-8eaf-bc7d3262065a' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2438185c-4208-49cd-963e-d825960bdd39' class='xr-section-summary-in' type='checkbox'  ><label for='section-2438185c-4208-49cd-963e-d825960bdd39' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c4f50a7b-d5c9-4357-80f9-a8f845828135' class='xr-index-data-in' type='checkbox'/><label for='index-c4f50a7b-d5c9-4357-80f9-a8f845828135' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-940a68c6-a37c-481e-8049-ca4ee2cd902d' class='xr-index-data-in' type='checkbox'/><label for='index-940a68c6-a37c-481e-8049-ca4ee2cd902d' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8bc14ece-54b1-4d63-95dc-bdb35bf41fc5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8bc14ece-54b1-4d63-95dc-bdb35bf41fc5' 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 (level: 78, date: 75)>\n",
       "array([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [34.91585922, 34.92390442, 34.92390442, ..., 34.93481064,\n",
       "        34.94081116, 34.94680786],\n",
       "       [34.91585922, 34.92390442, 34.92190552, ..., 34.93280792,\n",
       "        34.93680954, 34.94380951],\n",
       "       [34.91785812, 34.92390442, 34.92390442, ...,         nan,\n",
       "        34.93680954,         nan]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da_salinity = xr.DataArray(S, dims=['level', 'date'],\n",
    "                           coords={'level': levels,\n",
    "                                   'date': date},)\n",
    "da_salinity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9a91aca530>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "da_salinity.plot(yincrease=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attributes can be used to store metadata. What metadata should you store? The [CF Conventions](http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/cf-conventions.html#_description_of_the_data) are a great resource for thinking about climate metadata. Below we define two of the required CF-conventions attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --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 (level: 78, date: 75)&gt;\n",
       "array([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [34.91585922, 34.92390442, 34.92390442, ..., 34.93481064,\n",
       "        34.94081116, 34.94680786],\n",
       "       [34.91585922, 34.92390442, 34.92190552, ..., 34.93280792,\n",
       "        34.93680954, 34.94380951],\n",
       "       [34.91785812, 34.92390442, 34.92390442, ...,         nan,\n",
       "        34.93680954,         nan]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "Attributes:\n",
       "    units:          PSU\n",
       "    standard_name:  sea_water_salinity</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'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 78</li><li><span class='xr-has-index'>date</span>: 75</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-22300805-6c3d-40b9-a9a6-156d23f18316' class='xr-array-in' type='checkbox' checked><label for='section-22300805-6c3d-40b9-a9a6-156d23f18316' 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>35.64 35.51 35.57 35.4 35.45 35.5 ... 34.94 nan 34.94 nan 34.94 nan</span></div><div class='xr-array-data'><pre>array([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [34.91585922, 34.92390442, 34.92390442, ..., 34.93481064,\n",
       "        34.94081116, 34.94680786],\n",
       "       [34.91585922, 34.92390442, 34.92190552, ..., 34.93280792,\n",
       "        34.93680954, 34.94380951],\n",
       "       [34.91785812, 34.92390442, 34.92390442, ...,         nan,\n",
       "        34.93680954,         nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-69c00d1c-9530-4931-ab89-ec9ccd11d7f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-69c00d1c-9530-4931-ab89-ec9ccd11d7f9' 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'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-a2c06d31-0c10-4459-9029-67c329a09f41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2c06d31-0c10-4459-9029-67c329a09f41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40d13407-884c-418e-8755-50553eb21a40' class='xr-var-data-in' type='checkbox'><label for='data-40d13407-884c-418e-8755-50553eb21a40' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-715062dc-bba9-48d1-9eea-4b8e07c2ed8d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-715062dc-bba9-48d1-9eea-4b8e07c2ed8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3274832a-3668-4720-b6eb-a14a079d5692' class='xr-var-data-in' type='checkbox'><label for='data-3274832a-3668-4720-b6eb-a14a079d5692' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-774b7fe8-609b-43fa-834b-44592659fb81' class='xr-section-summary-in' type='checkbox'  ><label for='section-774b7fe8-609b-43fa-834b-44592659fb81' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c4bd9474-6597-4597-919f-dfdc0b61d404' class='xr-index-data-in' type='checkbox'/><label for='index-c4bd9474-6597-4597-919f-dfdc0b61d404' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f046bb57-8a15-4b3b-a405-73656926e3ce' class='xr-index-data-in' type='checkbox'/><label for='index-f046bb57-8a15-4b3b-a405-73656926e3ce' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c3dec2d2-0c39-4a04-8939-c19926c66c61' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c3dec2d2-0c39-4a04-8939-c19926c66c61' 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>units :</span></dt><dd>PSU</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (level: 78, date: 75)>\n",
       "array([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [34.91585922, 34.92390442, 34.92390442, ..., 34.93481064,\n",
       "        34.94081116, 34.94680786],\n",
       "       [34.91585922, 34.92390442, 34.92190552, ..., 34.93280792,\n",
       "        34.93680954, 34.94380951],\n",
       "       [34.91785812, 34.92390442, 34.92390442, ...,         nan,\n",
       "        34.93680954,         nan]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "Attributes:\n",
       "    units:          PSU\n",
       "    standard_name:  sea_water_salinity"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da_salinity.attrs['units'] = 'PSU'\n",
    "da_salinity.attrs['standard_name'] = 'sea_water_salinity'\n",
    "da_salinity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if we plot the data again, the name and units are automatically attached to the figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9a91a14430>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "da_salinity.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Datasets\n",
    "\n",
    "A Dataset holds many DataArrays which potentially can share coordinates. In analogy to pandas:\n",
    "\n",
    "    pandas.Series : pandas.Dataframe :: xarray.DataArray : xarray.Dataset\n",
    "    \n",
    "Constructing Datasets manually is a bit more involved in terms of syntax. The Dataset constructor takes three arguments:\n",
    "\n",
    "* `data_vars` should be a dictionary with each key as the name of the variable and each value as one of:\n",
    "  * A `DataArray` or Variable\n",
    "  * A tuple of the form `(dims, data[, attrs])`, which is converted into arguments for Variable\n",
    "  * A pandas object, which is converted into a `DataArray`\n",
    "  * A 1D array or list, which is interpreted as values for a one dimensional coordinate variable along the same dimension as it’s name\n",
    "* `coords` should be a dictionary of the same form as data_vars.\n",
    "* `attrs` should be a dictionary.\n",
    "\n",
    "Let's put together a Dataset with temperature, salinity and pressure all together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  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;\n",
       "Dimensions:      (level: 78, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan 34.94 nan\n",
       "    temperature  (level, date) float64 18.97 18.44 19.1 19.79 ... nan 3.714 nan\n",
       "    pressure     (level, date) float64 6.8 6.1 6.5 5.0 ... 2e+03 nan 2e+03 nan</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-6100215c-d735-436c-adb0-b9dc5c29f13b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6100215c-d735-436c-adb0-b9dc5c29f13b' 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'>level</span>: 78</li><li><span class='xr-has-index'>date</span>: 75</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-097452c3-4c62-4325-a8f1-4f67d4fcdd63' class='xr-section-summary-in' type='checkbox'  checked><label for='section-097452c3-4c62-4325-a8f1-4f67d4fcdd63' 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'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-a454a008-c7ef-4e04-ac9a-4d5957101e01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a454a008-c7ef-4e04-ac9a-4d5957101e01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dcdea0b-f07f-4d56-af97-e031b441b000' class='xr-var-data-in' type='checkbox'><label for='data-5dcdea0b-f07f-4d56-af97-e031b441b000' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-85c28dbe-db14-42ea-83de-9681f6915274' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85c28dbe-db14-42ea-83de-9681f6915274' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbb14149-c84d-4da1-82ed-99f4a2836e44' class='xr-var-data-in' type='checkbox'><label for='data-bbb14149-c84d-4da1-82ed-99f4a2836e44' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f5dce66b-526c-4d34-8000-6c4108b28ccb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f5dce66b-526c-4d34-8000-6c4108b28ccb' 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>salinity</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.64 35.51 35.57 ... nan 34.94 nan</div><input id='attrs-b53a5e9f-4819-40d4-8856-1747ca072493' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b53a5e9f-4819-40d4-8856-1747ca072493' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-637fcdf4-2aa4-4efc-a43d-9cd2c66ddf44' class='xr-var-data-in' type='checkbox'><label for='data-637fcdf4-2aa4-4efc-a43d-9cd2c66ddf44' title='Show/Hide data 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([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [34.91585922, 34.92390442, 34.92390442, ..., 34.93481064,\n",
       "        34.94081116, 34.94680786],\n",
       "       [34.91585922, 34.92390442, 34.92190552, ..., 34.93280792,\n",
       "        34.93680954, 34.94380951],\n",
       "       [34.91785812, 34.92390442, 34.92390442, ...,         nan,\n",
       "        34.93680954,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>18.97 18.44 19.1 ... nan 3.714 nan</div><input id='attrs-3f47c806-ca3e-4dd6-8a2c-db662ed4bdf3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f47c806-ca3e-4dd6-8a2c-db662ed4bdf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71649d20-a4d0-4390-b74a-1af0ad8520e2' class='xr-var-data-in' type='checkbox'><label for='data-71649d20-a4d0-4390-b74a-1af0ad8520e2' title='Show/Hide data 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([[18.97400093, 18.43700027, 19.09900093, ..., 19.11300087,\n",
       "        21.82299995, 20.13100052],\n",
       "       [18.74099922, 18.39999962, 19.08200073, ..., 18.47200012,\n",
       "        19.45999908, 20.125     ],\n",
       "       [18.37000084, 18.37400055, 19.06500053, ..., 18.22999954,\n",
       "        19.26199913, 20.07699966],\n",
       "       ...,\n",
       "       [ 3.79299998,  3.81399989,  3.80200005, ...,  3.80699992,\n",
       "         3.81100011,  3.8599999 ],\n",
       "       [ 3.76399994,  3.77800012,  3.75699997, ...,  3.75399995,\n",
       "         3.74600005,  3.80599999],\n",
       "       [ 3.74399996,  3.74600005,  3.7249999 , ...,         nan,\n",
       "         3.71399999,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.8 6.1 6.5 5.0 ... nan 2e+03 nan</div><input id='attrs-6ffef86f-4bc4-4e8f-a590-eea2f097b3d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ffef86f-4bc4-4e8f-a590-eea2f097b3d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad3ff1f4-9a52-4d58-8b1b-8b1343357439' class='xr-var-data-in' type='checkbox'><label for='data-ad3ff1f4-9a52-4d58-8b1b-8b1343357439' title='Show/Hide data 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([[   6.80000019,    6.0999999 ,    6.5       , ...,    7.0999999 ,\n",
       "           7.20000029,    6.5       ],\n",
       "       [  10.69999981,   10.59999943,   10.39999962, ...,   10.79999924,\n",
       "          11.09999943,   10.39999962],\n",
       "       [  15.69999981,   14.09999943,   14.89999962, ...,   15.89999962,\n",
       "          15.59999943,   15.89999962],\n",
       "       ...,\n",
       "       [1900.60009766, 1900.        , 1900.19995117, ..., 1899.70007324,\n",
       "        1900.40002441, 1899.80004883],\n",
       "       [1949.90002441, 1950.        , 1949.89990234, ..., 1950.59997559,\n",
       "        1950.20007324, 1949.70007324],\n",
       "       [1999.30004883, 1998.        , 1998.5       , ...,           nan,\n",
       "        2000.40002441,           nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d330a65e-4157-4a9f-a21f-c0ffc892a5e7' class='xr-section-summary-in' type='checkbox'  ><label for='section-d330a65e-4157-4a9f-a21f-c0ffc892a5e7' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-24e8adf9-963b-4dab-ae15-b4eff363ad68' class='xr-index-data-in' type='checkbox'/><label for='index-24e8adf9-963b-4dab-ae15-b4eff363ad68' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-19ca0725-9c91-447c-b5cb-390a4df10387' class='xr-index-data-in' type='checkbox'/><label for='index-19ca0725-9c91-447c-b5cb-390a4df10387' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c6a2be7d-6ba8-46eb-a898-cf7ee5e108f5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c6a2be7d-6ba8-46eb-a898-cf7ee5e108f5' 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.Dataset>\n",
       "Dimensions:      (level: 78, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan 34.94 nan\n",
       "    temperature  (level, date) float64 18.97 18.44 19.1 19.79 ... nan 3.714 nan\n",
       "    pressure     (level, date) float64 6.8 6.1 6.5 5.0 ... 2e+03 nan 2e+03 nan"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo = xr.Dataset(\n",
    "    data_vars={\n",
    "        'salinity':    (('level', 'date'), S),\n",
    "        'temperature': (('level', 'date'), T),\n",
    "        'pressure':    (('level', 'date'), P)\n",
    "    },\n",
    "    coords={\n",
    "        'level': levels,\n",
    "        'date': date\n",
    "    }\n",
    ")\n",
    "argo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about lon and lat? We forgot them in the creation process, but we can add them after the fact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (level: 78, date: 75, lon: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "  * lon          (lon) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan 34.94 nan\n",
       "    temperature  (level, date) float64 18.97 18.44 19.1 19.79 ... nan 3.714 nan\n",
       "    pressure     (level, date) float64 6.8 6.1 6.5 5.0 ... 2e+03 nan 2e+03 nan</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-14c3dfaf-3019-4fb9-84ce-d8b679d9f595' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-14c3dfaf-3019-4fb9-84ce-d8b679d9f595' 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'>level</span>: 78</li><li><span class='xr-has-index'>date</span>: 75</li><li><span class='xr-has-index'>lon</span>: 75</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ced24979-eace-45d4-94f9-0c7e9fd44653' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ced24979-eace-45d4-94f9-0c7e9fd44653' 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'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-3c183c24-f2ab-4c2c-8499-281d0cd174c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c183c24-f2ab-4c2c-8499-281d0cd174c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b04d8445-c15f-473d-8688-1cceb6139515' class='xr-var-data-in' type='checkbox'><label for='data-b04d8445-c15f-473d-8688-1cceb6139515' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-f616ff40-6663-4616-9c0c-3ae754fb3326' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f616ff40-6663-4616-9c0c-3ae754fb3326' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29b12e93-c4c7-4ce8-a120-c1b2e92c2af0' class='xr-var-data-in' type='checkbox'><label for='data-29b12e93-c4c7-4ce8-a120-c1b2e92c2af0' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</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'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-86f5e7b3-454e-4b9d-947c-1dbc8cebe7b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86f5e7b3-454e-4b9d-947c-1dbc8cebe7b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c3975d2-6614-4f18-9f5c-652b6384abb1' class='xr-var-data-in' type='checkbox'><label for='data-6c3975d2-6614-4f18-9f5c-652b6384abb1' title='Show/Hide data 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([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963, -35.184,\n",
       "       -34.462, -33.784, -32.972, -32.546, -32.428, -32.292, -32.169, -31.998,\n",
       "       -31.824, -31.624, -31.433, -31.312, -31.107, -31.147, -31.044, -31.14 ,\n",
       "       -31.417, -31.882, -32.145, -32.487, -32.537, -32.334, -32.042, -31.892,\n",
       "       -31.861, -31.991, -31.883, -31.89 , -31.941, -31.889, -31.724, -31.412,\n",
       "       -31.786, -31.561, -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 ,\n",
       "       -32.224, -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784, -32.607,\n",
       "       -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 , -33.621, -33.552,\n",
       "       -33.828, -34.11 , -34.38 ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b568b91-3a28-4350-a17e-d3aaddab2e25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7b568b91-3a28-4350-a17e-d3aaddab2e25' 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>salinity</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.64 35.51 35.57 ... nan 34.94 nan</div><input id='attrs-fc49f021-231b-483e-8ef9-af2c44ea6cff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc49f021-231b-483e-8ef9-af2c44ea6cff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d06af9c-1d3f-49b0-8526-6878e0e43809' class='xr-var-data-in' type='checkbox'><label for='data-4d06af9c-1d3f-49b0-8526-6878e0e43809' title='Show/Hide data 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([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [34.91585922, 34.92390442, 34.92390442, ..., 34.93481064,\n",
       "        34.94081116, 34.94680786],\n",
       "       [34.91585922, 34.92390442, 34.92190552, ..., 34.93280792,\n",
       "        34.93680954, 34.94380951],\n",
       "       [34.91785812, 34.92390442, 34.92390442, ...,         nan,\n",
       "        34.93680954,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>18.97 18.44 19.1 ... nan 3.714 nan</div><input id='attrs-7d12c9aa-d29a-4149-9e80-0b3ce141aeac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d12c9aa-d29a-4149-9e80-0b3ce141aeac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24b69a0b-4ba8-40fd-9782-602415721988' class='xr-var-data-in' type='checkbox'><label for='data-24b69a0b-4ba8-40fd-9782-602415721988' title='Show/Hide data 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([[18.97400093, 18.43700027, 19.09900093, ..., 19.11300087,\n",
       "        21.82299995, 20.13100052],\n",
       "       [18.74099922, 18.39999962, 19.08200073, ..., 18.47200012,\n",
       "        19.45999908, 20.125     ],\n",
       "       [18.37000084, 18.37400055, 19.06500053, ..., 18.22999954,\n",
       "        19.26199913, 20.07699966],\n",
       "       ...,\n",
       "       [ 3.79299998,  3.81399989,  3.80200005, ...,  3.80699992,\n",
       "         3.81100011,  3.8599999 ],\n",
       "       [ 3.76399994,  3.77800012,  3.75699997, ...,  3.75399995,\n",
       "         3.74600005,  3.80599999],\n",
       "       [ 3.74399996,  3.74600005,  3.7249999 , ...,         nan,\n",
       "         3.71399999,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.8 6.1 6.5 5.0 ... nan 2e+03 nan</div><input id='attrs-410881b3-c63f-4ad0-9991-4602c713085b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-410881b3-c63f-4ad0-9991-4602c713085b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ffd0d87-6c87-45fc-a0ae-64084afda8cd' class='xr-var-data-in' type='checkbox'><label for='data-8ffd0d87-6c87-45fc-a0ae-64084afda8cd' title='Show/Hide data 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([[   6.80000019,    6.0999999 ,    6.5       , ...,    7.0999999 ,\n",
       "           7.20000029,    6.5       ],\n",
       "       [  10.69999981,   10.59999943,   10.39999962, ...,   10.79999924,\n",
       "          11.09999943,   10.39999962],\n",
       "       [  15.69999981,   14.09999943,   14.89999962, ...,   15.89999962,\n",
       "          15.59999943,   15.89999962],\n",
       "       ...,\n",
       "       [1900.60009766, 1900.        , 1900.19995117, ..., 1899.70007324,\n",
       "        1900.40002441, 1899.80004883],\n",
       "       [1949.90002441, 1950.        , 1949.89990234, ..., 1950.59997559,\n",
       "        1950.20007324, 1949.70007324],\n",
       "       [1999.30004883, 1998.        , 1998.5       , ...,           nan,\n",
       "        2000.40002441,           nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-00552df4-11f9-478c-8073-8e3616a60532' class='xr-section-summary-in' type='checkbox'  ><label for='section-00552df4-11f9-478c-8073-8e3616a60532' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-053a20fc-9ca2-4e80-9f93-64f91912e4fd' class='xr-index-data-in' type='checkbox'/><label for='index-053a20fc-9ca2-4e80-9f93-64f91912e4fd' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-48d6d975-8943-4bc0-8c26-53e5bf662202' class='xr-index-data-in' type='checkbox'/><label for='index-48d6d975-8943-4bc0-8c26-53e5bf662202' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</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-3041722c-20c1-45e8-8044-8e849558b55f' class='xr-index-data-in' type='checkbox'/><label for='index-3041722c-20c1-45e8-8044-8e849558b55f' 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(Float64Index([             -39.13, -37.282000000000004,               -36.9,\n",
       "                           -36.89, -37.053000000000004,             -36.658,\n",
       "                          -35.963, -35.184000000000005,             -34.462,\n",
       "                          -33.784,             -32.972,             -32.546,\n",
       "              -32.428000000000004,             -32.292, -32.169000000000004,\n",
       "                          -31.998,             -31.824, -31.624000000000002,\n",
       "                          -31.433,             -31.312, -31.107000000000003,\n",
       "              -31.147000000000002,             -31.044,              -31.14,\n",
       "                          -31.417,             -31.882,             -32.145,\n",
       "                          -32.487,             -32.537,             -32.334,\n",
       "                          -32.042, -31.892000000000003,             -31.861,\n",
       "              -31.991000000000003, -31.883000000000003,              -31.89,\n",
       "              -31.941000000000003, -31.889000000000003,             -31.724,\n",
       "              -31.412000000000003,             -31.786,             -31.561,\n",
       "              -31.732000000000003,             -31.553, -31.862000000000002,\n",
       "                          -32.389, -32.318000000000005,              -32.19,\n",
       "              -32.224000000000004,             -32.368, -32.306000000000004,\n",
       "                          -32.305,              -32.65,             -33.093,\n",
       "                          -33.263,             -33.199,              -33.27,\n",
       "                          -33.237, -33.221000000000004,             -33.011,\n",
       "                          -32.844,             -32.981,             -32.784,\n",
       "                          -32.607,              -32.87, -33.196000000000005,\n",
       "                          -33.524,             -33.956,             -33.944,\n",
       "                           -33.71,             -33.621,             -33.552,\n",
       "                          -33.828,              -34.11,              -34.38],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-194668db-e417-4865-bd67-99e00584c3fb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-194668db-e417-4865-bd67-99e00584c3fb' 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.Dataset>\n",
       "Dimensions:      (level: 78, date: 75, lon: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "  * lon          (lon) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan 34.94 nan\n",
       "    temperature  (level, date) float64 18.97 18.44 19.1 19.79 ... nan 3.714 nan\n",
       "    pressure     (level, date) float64 6.8 6.1 6.5 5.0 ... 2e+03 nan 2e+03 nan"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo.coords['lon'] = lon\n",
    "argo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was not quite right...we want lon to have dimension `date`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "}\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;\n",
       "Dimensions:      (level: 78, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan 34.94 nan\n",
       "    temperature  (level, date) float64 18.97 18.44 19.1 19.79 ... nan 3.714 nan\n",
       "    pressure     (level, date) float64 6.8 6.1 6.5 5.0 ... 2e+03 nan 2e+03 nan</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-37a07e8a-460e-4bff-8afd-2a3c6220e6d4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-37a07e8a-460e-4bff-8afd-2a3c6220e6d4' 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'>level</span>: 78</li><li><span class='xr-has-index'>date</span>: 75</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e4d502f6-220b-46f0-b974-0f6e4a956165' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4d502f6-220b-46f0-b974-0f6e4a956165' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-2b05a87a-7f2d-4920-bd02-834a38b0a4e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b05a87a-7f2d-4920-bd02-834a38b0a4e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a5cd678-2013-499d-b4fe-3fbbd23fbe4e' class='xr-var-data-in' type='checkbox'><label for='data-3a5cd678-2013-499d-b4fe-3fbbd23fbe4e' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-978e4794-7ad5-4611-af73-1e23274de257' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-978e4794-7ad5-4611-af73-1e23274de257' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eaf80edd-94ea-4ffe-9ddd-993cc6414cc8' class='xr-var-data-in' type='checkbox'><label for='data-eaf80edd-94ea-4ffe-9ddd-993cc6414cc8' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-d6483ed6-b238-46cf-ad74-f3d3a25ee8d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6483ed6-b238-46cf-ad74-f3d3a25ee8d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-055acb14-9155-43fb-b0bb-903eb916c49b' class='xr-var-data-in' type='checkbox'><label for='data-055acb14-9155-43fb-b0bb-903eb916c49b' title='Show/Hide data 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([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-8deaf3f3-e262-4970-b979-f2a1cedc4e96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8deaf3f3-e262-4970-b979-f2a1cedc4e96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e4da2b9-0888-4b79-b97e-2c5da95f6123' class='xr-var-data-in' type='checkbox'><label for='data-0e4da2b9-0888-4b79-b97e-2c5da95f6123' title='Show/Hide data 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([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d50189e-2866-4b15-a279-f3dbc6c2a283' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0d50189e-2866-4b15-a279-f3dbc6c2a283' 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>salinity</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.64 35.51 35.57 ... nan 34.94 nan</div><input id='attrs-f6af3dab-09cd-4def-90a6-045a16362438' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6af3dab-09cd-4def-90a6-045a16362438' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b523981a-3595-419e-b176-634995db849a' class='xr-var-data-in' type='checkbox'><label for='data-b523981a-3595-419e-b176-634995db849a' title='Show/Hide data 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([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [34.91585922, 34.92390442, 34.92390442, ..., 34.93481064,\n",
       "        34.94081116, 34.94680786],\n",
       "       [34.91585922, 34.92390442, 34.92190552, ..., 34.93280792,\n",
       "        34.93680954, 34.94380951],\n",
       "       [34.91785812, 34.92390442, 34.92390442, ...,         nan,\n",
       "        34.93680954,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>18.97 18.44 19.1 ... nan 3.714 nan</div><input id='attrs-b302e1b9-b177-48d6-8d57-985df319acd0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b302e1b9-b177-48d6-8d57-985df319acd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-526e4ec8-b968-4715-a737-36bdf1c4ee29' class='xr-var-data-in' type='checkbox'><label for='data-526e4ec8-b968-4715-a737-36bdf1c4ee29' title='Show/Hide data 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([[18.97400093, 18.43700027, 19.09900093, ..., 19.11300087,\n",
       "        21.82299995, 20.13100052],\n",
       "       [18.74099922, 18.39999962, 19.08200073, ..., 18.47200012,\n",
       "        19.45999908, 20.125     ],\n",
       "       [18.37000084, 18.37400055, 19.06500053, ..., 18.22999954,\n",
       "        19.26199913, 20.07699966],\n",
       "       ...,\n",
       "       [ 3.79299998,  3.81399989,  3.80200005, ...,  3.80699992,\n",
       "         3.81100011,  3.8599999 ],\n",
       "       [ 3.76399994,  3.77800012,  3.75699997, ...,  3.75399995,\n",
       "         3.74600005,  3.80599999],\n",
       "       [ 3.74399996,  3.74600005,  3.7249999 , ...,         nan,\n",
       "         3.71399999,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.8 6.1 6.5 5.0 ... nan 2e+03 nan</div><input id='attrs-8c90d9c1-b014-43c2-96bb-954a6156d9b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c90d9c1-b014-43c2-96bb-954a6156d9b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-270d9002-857c-4584-8b28-25aa6eaaf8a2' class='xr-var-data-in' type='checkbox'><label for='data-270d9002-857c-4584-8b28-25aa6eaaf8a2' title='Show/Hide data 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([[   6.80000019,    6.0999999 ,    6.5       , ...,    7.0999999 ,\n",
       "           7.20000029,    6.5       ],\n",
       "       [  10.69999981,   10.59999943,   10.39999962, ...,   10.79999924,\n",
       "          11.09999943,   10.39999962],\n",
       "       [  15.69999981,   14.09999943,   14.89999962, ...,   15.89999962,\n",
       "          15.59999943,   15.89999962],\n",
       "       ...,\n",
       "       [1900.60009766, 1900.        , 1900.19995117, ..., 1899.70007324,\n",
       "        1900.40002441, 1899.80004883],\n",
       "       [1949.90002441, 1950.        , 1949.89990234, ..., 1950.59997559,\n",
       "        1950.20007324, 1949.70007324],\n",
       "       [1999.30004883, 1998.        , 1998.5       , ...,           nan,\n",
       "        2000.40002441,           nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-55a79df2-7b7c-4565-aaf3-d074ffeab6b1' class='xr-section-summary-in' type='checkbox'  ><label for='section-55a79df2-7b7c-4565-aaf3-d074ffeab6b1' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0309b951-c976-4da8-9af0-dcc9b09d2470' class='xr-index-data-in' type='checkbox'/><label for='index-0309b951-c976-4da8-9af0-dcc9b09d2470' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fcf79da4-9f08-4837-8754-5ee9c745688b' class='xr-index-data-in' type='checkbox'/><label for='index-fcf79da4-9f08-4837-8754-5ee9c745688b' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0ab472e-cfed-4435-baa6-f07dd5f2d408' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d0ab472e-cfed-4435-baa6-f07dd5f2d408' 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.Dataset>\n",
       "Dimensions:      (level: 78, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan 34.94 nan\n",
       "    temperature  (level, date) float64 18.97 18.44 19.1 19.79 ... nan 3.714 nan\n",
       "    pressure     (level, date) float64 6.8 6.1 6.5 5.0 ... 2e+03 nan 2e+03 nan"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del argo['lon']\n",
    "argo.coords['lon'] = ('date', lon)\n",
    "argo.coords['lat'] = ('date', lat)\n",
    "argo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coordinates vs. Data Variables\n",
    "\n",
    "Data variables can be modified through arithmentic operations or other functions. Coordinates are always keept the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "\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;\n",
       "Dimensions:      (level: 78, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 3.564e+05 3.551e+05 ... 3.494e+05 nan\n",
       "    temperature  (level, date) float64 1.897e+05 1.844e+05 ... 3.714e+04 nan\n",
       "    pressure     (level, date) float64 6.8e+04 6.1e+04 6.5e+04 ... nan 2e+07 nan</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-03855bc6-8a0e-4c9f-bb52-fc7e291a616c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-03855bc6-8a0e-4c9f-bb52-fc7e291a616c' 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'>level</span>: 78</li><li><span class='xr-has-index'>date</span>: 75</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b607787f-efb1-468e-a6f0-30109b5b17c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b607787f-efb1-468e-a6f0-30109b5b17c2' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-1d3bd706-8219-4c83-a4b1-4a02cfc87f51' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d3bd706-8219-4c83-a4b1-4a02cfc87f51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17a40d7e-b373-40a5-9232-27a6e35cead7' class='xr-var-data-in' type='checkbox'><label for='data-17a40d7e-b373-40a5-9232-27a6e35cead7' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-1633e065-9fac-4d39-874d-47be291fc31c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1633e065-9fac-4d39-874d-47be291fc31c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccce351f-9413-4f31-95e4-303f67960f02' class='xr-var-data-in' type='checkbox'><label for='data-ccce351f-9413-4f31-95e4-303f67960f02' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-9445188a-7015-4e86-b7dd-0757239fb2fb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9445188a-7015-4e86-b7dd-0757239fb2fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4a7644f-88b5-483a-baaf-b9d01ba10b09' class='xr-var-data-in' type='checkbox'><label for='data-f4a7644f-88b5-483a-baaf-b9d01ba10b09' title='Show/Hide data 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([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-2b059718-5684-4b08-bc26-aa5994e36f7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b059718-5684-4b08-bc26-aa5994e36f7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac2b6910-7b34-4739-b338-61aa80acac21' class='xr-var-data-in' type='checkbox'><label for='data-ac2b6910-7b34-4739-b338-61aa80acac21' title='Show/Hide data 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([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1df269cc-2035-4951-bbdc-ed3f9f1b38ca' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1df269cc-2035-4951-bbdc-ed3f9f1b38ca' 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>salinity</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.564e+05 3.551e+05 ... nan</div><input id='attrs-f509bdce-f568-448f-9128-272b6ae470fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f509bdce-f568-448f-9128-272b6ae470fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dbcabb9-e7d6-43c0-92a0-7525125f40fb' class='xr-var-data-in' type='checkbox'><label for='data-2dbcabb9-e7d6-43c0-92a0-7525125f40fb' title='Show/Hide data 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([[356389.38903809, 355149.57427979, 355729.71343994, ...,\n",
       "        358209.38110352, 357779.38842773, 356689.10980225],\n",
       "       [356339.37835693, 355219.57397461, 355739.70794678, ...,\n",
       "        358109.32159424, 355838.96636963, 356679.11529541],\n",
       "       [356819.45800781, 355259.59014893, 355729.71343994, ...,\n",
       "        357959.28955078, 356629.06646729, 356659.12628174],\n",
       "       ...,\n",
       "       [349158.59222412, 349239.04418945, 349239.04418945, ...,\n",
       "        349348.10638428, 349408.11157227, 349468.07861328],\n",
       "       [349158.59222412, 349239.04418945, 349219.05517578, ...,\n",
       "        349328.07922363, 349368.09539795, 349438.09509277],\n",
       "       [349178.58123779, 349239.04418945, 349239.04418945, ...,\n",
       "                    nan, 349368.09539795,             nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.897e+05 1.844e+05 ... nan</div><input id='attrs-a83f3a87-ce8a-4c93-81f5-1fbc76a19e64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a83f3a87-ce8a-4c93-81f5-1fbc76a19e64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07c1f880-6be1-4201-b925-d314dfa0036e' class='xr-var-data-in' type='checkbox'><label for='data-07c1f880-6be1-4201-b925-d314dfa0036e' title='Show/Hide data 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([[189740.00930786, 184370.00274658, 190990.00930786, ...,\n",
       "        191130.00869751, 218229.99954224, 201310.00518799],\n",
       "       [187409.99221802, 183999.9961853 , 190820.00732422, ...,\n",
       "        184720.0012207 , 194599.99084473, 201250.        ],\n",
       "       [183700.00839233, 183740.00549316, 190650.00534058, ...,\n",
       "        182299.99542236, 192619.99130249, 200769.99664307],\n",
       "       ...,\n",
       "       [ 37929.99982834,  38139.99891281,  38020.00045776, ...,\n",
       "         38069.99921799,  38110.00108719,  38599.99895096],\n",
       "       [ 37639.99938965,  37780.00116348,  37569.99969482, ...,\n",
       "         37539.99948502,  37460.00051498,  38059.99994278],\n",
       "       [ 37439.99958038,  37460.00051498,  37249.99904633, ...,\n",
       "                    nan,  37139.99986649,             nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.8e+04 6.1e+04 ... 2e+07 nan</div><input id='attrs-3bc3ef0e-8d5c-4872-95f0-7d1344be11dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3bc3ef0e-8d5c-4872-95f0-7d1344be11dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7653a2d-17bb-420a-b7a9-c39d1439cbd2' class='xr-var-data-in' type='checkbox'><label for='data-b7653a2d-17bb-420a-b7a9-c39d1439cbd2' title='Show/Hide data 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([[   68000.00190735,    60999.99904633,    65000.        , ...,\n",
       "           70999.99904633,    72000.00286102,    65000.        ],\n",
       "       [  106999.99809265,   105999.99427795,   103999.9961853 , ...,\n",
       "          107999.99237061,   110999.99427795,   103999.9961853 ],\n",
       "       [  156999.99809265,   140999.99427795,   148999.9961853 , ...,\n",
       "          158999.9961853 ,   155999.99427795,   158999.9961853 ],\n",
       "       ...,\n",
       "       [19006000.9765625 , 19000000.        , 19001999.51171875, ...,\n",
       "        18997000.73242188, 19004000.24414062, 18998000.48828125],\n",
       "       [19499000.24414062, 19500000.        , 19498999.0234375 , ...,\n",
       "        19505999.75585938, 19502000.73242188, 19497000.73242188],\n",
       "       [19993000.48828125, 19980000.        , 19985000.        , ...,\n",
       "                      nan, 20004000.24414062,               nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-93be7c07-2d70-4389-831d-c6bb994fd86f' class='xr-section-summary-in' type='checkbox'  ><label for='section-93be7c07-2d70-4389-831d-c6bb994fd86f' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a76e9d22-591f-454f-b73b-d6555bc86053' class='xr-index-data-in' type='checkbox'/><label for='index-a76e9d22-591f-454f-b73b-d6555bc86053' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2441f3df-1fd3-44ef-85d8-055008569835' class='xr-index-data-in' type='checkbox'/><label for='index-2441f3df-1fd3-44ef-85d8-055008569835' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c5a4f76-db7d-43b7-9657-1d018a6be9d9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3c5a4f76-db7d-43b7-9657-1d018a6be9d9' 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.Dataset>\n",
       "Dimensions:      (level: 78, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 3.564e+05 3.551e+05 ... 3.494e+05 nan\n",
       "    temperature  (level, date) float64 1.897e+05 1.844e+05 ... 3.714e+04 nan\n",
       "    pressure     (level, date) float64 6.8e+04 6.1e+04 6.5e+04 ... nan 2e+07 nan"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo * 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly lon and lat are coordinates rather than data variables. We can change their status as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "}\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;\n",
       "Dimensions:      (level: 78, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan 34.94 nan\n",
       "    temperature  (level, date) float64 18.97 18.44 19.1 19.79 ... nan 3.714 nan\n",
       "    pressure     (level, date) float64 6.8 6.1 6.5 5.0 ... 2e+03 nan 2e+03 nan</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-be43850e-d567-4520-b716-d0d3f213b649' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-be43850e-d567-4520-b716-d0d3f213b649' 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'>level</span>: 78</li><li><span class='xr-has-index'>date</span>: 75</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a20406dc-e5f4-42a3-a9f9-92fa14f00ff4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a20406dc-e5f4-42a3-a9f9-92fa14f00ff4' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-cc89233c-3c1a-44b5-a319-585c56791c7f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc89233c-3c1a-44b5-a319-585c56791c7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac16a676-8df4-4ab5-b4ad-e5104edd33f1' class='xr-var-data-in' type='checkbox'><label for='data-ac16a676-8df4-4ab5-b4ad-e5104edd33f1' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-acf1187a-624b-4877-9258-e4375c0764c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acf1187a-624b-4877-9258-e4375c0764c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57b2a050-7391-48a7-8582-b8fe88606094' class='xr-var-data-in' type='checkbox'><label for='data-57b2a050-7391-48a7-8582-b8fe88606094' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-2ecce9b8-46b3-47de-a0b9-8f30e3524b24' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ecce9b8-46b3-47de-a0b9-8f30e3524b24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89dd250a-b18c-4965-b943-fa9198a968a4' class='xr-var-data-in' type='checkbox'><label for='data-89dd250a-b18c-4965-b943-fa9198a968a4' title='Show/Hide data 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([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-ca33e001-f1f0-46b5-b4bc-2ac85ca2a3bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca33e001-f1f0-46b5-b4bc-2ac85ca2a3bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-559c92e2-1160-4230-84bc-81c1415a9b1c' class='xr-var-data-in' type='checkbox'><label for='data-559c92e2-1160-4230-84bc-81c1415a9b1c' title='Show/Hide data 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([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fee0527e-a64c-4318-891e-7b42b611baf8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fee0527e-a64c-4318-891e-7b42b611baf8' 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>salinity</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.64 35.51 35.57 ... nan 34.94 nan</div><input id='attrs-678b4314-cf8e-40b5-ae51-6bc2f85aece9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-678b4314-cf8e-40b5-ae51-6bc2f85aece9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f332398-cd66-488e-ad4e-92057cc9e9c8' class='xr-var-data-in' type='checkbox'><label for='data-3f332398-cd66-488e-ad4e-92057cc9e9c8' title='Show/Hide data 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([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [34.91585922, 34.92390442, 34.92390442, ..., 34.93481064,\n",
       "        34.94081116, 34.94680786],\n",
       "       [34.91585922, 34.92390442, 34.92190552, ..., 34.93280792,\n",
       "        34.93680954, 34.94380951],\n",
       "       [34.91785812, 34.92390442, 34.92390442, ...,         nan,\n",
       "        34.93680954,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>18.97 18.44 19.1 ... nan 3.714 nan</div><input id='attrs-d190bca5-b5cb-43fa-8f2b-a9d8a207ce9f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d190bca5-b5cb-43fa-8f2b-a9d8a207ce9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dfe7589-5eff-4535-9702-15b4c1fb7460' class='xr-var-data-in' type='checkbox'><label for='data-8dfe7589-5eff-4535-9702-15b4c1fb7460' title='Show/Hide data 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([[18.97400093, 18.43700027, 19.09900093, ..., 19.11300087,\n",
       "        21.82299995, 20.13100052],\n",
       "       [18.74099922, 18.39999962, 19.08200073, ..., 18.47200012,\n",
       "        19.45999908, 20.125     ],\n",
       "       [18.37000084, 18.37400055, 19.06500053, ..., 18.22999954,\n",
       "        19.26199913, 20.07699966],\n",
       "       ...,\n",
       "       [ 3.79299998,  3.81399989,  3.80200005, ...,  3.80699992,\n",
       "         3.81100011,  3.8599999 ],\n",
       "       [ 3.76399994,  3.77800012,  3.75699997, ...,  3.75399995,\n",
       "         3.74600005,  3.80599999],\n",
       "       [ 3.74399996,  3.74600005,  3.7249999 , ...,         nan,\n",
       "         3.71399999,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.8 6.1 6.5 5.0 ... nan 2e+03 nan</div><input id='attrs-6cbd5ee1-344d-45ee-9174-57d2ea637ca8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6cbd5ee1-344d-45ee-9174-57d2ea637ca8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b6c4774-5993-4755-85c9-05ff2416ddf4' class='xr-var-data-in' type='checkbox'><label for='data-7b6c4774-5993-4755-85c9-05ff2416ddf4' title='Show/Hide data 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([[   6.80000019,    6.0999999 ,    6.5       , ...,    7.0999999 ,\n",
       "           7.20000029,    6.5       ],\n",
       "       [  10.69999981,   10.59999943,   10.39999962, ...,   10.79999924,\n",
       "          11.09999943,   10.39999962],\n",
       "       [  15.69999981,   14.09999943,   14.89999962, ...,   15.89999962,\n",
       "          15.59999943,   15.89999962],\n",
       "       ...,\n",
       "       [1900.60009766, 1900.        , 1900.19995117, ..., 1899.70007324,\n",
       "        1900.40002441, 1899.80004883],\n",
       "       [1949.90002441, 1950.        , 1949.89990234, ..., 1950.59997559,\n",
       "        1950.20007324, 1949.70007324],\n",
       "       [1999.30004883, 1998.        , 1998.5       , ...,           nan,\n",
       "        2000.40002441,           nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee649f88-7f9a-418d-821c-53680d84f700' class='xr-section-summary-in' type='checkbox'  ><label for='section-ee649f88-7f9a-418d-821c-53680d84f700' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8242e042-530b-4925-bf81-8bc00bc3e4cd' class='xr-index-data-in' type='checkbox'/><label for='index-8242e042-530b-4925-bf81-8bc00bc3e4cd' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-76ff247c-9d28-45f8-8d83-4adf04baaefd' class='xr-index-data-in' type='checkbox'/><label for='index-76ff247c-9d28-45f8-8d83-4adf04baaefd' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68c147b6-c9f9-4ecf-9411-a5386d4354d3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68c147b6-c9f9-4ecf-9411-a5386d4354d3' 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.Dataset>\n",
       "Dimensions:      (level: 78, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan 34.94 nan\n",
       "    temperature  (level, date) float64 18.97 18.44 19.1 19.79 ... nan 3.714 nan\n",
       "    pressure     (level, date) float64 6.8 6.1 6.5 5.0 ... 2e+03 nan 2e+03 nan"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo = argo.set_coords(['lon', 'lat'])\n",
    "argo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `*` symbol in the representation above indicates that `level` and `date` are \"dimension coordinates\" (they describe the coordinates associated with data variable axes) while `lon` and `lat` are \"non-dimension coordinates\". We can make any variable a non-dimension coordiante.\n",
    "\n",
    "Alternatively, we could have assigned directly to coords as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "argo.coords['lon'] = ('date', lon)\n",
    "argo.coords['lat'] = ('date', lat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting Data (Indexing)\n",
    "\n",
    "We can always use regular numpy indexing and slicing on DataArrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
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       ".xr-icon-file-text2,\n",
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       "  stroke: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;salinity&#x27; (date: 75)&gt;\n",
       "array([35.6819458 , 35.52595901, 35.57297134, 35.40494537, 35.45091629,\n",
       "       35.50192261, 35.62397766, 35.51696014, 35.62797546, 35.52292252,\n",
       "       35.47383118, 35.33785629, 35.81896591, 35.88694   , 35.90187836,\n",
       "       36.02391815, 36.00475693, 35.94187927, 35.91583252, 35.86392212,\n",
       "       35.81995392, 35.88601303, 35.95079422, 35.84091568, 35.87992477,\n",
       "               nan, 35.92179108, 35.96979141, 36.0008316 , 35.98083115,\n",
       "       35.92887878, 35.98091888, 35.9838829 , 36.01884842, 35.99092484,\n",
       "       36.04689026, 36.04185867,         nan, 36.19193268, 36.22789764,\n",
       "       36.20986557, 35.97589874, 36.2779007 , 36.25889969, 36.2418251 ,\n",
       "       36.23685837, 36.19781876, 36.19785309, 36.17692184, 36.1048851 ,\n",
       "       36.11392212, 36.09080505,         nan, 36.05675888, 35.93374634,\n",
       "       36.04291534, 36.10183716, 35.97779083, 35.86592102, 35.87791824,\n",
       "       35.88392258, 35.92078781, 35.88601303, 36.05178833, 35.85883713,\n",
       "       35.94878769, 35.8938446 , 35.94379425, 35.90884018, 35.84893036,\n",
       "       35.83496857, 35.71691132, 35.79592896, 35.66290665, 35.66591263])\n",
       "Coordinates:\n",
       "    level    int64 2\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38</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'>'salinity'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 75</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b35685e6-62cf-46ce-b0d0-3c297fa23158' class='xr-array-in' type='checkbox' checked><label for='section-b35685e6-62cf-46ce-b0d0-3c297fa23158' 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>35.68 35.53 35.57 35.4 35.45 35.5 ... 35.83 35.72 35.8 35.66 35.67</span></div><div class='xr-array-data'><pre>array([35.6819458 , 35.52595901, 35.57297134, 35.40494537, 35.45091629,\n",
       "       35.50192261, 35.62397766, 35.51696014, 35.62797546, 35.52292252,\n",
       "       35.47383118, 35.33785629, 35.81896591, 35.88694   , 35.90187836,\n",
       "       36.02391815, 36.00475693, 35.94187927, 35.91583252, 35.86392212,\n",
       "       35.81995392, 35.88601303, 35.95079422, 35.84091568, 35.87992477,\n",
       "               nan, 35.92179108, 35.96979141, 36.0008316 , 35.98083115,\n",
       "       35.92887878, 35.98091888, 35.9838829 , 36.01884842, 35.99092484,\n",
       "       36.04689026, 36.04185867,         nan, 36.19193268, 36.22789764,\n",
       "       36.20986557, 35.97589874, 36.2779007 , 36.25889969, 36.2418251 ,\n",
       "       36.23685837, 36.19781876, 36.19785309, 36.17692184, 36.1048851 ,\n",
       "       36.11392212, 36.09080505,         nan, 36.05675888, 35.93374634,\n",
       "       36.04291534, 36.10183716, 35.97779083, 35.86592102, 35.87791824,\n",
       "       35.88392258, 35.92078781, 35.88601303, 36.05178833, 35.85883713,\n",
       "       35.94878769, 35.8938446 , 35.94379425, 35.90884018, 35.84893036,\n",
       "       35.83496857, 35.71691132, 35.79592896, 35.66290665, 35.66591263])</pre></div></div></li><li class='xr-section-item'><input id='section-536f9a1b-8f99-4506-a563-e6dade6f6c41' class='xr-section-summary-in' type='checkbox'  checked><label for='section-536f9a1b-8f99-4506-a563-e6dade6f6c41' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>level</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2</div><input id='attrs-775381b0-d409-4639-a30c-3275a2d73a13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-775381b0-d409-4639-a30c-3275a2d73a13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33cb16c1-bc9b-4d22-bf8b-85384b2ceae3' class='xr-var-data-in' type='checkbox'><label for='data-33cb16c1-bc9b-4d22-bf8b-85384b2ceae3' title='Show/Hide data 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(2)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-a804c00d-682c-45c4-b579-13a73081bd25' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a804c00d-682c-45c4-b579-13a73081bd25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4da97287-33bc-441a-9bb3-c8dc5726735b' class='xr-var-data-in' type='checkbox'><label for='data-4da97287-33bc-441a-9bb3-c8dc5726735b' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-4a91de89-dc6e-43dc-8e87-5e217d86734a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a91de89-dc6e-43dc-8e87-5e217d86734a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07d405d2-fae1-4230-9073-2cdeb040f56a' class='xr-var-data-in' type='checkbox'><label for='data-07d405d2-fae1-4230-9073-2cdeb040f56a' title='Show/Hide data 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([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-f9bac690-0d6a-4736-8ade-a33c962fc932' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9bac690-0d6a-4736-8ade-a33c962fc932' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-059678f0-be0b-4a9a-b359-f2e6c3bc084c' class='xr-var-data-in' type='checkbox'><label for='data-059678f0-be0b-4a9a-b359-f2e6c3bc084c' title='Show/Hide data 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([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e051cb3a-8b13-47a9-b61e-321b2141fb38' class='xr-section-summary-in' type='checkbox'  ><label for='section-e051cb3a-8b13-47a9-b61e-321b2141fb38' 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>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7ea39803-c141-4b82-b09f-85d9ed76a55d' class='xr-index-data-in' type='checkbox'/><label for='index-7ea39803-c141-4b82-b09f-85d9ed76a55d' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef856048-af8b-4dff-afa3-090ee736970b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ef856048-af8b-4dff-afa3-090ee736970b' 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 'salinity' (date: 75)>\n",
       "array([35.6819458 , 35.52595901, 35.57297134, 35.40494537, 35.45091629,\n",
       "       35.50192261, 35.62397766, 35.51696014, 35.62797546, 35.52292252,\n",
       "       35.47383118, 35.33785629, 35.81896591, 35.88694   , 35.90187836,\n",
       "       36.02391815, 36.00475693, 35.94187927, 35.91583252, 35.86392212,\n",
       "       35.81995392, 35.88601303, 35.95079422, 35.84091568, 35.87992477,\n",
       "               nan, 35.92179108, 35.96979141, 36.0008316 , 35.98083115,\n",
       "       35.92887878, 35.98091888, 35.9838829 , 36.01884842, 35.99092484,\n",
       "       36.04689026, 36.04185867,         nan, 36.19193268, 36.22789764,\n",
       "       36.20986557, 35.97589874, 36.2779007 , 36.25889969, 36.2418251 ,\n",
       "       36.23685837, 36.19781876, 36.19785309, 36.17692184, 36.1048851 ,\n",
       "       36.11392212, 36.09080505,         nan, 36.05675888, 35.93374634,\n",
       "       36.04291534, 36.10183716, 35.97779083, 35.86592102, 35.87791824,\n",
       "       35.88392258, 35.92078781, 35.88601303, 36.05178833, 35.85883713,\n",
       "       35.94878769, 35.8938446 , 35.94379425, 35.90884018, 35.84893036,\n",
       "       35.83496857, 35.71691132, 35.79592896, 35.66290665, 35.66591263])\n",
       "Coordinates:\n",
       "    level    int64 2\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo.salinity[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a91929960>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "argo.salinity[2].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  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;salinity&#x27; (level: 78)&gt;\n",
       "array([35.47483063, 35.47483063, 35.47383118, 35.47383118, 35.47383118,\n",
       "       35.47483063, 35.48183441, 35.47983551, 35.4948349 , 35.51083755,\n",
       "       36.13380051, 36.09579849, 35.95479965, 35.93979645, 35.8958931 ,\n",
       "       35.86388397, 35.87788773, 35.88188934, 35.90379333, 35.9067955 ,\n",
       "       35.86588669, 35.8588829 , 35.86088181, 35.85188293, 35.85788345,\n",
       "       35.82787323, 35.78786469, 35.73185349, 35.69784927, 35.67684174,\n",
       "       35.677845  , 35.65784073, 35.64083481, 35.6238327 , 35.59682846,\n",
       "       35.57782364, 35.56182098, 35.55781937, 35.52181625, 35.49881363,\n",
       "       35.51381302, 35.49981308, 35.47280884, 35.47880936, 35.44780731,\n",
       "       35.39379501, 35.35879135, 35.28778076, 35.21878052, 35.20677567,\n",
       "       35.17777252, 35.15076828, 35.07276535, 35.01475525, 34.9797554 ,\n",
       "       35.0117569 , 35.03975677, 35.05575562, 35.00975037, 34.96175385,\n",
       "       34.96775055, 34.95075226, 34.93775177, 34.93375015, 34.93775558,\n",
       "       34.9247551 , 34.92175674, 34.91975403, 34.91975403, 34.91975403,\n",
       "       34.92176056, 34.92375946, 34.92575836, 34.92575836, 34.92475891,\n",
       "       34.93076324, 34.92176437,         nan])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77\n",
       "    date     datetime64[ns] 2012-10-22T02:50:32.006400\n",
       "    lon      float64 -32.97\n",
       "    lat      float64 44.13</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'>'salinity'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 78</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ebd180ef-57cc-4a40-8ec3-027759d8b6ed' class='xr-array-in' type='checkbox' checked><label for='section-ebd180ef-57cc-4a40-8ec3-027759d8b6ed' 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>35.47 35.47 35.47 35.47 35.47 35.47 ... 34.93 34.92 34.93 34.92 nan</span></div><div class='xr-array-data'><pre>array([35.47483063, 35.47483063, 35.47383118, 35.47383118, 35.47383118,\n",
       "       35.47483063, 35.48183441, 35.47983551, 35.4948349 , 35.51083755,\n",
       "       36.13380051, 36.09579849, 35.95479965, 35.93979645, 35.8958931 ,\n",
       "       35.86388397, 35.87788773, 35.88188934, 35.90379333, 35.9067955 ,\n",
       "       35.86588669, 35.8588829 , 35.86088181, 35.85188293, 35.85788345,\n",
       "       35.82787323, 35.78786469, 35.73185349, 35.69784927, 35.67684174,\n",
       "       35.677845  , 35.65784073, 35.64083481, 35.6238327 , 35.59682846,\n",
       "       35.57782364, 35.56182098, 35.55781937, 35.52181625, 35.49881363,\n",
       "       35.51381302, 35.49981308, 35.47280884, 35.47880936, 35.44780731,\n",
       "       35.39379501, 35.35879135, 35.28778076, 35.21878052, 35.20677567,\n",
       "       35.17777252, 35.15076828, 35.07276535, 35.01475525, 34.9797554 ,\n",
       "       35.0117569 , 35.03975677, 35.05575562, 35.00975037, 34.96175385,\n",
       "       34.96775055, 34.95075226, 34.93775177, 34.93375015, 34.93775558,\n",
       "       34.9247551 , 34.92175674, 34.91975403, 34.91975403, 34.91975403,\n",
       "       34.92176056, 34.92375946, 34.92575836, 34.92575836, 34.92475891,\n",
       "       34.93076324, 34.92176437,         nan])</pre></div></div></li><li class='xr-section-item'><input id='section-a3e030e0-8968-4e8c-b82d-459c8b3e4c88' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a3e030e0-8968-4e8c-b82d-459c8b3e4c88' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-9d196b78-942e-4a7d-9ead-264b32b99d13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d196b78-942e-4a7d-9ead-264b32b99d13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de1a3d0b-7883-474f-8518-18cee32cc487' class='xr-var-data-in' type='checkbox'><label for='data-de1a3d0b-7883-474f-8518-18cee32cc487' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>date</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-10-22T02:50:32.006400</div><input id='attrs-8d1b91ee-6711-47c9-b287-b3af1af1722b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d1b91ee-6711-47c9-b287-b3af1af1722b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bd6104b-a78d-43e5-b42b-d67de40e7cfb' class='xr-var-data-in' type='checkbox'><label for='data-4bd6104b-a78d-43e5-b42b-d67de40e7cfb' title='Show/Hide data 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;2012-10-22T02:50:32.006400000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-32.97</div><input id='attrs-4211e28f-4b53-404d-ac20-fddc0256bfcb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4211e28f-4b53-404d-ac20-fddc0256bfcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b083860-8251-4519-a587-4f766f545602' class='xr-var-data-in' type='checkbox'><label for='data-6b083860-8251-4519-a587-4f766f545602' title='Show/Hide data 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(-32.972)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.13</div><input id='attrs-d2f5b31a-8821-4127-950d-46225288bb12' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d2f5b31a-8821-4127-950d-46225288bb12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5652628-9c7f-4e7a-9c70-4652a11a6dfe' class='xr-var-data-in' type='checkbox'><label for='data-f5652628-9c7f-4e7a-9c70-4652a11a6dfe' title='Show/Hide data 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(44.13)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15e2827d-7cb2-42aa-9377-f3fe9876ff95' class='xr-section-summary-in' type='checkbox'  ><label for='section-15e2827d-7cb2-42aa-9377-f3fe9876ff95' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-555f7a97-8e7b-4c59-ab0a-727338c8229f' class='xr-index-data-in' type='checkbox'/><label for='index-555f7a97-8e7b-4c59-ab0a-727338c8229f' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e788c2e-d38d-4b43-a059-64a426629dbf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9e788c2e-d38d-4b43-a059-64a426629dbf' 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 'salinity' (level: 78)>\n",
       "array([35.47483063, 35.47483063, 35.47383118, 35.47383118, 35.47383118,\n",
       "       35.47483063, 35.48183441, 35.47983551, 35.4948349 , 35.51083755,\n",
       "       36.13380051, 36.09579849, 35.95479965, 35.93979645, 35.8958931 ,\n",
       "       35.86388397, 35.87788773, 35.88188934, 35.90379333, 35.9067955 ,\n",
       "       35.86588669, 35.8588829 , 35.86088181, 35.85188293, 35.85788345,\n",
       "       35.82787323, 35.78786469, 35.73185349, 35.69784927, 35.67684174,\n",
       "       35.677845  , 35.65784073, 35.64083481, 35.6238327 , 35.59682846,\n",
       "       35.57782364, 35.56182098, 35.55781937, 35.52181625, 35.49881363,\n",
       "       35.51381302, 35.49981308, 35.47280884, 35.47880936, 35.44780731,\n",
       "       35.39379501, 35.35879135, 35.28778076, 35.21878052, 35.20677567,\n",
       "       35.17777252, 35.15076828, 35.07276535, 35.01475525, 34.9797554 ,\n",
       "       35.0117569 , 35.03975677, 35.05575562, 35.00975037, 34.96175385,\n",
       "       34.96775055, 34.95075226, 34.93775177, 34.93375015, 34.93775558,\n",
       "       34.9247551 , 34.92175674, 34.91975403, 34.91975403, 34.91975403,\n",
       "       34.92176056, 34.92375946, 34.92575836, 34.92575836, 34.92475891,\n",
       "       34.93076324, 34.92176437,         nan])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77\n",
       "    date     datetime64[ns] 2012-10-22T02:50:32.006400\n",
       "    lon      float64 -32.97\n",
       "    lat      float64 44.13"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo.salinity[:, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a917b30a0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "argo.salinity[:, 10].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, it is often much more powerful to use xarray's `.sel()` method to use label-based indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "}\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;salinity&#x27; (date: 75)&gt;\n",
       "array([35.6819458 , 35.52595901, 35.57297134, 35.40494537, 35.45091629,\n",
       "       35.50192261, 35.62397766, 35.51696014, 35.62797546, 35.52292252,\n",
       "       35.47383118, 35.33785629, 35.81896591, 35.88694   , 35.90187836,\n",
       "       36.02391815, 36.00475693, 35.94187927, 35.91583252, 35.86392212,\n",
       "       35.81995392, 35.88601303, 35.95079422, 35.84091568, 35.87992477,\n",
       "               nan, 35.92179108, 35.96979141, 36.0008316 , 35.98083115,\n",
       "       35.92887878, 35.98091888, 35.9838829 , 36.01884842, 35.99092484,\n",
       "       36.04689026, 36.04185867,         nan, 36.19193268, 36.22789764,\n",
       "       36.20986557, 35.97589874, 36.2779007 , 36.25889969, 36.2418251 ,\n",
       "       36.23685837, 36.19781876, 36.19785309, 36.17692184, 36.1048851 ,\n",
       "       36.11392212, 36.09080505,         nan, 36.05675888, 35.93374634,\n",
       "       36.04291534, 36.10183716, 35.97779083, 35.86592102, 35.87791824,\n",
       "       35.88392258, 35.92078781, 35.88601303, 36.05178833, 35.85883713,\n",
       "       35.94878769, 35.8938446 , 35.94379425, 35.90884018, 35.84893036,\n",
       "       35.83496857, 35.71691132, 35.79592896, 35.66290665, 35.66591263])\n",
       "Coordinates:\n",
       "    level    int64 2\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38</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'>'salinity'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 75</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e4dfe5d7-58e4-4c12-a5c8-4d5bd5dfbb5f' class='xr-array-in' type='checkbox' checked><label for='section-e4dfe5d7-58e4-4c12-a5c8-4d5bd5dfbb5f' 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>35.68 35.53 35.57 35.4 35.45 35.5 ... 35.83 35.72 35.8 35.66 35.67</span></div><div class='xr-array-data'><pre>array([35.6819458 , 35.52595901, 35.57297134, 35.40494537, 35.45091629,\n",
       "       35.50192261, 35.62397766, 35.51696014, 35.62797546, 35.52292252,\n",
       "       35.47383118, 35.33785629, 35.81896591, 35.88694   , 35.90187836,\n",
       "       36.02391815, 36.00475693, 35.94187927, 35.91583252, 35.86392212,\n",
       "       35.81995392, 35.88601303, 35.95079422, 35.84091568, 35.87992477,\n",
       "               nan, 35.92179108, 35.96979141, 36.0008316 , 35.98083115,\n",
       "       35.92887878, 35.98091888, 35.9838829 , 36.01884842, 35.99092484,\n",
       "       36.04689026, 36.04185867,         nan, 36.19193268, 36.22789764,\n",
       "       36.20986557, 35.97589874, 36.2779007 , 36.25889969, 36.2418251 ,\n",
       "       36.23685837, 36.19781876, 36.19785309, 36.17692184, 36.1048851 ,\n",
       "       36.11392212, 36.09080505,         nan, 36.05675888, 35.93374634,\n",
       "       36.04291534, 36.10183716, 35.97779083, 35.86592102, 35.87791824,\n",
       "       35.88392258, 35.92078781, 35.88601303, 36.05178833, 35.85883713,\n",
       "       35.94878769, 35.8938446 , 35.94379425, 35.90884018, 35.84893036,\n",
       "       35.83496857, 35.71691132, 35.79592896, 35.66290665, 35.66591263])</pre></div></div></li><li class='xr-section-item'><input id='section-92a1635b-a279-4419-b8c6-be156c30be65' class='xr-section-summary-in' type='checkbox'  checked><label for='section-92a1635b-a279-4419-b8c6-be156c30be65' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>level</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2</div><input id='attrs-54c657e5-ea8f-4ab1-8d80-2c93d9c317c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54c657e5-ea8f-4ab1-8d80-2c93d9c317c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b80f968-8d49-4060-ae71-64fd379486da' class='xr-var-data-in' type='checkbox'><label for='data-2b80f968-8d49-4060-ae71-64fd379486da' title='Show/Hide data 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(2)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-8aefd3b8-57b1-4c07-86f9-8c3fb078e26a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8aefd3b8-57b1-4c07-86f9-8c3fb078e26a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-892fe2dc-36e0-465e-8626-b4a8f201d883' class='xr-var-data-in' type='checkbox'><label for='data-892fe2dc-36e0-465e-8626-b4a8f201d883' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-2f6dbb4c-828b-40d0-b0fd-f13a9ad80579' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f6dbb4c-828b-40d0-b0fd-f13a9ad80579' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d336c35-9749-41a7-a97c-ce1b5c3ac240' class='xr-var-data-in' type='checkbox'><label for='data-0d336c35-9749-41a7-a97c-ce1b5c3ac240' title='Show/Hide data 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([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-c1b4af64-fa7e-4b2c-9fc5-8cece9b785c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1b4af64-fa7e-4b2c-9fc5-8cece9b785c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-008b8492-c320-4ed2-8313-14c4118b4404' class='xr-var-data-in' type='checkbox'><label for='data-008b8492-c320-4ed2-8313-14c4118b4404' title='Show/Hide data 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([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f3425a9f-0d85-4d5c-8cf8-daaa71f7ed1c' class='xr-section-summary-in' type='checkbox'  ><label for='section-f3425a9f-0d85-4d5c-8cf8-daaa71f7ed1c' 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>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ebff7234-9026-40b8-a1bf-e9f8e6df679c' class='xr-index-data-in' type='checkbox'/><label for='index-ebff7234-9026-40b8-a1bf-e9f8e6df679c' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5c3150d9-b96f-4482-81da-c2c055f4c582' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5c3150d9-b96f-4482-81da-c2c055f4c582' 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 'salinity' (date: 75)>\n",
       "array([35.6819458 , 35.52595901, 35.57297134, 35.40494537, 35.45091629,\n",
       "       35.50192261, 35.62397766, 35.51696014, 35.62797546, 35.52292252,\n",
       "       35.47383118, 35.33785629, 35.81896591, 35.88694   , 35.90187836,\n",
       "       36.02391815, 36.00475693, 35.94187927, 35.91583252, 35.86392212,\n",
       "       35.81995392, 35.88601303, 35.95079422, 35.84091568, 35.87992477,\n",
       "               nan, 35.92179108, 35.96979141, 36.0008316 , 35.98083115,\n",
       "       35.92887878, 35.98091888, 35.9838829 , 36.01884842, 35.99092484,\n",
       "       36.04689026, 36.04185867,         nan, 36.19193268, 36.22789764,\n",
       "       36.20986557, 35.97589874, 36.2779007 , 36.25889969, 36.2418251 ,\n",
       "       36.23685837, 36.19781876, 36.19785309, 36.17692184, 36.1048851 ,\n",
       "       36.11392212, 36.09080505,         nan, 36.05675888, 35.93374634,\n",
       "       36.04291534, 36.10183716, 35.97779083, 35.86592102, 35.87791824,\n",
       "       35.88392258, 35.92078781, 35.88601303, 36.05178833, 35.85883713,\n",
       "       35.94878769, 35.8938446 , 35.94379425, 35.90884018, 35.84893036,\n",
       "       35.83496857, 35.71691132, 35.79592896, 35.66290665, 35.66591263])\n",
       "Coordinates:\n",
       "    level    int64 2\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo.salinity.sel(level=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a918404f0>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "argo.salinity.sel(level=2).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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;salinity&#x27; (level: 78, date: 1)&gt;\n",
       "array([[35.47483063],\n",
       "       [35.47483063],\n",
       "       [35.47383118],\n",
       "       [35.47383118],\n",
       "       [35.47383118],\n",
       "       [35.47483063],\n",
       "       [35.48183441],\n",
       "       [35.47983551],\n",
       "       [35.4948349 ],\n",
       "       [35.51083755],\n",
       "       [36.13380051],\n",
       "       [36.09579849],\n",
       "       [35.95479965],\n",
       "       [35.93979645],\n",
       "       [35.8958931 ],\n",
       "       [35.86388397],\n",
       "       [35.87788773],\n",
       "       [35.88188934],\n",
       "       [35.90379333],\n",
       "       [35.9067955 ],\n",
       "...\n",
       "       [35.00975037],\n",
       "       [34.96175385],\n",
       "       [34.96775055],\n",
       "       [34.95075226],\n",
       "       [34.93775177],\n",
       "       [34.93375015],\n",
       "       [34.93775558],\n",
       "       [34.9247551 ],\n",
       "       [34.92175674],\n",
       "       [34.91975403],\n",
       "       [34.91975403],\n",
       "       [34.91975403],\n",
       "       [34.92176056],\n",
       "       [34.92375946],\n",
       "       [34.92575836],\n",
       "       [34.92575836],\n",
       "       [34.92475891],\n",
       "       [34.93076324],\n",
       "       [34.92176437],\n",
       "       [        nan]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77\n",
       "  * date     (date) datetime64[ns] 2012-10-22T02:50:32.006400\n",
       "    lon      (date) float64 -32.97\n",
       "    lat      (date) float64 44.13</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'>'salinity'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 78</li><li><span class='xr-has-index'>date</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7d6507b1-e3d4-40b5-b87c-ae3880bc854b' class='xr-array-in' type='checkbox' checked><label for='section-7d6507b1-e3d4-40b5-b87c-ae3880bc854b' 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>35.47 35.47 35.47 35.47 35.47 35.47 ... 34.93 34.92 34.93 34.92 nan</span></div><div class='xr-array-data'><pre>array([[35.47483063],\n",
       "       [35.47483063],\n",
       "       [35.47383118],\n",
       "       [35.47383118],\n",
       "       [35.47383118],\n",
       "       [35.47483063],\n",
       "       [35.48183441],\n",
       "       [35.47983551],\n",
       "       [35.4948349 ],\n",
       "       [35.51083755],\n",
       "       [36.13380051],\n",
       "       [36.09579849],\n",
       "       [35.95479965],\n",
       "       [35.93979645],\n",
       "       [35.8958931 ],\n",
       "       [35.86388397],\n",
       "       [35.87788773],\n",
       "       [35.88188934],\n",
       "       [35.90379333],\n",
       "       [35.9067955 ],\n",
       "...\n",
       "       [35.00975037],\n",
       "       [34.96175385],\n",
       "       [34.96775055],\n",
       "       [34.95075226],\n",
       "       [34.93775177],\n",
       "       [34.93375015],\n",
       "       [34.93775558],\n",
       "       [34.9247551 ],\n",
       "       [34.92175674],\n",
       "       [34.91975403],\n",
       "       [34.91975403],\n",
       "       [34.91975403],\n",
       "       [34.92176056],\n",
       "       [34.92375946],\n",
       "       [34.92575836],\n",
       "       [34.92575836],\n",
       "       [34.92475891],\n",
       "       [34.93076324],\n",
       "       [34.92176437],\n",
       "       [        nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-d78cf395-6900-4faf-b4f4-e362d21758df' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d78cf395-6900-4faf-b4f4-e362d21758df' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-f15edec9-a8a9-4504-889c-a9d21356a78e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f15edec9-a8a9-4504-889c-a9d21356a78e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f43ccce-a2ff-40a2-b2c1-6c6a1ec19009' class='xr-var-data-in' type='checkbox'><label for='data-9f43ccce-a2ff-40a2-b2c1-6c6a1ec19009' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-10-22T02:50:32.006400</div><input id='attrs-028755f0-169f-4ef3-9ca9-1ba6fbe81175' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-028755f0-169f-4ef3-9ca9-1ba6fbe81175' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73c2f6af-99f5-46ad-9ad2-421d102221a4' class='xr-var-data-in' type='checkbox'><label for='data-73c2f6af-99f5-46ad-9ad2-421d102221a4' title='Show/Hide data 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;2012-10-22T02:50:32.006400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-32.97</div><input id='attrs-cdd440f7-3777-43d7-8abb-c8e4b9c40e9c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdd440f7-3777-43d7-8abb-c8e4b9c40e9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70558905-c31b-45de-8c3b-b1ea4a7b9a07' class='xr-var-data-in' type='checkbox'><label for='data-70558905-c31b-45de-8c3b-b1ea4a7b9a07' title='Show/Hide data 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([-32.972])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.13</div><input id='attrs-c47f1678-d10c-4291-9755-d6b7d21a05c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c47f1678-d10c-4291-9755-d6b7d21a05c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2d42ac7-8789-4a08-ad8c-639b769ffa5a' class='xr-var-data-in' type='checkbox'><label for='data-b2d42ac7-8789-4a08-ad8c-639b769ffa5a' title='Show/Hide data 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([44.13])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70841c2d-22b9-4ed9-81f3-8f9dd6b261fc' class='xr-section-summary-in' type='checkbox'  ><label for='section-70841c2d-22b9-4ed9-81f3-8f9dd6b261fc' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d0a053a9-add5-465e-9d4f-2a145d2c81fc' class='xr-index-data-in' type='checkbox'/><label for='index-d0a053a9-add5-465e-9d4f-2a145d2c81fc' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-118edd9d-a868-4e20-b340-c46de55e85d6' class='xr-index-data-in' type='checkbox'/><label for='index-118edd9d-a868-4e20-b340-c46de55e85d6' 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;2012-10-22 02:50:32.006400&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-715c5705-8f8c-467d-b658-b3d8e73df0f7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-715c5705-8f8c-467d-b658-b3d8e73df0f7' 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 'salinity' (level: 78, date: 1)>\n",
       "array([[35.47483063],\n",
       "       [35.47483063],\n",
       "       [35.47383118],\n",
       "       [35.47383118],\n",
       "       [35.47383118],\n",
       "       [35.47483063],\n",
       "       [35.48183441],\n",
       "       [35.47983551],\n",
       "       [35.4948349 ],\n",
       "       [35.51083755],\n",
       "       [36.13380051],\n",
       "       [36.09579849],\n",
       "       [35.95479965],\n",
       "       [35.93979645],\n",
       "       [35.8958931 ],\n",
       "       [35.86388397],\n",
       "       [35.87788773],\n",
       "       [35.88188934],\n",
       "       [35.90379333],\n",
       "       [35.9067955 ],\n",
       "...\n",
       "       [35.00975037],\n",
       "       [34.96175385],\n",
       "       [34.96775055],\n",
       "       [34.95075226],\n",
       "       [34.93775177],\n",
       "       [34.93375015],\n",
       "       [34.93775558],\n",
       "       [34.9247551 ],\n",
       "       [34.92175674],\n",
       "       [34.91975403],\n",
       "       [34.91975403],\n",
       "       [34.91975403],\n",
       "       [34.92176056],\n",
       "       [34.92375946],\n",
       "       [34.92575836],\n",
       "       [34.92575836],\n",
       "       [34.92475891],\n",
       "       [34.93076324],\n",
       "       [34.92176437],\n",
       "       [        nan]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77\n",
       "  * date     (date) datetime64[ns] 2012-10-22T02:50:32.006400\n",
       "    lon      (date) float64 -32.97\n",
       "    lat      (date) float64 44.13"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo.salinity.sel(date='2012-10-22')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a916d24a0>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "argo.salinity.sel(date='2012-10-22').plot(y='level', yincrease=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`.sel()` also supports slicing. Unfortunately we have to use a somewhat awkward syntax, but it still works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;salinity&#x27; (level: 78, date: 7)&gt;\n",
       "array([[35.63097763, 35.52592468, 35.47483063, 35.33785629, 35.81896591,\n",
       "        35.8889389 , 35.90187836],\n",
       "       [35.63097763, 35.52292252, 35.47483063, 35.33685684, 35.81796646,\n",
       "        35.88793945, 35.90187836],\n",
       "       [35.62797546, 35.52292252, 35.47383118, 35.33785629, 35.81896591,\n",
       "        35.88694   , 35.90187836],\n",
       "       [35.62697601, 35.52192307, 35.47383118, 35.33785629, 35.81896591,\n",
       "        35.89193726, 35.90187836],\n",
       "       [35.62797546, 35.52192307, 35.47383118, 35.33785629, 35.81996536,\n",
       "        35.88993835, 35.90187836],\n",
       "       [35.62897873, 35.52292252, 35.47483063, 35.33785629, 35.81996536,\n",
       "        35.88993835, 35.90187836],\n",
       "       [35.62997818, 35.51892471, 35.48183441, 35.33785629, 35.81996536,\n",
       "        35.88993835, 35.90187836],\n",
       "       [35.63197708, 35.44991302, 35.47983551, 35.33785629, 35.81996536,\n",
       "        35.89683914, 35.90187836],\n",
       "       [35.63097763, 35.38090134, 35.4948349 , 35.33785629, 35.81896591,\n",
       "        35.89583969, 35.90187836],\n",
       "       [35.62697601, 35.58792114, 35.51083755, 35.33985519, 35.82497025,\n",
       "        35.89683914, 35.90187836],\n",
       "...\n",
       "       [34.91690445, 34.92385483, 34.91975403, 34.91980362, 34.92385483,\n",
       "        34.93680573, 34.93885422],\n",
       "       [34.92190552, 34.92485428, 34.91975403, 34.92080688, 34.92485428,\n",
       "        34.94480515, 34.9328537 ],\n",
       "       [34.92390442, 34.92285538, 34.92176056, 34.92280579, 34.92985535,\n",
       "        34.93280411, 34.92785645],\n",
       "       [34.92390442, 34.92385483, 34.92375946, 34.92480469, 34.92685318,\n",
       "        34.93780899, 34.92485428],\n",
       "       [34.92390442, 34.92285538, 34.92575836, 34.92181015, 34.92085648,\n",
       "        34.93680954, 34.92385483],\n",
       "       [34.92590332, 34.9288559 , 34.92575836, 34.92181015, 34.92685318,\n",
       "        34.93481064, 34.92585373],\n",
       "       [34.92490387, 34.92785645, 34.92475891, 34.92781067, 34.93385696,\n",
       "        34.93380737, 34.92385864],\n",
       "       [34.92190552, 34.92385864, 34.93076324, 34.9268074 , 34.93585968,\n",
       "        34.93481064, 34.92985916],\n",
       "       [34.92090607, 34.92185974, 34.92176437, 34.9228096 , 34.93285751,\n",
       "        34.93180847, 34.92786026],\n",
       "       [        nan, 34.91985703,         nan, 34.92181015,         nan,\n",
       "        34.92181015,         nan]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77\n",
       "  * date     (date) datetime64[ns] 2012-10-02T03:00:17.971200 ... 2012-12-01T...\n",
       "    lon      (date) float64 -34.46 -33.78 -32.97 -32.55 -32.43 -32.29 -32.17\n",
       "    lat      (date) float64 44.96 44.68 44.13 43.64 43.07 42.66 42.51</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'>'salinity'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 78</li><li><span class='xr-has-index'>date</span>: 7</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e9a4f8a0-edce-4579-a7e4-7b76aa5392db' class='xr-array-in' type='checkbox' checked><label for='section-e9a4f8a0-edce-4579-a7e4-7b76aa5392db' 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>35.63 35.53 35.47 35.34 35.82 35.89 ... 34.92 nan 34.92 nan 34.92 nan</span></div><div class='xr-array-data'><pre>array([[35.63097763, 35.52592468, 35.47483063, 35.33785629, 35.81896591,\n",
       "        35.8889389 , 35.90187836],\n",
       "       [35.63097763, 35.52292252, 35.47483063, 35.33685684, 35.81796646,\n",
       "        35.88793945, 35.90187836],\n",
       "       [35.62797546, 35.52292252, 35.47383118, 35.33785629, 35.81896591,\n",
       "        35.88694   , 35.90187836],\n",
       "       [35.62697601, 35.52192307, 35.47383118, 35.33785629, 35.81896591,\n",
       "        35.89193726, 35.90187836],\n",
       "       [35.62797546, 35.52192307, 35.47383118, 35.33785629, 35.81996536,\n",
       "        35.88993835, 35.90187836],\n",
       "       [35.62897873, 35.52292252, 35.47483063, 35.33785629, 35.81996536,\n",
       "        35.88993835, 35.90187836],\n",
       "       [35.62997818, 35.51892471, 35.48183441, 35.33785629, 35.81996536,\n",
       "        35.88993835, 35.90187836],\n",
       "       [35.63197708, 35.44991302, 35.47983551, 35.33785629, 35.81996536,\n",
       "        35.89683914, 35.90187836],\n",
       "       [35.63097763, 35.38090134, 35.4948349 , 35.33785629, 35.81896591,\n",
       "        35.89583969, 35.90187836],\n",
       "       [35.62697601, 35.58792114, 35.51083755, 35.33985519, 35.82497025,\n",
       "        35.89683914, 35.90187836],\n",
       "...\n",
       "       [34.91690445, 34.92385483, 34.91975403, 34.91980362, 34.92385483,\n",
       "        34.93680573, 34.93885422],\n",
       "       [34.92190552, 34.92485428, 34.91975403, 34.92080688, 34.92485428,\n",
       "        34.94480515, 34.9328537 ],\n",
       "       [34.92390442, 34.92285538, 34.92176056, 34.92280579, 34.92985535,\n",
       "        34.93280411, 34.92785645],\n",
       "       [34.92390442, 34.92385483, 34.92375946, 34.92480469, 34.92685318,\n",
       "        34.93780899, 34.92485428],\n",
       "       [34.92390442, 34.92285538, 34.92575836, 34.92181015, 34.92085648,\n",
       "        34.93680954, 34.92385483],\n",
       "       [34.92590332, 34.9288559 , 34.92575836, 34.92181015, 34.92685318,\n",
       "        34.93481064, 34.92585373],\n",
       "       [34.92490387, 34.92785645, 34.92475891, 34.92781067, 34.93385696,\n",
       "        34.93380737, 34.92385864],\n",
       "       [34.92190552, 34.92385864, 34.93076324, 34.9268074 , 34.93585968,\n",
       "        34.93481064, 34.92985916],\n",
       "       [34.92090607, 34.92185974, 34.92176437, 34.9228096 , 34.93285751,\n",
       "        34.93180847, 34.92786026],\n",
       "       [        nan, 34.91985703,         nan, 34.92181015,         nan,\n",
       "        34.92181015,         nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-38fa84d4-f8e4-4a2f-bf88-2ad61294f6fc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-38fa84d4-f8e4-4a2f-bf88-2ad61294f6fc' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-cbbe4257-6567-4a59-88fe-c0dd49cf3d3a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cbbe4257-6567-4a59-88fe-c0dd49cf3d3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0915d60-5fd2-412f-8b07-b46d5409c58c' class='xr-var-data-in' type='checkbox'><label for='data-e0915d60-5fd2-412f-8b07-b46d5409c58c' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-10-02T03:00:17.971200 ... 2...</div><input id='attrs-c6eec74d-80c2-4b29-9784-74687e59246f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6eec74d-80c2-4b29-9784-74687e59246f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad4bcdd1-bdf9-4e58-aeec-7f248ded55b7' class='xr-var-data-in' type='checkbox'><label for='data-ad4bcdd1-bdf9-4e58-aeec-7f248ded55b7' title='Show/Hide data 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;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-34.46 -33.78 ... -32.29 -32.17</div><input id='attrs-0e7f78a4-8865-43d3-9c29-4e8f64b2a956' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0e7f78a4-8865-43d3-9c29-4e8f64b2a956' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98140a6d-732b-48fe-8adc-f9cefba4980c' class='xr-var-data-in' type='checkbox'><label for='data-98140a6d-732b-48fe-8adc-f9cefba4980c' title='Show/Hide data 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([-34.462, -33.784, -32.972, -32.546, -32.428, -32.292, -32.169])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.96 44.68 44.13 ... 42.66 42.51</div><input id='attrs-2dc38385-2fc5-42f1-86a5-19975b0996fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2dc38385-2fc5-42f1-86a5-19975b0996fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-309c25c0-295e-4746-8e02-094011596673' class='xr-var-data-in' type='checkbox'><label for='data-309c25c0-295e-4746-8e02-094011596673' title='Show/Hide data 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([44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c961e92-c1ba-4c84-a45c-116fdcf302fe' class='xr-section-summary-in' type='checkbox'  ><label for='section-3c961e92-c1ba-4c84-a45c-116fdcf302fe' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9cb94c85-cdba-4c5a-8b38-1f7b0673d099' class='xr-index-data-in' type='checkbox'/><label for='index-9cb94c85-cdba-4c5a-8b38-1f7b0673d099' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f576a3d2-33fa-498a-96b9-9d5ef40cc001' class='xr-index-data-in' type='checkbox'/><label for='index-f576a3d2-33fa-498a-96b9-9d5ef40cc001' 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;2012-10-02 03:00:17.971200&#x27;, &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "               &#x27;2012-10-22 02:50:32.006400&#x27;, &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "               &#x27;2012-11-11 02:40:46.041600&#x27;, &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "               &#x27;2012-12-01 02:47:51.993600&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3c4996c-d62b-4572-9fa3-8006eaa9c462' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b3c4996c-d62b-4572-9fa3-8006eaa9c462' 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 'salinity' (level: 78, date: 7)>\n",
       "array([[35.63097763, 35.52592468, 35.47483063, 35.33785629, 35.81896591,\n",
       "        35.8889389 , 35.90187836],\n",
       "       [35.63097763, 35.52292252, 35.47483063, 35.33685684, 35.81796646,\n",
       "        35.88793945, 35.90187836],\n",
       "       [35.62797546, 35.52292252, 35.47383118, 35.33785629, 35.81896591,\n",
       "        35.88694   , 35.90187836],\n",
       "       [35.62697601, 35.52192307, 35.47383118, 35.33785629, 35.81896591,\n",
       "        35.89193726, 35.90187836],\n",
       "       [35.62797546, 35.52192307, 35.47383118, 35.33785629, 35.81996536,\n",
       "        35.88993835, 35.90187836],\n",
       "       [35.62897873, 35.52292252, 35.47483063, 35.33785629, 35.81996536,\n",
       "        35.88993835, 35.90187836],\n",
       "       [35.62997818, 35.51892471, 35.48183441, 35.33785629, 35.81996536,\n",
       "        35.88993835, 35.90187836],\n",
       "       [35.63197708, 35.44991302, 35.47983551, 35.33785629, 35.81996536,\n",
       "        35.89683914, 35.90187836],\n",
       "       [35.63097763, 35.38090134, 35.4948349 , 35.33785629, 35.81896591,\n",
       "        35.89583969, 35.90187836],\n",
       "       [35.62697601, 35.58792114, 35.51083755, 35.33985519, 35.82497025,\n",
       "        35.89683914, 35.90187836],\n",
       "...\n",
       "       [34.91690445, 34.92385483, 34.91975403, 34.91980362, 34.92385483,\n",
       "        34.93680573, 34.93885422],\n",
       "       [34.92190552, 34.92485428, 34.91975403, 34.92080688, 34.92485428,\n",
       "        34.94480515, 34.9328537 ],\n",
       "       [34.92390442, 34.92285538, 34.92176056, 34.92280579, 34.92985535,\n",
       "        34.93280411, 34.92785645],\n",
       "       [34.92390442, 34.92385483, 34.92375946, 34.92480469, 34.92685318,\n",
       "        34.93780899, 34.92485428],\n",
       "       [34.92390442, 34.92285538, 34.92575836, 34.92181015, 34.92085648,\n",
       "        34.93680954, 34.92385483],\n",
       "       [34.92590332, 34.9288559 , 34.92575836, 34.92181015, 34.92685318,\n",
       "        34.93481064, 34.92585373],\n",
       "       [34.92490387, 34.92785645, 34.92475891, 34.92781067, 34.93385696,\n",
       "        34.93380737, 34.92385864],\n",
       "       [34.92190552, 34.92385864, 34.93076324, 34.9268074 , 34.93585968,\n",
       "        34.93481064, 34.92985916],\n",
       "       [34.92090607, 34.92185974, 34.92176437, 34.9228096 , 34.93285751,\n",
       "        34.93180847, 34.92786026],\n",
       "       [        nan, 34.91985703,         nan, 34.92181015,         nan,\n",
       "        34.92181015,         nan]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77\n",
       "  * date     (date) datetime64[ns] 2012-10-02T03:00:17.971200 ... 2012-12-01T...\n",
       "    lon      (date) float64 -34.46 -33.78 -32.97 -32.55 -32.43 -32.29 -32.17\n",
       "    lat      (date) float64 44.96 44.68 44.13 43.64 43.07 42.66 42.51"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo.salinity.sel(date=slice('2012-10-01', '2012-12-01'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9a9172aad0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "argo.salinity.sel(date=slice('2012-10-01', '2012-12-01')).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`.sel()` also works on the whole Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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;\n",
       "Dimensions:      (level: 78, date: 1)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-10-22T02:50:32.006400\n",
       "    lon          (date) float64 -32.97\n",
       "    lat          (date) float64 44.13\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.47 35.47 35.47 ... 34.93 34.92 nan\n",
       "    temperature  (level, date) float64 17.13 17.13 17.13 ... 3.736 3.639 nan\n",
       "    pressure     (level, date) float64 6.4 10.3 15.4 ... 1.9e+03 1.951e+03 nan</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-9fc9cc8b-af76-43bb-8252-c9c5dc0f495a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9fc9cc8b-af76-43bb-8252-c9c5dc0f495a' 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'>level</span>: 78</li><li><span class='xr-has-index'>date</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-69721dc4-0d4a-49e8-8184-f3a03a5ee603' class='xr-section-summary-in' type='checkbox'  checked><label for='section-69721dc4-0d4a-49e8-8184-f3a03a5ee603' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-3f16eb2e-aa67-4b9e-ae0e-e8c768ff96ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f16eb2e-aa67-4b9e-ae0e-e8c768ff96ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d093a5d-5f3a-4e55-b67b-b7015d36be97' class='xr-var-data-in' type='checkbox'><label for='data-4d093a5d-5f3a-4e55-b67b-b7015d36be97' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-10-22T02:50:32.006400</div><input id='attrs-fa17a0c3-9f48-4564-b619-320d9048d3fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa17a0c3-9f48-4564-b619-320d9048d3fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d3a7dd3-da9b-4bd6-b2b3-a6a2c537dc42' class='xr-var-data-in' type='checkbox'><label for='data-9d3a7dd3-da9b-4bd6-b2b3-a6a2c537dc42' title='Show/Hide data 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;2012-10-22T02:50:32.006400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-32.97</div><input id='attrs-b9908939-beb4-4ab8-869c-17c9fdd80c5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9908939-beb4-4ab8-869c-17c9fdd80c5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-450faa1c-6c47-4e99-999f-75df8cd38328' class='xr-var-data-in' type='checkbox'><label for='data-450faa1c-6c47-4e99-999f-75df8cd38328' title='Show/Hide data 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([-32.972])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.13</div><input id='attrs-c5dd8b88-36b6-4cb5-bd9d-8c26eb8247b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5dd8b88-36b6-4cb5-bd9d-8c26eb8247b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-478aaa3c-8f31-4aa3-b4db-d7e764a57c7f' class='xr-var-data-in' type='checkbox'><label for='data-478aaa3c-8f31-4aa3-b4db-d7e764a57c7f' title='Show/Hide data 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([44.13])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2dee7cc4-e145-4257-8849-e21e8b9eaaeb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2dee7cc4-e145-4257-8849-e21e8b9eaaeb' 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>salinity</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.47 35.47 35.47 ... 34.92 nan</div><input id='attrs-03f8303c-aaa4-4bbe-bebf-6dd8b4c7f8e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-03f8303c-aaa4-4bbe-bebf-6dd8b4c7f8e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef03932c-8938-4d11-aca5-1cd7d6c020b7' class='xr-var-data-in' type='checkbox'><label for='data-ef03932c-8938-4d11-aca5-1cd7d6c020b7' title='Show/Hide data 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([[35.47483063],\n",
       "       [35.47483063],\n",
       "       [35.47383118],\n",
       "       [35.47383118],\n",
       "       [35.47383118],\n",
       "       [35.47483063],\n",
       "       [35.48183441],\n",
       "       [35.47983551],\n",
       "       [35.4948349 ],\n",
       "       [35.51083755],\n",
       "       [36.13380051],\n",
       "       [36.09579849],\n",
       "       [35.95479965],\n",
       "       [35.93979645],\n",
       "       [35.8958931 ],\n",
       "       [35.86388397],\n",
       "       [35.87788773],\n",
       "       [35.88188934],\n",
       "       [35.90379333],\n",
       "       [35.9067955 ],\n",
       "...\n",
       "       [35.00975037],\n",
       "       [34.96175385],\n",
       "       [34.96775055],\n",
       "       [34.95075226],\n",
       "       [34.93775177],\n",
       "       [34.93375015],\n",
       "       [34.93775558],\n",
       "       [34.9247551 ],\n",
       "       [34.92175674],\n",
       "       [34.91975403],\n",
       "       [34.91975403],\n",
       "       [34.91975403],\n",
       "       [34.92176056],\n",
       "       [34.92375946],\n",
       "       [34.92575836],\n",
       "       [34.92575836],\n",
       "       [34.92475891],\n",
       "       [34.93076324],\n",
       "       [34.92176437],\n",
       "       [        nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>17.13 17.13 17.13 ... 3.639 nan</div><input id='attrs-e0f83443-bf08-4591-aef2-a37c306c50ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0f83443-bf08-4591-aef2-a37c306c50ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5244bf6-2528-4713-8123-36cae46fd6b2' class='xr-var-data-in' type='checkbox'><label for='data-f5244bf6-2528-4713-8123-36cae46fd6b2' title='Show/Hide data 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([[17.13299942],\n",
       "       [17.13199997],\n",
       "       [17.13299942],\n",
       "       [17.13400078],\n",
       "       [17.13500023],\n",
       "       [17.13500023],\n",
       "       [17.13199997],\n",
       "       [17.13400078],\n",
       "       [17.11300087],\n",
       "       [17.09700012],\n",
       "       [16.67099953],\n",
       "       [16.40099907],\n",
       "       [15.76000023],\n",
       "       [15.58600044],\n",
       "       [15.35299969],\n",
       "       [15.18299961],\n",
       "       [15.15299988],\n",
       "       [15.05799961],\n",
       "       [15.04399967],\n",
       "       [14.99300003],\n",
       "...\n",
       "       [ 5.04799986],\n",
       "       [ 4.69399977],\n",
       "       [ 4.61399984],\n",
       "       [ 4.42500019],\n",
       "       [ 4.30600023],\n",
       "       [ 4.23099995],\n",
       "       [ 4.19500017],\n",
       "       [ 4.07600021],\n",
       "       [ 4.0170002 ],\n",
       "       [ 3.95000005],\n",
       "       [ 3.92499995],\n",
       "       [ 3.87899995],\n",
       "       [ 3.86800003],\n",
       "       [ 3.84500003],\n",
       "       [ 3.8210001 ],\n",
       "       [ 3.77800012],\n",
       "       [ 3.73799992],\n",
       "       [ 3.73600006],\n",
       "       [ 3.63899994],\n",
       "       [        nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.4 10.3 15.4 ... 1.951e+03 nan</div><input id='attrs-174c46ee-f4cb-43b4-8324-dc1766575aeb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-174c46ee-f4cb-43b4-8324-dc1766575aeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7d24634-18bf-4d09-9745-4d1b6bc87715' class='xr-var-data-in' type='checkbox'><label for='data-c7d24634-18bf-4d09-9745-4d1b6bc87715' title='Show/Hide data 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([[   6.4000001 ],\n",
       "       [  10.30000019],\n",
       "       [  15.39999962],\n",
       "       [  21.        ],\n",
       "       [  26.        ],\n",
       "       [  30.20000076],\n",
       "       [  35.40000153],\n",
       "       [  40.79999924],\n",
       "       [  44.79999924],\n",
       "       [  51.29999924],\n",
       "       [  60.20000076],\n",
       "       [  71.        ],\n",
       "       [  80.59999847],\n",
       "       [  91.        ],\n",
       "       [ 100.80000305],\n",
       "       [ 110.90000153],\n",
       "       [ 121.09999847],\n",
       "       [ 130.8999939 ],\n",
       "       [ 140.69999695],\n",
       "       [ 150.80000305],\n",
       "...\n",
       "       [1050.        ],\n",
       "       [1100.59997559],\n",
       "       [1150.5       ],\n",
       "       [1200.40002441],\n",
       "       [1250.69995117],\n",
       "       [1300.40002441],\n",
       "       [1350.40002441],\n",
       "       [1400.90002441],\n",
       "       [1447.90002441],\n",
       "       [1500.80004883],\n",
       "       [1550.40002441],\n",
       "       [1600.90002441],\n",
       "       [1650.09997559],\n",
       "       [1700.40002441],\n",
       "       [1750.5       ],\n",
       "       [1800.80004883],\n",
       "       [1850.69995117],\n",
       "       [1900.5       ],\n",
       "       [1950.80004883],\n",
       "       [          nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e9499c9b-0e28-4981-855b-c50c9ccfa15a' class='xr-section-summary-in' type='checkbox'  ><label for='section-e9499c9b-0e28-4981-855b-c50c9ccfa15a' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ca55de22-d754-4f0c-a256-da8b5e41ea17' class='xr-index-data-in' type='checkbox'/><label for='index-ca55de22-d754-4f0c-a256-da8b5e41ea17' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-85b6109d-8201-45c4-a63e-d1a9775278ca' class='xr-index-data-in' type='checkbox'/><label for='index-85b6109d-8201-45c4-a63e-d1a9775278ca' 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;2012-10-22 02:50:32.006400&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-441eac10-a42b-4354-bcc3-6fcf8b2dc903' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-441eac10-a42b-4354-bcc3-6fcf8b2dc903' 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.Dataset>\n",
       "Dimensions:      (level: 78, date: 1)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-10-22T02:50:32.006400\n",
       "    lon          (date) float64 -32.97\n",
       "    lat          (date) float64 44.13\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.47 35.47 35.47 ... 34.93 34.92 nan\n",
       "    temperature  (level, date) float64 17.13 17.13 17.13 ... 3.736 3.639 nan\n",
       "    pressure     (level, date) float64 6.4 10.3 15.4 ... 1.9e+03 1.951e+03 nan"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo.sel(date='2012-10-22')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computation\n",
    "\n",
    "Xarray dataarrays and datasets work seamlessly with arithmetic operators and numpy array functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9a9160b400>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_kelvin = argo.temperature + 273.15\n",
    "temp_kelvin.plot(yincrease=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also combine multiple xarray datasets in arithemtic operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9a9150d4b0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = 9.8\n",
    "buoyancy = g * (2e-4 * argo.temperature - 7e-4 * argo.salinity)\n",
    "buoyancy.plot(yincrease=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Broadcasting, Aligment, and Combining Data\n",
    "\n",
    "### Broadcasting\n",
    "\n",
    "Broadcasting arrays in numpy is a nightmare. It is much easier when the data axes are labeled!\n",
    "\n",
    "This is a useless calculation, but it illustrates how perfoming an operation on arrays with differenty coordinates will result in automatic broadcasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (level: 78, date: 75)&gt;\n",
       "array([[   0.   ,    0.   ,    0.   , ...,    0.   ,    0.   ,    0.   ],\n",
       "       [  47.187,   46.716,   46.45 , ...,   42.601,   42.457,   42.379],\n",
       "       [  94.374,   93.432,   92.9  , ...,   85.202,   84.914,   84.758],\n",
       "       ...,\n",
       "       [3539.025, 3503.7  , 3483.75 , ..., 3195.075, 3184.275, 3178.425],\n",
       "       [3586.212, 3550.416, 3530.2  , ..., 3237.676, 3226.732, 3220.804],\n",
       "       [3633.399, 3597.132, 3576.65 , ..., 3280.277, 3269.189, 3263.183]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38</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'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 78</li><li><span class='xr-has-index'>date</span>: 75</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4088dc96-2ef2-423f-b4f7-09bd30d05a69' class='xr-array-in' type='checkbox' checked><label for='section-4088dc96-2ef2-423f-b4f7-09bd30d05a69' 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>0.0 0.0 0.0 0.0 0.0 ... 3.29e+03 3.28e+03 3.269e+03 3.263e+03</span></div><div class='xr-array-data'><pre>array([[   0.   ,    0.   ,    0.   , ...,    0.   ,    0.   ,    0.   ],\n",
       "       [  47.187,   46.716,   46.45 , ...,   42.601,   42.457,   42.379],\n",
       "       [  94.374,   93.432,   92.9  , ...,   85.202,   84.914,   84.758],\n",
       "       ...,\n",
       "       [3539.025, 3503.7  , 3483.75 , ..., 3195.075, 3184.275, 3178.425],\n",
       "       [3586.212, 3550.416, 3530.2  , ..., 3237.676, 3226.732, 3220.804],\n",
       "       [3633.399, 3597.132, 3576.65 , ..., 3280.277, 3269.189, 3263.183]])</pre></div></div></li><li class='xr-section-item'><input id='section-442c15ef-6e46-42ad-b55c-1de16ef52f14' class='xr-section-summary-in' type='checkbox'  checked><label for='section-442c15ef-6e46-42ad-b55c-1de16ef52f14' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-2741c407-b6e0-4953-bbc2-0fb2f30713ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2741c407-b6e0-4953-bbc2-0fb2f30713ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a391865-c483-4f06-a935-7f861d2a162d' class='xr-var-data-in' type='checkbox'><label for='data-9a391865-c483-4f06-a935-7f861d2a162d' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-9a1fce13-12c8-4842-b642-f4038e75ca54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a1fce13-12c8-4842-b642-f4038e75ca54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61e79c2b-f158-4b0d-bc0f-158780073033' class='xr-var-data-in' type='checkbox'><label for='data-61e79c2b-f158-4b0d-bc0f-158780073033' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-32961d80-ac9f-4354-af33-e1e472487b58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32961d80-ac9f-4354-af33-e1e472487b58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1a616a4-942b-4e4b-a2b2-e105e3cff66b' class='xr-var-data-in' type='checkbox'><label for='data-e1a616a4-942b-4e4b-a2b2-e105e3cff66b' title='Show/Hide data 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([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-70846f49-ae55-4f80-b58e-c434e8797f62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70846f49-ae55-4f80-b58e-c434e8797f62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9a15277-2b96-49ce-baa7-d4d4f4d79cb2' class='xr-var-data-in' type='checkbox'><label for='data-a9a15277-2b96-49ce-baa7-d4d4f4d79cb2' title='Show/Hide data 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([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-09a0803a-e423-436d-8567-54c2fec9d8ea' class='xr-section-summary-in' type='checkbox'  ><label for='section-09a0803a-e423-436d-8567-54c2fec9d8ea' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a813cb24-80c5-4bdb-bbc1-782dea425bcf' class='xr-index-data-in' type='checkbox'/><label for='index-a813cb24-80c5-4bdb-bbc1-782dea425bcf' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b7605750-9925-4e7a-ac17-6db53ebbb394' class='xr-index-data-in' type='checkbox'/><label for='index-b7605750-9925-4e7a-ac17-6db53ebbb394' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dc447781-b30e-4f6d-b46e-e6500a91f5c4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dc447781-b30e-4f6d-b46e-e6500a91f5c4' 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 (level: 78, date: 75)>\n",
       "array([[   0.   ,    0.   ,    0.   , ...,    0.   ,    0.   ,    0.   ],\n",
       "       [  47.187,   46.716,   46.45 , ...,   42.601,   42.457,   42.379],\n",
       "       [  94.374,   93.432,   92.9  , ...,   85.202,   84.914,   84.758],\n",
       "       ...,\n",
       "       [3539.025, 3503.7  , 3483.75 , ..., 3195.075, 3184.275, 3178.425],\n",
       "       [3586.212, 3550.416, 3530.2  , ..., 3237.676, 3226.732, 3220.804],\n",
       "       [3633.399, 3597.132, 3576.65 , ..., 3280.277, 3269.189, 3263.183]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "level_times_lat = argo.level * argo.lat\n",
    "level_times_lat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9a913e9cc0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "level_times_lat.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alignment\n",
    "\n",
    "If you try to perform operations on DataArrays that share a dimension name, Xarray will try to _align_ them first.\n",
    "This works nearly identically to Pandas, except that there can be multiple dimensions to align over.\n",
    "\n",
    "To see how alignment works, we will create some subsets of our original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sa_surf = argo.salinity.isel(level=slice(0, 20))\n",
    "sa_mid = argo.salinity.isel(level=slice(10, 30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, when we combine multiple arrays in mathematical operations, Xarray performs an \"inner join\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       ".xr-section-item input + label {\n",
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       "\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
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       "  color: var(--xr-font-color0);\n",
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       "\n",
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       "\n",
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       "\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",
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       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "  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",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
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       "\n",
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       "\n",
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       ".xr-var-item label,\n",
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       "  background-color: var(--xr-background-color-row-even);\n",
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       "\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",
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       "\n",
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       "\n",
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       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
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       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
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       "\n",
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       ".xr-attrs dt:hover {\n",
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       "\n",
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       ".xr-var-data,\n",
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       "  display: none;\n",
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       "\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",
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       "\n",
       "dl.xr-attrs {\n",
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       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "  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;level&#x27; (level: 10)&gt;\n",
       "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])\n",
       "Coordinates:\n",
       "  * level    (level) int64 10 11 12 13 14 15 16 17 18 19</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'>'level'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 10</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f1b725bb-c6e0-4bcf-81a9-6bb9d4c88595' class='xr-array-in' type='checkbox' checked><label for='section-f1b725bb-c6e0-4bcf-81a9-6bb9d4c88595' 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>10 11 12 13 14 15 16 17 18 19</span></div><div class='xr-array-data'><pre>array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])</pre></div></div></li><li class='xr-section-item'><input id='section-ff98fda8-8b67-4cba-9583-bed6425c64aa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff98fda8-8b67-4cba-9583-bed6425c64aa' 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'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>10 11 12 13 14 15 16 17 18 19</div><input id='attrs-24d96cef-36ad-4822-aa96-e1dac0647993' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24d96cef-36ad-4822-aa96-e1dac0647993' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c282ab61-57cb-47f3-8946-57d6cf1862a7' class='xr-var-data-in' type='checkbox'><label for='data-c282ab61-57cb-47f3-8946-57d6cf1862a7' title='Show/Hide data 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([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f6cd530-7e84-4f92-b2a9-06bb033c13ff' class='xr-section-summary-in' type='checkbox'  ><label for='section-5f6cd530-7e84-4f92-b2a9-06bb033c13ff' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-31c60fd3-c0fa-4b62-9350-dd6d16a4bc23' class='xr-index-data-in' type='checkbox'/><label for='index-31c60fd3-c0fa-4b62-9350-dd6d16a4bc23' 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(Int64Index([10, 11, 12, 13, 14, 15, 16, 17, 18, 19], dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e39f3d9-53a8-4ce9-a32d-7daecd3579e1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6e39f3d9-53a8-4ce9-a32d-7daecd3579e1' 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 'level' (level: 10)>\n",
       "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])\n",
       "Coordinates:\n",
       "  * level    (level) int64 10 11 12 13 14 15 16 17 18 19"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sa_surf * sa_mid).level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can override this behavior by manually aligning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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;level&#x27; (level: 30)&gt;\n",
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 29</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'>'level'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 30</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ca19a1d5-4a61-40b1-9b25-db396c3a77ef' class='xr-array-in' type='checkbox' checked><label for='section-ca19a1d5-4a61-40b1-9b25-db396c3a77ef' 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>0 1 2 3 4 5 6 7 8 9 10 11 12 ... 18 19 20 21 22 23 24 25 26 27 28 29</span></div><div class='xr-array-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])</pre></div></div></li><li class='xr-section-item'><input id='section-bfea243e-a52f-4a2e-b4ef-33442eb5c919' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bfea243e-a52f-4a2e-b4ef-33442eb5c919' 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'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 24 25 26 27 28 29</div><input id='attrs-541c1951-e9f5-41fc-83c1-51b56be35db6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-541c1951-e9f5-41fc-83c1-51b56be35db6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2da096bb-9716-42fb-8619-fde6277045a2' class='xr-var-data-in' type='checkbox'><label for='data-2da096bb-9716-42fb-8619-fde6277045a2' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ac69d8ab-d5d6-487a-abed-82914405d512' class='xr-section-summary-in' type='checkbox'  ><label for='section-ac69d8ab-d5d6-487a-abed-82914405d512' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ffe3553c-e431-46a2-9017-93cef5eeff81' class='xr-index-data-in' type='checkbox'/><label for='index-ffe3553c-e431-46a2-9017-93cef5eeff81' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c0c846b4-afea-4c67-9c9c-5ff2e291f395' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c0c846b4-afea-4c67-9c9c-5ff2e291f395' 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 'level' (level: 30)>\n",
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 29"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa_surf_outer, sa_mid_outer = xr.align(sa_surf, sa_mid, join='outer')\n",
    "sa_surf_outer.level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, missing data (NaNs) have been filled in where the array was extended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9a912bf6d0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sa_surf_outer.plot(yincrease=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use `join='right'` and `join='left'`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combing Data: Concat and Merge\n",
    "\n",
    "The ability to combine many smaller arrays into a single big Dataset is one of the main advantages of Xarray.\n",
    "To take advantage of this, we need to learn two operations that help us combine data:\n",
    "- `xr.contact`: to concatenate multiple arrays into one bigger array along their dimensions\n",
    "- `xr.merge`: to combine multiple different arrays into a dataset\n",
    "\n",
    "First let's look at concat. Let's re-combine the subsetted data from the previous step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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",
       "  display: inline-block;\n",
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;salinity&#x27; (level: 40, date: 75)&gt;\n",
       "array([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [35.78895187, 35.7829895 , 35.85100555, ..., 35.84291458,\n",
       "        35.81891251, 35.7779007 ],\n",
       "       [35.76794815, 35.75598526, 35.84500504, ..., 35.84891891,\n",
       "        35.83391571, 35.76390076],\n",
       "       [35.75194168, 35.71097565, 35.83100128, ..., 35.80690765,\n",
       "        35.85292053, 35.75489807]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 29\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38</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'>'salinity'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 40</li><li><span class='xr-has-index'>date</span>: 75</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-dda54dfe-fe61-47ba-ac7b-113cdded7a45' class='xr-array-in' type='checkbox' checked><label for='section-dda54dfe-fe61-47ba-ac7b-113cdded7a45' 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>35.64 35.51 35.57 35.4 35.45 35.5 ... 35.64 35.82 35.81 35.85 35.75</span></div><div class='xr-array-data'><pre>array([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [35.78895187, 35.7829895 , 35.85100555, ..., 35.84291458,\n",
       "        35.81891251, 35.7779007 ],\n",
       "       [35.76794815, 35.75598526, 35.84500504, ..., 35.84891891,\n",
       "        35.83391571, 35.76390076],\n",
       "       [35.75194168, 35.71097565, 35.83100128, ..., 35.80690765,\n",
       "        35.85292053, 35.75489807]])</pre></div></div></li><li class='xr-section-item'><input id='section-00d9fbd8-8a44-4019-803d-f48417546712' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00d9fbd8-8a44-4019-803d-f48417546712' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 24 25 26 27 28 29</div><input id='attrs-225e1edd-b788-42c3-a702-0d4bdc61a402' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-225e1edd-b788-42c3-a702-0d4bdc61a402' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b917f5d-9bf7-4972-b478-09ed1c416e1f' class='xr-var-data-in' type='checkbox'><label for='data-6b917f5d-9bf7-4972-b478-09ed1c416e1f' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
       "       26, 27, 28, 29])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-4885ca2b-068c-4ad4-b133-4358fe0f98ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4885ca2b-068c-4ad4-b133-4358fe0f98ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7b42540-a7ed-40f8-9c09-497c26bf9016' class='xr-var-data-in' type='checkbox'><label for='data-b7b42540-a7ed-40f8-9c09-497c26bf9016' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-059876d4-5b5b-4a75-bb98-3d6f15e22be2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-059876d4-5b5b-4a75-bb98-3d6f15e22be2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5c8b19a-659b-4fc5-8f65-fe40692bb322' class='xr-var-data-in' type='checkbox'><label for='data-b5c8b19a-659b-4fc5-8f65-fe40692bb322' title='Show/Hide data 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([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-4004097c-0ffc-4bb8-8ee3-9de70184757e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4004097c-0ffc-4bb8-8ee3-9de70184757e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76614e50-6621-46da-9d83-8abb097bfee4' class='xr-var-data-in' type='checkbox'><label for='data-76614e50-6621-46da-9d83-8abb097bfee4' title='Show/Hide data 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([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48416258-d51b-4ed0-8774-5cb146b67410' class='xr-section-summary-in' type='checkbox'  ><label for='section-48416258-d51b-4ed0-8774-5cb146b67410' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c9e6a7f2-8918-4fb0-beca-1bf36dc067a6' class='xr-index-data-in' type='checkbox'/><label for='index-c9e6a7f2-8918-4fb0-beca-1bf36dc067a6' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n",
       "            24, 25, 26, 27, 28, 29],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ba3e54df-983e-47fc-ac84-e5e5d9bece2b' class='xr-index-data-in' type='checkbox'/><label for='index-ba3e54df-983e-47fc-ac84-e5e5d9bece2b' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7398acfb-b7a7-4eb2-abbf-5ce70a70b503' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7398acfb-b7a7-4eb2-abbf-5ce70a70b503' 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 'salinity' (level: 40, date: 75)>\n",
       "array([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [35.78895187, 35.7829895 , 35.85100555, ..., 35.84291458,\n",
       "        35.81891251, 35.7779007 ],\n",
       "       [35.76794815, 35.75598526, 35.84500504, ..., 35.84891891,\n",
       "        35.83391571, 35.76390076],\n",
       "       [35.75194168, 35.71097565, 35.83100128, ..., 35.80690765,\n",
       "        35.85292053, 35.75489807]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 29\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa_surf_mid = xr.concat([sa_surf, sa_mid], dim='level')\n",
    "sa_surf_mid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{warning}\n",
    "Xarray won't check the values of the coordinates before `concat`. It will just stick everything together into a new array.\n",
    "```\n",
    "\n",
    "In this case, we had overlapping data. We can see this by looking at the `level` coordinate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "  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;level&#x27; (level: 40)&gt;\n",
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
       "       26, 27, 28, 29])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 29</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'>'level'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 40</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6b8fc705-4b5c-43ae-b0b3-7aad64f51312' class='xr-array-in' type='checkbox' checked><label for='section-6b8fc705-4b5c-43ae-b0b3-7aad64f51312' 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>0 1 2 3 4 5 6 7 8 9 10 11 12 ... 18 19 20 21 22 23 24 25 26 27 28 29</span></div><div class='xr-array-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
       "       26, 27, 28, 29])</pre></div></div></li><li class='xr-section-item'><input id='section-8f9f588e-085e-4e11-959d-115b0f06c10c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f9f588e-085e-4e11-959d-115b0f06c10c' 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'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 24 25 26 27 28 29</div><input id='attrs-7bbc6531-508d-40f6-9e0d-98cccf568969' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7bbc6531-508d-40f6-9e0d-98cccf568969' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87d7f576-0a39-45f5-bb20-e08f9903e12b' class='xr-var-data-in' type='checkbox'><label for='data-87d7f576-0a39-45f5-bb20-e08f9903e12b' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
       "       26, 27, 28, 29])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-30a9d180-610e-4583-b075-8c47be332019' class='xr-section-summary-in' type='checkbox'  ><label for='section-30a9d180-610e-4583-b075-8c47be332019' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-65a1ba4c-1a5d-4493-b7ff-c2a816fe8eed' class='xr-index-data-in' type='checkbox'/><label for='index-65a1ba4c-1a5d-4493-b7ff-c2a816fe8eed' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n",
       "            24, 25, 26, 27, 28, 29],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7eeb9878-deb5-4d73-b0e2-307da96f4909' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7eeb9878-deb5-4d73-b0e2-307da96f4909' 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 'level' (level: 40)>\n",
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
       "       26, 27, 28, 29])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 29"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa_surf_mid.level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a91a9fc70>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sa_surf_mid.level.values, marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also concat data along a _new_ dimension, e.g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       ".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;salinity&#x27; (newdim: 2, level: 30, date: 75)&gt;\n",
       "array([[[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "         35.77793884, 35.66891098],\n",
       "        [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "         35.58389664, 35.66791153],\n",
       "        [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "         35.66290665, 35.66591263],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [35.78895187, 35.7829895 , 35.85100555, ..., 35.84291458,\n",
       "         35.81891251, 35.7779007 ],\n",
       "        [35.76794815, 35.75598526, 35.84500504, ..., 35.84891891,\n",
       "         35.83391571, 35.76390076],\n",
       "        [35.75194168, 35.71097565, 35.83100128, ..., 35.80690765,\n",
       "         35.85292053, 35.75489807]]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 29\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38\n",
       "Dimensions without coordinates: newdim</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'>'salinity'</div><ul class='xr-dim-list'><li><span>newdim</span>: 2</li><li><span class='xr-has-index'>level</span>: 30</li><li><span class='xr-has-index'>date</span>: 75</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d237c30f-335b-411e-9c16-543b38a3a91d' class='xr-array-in' type='checkbox' checked><label for='section-d237c30f-335b-411e-9c16-543b38a3a91d' 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>35.64 35.51 35.57 35.4 35.45 35.5 ... 35.64 35.82 35.81 35.85 35.75</span></div><div class='xr-array-data'><pre>array([[[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "         35.77793884, 35.66891098],\n",
       "        [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "         35.58389664, 35.66791153],\n",
       "        [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "         35.66290665, 35.66591263],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [35.78895187, 35.7829895 , 35.85100555, ..., 35.84291458,\n",
       "         35.81891251, 35.7779007 ],\n",
       "        [35.76794815, 35.75598526, 35.84500504, ..., 35.84891891,\n",
       "         35.83391571, 35.76390076],\n",
       "        [35.75194168, 35.71097565, 35.83100128, ..., 35.80690765,\n",
       "         35.85292053, 35.75489807]]])</pre></div></div></li><li class='xr-section-item'><input id='section-ce85c525-9e29-449d-ae56-1fbb4e1e3461' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ce85c525-9e29-449d-ae56-1fbb4e1e3461' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 24 25 26 27 28 29</div><input id='attrs-0fecac3a-8b01-4fbd-82bb-5f65453da52a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0fecac3a-8b01-4fbd-82bb-5f65453da52a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b5a7ac6-eedf-4c56-96b5-1e207cb753fd' class='xr-var-data-in' type='checkbox'><label for='data-0b5a7ac6-eedf-4c56-96b5-1e207cb753fd' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-1edef4bf-893f-4998-8088-3ab15c131aa8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1edef4bf-893f-4998-8088-3ab15c131aa8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dfe48c3-018f-4d20-9337-ba2807457070' class='xr-var-data-in' type='checkbox'><label for='data-4dfe48c3-018f-4d20-9337-ba2807457070' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-4c5a3e57-5a28-4359-a73f-572262905f50' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c5a3e57-5a28-4359-a73f-572262905f50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f2da320-3c9f-41d7-be57-a195bb10af24' class='xr-var-data-in' type='checkbox'><label for='data-4f2da320-3c9f-41d7-be57-a195bb10af24' title='Show/Hide data 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([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-708af0db-8ea9-4c77-a881-ddf6ac9b05a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-708af0db-8ea9-4c77-a881-ddf6ac9b05a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d4cf121-ec8f-4c1b-8934-de8ab6c9bee6' class='xr-var-data-in' type='checkbox'><label for='data-5d4cf121-ec8f-4c1b-8934-de8ab6c9bee6' title='Show/Hide data 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([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2eb4f2d3-4add-4552-b6f8-9eaf1e436140' class='xr-section-summary-in' type='checkbox'  ><label for='section-2eb4f2d3-4add-4552-b6f8-9eaf1e436140' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d91b2caa-2d66-4bc2-9e32-d0df62ffd3f1' class='xr-index-data-in' type='checkbox'/><label for='index-d91b2caa-2d66-4bc2-9e32-d0df62ffd3f1' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cd4df7d5-2895-4269-b313-6c63b501917b' class='xr-index-data-in' type='checkbox'/><label for='index-cd4df7d5-2895-4269-b313-6c63b501917b' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c77d0be-3025-488b-800a-5b1baf6a50f1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1c77d0be-3025-488b-800a-5b1baf6a50f1' 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 'salinity' (newdim: 2, level: 30, date: 75)>\n",
       "array([[[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "         35.77793884, 35.66891098],\n",
       "        [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "         35.58389664, 35.66791153],\n",
       "        [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "         35.66290665, 35.66591263],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [35.78895187, 35.7829895 , 35.85100555, ..., 35.84291458,\n",
       "         35.81891251, 35.7779007 ],\n",
       "        [35.76794815, 35.75598526, 35.84500504, ..., 35.84891891,\n",
       "         35.83391571, 35.76390076],\n",
       "        [35.75194168, 35.71097565, 35.83100128, ..., 35.80690765,\n",
       "         35.85292053, 35.75489807]]])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 29\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38\n",
       "Dimensions without coordinates: newdim"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa_concat_new = xr.concat([sa_surf, sa_mid], dim='newdim')\n",
    "sa_concat_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the data were aligned using an _outer_ join along the non-concat dimensions.\n",
    "\n",
    "Instead of specifying a new dimension name, we can pass a new Pandas index object explicitly to `concat`.\n",
    "This will create a new dimension coordinate and corresponding index.\n",
    "\n",
    "We can merge both DataArrays and Datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "\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;\n",
       "Dimensions:      (level: 78, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan 34.94 nan\n",
       "    temperature  (level, date) float64 18.97 18.44 19.1 19.79 ... nan 3.714 nan</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-1aa9983f-b5f9-4f86-8b78-238499919bbc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1aa9983f-b5f9-4f86-8b78-238499919bbc' 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'>level</span>: 78</li><li><span class='xr-has-index'>date</span>: 75</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d18235cf-cd72-48a9-b2f9-050eec34bd3c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d18235cf-cd72-48a9-b2f9-050eec34bd3c' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-8285eb22-bd0b-4c57-a786-4e656d16594c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8285eb22-bd0b-4c57-a786-4e656d16594c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c15f05c3-33e2-4ecb-b8d3-66de2b19623f' class='xr-var-data-in' type='checkbox'><label for='data-c15f05c3-33e2-4ecb-b8d3-66de2b19623f' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-1fc13068-a133-4400-9b88-cff4a5060184' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1fc13068-a133-4400-9b88-cff4a5060184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f43840dc-a96e-4a33-b3a4-fa8895de6d01' class='xr-var-data-in' type='checkbox'><label for='data-f43840dc-a96e-4a33-b3a4-fa8895de6d01' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-7c921190-7cc3-400e-bb37-9d29d64d69d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c921190-7cc3-400e-bb37-9d29d64d69d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c99c2a71-cec4-4ed2-a79a-7d07975511a3' class='xr-var-data-in' type='checkbox'><label for='data-c99c2a71-cec4-4ed2-a79a-7d07975511a3' title='Show/Hide data 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([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-b8713977-6a15-40bd-a8bf-32a7ba3673ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8713977-6a15-40bd-a8bf-32a7ba3673ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37c29798-1c21-4a0e-8228-99b59cefd974' class='xr-var-data-in' type='checkbox'><label for='data-37c29798-1c21-4a0e-8228-99b59cefd974' title='Show/Hide data 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([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2273bad2-84d6-4a8f-beef-b4f5fa4c5b52' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2273bad2-84d6-4a8f-beef-b4f5fa4c5b52' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.64 35.51 35.57 ... nan 34.94 nan</div><input id='attrs-b6579b7e-1170-494e-b249-5d3585d8b530' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6579b7e-1170-494e-b249-5d3585d8b530' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4842b40a-8cac-4b68-96d7-3ed89e5c740c' class='xr-var-data-in' type='checkbox'><label for='data-4842b40a-8cac-4b68-96d7-3ed89e5c740c' title='Show/Hide data 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([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [34.91585922, 34.92390442, 34.92390442, ..., 34.93481064,\n",
       "        34.94081116, 34.94680786],\n",
       "       [34.91585922, 34.92390442, 34.92190552, ..., 34.93280792,\n",
       "        34.93680954, 34.94380951],\n",
       "       [34.91785812, 34.92390442, 34.92390442, ...,         nan,\n",
       "        34.93680954,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>18.97 18.44 19.1 ... nan 3.714 nan</div><input id='attrs-5d2122a8-5086-4af9-af29-3dd73c7ce031' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d2122a8-5086-4af9-af29-3dd73c7ce031' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba394d9c-9382-495a-969b-19674fa3edf1' class='xr-var-data-in' type='checkbox'><label for='data-ba394d9c-9382-495a-969b-19674fa3edf1' title='Show/Hide data 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([[18.97400093, 18.43700027, 19.09900093, ..., 19.11300087,\n",
       "        21.82299995, 20.13100052],\n",
       "       [18.74099922, 18.39999962, 19.08200073, ..., 18.47200012,\n",
       "        19.45999908, 20.125     ],\n",
       "       [18.37000084, 18.37400055, 19.06500053, ..., 18.22999954,\n",
       "        19.26199913, 20.07699966],\n",
       "       ...,\n",
       "       [ 3.79299998,  3.81399989,  3.80200005, ...,  3.80699992,\n",
       "         3.81100011,  3.8599999 ],\n",
       "       [ 3.76399994,  3.77800012,  3.75699997, ...,  3.75399995,\n",
       "         3.74600005,  3.80599999],\n",
       "       [ 3.74399996,  3.74600005,  3.7249999 , ...,         nan,\n",
       "         3.71399999,         nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-af7fdade-4d8d-4f1a-baa3-5284c99fe3e0' class='xr-section-summary-in' type='checkbox'  ><label for='section-af7fdade-4d8d-4f1a-baa3-5284c99fe3e0' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e541cba8-11a3-4035-8360-634a7f524863' class='xr-index-data-in' type='checkbox'/><label for='index-e541cba8-11a3-4035-8360-634a7f524863' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-34b3f472-1917-4311-93ec-85474c074713' class='xr-index-data-in' type='checkbox'/><label for='index-34b3f472-1917-4311-93ec-85474c074713' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56419724-5ab0-4c53-a6a5-12a6b36f6fbd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-56419724-5ab0-4c53-a6a5-12a6b36f6fbd' 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.Dataset>\n",
       "Dimensions:      (level: 78, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan 34.94 nan\n",
       "    temperature  (level, date) float64 18.97 18.44 19.1 19.79 ... nan 3.714 nan"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.merge([argo.salinity, argo.temperature])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the data are not aligned, they will be aligned before merge.\n",
    "We can specify the join options in `merge`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "}\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;\n",
       "Dimensions:      (level: 78, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan nan nan\n",
       "    temperature  (level, date) float64 nan nan nan nan ... 3.728 nan 3.714 nan</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-d08f7f2d-05d1-453e-8ba4-dd75cd762c0f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d08f7f2d-05d1-453e-8ba4-dd75cd762c0f' 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'>level</span>: 78</li><li><span class='xr-has-index'>date</span>: 75</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-71c54eea-c3f8-4c25-8fb8-8d76fa3ddc87' class='xr-section-summary-in' type='checkbox'  checked><label for='section-71c54eea-c3f8-4c25-8fb8-8d76fa3ddc87' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-2a473743-a0c4-4f0e-bcf9-63c3586d1e39' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a473743-a0c4-4f0e-bcf9-63c3586d1e39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51c8a152-2ded-4ca8-b8aa-6e5807d01d82' class='xr-var-data-in' type='checkbox'><label for='data-51c8a152-2ded-4ca8-b8aa-6e5807d01d82' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-ba26cb00-96d1-43c5-98b0-34d18a16f29e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba26cb00-96d1-43c5-98b0-34d18a16f29e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b81114c2-d45a-4017-a6d0-83502ff281df' class='xr-var-data-in' type='checkbox'><label for='data-b81114c2-d45a-4017-a6d0-83502ff281df' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-71fe346f-5a8e-449a-aa3d-45cb424bda7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71fe346f-5a8e-449a-aa3d-45cb424bda7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efb2c4b6-3816-43b7-8716-1c2f33bd0e45' class='xr-var-data-in' type='checkbox'><label for='data-efb2c4b6-3816-43b7-8716-1c2f33bd0e45' title='Show/Hide data 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([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-b18bc493-643b-429b-8a59-ac248363510f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b18bc493-643b-429b-8a59-ac248363510f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c14add3-60dd-4aa5-854b-f6d1dc802047' class='xr-var-data-in' type='checkbox'><label for='data-2c14add3-60dd-4aa5-854b-f6d1dc802047' title='Show/Hide data 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([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a28f452c-7aef-4b0d-96c5-d60e1d509cc1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a28f452c-7aef-4b0d-96c5-d60e1d509cc1' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.64 35.51 35.57 ... nan nan nan</div><input id='attrs-bba4238e-201f-497e-a09c-48db0513ec1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bba4238e-201f-497e-a09c-48db0513ec1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3931215a-547b-428c-a269-f631222d3925' class='xr-var-data-in' type='checkbox'><label for='data-3931215a-547b-428c-a269-f631222d3925' title='Show/Hide data 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([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan 3.714 nan</div><input id='attrs-e1359a7a-4b88-4333-8ec5-be7757a41adf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1359a7a-4b88-4333-8ec5-be7757a41adf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64e29bc2-5669-4a7e-8d8a-4f00457d0af3' class='xr-var-data-in' type='checkbox'><label for='data-64e29bc2-5669-4a7e-8d8a-4f00457d0af3' title='Show/Hide data 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,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       ...,\n",
       "       [3.79299998, 3.81399989, 3.80200005, ..., 3.80699992, 3.81100011,\n",
       "        3.8599999 ],\n",
       "       [3.76399994, 3.77800012, 3.75699997, ..., 3.75399995, 3.74600005,\n",
       "        3.80599999],\n",
       "       [3.74399996, 3.74600005, 3.7249999 , ...,        nan, 3.71399999,\n",
       "               nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-50c4e7ba-a404-4a51-b9cf-fe5c8c15ff01' class='xr-section-summary-in' type='checkbox'  ><label for='section-50c4e7ba-a404-4a51-b9cf-fe5c8c15ff01' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c7d54112-a75a-4024-9a9c-1a97bb002589' class='xr-index-data-in' type='checkbox'/><label for='index-c7d54112-a75a-4024-9a9c-1a97bb002589' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e9c969fd-6bb0-412e-a0b4-7e706a8ff9ec' class='xr-index-data-in' type='checkbox'/><label for='index-e9c969fd-6bb0-412e-a0b4-7e706a8ff9ec' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f6f8f02-fb9e-4397-a409-22a8a339d1bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0f6f8f02-fb9e-4397-a409-22a8a339d1bf' 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.Dataset>\n",
       "Dimensions:      (level: 78, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan nan nan\n",
       "    temperature  (level, date) float64 nan nan nan nan ... 3.728 nan 3.714 nan"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.merge([\n",
    "    argo.salinity.sel(level=slice(0, 30)),\n",
    "    argo.temperature.sel(level=slice(30, None))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "  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;\n",
       "Dimensions:      (level: 31, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 22 23 24 25 26 27 28 29 30\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 ... 35.78 35.83 35.76\n",
       "    temperature  (level, date) float64 nan nan nan nan ... 13.59 13.74 13.31</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-bd6e507a-da39-496d-9944-7ddaebef4b41' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd6e507a-da39-496d-9944-7ddaebef4b41' 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'>level</span>: 31</li><li><span class='xr-has-index'>date</span>: 75</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-14f0cd72-15a7-4c7f-9ec1-f8caf46b3499' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14f0cd72-15a7-4c7f-9ec1-f8caf46b3499' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 25 26 27 28 29 30</div><input id='attrs-f683de95-2529-4a1d-bdc8-38c869fae55e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f683de95-2529-4a1d-bdc8-38c869fae55e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7f1f3fc-28ad-4a74-b377-af8a1fed3748' class='xr-var-data-in' type='checkbox'><label for='data-f7f1f3fc-28ad-4a74-b377-af8a1fed3748' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-b7308812-d749-47fb-87a7-b47f1701b022' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7308812-d749-47fb-87a7-b47f1701b022' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9437e411-e346-4d61-a66b-051406d8e2ee' class='xr-var-data-in' type='checkbox'><label for='data-9437e411-e346-4d61-a66b-051406d8e2ee' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-1c0bdf0a-f97a-4c8a-8a97-a2cb4b0a2629' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c0bdf0a-f97a-4c8a-8a97-a2cb4b0a2629' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4b3c601-9128-4551-b4df-2ba70f4d4b35' class='xr-var-data-in' type='checkbox'><label for='data-d4b3c601-9128-4551-b4df-2ba70f4d4b35' title='Show/Hide data 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([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-e49bd8bc-d4db-42c0-bd18-59af4ac07285' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e49bd8bc-d4db-42c0-bd18-59af4ac07285' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b36a65a6-65b3-4abd-96ed-262df3588bec' class='xr-var-data-in' type='checkbox'><label for='data-b36a65a6-65b3-4abd-96ed-262df3588bec' title='Show/Hide data 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([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ed18381e-ac3e-4d8a-8b1b-5d4a1236441f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed18381e-ac3e-4d8a-8b1b-5d4a1236441f' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.64 35.51 35.57 ... 35.83 35.76</div><input id='attrs-db93ddd7-19fb-4c63-bc11-ddacf2f63216' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db93ddd7-19fb-4c63-bc11-ddacf2f63216' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc5a59d5-536a-4734-8c61-82523a18344c' class='xr-var-data-in' type='checkbox'><label for='data-bc5a59d5-536a-4734-8c61-82523a18344c' title='Show/Hide data 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([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
       "        35.77793884, 35.66891098],\n",
       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
       "        35.58389664, 35.66791153],\n",
       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
       "        35.66290665, 35.66591263],\n",
       "       ...,\n",
       "       [35.76794815, 35.75598526, 35.84500504, ..., 35.84891891,\n",
       "        35.83391571, 35.76390076],\n",
       "       [35.75194168, 35.71097565, 35.83100128, ..., 35.80690765,\n",
       "        35.85292053, 35.75489807],\n",
       "       [35.73593903, 35.69297409, 35.81199646, ..., 35.78390503,\n",
       "        35.82591248, 35.75789642]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 13.59 13.74 13.31</div><input id='attrs-6ae76f26-b058-4d8e-9401-46e033b08a09' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ae76f26-b058-4d8e-9401-46e033b08a09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3851527-033d-4f01-8180-b96922eba9ca' class='xr-var-data-in' type='checkbox'><label for='data-c3851527-033d-4f01-8180-b96922eba9ca' title='Show/Hide data 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,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       ...,\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [13.70600033, 13.44799995, 14.14000034, ..., 13.58699989,\n",
       "        13.73700047, 13.31000042]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c15af1e7-5de2-4bce-8417-5674d75f87c7' class='xr-section-summary-in' type='checkbox'  ><label for='section-c15af1e7-5de2-4bce-8417-5674d75f87c7' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-234897bc-0d64-4ec7-bb85-9cc7efb8d51a' class='xr-index-data-in' type='checkbox'/><label for='index-234897bc-0d64-4ec7-bb85-9cc7efb8d51a' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-925d2b64-c0f7-4114-9df1-b8041af75ce9' class='xr-index-data-in' type='checkbox'/><label for='index-925d2b64-c0f7-4114-9df1-b8041af75ce9' 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;2012-07-13 22:33:06.019200&#x27;,\n",
       "                  &#x27;2012-07-23 22:54:59.990400&#x27;,\n",
       "                  &#x27;2012-08-02 22:55:52.003200&#x27;,\n",
       "                  &#x27;2012-08-12 23:08:59.971200&#x27;,\n",
       "                  &#x27;2012-08-22 23:29:01.968000&#x27;,\n",
       "                  &#x27;2012-09-01 23:17:38.976000&#x27;,\n",
       "                  &#x27;2012-09-12 02:59:18.960000&#x27;,\n",
       "                  &#x27;2012-09-21 23:18:37.036800&#x27;,\n",
       "                  &#x27;2012-10-02 03:00:17.971200&#x27;,\n",
       "                  &#x27;2012-10-11 23:13:27.984000&#x27;,\n",
       "                  &#x27;2012-10-22 02:50:32.006400&#x27;,\n",
       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
       "                  &#x27;2012-11-20 23:08:29.990400&#x27;,\n",
       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
       "                  &#x27;2012-12-10 23:23:16.972800&#x27;,\n",
       "                  &#x27;2012-12-21 02:58:48.979200&#x27;,\n",
       "                  &#x27;2012-12-30 23:07:23.030400&#x27;,\n",
       "                  &#x27;2013-01-10 02:56:43.008000&#x27;,\n",
       "                  &#x27;2013-01-19 23:24:26.956800&#x27;,\n",
       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
       "                  &#x27;2013-02-08 23:15:27.043200&#x27;,\n",
       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b11c9c9-1b98-467d-aa6a-acfbdc35fb69' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b11c9c9-1b98-467d-aa6a-acfbdc35fb69' 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.Dataset>\n",
       "Dimensions:      (level: 31, date: 75)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 22 23 24 25 26 27 28 29 30\n",
       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
       "Data variables:\n",
       "    salinity     (level, date) float64 35.64 35.51 35.57 ... 35.78 35.83 35.76\n",
       "    temperature  (level, date) float64 nan nan nan nan ... 13.59 13.74 13.31"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.merge([\n",
    "    argo.salinity.sel(level=slice(0, 30)),\n",
    "    argo.temperature.sel(level=slice(30, None))\n",
    "], join='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Reductions\n",
    "\n",
    "Just like in numpy, we can reduce xarray DataArrays along any number of axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       ".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;temperature&#x27; (date: 75)&gt;\n",
       "array([10.88915385, 10.7282564 , 10.9336282 , 10.75679484, 10.38166666,\n",
       "       10.08619236, 10.58194804, 10.50066671, 10.56841555, 10.53705122,\n",
       "       10.81131168, 11.01932052, 11.39205196, 11.40823073, 11.3642208 ,\n",
       "       11.35821797, 11.39444157, 11.10514098, 11.02870125, 10.80894868,\n",
       "       10.93076625, 11.01069231, 11.88195654, 10.57373078, 10.66359736,\n",
       "       10.56573237, 11.08854546, 10.87921792, 11.21384416, 11.24991028,\n",
       "       11.29168825, 11.06203848, 11.32829864, 11.20401279, 11.25300001,\n",
       "       11.32106403, 11.40112986,  6.07053117, 11.7748052 , 11.7466795 ,\n",
       "       12.03732056, 11.92653251, 12.08844156, 12.20543591, 12.23402598,\n",
       "       12.03365387, 11.9919221 , 11.92087012, 11.84273071, 11.79711684,\n",
       "       11.7895325 , 11.55385894, 11.19083561, 11.266282  , 11.0611948 ,\n",
       "       11.0307179 , 11.06566232, 10.79799995, 10.787     , 10.41173077,\n",
       "       10.44170127, 10.32649998, 10.38242857, 10.88080769, 10.86177921,\n",
       "       10.98787178, 10.93602596, 10.73039743, 11.09251948, 10.93983334,\n",
       "       10.65942862, 11.01814097, 11.21918184, 11.19080765, 11.13364934])\n",
       "Coordinates:\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38</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'>'temperature'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 75</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-24ee3582-7b5e-4b25-851f-b966b16cfd9d' class='xr-array-in' type='checkbox' checked><label for='section-24ee3582-7b5e-4b25-851f-b966b16cfd9d' 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>10.89 10.73 10.93 10.76 10.38 10.09 ... 10.66 11.02 11.22 11.19 11.13</span></div><div class='xr-array-data'><pre>array([10.88915385, 10.7282564 , 10.9336282 , 10.75679484, 10.38166666,\n",
       "       10.08619236, 10.58194804, 10.50066671, 10.56841555, 10.53705122,\n",
       "       10.81131168, 11.01932052, 11.39205196, 11.40823073, 11.3642208 ,\n",
       "       11.35821797, 11.39444157, 11.10514098, 11.02870125, 10.80894868,\n",
       "       10.93076625, 11.01069231, 11.88195654, 10.57373078, 10.66359736,\n",
       "       10.56573237, 11.08854546, 10.87921792, 11.21384416, 11.24991028,\n",
       "       11.29168825, 11.06203848, 11.32829864, 11.20401279, 11.25300001,\n",
       "       11.32106403, 11.40112986,  6.07053117, 11.7748052 , 11.7466795 ,\n",
       "       12.03732056, 11.92653251, 12.08844156, 12.20543591, 12.23402598,\n",
       "       12.03365387, 11.9919221 , 11.92087012, 11.84273071, 11.79711684,\n",
       "       11.7895325 , 11.55385894, 11.19083561, 11.266282  , 11.0611948 ,\n",
       "       11.0307179 , 11.06566232, 10.79799995, 10.787     , 10.41173077,\n",
       "       10.44170127, 10.32649998, 10.38242857, 10.88080769, 10.86177921,\n",
       "       10.98787178, 10.93602596, 10.73039743, 11.09251948, 10.93983334,\n",
       "       10.65942862, 11.01814097, 11.21918184, 11.19080765, 11.13364934])</pre></div></div></li><li class='xr-section-item'><input id='section-1c7abaa9-2b9c-4cb1-98af-be3ed44509ef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1c7abaa9-2b9c-4cb1-98af-be3ed44509ef' 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'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-56ee0b70-7b8f-4f35-9537-36f2dbad7011' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56ee0b70-7b8f-4f35-9537-36f2dbad7011' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f74c0cc2-82f3-4802-90aa-58020037a62c' class='xr-var-data-in' type='checkbox'><label for='data-f74c0cc2-82f3-4802-90aa-58020037a62c' title='Show/Hide data 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;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
       "       &#x27;2012-10-22T02:50:32.006400000&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
       "       &#x27;2013-01-30T02:43:53.011200000&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
       "       &#x27;2013-07-09T03:28:30.979199999&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200000&#x27;,\n",
       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020800000&#x27;,\n",
       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-3bde02c8-f01d-48f4-95d1-46a7e271027a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3bde02c8-f01d-48f4-95d1-46a7e271027a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dd7ce86-f26e-4c13-8d95-f0ea82afab6a' class='xr-var-data-in' type='checkbox'><label for='data-0dd7ce86-f26e-4c13-8d95-f0ea82afab6a' title='Show/Hide data 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([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-eeb752ec-145c-470a-833d-b4e6e1c96d66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eeb752ec-145c-470a-833d-b4e6e1c96d66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83b90391-e628-4ada-9214-741e0b326be7' class='xr-var-data-in' type='checkbox'><label for='data-83b90391-e628-4ada-9214-741e0b326be7' title='Show/Hide data 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([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-58887aa9-2ff6-4cc3-88e0-be3a32a6f25c' class='xr-section-summary-in' type='checkbox'  ><label for='section-58887aa9-2ff6-4cc3-88e0-be3a32a6f25c' 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>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9c603b43-ab24-4eb5-a453-1e3ce39d5792' class='xr-index-data-in' type='checkbox'/><label for='index-9c603b43-ab24-4eb5-a453-1e3ce39d5792' 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;2012-07-13 22:33:06.019200&#x27;,\n",
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       "                  &#x27;2012-10-31 23:36:39.974400&#x27;,\n",
       "                  &#x27;2012-11-11 02:40:46.041600&#x27;,\n",
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       "                  &#x27;2012-12-01 02:47:51.993600&#x27;,\n",
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       "                  &#x27;2013-01-30 02:43:53.011200&#x27;,\n",
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       "                  &#x27;2013-02-19 01:12:50.976000&#x27;,\n",
       "                  &#x27;2013-02-28 23:07:13.008000&#x27;,\n",
       "                  &#x27;2013-03-11 02:43:30.979200&#x27;,\n",
       "                  &#x27;2013-03-20 23:17:22.992000&#x27;,\n",
       "                  &#x27;2013-03-31 01:50:38.025600&#x27;,\n",
       "                  &#x27;2013-04-09 23:19:07.968000&#x27;,\n",
       "                  &#x27;2013-04-20 02:53:29.990400&#x27;,\n",
       "                  &#x27;2013-04-29 23:28:33.024000&#x27;,\n",
       "                  &#x27;2013-05-10 02:50:18.009600&#x27;,\n",
       "                  &#x27;2013-05-19 23:21:05.990400&#x27;,\n",
       "                  &#x27;2013-05-30 02:50:30.969600&#x27;,\n",
       "                  &#x27;2013-06-08 23:32:49.027200&#x27;,\n",
       "                  &#x27;2013-06-19 03:42:51.004800&#x27;,\n",
       "                  &#x27;2013-06-28 23:32:16.022400&#x27;,\n",
       "               &#x27;2013-07-09 03:28:30.979199999&#x27;,\n",
       "                  &#x27;2013-07-18 23:33:57.974400&#x27;,\n",
       "                  &#x27;2013-07-29 03:15:42.019200&#x27;,\n",
       "                  &#x27;2013-08-07 23:25:02.035200&#x27;,\n",
       "                  &#x27;2013-08-18 01:47:44.966400&#x27;,\n",
       "                  &#x27;2013-08-28 03:02:59.020800&#x27;,\n",
       "                  &#x27;2013-09-07 03:03:51.984000&#x27;,\n",
       "                  &#x27;2013-09-16 23:32:07.987200&#x27;,\n",
       "                  &#x27;2013-09-27 04:08:27.974400&#x27;,\n",
       "                  &#x27;2013-10-06 23:25:39.964800&#x27;,\n",
       "                  &#x27;2013-10-17 02:55:50.995200&#x27;,\n",
       "                  &#x27;2013-10-27 03:45:47.001600&#x27;,\n",
       "                  &#x27;2013-11-06 01:14:52.022400&#x27;,\n",
       "                  &#x27;2013-11-16 03:29:54.009600&#x27;,\n",
       "                  &#x27;2013-11-26 03:03:56.995200&#x27;,\n",
       "                  &#x27;2013-12-05 23:33:59.011200&#x27;,\n",
       "                  &#x27;2013-12-16 02:58:01.977600&#x27;,\n",
       "                  &#x27;2013-12-25 23:22:43.017600&#x27;,\n",
       "                  &#x27;2014-01-05 02:52:06.009600&#x27;,\n",
       "                  &#x27;2014-01-14 23:41:18.009600&#x27;,\n",
       "                  &#x27;2014-01-25 03:00:43.977600&#x27;,\n",
       "                  &#x27;2014-02-03 23:29:13.977600&#x27;,\n",
       "                  &#x27;2014-02-14 02:50:11.961600&#x27;,\n",
       "                  &#x27;2014-02-23 23:03:21.974400&#x27;,\n",
       "                  &#x27;2014-03-06 02:58:03.964800&#x27;,\n",
       "                  &#x27;2014-03-15 23:10:28.012800&#x27;,\n",
       "                  &#x27;2014-03-26 02:51:22.032000&#x27;,\n",
       "                  &#x27;2014-04-04 23:25:58.972800&#x27;,\n",
       "                  &#x27;2014-04-15 03:00:45.964800&#x27;,\n",
       "                  &#x27;2014-04-24 23:24:40.003200&#x27;,\n",
       "                  &#x27;2014-05-05 02:56:22.012800&#x27;,\n",
       "                  &#x27;2014-05-15 00:10:06.009600&#x27;,\n",
       "                  &#x27;2014-05-25 03:02:43.036800&#x27;,\n",
       "                  &#x27;2014-06-03 23:34:53.961600&#x27;,\n",
       "                  &#x27;2014-06-14 03:01:23.980800&#x27;,\n",
       "                  &#x27;2014-06-23 23:24:31.968000&#x27;,\n",
       "                  &#x27;2014-07-04 03:08:30.019200&#x27;,\n",
       "                  &#x27;2014-07-13 23:47:43.008000&#x27;,\n",
       "                  &#x27;2014-07-24 03:02:33.014400&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fc035c0d-aeea-4e6a-bd2e-ef988ce62b4c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fc035c0d-aeea-4e6a-bd2e-ef988ce62b4c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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       "<xarray.DataArray 'temperature' (date: 75)>\n",
       "array([10.88915385, 10.7282564 , 10.9336282 , 10.75679484, 10.38166666,\n",
       "       10.08619236, 10.58194804, 10.50066671, 10.56841555, 10.53705122,\n",
       "       10.81131168, 11.01932052, 11.39205196, 11.40823073, 11.3642208 ,\n",
       "       11.35821797, 11.39444157, 11.10514098, 11.02870125, 10.80894868,\n",
       "       10.93076625, 11.01069231, 11.88195654, 10.57373078, 10.66359736,\n",
       "       10.56573237, 11.08854546, 10.87921792, 11.21384416, 11.24991028,\n",
       "       11.29168825, 11.06203848, 11.32829864, 11.20401279, 11.25300001,\n",
       "       11.32106403, 11.40112986,  6.07053117, 11.7748052 , 11.7466795 ,\n",
       "       12.03732056, 11.92653251, 12.08844156, 12.20543591, 12.23402598,\n",
       "       12.03365387, 11.9919221 , 11.92087012, 11.84273071, 11.79711684,\n",
       "       11.7895325 , 11.55385894, 11.19083561, 11.266282  , 11.0611948 ,\n",
       "       11.0307179 , 11.06566232, 10.79799995, 10.787     , 10.41173077,\n",
       "       10.44170127, 10.32649998, 10.38242857, 10.88080769, 10.86177921,\n",
       "       10.98787178, 10.93602596, 10.73039743, 11.09251948, 10.93983334,\n",
       "       10.65942862, 11.01814097, 11.21918184, 11.19080765, 11.13364934])\n",
       "Coordinates:\n",
       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo.temperature.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;temperature&#x27; (level: 78)&gt;\n",
       "array([17.60172602, 17.57223609, 17.5145833 , 17.42326395, 17.24943838,\n",
       "       17.03730134, 16.76787661, 16.44609588, 16.17439195, 16.04501356,\n",
       "       15.65827023, 15.4607296 , 15.26114862, 15.12489191, 14.99133783,\n",
       "       14.90160808, 14.81990544, 14.74535139, 14.66822971, 14.585027  ,\n",
       "       14.49732434, 14.41904053, 14.35412163, 14.27102702, 14.19081082,\n",
       "       14.11487838, 14.04347293, 13.98067566, 13.90994595, 13.83274319,\n",
       "       13.76139196, 13.69836479, 13.62335132, 13.54185131, 13.46647295,\n",
       "       13.39395946, 13.32541891, 13.25205403, 13.18131082, 13.10233782,\n",
       "       12.89268916, 12.67795943, 12.4649189 , 12.2178513 , 11.98270268,\n",
       "       11.1281081 , 10.80430666, 10.49702667, 10.1749066 ,  9.83453334,\n",
       "        9.48625332,  9.19793334,  8.66010666,  8.12324001,  7.60221333,\n",
       "        7.15289333,  6.74250667,  6.39543999,  6.04598667,  5.74538665,\n",
       "        5.48913333,  5.26604001,  5.08768   ,  4.93479998,  4.77769334,\n",
       "        4.65368   ,  4.54237334,  4.44274664,  4.35933333,  4.2653784 ,\n",
       "        4.17290539,  4.08902703,  3.99864865,  3.92163514,  3.85617567,\n",
       "        3.78916217,  3.72950001,  3.66207691])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77</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'>'temperature'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 78</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-619796ee-fb6c-46df-b598-d393a6601f77' class='xr-array-in' type='checkbox' checked><label for='section-619796ee-fb6c-46df-b598-d393a6601f77' 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>17.6 17.57 17.51 17.42 17.25 17.04 ... 3.922 3.856 3.789 3.73 3.662</span></div><div class='xr-array-data'><pre>array([17.60172602, 17.57223609, 17.5145833 , 17.42326395, 17.24943838,\n",
       "       17.03730134, 16.76787661, 16.44609588, 16.17439195, 16.04501356,\n",
       "       15.65827023, 15.4607296 , 15.26114862, 15.12489191, 14.99133783,\n",
       "       14.90160808, 14.81990544, 14.74535139, 14.66822971, 14.585027  ,\n",
       "       14.49732434, 14.41904053, 14.35412163, 14.27102702, 14.19081082,\n",
       "       14.11487838, 14.04347293, 13.98067566, 13.90994595, 13.83274319,\n",
       "       13.76139196, 13.69836479, 13.62335132, 13.54185131, 13.46647295,\n",
       "       13.39395946, 13.32541891, 13.25205403, 13.18131082, 13.10233782,\n",
       "       12.89268916, 12.67795943, 12.4649189 , 12.2178513 , 11.98270268,\n",
       "       11.1281081 , 10.80430666, 10.49702667, 10.1749066 ,  9.83453334,\n",
       "        9.48625332,  9.19793334,  8.66010666,  8.12324001,  7.60221333,\n",
       "        7.15289333,  6.74250667,  6.39543999,  6.04598667,  5.74538665,\n",
       "        5.48913333,  5.26604001,  5.08768   ,  4.93479998,  4.77769334,\n",
       "        4.65368   ,  4.54237334,  4.44274664,  4.35933333,  4.2653784 ,\n",
       "        4.17290539,  4.08902703,  3.99864865,  3.92163514,  3.85617567,\n",
       "        3.78916217,  3.72950001,  3.66207691])</pre></div></div></li><li class='xr-section-item'><input id='section-d3b0a9bc-db7c-4b4a-953a-7a3010b001e6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3b0a9bc-db7c-4b4a-953a-7a3010b001e6' 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'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-c786c601-f70d-4295-9c34-fc65f56300c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c786c601-f70d-4295-9c34-fc65f56300c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcb97922-a31d-4197-acb5-f10cdf7e4f19' class='xr-var-data-in' type='checkbox'><label for='data-fcb97922-a31d-4197-acb5-f10cdf7e4f19' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-acfb1f2e-6070-40ec-b17d-c9485d3ab546' class='xr-section-summary-in' type='checkbox'  ><label for='section-acfb1f2e-6070-40ec-b17d-c9485d3ab546' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0c0712c0-c71f-4bcf-ab74-d9c72a31a851' class='xr-index-data-in' type='checkbox'/><label for='index-0c0712c0-c71f-4bcf-ab74-d9c72a31a851' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4841a59a-c584-4ca1-b73e-3e346c09836b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4841a59a-c584-4ca1-b73e-3e346c09836b' 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 'temperature' (level: 78)>\n",
       "array([17.60172602, 17.57223609, 17.5145833 , 17.42326395, 17.24943838,\n",
       "       17.03730134, 16.76787661, 16.44609588, 16.17439195, 16.04501356,\n",
       "       15.65827023, 15.4607296 , 15.26114862, 15.12489191, 14.99133783,\n",
       "       14.90160808, 14.81990544, 14.74535139, 14.66822971, 14.585027  ,\n",
       "       14.49732434, 14.41904053, 14.35412163, 14.27102702, 14.19081082,\n",
       "       14.11487838, 14.04347293, 13.98067566, 13.90994595, 13.83274319,\n",
       "       13.76139196, 13.69836479, 13.62335132, 13.54185131, 13.46647295,\n",
       "       13.39395946, 13.32541891, 13.25205403, 13.18131082, 13.10233782,\n",
       "       12.89268916, 12.67795943, 12.4649189 , 12.2178513 , 11.98270268,\n",
       "       11.1281081 , 10.80430666, 10.49702667, 10.1749066 ,  9.83453334,\n",
       "        9.48625332,  9.19793334,  8.66010666,  8.12324001,  7.60221333,\n",
       "        7.15289333,  6.74250667,  6.39543999,  6.04598667,  5.74538665,\n",
       "        5.48913333,  5.26604001,  5.08768   ,  4.93479998,  4.77769334,\n",
       "        4.65368   ,  4.54237334,  4.44274664,  4.35933333,  4.2653784 ,\n",
       "        4.17290539,  4.08902703,  3.99864865,  3.92163514,  3.85617567,\n",
       "        3.78916217,  3.72950001,  3.66207691])\n",
       "Coordinates:\n",
       "  * level    (level) int64 0 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75 76 77"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo.temperature.mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, rather than performing reductions on axes (as in numpy), we can perform them on dimensions. This turns out to be a huge convenience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
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       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (level: 78)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "Data variables:\n",
       "    salinity     (level) float64 35.91 35.9 35.9 35.9 ... 34.94 34.94 34.93\n",
       "    temperature  (level) float64 17.6 17.57 17.51 17.42 ... 3.789 3.73 3.662\n",
       "    pressure     (level) float64 6.435 10.57 15.54 ... 1.95e+03 1.999e+03</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-fc193679-3e90-4e3b-8930-1aaca7d56e7e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fc193679-3e90-4e3b-8930-1aaca7d56e7e' 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'>level</span>: 78</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-db270744-9caa-44c4-bc7a-7d06ae11b48f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db270744-9caa-44c4-bc7a-7d06ae11b48f' 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'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-4d54a0bd-d852-4f1a-a84d-390351751c10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d54a0bd-d852-4f1a-a84d-390351751c10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-514220f8-bbf2-4eb3-b99b-8c953ab0d2d0' class='xr-var-data-in' type='checkbox'><label for='data-514220f8-bbf2-4eb3-b99b-8c953ab0d2d0' title='Show/Hide data 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ffb16212-f263-4575-98f0-c1e94c705cdf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ffb16212-f263-4575-98f0-c1e94c705cdf' 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>salinity</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.91 35.9 35.9 ... 34.94 34.93</div><input id='attrs-e22d8754-7b39-4ef1-9c5b-e6362f343b29' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e22d8754-7b39-4ef1-9c5b-e6362f343b29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb621d1f-c653-4d2b-a7dd-76f944364e63' class='xr-var-data-in' type='checkbox'><label for='data-fb621d1f-c653-4d2b-a7dd-76f944364e63' title='Show/Hide data 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([35.9063218 , 35.90223138, 35.90313435, 35.90173139, 35.90544583,\n",
       "       35.9100359 , 35.90946015, 35.91343146, 35.91967712, 35.92615988,\n",
       "       35.93195456, 35.94055356, 35.94091596, 35.93905366, 35.93931069,\n",
       "       35.93786745, 35.93525794, 35.93118039, 35.92534328, 35.91652257,\n",
       "       35.90671895, 35.89617843, 35.88888019, 35.8789927 , 35.86946183,\n",
       "       35.8598671 , 35.85061713, 35.84211978, 35.83150467, 35.81969395,\n",
       "       35.80945061, 35.80092265, 35.79078674, 35.77886525, 35.76833627,\n",
       "       35.75838795, 35.74923783, 35.73923559, 35.73000444, 35.71877237,\n",
       "       35.68864513, 35.65607159, 35.62678265, 35.59231774, 35.56205662,\n",
       "       35.45401408, 35.41392634, 35.3810557 , 35.34845245, 35.31531555,\n",
       "       35.28392568, 35.26568334, 35.2389473 , 35.21583745, 35.19686081,\n",
       "       35.18231257, 35.1648436 , 35.15073542, 35.12509338, 35.10155869,\n",
       "       35.08199799, 35.06317012, 35.0490097 , 35.03678253, 35.02174266,\n",
       "       35.01135579, 35.00212936, 34.99386297, 34.98810328, 34.98008094,\n",
       "       34.97214884, 34.96517645, 34.95664983, 34.9507985 , 34.9465696 ,\n",
       "       34.94198907, 34.93844852, 34.93290652])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>17.6 17.57 17.51 ... 3.73 3.662</div><input id='attrs-695ab1b8-df32-4653-9afe-fe815fc7aadd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-695ab1b8-df32-4653-9afe-fe815fc7aadd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-128a5fdc-d8ff-4c03-8a7f-97b2076ca334' class='xr-var-data-in' type='checkbox'><label for='data-128a5fdc-d8ff-4c03-8a7f-97b2076ca334' title='Show/Hide data 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([17.60172602, 17.57223609, 17.5145833 , 17.42326395, 17.24943838,\n",
       "       17.03730134, 16.76787661, 16.44609588, 16.17439195, 16.04501356,\n",
       "       15.65827023, 15.4607296 , 15.26114862, 15.12489191, 14.99133783,\n",
       "       14.90160808, 14.81990544, 14.74535139, 14.66822971, 14.585027  ,\n",
       "       14.49732434, 14.41904053, 14.35412163, 14.27102702, 14.19081082,\n",
       "       14.11487838, 14.04347293, 13.98067566, 13.90994595, 13.83274319,\n",
       "       13.76139196, 13.69836479, 13.62335132, 13.54185131, 13.46647295,\n",
       "       13.39395946, 13.32541891, 13.25205403, 13.18131082, 13.10233782,\n",
       "       12.89268916, 12.67795943, 12.4649189 , 12.2178513 , 11.98270268,\n",
       "       11.1281081 , 10.80430666, 10.49702667, 10.1749066 ,  9.83453334,\n",
       "        9.48625332,  9.19793334,  8.66010666,  8.12324001,  7.60221333,\n",
       "        7.15289333,  6.74250667,  6.39543999,  6.04598667,  5.74538665,\n",
       "        5.48913333,  5.26604001,  5.08768   ,  4.93479998,  4.77769334,\n",
       "        4.65368   ,  4.54237334,  4.44274664,  4.35933333,  4.2653784 ,\n",
       "        4.17290539,  4.08902703,  3.99864865,  3.92163514,  3.85617567,\n",
       "        3.78916217,  3.72950001,  3.66207691])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.435 10.57 ... 1.95e+03 1.999e+03</div><input id='attrs-efbebede-f778-4d07-aab2-5dfc73777546' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-efbebede-f778-4d07-aab2-5dfc73777546' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d6916de-700e-4d11-8b59-630f54d7de3d' class='xr-var-data-in' type='checkbox'><label for='data-4d6916de-700e-4d11-8b59-630f54d7de3d' title='Show/Hide data 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([   6.43466671,   10.56891882,   15.54246568,   20.46301361,\n",
       "         25.42567552,   30.44459441,   35.44324375,   40.4391894 ,\n",
       "         45.40810832,   50.37837879,   60.47297323,   70.48378413,\n",
       "         80.40270347,   90.48243311,  100.51216311,  110.46081151,\n",
       "        120.52702795,  130.49459282,  140.51216064,  150.40540376,\n",
       "        160.40810559,  170.36216035,  180.41080949,  190.4108097 ,\n",
       "        200.39999761,  210.34729499,  220.32026858,  230.31351224,\n",
       "        240.28918808,  250.41486297,  260.39999843,  270.36891752,\n",
       "        280.42432136,  290.42297075,  300.4229691 ,  310.46351087,\n",
       "        320.50675346,  330.5297266 ,  340.41891521,  350.49729383,\n",
       "        375.41080867,  400.3797294 ,  425.29864626,  450.38378205,\n",
       "        475.30675403,  550.47703016,  575.68400146,  600.42400716,\n",
       "        625.30800456,  650.34533773,  675.33333984,  700.37067546,\n",
       "        750.42400716,  800.36666992,  850.38534017,  900.4613387 ,\n",
       "        950.45067383, 1000.38534261, 1050.38534668, 1100.45734212,\n",
       "       1150.45201335, 1200.40534505, 1250.25067383, 1300.49467773,\n",
       "       1350.40268392, 1400.41734538, 1450.25734212, 1500.40267253,\n",
       "       1550.46401367, 1600.44055011, 1650.34595056, 1700.42163251,\n",
       "       1750.42299013, 1800.35677358, 1850.33379467, 1900.20676401,\n",
       "       1950.20946771, 1999.28975423])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d18e0b5-ac98-4b1d-a898-37945843cdfd' class='xr-section-summary-in' type='checkbox'  ><label for='section-6d18e0b5-ac98-4b1d-a898-37945843cdfd' 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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-58dff5c5-90d3-49cf-a971-75c42dbb92c5' class='xr-index-data-in' type='checkbox'/><label for='index-58dff5c5-90d3-49cf-a971-75c42dbb92c5' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17c1d2e6-eaa2-40bd-aa86-aeafd8f933f1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-17c1d2e6-eaa2-40bd-aa86-aeafd8f933f1' 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.Dataset>\n",
       "Dimensions:      (level: 78)\n",
       "Coordinates:\n",
       "  * level        (level) int64 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
       "Data variables:\n",
       "    salinity     (level) float64 35.91 35.9 35.9 35.9 ... 34.94 34.94 34.93\n",
       "    temperature  (level) float64 17.6 17.57 17.51 17.42 ... 3.789 3.73 3.662\n",
       "    pressure     (level) float64 6.435 10.57 15.54 ... 1.95e+03 1.999e+03"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo_mean = argo.mean(dim='date')\n",
    "argo_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a91266560>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "argo_mean.salinity.plot(y='level', yincrease=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a910f5030>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "argo_std = argo.std(dim='date')\n",
    "argo_std.salinity.plot(y='level', yincrease=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weighted Reductions\n",
    "\n",
    "Sometimes we want to perform a reduction (e.g. a mean) where we assign different weight factors to each point in the array.\n",
    "Xarray supports this via [weighted array reductions](http://xarray.pydata.org/en/stable/user-guide/computation.html#weighted-array-reductions).\n",
    "\n",
    "As a toy example, imagine we want to weight values in the upper ocean more than the lower ocean.\n",
    "We could imagine creating a weight array exponentially proportional to pressure as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a91153910>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_pressure = argo.pressure.mean(dim='date')\n",
    "p0 = 250  # dbat\n",
    "weights = np.exp(-mean_pressure / p0)\n",
    "weights.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The weighted mean over the `level` dimensions is calculated as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_weighted_mean = argo.temperature.weighted(weights).mean('level')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing to the unweighted mean, we see the difference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9a90fc6770>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_weighted_mean.plot(label='weighted')\n",
    "argo.temperature.mean(dim='level').plot(label='unweighted')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Loading Data from netCDF Files\n",
    "\n",
    "NetCDF (Network Common Data Format) is the most widely used format for distributing geoscience data. NetCDF is maintained by the [Unidata](https://www.unidata.ucar.edu/) organization.\n",
    "\n",
    "Below we quote from the [NetCDF website](https://www.unidata.ucar.edu/software/netcdf/docs/faq.html#whatisit):\n",
    "\n",
    ">NetCDF (network Common Data Form) is a set of interfaces for array-oriented data access and a freely distributed collection of data access libraries for C, Fortran, C++, Java, and other languages. The netCDF libraries support a machine-independent format for representing scientific data. Together, the interfaces, libraries, and format support the creation, access, and sharing of scientific data.\n",
    ">\n",
    ">NetCDF data is:\n",
    ">\n",
    "> - Self-Describing. A netCDF file includes information about the data it contains.\n",
    "> - Portable. A netCDF file can be accessed by computers with different ways of storing integers, characters, and floating-point numbers.\n",
    "> - Scalable. A small subset of a large dataset may be accessed efficiently.\n",
    "> - Appendable. Data may be appended to a properly structured netCDF file without copying the dataset or redefining its structure.\n",
    "> - Sharable. One writer and multiple readers may simultaneously access the same netCDF file.\n",
    "> - Archivable. Access to all earlier forms of netCDF data will be supported by current and future versions of the software.\n",
    "\n",
    "Xarray was designed to make reading netCDF files in python as easy, powerful, and flexible as possible. (See [xarray netCDF docs](http://xarray.pydata.org/en/latest/io.html#netcdf) for more details.)\n",
    "\n",
    "Below we download and load some the NASA [GISSTemp](https://data.giss.nasa.gov/gistemp/) global temperature anomaly dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "gistemp_file = pooch.retrieve(\n",
    "    'https://data.giss.nasa.gov/pub/gistemp/gistemp1200_GHCNv4_ERSSTv5.nc.gz',\n",
    "    known_hash='52e5c2d0b601ccb5188301ca5247b0563c0000dc6086dffa8489e555a109f3d0',\n",
    "    processor=pooch.Decompress(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (lat: 90, lon: 180, time: 1715, nv: 2)\n",
       "Coordinates:\n",
       "  * lat          (lat) float32 -89.0 -87.0 -85.0 -83.0 ... 83.0 85.0 87.0 89.0\n",
       "  * lon          (lon) float32 -179.0 -177.0 -175.0 -173.0 ... 175.0 177.0 179.0\n",
       "  * time         (time) datetime64[ns] 1880-01-15 1880-02-15 ... 2022-11-15\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    time_bnds    (time, nv) datetime64[ns] ...\n",
       "    tempanomaly  (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    title:        GISTEMP Surface Temperature Analysis\n",
       "    institution:  NASA Goddard Institute for Space Studies\n",
       "    source:       http://data.giss.nasa.gov/gistemp/\n",
       "    Conventions:  CF-1.6\n",
       "    history:      Created 2022-12-12 11:35:15 by SBBX_to_nc 2.0 - ILAND=1200,...</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-6697f445-5223-4ad7-a138-44d7378787ca' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6697f445-5223-4ad7-a138-44d7378787ca' 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'>lat</span>: 90</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 1715</li><li><span>nv</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-879dfff6-22ee-4ca9-b605-52e20bcfbbd3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-879dfff6-22ee-4ca9-b605-52e20bcfbbd3' 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'>float32</div><div class='xr-var-preview xr-preview'>-89.0 -87.0 -85.0 ... 87.0 89.0</div><input id='attrs-23407ed0-8f8b-4cbc-a2c8-470b99b5056a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23407ed0-8f8b-4cbc-a2c8-470b99b5056a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd73aa3f-5ff7-4057-9a71-061b8c4913d4' class='xr-var-data-in' type='checkbox'><label for='data-dd73aa3f-5ff7-4057-9a71-061b8c4913d4' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-89., -87., -85., -83., -81., -79., -77., -75., -73., -71., -69., -67.,\n",
       "       -65., -63., -61., -59., -57., -55., -53., -51., -49., -47., -45., -43.,\n",
       "       -41., -39., -37., -35., -33., -31., -29., -27., -25., -23., -21., -19.,\n",
       "       -17., -15., -13., -11.,  -9.,  -7.,  -5.,  -3.,  -1.,   1.,   3.,   5.,\n",
       "         7.,   9.,  11.,  13.,  15.,  17.,  19.,  21.,  23.,  25.,  27.,  29.,\n",
       "        31.,  33.,  35.,  37.,  39.,  41.,  43.,  45.,  47.,  49.,  51.,  53.,\n",
       "        55.,  57.,  59.,  61.,  63.,  65.,  67.,  69.,  71.,  73.,  75.,  77.,\n",
       "        79.,  81.,  83.,  85.,  87.,  89.], 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'>-179.0 -177.0 ... 177.0 179.0</div><input id='attrs-0b816966-a652-458a-970b-0435122fc686' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b816966-a652-458a-970b-0435122fc686' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-018406f1-9794-4bb6-853c-b7f578bb69ee' class='xr-var-data-in' type='checkbox'><label for='data-018406f1-9794-4bb6-853c-b7f578bb69ee' title='Show/Hide data repr'><svg 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>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179., -177., -175., -173., -171., -169., -167., -165., -163., -161.,\n",
       "       -159., -157., -155., -153., -151., -149., -147., -145., -143., -141.,\n",
       "       -139., -137., -135., -133., -131., -129., -127., -125., -123., -121.,\n",
       "       -119., -117., -115., -113., -111., -109., -107., -105., -103., -101.,\n",
       "        -99.,  -97.,  -95.,  -93.,  -91.,  -89.,  -87.,  -85.,  -83.,  -81.,\n",
       "        -79.,  -77.,  -75.,  -73.,  -71.,  -69.,  -67.,  -65.,  -63.,  -61.,\n",
       "        -59.,  -57.,  -55.,  -53.,  -51.,  -49.,  -47.,  -45.,  -43.,  -41.,\n",
       "        -39.,  -37.,  -35.,  -33.,  -31.,  -29.,  -27.,  -25.,  -23.,  -21.,\n",
       "        -19.,  -17.,  -15.,  -13.,  -11.,   -9.,   -7.,   -5.,   -3.,   -1.,\n",
       "          1.,    3.,    5.,    7.,    9.,   11.,   13.,   15.,   17.,   19.,\n",
       "         21.,   23.,   25.,   27.,   29.,   31.,   33.,   35.,   37.,   39.,\n",
       "         41.,   43.,   45.,   47.,   49.,   51.,   53.,   55.,   57.,   59.,\n",
       "         61.,   63.,   65.,   67.,   69.,   71.,   73.,   75.,   77.,   79.,\n",
       "         81.,   83.,   85.,   87.,   89.,   91.,   93.,   95.,   97.,   99.,\n",
       "        101.,  103.,  105.,  107.,  109.,  111.,  113.,  115.,  117.,  119.,\n",
       "        121.,  123.,  125.,  127.,  129.,  131.,  133.,  135.,  137.,  139.,\n",
       "        141.,  143.,  145.,  147.,  149.,  151.,  153.,  155.,  157.,  159.,\n",
       "        161.,  163.,  165.,  167.,  169.,  171.,  173.,  175.,  177.,  179.],\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'>1880-01-15 ... 2022-11-15</div><input id='attrs-8ce613d4-406f-45d7-9ba0-55d930907423' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ce613d4-406f-45d7-9ba0-55d930907423' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55b39309-e32a-4de0-9165-8b474288880c' class='xr-var-data-in' type='checkbox'><label for='data-55b39309-e32a-4de0-9165-8b474288880c' title='Show/Hide data repr'><svg 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>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1880-01-15T00:00:00.000000000&#x27;, &#x27;1880-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1880-03-15T00:00:00.000000000&#x27;, ..., &#x27;2022-09-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-10-15T00:00:00.000000000&#x27;, &#x27;2022-11-15T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf162811-b6a8-4f9d-aad1-3f4583d793bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cf162811-b6a8-4f9d-aad1-3f4583d793bf' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e32387a4-c9f9-43a3-b6eb-2eec2875fa0d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e32387a4-c9f9-43a3-b6eb-2eec2875fa0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-118256f8-ae47-41eb-9e5e-b49d391ef9e3' class='xr-var-data-in' type='checkbox'><label for='data-118256f8-ae47-41eb-9e5e-b49d391ef9e3' title='Show/Hide data 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>[3430 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tempanomaly</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-dfc0f3e7-0811-426d-8130-dc327311abf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfc0f3e7-0811-426d-8130-dc327311abf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19c32740-d861-477e-a970-c43c913881c3' class='xr-var-data-in' type='checkbox'><label for='data-19c32740-d861-477e-a970-c43c913881c3' title='Show/Hide data repr'><svg 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>Surface temperature anomaly</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>[27783000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7a888f3-a3d9-48a3-b877-a646a3aef494' class='xr-section-summary-in' type='checkbox'  ><label for='section-e7a888f3-a3d9-48a3-b877-a646a3aef494' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0359ce12-a8fd-4ae2-b620-b58b8fa67145' class='xr-index-data-in' type='checkbox'/><label for='index-0359ce12-a8fd-4ae2-b620-b58b8fa67145' 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(Float64Index([-89.0, -87.0, -85.0, -83.0, -81.0, -79.0, -77.0, -75.0, -73.0,\n",
       "              -71.0, -69.0, -67.0, -65.0, -63.0, -61.0, -59.0, -57.0, -55.0,\n",
       "              -53.0, -51.0, -49.0, -47.0, -45.0, -43.0, -41.0, -39.0, -37.0,\n",
       "              -35.0, -33.0, -31.0, -29.0, -27.0, -25.0, -23.0, -21.0, -19.0,\n",
       "              -17.0, -15.0, -13.0, -11.0,  -9.0,  -7.0,  -5.0,  -3.0,  -1.0,\n",
       "                1.0,   3.0,   5.0,   7.0,   9.0,  11.0,  13.0,  15.0,  17.0,\n",
       "               19.0,  21.0,  23.0,  25.0,  27.0,  29.0,  31.0,  33.0,  35.0,\n",
       "               37.0,  39.0,  41.0,  43.0,  45.0,  47.0,  49.0,  51.0,  53.0,\n",
       "               55.0,  57.0,  59.0,  61.0,  63.0,  65.0,  67.0,  69.0,  71.0,\n",
       "               73.0,  75.0,  77.0,  79.0,  81.0,  83.0,  85.0,  87.0,  89.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6eb3fc3e-f6da-42b3-915d-d045b9f61c2b' class='xr-index-data-in' type='checkbox'/><label for='index-6eb3fc3e-f6da-42b3-915d-d045b9f61c2b' 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(Float64Index([-179.0, -177.0, -175.0, -173.0, -171.0, -169.0, -167.0, -165.0,\n",
       "              -163.0, -161.0,\n",
       "              ...\n",
       "               161.0,  163.0,  165.0,  167.0,  169.0,  171.0,  173.0,  175.0,\n",
       "               177.0,  179.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=180))</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-23ecc9f6-da6b-4030-aebe-ee5a1247184d' class='xr-index-data-in' type='checkbox'/><label for='index-23ecc9f6-da6b-4030-aebe-ee5a1247184d' 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;1880-01-15&#x27;, &#x27;1880-02-15&#x27;, &#x27;1880-03-15&#x27;, &#x27;1880-04-15&#x27;,\n",
       "               &#x27;1880-05-15&#x27;, &#x27;1880-06-15&#x27;, &#x27;1880-07-15&#x27;, &#x27;1880-08-15&#x27;,\n",
       "               &#x27;1880-09-15&#x27;, &#x27;1880-10-15&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-02-15&#x27;, &#x27;2022-03-15&#x27;, &#x27;2022-04-15&#x27;, &#x27;2022-05-15&#x27;,\n",
       "               &#x27;2022-06-15&#x27;, &#x27;2022-07-15&#x27;, &#x27;2022-08-15&#x27;, &#x27;2022-09-15&#x27;,\n",
       "               &#x27;2022-10-15&#x27;, &#x27;2022-11-15&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1715, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3e9ef97-4c5e-41d7-b463-52ba80354356' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3e9ef97-4c5e-41d7-b463-52ba80354356' 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>title :</span></dt><dd>GISTEMP Surface Temperature Analysis</dd><dt><span>institution :</span></dt><dd>NASA Goddard Institute for Space Studies</dd><dt><span>source :</span></dt><dd>http://data.giss.nasa.gov/gistemp/</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>Created 2022-12-12 11:35:15 by SBBX_to_nc 2.0 - ILAND=1200, IOCEAN=NCDC/ER5, Base: 1951-1980</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (lat: 90, lon: 180, time: 1715, nv: 2)\n",
       "Coordinates:\n",
       "  * lat          (lat) float32 -89.0 -87.0 -85.0 -83.0 ... 83.0 85.0 87.0 89.0\n",
       "  * lon          (lon) float32 -179.0 -177.0 -175.0 -173.0 ... 175.0 177.0 179.0\n",
       "  * time         (time) datetime64[ns] 1880-01-15 1880-02-15 ... 2022-11-15\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    time_bnds    (time, nv) datetime64[ns] ...\n",
       "    tempanomaly  (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    title:        GISTEMP Surface Temperature Analysis\n",
       "    institution:  NASA Goddard Institute for Space Studies\n",
       "    source:       http://data.giss.nasa.gov/gistemp/\n",
       "    Conventions:  CF-1.6\n",
       "    history:      Created 2022-12-12 11:35:15 by SBBX_to_nc 2.0 - ILAND=1200,..."
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset(gistemp_file)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9a875fd810>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.tempanomaly.isel(time=-1).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a8722bbe0>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.tempanomaly.mean(dim=('lon', 'lat')).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: src/lectures/xarray/xarray_intro.md
================================================
# Xarray for multidimensional gridded data

In the previous set of lectures, we saw how Pandas provided a way to keep track of additional "metadata" surrounding tabular datasets, including "indexes" for each row and labels for each column. These features, together with Pandas' many useful routines for all kinds of data munging and analysis, have made Pandas one of the most popular python packages in the world.

However, not all Earth science datasets easily fit into the "tabular" model (i.e. rows and columns) imposed by Pandas. In particular, we often deal with _multidimensional data_. By _multidimensional data_ (also often called _N-dimensional_), I mean data with many independent dimensions or axes. For example, we might represent Earth's surface temperature $T$ as a three dimensional variable

$$ T(x, y, t) $$

where $x$ is longitude, $y$ is latitude, and $t$ is time.

The point of xarray is to provide pandas-level convenience for working with this type of data.

![xarray data model](http://xarray.pydata.org/en/stable/_images/dataset-diagram.png)

## Learning Goals for Xarray

Because of the importance of xarray for data analysis in geoscience, we are going to spend a long time on it.
The goals of this section include the following.

### Lesson 1: Xarray Fundamentals

#### Dataset Creation

1. Describe the core xarray data structures, the `DataArray` and the `Dataset`, and the components that make them up, including: Data Variables, Dimensions, Coordinates, Indexes, and Attributes
1. Create xarray `DataArrays` and `DataSets` out of raw numpy arrays
1. Create xarray objects with and without indexes
1. Load xarray datasets from netCDF files and openDAP servers
1. View and set attributes

#### Basic Indexing and Interpolation

1. Select data by position using `.isel` with values or slices
1. Select data by label using `.sel` with values or slices
1. Select timeseries data by date/time with values or slices
1. Use nearest-neighbor lookups with `.sel`
1. Mask data with `.where`
1. Interpolate data in one and several dimensions

#### Basic Computation

1. Do basic arithmetic with DataArrays and Datasets
1. Use numpy universal function on DataArrays and Datasets, or use corresponding built-in xarray methods
1. Combine multiple xarray objects in arithmetic operations and understand how they are broadcasted / aligned
1. Perform aggregation (reduction) along one or multiple dimensions of a DataArray or Dataset

#### Basic Plotting

1. Use built-in xarray plotting for 1D and 2D DataArrays
1. Customize plots with options

### Lesson 2: Advanced Usage


#### Xarray's groupby, resample, and rolling

1. Split xarray objects into groups using `groupby`
1. Apply reduction operations to groups (e.g. mean)
1. Apply non-reducing functions to groups (e.g. standardize)
1. Use `groupby` with time coordinates (e.g. to create climatologies)
1. Use artimetic between `GroupBy` objects and regular DataArrays / Datasets
1. Use `groupby_bins` to aggregate data in bins
1. Use `resample` on time dimensions
1. Use `rolling` to apply rolling aggregations

#### Merging Combining Datasets

1. Concatentate DataArrays and Datasets along a new or existing dimension
1. Merge multiple datasets with different variables
1. Add a new data variable to an existing Dataset

#### Reshaping Data

1. Transpose dimension order
1. Swap coordinates
1. Expand and squeeze dimensions
1. Convert between DataArray and Dataset
1. Use `stack` and `unstack` to transform data

#### Advanced Computations

1. Use `differentiate` to take derivatives of data
1. Use `apply_ufunc` to apply custom or specialized operations to data


#### Plotting

1. Show multiple line plots over a dimension using the `hue` keyword
1. Create multiple 2D plots using faceting


================================================
FILE: src/lectures/xarray/xarray_tips_and_tricks.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Xarray Tips and Tricks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a multi-file dataset from an OpenDAP server\n",
    "\n",
    "One thing we love about xarray is the `open_mfdataset` function, which combines many netCDF files into a single xarray Dataset.\n",
    "\n",
    "But what if the files are stored on a remote server and accessed over OpenDAP. An example can be found in NOAA's NCEP Reanalysis catalog.\n",
    "\n",
    "https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/catalog.html\n",
    "\n",
    "The dataset is split into different files for each variable and year. For example, a single file for surface air temperature looks like:\n",
    "\n",
    "http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1948.nc\n",
    "\n",
    "We can't just call\n",
    "\n",
    "    open_mfdataset(\"http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.*.nc\")\n",
    "    \n",
    "Because wildcard expansion doesn't work with OpenDAP endpoints. The solution is to manually create a list of files to open."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1948.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1949.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1950.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1951.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1952.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1953.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1954.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1955.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1956.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1957.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1958.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1959.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1960.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1961.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1962.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1963.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1964.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1965.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1966.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1967.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1968.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1969.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1970.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1971.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1972.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1973.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1974.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1975.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1976.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1977.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1978.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1979.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1980.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1981.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1982.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1983.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1984.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1985.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1986.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1987.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1988.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1989.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1990.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1991.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1992.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1993.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1994.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1995.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1996.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1997.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1998.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.1999.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2000.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2001.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2002.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2003.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2004.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2005.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2006.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2007.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2008.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2009.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2010.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2011.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2012.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2013.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2014.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2015.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2016.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2017.nc',\n",
       " 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2018.nc']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_url = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995'\n",
    "\n",
    "files = [f'{base_url}.{year}.nc' for year in range(1948, 2019)]\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 73, lon: 144, time: 103596)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...\n",
       "  * lat      (lat) float32 90.0 87.5 85.0 82.5 80.0 77.5 75.0 72.5 70.0 67.5 ...\n",
       "  * time     (time) datetime64[ns] 1948-01-01 1948-01-01T06:00:00 ...\n",
       "Data variables:\n",
       "    air      (time, lat, lon) float32 dask.array<shape=(103596, 73, 144), chunksize=(1464, 73, 144)>\n",
       "Attributes:\n",
       "    Conventions:                     COARDS\n",
       "    title:                           4x daily NMC reanalysis (1948)\n",
       "    description:                     Data is from NMC initialized reanalysis\\...\n",
       "    platform:                        Model\n",
       "    history:                         created 99/05/11 by Hoop (netCDF2.3)\\nCo...\n",
       "    References:                      http://www.esrl.noaa.gov/psd/data/gridde...\n",
       "    dataset_title:                   NCEP-NCAR Reanalysis 1\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "%matplotlib inline\n",
    "\n",
    "ds = xr.open_mfdataset(files)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now opened the entire ensemble of files on the remote server as a single xarray Dataset!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform Correlation Analysis\n",
    "\n",
    "Many people want to look at correlations (usually in time) in their final projects.\n",
    "Unfortunately, this is not currently part of xarray (but it will be added soon! -- see [open issue](https://github.com/pydata/xarray/issues/1115)).\n",
    "\n",
    "In the meantime, the following functions works pretty well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def covariance(x, y, dims=None):\n",
    "    return xr.dot(x - x.mean(dims), y - y.mean(dims), dims=dims) / x.count(dims)\n",
    "\n",
    "def corrrelation(x, y, dims=None):\n",
    "    return covariance(x, y, dims) / (x.std(dims) * y.std(dims))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 89, lon: 180, time: 684)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -88.0 -86.0 -84.0 -82.0 -80.0 -78.0 -76.0 -74.0 ...\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0 ...\n",
       "  * time     (time) datetime64[ns] 1960-01-15 1960-02-15 1960-03-15 ...\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL\n",
       "    source:       https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCDC/.ERSST/...\n",
       "    history:      extracted and cleaned by Ryan Abernathey for Research Compu..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset('NOAA_NCDC_ERSST_v3b_SST.nc')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fc6018af5f8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sst = ds.sst\n",
    "sst_clim = sst.groupby('time.month').mean(dim='time')\n",
    "sst_anom = sst.groupby('time.month') - sst_clim\n",
    "\n",
    "%matplotlib inline\n",
    "sst_anom[0].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc6017de7f0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sst_ref = sst_anom.sel(lon=200, lat=0, method='nearest')\n",
    "sst_ref.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Correlation btw. global SST Anomaly and SST Anomaly at one point')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sst_cor = corrrelation(sst_anom, sst_ref, dims='time')\n",
    "pc = sst_cor.plot()\n",
    "pc.axes.set_title('Correlation btw. global SST Anomaly and SST Anomaly at one point')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fix poorly encoded time coordinates\n",
    "\n",
    "Many datasets, especially from INGRID, are encoded with \"months since\" as the time unit. Trying to open these in xarray currently gives an error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.3'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cftime\n",
    "cftime.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "unable to decode time units 'months since 1960-01-01' with calendar '360'. Try opening your dataset with decode_times=False.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOutOfBoundsDatetime\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/coding/times.py\u001b[0m in \u001b[0;36mdecode_cf_datetime\u001b[0;34m(num_dates, units, calendar, enable_cftimeindex)\u001b[0m\n\u001b[1;32m    172\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcalendar\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_STANDARD_CALENDARS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mOutOfBoundsDatetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOutOfBoundsDatetime\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/coding/times.py\u001b[0m in \u001b[0;36m_decode_cf_datetime_dtype\u001b[0;34m(data, units, calendar, enable_cftimeindex)\u001b[0m\n\u001b[1;32m    131\u001b[0m         result = decode_cf_datetime(example_value, units, calendar,\n\u001b[0;32m--> 132\u001b[0;31m                                     enable_cftimeindex)\n\u001b[0m\u001b[1;32m    133\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/coding/times.py\u001b[0m in \u001b[0;36mdecode_cf_datetime\u001b[0;34m(num_dates, units, calendar, enable_cftimeindex)\u001b[0m\n\u001b[1;32m    200\u001b[0m             \u001b[0mflat_num_dates\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcalendar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m             enable_cftimeindex)\n\u001b[0m\u001b[1;32m    202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/coding/times.py\u001b[0m in \u001b[0;36m_decode_datetime_with_cftime\u001b[0;34m(num_dates, units, calendar, enable_cftimeindex)\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mdates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum2date\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_dates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcalendar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mcftime/_cftime.pyx\u001b[0m in \u001b[0;36mcftime._cftime.num2date\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mcftime/_cftime.pyx\u001b[0m in \u001b[0;36mcftime._cftime.utime.__init__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: calendar must be one of ['standard', 'gregorian', 'proleptic_gregorian', 'noleap', 'julian', 'all_leap', '365_day', '366_day', '360_day'], got '360'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-95c7bea581ab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.CAMS_OPI/.v0208/.mean/.prcp/dods'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/backends/api.py\u001b[0m in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, autoclose, concat_characters, decode_coords, engine, chunks, lock, cache, drop_variables, backend_kwargs)\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0mlock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_default_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mclose_on_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 346\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_decode_store\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    347\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'scipy'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/backends/api.py\u001b[0m in \u001b[0;36mmaybe_decode_store\u001b[0;34m(store, lock)\u001b[0m\n\u001b[1;32m    256\u001b[0m             \u001b[0mstore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_and_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask_and_scale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecode_times\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m             \u001b[0mconcat_characters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconcat_characters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_coords\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecode_coords\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 258\u001b[0;31m             drop_variables=drop_variables)\n\u001b[0m\u001b[1;32m    259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0m_protect_dataset_variables_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/conventions.py\u001b[0m in \u001b[0;36mdecode_cf\u001b[0;34m(obj, concat_characters, mask_and_scale, decode_times, decode_coords, drop_variables)\u001b[0m\n\u001b[1;32m    427\u001b[0m     vars, attrs, coord_names = decode_cf_variables(\n\u001b[1;32m    428\u001b[0m         \u001b[0mvars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconcat_characters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_and_scale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_times\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 429\u001b[0;31m         decode_coords, drop_variables=drop_variables)\n\u001b[0m\u001b[1;32m    430\u001b[0m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_coords\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoord_names\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextra_coords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/conventions.py\u001b[0m in \u001b[0;36mdecode_cf_variables\u001b[0;34m(variables, attributes, concat_characters, mask_and_scale, decode_times, decode_coords, drop_variables)\u001b[0m\n\u001b[1;32m    360\u001b[0m             \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconcat_characters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconcat_characters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m             \u001b[0mmask_and_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask_and_scale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecode_times\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 362\u001b[0;31m             stack_char_dim=stack_char_dim)\n\u001b[0m\u001b[1;32m    363\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdecode_coords\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m             \u001b[0mvar_attrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_vars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/conventions.py\u001b[0m in \u001b[0;36mdecode_cf_variable\u001b[0;34m(name, var, concat_characters, mask_and_scale, decode_times, decode_endianness, stack_char_dim)\u001b[0m\n\u001b[1;32m    298\u001b[0m         for coder in [times.CFTimedeltaCoder(),\n\u001b[1;32m    299\u001b[0m                       times.CFDatetimeCoder()]:\n\u001b[0;32m--> 300\u001b[0;31m             \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m     dimensions, data, attributes, encoding = (\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/coding/times.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, variable, name)\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0mcalendar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpop_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'calendar'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m             dtype = _decode_cf_datetime_dtype(\n\u001b[0;32m--> 404\u001b[0;31m                 data, units, calendar, enable_cftimeindex)\n\u001b[0m\u001b[1;32m    405\u001b[0m             transform = partial(\n\u001b[1;32m    406\u001b[0m                 \u001b[0mdecode_cf_datetime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcalendar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcalendar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/coding/times.py\u001b[0m in \u001b[0;36m_decode_cf_datetime_dtype\u001b[0;34m(data, units, calendar, enable_cftimeindex)\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mPY3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m' Full traceback:\\n'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_exc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'dtype'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'object'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: unable to decode time units 'months since 1960-01-01' with calendar '360'. Try opening your dataset with decode_times=False."
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "url = 'http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.CAMS_OPI/.v0208/.mean/.prcp/dods'\n",
    "ds = xr.open_dataset(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to avoid this is to simply not decode the times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (T: 478, X: 144, Y: 72)\n",
       "Coordinates:\n",
       "  * X        (X) float32 1.25 3.75 6.25 8.75 11.25 13.75 16.25 18.75 21.25 ...\n",
       "  * T        (T) float32 228.5 229.5 230.5 231.5 232.5 233.5 234.5 235.5 ...\n",
       "  * Y        (Y) float32 -88.75 -86.25 -83.75 -81.25 -78.75 -76.25 -73.75 ...\n",
       "Data variables:\n",
       "    prcp     (T, Y, X) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset(url, decode_times=False)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, this is not satisfying, because now we can't use xarray's time aware features (like resample).\n",
    "\n",
    "A better option has recently become possible. First we need the latest development version of the [cftime](https://github.com/Unidata/cftime) package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (T: 478, X: 144, Y: 72)\n",
       "Coordinates:\n",
       "  * X        (X) float32 1.25 3.75 6.25 8.75 11.25 13.75 16.25 18.75 21.25 ...\n",
       "  * T        (T) datetime64[ns] 1979-01-16 1979-02-16 1979-03-16 1979-04-16 ...\n",
       "  * Y        (Y) float32 -88.75 -86.25 -83.75 -81.25 -78.75 -76.25 -73.75 ...\n",
       "Data variables:\n",
       "    prcp     (T, Y, X) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fix_calendar(ds, timevar='T'):\n",
    "    if ds[timevar].attrs['calendar'] == '360':\n",
    "        ds[timevar].attrs['calendar'] = '360_day'\n",
    "    return ds\n",
    "\n",
    "ds = fix_calendar(ds)\n",
    "ds = xr.decode_cf(ds)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
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 "nbformat": 4,
 "nbformat_minor": 2
}


================================================
FILE: src/projects.md
================================================
# Final Projects

When taught at Columbia, the capstone of this course has been a final project.
These final projects are archived here.
Feel free to open a [pull request] to add your class's projects to the list.

## Project Archive

### Columbia, Fall 2019

- _Deep earthquakes at Axial Seamount_ by Michelle Lee
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/mkhuulee/RC_Final_Project) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/mkhuulee/RC_Final_Project/master)
- _Correlation between sea surface temperature and sea ice from 1990_ by Shengtao Wang
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/wst1997/final_project) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/wst1997/final_project/master)
- _An analysis of eddies in the Bay of Bengal_ by Shannon Bohman
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/shannonbohman/Final-Project) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/shannonbohman/Final-Project/master)
- _The variation trend and distribution study of CO2 concentration and temperature in North America_ by Lin Xing
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/lx2251/final_project) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/lx2251/final_project.git/master)
- _The correlation of global distribution of soil moisture and precipitation_ by Jiahao Zhang
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/Steven19970523/final-project.git) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Steven19970523/final-project.git/master)
- _Temperature Polarization in Different Membrane Distillation Operations_ by Stephanie McCartney
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/sm4386/Project) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sm4386/Project/master)
- _PTLC vs S&P500_ by Thomas Kovar
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/tk2749/Project) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tk2749/Project/master)
- _Are Trees in the South Eastern Brooks Range suffering from Temperature-induced Drought Stress?_ by Rose Oelkers
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/roho91/Final-Project-2019) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/roho91/Final-Project-2019/master)
- _Analysis of the risks to submarine cables_ by Amelie Latreille
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/alatreille/my_project) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/alatreille/my_project/master)
- _Recent Trends in Global Daily Temperature Variability_ by Casey Ivanovich
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/ccivanovich/RCES-Final_Assignment) [![Binder](https://mybinder.org/badge_logo.svg)](nan)
- _Aerosol Change Effect on China's Precipitation_ by Patrick Cho
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/pgc2113/Research-Computing-Final-Project) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pgc2113/Research-Computing-Final-Project/master)
- _Could CSIF better reveal drought mechanisms?_ by Weiwei Zhan
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/Weiwei047/Final_Project) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Weiwei047/Final_Project/master)
- _Seasonality in Salinity, Temperature, and Currents of the North Atlantic_ by Joohee Kim
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/jk3652/rces-final-project-jkim) [![Binder](https://mybinder.org/badge_logo.svg)](https://binder.pangeo.io/v2/gh/jk3652/rces-final-project-jkim/master)
- _Analyzing the total emissions and the spatial distributions of the different greenhouse gases in the U.S._ by Haokai Zhao
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/hkzhao7/rces-final-project) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/hkzhao7/rces-final-project/master)
- _Sediment provenance variations in Southern Africa during the last 150 kyr_ by Chiza Mwinde
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/mwindec/Site_U1474) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/mwindec/Site_U1474/master)
- _The affect of geopotential height anomalies on climate extremes_ by Patric Ryser
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/Patricryser/Project.git) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Patricryser/Project/master)
- _Local Effects of Farmland Irrigation on Groundwater Availability_ by Matt Harrington
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/M-Harrington/final_project_ResComp/blob/master/README.md) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/M-Harrington/final_project_ResComp/master)
- _Greenhouse gas ecosystem fluxes in the YK Delta of Alaska_ by Sarah Ludwig
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/LuddaLudwig/rces_final_project) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/LuddaLudwig/rces_final_project/master)
- _Quantifying dynamic topography change in Argentina based on model results_ by Andrew
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/ahollyday/RCES_final_project/blob/master/Project_proposal_RCES.md) [![Binder](https://mybinder.org/badge_logo.svg)](nan)
- _Implied Uncertainty in Impacts Projections from Internal Model Variability_ by Kevin Schwarzwald
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/ks905383/impact-variability) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ks905383/impact-variability/master)
- _Effect of Wind Extremes on Aboveground Biomass: Case Study of Hurricanes Maria and Irma_ by Jashvina Devadoss
  [![github](https://img.shields.io/badge/view-github-green?logo=github)](https://github.com/jdeva/wind-extremes) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jdeva/wind-extremes/master)

## Final Project Requirements

For instructors wishing to re-create the final project experience, below we include some specification used in past iterations of the course.

### Learning Goals

The goal of the final project is to assess your ability to combine and apply the skills you have learned in class in the context of a real-world research problem. Our class has mostly focused on tools for data analysis and visualization, so this must be the focus of your final project. Specifically, we seek to assess your ability to do the following tasks:

*   Discover and download real datasets in standard formats (e.g. CSV, netCDF)
*   Load the data into Pandas or Xarray, performing any necessary data cleanup (dealing with missing values, proper time encoding, etc.) along the way.
*   Perform realistic scientific calculation involving, for example tasks such as grouping, aggregating, and applying mathematical formulas.
*   Visualize your results in well-formatted plots.

### Dataset Requirements

Your datasets can involve data collected by yourself or your lab, or can come from a public data repository. Ideally, your choice of dataset should be driven by your research. It is acceptable (and encouraged) to have your final project for this class involve an ongoing research project. _However, it is not acceptable to have your final project overlap with the final project for another class._

If you don't know what dataset to use, here are a couple of links to get you started:

*   [IRI Data Library](http://iridl.ldeo.columbia.edu/)
*   [USGS Data Catalog](https://data.usgs.gov/datacatalog/)
*   [NOAA National Climate Data Center](https://www.ncdc.noaa.gov/)
*   [NASA Earth Science Data](https://earthdata.nasa.gov/)

You may use just one dataset, or you may choose to combine multiple datasets, depending on your scientific question.

### Analysis Requirements

The goal here is the same as with any science project: to use the data to investigate a scientific question or hypothesis. In order to succeed on the project, you will have to draw on your experience _outside_ our class, from your science-focused classes or independent research, in order to define a scientifically interesting question. It is also acceptable to use this project to reproduce the results from a published study that you find interesting, provided you have access to the original data.

Whatever you choose, you should _clearly define a hypothesis or scientific question that you aim to investigate with your analysis_. This will determine what you have to do.

The results of this analysis will be figures. Beautiful figures which clearly provide answers to your question / hypothesis. Your notebook should contain at least 4 and no more than 8 figures. If you have closer to 4, they should be complex, multi-panel figures. All figures must have titles, clearly labeled axes, informative colormaps / colorbars, and legends, where appropriate.

### Technical Requirements

Your final project must meet the following technical requirements

*   A _single jupyter notebook_
*   Stored in a standalone public github repo
*   All data is either stored in the repo itself or downloaded / accessed from within the notebook (no manual download steps)
*   Complete explanatory text / equations included in the notebook as markdown cells
*   Notebook must execute in sequence with no errors
*   The whole github repo must be configured to run on [mybinder.org](https://mybinder.org/) or, for analysis involving dask, [binder.pangeo.io](http://binder.pangeo.io/)

You _must_ use either Pandas or Xarray (or both) in some part of your project. You _may_ use other scientific python libraries as well, if you wish, to facilitate some analysis that is not possible with Xarray / Pandas alone. Some libraries you may wish to consider are:

*   [SciPy](https://docs.scipy.org/doc/scipy/reference/) for interpolation, signal processing, spectral analysis, linear algebra, and other general purpose scientific computing routines
*   [Statsmodels](https://github.com/statsmodels/statsmodels) for advanced statistical analysis
*   [Scikit-image](https://scikit-image.org/) for image processing
*   [Scikit-learn](https://scikit-learn.org/stable/) for machine learning
*   [XGCM](https://xgcm.readthedocs.io/en/latest/) for working with finite-volume data from general circulation models
*   [XESMF](https://xesmf.readthedocs.io/en/latest/) for regridding of gridded data
*   [Pyresample](https://pyresample.readthedocs.io/en/latest/) for resampling (reprojection) of satellite data
*   [EOFS](https://ajdawson.github.io/eofs/) for empirical orthogonal function analysis
*   [windspharm](https://ajdawson.github.io/windspharm/latest/) for spherical harmonic analysis of global atmospheric wind data
*   [metpy](https://unidata.github.io/MetPy/latest/index.html) a collection of tools in Python for reading, visualizing, and performing calculations with weather data

### Project Approval

You must have your dataset(s) and general scope for your project improved by the instructor. The approval process works like this:

*   Create a new public github repo for your project
*   Add a `README.md` file which contains the scientific question / hypothesis you plan to investigate, links to the relevant datasets, and a three sentence summary of the analysis you plan to do.
*   Submit a link to your project repo using the method indicated by the instructor. 

### In-Class Presentations

You are asked to give a 5-minute presentation about your project. Do not prepare any slides. Instead, make your presentation by opening your notebook from GitHub on the presentation computer and walking us through parts of it. Your presentation should be concise and cover the following topics:

*   What data did you analyze and how did you load it?
*   What is the most interesting figure you made? (Show us the figure.)
*   What was the biggest challenge you faced in completing your project.

_Don't forget to make your project repo **public**; otherwise we won't be able to see it._