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‎.github/workflows/deploy-book.yml

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jobs:
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deploy-book:
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runs-on: ubuntu-latest
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defaults:
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run:
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shell: bash -l {0}
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steps:
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- uses: actions/[email protected]
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# Checkout current git repository
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- name: Checkout
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uses: actions/[email protected]
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# Install dependencies
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- name: Set up Python 3.9
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uses: actions/setup-python@v3.1.2
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# Install Mambaforge with conda-forge dependencies
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- name: Setup Mambaforge
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uses: conda-incubator/setup-miniconda@v2.1.1
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with:
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python-version: 3.9
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activate-environment: egu22pygmt
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environment-file: environment.yml
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python-version: ${{ matrix.python-version }}
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channels: conda-forge
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channel-priority: strict
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miniforge-version: latest
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miniforge-variant: Mambaforge
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mamba-version: "*"
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use-mamba: true
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- name: Install dependencies
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run: pip install -r book/requirements.txt
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# Show installed pkg information for postmortem diagnostic
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- name: List installed packages
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run: mamba list
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# Build the book
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- name: Build the book

‎.gitignore

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# Data files
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*.laz
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*.nc
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*.png
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*.tif
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# Jupyter Book
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/book/_build/
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‎book/_toc.yml

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url: https://github.com/GenericMappingTools/egu22pygmt
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- caption: 🧑‍🏫 Tutorials
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chapters:
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- file: markdown
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- file: notebooks
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- file: markdown-notebooks
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- file: first-figure
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- file: ecosystem
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- file: lidar_to_surface

‎book/ecosystem.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b862b964-62fc-48b7-96db-d7f3b868ced1",
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"metadata": {
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"tags": []
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},
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"source": [
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"# Integration with the scientific Python ecosystem 🐍\n",
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"\n",
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"In this tutorial, we'll try out the integration between PyGMT and other common packages in the scientific Python ecosystem.\n",
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"\n",
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"\n",
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"Besides [pygmt](https://www.pygmt.org), we'll also be using:\n",
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"\n",
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"- [GeoPandas](https://geopandas.org/en/stable/) for managing geospatial tabular data\n",
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"- [Panel](https://panel.holoviz.org/index.html) for interactive visualizations\n",
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"- [Xarray](https://xarray.dev/) for managing n-dimensional labelled arrays\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "62d489cd-c901-42a2-b51a-9b21842b34df",
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"metadata": {},
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"source": [
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"## Plotting geospatial vector data with GeoPandas and PyGMT\n",
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"\n",
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"We'll extend the GeoPandas [Mapping and Plotting Tools Examples](https://geopandas.org/en/stable/docs/user_guide/mapping.html) to show how to create choropleth maps using PyGMT.\n",
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"\n",
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"**References**:\n",
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"\n",
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" - GeoPandas User Guide - https://geopandas.org/en/stable/docs/user_guide/"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6436dfbb-be9c-4c2b-901a-a8fc7cde4ae8",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pygmt\n",
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"import geopandas as gpd"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a0f88acb-e463-4193-a7ef-33837b2f5fdf",
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"metadata": {},
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"source": [
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"We'll load sample data provided through the GeoPandas package and inspect the GeoDataFrame."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8cf79e91-8ce0-48ec-8812-1395d3d0eddf",
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"metadata": {},
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"outputs": [],
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"source": [
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"world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
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"world.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "65adae6a-3933-453b-bd4b-4e04e018d02d",
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"metadata": {},
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"source": [
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"Following the [GeoPandas example](https://geopandas.org/en/stable/docs/user_guide/mapping.html#choropleth-maps), we'll create a Choropleth map showing world population estimates, but will use PyGMT to plot the data using the [Hammer projection](https://www.pygmt.org/latest/projections/misc/misc_hammer.html#hammer)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "76b850c4-e5bc-42bd-ba13-7b52b4f60dc2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calculate the populations in millions per capita\n",
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"world = world[(world.pop_est>0) & (world.name!=\"Antarctica\")]\n",
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"world['pop_est'] = world.pop_est * 1e-6\n",
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"\n",
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"# Find the range of data values for creating a colormap\n",
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"cmap_bounds = pygmt.info(data=world['pop_est'], per_column=True)\n",
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"cmap_bounds"
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]
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},
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{
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"cell_type": "markdown",
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"id": "864091e5-85e2-4452-ab44-5cbeaa2ac8a9",
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"metadata": {},
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"source": [
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"Now, we'll plot the data on a PyGMT figure, by creating a figure instance, laying down a basemap, plotting the GeoDataFrame, and adding a colorbar!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "15a21a74-8531-4e74-86e1-a85e6eaef793",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create an instance of the pygmt.Figure class\n",
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"fig = pygmt.Figure()\n",
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"# Create a colormap for the figure\n",
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"pygmt.makecpt(cmap=\"bilbao\", series=cmap_bounds)\n",
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"# Create a basemap\n",
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"fig.basemap(region=\"d\", projection=\"H15c\", frame=True)\n",
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"# Plot the GeoDataFrame\n",
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"# - Use `close=True` to specify that the polygons should be forced closed\n",
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"# - Plot the polygon outlines with a 1 point, black pen\n",
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"# - Set that the color should be based on the `pop_est` using the `color, `cmap`, and `aspatial` parameters\n",
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"fig.plot(data=world, pen=\"1p,black\", close=True, color=\"+z\", cmap=True, aspatial=\"Z=pop_est\")\n",
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"# Add a colorbar\n",
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"fig.colorbar(position=\"JMR\", frame='a200+lPopulation (millions)')\n",
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"# Display the output\n",
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"fig.show()\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b73a6666-6c2c-4f49-a92b-995ade576ccc",
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"metadata": {},
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"source": [
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"## Interactive data visualization with Xarray, Panel, and PyGMT"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1bf15df6-6a4f-4221-af66-0db1c5b1e328",
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"metadata": {},
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"source": [
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"In this section, we'll create some interactive visualizations of oceanographic data!\n",
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"\n",
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"We'll use [Panel](https://panel.holoviz.org/index.html), which is a Python library\n",
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"for connecting interactive widgets with plots! We'll use Panel with\n",
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"[PyGMT](https://www.pygmt.org) and [xarray](https://www.xarray.dev) to visualize\n",
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"the objectively interpolated mean field for in-situ temperature from the World Ocean Atlas.\n",
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"\n",
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"**References**:\n",
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"\n",
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"- Temperature visualization based on https://rabernat.github.io/intro_to_physical_oceanography/02-c_ocean_temperature_salinity_stratification.html\n",
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"- Interactive setup based on https://github.com/weiji14/30DayMapChallenge2021/blob/main/day25_interactive.py\n",
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"- Data from the NOAA World Ocean Atlas, stored on the IRI Data Library at http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dbcff01f-fac0-4c1d-bb66-89dcb2e7711b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import panel as pn\n",
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"import xarray as xr\n",
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"import pygmt\n",
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"pn.extension()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7b2f9cb8-add6-45c2-9f54-8e33ed9e6f02",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Download the dataset from the IRI Data Library\n",
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"url = 'https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/.Grid-1x1/.Annual/.temperature/.t_an/data.nc'\n",
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"netcdf_file = pygmt.which(fname=url, download=True)\n",
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"woa_temp = xr.open_dataset(netcdf_file).isel(time=0)\n",
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"woa_temp"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1c72a673",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Make a static plot of sea surface temperature\n",
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"fig = pygmt.Figure()\n",
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"fig.grdimage(grid=woa_temp.t_an.sel(depth=0), cmap=\"vik\", projection=\"R15c\", frame=True)\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b11ea510-7540-475e-9217-be4204cb1ea3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Make a panel widget for controlling the depth plotted\n",
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"depth_slider = pn.widgets.DiscreteSlider(name='Depth (m)', options=woa_temp.depth.values.astype(int).tolist(), value=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3496d4db-92d9-45ee-9d48-6b4f00a19361",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Make a function for plotting the depth slice with PyGMT\n",
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"\n",
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"@pn.depends(depth=depth_slider)\n",
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"def view(depth: int):\n",
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" fig = pygmt.Figure()\n",
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" pygmt.makecpt(cmap=\"vik\", series=[-2,30])\n",
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" fig.grdimage(grid=woa_temp.t_an.sel(depth=depth), cmap=True, projection=\"R15c\", frame=True)\n",
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" fig.colorbar(frame=\"a5\")\n",
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" return fig"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d339433c-1c7a-4769-8986-edfb2a9897ed",
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"metadata": {},
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"source": [
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"### Make the interactive dashboard!\n",
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"\n",
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"Now to put everything together! The 'dashboard' will be very simple.\n",
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"The 'depth' slider is placed next to the map using `panel.Column`.\n",
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"Selecting different depths will update the data plotted! Find out more at\n",
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"https://panel.holoviz.org/getting_started/index.html#using-panel.\n",
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"\n",
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"Note: This is meant to run in a Jupyter lab/notebook environment.\n",
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"The grdinfo warning can be ignored."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cc8d2f88-6e37-4ca4-9ebc-4bdf1e00ec8e",
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"metadata": {},
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"outputs": [],
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"source": [
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"pn.Column(depth_slider, view)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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