Start tutorial showing how to use PyGMT with other packages

book/_toc.yml

@@ -14,3 +14,4 @@ parts:
     - file: notebooks
     - file: markdown-notebooks
     - file: first-figure
+    - file: ecosystem

book/ecosystem.ipynb

@@ -0,0 +1,266 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b862b964-62fc-48b7-96db-d7f3b868ced1",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Integration with the scientific Python ecosystem 🐍\n",
+    "\n",
+    "In this tutorial, we'll try out the integration between PyGMT and other common packages in the scientific Python ecosystem.\n",
+    "\n",
+    "\n",
+    "Besides [pygmt](https://www.pygmt.org), we'll also be using:\n",
+    "\n",
+    "- [GeoPandas](https://geopandas.org/en/stable/) for managing geospatial tabular data\n",
+    "- [Panel](https://panel.holoviz.org/index.html) for interactive visualizations\n",
+    "- [Xarray](https://xarray.dev/) for managing n-dimensional labelled arrays\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62d489cd-c901-42a2-b51a-9b21842b34df",
+   "metadata": {},
+   "source": [
+    "## Plotting geospatial vector data with GeoPandas and PyGMT\n",
+    "\n",
+    "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",
+    "\n",
+    "**References**:\n",
+    "\n",
+    " - GeoPandas User Guide - https://geopandas.org/en/stable/docs/user_guide/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6436dfbb-be9c-4c2b-901a-a8fc7cde4ae8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pygmt\n",
+    "import geopandas as gpd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0f88acb-e463-4193-a7ef-33837b2f5fdf",
+   "metadata": {},
+   "source": [
+    "We'll load sample data provided through the GeoPandas package and inspect the GeoDataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cf79e91-8ce0-48ec-8812-1395d3d0eddf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
+    "world.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65adae6a-3933-453b-bd4b-4e04e018d02d",
+   "metadata": {},
+   "source": [
+    "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)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "76b850c4-e5bc-42bd-ba13-7b52b4f60dc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate the populations in millions per capita\n",
+    "world = world[(world.pop_est>0) & (world.name!=\"Antarctica\")]\n",
+    "world['pop_est'] = world.pop_est * 1e-6\n",
+    "\n",
+    "# Find the range of data values for creating a colormap\n",
+    "cmap_bounds = pygmt.info(data=world['pop_est'], per_column=True)\n",
+    "cmap_bounds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "864091e5-85e2-4452-ab44-5cbeaa2ac8a9",
+   "metadata": {},
+   "source": [
+    "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!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15a21a74-8531-4e74-86e1-a85e6eaef793",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an instance of the pygmt.Figure class\n",
+    "fig = pygmt.Figure()\n",
+    "# Create a colormap for the figure\n",
+    "pygmt.makecpt(cmap=\"bilbao\", series=cmap_bounds)\n",
+    "# Create a basemap\n",
+    "fig.basemap(region=\"d\", projection=\"H15c\", frame=True)\n",
+    "# Plot the GeoDataFrame\n",
+    "# - Use `close=True` to specify that the polygons should be forced closed\n",
+    "# - Plot the polygon outlines with a 1 point, black pen\n",
+    "# - Set that the color should be based on the `pop_est` using the `color, `cmap`, and `aspatial` parameters\n",
+    "fig.plot(data=world, pen=\"1p,black\", close=True, color=\"+z\", cmap=True, aspatial=\"Z=pop_est\")\n",
+    "# Add a colorbar\n",
+    "fig.colorbar(position=\"JMR\", frame='a200+lPopulation (millions)')\n",
+    "# Display the output\n",
+    "fig.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b73a6666-6c2c-4f49-a92b-995ade576ccc",
+   "metadata": {},
+   "source": [
+    "## Interactive data visualization with Xarray, Panel, and PyGMT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bf15df6-6a4f-4221-af66-0db1c5b1e328",
+   "metadata": {},
+   "source": [
+    "In this section, we'll create some interactive visualizations of oceanographic data!\n",
+    "\n",
+    "We'll use [Panel](https://panel.holoviz.org/index.html), which is a Python library\n",
+    "for connecting interactive widgets with plots! We'll use Panel with\n",
+    "[PyGMT](https://www.pygmt.org) and [xarray](https://www.xarray.dev) to visualize\n",
+    "the objectively interpolated mean field for in-situ temperature from the World Ocean Atlas.\n",
+    "\n",
+    "**References**:\n",
+    "\n",
+    "- Temperature visualization based on https://rabernat.github.io/intro_to_physical_oceanography/02-c_ocean_temperature_salinity_stratification.html\n",
+    "- Interactive setup based on https://github.com/weiji14/30DayMapChallenge2021/blob/main/day25_interactive.py\n",
+    "- Data from the NOAA World Ocean Atlas, stored on the IRI Data Library at http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbcff01f-fac0-4c1d-bb66-89dcb2e7711b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import panel as pn\n",
+    "import xarray as xr\n",
+    "import pygmt\n",
+    "pn.extension()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b2f9cb8-add6-45c2-9f54-8e33ed9e6f02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Download the dataset from the IRI Data Library\n",
+    "url = 'https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/.Grid-1x1/.Annual/.temperature/.t_an/data.nc'\n",
+    "netcdf_file = pygmt.which(fname=url, download=True)\n",
+    "woa_temp = xr.open_dataset(netcdf_file).isel(time=0)\n",
+    "woa_temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c72a673",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make a static plot of sea surface temperature\n",
+    "fig = pygmt.Figure()\n",
+    "fig.grdimage(grid=woa_temp.t_an.sel(depth=0), cmap=\"vik\", projection=\"R15c\", frame=True)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b11ea510-7540-475e-9217-be4204cb1ea3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make a panel widget for controlling the depth plotted\n",
+    "depth_slider = pn.widgets.DiscreteSlider(name='Depth (m)', options=woa_temp.depth.values.astype(int).tolist(), value=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3496d4db-92d9-45ee-9d48-6b4f00a19361",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make a function for plotting the depth slice with PyGMT\n",
+    "\n",
+    "@pn.depends(depth=depth_slider)\n",
+    "def view(depth: int):\n",
+    "    fig = pygmt.Figure()\n",
+    "    pygmt.makecpt(cmap=\"vik\", series=[-2,30])\n",
+    "    fig.grdimage(grid=woa_temp.t_an.sel(depth=depth), cmap=True, projection=\"R15c\", frame=True)\n",
+    "    fig.colorbar(frame=\"a5\")\n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d339433c-1c7a-4769-8986-edfb2a9897ed",
+   "metadata": {},
+   "source": [
+    "### Make the interactive dashboard!\n",
+    "\n",
+    "Now to put everything together! The 'dashboard' will be very simple.\n",
+    "The 'depth' slider is placed next to the map using `panel.Column`.\n",
+    "Selecting different depths will update the data plotted! Find out more at\n",
+    "https://panel.holoviz.org/getting_started/index.html#using-panel.\n",
+    "\n",
+    "Note: This is meant to run in a Jupyter lab/notebook environment.\n",
+    "The grdinfo warning can be ignored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc8d2f88-6e37-4ca4-9ebc-4bdf1e00ec8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pn.Column(depth_slider, view)"
+   ]
+  }
+ ],
+ "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

environment.yml

@@ -9,6 +9,7 @@ dependencies:
   - python=3.9
   - pip=22
   # Optional dependencies
+  - panel=0.13.0
   - geopandas=0.10.2
   - jupyter-book=0.12.3
   - laspy=2.1.2