-Advanced Visualization Cookbook
-
-
This Project Pythia Cookbook covers advanced visualization techniques building upon and combining various Python packages.
-Motivation
-The possibilities of data visualization in Python are almost endless. Already using matplotlib the workhorse behind many visualization packages, the user has a lot of customization options available to them. cartopy, metpy, seaborn, geocat-viz, and datashader are all also great packages that can offer unique additions to your Python visualization toolbox.
This Cookbook will house various visualization workflow examples that use different visualization packages, highlight the differences in functionality between the packages, any noteable syntax distinctions, and demonstrate combining tools to achieve a specific outcome.
-Authors
-Julia Kent, Anissa Zacharias, Orhan Eroglu, Philip Chmielowiec, John Clyne
-Contributors
- -Structure
-This cookbook is broken up into a few sections - a “Basics of Geoscience Visualization” intro that compares different visualization packages and plot elements, and then example workflows of advanced visualization applications that are further subdivided.
-Basics of Geoscience Visualization
-Here we introduce the basics of geoscience visualization, the elements of a plot, different types of plots, and some unique considerations when dealing with model and measured data. Here we also share a comparison of different visualization packages available to the Scientific Python programmer.
-Specialty Plots
-There are some plot types that are unique to atmospheric science such as Taylor Diagrams or Skew-T plots. Here we will use metpy and geocat-viz to demonstrate these specialty plots.
Visualization of Structured Grids
-In this section we will demonstrate how to visualize data that is on a structured grid. Namely, we will look at Spaghetti Hurricane plots. Here we will have workflows that utilize packages such as cartopy and geocat-viz.
Animation
-Animated plots are great tools for science communication and outreach. We will demonstrate how to make your plots come to life. In this book, we use “animated plots” to refer to stable animations, such as the creation of gifs or videos.
-Interactivity
-Dynamically rendering, animating, panning & zooming over a plot can be great to increase data fidelity. We will showcase how to use Holoviz technologies with Bokeh backend to create interactive plots, utilizing an unstructured grid data in the Model for Prediction Across Scales (MPAS) format.
-Running the Notebooks
-You can either run the notebook using Binder or on your local machine.
-Running on Binder
-The simplest way to interact with a Jupyter Notebook is through -Binder, which enables the execution of a -Jupyter Book in the cloud. The details of how this works are not -important for now. All you need to know is how to launch a Pythia -Cookbooks chapter via Binder. Simply navigate your mouse to -the top right corner of the book chapter you are viewing and click -on the rocket ship icon, (see figure below), and be sure to select -“launch Binder”. After a moment you should be presented with a -notebook that you can interact with. I.e. you’ll be able to execute -and even change the example programs. You’ll see that the code cells -have no output at first, until you execute them by pressing -Shift+Enter. Complete details on how to interact with -a live Jupyter notebook are described in Getting Started with -Jupyter.
-Running on Your Own Machine
-If you are interested in running this material locally on your com
--
-
Clone the
-https://github.com/ProjectPythia/advanced-viz-cookbookrepository:--git clone https://github.com/ProjectPythia/advanced-viz-cookbook.git -
-Move into the
-advanced-viz-cookbookdirectory--cd advanced-viz-cookbook -
-Create and activate your conda environment from the
-environment.ymlfile--conda env create -f environment.yml -conda activate advanced-viz-cookbook -
-Move into the
-notebooksdirectory and start up Jupyterlab--cd notebooks/ -jupyter lab -
-
-
-# Advanced Visualization Cookbook
-
-[](https://binder.projectpythia.org/v2/gh/ProjectPythia/advanced-viz-cookbook.git/main?labpath=notebooks)
-[](https://zenodo.org/badge/latestdoi/671205314)
-
-This Project Pythia Cookbook covers advanced visualization techniques building upon and combining various Python packages.
-
-## Motivation
-
-The possibilities of data visualization in Python are almost endless. Already using `matplotlib` the workhorse behind many visualization packages, the user has a lot of customization options available to them. `cartopy`, `metpy`, `seaborn`, `geocat-viz`, and `datashader` are all also great packages that can offer unique additions to your Python visualization toolbox.
-
-This Cookbook will house various visualization workflow examples that use different visualization packages, highlight the differences in functionality between the packages, any noteable syntax distinctions, and demonstrate combining tools to achieve a specific outcome.
-
-## Authors
-
-[Julia Kent](@jukent), [Anissa Zacharias](@anissa111), [Orhan Eroglu](@erogluorhan), [Philip Chmielowiec](@philipc2), [John Clyne](@clyne)
-
-### Contributors
-
-![\"Matplotlib](\"images/logos/matplotlib.svg\")
\n",
- "\n",
- "Matplotlib is the workhorse of Python visualization needs. It is a comprehensive plotting library that has the capacity to make static, animated, or interactive visualizations. It is hard to imagine plotting in Python without first getting comfortable with Matplotlib. Be sure to check out the [Matplotlib documentation](https://matplotlib.org/) as well as the [Pythia foundations chapter on Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) for guidance.\n",
- "\n",
- "Matplotlib's syntax should feel familiar to anyone who has plotted data in Matlab.\n",
- "\n",
- "Here is a [simple plotting example from Matplotlib](https://matplotlib.org/stable/gallery/lines_bars_and_markers/simple_plot.html):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "\n",
- "# Data for plotting\n",
- "t = np.arange(0.0, 2.0, 0.01)\n",
- "s = 1 + np.sin(2 * np.pi * t)\n",
- "\n",
- "fig, ax = plt.subplots()\n",
- "ax.plot(t, s)\n",
- "\n",
- "ax.set(xlabel='time (s)', ylabel='voltage (mV)',\n",
- " title='About as simple as it gets, folks')\n",
- "ax.grid()\n",
- "\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Cartopy\n",
- "\n",
- "![\"Cartopy](\"images/logos/cartopy.jpeg\")
\n",
- "\n",
- "Cartopy is a Python package for plotting data on the globe. It is the go-to package for plotting maps, dealing with different projections, and adding surface features to your plot. Cartopy is buit on top of [PROJ](https://proj.org/en/9.2/), NumPy and [Shapely](https://shapely.readthedocs.io/en/stable/manual.html), and Matplotlib. To learn more about what Cartopy can do, check out the [Cartopy documentation](https://scitools.org.uk/cartopy/docs/latest/) and the [Pythia foundations Cartopy chapter](https://foundations.projectpythia.org/core/cartopy.html).\n",
- "\n",
- "You may have heard about [Basemap](https://matplotlib.org/basemap/index.html), another geoscience plotting library, which was deprecated in favor of Cartopy.\n",
- "\n",
- "Here is a [simple plotting example from Cartopy](https://scitools.org.uk/cartopy/docs/v0.15/matplotlib/intro.html):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import cartopy.crs as ccrs\n",
- "\n",
- "ax = plt.axes(projection=ccrs.PlateCarree())\n",
- "ax.coastlines()\n",
- "\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## GeoCAT-Viz\n",
- "\n",
- "![\"GeoCAT](\"images/logos/geocat.png\")
\n",
- "\n",
- "The GeoCAT team at the National Center for Atmospheric Research (NCAR) aims to help scientists transitioning from [NCL](https://www.ncl.ucar.edu/) to Python. Out of this team come two different visualization aids: the [GeoCAT-examples Visualization Gallery](https://geocat-examples.readthedocs.io/en/latest/) which contains tons of different plotting examples that you can use as a starting place for your figures, and the [GeoCAT-Viz package (documentation)](https://geocat-viz.readthedocs.io/en/latest/) which contains many convenience functions that formerly existed in NCL or for making Python plots look publication-ready."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## MetPy\n",
- "\n",
- "![\"Metpy](\"images/logos/metpy.png\")
\n",
- "\n",
- "Metpy is a collection of tools for data reading, analysis, and visualization with weather data. Matplotlib offers some useful functionality for unique plots such as Skew-T diagrams, as well as declaritive plotting functionality. Check out the [MetPy documentation](https://unidata.github.io/MetPy/latest/index.html).\n",
- "\n",
- "Here is a simple Skew-T plot from their [Getting Started documentation](https://unidata.github.io/MetPy/latest/userguide/startingguide.html):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import metpy.calc as mpcalc\n",
- "from metpy.plots import SkewT\n",
- "from metpy.units import units\n",
- "\n",
- "fig = plt.figure(figsize=(9, 9))\n",
- "skew = SkewT(fig)\n",
- "\n",
- "# Create arrays of pressure, temperature, dewpoint, and wind components\n",
- "p = [902, 897, 893, 889, 883, 874, 866, 857, 849, 841, 833, 824, 812, 796, 776, 751,\n",
- " 727, 704, 680, 656, 629, 597, 565, 533, 501, 468, 435, 401, 366, 331, 295, 258,\n",
- " 220, 182, 144, 106] * units.hPa\n",
- "t = [-3, -3.7, -4.1, -4.5, -5.1, -5.8, -6.5, -7.2, -7.9, -8.6, -8.9, -7.6, -6, -5.1,\n",
- " -5.2, -5.6, -5.4, -4.9, -5.2, -6.3, -8.4, -11.5, -14.9, -18.4, -21.9, -25.4,\n",
- " -28, -32, -37, -43, -49, -54, -56, -57, -58, -60] * units.degC\n",
- "td = [-22, -22.1, -22.2, -22.3, -22.4, -22.5, -22.6, -22.7, -22.8, -22.9, -22.4,\n",
- " -21.6, -21.6, -21.9, -23.6, -27.1, -31, -38, -44, -46, -43, -37, -34, -36,\n",
- " -42, -46, -49, -48, -47, -49, -55, -63, -72, -88, -93, -92] * units.degC\n",
- "\n",
- "# Calculate parcel profile\n",
- "prof = mpcalc.parcel_profile(p, t[0], td[0]).to('degC')\n",
- "u = np.linspace(-10, 10, len(p)) * units.knots\n",
- "v = np.linspace(-20, 20, len(p)) * units.knots\n",
- "\n",
- "skew.plot(p, t, 'r')\n",
- "skew.plot(p, td, 'g')\n",
- "skew.plot(p, prof, 'k') # Plot parcel profile\n",
- "skew.plot_barbs(p[::5], u[::5], v[::5])\n",
- "\n",
- "skew.ax.set_xlim(-50, 15)\n",
- "skew.ax.set_ylim(1000, 100)\n",
- "\n",
- "# Add the relevant special lines\n",
- "skew.plot_dry_adiabats()\n",
- "skew.plot_moist_adiabats()\n",
- "skew.plot_mixing_lines()\n",
- "\n",
- "plt.show();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## VAPOR\n",
- "\n",
- "![\"VAPOR](\"images/logos/vapor.png\")
\n",
- "\n",
- "VAPOR stands for the Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Researchers and is another project from NCAR. VAPOR provides an interactive 3D visualization environment. Learn more at the [VAPOR documentation](https://www.vapor.ucar.edu/) and the [VAPOR Pythia Cookbook](https://projectpythia.org/vapor-python-cookbook/README.html). VAPOR requires a GPU-enabled environment to run."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Plotly\n",
- "\n",
- "![\"Plotly](\"images/logos/plotly.png\")
\n",
- "\n",
- "Plotly is solid choice for interactive plotting. Plotly has functionality in several languags. Here is the [Plotly Python documentation](https://plotly.com/python/).\n",
- "\n",
- "Here is an example using their \"Express\" functionality:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import plotly.express as px\n",
- "\n",
- "fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])\n",
- "fig.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Seaborn\n",
- "\n",
- "![\"Seaborn](\"images/logos/seaborn.svg\")
\n",
- "\n",
- "Seaborn is a high level interactive interface for creating statistical visualizations built on matplotlib. Check out the [Seaborn documentation](https://seaborn.pydata.org/).\n",
- "\n",
- "Here is their [heatmap example](https://seaborn.pydata.org/examples/spreadsheet_heatmap.html):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import seaborn as sns\n",
- "sns.set_theme()\n",
- "\n",
- "# Load the example flights dataset and convert to long-form\n",
- "flights_long = sns.load_dataset(\"flights\")\n",
- "flights = flights_long.pivot(index=\"month\", columns=\"year\", values=\"passengers\")\n",
- "\n",
- "# Draw a heatmap with the numeric values in each cell\n",
- "f, ax = plt.subplots(figsize=(9, 6))\n",
- "sns.heatmap(flights, annot=True, fmt=\"d\", linewidths=.5, ax=ax)\n",
- "\n",
- "plt.show();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Bokeh\n",
- "\n",
- "![\"Bokeh](\"images/logos/bokeh.jpeg\")
\n",
- "\n",
- "Bokeh is a Javascript-powered tool for creating interactive visualizations in modern web browsers. Check out the [Bokeh documentation](https://bokeh.org/)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## UXarray\n",
- "\n",
- "![\"UXarray](\"images/logos/uxarray.png\")
\n",
- "\n",
- "UXarray specializes in unstructured grids, built around [UGRID conventions](https://ugrid-conventions.github.io/ugrid-conventions/) and Xarray syntax. See the [UXarray documentation](https://uxarray.readthedocs.io/en/latest/) and check out the the [UXarray Cookbook](https://projectpythia.org/unstructured-grid-viz-cookbook)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## hvPlot\n",
- "\n",
- "![\"Datashader](\"images/logos/hvplot.svg\")
\n",
- "\n",
- "hvPlot wraps both [Datashader](https://datashader.org/), a graphics pipeline, and [Holoviews](https://holoviews.org/), a tool for bundling data and metadata for intuitive interactive plotting, at a higher level. All 3 tools are by [Holoviz](https://holoviz.org/). Reference the [hvPlot documentation](https://hvplot.holoviz.org/).\n",
- "\n",
- "Here is a simple example from their [user guide](https://hvplot.holoviz.org/user_guide/Introduction.html):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import hvplot.pandas\n",
- "\n",
- "pd.options.plotting.backend = 'holoviews'\n",
- "\n",
- "index = pd.date_range('1/1/2000', periods=1000)\n",
- "df = pd.DataFrame(np.random.randn(1000, 4), index=index, columns=list('ABCD')).cumsum()\n",
- "\n",
- "df.plot()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This useful diagram from [hvPlot's documentation](https://hvplot.holoviz.org/index.html) details how different high-level tools for data visualization interact.\n",
- "\n",
- "![\"Datashader](\"images/hvplot_diagram.svg\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Summary\n",
- "\n",
- "Each Python plotting library offers a slightly different niche in the data visualization world. Some are better for creating publication figures (matplotlib, cartopy, metpy, geocat-viz, uxarray) while others offer interactive functionality that is great for websites, demonstrations, and other forms of engagement (holoviews, seaborn, plotly, bokeh, and vapor). Hopefully the mini examples on this page allow you to play around and see which user interfaces you like best for your visualization needs.\n",
- "\n",
- "\n",
- "### What's next?\n",
- "\n",
- "Next up let's discuss elements of [good data visualization](2-good-viz)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Resources and references\n",
- "\n",
- "- [Matplotlib documentation](https://matplotlib.org/)\n",
- "- [Cartopy documentation](https://scitools.org.uk/cartopy/docs/latest/)\n",
- "- [GeoCat-examples Visualization Gallery](https://geocat-examples.readthedocs.io/en/latest/)\n",
- "- [GeoCAT-Viz documentation](https://geocat-viz.readthedocs.io/en/latest/)\n",
- "- [MetPy documentation](https://unidata.github.io/MetPy/latest/index.html)\n",
- "- [Vapor documentation](https://www.vapor.ucar.edu/)\n",
- "- [Plotly Python documentation](https://plotly.com/python/)\n",
- "- [Seaborn documentation](https://seaborn.pydata.org/)\n",
- "- [Bokeh documentation](https://bokeh.org/)\n",
- "- [UXarray documentation](https://uxarray.readthedocs.io/en/latest/)\n",
- "- [hvPlot documentation](https://hvplot.holoviz.org/index.html)\n",
- "- [Holoviews documentation](https://holoviews.org/)\n",
- "- [Datashader documentation](https://datashader.org/)"
- ]
- }
- ],
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- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
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- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
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diff --git a/_preview/51/_sources/notebooks/2-good-viz.ipynb b/_preview/51/_sources/notebooks/2-good-viz.ipynb
deleted file mode 100644
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--- a/_preview/51/_sources/notebooks/2-good-viz.ipynb
+++ /dev/null
@@ -1,325 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# What Makes for Good Data Visualization?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Overview\n",
- "What Makes a Good Visualization? We want graphics to be eye catching and informative. In this chapter we'll discuss different aspects that can affect the quality of your figures and specific considerations relevant to the geosciences.\n",
- "\n",
- "1. The Importance of Data Visualization\n",
- "1. Publication Ready Figures\n",
- "1. The Problem with Rainbow Colormaps\n",
- "1. Misleading Visualizations"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Prerequisites\n",
- "\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) | Necessary | |\n",
- "| [Cartopy](https://foundations.projectpythia.org/core/cartopy.html) | Necessary | |\n",
- "\n",
- "- **Time to learn**: 3 hours\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib as mpl\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "import geocat.viz as gv"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## The Importance of Data Visualization\n",
- "\n",
- "It is important to use pictures to show data because we can visually detect patterns that could be lost in statistical analysis. All scientific disciplines use data visualizations to communicate concepts. \n",
- "\n",
- "Here we have a figure from [Autodesk](https://www.research.autodesk.com/publications/same-stats-different-graphs/) that shows a \"Datasaurus\" and 12 other datasets that share the same statistical information (mean, standard deviation, etc). We can see immediately that visually are telling very different stories: be it a dinosaur, a star, an oval, concentric ovals, or a series of lines (perhaps weather fronts).\n",
- "\n",
- "![\"Same](\"images/same_stats.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Publication Ready Figures\n",
- "\n",
- "For your figure to be publication rady, you probably want to change some of Matplotlib's default plotting settings: selecting fontsizes for your titles and labels, changing figure sizes, or subplot/colormap layout.\n",
- "\n",
- "To demonstrate this, let's look at an example:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# fake data\n",
- "x = [0, 1, 2, 3, 4, 5]\n",
- "y = [0, 3, 6, 9, 12, 15]\n",
- " \n",
- "# plot\n",
- "plt.plot(x, y)\n",
- "\n",
- "# annotate\n",
- "plt.title('Title')\n",
- "plt.xlabel('X Label')\n",
- "plt.ylabel('Y Label')\n",
- "\n",
- "plt.show();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now let's show some customization options:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# fake data\n",
- "x = [0, 1, 2, 3, 4, 5]\n",
- "y = [0, 3, 6, 9, 12, 15]\n",
- " \n",
- "# plot\n",
- "plt.plot(x, y, '--', color='red')\n",
- "\n",
- "# annotate\n",
- "plt.title('Title', fontsize=20)\n",
- "plt.xlabel('X Label', fontsize=16)\n",
- "plt.ylabel('Y Label', fontsize=16)\n",
- "\n",
- "plt.show();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Matplotlib Global Parameters\n",
- "\n",
- "Matplotlib has defaults for fontsizes and all sorts of attributes of a plot. Instead of setting your fontsize in every script, it is possible to set global parameters to change the default values of these attributes.\n",
- "\n",
- "You can veiw the globoal parameters options and their current settings with:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mpl.rcParams.keys"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To change any given parameter you would use the following command (replacing your parameter and value, of course):"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "```\n",
- "mpl.rcParams['font.family'] = 'Arial'\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Using GeoCAT-Viz\n",
- "\n",
- "The GeoCAT-Viz package also has many utility functions for making your plots looks publication ready in fewer lines of code. The defaults of GeoCAT-viz keword-arguments are set to resemble the style of NCL."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# fake data\n",
- "x = [0, 1, 2, 3, 4, 5]\n",
- "y = [0, 3, 6, 9, 12, 15]\n",
- " \n",
- "# plot\n",
- "plt.plot(x, y)\n",
- "\n",
- "# annotate\n",
- "plt.title('Title')\n",
- "plt.xlabel('X Label')\n",
- "plt.ylabel('Y Label')\n",
- "\n",
- "gv.set_titles_and_labels(plt.gca())\n",
- "\n",
- "plt.show();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## The Problem with Rainbow Colormaps\n",
- "\n",
- "Rainbow colormaps are visually beautiful, but are falling out of favor because\n",
- "1. They are not colorblind friendly and\n",
- "2. They do not print out in grayscale in a meaningful way.\n",
- "\n",
- "Both of these issues can be addressed by bing careful about you colormaps lightness-values.\n",
- "\n",
- "Some colormaps options are perceptually uniform (the same lightness value), sequentially ordered (goes from lighter to darker), or diverging (lightest or darkest at a set point and uniformly changes lightness going out). A rainbow colormap however is lighter or darker in arbitrary places and it affects how we interpret data (especially if it was printed out in grayscale).\n",
- "\n",
- "For example, from [Matplotlib's Choosing a Colormap documentation](https://matplotlib.org/stable/tutorials/colors/colormaps.html) here are some \"good\" colormaps:\n",
- "\n",
- "![\"Matplotlib](\"https://matplotlib.org/stable/_images/sphx_glr_colormaps_016_2_00x.png\")
\n",
- "\n",
- "And here are miscellaneous colormaps:\n",
- "\n",
- "![\"Matplotlib](\"https://matplotlib.org/stable/_images/sphx_glr_colormaps_021_2_00x.png\")
\n",
- "\n",
- "Looking at the colors in grayscale helps to understand why we might prefer a sequentially ordered colormap. Some grayscale values are duplicated and the reader will not know if it is a high or low value.\n",
- "\n",
- "Another consideration that can help those who are visually impaired is to make sure your figure comments are substantial. Use words to paint the picture of what is displayed, not just the conclusions you want the reader to get."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Misleading Visualizations\n",
- "\n",
- "The scales or colors we choose to use for data visualization affect how people interpret figures. You should strive to make your visualizations as accurate and as informative as possible. Here are some examples that demonstrate just how different a figure can look based on these choices you make. Do not intentionally mislead your audience!\n",
- "\n",
- "Perhaps the most common data visualization manipulation is to change the Y-scale. If a plot does not begin at 0, small changes in magnitude can be exhaggerated. Similarly a logarithmic scale will amplify changes. This is not always disingenuous, sometimes these changes are what you want to highlight, the pattern you want to draw attention to. Just make sure it is appropriate for your use case and documented. Alternatively, extending the Y-axis too large has the opposite affect and smooths out the differences in data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "x = [1, 2, 3, 4, 5]\n",
- "y = [1101, 1011, 1111, 1070, 1050]\n",
- "\n",
- "\n",
- "fig, (ax1, ax2, ax3, ax4) = plt.subplots(4)\n",
- "fig.tight_layout()\n",
- "\n",
- "ax1.bar(x,y)\n",
- "ax1.set_title(\"Default Y-Scale Starts at 0\")\n",
- "\n",
- "ax2.bar(x,y)\n",
- "ax2.set_ylim(1000)\n",
- "ax2.set_title(\"Y-Scale Starts at 1000\")\n",
- "\n",
- "ax3.bar(x,y)\n",
- "ax3.set_yscale(\"log\")\n",
- "ax3.set_title(\"Y-Scale is Logarithmic\");\n",
- "\n",
- "ax4.bar(x,y)\n",
- "ax4.set_ylim(0, 2000)\n",
- "ax4.set_title(\"Y-Scale is Extended\");"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Other examples of data visualization manipulation include improper scaling, cherry picking a small non-representative subset of the data to display, displaying pie charts at a slant (pie charts are hard to interpet accurately as is), and unusing unexpected colormaps."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Summary\n",
- "It is important to have accurate, engaging, and representative data visualization to accumpany your research, both for data exploration as part of the scientific process, for communication of results, and education/outreach efforts. Visually we pick up on patterns that statistics alone may not convey. However, an over reliance on data visualization can make science less accessible to those with vision disabilities. It is important to be cognicent of the patterns our minds pick up, be it based on color or y-axis scaling, so that we can avoid misleading our audience and more accurately convey the narrative inherent to the data.\n",
- "\n",
- "### What's next?\n",
- "\n",
- "Let's break down the different components of data visualization in [Plot Elements](3-plot-elements).\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Resources and references\n",
- "\n",
- "- [Autodesk](https://www.research.autodesk.com/publications/same-stats-different-graphs/)\n",
- "- [Matplotlib's Choosing a Colormap documentation](https://matplotlib.org/stable/tutorials/colors/colormaps.html)\n",
- "- [GeoCAT-examples Gallery](https://geocat-examples.readthedocs.io/en/latest/)\n",
- "- [NWSC Script](https://docs.google.com/document/d/1PxJBbYJsI5nmR9pDQmcRM0ZghRPSI_xlzGVhw3AEUms/edit)\n",
- "- [Beyond Visuals: Examining the Experiences of Geoscience\n",
- "Professionals With Vision Disabilities in Accessing Data Visualizations](https://arxiv.org/pdf/2207.13220.pdf)\n",
- "- [Same Stats Different Graphs: Generating Datasets with Varied Appearance and\n",
- "Identical Statistics through Simulated Annealing](https://www.research.autodesk.com/app/uploads/2023/03/same-stats-different-graphs.pdf_rec2hRjLLGgM7Cn2T.pdf)\n",
- "- Alberto Cairo's How Charts Lie: Getting Smarter about Visual Information\n",
- "- [Misleading Data Visualization – What to Avoid](https://blog.coupler.io/misleading-data-visualization-examples/)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "advanced-viz-cookbook",
- "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": "3.10.12"
- },
- "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/_preview/51/_sources/notebooks/3-plot-elements.ipynb b/_preview/51/_sources/notebooks/3-plot-elements.ipynb
deleted file mode 100644
index 27fec96..0000000
--- a/_preview/51/_sources/notebooks/3-plot-elements.ipynb
+++ /dev/null
@@ -1,308 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Plot Elements"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Prerequisites\n",
- "\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) | Necessary | |\n",
- "| [Cartopy](https://foundations.projectpythia.org/core/cartopy.html) | Necessary | |\n",
- "\n",
- "- **Time to learn**: 40 minutes\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import xarray as xr\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import geocat.viz as gv"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Data\n",
- "\n",
- "The first piece of data visualization is the data!\n",
- "\n",
- "Let's generate some dummy data to work with:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "x = np.linspace(0, 20, 1000)\n",
- "y1 = np.sin(x)\n",
- "y2 = np.cos(x)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Figure\n",
- "\n",
- "The figure is the object that contains your entire visualization. Creating a figure tends to be the first step in plotting, even if it doesn't currently show anything:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig = plt.figure(figsize=(9.5, 8))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Axis\n",
- "\n",
- "We then add axes to our plot. You can add multiple axes to one plot in order to produce subplots, or just one. Axes will automatically inherit their limits from the data plotted, or can be manually set."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig = plt.figure(figsize=(9.5, 8))\n",
- "ax = plt.axes()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Plot\n",
- "\n",
- "Adding the data to the figure can be done through several different plot types: line, contour, bar, histogram. Here we use two line plots:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig = plt.figure(figsize=(9.5, 8))\n",
- "ax = plt.axes()\n",
- "\n",
- "ax.plot(x,y1)\n",
- "ax.plot(x,y2);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Titles and Labels\n",
- "\n",
- "Titles and labels are important for indicating what the figure is plotting. It is a good idea to include relevant units in your axis labels."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig = plt.figure(figsize=(9.5, 8))\n",
- "ax = plt.axes()\n",
- "\n",
- "ax.plot(x,y1)\n",
- "ax.plot(x,y2)\n",
- "\n",
- "ax.set_title(\"Dummy Data\")\n",
- "ax.set_xlabel(\"X (radians)\");"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Legends\n",
- "\n",
- "If you're plotting multiple lines of data, it's a good idea to include a legend. Here is how you call or point to the legend:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig = plt.figure(figsize=(9.5, 8))\n",
- "ax = plt.axes()\n",
- "\n",
- "ax.plot(x,y1,label='sine')\n",
- "ax.plot(x,y2,label='cosine')\n",
- "\n",
- "ax.set_title(\"Dummy Data\")\n",
- "ax.set_xlabel(\"X (radians)\")\n",
- "\n",
- "plt.legend(loc=\"upper left\");"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Colorbars\n",
- "\n",
- "While legends are more appropriate for line or bar plots, colorbars are most commonly used for contour plots and sometimes to apply a third level of dimension to a scatter plot.\n",
- "\n",
- "Let's shift our example to better demonstrate a colorbar by workign with a filled contour plot:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Generate dummy data\n",
- "data = [[1, 4, 5, 6, 8.2],\n",
- " [9, 8.4, 10, 10.6, 9.7],\n",
- " [4.4, 5, 0, 6.6, 1.4],\n",
- " [4.6, 5.2, 1.5, 7.6, 2.4]]\n",
- "\n",
- "# Convert data into type xarray.DataArray\n",
- "data = xr.DataArray(data,\n",
- " dims=[\"lat\", \"lon\"],\n",
- " coords=dict(lat=np.arange(4), lon=np.arange(5)))\n",
- "\n",
- "data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig = plt.figure(figsize=(9.5, 8))\n",
- "ax = plt.axes()\n",
- "\n",
- "pcm = ax.contourf(data,cmap='viridis')\n",
- "\n",
- "ax.set_title(\"Dummy Data\")\n",
- "ax.set_xlabel(\"Longitude (\\N{DEGREE SIGN})\")\n",
- "ax.set_ylabel(\"Latitude (\\N{DEGREE SIGN})\")\n",
- "\n",
- "fig.colorbar(pcm,ax=ax);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Annotations\n",
- "\n",
- "Additional annotations allow you to specify some text and a location to indicate almost anything. \n",
- "\n",
- "Here we use GeoCAT-viz to add annotations to the maxima in a contour plot:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig = plt.figure(figsize=(9.5, 8))\n",
- "ax = plt.axes()\n",
- "\n",
- "pcm = ax.contourf(data,cmap='viridis')\n",
- "\n",
- "ax.set_title(\"Dummy Data\")\n",
- "ax.set_xlabel(\"Longitude (\\N{DEGREE SIGN})\")\n",
- "ax.set_ylabel(\"Latitude (\\N{DEGREE SIGN})\")\n",
- "\n",
- "fig.colorbar(pcm,ax=ax)\n",
- "\n",
- "# Find local maximum with GeoCAT-Viz find_local_extrema\n",
- "lmax = gv.find_local_extrema(data, eType='High')[0]\n",
- "\n",
- "# Plot labels for local mins\n",
- "max_value = data.data[lmax[1]][lmax[0]]\n",
- "ax.text(lmax[0], lmax[1],'Maxima = '+str(max_value))\n",
- "\n",
- "# Show plot\n",
- "plt.show();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Summary\n",
- "\n",
- "There are several key elements to a Python plot and knowing what they are called is instrumental to begin your journey for further customization.\n",
- "\n",
- "### What's next?\n",
- "\n",
- "Next up, the specialty plots called [Taylor Diagrams](4-taylor-diagrams)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Resources and references"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "advanced-viz-cookbook",
- "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": "3.10.12"
- },
- "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/_preview/51/_sources/notebooks/4-taylor-diagrams.ipynb b/_preview/51/_sources/notebooks/4-taylor-diagrams.ipynb
deleted file mode 100644
index ec53e57..0000000
--- a/_preview/51/_sources/notebooks/4-taylor-diagrams.ipynb
+++ /dev/null
@@ -1,588 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Taylor Diagrams"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Overview\n",
- "\n",
- "Taylor diagrams are a visual way of representing a statistical summary of how at least two datasets compare, where all plotted datasets are statistically compared to the same reference dataset (typically climate observations). Taylor diagrams are radial plots, with distance from the origin determined by a normalized standard deviation of your dataset (normalized by dividing it by the standard deviation of the reference or observational dataset) and the angle determined by the correlation coefficient between your dataset and the reference.\n",
- "\n",
- "Taylor diagrams are popular for displaying climatological data because the normalization of variances helps account for the widely varying numerical values of geoscientific variables such as temperature or precipitation.\n",
- "\n",
- "This notebook explores how to create and customize Taylor diagrams using `geocat-viz`. See the more information on [`geocat-viz.TaylorDiagram`](https://geocat-viz.readthedocs.io/en/latest/user_api/generated/geocat.viz.taylor.TaylorDiagram.html).\n",
- "\n",
- "1. Creating a Simple Taylor Diagram\n",
- "1. Necessary Statistical Analysis\n",
- "1. Plotting Different Ensemble Members\n",
- "1. Plotting Multiple Models\n",
- "1. Plotting Multiple Variables\n",
- "1. Plotting Bias\n",
- "1. Variants"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Prerequisites\n",
- "\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) | Necessary | |\n",
- "\n",
- "- **Time to learn**: 10 minutes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "import xarray as xr\n",
- "\n",
- "import cftime\n",
- "\n",
- "import geocat.viz as gv\n",
- "import geocat.datafiles as gdf"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Creating a Simple Taylor Diagram\n",
- "\n",
- "Before getting into the data computation necessary to create a Taylor diagram, let's demonstrate how to make the simplest Taylor Diagram plot. Here we are using sample data with a normalized standard deviation of 0.6 and a correlation coefficient of 0.24."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create figure and Taylor Diagram instance\n",
- "fig = plt.figure(figsize=(12, 12))\n",
- "taylor = gv.TaylorDiagram(fig=fig, label='REF')\n",
- "\n",
- "# Draw diagonal dashed lines from origin to correlation values\n",
- "# Also enforces proper X-Y ratio\n",
- "taylor.add_xgrid(np.array([0.6, 0.9]))\n",
- "\n",
- "# Add a model dataset of one point\n",
- "taylor.add_model_set(stddev=[.6], corrcoef=[.24]);\n",
- "\n",
- "plt.title(\"Simple Taylor Diagram\", size=26, pad=45); # Need to move title up"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Necessary Statistical Analysis\n",
- "\n",
- "To make understanding a Taylor Diagram more meaningful or intuitive, let's use some real data. Here we are going to use ERA5 reanalysis data as our observational dataset. CMIP5 temperature data from various representative concentration pathways (RCPs) and ensemble members as our model data.\n",
- "\n",
- "Because these dataset can be so large, some data pre-processing has been done already to the datasets used in this example. \n",
- "\n",
- " - ERA5 and CMIP5 data have been spatially averaged (removing latitudinal and longitudinal dimensions)\n",
- " - ERA5 and CMIP5 data have been indexed to only include the year 2022\n",
- " - All ensembles from a given CMIP5 RCP model have been combined into one dataset.\n",
- " - Temperature and pressure variables from ERA5 have been combined into one dataset.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Preparing the Reference (Observed) Dataset\n",
- "\n",
- "Let's use geocat.datafiles to open up our ERA5 reanalysis data.\n",
- "\n",
- "This dataset has two variables:\n",
- " - `2T_GDSO_SFC` which refers to the air temperature 2 meters above the surface, and \n",
- " - `SP_GDSO_SFC` which is surface pressure.\n",
- "\n",
- "For this dataset, we still need to resample our data to monthly to match the monthly CMIP5 data.\n",
- "\n",
- "Notice that our time coordinate is in `datetime64`, we will have to manipulate either ERA5 or CMIP5 data to use the same time formatting system."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "era5 = xr.open_dataset(gdf.get('netcdf_files/era5_2022_2mtemp_spres_xyav.nc'))\n",
- "era5"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Change hourly data to monthly\n",
- "era5 = era5.rename({'initial_time0_hours': 'time'}) # Changing dimension name for convenience\n",
- "era5_resampled = era5.resample(time='MS', loffset='15D').mean() # loffsest to set resampling to the center of each month as in CMIP5 data\n",
- "\n",
- "era5_resampled"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "era5_temp = era5_resampled['2T_GDS0_SFC'] # Because this variable name starts with a number `era5_resampled.2T_GDS0_SFC` would give an error s\n",
- "\n",
- "# Take a look at our final temperature data\n",
- "era5_temp"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Preparing the model datasets\n",
- "\n",
- "Here our CMIP5 data is originally sourced from the [Earth System Grid Federation](https://esgf.llnl.gov/). Let's first look at our RCP8.5 model, typically referred to as \"business as usual\" because it is expected to be the mostly likely outcome without improving greenhouse gas mitigation efforts.\n",
- "\n",
- "To compare this data with the ERA5 data we need to convert our data to `datetime64`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "tas_rcp85 = xr.open_dataset(gdf.get('netcdf_files/tas_Amon_CanESM2_rcp85_2022_xyav.nc'))\n",
- "\n",
- "tas_rcp85"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "tas_rcp85['time'] = tas_rcp85.indexes['time'].to_datetimeindex()\n",
- "tas_rcp85"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Perform the statistical calculations\n",
- "\n",
- "1. We need to compute the standard deviation for both our ERA5 observed temperature data and our CMIP5 RCP8.5 modeled temperature.\n",
- "2. Find the correlation coefficient between them.\n",
- "3. Then, divide the model standard deviation by the observed standard deviation to normalize it around the value 1.\n",
- "\n",
- "In the next cell we will perform this calculation for all ensemble members."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "temp_rcp85_std = []\n",
- "temp_rcp85_corr = []\n",
- "\n",
- "std_temp_obsv = float(era5_temp.std().values)\n",
- "\n",
- "for em in list(tas_rcp85.data_vars): # for each ensemble member\n",
- " std = float(tas_rcp85[em].std().values)\n",
- " std_norm = std / std_temp_obsv\n",
- "\n",
- " corr= float(xr.corr(era5_temp, tas_rcp85[em]).values)\n",
- "\n",
- " temp_rcp85_std.append(std_norm)\n",
- " temp_rcp85_corr.append(corr)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Plotting Different Ensemble Members\n",
- "\n",
- "One application of a Taylor Diagram application is to plot the same variable from different ensembles of the same climate model.\n",
- "\n",
- "This Taylor diagram differs from our simple example in that we've specified more keyword arguments in our `taylor.add_model_set()` call, specifying how we want our dots to be drawn. We've also added a legend of ensemble members with `taylor.add_model_name()`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create figure and Taylor Diagram instance\n",
- "fig = plt.figure(figsize=(12, 12))\n",
- "taylor = gv.TaylorDiagram(fig=fig, label='REF')\n",
- "ax = plt.gca()\n",
- "\n",
- "# Draw diagonal dashed lines from origin to correlation values\n",
- "# Also enforces proper X-Y ratio\n",
- "taylor.add_xgrid(np.array([0.6, 0.9]))\n",
- "\n",
- "# Add model sets for p and t datasets\n",
- "taylor.add_model_set(\n",
- " temp_rcp85_std,\n",
- " temp_rcp85_corr,\n",
- " fontsize=20, # specify font size\n",
- " xytext=(-5, 10), # marker label location, in pixels\n",
- " color='red', # specify marker color\n",
- " marker='o', # specify marker shape\n",
- " facecolors='none', # specify marker fill\n",
- " s=100) # marker size\n",
- "\n",
- "# Add legend of ensemble names\n",
- "namearr = list(tas_rcp85.data_vars)\n",
- "taylor.add_model_name(namearr, fontsize=16)\n",
- "\n",
- "# Add figure title\n",
- "plt.title(\"RCP85 Temperature\", size=26, pad=45);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Plotting Multiple Models\n",
- "\n",
- "Another potential use case for a Taylor diagram is to plot multiple models. Here we compare RCP2.6, RCP4.5, and RCP8.5 together. \n",
- "\n",
- "Because it isn't meaningful to compare ensemble members across model runs (the nature of the perturbations isn't reliably similar across RCPs or labs), we will look at the first ensemble `r1i1p1` for all models. For your analysis, you might find it more meaningful to average across ensemble members, but we'll keep it simple for this plotting example.\n",
- "\n",
- "Of course, you could still chose to display more information on one graph, but there is no real conection between the first ensemble of one model versus another.\n",
- "\n",
- "In this final example, we'll add another layer of complexity to our Taylor Diagram plot with contour lines of constant root mean squared error (RMSE)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Open RCP26 and RCP45 files\n",
- "tas_rcp26 = xr.open_dataset(gdf.get('netcdf_files/tas_Amon_CanESM2_rcp26_2022_xyav.nc'))\n",
- "tas_rcp26['time'] = tas_rcp26.indexes['time'].to_datetimeindex()\n",
- "\n",
- "tas_rcp45 = xr.open_dataset(gdf.get('netcdf_files/tas_Amon_CanESM2_rcp45_2022_xyav.nc'))\n",
- "tas_rcp45['time'] = tas_rcp45.indexes['time'].to_datetimeindex()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Perform statistical analysis to create our standard deviation and correlation coefficient lists\n",
- "temp_rcp26_std = float(tas_rcp26['r1i1p1'].std().values) \n",
- "temp_rcp26_std_norm = temp_rcp26_std / std_temp_obsv\n",
- "temp_rcp26_corr = float(xr.corr(era5_temp, tas_rcp26['r1i1p1']).values)\n",
- "\n",
- "temp_rcp45_std = float(tas_rcp45['r1i1p1'].std().values)\n",
- "temp_rcp45_std_norm = temp_rcp45_std / std_temp_obsv\n",
- "temp_rcp45_corr = float(xr.corr(era5_temp, tas_rcp45['r1i1p1']).values)\n",
- "\n",
- "temp_std = [temp_rcp26_std_norm, temp_rcp45_std_norm, temp_rcp85_std[0]]\n",
- "temp_corr = [temp_rcp26_corr, temp_rcp45_corr, temp_rcp85_corr[0]]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create figure and Taylor Diagram instance\n",
- "fig = plt.figure(figsize=(12, 12))\n",
- "taylor = gv.TaylorDiagram(fig=fig, label='REF')\n",
- "ax = plt.gca()\n",
- "\n",
- "# Draw diagonal dashed lines from origin to correlation values\n",
- "# Also enforces proper X-Y ratio\n",
- "taylor.add_xgrid(np.array([0.6, 0.9]))\n",
- "\n",
- "# Add model set for temp dataset\n",
- "taylor.add_model_set(\n",
- " temp_std,\n",
- " temp_corr,\n",
- " fontsize=20,\n",
- " xytext=(-5, 10), # marker label location, in pixels\n",
- " color='red',\n",
- " marker='o',\n",
- " facecolors='none',\n",
- " s=100) # marker size\n",
- "\n",
- "#gv.util.set_axes_limits_and_ticks(ax, xlim=[0,2])\n",
- "\n",
- "namearr = ['rcp26', 'rcp45', 'rcp85']\n",
- "taylor.add_model_name(namearr, fontsize=16)\n",
- "\n",
- "# Add figure title\n",
- "plt.title(\"CMIP5 Temperature - First Ensemble Member\", size=26, pad=45)\n",
- "\n",
- "# Add constant centered RMS difference contours.\n",
- "taylor.add_contours(levels=np.arange(0, 1.1, 0.25),\n",
- " colors='lightgrey',\n",
- " linewidths=0.5);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Based on these three RCPs it looks like RCP8.5 has the closest correlation to our observed climate behavior, but RCP2.6 has a closer standard deviation to what we experience. Based on your selected data, scientific interpretations may vary."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Plotting Multiple Variables\n",
- "\n",
- "A Taylor Diagram can support multiple model sets, you simply need to call `taylor.add_model_set()` multiple times. By adding the `label` kwarg and calling `taylor.add_legend()` you can add a label distinguishing between the two sets.\n",
- "\n",
- "Since we've already demonstrated the statistical analysis necessary to perform Taylor Diagrams, the following example will be using sample data.\n",
- "\n",
- "Here we make sample data for 7 common climate model variables, for two different models."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create sample data\n",
- "\n",
- "# Model A\n",
- "a_sdev = [1.230, 0.988, 1.092, 1.172, 1.064, 0.966, 1.079] # normalized standard deviation\n",
- "a_ccorr = [0.958, 0.973, 0.740, 0.743, 0.922, 0.982, 0.952] # correlation coefficient\n",
- "\n",
- "# Model B\n",
- "b_sdev = [1.129, 0.996, 1.016, 1.134, 1.023, 0.962, 1.048] # normalized standard deviation\n",
- "b_ccorr = [0.963, 0.975, 0.801, 0.814, 0.946, 0.984, 0.968] # correlation coefficient\n",
- "\n",
- "# Sample Variable List\n",
- "var_list = ['Surface Pressure', '2m Temp', 'Dew Point Temp', 'U Wind', 'V Wind', 'Precip', 'Cloud Cov']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create figure and TaylorDiagram instance\n",
- "fig = plt.figure(figsize=(10, 10))\n",
- "taylor = gv.TaylorDiagram(fig=fig, label='REF')\n",
- "\n",
- "# Draw diagonal dashed lines from origin to correlation values\n",
- "# Also enforces proper X-Y ratio\n",
- "taylor.add_xgrid(np.array([0.6, 0.9]))\n",
- "\n",
- "# Add models to Taylor diagram\n",
- "taylor.add_model_set(a_sdev,\n",
- " a_ccorr,\n",
- " color='red',\n",
- " marker='o',\n",
- " label='Model A', # add model set legend label\n",
- " fontsize=16)\n",
- "\n",
- "taylor.add_model_set(b_sdev,\n",
- " b_ccorr,\n",
- " color='blue',\n",
- " marker='o',\n",
- " label='Model B',\n",
- " fontsize=16)\n",
- "\n",
- "# Add model name\n",
- "taylor.add_model_name(var_list, fontsize=16)\n",
- "\n",
- "# Add figure legend\n",
- "taylor.add_legend(fontsize=16)\n",
- "\n",
- "# Add constant centered RMS difference contours.\n",
- "taylor.add_contours(levels=np.arange(0, 1.1, 0.25),\n",
- " colors='lightgrey',\n",
- " linewidths=0.5);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Plotting Bias\n",
- "\n",
- "We can add another layer of information to the Taylor Diagram by changing the marker size and shape depending on a third variable. Most commonly this is done to demonstrate bias, a statistical definition of the difference between the observed and estimated values.\n",
- "\n",
- "We do this by adding a `bias_array` kwarg to the `add_model_set()` method. Doing so necessitates removing the `marker` specification, since they are overriden with up or down arrows of varrying sizes. Bias values are in percentages.\n",
- "\n",
- "Indicate the meaning of these new bias symbols with a third legend with the call `add_bias_legend()`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Sample corresponding bias data.\n",
- "\n",
- "# Case A\n",
- "a_bias = [2.7, -1.5, 17.31, -20.11, 12.5, 8.341, -4.7] # bias (%)\n",
- "\n",
- "# Case B\n",
- "b_bias = [1.7, 2.5, -17.31, 20.11, 19.5, 7.341, 9.2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create figure and TaylorDiagram instance\n",
- "fig = plt.figure(figsize=(10, 10))\n",
- "taylor = gv.TaylorDiagram(fig=fig, label='REF')\n",
- "\n",
- "# Draw diagonal dashed lines from origin to correlation values\n",
- "# Also enforces proper X-Y ratio\n",
- "taylor.add_xgrid(np.array([0.6, 0.9]))\n",
- "\n",
- "# Add models to Taylor diagram\n",
- "taylor.add_model_set(a_sdev,\n",
- " a_ccorr,\n",
- " percent_bias_on=True, # indicate marker and size to be plotted based on bias_array\n",
- " bias_array=a_bias, # specify bias array\n",
- " color='red',\n",
- " label='Model A',\n",
- " fontsize=16)\n",
- "\n",
- "taylor.add_model_set(b_sdev,\n",
- " b_ccorr,\n",
- " percent_bias_on=True,\n",
- " bias_array=b_bias,\n",
- " color='blue',\n",
- " label='Model B',\n",
- " fontsize=16)\n",
- "\n",
- "# Add model name\n",
- "taylor.add_model_name(var_list, fontsize=16)\n",
- "\n",
- "# Add figure legend\n",
- "taylor.add_legend(fontsize=16)\n",
- "\n",
- "# Add bias legend\n",
- "taylor.add_bias_legend()\n",
- "\n",
- "# Add constant centered RMS difference contours.\n",
- "taylor.add_contours(levels=np.arange(0, 1.1, 0.25),\n",
- " colors='lightgrey',\n",
- " linewidths=0.5);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Variants"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Taylor Diagram's can be altered in the following variations (not all of which are supported yet by GeoCAT-viz, please consider this [feature request form](https://docs.google.com/forms/d/e/1FAIpQLSeWuogl8Z671et3DdrmglenBPKGFmHVHkpSamTQeoPGuytLZw/viewform)). Coming soon:\n",
- "\n",
- "- Supporting display of negative correlations by extending the diagram into a second quandrant to the left.\n",
- "- Supporting automatic notations connecting related points, say the same variable in two different models to see how it moves towards truth."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Summary\n",
- "\n",
- "Taylor Diagrams allow you to display and compare statistical information about several models, variables, ensembles, or other dataset categorizations on a single plot. They are commonly used in climate analysis. With these tools under your belt, you're ready to include stronger data visualizations in your research.\n",
- "\n",
- "### What's next?\n",
- "\n",
- "Let's look at the meteorology specialty plots [Skew T Diagrams](5-skewt)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Resources and references\n",
- "\n",
- "- Karl E. Taylor - \"Summarizing multiple aspects of model performance in a single diagram\", AGU 2001\n",
- "- [Plotting with GeoCAT Tutorial](https://github.com/anissa111/plotting-with-geocat-tutorial/blob/main/notebooks/02-geocat-viz.ipynb)\n",
- "- [NCL Graphics: Taylor Diagrams](https://www.ncl.ucar.edu/Applications/taylor.shtml)"
- ]
- }
- ],
- "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": "3.10.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/51/_sources/notebooks/5-skewt.ipynb b/_preview/51/_sources/notebooks/5-skewt.ipynb
deleted file mode 100644
index cdda0bf..0000000
--- a/_preview/51/_sources/notebooks/5-skewt.ipynb
+++ /dev/null
@@ -1,398 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Skew T Diagrams"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Overview\n",
- "\n",
- "Skew-T plots are effectively thermodynamic diagrams used in meteorology. They display data collected from radiosonde balloons collecting atmospheric data including pressure level, temperature, relative humidity, and wind speed and direction. \n",
- "\n",
- "In this notebook, we'll learn about the structural and data components of Skew-T diagrams and how to plot them in Python using the [`MetPy`](https://unidata.github.io/MetPy/latest/index.html) package.\n",
- "\n",
- "1. Elements of a Skew-T Diagram\n",
- "2. Acquiring Sounding Data\n",
- "3. Making a Skew-T plot in Python (with MetPy!)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Prerequisites\n",
- "\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) | Necessary | |\n",
- "| [Metpy](https://unidata.github.io/MetPy/latest/index.html) | Useful | |\n",
- "\n",
- "- **Time to learn**: 10 minutes\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Elements of a Skew-T Plot\n",
- "Let's start out by talking about the structural elements of a Skew-T plot.\n",
- "\n",
- "1. **Temperature Lines** are drawn at an angle up from the x-axis and are where the name \"Skew-T\" comes from.\n",
- "2. **Pressure Lines** are horizontal from the y-axis, where pressure is plotted at a logarithmic scale.\n",
- "3. **Dry Adiabats**: are lines of constant potential temperature as hypotethical air with no moisture content rises isentropically (with constant entropy).\n",
- "4. **Moist Adiabats**: are lines of constant equivalent potential temperature - the change in temperature of fully saturated air as it rises, undergoing cooling due to adiabatic expansion.\n",
- "5. **Mixing Ratio Lines**: represent lines of constant mixing ratio, the mass of water vapor relative to the mass of dry air.\n",
- "\n",
- "On all those structural elements, Skew-T plots have two lines plotted on them, **air temperature** and **dew point**. In this notebook, we'll be plotting the air temperature in red and the dew point in blue.\n",
- "\n",
- "Additionally, Skew-T plots have **wind barbs**. These describe the wind speed and direction at different pressure levels and are plotted on the right side of the diagram.\n",
- "\n",
- ":::{tip}\n",
- "For a more detailed description and a cool interactive diagram, visit [NOAA's Skew-T page](https://www.noaa.gov/jetstream/upperair/skew-t-log-p-diagrams).\n",
- ":::"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
- "import pandas as pd\n",
- "\n",
- "from metpy.plots import SkewT, Hodograph\n",
- "import metpy.calc as mpcalc\n",
- "from metpy.units import units"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Acquiring Sounding Data\n",
- "If you want to get your own sounding data, run the following code in a new cell using the date and station of your choice:\n",
- "\n",
- "```python\n",
- "from datetime import datetime\n",
- "from siphon.simplewebservice.wyoming import WyomingUpperAir\n",
- "\n",
- "date = datetime(2023, 7, 7, 0)\n",
- "station = 'JAX'\n",
- "df = WyomingUpperAir.request_data(date, station)\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We've already done this for you and saved the data in a file, `notebooks/data/jax_sounding.csv` for you to use. We'll use that file's data for the rest of the notebook"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df = pd.read_csv('data/jax_sounding.csv')\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "h = df['height'].values\n",
- "p = df['pressure'].values\n",
- "T = df['temperature'].values\n",
- "Td = df['dewpoint'].values\n",
- "u = df['u_wind'].values\n",
- "v = df['v_wind'].values"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Making a Skew-T plot in Python (with MetPy!)\n",
- "So, all of that might seem a little abstract without a visual. We're going to use MetPy's SkewT module to make an actual Skew-T plot with the sounding data we downloaded earlier.\n",
- "\n",
- "From the [MetPy documentation](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html):\n",
- "> \"This class simplifies the process of creating Skew-T log-P plots in using matplotlib. It handles requesting the appropriate skewed projection, and provides simplified wrappers to make it easy to plot data, add wind barbs, and add other lines to the plots (e.g. dry adiabats)\"\n",
- "\n",
- "### Just the basics\n",
- "To start with, let's create a very minimal Skew-T plot with just the pressure and temperature lines under the sounding data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# make figure and `SkewT` object\n",
- "fig = plt.figure(figsize=(9, 9))\n",
- "skewt = SkewT(fig=fig, rotation=45)\n",
- "\n",
- "# plot sounding data\n",
- "skewt.plot(p, T, 'r') # air temperature\n",
- "skewt.plot(p, Td, 'b') # dew point\n",
- "skewt.plot_barbs(p[p >= 100], u[p >= 100], v[p >= 100]) # wind barbs"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's talk break that down a bit.\n",
- " \n",
- "```python\n",
- "# make figure and `SkewT` object\n",
- "fig = plt.figure(figsize=(9, 9))\n",
- "skewt = SkewT(fig=fig, rotation=45)\n",
- "```\n",
- "First, we made a new figure and used it to make a new skew-T plot. If you don't provide a figure to `SkewT`, one will be created for you, but it's useful to make the default figure size a bit larger for this tutorial.\n",
- "\n",
- "Additionally, we've also set the `rotation` kwarg to be 45 degrees. This is the angle that the temperature lines will be drawn at. `MetPy`'s default is 30 degrees, but we're going to use a more traditional 45 degrees for this tutorial.\n",
- "\n",
- "```python\n",
- "# plot sounding data\n",
- "skewt.plot(p, T, 'r') # air temperature\n",
- "skewt.plot(p, Td, 'b') # dew point\n",
- "```\n",
- "\n",
- "For air temperature and dew point, we can use the standard `plot` method. The `SkewT` object provides a wrapper around matplotlib's `plot` method, and can be used in the same way. Note that even though pressure is on the y-axis, we still provide it as the first argument to `plot` because it is the independent variable. \n",
- "\n",
- "```python\n",
- "skewt.plot_barbs(p[p >= 100], u[p >= 100], v[p >= 100]) # wind barbs\n",
- "```\n",
- "\n",
- "Finally, we use `SkewT`'s [`plot_barbs`](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html#metpy.plots.SkewT.plot_barbs) method to add the wind barbs to the right side of the plot. This is a wrapper around matplotlib's [`barbs`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.barbs.html#matplotlib.pyplot.barbs) method that applies the appropriate transformation and positions the barbs as expected for a Skew-T plot. Note that we only plot the wind barbs for pressure levels greater than 100 hPa. This is just to keep the wind barbs from extending off the plot.\n",
- "\n",
- "In addition to the elements we have added specifically, you can see that the `SkewT` object also added some of the structural elements we discussed previously. By default, `SkewT` adds the horizontal pressure and skewed temperature lines. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Adding more structural elements\n",
- "Next, let's add the rest of the structural elements to the plot."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# make figure and `SkewT` object\n",
- "fig = plt.figure(figsize=(9, 9))\n",
- "skewt = SkewT(fig=fig, rotation=45)\n",
- "\n",
- "# plot sounding data\n",
- "skewt.plot(p, T, 'r') # air temperature\n",
- "skewt.plot(p, Td, 'b') # dew point\n",
- "skewt.plot_barbs(p[p >= 100], u[p >= 100], v[p >= 100]) # wind barbs\n",
- "\n",
- "# add dry adiabats, moist adiabats, and mixing ratio lines\n",
- "skewt.plot_dry_adiabats()\n",
- "skewt.plot_moist_adiabats()\n",
- "skewt.plot_mixing_lines()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Similarly to the `plot_barbs` command, the `SkewT` object provides convenient methods for adding the remaining structural elements to the plot.\n",
- "\n",
- "The default appearance of these elements is:\n",
- "- **Dry Adiabats**: dashed red/pinkish lines with an alpha value of 0.5\n",
- "- **Moist Adiabats**: dashed blue lines with an alpha value of 0.5\n",
- "- **Mixing Ratio Lines**: dashed green lines with an alpha value of 0.8\n",
- "\n",
- "These defaults can be overwritten by providing additional keyword arguments to the methods."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Polishing the plot\n",
- "Now that we have all the structural elements on the plot, let's make it look a little nicer. The previous plot has all the necessary information, but it's a little cluttered and hard to read."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# make figure and `SkewT` object\n",
- "fig = plt.figure(figsize=(8,12))\n",
- "skewt = SkewT(fig=fig)\n",
- "skewt.ax.set_ylim(1000, 10)\n",
- "\n",
- "# plot sounding data\n",
- "skewt.plot(p, T, 'r') # air temperature\n",
- "skewt.plot(p, Td, 'b') # dew point\n",
- "skewt.plot_barbs(p[::5], u[::5], v[::5]) # add a wind barb every fifth level\n",
- "\n",
- "# add dry adiabats, moist adiabats, and mixing ratio lines\n",
- "skewt.plot_dry_adiabats(linewidth=0.5)\n",
- "skewt.plot_moist_adiabats(linewidth=0.5)\n",
- "skewt.plot_mixing_lines(linewidth=0.5)\n",
- "\n",
- "# add axis and figure titles\n",
- "plt.title(df['station'][0] + ' ' + str(df['time'][0]))\n",
- "plt.xlabel('temperature (degC)')\n",
- "plt.ylabel('pressure (hPa)')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here, we've made the following changes:\n",
- "- changed the figsize to `figsize=(8,12)`\n",
- "- removed the `rotation` kwarg from the `SkewT` object to allow the upper air temp and dew point lines to be seen without being cut off or expanding the x-axis limits\n",
- "- `skewt.ax.set_ylim(1000, 10)`: sets the y-axis limits to 1000 hPa at the bottom and 10 hPa at the top to include the entire sounding\n",
- "- `skewt.plot_barbs(p[::5], u[::5], v[::5])`: plots every fifth wind barb to reduce clutter, also removes limiting the wind barbs to pressure levels greater than 100 hPa\n",
- "- reduced the linewidth of the dry adiabats, moist adiabats, and mixing ratio lines to 0.5\n",
- "- added axes labels\n",
- "- added a title including the station name and date of the sounding pulled from the data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Additional Skew-T Options\n",
- "There are a few additional options that can be used to customize the appearance of the Skew-T plot that we haven't covered here. For more information, check out the [MetPy documentation](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html).\n",
- "\n",
- "Here's a few quick examples of some of those additional options:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig = plt.figure(figsize=(15, 4))\n",
- "\n",
- "# set up some subplots\n",
- "skewt_plots = []\n",
- "for i in range(0,4):\n",
- " skewt_plots.append(SkewT(fig=fig, subplot=(1,4,i+1), rotation=45))\n",
- " skewt_plots[i].plot(p, T, 'r') # air temperature\n",
- " skewt_plots[i].plot(p, Td, 'b') # dew point\n",
- " skewt_plots[i].plot_barbs(p[::5], u[::5], v[::5], length=5, linewidth=0.5)\n",
- " skewt_plots[i].plot_dry_adiabats(linewidth=0.5)\n",
- " skewt_plots[i].plot_moist_adiabats(linewidth=0.5)\n",
- " skewt_plots[i].plot_mixing_lines(linewidth=0.5)\n",
- " skewt_plots[i].ax.set_xlabel('')\n",
- " skewt_plots[i].ax.set_ylabel('')\n",
- "\n",
- "# calculate LCL and parcel profile\n",
- "lcl_p, lcl_t = mpcalc.lcl(p[0]*units.hPa, T[0]*units.degC, Td[0]*units.degC)\n",
- "lcl_prof = mpcalc.parcel_profile(p*units.hPa, T[0]*units.degC, Td[0]*units.degC).to('degC')\n",
- "\n",
- "\n",
- "# LCL and parcel profile skew-T\n",
- "# At what point an air parcel lifted as a dry parcel becomes saturated\n",
- "skewt_plots[0].ax.set_title('LCL and Parcel Profile')\n",
- "skewt_plots[0].plot(p, lcl_prof, 'k')\n",
- "skewt_plots[0].plot(lcl_p, lcl_t, 'ko') # Lifted Condensation Level\n",
- "\n",
- "\n",
- "# add constant temperature line at t=0\n",
- "skewt_plots[1].ax.set_title('Constant T Line at 0$^\\circ$C')\n",
- "skewt_plots[1].ax.axvline(0, color='k', ls='--')\n",
- "\n",
- "\n",
- "# shade CAPE and CIN\n",
- "# Area above and below the Level of Free Convection (LFC) - where the temeprature line crosses the moist adiabat\n",
- "# Updraft energy for thunderstorms (CAPE) and energy needed for the storm to start (CIN)\n",
- "skewt_plots[2].ax.set_title('Shade CAPE and CIN')\n",
- "skewt_plots[2].plot(p, lcl_prof, 'k')\n",
- "skewt_plots[2].shade_cin(p*units.hPa, T*units.degC, lcl_prof, Td*units.degC) # Convective INhibition\n",
- "skewt_plots[2].shade_cape(p*units.hPa, T*units.degC, lcl_prof) # Convective Available Potential Energy\n",
- "\n",
- "\n",
- "# Hodograph\n",
- "# A line that connects the tips of wind vectors between two atmospheric heights\n",
- "# # Used for understanding wind sheer\n",
- "skewt_plots[3].ax.set_title('Hodograph')\n",
- "ax_hod = inset_axes(skewt_plots[3].ax, '30%', '30%')\n",
- "hod = Hodograph(ax_hod, component_range=50)\n",
- "hod.add_grid(increment=10)\n",
- "hod.plot_colormapped(u, v, h)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Summary\n",
- "Skew-T plots are a useful tool for visualizing and understanding sounding data. Creating Skew-T plots in python might seem challenging given their unique structural characteristics, but `MetPy`'s `SkewT` module greatly simplifies the process.\n",
- "\n",
- "### What's next?\n",
- "Next up let’s discuss [spaghetti plots](6-spaghetti)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Resources and references\n",
- "\n",
- "- [Wyoming Upper Air](https://weather.uwyo.edu/upperair/)\n",
- "- [Siphon](https://unidata.github.io/siphon/latest/examples/upperair/Wyoming_Request.html)\n",
- "- [MetPy's SkewT documentation](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html)\n",
- "- [NOAA's JetStream](https://www.noaa.gov/jetstream)'s [Skew-T Plot](https://www.noaa.gov/jetstream/upperair/skew-t-plots) page"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "advanced-viz-cookbook",
- "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": "3.10.12"
- },
- "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/_preview/51/_sources/notebooks/6-spaghetti.ipynb b/_preview/51/_sources/notebooks/6-spaghetti.ipynb
deleted file mode 100644
index ed2e8c3..0000000
--- a/_preview/51/_sources/notebooks/6-spaghetti.ipynb
+++ /dev/null
@@ -1,942 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Spaghetti Plots"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Overview\n",
- "Spaghetti Plots are a tool typically used to visualize movement. Essentially they are many line plots displayed on the same axes. By drawing the same path at different times or from different forecasts, we can see the patterns and chaos associated with the plotted variable.\n",
- "\n",
- "1. Spaghetti Hurricane Plot\n",
- "1. Spaghetti Contour Plot\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Prerequisites\n",
- "\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) | Necessary | |\n",
- "| [Cartopy](https://foundations.projectpythia.org/core/cartopy.html) | Necessary | |\n",
- "\n",
- "- **Time to learn**: 10 minutes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "jupyter": {
- "outputs_hidden": false
- }
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import xarray as xr\n",
- "import datetime\n",
- "\n",
- "import matplotlib as mpl\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib.ticker as mticker\n",
- "import matplotlib.pylab as pl\n",
- "\n",
- "import cartopy.crs as ccrs\n",
- "import cartopy.feature as cfeature\n",
- "\n",
- "import geocat.viz as gv\n",
- "import geocat.datafiles as gdf\n",
- "\n",
- "import tropycal.tracks as tracks\n",
- "\n",
- "import warnings\n",
- "warnings.filterwarnings('ignore')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Spaghetti Hurricane Plot\n",
- "\n",
- "Visualizing the predicted path of an incoming hurricane is both complicated and important. There are many plots that meteorologists are trained to read, but when shared with the public can be confusing or alarming. There are strengths and weaknesses to each hurricane visualization approach. The cone of uncertainty, for example, is often misinterpreted to suggest the hurricane growth in time rather than widening of path possibilities. Spaghetti plots on the other hand, clearly show hurricane paths but show them as equal to each other.\n",
- "\n",
- "In this example we will plot some forecasted paths from the 2012 North-Atlantic storm Hurricane Sandy. Each forecast is from the Global Ensemble Forecast System (GEFS) provided by the National Centers for Environmental Prediction at NOAA.\n",
- "\n",
- "We'll use the package [`tropycal`](tropycal.github.io/tropycal/) to easily access HURDAT2 and IBTrACS reanalysis data and operational National Hurricane Center (NHC) Best Track data. `tropycal` has a lot of great features for real time hurricane visualization, but since this Cookbook is comparatively static we're using a past hurricane and only using this package to access the data. Our plotting will be done with `matplotlib` and `cartopy`."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Read in Data\n",
- "\n",
- "First, to grab our hurricane data from tropycal we need to specify a basin:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "basin = tracks.TrackDataset(basin='north_atlantic')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Find your storm by name and year:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "storm = basin.get_storm(('sandy',2012))\n",
- "\n",
- "sandy_ds = storm.to_xarray()\n",
- "sandy_ds"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And we can grab any of a number of forecasts:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "forecasts = storm.get_operational_forecasts()\n",
- "print(forecasts.keys())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Each key represents a forecast model, we'll select the GFS AP01 forecast which has many initializations. These initializations are named by time in YYYYMMDDHH format:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "forecasts_AP01 = forecasts['AP01']\n",
- "print(forecasts_AP01.keys())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Spaghetti Plot of One Esemble Member\n",
- "\n",
- "Let's set up our Cartopy grid to plot one ensemble member of the hurricane model.\n",
- "\n",
- "These steps might be familiar to you but we:\n",
- " - create our axes with a Plate Caree projection\n",
- " - add land features\n",
- " - add grid lines\n",
- " - edit our gridline labels to not duplicate on all four sides\n",
- " - edit our gridline label fontsize"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set up Cartopy Projection with land features\n",
- "ax = plt.axes(projection=ccrs.PlateCarree())\n",
- "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n",
- "\n",
- "# Add Gridlines to right and bottom\n",
- "gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n",
- " linewidth=.25, color='gray', alpha=0.5, linestyle='--')\n",
- "gl.xlabels_top = False\n",
- "gl.ylabels_left = False\n",
- "gl.xlabel_style = {'size': 8,}\n",
- "gl.ylabel_style = {'size': 8,}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Looking at GFS Ensemble Member Forecast AP01, we can make a spaghetti plot of each of these initializations.\n",
- "\n",
- "The crux of the visualization is that we loop through and plot each initialization (in `for i in forecasts_AP01`), plot the true hurricane path, and add a legend (`plt.legend()`)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set up Cartopy Projection with land features\n",
- "ax = plt.axes(projection=ccrs.PlateCarree())\n",
- "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n",
- "\n",
- "# Add Gridlines to right and bottom\n",
- "gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n",
- " linewidth=.25, color='gray', alpha=0.5, linestyle='--')\n",
- "gl.xlabels_top = False\n",
- "gl.ylabels_left = False\n",
- "gl.xlabel_style = {'size': 8,}\n",
- "gl.ylabel_style = {'size': 8,}\n",
- "\n",
- "# Spaghetti Plot of AP01 forecasts\n",
- "forecasts_AP01 = forecasts['AP01']\n",
- "for i in forecasts_AP01:\n",
- " # We're naming this line even though it is over-written each loop,\n",
- " # so that we can reference the last line in the legend\n",
- " # (as they all share the same formatting)\n",
- " forecast_path = plt.plot(forecasts_AP01[i]['lon'],\n",
- " forecasts_AP01[i]['lat'],\n",
- " color='cornflowerblue',\n",
- " linewidth=0.5)\n",
- "\n",
- "# Plot the real storm path in a thicker black line\n",
- "true_path = plt.plot(sandy_ds.lon,\n",
- " sandy_ds.lat,\n",
- " color='k',\n",
- " linewidth=1) # Make it thicker than the ensemble paths\n",
- "\n",
- "# Add a legend with only one forecast_path and the true_path\n",
- "plt.legend([true_path[0], forecast_path[0]], ['True Path', 'GFS AP01 Forecasts'])\n",
- "\n",
- "plt.title('Hurricane Sandy (2012)');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This plot is a great example of a spaghetti plot, but is it super useful? Is it confusing? Each line looks like it carries the same weight, when some of these possible paths are from hours before Sandy hit the American NorthEast and others are from days before.\n",
- "\n",
- "Maybe it is better to show the user some indication of how the forecast for this ensemble converged on the true path with later and later initialization times."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Spaghetti Plot of One Esemble Member with Temporal Colormapping\n",
- "\n",
- "Some additions to look out for:\n",
- " - we grab the time information from the initialization name using `datetime`\n",
- " - normalize a colormap by the time information with `cmap = mpl.colors.ListedColormap(plt.cm.autumn_r(normalized_times))`\n",
- " - loop through the colormap as we loop through the time steps within the ensemble member\n",
- " - add a colorbar with `plt.colorbar(plt.cm.ScalarMappable(cmap=cmap))`, where `ScalarMappable` is used to map scalar data to RGBA."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set up Cartopy Projection with land features\n",
- "ax = plt.axes(projection=ccrs.PlateCarree())\n",
- "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n",
- "\n",
- "# Add Gridlines to right and bottom\n",
- "gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n",
- " linewidth=.25, color='gray', alpha=0.5, linestyle='--')\n",
- "gl.xlabels_top = False\n",
- "gl.ylabels_left = False\n",
- "gl.xlabel_style = {'size': 8,}\n",
- "gl.ylabel_style = {'size': 8,}\n",
- "\n",
- "# Spaghetti Plot of AP01 forecasts\n",
- "forecasts_AP01 = forecasts['AP01']\n",
- "\n",
- "# Get time information from initialization name\n",
- "format = '%Y%m%d%H'\n",
- "times = [datetime.datetime.strptime(i, format) for i in list(forecasts_AP01.keys())]\n",
- "normalized_times = [(i - times[0]) / (times[-1] - times[0]) for i in times]\n",
- "\n",
- "# Create a color list for forecast iteration\n",
- "cmap = mpl.colors.ListedColormap(plt.cm.autumn_r(normalized_times))\n",
- "\n",
- "c = 0\n",
- "for i in forecasts_AP01:\n",
- " plt.plot(forecasts_AP01[i]['lon'],\n",
- " forecasts_AP01[i]['lat'],\n",
- " color=cmap(c),\n",
- " linewidth=0.5)\n",
- " c += 1\n",
- "\n",
- "# Plot the real storm path\n",
- "true_path = plt.plot(sandy_ds.lon,\n",
- " sandy_ds.lat,\n",
- " color='red', # Selecting a color matching one of the cmap extremes\n",
- " linewidth=1,\n",
- " label='True Path') # The easiest way to add a plot to the legend is with the label kwarg\n",
- "\n",
- "# Add a legend with only one the true_path\n",
- "# Forecasted paths will be shown in a colorbar\n",
- "plt.legend()\n",
- "\n",
- "plt.title('Hurricane Sandy')\n",
- "\n",
- "# Add colorbar\n",
- "cbar = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap), ax=ax, orientation='horizontal', shrink=0.8, pad=0.075)\n",
- "cbar.set_label('GFS AP01 Forecasts', labelpad=6)\n",
- "\n",
- "# Set tick locations and labels for every 4th tick\n",
- "# i.e. once a day (a new initialiation every 6 hours)\n",
- "tick_indices = range(0, len(times), 4)\n",
- "cbar.set_ticks([normalized_times[i] for i in tick_indices])\n",
- "cbar.set_ticklabels([times[i].strftime('%d') for i in tick_indices], fontsize=8)\n",
- "cbar.ax.text(1.02, 0.5, 'OCT-2012', va='top', ha='left', transform=cbar.ax.transAxes);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we can see that as the storm progressed, the AP01 GFS Forecast Ensemble Member converges on Sandy's true path as the storm progresses through October, 2012."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Alternatively, we may want to plot the possible hurricane paths from multiple GFS Forecast Ensemble members from the same iteration timestamp as a spaghetti plot."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Spaghetti Plot of All Esemble Members at One Point in Time\n",
- "\n",
- "First, we need to grab all of the relevant forecast keys to GFS models (the ones that are titled `AP##` from 0 to 20):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# List of valid AP## keys from 0 to 20\n",
- "GFS_keys = ['AP' + str(i).zfill(2) for i in range(1, 21)]\n",
- "\n",
- "# Arbitrarily selected midnight on October 27, 2012 to plot all forecasts at\n",
- "time = '2012102700'\n",
- "\n",
- "# Set up Cartopy Projection with land features\n",
- "ax = plt.axes(projection=ccrs.PlateCarree())\n",
- "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n",
- "\n",
- "# Add Gridlines to right and bottom\n",
- "gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n",
- " linewidth=.25, color='gray', alpha=0.5, linestyle='--')\n",
- "gl.xlabels_top = False\n",
- "gl.ylabels_left = False\n",
- "gl.xlabel_style = {'size': 8,}\n",
- "gl.ylabel_style = {'size': 8,}\n",
- "\n",
- "# Spaghetti Plot of forecasts\n",
- "for i in range(20):\n",
- " ap = forecasts[GFS_keys[i]]\n",
- " forecast_path = plt.plot(ap[time]['lon'],\n",
- " ap[time]['lat'],\n",
- " color='cornflowerblue',\n",
- " linewidth=0.5)\n",
- "\n",
- "# Plot the real storm path in a thicker black line\n",
- "true_path = plt.plot(sandy_ds.lon, sandy_ds.lat, color='k', linewidth=1)\n",
- "\n",
- "# Add a legend with only one forecast_path and the true_path\n",
- "plt.legend([true_path[0], forecast_path[0]],\n",
- " ['True Path', 'AP01 - AP20'],\n",
- " loc='lower right')\n",
- "\n",
- "plt.title('Hurricane Sandy - GFS Forecasts from Oct-27-2012');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Hurricane Sandy hit the NorthEast on October 29, 2012. From this spaghetti plot we can see that by the 27th most ensemble members of the GFS forecast predicted a similar behavior for the storm.\n",
- "\n",
- "There is more analysis that could be done on hurriane trajectories. We have covered some plotting customization that might be useful for your analysis and data visualization."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Spaghetti Contour Plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this example we will read in the geopotential height datafile `HGT500_MON_1958-1997.nc` from using geocat-datafiles. Then we will look at different timesteps of the `HGT` geopotential height variable at the 5500 gpm level, plotting this contour's locations through time. This example is adapted from [GeoCAT](https://geocat.ucar.edu/)'s [NCL_conOncon_5](https://geocat-examples.readthedocs.io/en/latest/gallery/Contours/NCL_conOncon_5.html) script."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Read in data:\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "jupyter": {
- "outputs_hidden": false
- }
- },
- "outputs": [],
- "source": [
- "ds = xr.open_dataset(gdf.get(\"netcdf_files/HGT500_MON_1958-1997.nc\"),\n",
- " decode_times=False)\n",
- "\n",
- "ds"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Initial Spaghetti Plot on North Polar Stereographic Projection\n",
- "\n",
- "Again, first let's set up our Cartopy axes. This time setting our projection to `NorthPolarStereo`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set up Cartopy Map Projection\n",
- "fig = plt.figure(figsize=(8, 8))\n",
- "ax = plt.axes(projection=ccrs.NorthPolarStereo())\n",
- "\n",
- "gv.set_map_boundary(ax, [-180, 180], [0, 40], south_pad=1)\n",
- "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n",
- "\n",
- "# Set draw_labels to False so that we can manually manipulate it\n",
- "gl = ax.gridlines(ccrs.PlateCarree(),\n",
- " draw_labels=False,\n",
- " linestyle=\"--\",\n",
- " linewidth=1,\n",
- " color='darkgray',\n",
- " zorder=2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Then let's add our data to this plot.\n",
- "\n",
- "We will iterate through every 12th timestep\n",
- " - handling any artifacts of the global wrapping at 0 or 360 degrees with `gv.xr_add_cyclic_longitudes(p, \"lon\")`\n",
- " - and calling a contour plot on a single level."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set up Cartopy Map Projection\n",
- "fig = plt.figure(figsize=(8, 8))\n",
- "ax = plt.axes(projection=ccrs.NorthPolarStereo())\n",
- "\n",
- "gv.set_map_boundary(ax, [-180, 180], [0, 40], south_pad=1)\n",
- "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n",
- "\n",
- "# Set draw_labels to False so that we can manually manipulate it\n",
- "gl = ax.gridlines(ccrs.PlateCarree(),\n",
- " draw_labels=False,\n",
- " linestyle=\"--\",\n",
- " linewidth=1,\n",
- " color='darkgray',\n",
- " zorder=2)\n",
- "\n",
- "# Iterate through the 19 timesteps, plotting the data\n",
- "n = 19\n",
- "for x in range(n):\n",
- "\n",
- " # Get a slice of data at the 12*x timestep\n",
- " p = ds.HGT.isel(time=12*x)\n",
- "\n",
- " # Use geocat-viz utility function to handle the no-shown-data artifact\n",
- " # of 0 and 360-degree longitudes\n",
- " slon = gv.xr_add_cyclic_longitudes(p, \"lon\")\n",
- "\n",
- " # Plot contour data at pressure level 5500 for the 12*x timestep\n",
- " p = slon.plot.contour(ax=ax,\n",
- " transform=ccrs.PlateCarree(),\n",
- " linewidths=0.5,\n",
- " levels=[5500],\n",
- " colors='blue',\n",
- " add_labels=False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Adding Directional Labels to Polar Stereographic Projection\n",
- "\n",
- "Adding labels to a map projection that aren't lat/lon coordinates is less than intuitive. In this example we manually add labels and select their locations so that you can see NESW labels.\n",
- "\n",
- "For this we use `mticker.FixedLocator()` to manipulare gridline spacing, manipulate East and West tick labels separately, and specify tick locations with `ax.text(transform=ccrs.Geodetic())`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Generate a figure\n",
- "fig = plt.figure(figsize=(8, 8))\n",
- "\n",
- "# Create an axis with a polar stereographic projection\n",
- "ax = plt.axes(projection=ccrs.NorthPolarStereo())\n",
- "\n",
- "# Add land feature to map\n",
- "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n",
- "\n",
- "# Set map boundary to include latitudes between 0 and 40 and longitudes\n",
- "# between -180 and 180 only\n",
- "gv.set_map_boundary(ax, [-180, 180], [0, 40], south_pad=1)\n",
- "\n",
- "# Set draw_labels to False so that you can manually manipulate it later\n",
- "gl = ax.gridlines(ccrs.PlateCarree(),\n",
- " draw_labels=False,\n",
- " linestyle=\"--\",\n",
- " linewidth=1,\n",
- " color='darkgray',\n",
- " zorder=2)\n",
- "\n",
- "# Manipulate latitude and longitude gridline numbers and spacing\n",
- "gl.ylocator = mticker.FixedLocator(np.arange(0, 90, 15))\n",
- "gl.xlocator = mticker.FixedLocator(np.arange(-180, 180, 30))\n",
- "\n",
- "# Manipulate longitude labels (0, 30 E, 60 E, ..., 30 W, etc.)\n",
- "ticks = np.arange(0, 210, 30)\n",
- "etick = ['0'] + [\n",
- " r'%dE' % tick for tick in ticks if (tick != 0) & (tick != 180)\n",
- "] + ['180']\n",
- "wtick = [r'%dW' % tick for tick in ticks if (tick != 0) & (tick != 180)]\n",
- "labels = etick + wtick\n",
- "xticks = np.arange(0, 360, 30)\n",
- "yticks = np.full_like(xticks, -5) # Latitude where the labels will be drawn\n",
- "for xtick, ytick, label in zip(xticks, yticks, labels):\n",
- " if label == '180':\n",
- " ax.text(xtick,\n",
- " ytick,\n",
- " label,\n",
- " fontsize=12,\n",
- " horizontalalignment='center',\n",
- " verticalalignment='top',\n",
- " transform=ccrs.Geodetic())\n",
- " elif label == '0':\n",
- " ax.text(xtick,\n",
- " ytick,\n",
- " label,\n",
- " fontsize=12,\n",
- " horizontalalignment='center',\n",
- " verticalalignment='bottom',\n",
- " transform=ccrs.Geodetic())\n",
- " else:\n",
- " ax.text(xtick,\n",
- " ytick,\n",
- " label,\n",
- " fontsize=12,\n",
- " horizontalalignment='center',\n",
- " verticalalignment='center',\n",
- " transform=ccrs.Geodetic())\n",
- "\n",
- "# Iterate through 18 different timesteps\n",
- "for x in range(19):\n",
- "\n",
- " # Get a slice of data at the 12*x+1 timestep\n",
- " p = ds.HGT.isel(time=12 * x + 1)\n",
- "\n",
- " # Use geocat-viz utility function to handle the no-shown-data artifact\n",
- " # of 0 and 360-degree longitudes\n",
- " slon = gv.xr_add_cyclic_longitudes(p, \"lon\")\n",
- "\n",
- " # Plot contour data at pressure level 5500 for the 12*x+1 timestep\n",
- " p = slon.plot.contour(ax=ax,\n",
- " transform=ccrs.PlateCarree(),\n",
- " linewidths=0.5,\n",
- " levels=[5500],\n",
- " colors='blue',\n",
- " add_labels=False)\n",
- "\n",
- "# Use geocat.viz.util convenience function to add titles\n",
- "gv.set_titles_and_labels(ax,\n",
- " maintitle=r\"$\\bf{Spaghetti}$\" + \" \" + r\"$\\bf{Plot}$\",\n",
- " lefttitle=slon.long_name,\n",
- " righttitle='5500 '+slon.units)\n",
- "\n",
- "# Make tight layout\n",
- "plt.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now in this example, there isn't necessarily a temporal progression of geopotential height, but to be sure let's add a colormap component to each of our loops. \n",
- "\n",
- "This is also useful because for your data visualization application there might be, and the commands are slightly different for a contour plot as for a line plot in the above example."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Contour Spaghetti Plot Temporal Colorbar Manipulation\n",
- "\n",
- "Let's update add a discrete colorbar that has yearly ticklabels. One challenge addressed in this example is setting the ticklabels to be in the center of each discrete color box.\n",
- "\n",
- "New code lines here are:\n",
- " - creating a discrete colormap with `plt.get_cmap('winter_r', n)` and color bounds with `np.linspace()`\n",
- " - specifying the color in each contour call with `colors=[cmap(bounds)[x]]`\n",
- " - adjusting the time unit for the colorbar ticks\n",
- " - adding a colorbar for the normalized colormap, calling the `orientation`, `shrink`, and `pad` keyword arguments to make it display well\n",
- " - setting colorbar tick location to be at color midpoints with `cbar.set_ticks()`, yet forcing their labels to be years (not year midpoints) with `cbar.set_ticklabels()`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set up Cartopy Map Projection\n",
- "fig = plt.figure(figsize=(8, 8))\n",
- "ax = plt.axes(projection=ccrs.NorthPolarStereo())\n",
- "\n",
- "gv.set_map_boundary(ax, [-180, 180], [0, 40], south_pad=1)\n",
- "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n",
- "\n",
- "# Set draw_labels to False so that we can manually manipulate it\n",
- "gl = ax.gridlines(ccrs.PlateCarree(),\n",
- " draw_labels=False,\n",
- " linestyle=\"--\",\n",
- " linewidth=1,\n",
- " color='darkgray',\n",
- " zorder=2)\n",
- "\n",
- "# Manipulate latitude and longitude gridline numbers and spacing\n",
- "gl.ylocator = mticker.FixedLocator(np.arange(0, 90, 15))\n",
- "gl.xlocator = mticker.FixedLocator(np.arange(-180, 180, 30))\n",
- "\n",
- "# Manipulate longitude labels (0, 30 E, 60 E, ..., 30 W, etc.)\n",
- "ticks = np.arange(0, 210, 30)\n",
- "etick = ['0'] + [\n",
- " r'%dE' % tick for tick in ticks if (tick != 0) & (tick != 180)\n",
- "] + ['180']\n",
- "wtick = [r'%dW' % tick for tick in ticks if (tick != 0) & (tick != 180)]\n",
- "labels = etick + wtick\n",
- "xticks = np.arange(0, 360, 30)\n",
- "yticks = np.full_like(xticks, -5) # Latitude where the labels will be drawn\n",
- "for xtick, ytick, label in zip(xticks, yticks, labels):\n",
- " if label == '180':\n",
- " ax.text(xtick,\n",
- " ytick,\n",
- " label,\n",
- " fontsize=12,\n",
- " horizontalalignment='center',\n",
- " verticalalignment='top',\n",
- " transform=ccrs.Geodetic())\n",
- " elif label == '0':\n",
- " ax.text(xtick,\n",
- " ytick,\n",
- " label,\n",
- " fontsize=12,\n",
- " horizontalalignment='center',\n",
- " verticalalignment='bottom',\n",
- " transform=ccrs.Geodetic())\n",
- " else:\n",
- " ax.text(xtick,\n",
- " ytick,\n",
- " label,\n",
- " fontsize=12,\n",
- " horizontalalignment='center',\n",
- " verticalalignment='center',\n",
- " transform=ccrs.Geodetic())\n",
- "\n",
- "# Create a color list for each of the 19 contours\n",
- "n = 19\n",
- "cmap = plt.get_cmap('winter_r', n) # the `, n` makes the colormap display discretized\n",
- "bounds = np.linspace(0, 1, n)\n",
- "\n",
- "# Iterate through the timesteps\n",
- "for x in range(n):\n",
- "\n",
- " # Get a slice of data at the 12*x timestep\n",
- " p = ds.HGT.isel(time=12*x)\n",
- "\n",
- " # Handle wrapping artifacts\n",
- " slon = gv.xr_add_cyclic_longitudes(p, \"lon\")\n",
- "\n",
- " # Plot contour data at pressure level 5500 for the 12*x timestep\n",
- " p = slon.plot.contour(ax=ax,\n",
- " transform=ccrs.PlateCarree(),\n",
- " linewidths=0.5,\n",
- " levels=[5500],\n",
- " colors=[cmap(bounds)[x]], # set colors to use our new cmap\n",
- " add_labels=False)\n",
- "\n",
- "# Add a colorbar\n",
- "# The default time unit is in months since 1958, years is more intuitive\n",
- "year_0 = 1958\n",
- "year_n = (ds.time.isel(time=12*n) / 12).astype(int) + year_0\n",
- "\n",
- "norm = plt.Normalize(vmin=year_0, vmax=year_n)\n",
- "cbar = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap, norm=norm),\n",
- " ax=ax,\n",
- " orientation='vertical',\n",
- " shrink=0.8, # Shrink to the approximate size of the map\n",
- " pad = 0.1) # Pad so colorbar doesn't overlap with directional label\n",
- "\n",
- "cbar.set_ticks(np.arange(year_0+0.5, year_n)) # Set tick locations to be at color midpoints\n",
- "cbar.set_ticklabels(np.arange(year_0, year_n)) # Set tick labels to be years (despite their location value being year + 0.5)\n",
- "cbar.set_label('Time (years)')\n",
- "\n",
- "# Use geocat.viz.util convenience function to add titles\n",
- "gv.set_titles_and_labels(ax,\n",
- " maintitle=r\"$\\bf{Spaghetti}$\" + \" \" + r\"$\\bf{Plot}$\",\n",
- " lefttitle=slon.long_name,\n",
- " righttitle='5500 '+slon.units)\n",
- "\n",
- "# Make tight layout\n",
- "plt.tight_layout();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Contour Spaghetti Plot with Hand-Picked Colors\n",
- "\n",
- "If you want your plot to be visually appealing it might be worth selecting different colors for each contour plot in the for-loop, however these do not have to be sequentially ordered or time-aware. It is actually simplest to hand-pick colors for each loop. In this iteration of the plot we hand pick colors in a `colorlist` and plot the first time step on its own to demonstrate plotting one loop unlike the others."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Generate a figure\n",
- "fig = plt.figure(figsize=(8, 8))\n",
- "\n",
- "# Create an axis with a polar stereographic projection\n",
- "ax = plt.axes(projection=ccrs.NorthPolarStereo())\n",
- "\n",
- "# Add land feature to map\n",
- "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n",
- "\n",
- "# Set map boundary to include latitudes between 0 and 40 and longitudes\n",
- "# between -180 and 180 only\n",
- "gv.set_map_boundary(ax, [-180, 180], [0, 40], south_pad=1)\n",
- "\n",
- "# Set draw_labels to False so that you can manually manipulate it later\n",
- "gl = ax.gridlines(ccrs.PlateCarree(),\n",
- " draw_labels=False,\n",
- " linestyle=\"--\",\n",
- " linewidth=1,\n",
- " color='darkgray',\n",
- " zorder=2)\n",
- "\n",
- "# Manipulate latitude and longitude gridline numbers and spacing\n",
- "gl.ylocator = mticker.FixedLocator(np.arange(0, 90, 15))\n",
- "gl.xlocator = mticker.FixedLocator(np.arange(-180, 180, 30))\n",
- "\n",
- "# Manipulate longitude labels (0, 30 E, 60 E, ..., 30 W, etc.)\n",
- "ticks = np.arange(0, 210, 30)\n",
- "etick = ['0'] + [\n",
- " r'%dE' % tick for tick in ticks if (tick != 0) & (tick != 180)\n",
- "] + ['180']\n",
- "wtick = [r'%dW' % tick for tick in ticks if (tick != 0) & (tick != 180)]\n",
- "labels = etick + wtick\n",
- "xticks = np.arange(0, 360, 30)\n",
- "yticks = np.full_like(xticks, -5) # Latitude where the labels will be drawn\n",
- "for xtick, ytick, label in zip(xticks, yticks, labels):\n",
- " if label == '180':\n",
- " ax.text(xtick,\n",
- " ytick,\n",
- " label,\n",
- " fontsize=12,\n",
- " horizontalalignment='center',\n",
- " verticalalignment='top',\n",
- " transform=ccrs.Geodetic())\n",
- " elif label == '0':\n",
- " ax.text(xtick,\n",
- " ytick,\n",
- " label,\n",
- " fontsize=12,\n",
- " horizontalalignment='center',\n",
- " verticalalignment='bottom',\n",
- " transform=ccrs.Geodetic())\n",
- " else:\n",
- " ax.text(xtick,\n",
- " ytick,\n",
- " label,\n",
- " fontsize=12,\n",
- " horizontalalignment='center',\n",
- " verticalalignment='center',\n",
- " transform=ccrs.Geodetic())\n",
- "\n",
- "# Get slice of data at the 0th timestep - plot this contour line separately\n",
- "# because it will be thicker than the other contour lines\n",
- "p = ds.HGT.isel(time=0)\n",
- "\n",
- "# Use geocat-viz utility function to handle the no-shown-data\n",
- "# artifact of 0 and 360-degree longitudes\n",
- "slon = gv.xr_add_cyclic_longitudes(p, \"lon\")\n",
- "\n",
- "# Plot contour data at pressure level 5500 at the first timestep\n",
- "p = slon.plot.contour(ax=ax,\n",
- " transform=ccrs.PlateCarree(),\n",
- " linewidths=1.5,\n",
- " levels=[5500],\n",
- " colors='black',\n",
- " add_labels=False)\n",
- "\n",
- "# Create a color list for each of the next 18 contours\n",
- "colorlist = [\n",
- " \"crimson\", \"green\", \"blue\", \"yellow\", \"cyan\", \"hotpink\", \"crimson\",\n",
- " \"skyblue\", \"navy\", \"lightyellow\", \"mediumorchid\", \"orange\", \"slateblue\",\n",
- " \"palegreen\", \"magenta\", \"springgreen\", \"pink\", \"forestgreen\", \"violet\"\n",
- "]\n",
- "\n",
- "# Iterate through 18 different timesteps\n",
- "for x in range(18):\n",
- "\n",
- " # Get a slice of data at the 12*x+1 timestep\n",
- " p = ds.HGT.isel(time=12 * x + 1)\n",
- "\n",
- " # Use geocat-viz utility function to handle the no-shown-data artifact\n",
- " # of 0 and 360-degree longitudes\n",
- " slon = gv.xr_add_cyclic_longitudes(p, \"lon\")\n",
- "\n",
- " # Plot contour data at pressure level 5500 for the 12*x+1 timestep\n",
- " p = slon.plot.contour(ax=ax,\n",
- " transform=ccrs.PlateCarree(),\n",
- " linewidths=0.5,\n",
- " levels=[5500],\n",
- " colors=colorlist[x],\n",
- " add_labels=False)\n",
- "\n",
- "# Use geocat.viz.util convenience function to add titles\n",
- "gv.set_titles_and_labels(ax,\n",
- " maintitle=r\"$\\bf{Spaghetti}$\" + \" \" + r\"$\\bf{Plot}$\",\n",
- " lefttitle=slon.long_name,\n",
- " righttitle='5500 '+slon.units)\n",
- "\n",
- "# Make tight layout\n",
- "plt.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Summary\n",
- "\n",
- "Spaghetti Plots are many lines drawn on the same figure. They have pros and cons. They are visually stunning but can be confusing, so it is important to make sure your data visualization conveys accurate information either by manipulating color or linewidth. Since the manipulation of spaghetti plots have their own considerations, this chapter shows several design choices that you can use to jumpstart your visualization needs.\n",
- "\n",
- "\n",
- "### What's next?\n",
- "\n",
- "Next up let's discuss [Animation](7-animation)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Resources and references\n",
- "\n",
- "- [Tropycal documentation](https://tropycal.github.io/tropycal/)\n",
- "- [GeoCat-examples Visualization Gallery](https://geocat-examples.readthedocs.io/en/latest/)\n",
- "- [GeoCAT-Viz documentation](https://geocat-viz.readthedocs.io/en/latest/)\n"
- ]
- }
- ],
- "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": "3.10.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/51/_sources/notebooks/7-animation.ipynb b/_preview/51/_sources/notebooks/7-animation.ipynb
deleted file mode 100644
index 24585bd..0000000
--- a/_preview/51/_sources/notebooks/7-animation.ipynb
+++ /dev/null
@@ -1,514 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Animation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Overview\n",
- "\n",
- "Animations can be a useful and effective tool to visualize data, especially when that data changes over time. In this notebook, we will explore how to create animations using the `matplotlib` library. \n",
- "\n",
- "We will cover the two methods for creating animations in `matplotlib`, how to set up the elements of both types of animation, how to show the animation in jupyter notebooks, and how to save the animation to a file.\n",
- "\n",
- "1. Animation fundamentals with `matplotlib`\n",
- "2. Artist Animation\n",
- "3. Function Animation\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Prerequisites\n",
- "\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) | Necessary | |\n",
- "| [Cartopy](https://foundations.projectpythia.org/core/cartopy.html) | Useful | Not necessary for animations in general, but useful for the examples in this notebook |\n",
- "\n",
- "- **Time to learn**: 10 minutes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "jupyter": {
- "outputs_hidden": false
- }
- },
- "outputs": [],
- "source": [
- "import cartopy.crs as ccrs\n",
- "import matplotlib.animation as animation\n",
- "import xarray as xr\n",
- "from matplotlib import pyplot as plt\n",
- "import os\n",
- "from PIL import Image\n",
- "\n",
- "import geocat.datafiles as gdf"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Animation fundamentals with `matplotlib`\n",
- "\n",
- "There are two different methods of creating animations with `matplotlib`:\n",
- "1. Artist animations pulls from a list of pre-made [artists](https://matplotlib.org/stable/api/artist_api.html#matplotlib.artist.Artist) to draw in each frame to produce an animation\n",
- "2. Function animation iteratively modifies data on a pre-existing figure to produce an animation\n",
- "\n",
- "Generally, function animation is easier to use, but artist animation can be more flexible for certain applications. We'll cover both methods in this notebook."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Animating within jupyter notebooks\n",
- "Before we get into either type of animation, we're going to alter [`matplotlib`'s runtime configuration settings](https://matplotlib.org/stable/users/explain/customizing.html) (or `rcParams`) to allow us to use `matplotlib`'s animation functions within jupyter notebooks. This next cell is not necessary if you want to create animations in a script or just want to save the animation to a file.\n",
- "\n",
- "Here, we're changing default animation html output setting to `\"jshtml\"`, which creates a JavaScript animation that can be displayed in a jupyter notebook. The default setting is `\"none\"`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "plt.rcParams[\"animation.html\"] = \"jshtml\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Artist Animation\n",
- "Artist animation uses pre-made artists to cycle through to produce an animation.\n",
- "\n",
- "In this example, we're going to plot images using `matplotlib`'s [`imshow`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html) function and save the resulting artist object to a list. Then, we'll use the [`ArtistAnimation`](https://matplotlib.org/stable/api/_as_gen/matplotlib.animation.ArtistAnimation.html) function to create an animation from that list of artists.\n",
- "\n",
- "### Data\n",
- "Before we get into those steps, let's get some images to animate. First, we'll be looking at some [GeoColor](https://www.star.nesdis.noaa.gov/GOES/documents/QuickGuide_CIRA_Geocolor_20171019.pdf) satellite imagery from [GOES-16](https://eospso.nasa.gov/missions/geostationary-operational-environmental-satellite-16).\n",
- "\n",
- "In this repository, there is a script, `notebooks/scripts/goes_getter.py`, that will download hourly images from the last 24 hours from the [GOES-16 archive](https://cdn.star.nesdis.noaa.gov/GOES16/ABI/FD/GEOCOLOR/) if you'd like to play around with this yourself. We have already downloaded some images for this example in the `notebooks/data/goes16_hr/` directory and will be using those for this part of the notebook."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "### Get the images into a list\n",
- "First, we need to ge the images from the directory into a list. We know the only files in this directory are the images we want to plot, so let's get get a list of all the files ending in `.jpg` from that path using `os.listdir()`.\n",
- "\n",
- "We'll also sort them using the built in `sorted` function to make sure that our list of images ends up in chronological order."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "im_dir = \"./data/goes16_hr/\"\n",
- "im_paths = sorted([p for p in os.listdir(im_dir) if p.endswith(\".jpg\")])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Creating the figure\n",
- "Now, we need to set up the figure we'll be plotting our animation on.\n",
- "\n",
- ":::{note}\n",
- "Running the next cell will produce a blank figure in the jupyter notebook. This is expected since we've only created a blank figure to plot on, but haven't actually plotted anything yet.\n",
- ":::"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "dpi = 100\n",
- "figsize = tuple(t / dpi for t in Image.open(im_dir + im_paths[0]).size)\n",
- "fig = plt.figure(figsize=figsize, dpi=dpi)\n",
- "ax = fig.add_axes([0, 0, 1, 1]) # span the whole figure\n",
- "ax.set_axis_off()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "First, we set out dpi to be 100. This is technically not necessary at this point in creating the animation, but we're going to use it to set the size of the figure. Since we're plotting images, we want to make sure that the figure size is the same size as the images we're plotting.\n",
- "\n",
- "Next, we're going to figure out what our figsize should be based on the dpi and the size of the images we're about to plot. `tuple(t / dpi for t in Image.open(im_dir + im_paths[0]).size)` divides each dimension of the size of the image by the intended dpi and then returns the result as a tuple. We then use that tuple to create our figsize.\n",
- "\n",
- "Next, we create the figure using `plt.figure()` and the `figsize` and `dpi` we just calculated.\n",
- "\n",
- "Then, we create the axes object using `fig.add_axes([0, 0, 1, 1])`. This adds axes to our figure that span the entire range of the figure, allowing the images plot on these axes to take up the entire figure space.\n",
- "\n",
- "Finally, we turn off the axes using `ax.axis(\"off\")`. This is because we don't want to see axes on our final image plot.\n",
- "\n",
- ":::{tip}\n",
- "Customizing the figure size, dpi, and axes is not necessary for creating an artist animation, but it will make our end result look nicer.\n",
- ":::"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Creating a list of artists\n",
- "Now that we have a list of the filepaths for all the images we want to plot and axes to plot them on, we can use `imshow` to plot each image and save the resulting artist object to a list."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ims = [[ax.imshow(Image.open(im_dir + im_path), animated=True)] for im_path in im_paths]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "A couple of things to note here:\n",
- "- You may notice that we're using list generation to create our final list, `ims`. But what might not be obvious is that we're actually making a list of lists. Each created `implot` object is put into its own one-item list, and then all of those lists are used to create `ims`. This is because `ArtistAnimation` expects a list of lists, where each inner list is a list of artists to be plotted in a single frame.\n",
- "- We're using a kwarg you may not have seen before using `imshow`: `animated=True`. This allows the artist to only be drawn when called as part of an animation. This is a kwarg that is common to all artist objects."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Creating the artist animation\n",
- "Now that we've set up our figure and list (of lists) of artists, we can create the animation using `ArtistAnimation`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ani = animation.ArtistAnimation(fig, ims, interval=150, repeat_delay=1000)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "`ArtistAnimation` takes two required arguments: \n",
- "- the figure to plot on (`fig` in our case) and \n",
- "- the list of pre-created artist objects (`ims`) to plot. \n",
- "\n",
- "We've also provided a few optional arguments:\n",
- "- `interval`: The time between frames in milliseconds. We've set this to 150 milliseconds, or 0.15 seconds.\n",
- "- `repeat_delay`: The time in milliseconds to wait before repeating the animation. We've set this to 1000 milliseconds, or 1 second."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And that's it! We've created an animation in `matplotlib` using artist animation. Let's take a look at it."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ani"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Saving the animation\n",
- "If displaying your animation in a jupyter notebook was your end goal, then you're done! But if you want to save your animation to a file for later use, you can use the `save` method of the `ArtistAnimation` object."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ani.save(\"goes16_hr.gif\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Function animation\n",
- "\n",
- "The steps for function animation in `matplotlib` are:\n",
- "1. Set up all the artists that will be used in the animation and the initial frame of the animation\n",
- "2. Create a function that updates the data in the plot to create each frame of the animation\n",
- "3. Create a `FuncAnimation` object with the the previously created elements\n",
- "4. Display and save the animation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Data\n",
- "For this example, we're going to be looking at sample surface temperature data from our sample data repository, [geocat-datafiles](https://github.com/NCAR/geocat-datafiles)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds = xr.open_dataset(gdf.get(\"netcdf_files/meccatemp.cdf\"))\n",
- "t = ds.t"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Creating the figure\n",
- "The first step in creating a function animation is to create the figure that will be used to plot the animation.\n",
- "\n",
- "Since we're working with data represented by latitude and longitude coordinates, we're going to set up our figure and axes using [`cartopy`](https://scitools.org.uk/cartopy/docs/latest/)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set up Axes with Cartopy Projection\n",
- "fig = plt.figure()\n",
- "ax = plt.axes(projection=ccrs.PlateCarree())\n",
- "ax.coastlines()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Just like before, we used `plt.figure()` to create the figure. Then, we used `plt.axes` with the `projection` kwarg set to `ccrs.PlateCarree()` to create the axes object. This sets up the axes to use the [Plate Carree](https://scitools.org.uk/cartopy/docs/latest/reference/projections.html#cartopy.crs.PlateCarree) projection.\n",
- "\n",
- "Finally, we used `ax.coastlines()` to add coastlines to the plot."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Creating the initial frame\n",
- "Now that we have our figure and axes set up, we can plot the initial frame of our animation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "vmin = t.min()\n",
- "vmax = t.max()\n",
- "levels = 30\n",
- "\n",
- "# create initial plot that we will update\n",
- "t[0, :, :].plot.contourf(\n",
- " ax=ax,\n",
- " transform=ccrs.PlateCarree(),\n",
- " vmin=vmin,\n",
- " vmax=vmax,\n",
- " levels=levels,\n",
- " cmap=\"inferno\",\n",
- " cbar_kwargs={\n",
- " \"location\": \"bottom\",\n",
- " },\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In addition to plotting the initial frame, we also set up the colorbar. In order for every frame of the animation to use the same colorbar, we need to create the colorbar before we create the animation.\n",
- "\n",
- "The `vmin` and `vmax` arguments of `plt.contourf` are set to the minimum and maximum values of the data, instead of each individual time slice. This ensures that the colorbar remains consistent across all frames of the animation. We also set a `levels` kwarg to similarly ensure that the contour levels are consistent, as well."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Creating the update function\n",
- "Now that we have our initial frame, we need to create an update function that will update the data in the plot to create each frame of the animation. This function will be passed in as an argument to `FuncAnimation`, which expects a function that takes in a single argument, `i`, which is the frame number of the animation. The argument `i` is used to index into the data to get the data for the current frame to plot."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# create function to update plot\n",
- "def animate(i):\n",
- " # Plot the new frame\n",
- " t[i, :, :].plot.contourf(\n",
- " ax=ax,\n",
- " transform=ccrs.PlateCarree(),\n",
- " vmin=vmin,\n",
- " vmax=vmax,\n",
- " levels=levels,\n",
- " cmap=\"inferno\",\n",
- " add_colorbar=False,\n",
- " )\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note here that that the only differences from our initial frame set up are the indexing on the data to plot and the `add_colorbar = False` kwarg in `plt.contourf`. This is because we've already created a colorbar in the initial frame that we want to use for all frames of the animation.\n",
- "\n",
- ":::{warning}\n",
- "You can accidentally create some funny looking animations if you forget the `add_colorbar = False` kwarg in the update function.\n",
- "\n",
- "![](\"./images/animation_colorbar_oops.gif\")
\n",
- ":::"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Creating the animation\n",
- "Now it's time to create the animation using `FuncAnimation`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ani = animation.FuncAnimation(fig, animate, frames=len(t), interval=150, repeat_delay=1000)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This function looks similar to `ArtistAnimation`, but with one main difference: instead of a list of artists to plot, `FuncAnimation` takes the update function we created as the second argument. Also, we've provided the `frames` kwarg, which is the number of times the update function will be called. We've set it to the number of time slices in the data to make sure the FuncAnimation object does not try to go outside the bounds of the data.\n",
- "\n",
- "And that's it! We've created a function animation in `matplotlib`. Let's take a look at it.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ani"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Saving the animation\n",
- "Just like with `ArtistAnimation`, we can save the animation using the `save` method of the `FuncAnimation` object."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ani.save(\"temp.gif\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Summary\n",
- "\n",
- "Creating animations in `matplotlib` might seem intimidating, but is easier when you know the options and purpose of each method. These visualizations can be a powerful tool to display and understand time-dependent data.\n",
- "\n",
- "### What's next?\n",
- "\n",
- "In the final section of this cookbook, let’s look at [interactive plotting with Holoviz](8-interactive-holoviz-mpas) tools."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Resources and references\n",
- "\n",
- "- [`matplotlib` animation documentation](https://matplotlib.org/stable/api/animation_api.html)\n",
- "- [`FuncAnimation` documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.animation.FuncAnimation.html)\n",
- "- [`ArtistAnimation` documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.animation.ArtistAnimation.html)\n",
- "- [`imshow` documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html)\n",
- "- [GeoColor](https://www.star.nesdis.noaa.gov/GOES/documents/QuickGuide_CIRA_Geocolor_20171019.pdf) fact sheet\n",
- "- [GOES-16](https://eospso.nasa.gov/missions/geostationary-operational-environmental-satellite-16) mission page\n",
- "- [GOES Image Viewer](https://www.star.nesdis.noaa.gov/GOES/index.php)"
- ]
- }
- ],
- "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": "3.11.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/51/_sources/notebooks/8-interactive-holoviz-mpas.ipynb b/_preview/51/_sources/notebooks/8-interactive-holoviz-mpas.ipynb
deleted file mode 100644
index 7459536..0000000
--- a/_preview/51/_sources/notebooks/8-interactive-holoviz-mpas.ipynb
+++ /dev/null
@@ -1,737 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "tags": []
- },
- "source": [
- "# HoloViz Visualization"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Overview\n",
- "\n",
- "The ability to dynamically render, pan, zoom, animate and perform other dynamic operations on data can provide many benefits, such as providing greater data fidelity within the same plot. [HoloViz](https://holoviz.org/) provides high-level tools (such as Holoviews, Datashader, Geoviews, etc.) to visualize even the very large datasets efficiently.\n",
- "\n",
- "This notebook explores interactively plotting using an unstructured grid dataset in the [MPAS](https://mpas-dev.github.io/) format with Holoviews, Datashader, and Geoviews."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ":::{attention}\n",
- "If you are in this notebook to actually learn about and/or visualize unstructured grids, we highly recommend checking out the [UXarray Cookbook](https://projectpythia.org/unstructured-grid-viz-cookbook/README.html) that is a comprehensive showcase of workflows & techniques for visualizing Unstructured Grids using [UXarray](https://uxarray.readthedocs.io/). UXarray is a Python package that:\n",
- "- Provides Xarray-styled functionality for working with unstructured grids built around the UGRID conventions\n",
- "- Supports not only MPAS but also several other formats such as UGRID, SCRIP, and Exodus\n",
- "- Enables significant data analysis and visualization functionality for unstructured grid research, which makes working with unstructured grids a breeze\n",
- " - e.g. UXarray internally handles majority of the utility functions and data preparation steps mentioned throughout this notebook and provides user with convenient visualization and analysis functions\n",
- ":::"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This notebook demonstrates:\n",
- "1. Use of HoloViz tools for interactive plotting\n",
- "2. Different interactivity schemes\n",
- "3. Use of the MPAS format's connectivity information to render data on the native grid (hence avoiding costly Delaunay triangulation)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The flow of the content is as follows:\n",
- "\n",
- "1. Package imports\n",
- "2. MPAS preprocessing for visualization\n",
- " - Utility functions\n",
- " - Data loading\n",
- " - Triangular mesh generation using MPAS's cell connectivity array from the primal mesh\n",
- "3. Interactive Holoviz Plots"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Prerequisites\n",
- "\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Holoviews](https://holoviews.org/) | Necessary | |\n",
- "| [Geoviews](https://geoviews.org/) | Useful | Not necessary for plotting but useful for adding features |\n",
- "| [Matplotlib](https://matplotlib.org/) | Useful | |\n",
- "| [MPAS](https://mpas-dev.github.io/) | Useful | Not necessary for interactive plotting but useful for understanding the data used |\n",
- "| [Xarray](https://xarray.pydata.org/) | Useful | |\n",
- "\n",
- "- **Time to learn**: 60 minutes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Imports"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import math as math\n",
- "\n",
- "import cartopy.crs as ccrs\n",
- "import dask.dataframe as dd\n",
- "import geocat.datafiles as gdf # Only for reading-in datasets\n",
- "import geoviews.feature as gf # Only for displaying coastlines\n",
- "import holoviews as hv\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "from holoviews import opts\n",
- "from holoviews.operation.datashader import rasterize as hds_rasterize\n",
- "from numba import jit\n",
- "from xarray import open_mfdataset\n",
- "\n",
- "n_workers = 1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## MPAS Preprocessing"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The MPAS data requires some preprocessing to get it ready for visualization such as implementation \n",
- "of a few utility functions, loading the data, and creating triangular mesh out of the data to rasterize."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Utility functions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "def unzipMesh(x, tris, t):\n",
- " \"\"\"Splits a global mesh along longitude.\n",
- "\n",
- " Examine the X coordinates of each triangle in 'tris'. Return an array of 'tris' where\n",
- " only those triangles with legs whose length is less than 't' are returned.\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " x: numpy.ndarray\n",
- " x-coordinates of each triangle in 'tris'\n",
- " tris: numpy.ndarray\n",
- " Triangle indices for each vertex in the MPAS file, in counter-clockwise order\n",
- " t: float\n",
- " Threshold value\n",
- " \"\"\"\n",
- " return tris[\n",
- " (np.abs((x[tris[:, 0]]) - (x[tris[:, 1]])) < t)\n",
- " & (np.abs((x[tris[:, 0]]) - (x[tris[:, 2]])) < t)\n",
- " ]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "def triArea(x, y, tris):\n",
- " \"\"\"Computes the signed area of a triangle.\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " x: numpy.ndarray\n",
- " x-coordinates of each triangle in 'tris'\n",
- " y: numpy.ndarray\n",
- " y-coordinates of each triangle in 'tris'\n",
- " tris: numpy.ndarray\n",
- " Triangle indices for each vertex in the MPAS file\n",
- " \"\"\"\n",
- " return ((x[tris[:, 1]] - x[tris[:, 0]]) * (y[tris[:, 2]] - y[tris[:, 0]])) - (\n",
- " (x[tris[:, 2]] - x[tris[:, 0]]) * (y[tris[:, 1]] - y[tris[:, 0]])\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "def orderCCW(x, y, tris):\n",
- " \"\"\"Reorder triangles as necessary so they all have counter clockwise winding order.\n",
- " CCW is what Datashader and MPL require.\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " x: numpy.ndarray\n",
- " x-coordinates of each triangle in 'tris'\n",
- " y: numpy.ndarray\n",
- " y-coordinates of each triangle in 'tris'\n",
- " tris: numpy.ndarray\n",
- " Triangle indices for each vertex in the MPAS file\n",
- " \"\"\"\n",
- " tris[triArea(x, y, tris) < 0.0, :] = tris[triArea(x, y, tris) < 0.0, ::-1]\n",
- " return tris"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "def createHVTriMesh(x, y, triangle_indices, var, n_workers=1):\n",
- " \"\"\"Create a Holoviews Triangle Mesh suitable for rendering with Datashader\n",
- "\n",
- " This function returns a Holoviews TriMesh that is created from a list of coordinates, 'x'\n",
- " and 'y', an array of triangle indices that addresses the coordinates in 'x' and 'y', and\n",
- " a data variable 'var'. The data variable's values will annotate the triangle vertices\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " x: numpy.ndarray\n",
- " Projected x-coordinates of each triangle in 'tris'\n",
- " y: numpy.ndarray\n",
- " Projected y-coordinates of each triangle in 'tris'\n",
- " triangle_indices: numpy.ndarray\n",
- " Triangle indices for each vertex in the MPAS file, in counter-clockwise order\n",
- " var: numpy.ndarray\n",
- " Data variable from which the triangle vertex values are read.\n",
- " n_workers: int\n",
- " Number of workers, for Dask\n",
- " \"\"\"\n",
- " # Declare verts array\n",
- " verts = np.column_stack([x, y, var])\n",
- "\n",
- " # Convert to pandas\n",
- " verts_df = pd.DataFrame(verts, columns=[\"x\", \"y\", \"z\"])\n",
- " tris_df = pd.DataFrame(triangle_indices, columns=[\"v0\", \"v1\", \"v2\"])\n",
- "\n",
- " # Convert to dask\n",
- " verts_ddf = dd.from_pandas(verts_df, npartitions=n_workers)\n",
- " tris_ddf = dd.from_pandas(tris_df, npartitions=n_workers)\n",
- "\n",
- " # Declare HoloViews element\n",
- " tri_nodes = hv.Nodes(verts_ddf, [\"x\", \"y\", \"index\"], [\"z\"])\n",
- " trimesh = hv.TriMesh((tris_ddf, tri_nodes))\n",
- " return trimesh"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "@jit(nopython=True)\n",
- "def triangulatePoly(verticesOnCell, nEdgesOnCell):\n",
- " \"\"\"Triangulate MPAS dual mesh:\n",
- "\n",
- " Triangulate each polygon in a heterogenous mesh of n-gons by connecting\n",
- " each internal polygon vertex to the first vertex. Uses the MPAS\n",
- " auxilliary variables verticesOnCell, and nEdgesOnCell.\n",
- "\n",
- " The function is decorated with Numba's just-in-time compiler so that it is translated into\n",
- " optimized machine code for better peformance\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " verticesOnCell: numpy.ndarray\n",
- " Connectivity array that stores the vertex indices that surround a given cell\n",
- " nEdgesOnCell: numpy.ndarray\n",
- " Number of edges on a given cell.\n",
- " \"\"\"\n",
- "\n",
- " # Calculate the number of triangles. nEdgesOnCell gives the number of vertices for each cell (polygon)\n",
- " # The number of triangles per polygon is the number of vertices minus 2.\n",
- " nTriangles = np.sum(nEdgesOnCell - 2)\n",
- "\n",
- " triangles = np.ones((nTriangles, 3), dtype=np.int64)\n",
- " nCells = verticesOnCell.shape[0]\n",
- " triIndex = 0\n",
- " for j in range(nCells):\n",
- " for i in range(nEdgesOnCell[j] - 2):\n",
- " triangles[triIndex][0] = verticesOnCell[j][0]\n",
- " triangles[triIndex][1] = verticesOnCell[j][i + 1]\n",
- " triangles[triIndex][2] = verticesOnCell[j][i + 2]\n",
- " triIndex += 1\n",
- "\n",
- " return triangles"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Load data and coordinates"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ":::{info}\n",
- "The \"dyamond_1\" global datasets used in this example are courtesy of NCAR's Falko Judt and were produced as part of the \n",
- "[DYAMOND](http://dx.doi.org/10.1186/s40645-019-0304-z) initiative. They are all from the same experiment but run at several \n",
- "resolutions from 30km to 3.75km.\n",
- ":::"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Currently, the 30-km resolution dataset is used in this example and is available at [geocat-datafiles](https://github.com/NCAR/geocat-datafiles).\n",
- "However, the other resolutions of these data are stored on NCAR's Glade data storage because of their size. Due to their size, \n",
- "the higher resolution data sets are only distributed with two variables in them:\n",
- "\n",
- "+ relhum_200hPa: Relative humidity vertically interpolated to 200 hPa\n",
- "+ vorticity_200hPa: Relative vorticity vertically interpolated to 200 hPa\n",
- "\n",
- "The \"relhum_200hPa\" variable is computed on the MPAS 'primal' mesh, while \"vorticity_200hPa\" is computed on the MPAS\n",
- "'dual' mesh."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Load data\n",
- "datafiles = (\n",
- " gdf.get(\n",
- " \"netcdf_files/MPAS/FalkoJudt/dyamond_1/30km/diag.2016-08-20_00.00.00_subset.nc\"\n",
- " ),\n",
- " gdf.get(\"netcdf_files/MPAS/FalkoJudt/dyamond_1/30km/x1.655362.grid_subset.nc\"),\n",
- ")\n",
- "\n",
- "primalVarName = \"relhum_200hPa\"\n",
- "dualVarName = \"vorticity_200hPa\"\n",
- "central_longitude = 0.0\n",
- "\n",
- "ds = open_mfdataset(datafiles, decode_times=False)\n",
- "primalVar = ds[primalVarName].isel(Time=0).values\n",
- "dualVar = ds[dualVarName].isel(Time=0).values\n",
- "\n",
- "# Fetch lat and lon coordinates for the primal and dual mesh.\n",
- "lonCell = ds[\"lonCell\"].values * 180.0 / math.pi\n",
- "latCell = ds[\"latCell\"].values * 180.0 / math.pi\n",
- "lonCell = ((lonCell - 180.0) % 360.0) - 180.0\n",
- "\n",
- "lonVertex = ds[\"lonVertex\"].values * 180.0 / math.pi\n",
- "latVertex = ds[\"latVertex\"].values * 180.0 / math.pi\n",
- "lonVertex = ((lonVertex - 180.0) % 360.0) - 180.0"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Generate triangular mesh using MPAS connectivity information"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "tags": []
- },
- "source": [
- "#### Primal mesh\n",
- "\n",
- "In this example, we use the MPAS `cellsOnVertex` auxilliary variable, which defines mesh connectivity for the MPAS grid. Specifically, this variable tells us the cell indices contained by each cell."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "tags": []
- },
- "source": [
- ":::{important}\n",
- "The benefits of this are twofold: \n",
- "1. We're using the actual mesh description from the MPAS output file\n",
- "2. For large grid this is *much* faster than synthesizing the connectivity information (e.g. by triangulating them with, for example, Delaunay triangulation).\n",
- ":::"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "So, first let's: \n",
- "- Get the triangle indices (i.e. MPAS connectivity),\n",
- "- Make sure it is all in counter-clockwise order,\n",
- "- \"Unzip\" the mesh along the longitude (for now, assuming the central longitude from the map projection is 0.0, i.e. cutting the mesh where longitude wraps around from -180.0 to 180.0)\n",
- "- Project vertices from geographic to PCS coordinates\n",
- "- Create Holoviews TriMesh"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ":::{note}\n",
- "Indexing in MPAS starts from 1, not zero :-(\n",
- ":::"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "tris = ds.cellsOnVertex.values - 1\n",
- "\n",
- "tris = orderCCW(lonCell, latCell, tris)\n",
- "\n",
- "tris = unzipMesh(lonCell, tris, 90.0)\n",
- "\n",
- "projection = ccrs.Robinson(central_longitude=central_longitude)\n",
- "xPCS, yPCS, _ = projection.transform_points(ccrs.PlateCarree(), lonCell, latCell).T\n",
- "\n",
- "trimesh = createHVTriMesh(xPCS, yPCS, tris, primalVar, n_workers=n_workers)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Dual mesh\n",
- "\n",
- "In this example though, we use the MPAS `verticesOnCell` and `nEdgesOnCell` auxilliary variables, which defines mesh connectivity for the MPAS dual grid. A lot of details in the following code is similarv to those in the primal mesh's case except `triangulatePoly()` wheere we decompose each cell into triangles since for the dual mesh, the data are located on triangle centers, which correspond to cell (polygon) vertices."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "verticesOnCell = ds.verticesOnCell.values - 1\n",
- "nEdgesOnCell = ds.nEdgesOnCell.values\n",
- "\n",
- "tris_dual = triangulatePoly(verticesOnCell, nEdgesOnCell)\n",
- "\n",
- "tris_dual = unzipMesh(lonVertex, tris_dual, 90.0)\n",
- "\n",
- "projection = ccrs.Robinson(central_longitude=central_longitude)\n",
- "xPCS_dual, yPCS_dual, _ = projection.transform_points(\n",
- " ccrs.PlateCarree(), lonVertex, latVertex\n",
- ").T\n",
- "\n",
- "trimesh_dual = createHVTriMesh(\n",
- " xPCS_dual, yPCS_dual, tris_dual, dualVar, n_workers=n_workers\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Interactive HoloViz Plots"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ":::{note} \n",
- "Since the emphasis of these plots is the interactivity, we will not provide much detail on the features/arguments other than interactivity-related ones, but such details to some extent can still be found in the code comments.\n",
- ":::"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "HoloViz tools provide essential interactivity features such as panning, zooming, \n",
- "hovering, clickable/selectable legends, etc. on a plot."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ":::{note} \n",
- "The first step to achieve interactivity with HoloViz tools is to choose `bokeh` as \n",
- "the backend: \n",
- ":::"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "hv.extension(\"bokeh\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's define keyword arguments that will be commonly used for all of the plots and their `opts` throughout. For that, the plotting arguments are `aggregator=mean` to use the average of node values for shading in the rasterization and `precompute=True` to cache the data internally; the `opts` arguments are nothing more than opting in to showing the colorbar and using a colormap called \"coolwarm\":"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "plot_kwargs = {\n",
- " \"aggregator\": \"mean\",\n",
- " \"precompute\": True,\n",
- "}\n",
- "\n",
- "opts_kwargs = {\n",
- " \"colorbar\": True,\n",
- " \"cmap\": \"coolwarm\",\n",
- "}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Holoviews' options system: `opts`\n",
- "\n",
- "HoloViz packages provide high-level visualization functions that allow the customization of plot features through optional arguments. Furthermore, the HoloViews options system allows customizing a plot from title to size, legend, axes, and more. This system works by calling the `.opts()` method through a HoloViews plot object. We will set the `opts` defaults below and then customize plots throughout the notebook via `.opts()`. Keep an eye on them!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "opts.defaults(\n",
- " opts.Image(frame_width=600, data_aspect=1),\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Pan & zoom\n",
- "\n",
- "Bokeh-rendered HoloViz plots come with default pan and zoom (box & wheel), tools as well as save and reset options in the very right. Just look at the following plot for those features, which uses Datashader's rasterization method to visualize the data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "rasterized = hds_rasterize(trimesh, **plot_kwargs)\n",
- "rasterized.opts(**opts_kwargs) * gf.coastline(projection=projection)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Hover\n",
- "\n",
- "Use `tools=['hover']` can be used to get the plot to display data values while hovering over the plot (there are other tools that can be selected, too, but those will not be covered in this notebook). See the below plot (let's use the dual mesh data this time just for the sake of assortment of visualizations) and hover you cursor over it:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "rasterized = hds_rasterize(trimesh_dual, **plot_kwargs)\n",
- "rasterized.opts(tools=[\"hover\"], clim=(-4e-4, 4e-4), **opts_kwargs) * gf.coastline(\n",
- " projection=projection\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Dynamic plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can decide whether to return a dynamic plot that sends updates on widget and zoom/pan \n",
- "events or whether all the data should be embedded by using the boolean `dynamic` argument. \n",
- "The default value of this argument is `True`, so all the previously rendered plots were \n",
- "already dynamic. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ":::{warning}\n",
- "If you are looking at this through the Pythia Cookbook gallery webpage (i.e. this is a rendered notebook as html), you cannot experience how the \"dynamic\" option works. To do that, you have to execute the notebook live (e.g. through the Binder link we provide for the cookbook or by executing it in your local).\n",
- ":::"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now let's look at a non-dynamic plot (i.e. `dynamic=False`):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "rasterized = hds_rasterize(trimesh, **plot_kwargs, dynamic=False)\n",
- "rasterized.opts(**opts_kwargs) * gf.coastline(projection=projection)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ":::{tip}\n",
- "This plot does not look immediately different than the prior ones where `dynamic=True`, right? Yes, in the global view, but try zooming into the plot (again, if you are executing notebook rather than looking at the rendered one), and see how dynamic vs. non-dynamic plots look like. See the below comparison for instance:\n",
- ":::"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Non-Dynamic-Zoomed | Dynamic-Zoomed\n",
- ":-------------------------:|:-------------------------:\n",
- " | "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Further interactivity considerations\n",
- "\n",
- "Even though we will not cover in the scope of this cookbook, you may want to have a look at the following HoloViz resources for further interactivity in your plots:\n",
- "\n",
- "- [Mandelbrot](https://holoviews.org/gallery/apps/bokeh/mandelbrot.html)\n",
- "- [Player](https://holoviews.org/reference/apps/bokeh/player.html)\n",
- "- [HoloMap](https://holoviews.org/reference/containers/bokeh/HoloMap.html)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Summary\n",
- "\n",
- "HoloViz technologies can be used in order to create interactive plots when Bokeh is used as the backend. Even though a really large dataset (e.g. km-scale) is not \n",
- "showcased in this notebook, HoloViz packages are reasonably performant with visualization of such data, too.\n",
- "\n",
- "### What's next?\n",
- "\n",
- "The end of this notebook wraps up this cookbook as well. Thanks for reading!\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Resources and references\n",
- "\n",
- "- [MPAS Mesh Specification](https://mpas-dev.github.io/files/documents/MPAS-MeshSpec.pdf) for those interested"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
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- "name": "ipython",
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- },
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- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.6"
- },
- "widgets": {
- "application/vnd.jupyter.widget-state+json": {
- "state": {},
- "version_major": 2,
- "version_minor": 0
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/51/_sources/notebooks/how-to-cite.md b/_preview/51/_sources/notebooks/how-to-cite.md
deleted file mode 100644
index e2ca57e..0000000
--- a/_preview/51/_sources/notebooks/how-to-cite.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# How to Cite This Cookbook
-
-The material in this Project Pythia Cookbook is licensed for free and open consumption and reuse. All code is served under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), while all non-code content is licensed under [Creative Commons BY 4.0 (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). Effectively, this means you are free to share and adapt this material so long as you give appropriate credit to the Cookbook authors and the Project Pythia community.
-
-The source code for the book is [released on GitHub](https://github.com/ProjectPythia/advanced-viz-cookbook) and archived on Zenodo. This DOI will always resolve to the latest release of the book source:
-
-[](https://zenodo.org/badge/latestdoi/671205314)
diff --git a/_preview/51/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_preview/51/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css
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diff --git a/_preview/51/_sphinx_design_static/design-tabs.js b/_preview/51/_sphinx_design_static/design-tabs.js
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index 36b38cf..0000000
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diff --git a/_preview/51/_static/__init__.py b/_preview/51/_static/__init__.py
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diff --git a/_preview/51/_static/basic.css b/_preview/51/_static/basic.css
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diff --git a/_preview/51/_static/check-solid.svg b/_preview/51/_static/check-solid.svg
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diff --git a/_preview/51/_static/clipboard.min.js b/_preview/51/_static/clipboard.min.js
deleted file mode 100644
index 54b3c46..0000000
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