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16 changes: 8 additions & 8 deletions README.md
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Expand Up @@ -10,9 +10,9 @@ This Project Pythia Cookbook covers advanced visualization techniques building u

## 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 you Python visualization toolbox.
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 image.
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

Expand All @@ -34,27 +34,27 @@ Here we introduce the basics of geoscience visualization, the elements of a plot

### 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.
There are some plot types that are unique to atmospheric science such as Taylor Diagrams or Skew-T plots. Here we will use [`metpy`](https://unidata.github.io/MetPy/latest/index.html) and [`geocat-viz`](https://geocat-viz.readthedocs.io/en/latest/) 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. Here we will have workflows that utilize packages such as `cartopy` and `geocat-viz`.
In this section we will demonstrate how to visualize data that is on a structured grid. Here we will have workflows that utilize packages such as [`cartopy`](https://scitools.org.uk/cartopy/docs/latest/) and [`geocat-viz`](https://geocat-viz.readthedocs.io/en/latest/).

### Visualization of Unstructured Grids

There are lots of compelling reasons to use unstructured data. In this section we will go over these points and demonstrate how to visualizate unstructured grids using `uxarray`.
There are lots of compelling reasons to use unstructured data. In this section we will go over these points and demonstrate how to visualizate unstructured grids using [`uxarray`](https://uxarray.readthedocs.io/en/latest/).

### Interactive Visualization

When on the cloud, some plots allow users to iteract with them by toggling certain constants or changing the viewing angle. Here we use `datashader` to iteract with some plots.
Some plots allow users to iteract with them by toggling certain constants or changing the viewing angle. Here we use [`datashader`](ttps://datashader.org/) to iteract with some plots.

### 3D Visualization

A lot of geoscience data is 3-dimensional. Here we discuss tools such as `vapor` that are designed for 3d data visualization.
A lot of geoscience data is 3-dimensional. Here we discuss tools such as [`vapor`](https://www.vapor.ucar.edu/) that are designed for multidimensional data visualization.

### Animation

Animated plots are great tools for science communication and outreach. We will demonstrate how to make your plots come to life.
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.

## Running the Notebooks

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Expand Up @@ -62,7 +62,7 @@
"\n",
"<img src=\"images/logos/matplotlib.svg\" width=250 alt=\"Matplotlib Logo\"></img>\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 plottig in Python without first getting comfortable with Matplotlib. Be sure to check out their [documentation](https://matplotlib.org/) as well as the [Pythia foundations chapter on Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) for guidance.\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",
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"\n",
"<img src=\"images/logos/cartopy.jpeg\" width=250 alt=\"Cartopy Logo\"></img>\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 their [documentation](https://scitools.org.uk/cartopy/docs/latest/) and the [Pythia foundations Cartopy chapter](https://foundations.projectpythia.org/core/cartopy.html).\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",
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"\n",
"<img src=\"images/logos/geocat.png\" width=250 alt=\"GeoCAT Logo\"></img>\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](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."
"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."
]
},
{
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"\n",
"<img src=\"images/logos/metpy.png\" width=250 alt=\"Metpy Logo\"></img>\n",
"\n",
"Metpy is a collection of tools for data reading, analysis, and visualization with weather data. Matploblib 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",
"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):"
]
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Vapor\n",
"## VAPOR\n",
"\n",
"<img src=\"images/logos/vapor.png\" width=250 alt=\"Vapor Logo\"></img>\n",
"<img src=\"images/logos/vapor.png\" width=250 alt=\"VAPOR Logo\"></img>\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."
"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). VAPORrequires a GPU-enabled environment to run."
]
},
{
Expand All @@ -209,7 +209,7 @@
"\n",
"<img src=\"images/logos/plotly.png\" width=250 alt=\"Plotly Logo\"></img>\n",
"\n",
"Plotly is another choice for interacting plotting. Plotly has functionality in several languags. Here is the [Plotly Python documentation](https://plotly.com/python/).\n",
"Plotly is another 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:"
]
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"\n",
"<img src=\"images/logos/seaborn.svg\" width=250 alt=\"Seaborn Logo\"></img>\n",
"\n",
"Seaborn is another interactive interface for plotting built on top of matplotlib. Seaborn's specialty is with statistical graphhics. Check out the [Seaborn documentation](https://seaborn.pydata.org/). Seaborn requires a GPU-enabled environment to run.\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):"
]
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"\n",
"<img src=\"images/logos/hvplot.svg\" width=250 alt=\"Datashader Logo\"></img>\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/)\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):"
]
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