{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Colormap Reference\n", "\n", "FanInSAR provides four well-known colormap categories, including:\n", "\n", "- **SCM**: Scientific Colour Maps (SCM) is a set of perceptually uniform and color-vision deficiency friendly palettes designed for scientific visualization. These color maps ensure that visual errors are minimized, preventing distortion of underlying data and misleading the reader. (See [here](https://www.fabiocrameri.ch/colourmaps.php) for more information.)\n", "- **GMT**: Colormaps from the Generic Mapping Tools (GMT) software package. (See [here](https://docs.generic-mapping-tools.org/6.3/cookbook/cpts.html) for more information.)\n", "- **cmocean**: cmocean contains colormaps for commonly-used oceanographic variables. Most of the colormaps started from matplotlib colormaps, but have now been adjusted using the viscm tool to be perceptually uniform.(See [here](https://matplotlib.org/cmocean/) for more information.)\n", "- **colorcet**: A collection of perceptually accurate 256-color colormaps for use with Python plotting programs like Bokeh, Matplotlib, HoloViews, and Datashader. (See [here](https://colorcet.holoviz.org/) for more information.)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Usage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In FanInSAR, the colormap can be accessed by attributes of ``faninsar.cmaps`` module. For example, to use the ``earth`` colormap of ``GMT``, you can use the following code:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/html": [ "