Add support for raster custom colormap

docs/notebooks/103_raster_colormap.ipynb

@@ -0,0 +1,206 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![image](https://jupyterlite.rtfd.io/en/latest/_static/badge.svg)](https://demo.leafmap.org/lab/index.html?path=notebooks/103_raster_colormap.ipynb)\n",
+    "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/leafmap/blob/master/docs/notebooks/103_raster_colormap.ipynb)\n",
+    "[![image](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/opengeos/leafmap/HEAD)\n",
+    "\n",
+    "**Applying a custom colormap to a raster dataset**\n",
+    "\n",
+    "Uncomment the following line to install the `leafmap` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U \"leafmap[raster]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import leafmap\n",
+    "import rioxarray as rxr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Download a sample dataset from GitHub. This dataset is a GeoTIFF file containing the surface water extent in Las Vegas. This dataset is a [NASA OPERA DSWx](https://www.jpl.nasa.gov/go/opera/products/dswx-product-suite/) product. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://github.com/opengeos/datasets/releases/download/raster/OPERA_L3_DSWx_WTR.tif\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filepath = leafmap.download_file(url, quiet=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the dataset as an xarray DataArray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da = rxr.open_rasterio(filepath)\n",
+    "# da"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The original raster file contains a colormap. We can get the colormap from the raster file as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colormap = leafmap.get_image_colormap(filepath)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, we can define a custom colormap as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colormap = {\n",
+    "    0: (255, 255, 255),\n",
+    "    1: (0, 0, 255),\n",
+    "    2: (180, 213, 244),\n",
+    "    252: (0, 255, 255),\n",
+    "    253: (175, 175, 175),\n",
+    "    254: (0, 0, 127),\n",
+    "    255: (0, 0, 0),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can apply any data processing types to the xarray DataArray. After that, convert the xarray DataArray to an image in the memory and apply the custom colormap."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image = leafmap.array_to_image(da, colormap=colormap)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a legend dictionary to display the legend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legend_dict = {\n",
+    "    \"0: Not water\": (255, 255, 255),\n",
+    "    \"1: Open water\": (0, 0, 255),\n",
+    "    \"2: Partial surface water\": (180, 213, 244),\n",
+    "    \"252: Snow/ice\": (0, 255, 255),\n",
+    "    \"253: Cloud/cloud shadow\": (175, 175, 175),\n",
+    "    \"254: Ocean masked\": (0, 0, 127),\n",
+    "    \"255: Fill value (no data)\": (0, 0, 0),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualize the raster dataset with the custom colormap and the legend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = leafmap.Map()\n",
+    "m.add_basemap(\"HYBRID\")\n",
+    "m.add_raster(image, layer_name=\"Water\", nodata=255)\n",
+    "m.add_legend(legend_dict=legend_dict)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image](https://github.com/user-attachments/assets/495c6e91-a722-4618-a2f7-3bbca64adca9)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "geo",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/tutorials.md

@@ -113,6 +113,8 @@
 99. Retrieving wetland boundaries from the National Wetlands Inventory (NWI) ([notebook](https://leafmap.org/notebooks/99_wetlands))
 100. Visualizing the National Land Cover Database (NLCD) data products with Leafmap ([notebook](https://leafmap.org/notebooks/100_nlcd))
 101. Searching and Visualizing NASA OPERA Data Products Interactively ([notebook](https://leafmap.org/notebooks/101_nasa_opera))
+102. Mapping Overture Buildings and Foursquare Places with Leafmap + Fused ([notebook](https://leafmap.org/notebooks/102_fused))
+103. Applying a custom colormap to a raster dataset ([notebook](https://leafmap.org/notebooks/103_raster_colormap))
 
 ## Demo
 

examples/README.md

@@ -120,6 +120,8 @@
 99. Retrieving wetland boundaries from the National Wetlands Inventory (NWI) ([notebook](https://leafmap.org/notebooks/99_wetlands))
 100. Visualizing the National Land Cover Database (NLCD) data products with Leafmap ([notebook](https://leafmap.org/notebooks/100_nlcd))
 101. Searching and Visualizing NASA OPERA Data Products Interactively ([notebook](https://leafmap.org/notebooks/101_nasa_opera))
+102. Mapping Overture Buildings and Foursquare Places with Leafmap + Fused ([notebook](https://leafmap.org/notebooks/102_fused))
+103. Applying a custom colormap to a raster dataset ([notebook](https://leafmap.org/notebooks/103_raster_colormap))
 
 ## Demo