Add notebook example

docs/notebooks/141_image_array_viz.ipynb

@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://githubtocolab.com/gee-community/geemap/blob/master/examples/notebooks/141_image_array_viz.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/></a>\n",
+    "\n",
+    "**Visualizing in-memory raster datasets and image arrays**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install -U geemap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ee\n",
+    "import geemap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use a Landsat image covering San Francisco Bay Area."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = geemap.Map()\n",
+    "image = ee.Image(\"LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318\").select(\n",
+    "    ['B5', 'B4', 'B3']\n",
+    ")\n",
+    "vis_params = {\n",
+    "    'min': 0.0,\n",
+    "    'max': 0.4,\n",
+    "}\n",
+    "m.add_layer(image, vis_params, 'False Color (543)')\n",
+    "m.centerObject(image)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Retrieve the projection information from the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "projection = image.select(0).projection().getInfo()\n",
+    "crs = projection['crs']\n",
+    "crs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read the Earth Engine image into an Xarray DataArray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = geemap.ee_to_xarray(\n",
+    "    image, \n",
+    "    crs=\"EPSG:32610\", \n",
+    "    scale=300,\n",
+    "    geometry=image.geometry(),\n",
+    "    ee_mask_value=0\n",
+    ")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the DataArray as a Cloud Optimized GeoTIFF."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geemap.xee_to_image(ds, filenames=['landsat.tif'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the NDVI and save it to the DataArray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ndvi = (ds['B5'] - ds['B4']) / (ds['B5'] + ds['B4'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create an in-memory raster dataset from the DataArray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ndvi_image = geemap.array_to_image(ndvi, source=\"landsat.tif\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualize the in-memory raster dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m.add_raster('landsat.tif', band=[1, 2, 3], nodata=-1, layer_name=\"Landsat 7\")\n",
+    "m.add_raster(ndvi_image, cmap=\"Greens\", layer_name=\"NDVI\")\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the in-memory raster dataset to a GeoTIFF file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geemap.array_to_image(ndvi, output=\"ndvi.tif\", source=\"landsat.tif\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/tutorials.md

@@ -152,3 +152,4 @@ More video tutorials for geemap and Earth Engine are available on my [YouTube ch
 138. Clipping Earth Engine images interactively with the Draw Control ([notebook](https://geemap.org/notebooks/138_draw_control))
 139. Converting an Earth Engine to an image ([notebook](https://geemap.org/notebooks/139_layer_to_image))
 140. Converting Earth Engine images to an Xarray Dataset ([notebook](https://geemap.org/notebooks/140_ee_to_xarray))
+141. Visualizing in-memory raster datasets and image arrays ([notebook](https://geemap.org/notebooks/141_image_array_viz))

examples/README.md

@@ -157,6 +157,7 @@ More video tutorials for geemap and Earth Engine are available on my [YouTube ch
 138. Clipping Earth Engine images interactively with the Draw Control ([notebook](https://geemap.org/notebooks/138_draw_control))
 139. Converting an Earth Engine to an image ([notebook](https://geemap.org/notebooks/139_layer_to_image))
 140. Converting Earth Engine images to an Xarray Dataset ([notebook](https://geemap.org/notebooks/140_ee_to_xarray))
+141. Visualizing in-memory raster datasets and image arrays ([notebook](https://geemap.org/notebooks/141_image_array_viz))
 
 ### 1. Introducing the geemap Python package for interactive mapping with Google Earth Engine