EarthPy is a python package that makes it easier to plot and work with spatial raster and vector data using open source tools. Earthpy depends upon geopandas which has a focus on vector data and rasterio with facilitates input and output of raster data files.

earthpy.plot. plot_rgb (arr, rgb = (0, 1, 2), figsize = (10, 10), str_clip = 2, ax = None, extent = None, title = '', stretch = None) [source] Plot three bands in a numpy array as a composite RGB image. Parameters. arr (numpy array) – An n-dimensional array in rasterio band order (bands, rows, columns) containing the layers to plot.

Learn how to use the EarthPy plot_bands() function to quickly plot raster bands for an image. Plot_bands() can be used to both plot many bands with one command with custom titles and legends OR to plot a single raster layer with (or without) a legend.

The examples below will show you how to use the plot_rgb() function from EarthPy to plot different band combinations and create composite images from satellite imagery such as Landsat.

Plot Combinations of Raster Bands Using EarthPy Stack and Crop Raster Data Using EarthPy Download all examples in Python source code: gallery_vignettes_python.zip

Hillshades are often used as an underlay in a map, to make the data appear more 3-Dimensional and thus visually interesting. This vignette will show you how to create a hillshade from a DEM using EarthPy.

EarthPy is a python package devoted to working with spatial and remote sensing data. EarthPy also contains an IO module that supports downloading data for the Earth Lab earth analytics courses and any user with a url and a zip file.

Learn how to calculate and classify normalized difference indices in Python using EarthPy. This example shows how to calculate and classify the normalized difference vegetation index (NDVI) using Landsat 8 data.

Examples-----.. plot:: >>> import matplotlib.pyplot as plt >>> import rasterio as rio >>> import earthpy.plot as ep >>> from earthpy.io import path_to_example >>> with rio.open(path_to_example('rmnp-dem.tif')) as src:...

EarthPy has a stack() function that allows you to take a set of .tif files that are all in the same spatial extent, CRS and resolution and either export them together a single stacked .tif file or work with them in Python directly as a stacked numpy array.