How to use the earthpy.data function in earthpy

import os
import matplotlib.pyplot as plt
import geopandas as gpd
import earthpy as et
import earthpy.clip as ec

###############################################################################
# Import Example Data
# -------------------
#
# Once the packages have been imported, download the data needed for this
# example: one line shapefile containing all major roads in North America,
# and one polygon shapefile containing the United States boundary.

data = et.data.get_data("spatial-vector-lidar")

###############################################################################
# Open Files with GeoPandas and Reproject the Data
# -------------------------------------------------
#
# Start by setting your working directory. Then, import the data files to
# GeoDataFrames using GeoPandas.
#
# Recall that the data must be in the same CRS in order to use the
# ``clip_shp()`` function. If the data are not in the same CRS, be sure to use
# the ``to_crs()`` function from GeoPandas to match the projects between the
# two objects, as shown below.

# Set your home environment
os.chdir(os.path.join(et.io.HOME, "earth-analytics"))

# Import Packages
# ----------------
#
# You will need several packages to get started. You will use Rasterio to
# open up the DEM file needed to create the hillshade layer.

import os
import numpy as np
import matplotlib.pyplot as plt
import earthpy as et
import earthpy.spatial as es
import earthpy.plot as ep
import rasterio as rio

# Download the data needed for this vignette
data = et.data.get_data("vignette-elevation")

####################################################################################
# Open up the DEM
# ---------------
# To begin, open your DEM layer as a numpy array using Rasterio. Below you set all
# terrain values < 0 to ``nan``. Next, plot the data using ``ep.plot_bands()``.

# Set the home directory and get the data for the exercise
os.chdir(os.path.join(et.io.HOME, "earth-analytics"))
dtm = "data/vignette-elevation/pre_DTM.tif"

# Open the DEM with Rasterio
with rio.open(dtm) as src:
    elevation = src.read(1)
    # Set masked values to np.nan
    elevation[elevation < 0] = np.nan

# ---------------
#
# To begin, import the needed packages. You will use a combination of several EarthPy
# modules including spatial and plot.

import os
from glob import glob
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import earthpy as et
import earthpy.spatial as es
import earthpy.plot as ep

# Get data and set your home working directory
data = et.data.get_data("cold-springs-fire")


###############################################################################
# Import Example Data
# -------------------
#
# To get started, make sure your directory is set. Then, create a stack from all of
# the Landsat .tif files (one per band).

os.chdir(os.path.join(et.io.HOME, "earth-analytics"))

# Stack the Landsat 8 bands
# This creates a numpy array with each "layer" representing a single band
landsat_path = glob(
    "data/cold-springs-fire/landsat_collect/LC080340322016072301T1-SC20180214145802/crop/*band*.tif"
)

import os
from glob import glob
import matplotlib.pyplot as plt
import earthpy as et
import earthpy.spatial as es
import earthpy.plot as ep

###############################################################################
# Import Example Data
# -------------------
#
# To get started, make sure your directory is set. Then, create a stack from all
# of the Landsat .tif files (one per band).

# Get data for example
data = et.data.get_data("vignette-landsat")

# Set working directory
os.chdir(os.path.join(et.io.HOME, "earth-analytics"))

# Stack the Landsat 8 bands
# This creates a numpy array with each "layer" representing a single band
# You can use the nodata parameter to mask nodata values
landsat_path = glob(os.path.join("data", "vignette-landsat", "*band*.tif"))
landsat_path.sort()
array_stack, meta_data = es.stack(landsat_path, nodata=-9999)

###############################################################################
# Plot All Histograms in a Stack With Custom Titles and Colors
# -------------------------------------------------------------
#
# You can create histograms for each band with unique colors and titles by

# the bands of a satellite image such as those from Landsat, need to be stacked
# into one file. With EarthPy, you can create an image stack from all of the
# Landsat .tif files (one per band) in a directory using the ``stack()`` function
# from the ``earthpy.spatial`` module.

###################################################################################
# Error found on Windows systems
# -------------------------------
# .. note::
#       If you are running this script on a Windows system, there is a
#       known bug with ``.to_crs()``, which is used in this script. If an error
#       occurs, you have to reset your os environment with the command
#       ``os.environ["PROJ_LIB"] = r"path-to-share-folder-in-environment"``.

# Get sample data from EarthPy and set working directory
data_path = et.data.get_data("vignette-landsat")
os.chdir(os.path.join(et.io.HOME, "earth-analytics"))

# Get list of bands and sort by ascending band number
landsat_bands_data_path = "data/vignette-landsat/LC08_L1TP_034032_20160621_20170221_01_T1_sr_band*[1-7]*.tif"
stack_band_paths = glob(landsat_bands_data_path)
stack_band_paths.sort()

# Create image stack and apply nodata value for Landsat
arr_st, meta = es.stack(stack_band_paths, nodata=-9999)

###############################################################################
# Plot RGB Composite Image
# --------------------------
# You can use the ``plot_rgb()`` function from the ``earthpy.plot`` module to quickly
# plot three band composite images. For RGB composite images, you will plot the red,
# green, and blue bands, which are bands 4, 3, and 2, respectively, in the image

#
# To begin, import the needed packages. You will use a combination of several EarthPy
# modules including spatial, plot and mask.

from glob import glob
import os
import matplotlib.pyplot as plt
import rasterio as rio
from rasterio.plot import plotting_extent
import earthpy as et
import earthpy.spatial as es
import earthpy.plot as ep
import earthpy.mask as em

# Get data and set your home working directory
data = et.data.get_data("cold-springs-fire")

###############################################################################
# Import Example Data
# ------------------------------
# To get started, make sure your directory is set. Create a stack from all of the
# Landsat .tif files (one per band) and import the ``landsat_qa`` layer which provides
# the locations of cloudy and shadowed pixels in the scene.

os.chdir(os.path.join(et.io.HOME, "earth-analytics"))

# Stack the landsat bands
# This creates a numpy array with each "layer" representing a single band
landsat_paths_pre = glob(
    "data/cold-springs-fire/landsat_collect/LC080340322016070701T1-SC20180214145604/crop/*band*.tif"
)
landsat_paths_pre.sort()

# ---------------
#
# To begin, import the needed packages. You will use a combination of several EarthPy
# modules including spatial and plot.

import os
from glob import glob
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import earthpy as et
import earthpy.spatial as es
import earthpy.plot as ep

# Get data and set your home working directory
data = et.data.get_data("cold-springs-fire")


###############################################################################
# Import Example Data
# -------------------
#
# To get started, make sure your directory is set. Create a stack from all of the
# Landsat .tif files (one per band).

os.chdir(os.path.join(et.io.HOME, "earth-analytics"))

# Stack the Landsat 8 bands
# This creates a numpy array with each "layer" representing a single band
landsat_path = glob(
    "data/cold-springs-fire/landsat_collect/LC080340322016072301T1-SC20180214145802/crop/*band*.tif"
)

# ----------------------------------
# With EarthPy you can create a stack from all of the Landsat .tif files (one per band)
# in a folder with the ``es.stack()`` function.

###################################################################################
# Error found on Windows systems
# -------------------------------
# .. note::
#       If you are running this script on a Windows system, there is a
#       known bug with ``.to_crs()``, which is used in this script. If an error
#       occurs, you have to reset your os environment with the command
#       ``os.environ["PROJ_LIB"] = r"path-to-share-folder-in-environment"``.

# Get sample data from EarthPy and setting your home working directory

data_path = et.data.get_data("vignette-landsat")
os.chdir(os.path.join(et.io.HOME, "earth-analytics"))

# Prepare the landsat bands to be stacked using glob and sort

landsat_bands_data_path = "data/vignette-landsat/LC08_L1TP_034032_20160621_20170221_01_T1_sr_band*[2-4]*_crop.tif"
stack_band_paths = glob(landsat_bands_data_path)
stack_band_paths.sort()

# Create output directory and the output path

output_dir = os.path.join("data", "outputs")
if os.path.isdir(output_dir) == False:
    os.mkdir(output_dir)

raster_out_path = os.path.join(output_dir, "raster.tiff")

import os
from glob import glob
import matplotlib.pyplot as plt
import earthpy as et
import earthpy.spatial as es
import earthpy.plot as ep

###############################################################################
# Import Example Data
# -------------------
#
# To get started, make sure your directory is set. Then, create a stack from all of
# the Landsat .tif files (one per band).

# Get data for example
data = et.data.get_data("vignette-landsat")

# Set working directory
os.chdir(os.path.join(et.io.HOME, "earth-analytics"))

# Stack the Landsat 8 bands
# This creates a numpy array with each "layer" representing a single band
# You can use the nodata= parameter to mask nodata values
landsat_path = glob(
    "data/vignette-landsat/LC08_L1TP_034032_20160621_20170221_01_T1_sr_band*_crop.tif"
)
landsat_path.sort()
array_stack, meta_data = es.stack(landsat_path, nodata=-9999)

###############################################################################
# Plot All Bands in a Stack
# --------------------------