How to use the earthpy.spatial function in earthpy

To help you get started, we’ve selected a few earthpy examples, based on popular ways it is used in public projects.

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github earthlab / earthpy / examples / plot_raster_stack_crop.py View on Github external
# `es.stack()`.

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

band_paths_list = es.crop_all(
    stack_band_paths, output_dir, crop_bound_utm13N, overwrite=True
)

#############################################################################
# Stack All Bands
# ---------------
# Once the data are cropped, you are ready to create a new stack.

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

cropped_array, array_raster_profile = es.stack(band_paths_list, nodata=-9999)
crop_extent = plotting_extent(
    cropped_array[0], array_raster_profile["transform"]
)

# Plotting the cropped image
# sphinx_gallery_thumbnail_number = 5
fig, ax = plt.subplots(figsize=(12, 6))
crop_bound_utm13N.boundary.plot(ax=ax, color="red", zorder=10)
ep.plot_rgb(
    cropped_array,
    ax=ax,
    stretch=True,
    extent=crop_extent,
    title="Cropped Raster and Fire Boundary",
)
plt.show()
github earthlab / earthpy / examples / plot_calculate_classify_ndvi.py View on Github external
###############################################################################
# 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"
)
landsat_path.sort()
arr_st, meta = es.stack(landsat_path)


###############################################################################
# Calculate Normalized Difference Vegetation Index (NDVI)
# -------------------------------------------------------
#
# You can calculate NDVI for your dataset using the
# ``normalized_diff`` function from the ``earthpy.spatial`` module.
# Math will be calculated (b1-b2) / (b1 + b2).

# Landsat 8 red band is band 4 at [3]
# Landsat 8 near-infrared band is band 5 at [4]
ndvi = es.normalized_diff(arr_st[4], arr_st[3])


###############################################################################
github earthlab / earthpy / examples / plot_bands_functionality.py View on Github external
# 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
# --------------------------
#
# When you give ``ep.plot_bands()`` a three dimensional numpy array,
# it will plot all layers in the numpy array. You can create unique titles for
# each image by providing a list of titles using the ``title=`` parameter.
# The list must contain the same number of strings as there are bands in the stack.

titles = ["Ultra Blue", "Blue", "Green", "Red", "NIR", "SWIR 1", "SWIR 2"]
# sphinx_gallery_thumbnail_number = 1
ep.plot_bands(array_stack, title=titles)
plt.show()

##################################################################################
github earthlab / earthpy / examples / plot_hist_functionality.py View on Github external
#
# 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
# first creating a list of colors and titles that will be provided to the
# ep.hist() function. The list for titles must contain the same number of
# strings as there are bands in the stack. You can also modify the colors for
# each image with a list of Matplotlib colors. If one color is provided in
# the list, every band will inherit that color. Otherwise, the list must
# contain the same number of strings as there are bands in the stack.

# Create the list of color names for each band
colors_list = [
    "midnightblue",

earthpy

A set of helper functions to make working with spatial data in open source tools easier. This package is maintained by Earth Lab and was originally designed to support the earth analytics education program.

BSD-3-Clause
Latest version published 3 years ago

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