How to use the earthpy.plot.plot_rgb 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_rgb.py View on Github external
###############################################################################
# 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
# stack you created. Python uses a zero-based index system, so you need to subtract
# a value of 1 from each index. Thus, the index for the red band is 3, green is 2,
# and blue is 1. These index values are provided to the ``rgb`` argument to identify
# the bands for the composite image.

# Create figure with one plot
fig, ax = plt.subplots(figsize=(12, 12))

# Plot red, green, and blue bands, respectively
ep.plot_rgb(arr_st, rgb=(3, 2, 1), ax=ax, title="Landsat 8 RGB Image")
plt.show()

###############################################################################
# Stretch Composite Images
# -------------------------
# Composite images can sometimes be dark if the pixel brightness values are
# skewed toward the value of zero. You can stretch the pixel brightness values
# in an image using the argument ``stretch=True`` to extend the values to the
# full 0-255 range of potential values to increase the visual contrast of the
# image. In addition, the ``str_clip`` argument allows you to specify how much of
# the tails of the data that you want to clip off. The larger the number, the
# more the data will be stretched or brightened.

# Create figure with one plot
fig, ax = plt.subplots(figsize=(12, 12))
github earthlab / earthpy / examples / plot_raster_stack_crop.py View on Github external
# array from ``es.stack()`` and the Rasterio profile or metadata object. The function
# needs a single
# layer of a numpy array, which is why we use ``arr[0]``. The function also
# needs the spatial transformation for the Rasterio object, which can be acquired by accessing
# the ``"transform"`` key within the Rasterio Profile.

extent = plotting_extent(array[0], raster_prof["transform"])

################################################################################
# Plot Un-cropped Data
# ------------------------------
# You can see the boundary and the raster before the crop using ``ep.plot_rgb()``
# Notice that the data appear washed out.

fig, ax = plt.subplots(figsize=(12, 12))
ep.plot_rgb(
    array,
    ax=ax,
    stretch=True,
    extent=extent,
    str_clip=0.5,
    title="RGB Image of Un-cropped Raster",
)
plt.show()


################################################################################
# Explore the Range of Values in the Data
# ---------------------------------------
# You can explore the range of values found in the data using the EarthPy ``hist()``
# function. Do you notice any extreme values that may be impacting the stretch
# of the image?
github earthlab / earthpy / examples / plot_rgb.py View on Github external
# Plot of RGB composite image with polygon boundary
bound_utm13N.boundary.plot(ax=ax1, color="black", zorder=10)
ax1 = ep.plot_rgb(
    arr_st,
    rgb=(3, 2, 1),
    ax=ax1,
    stretch=True,
    extent=extent,
    str_clip=0.5,
    title="Landsat 8 RGB Image with Polygon Boundary",
)

# Plot of CIR composite image with polygon boundary
bound_utm13N.boundary.plot(ax=ax2, color="black", zorder=10)
ax2 = ep.plot_rgb(
    arr_st,
    rgb=(4, 3, 2),
    ax=ax2,
    stretch=True,
    extent=extent,
    str_clip=0.5,
    title="Landsat 8 CIR Image with Polygon Boundary",
)
plt.show()
github earthlab / earthpy / examples / plot_raster_stack_crop.py View on Github external
# 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_raster_stack_crop.py View on Github external
)
plt.show()

# Recreate extent object for the No Data array

extent_nodata = plotting_extent(
    array_nodata[0], raster_prof_nodata["transform"]
)

################################################################################
# Plot Un-cropped Data
# ------------------------------
# Plot the data again after the nodata values are removed.

fig, ax = plt.subplots(figsize=(12, 12))
ep.plot_rgb(
    array_nodata,
    ax=ax,
    stretch=True,
    extent=extent,
    str_clip=0.5,
    title="RGB image of Un-cropped Raster, No Data Value Selected",
)
plt.show()

#############################################################################
# Crop the Data
# ------------------
# Sometimes you have data for an area that is larger than your study area.
# It is more efficient to first crop the data to your study area before processing
# it in Python. The fastest and most efficient option is to crop each file
# individually, write out the cropped raster to a new file, and then stack
github earthlab / earthpy / examples / plot_stack_masks.py View on Github external
# ~~~~~~~~~~~~~~~~~~~~~
# Now apply the mask and plot the masked data. The mask applies to every band in your data.
# The mask values below are values documented in the Landsat 8 documentation that represent
# clouds and cloud shadows.

# Generate array of all possible cloud / shadow values
cloud_shadow = [328, 392, 840, 904, 1350]
cloud = [352, 368, 416, 432, 480, 864, 880, 928, 944, 992]
high_confidence_cloud = [480, 992]

# Mask the data
all_masked_values = cloud_shadow + cloud + high_confidence_cloud
arr_ma = em.mask_pixels(arr_st, landsat_qa, vals=all_masked_values)

# sphinx_gallery_thumbnail_number = 5
ep.plot_rgb(
    arr_ma, rgb=[4, 3, 2], title="Array with Clouds and Shadows Masked"
)
plt.show()
github earthlab / earthpy / examples / plot_rgb.py View on Github external
###############################################################################
# Stretch Composite Images
# -------------------------
# Composite images can sometimes be dark if the pixel brightness values are
# skewed toward the value of zero. You can stretch the pixel brightness values
# in an image using the argument ``stretch=True`` to extend the values to the
# full 0-255 range of potential values to increase the visual contrast of the
# image. In addition, the ``str_clip`` argument allows you to specify how much of
# the tails of the data that you want to clip off. The larger the number, the
# more the data will be stretched or brightened.

# Create figure with one plot
fig, ax = plt.subplots(figsize=(12, 12))

# Plot bands with stretched applied
ep.plot_rgb(
    arr_st,
    rgb=(3, 2, 1),
    ax=ax,
    stretch=True,
    str_clip=0.5,
    title="Landsat 8 RGB Image with Stretch Applied",
)
plt.show()

###############################################################################
# Plot Color Infrared (CIR) Composite Image
# ------------------------------------------
# For color infrared (CIR) composite images, you will plot the near-infrared (NIR),
# red, and green bands, which are bands 5, 4, 2, respectively. Once again, the
# Python index values will be the original band number minus 1, thus, 4, 3, and 2
# for NIR, red, and green, respectively.
github earthlab / earthpy / examples / plot_rgb.py View on Github external
)
plt.show()

###############################################################################
# Plot Color Infrared (CIR) Composite Image
# ------------------------------------------
# For color infrared (CIR) composite images, you will plot the near-infrared (NIR),
# red, and green bands, which are bands 5, 4, 2, respectively. Once again, the
# Python index values will be the original band number minus 1, thus, 4, 3, and 2
# for NIR, red, and green, respectively.

# Create figure with one plot
fig, ax = plt.subplots(figsize=(12, 12))

# Plot NIR, red, and green bands, respectively, with stretch
ep.plot_rgb(
    arr_st,
    rgb=(4, 3, 2),
    ax=ax,
    stretch=True,
    str_clip=0.5,
    title="Landsat 8 CIR Image with Stretch Applied",
)
plt.show()

#############################################################################
# Plot Boundary Over Composite Image
# -----------------------------------
# .. note::
#       If you are on windows, you may need to add the crs issue discussed above
#       here!
#
github earthlab / earthpy / examples / plot_rgb.py View on Github external
###############################################################################
# Create Figure with Multiple Axes or Subplots
# --------------------------------------------
# ```plot_rgb()`` has an ``ax=`` parameter which supports subplots. You can 
# create figures that contain multiple plots by creating multiple ax
# objects, one for each plot. You can also specify the number of rows and
# columns in which to display the plots. In the example below, the two plots
# will be displayed side-by-side along one row with two columns.

# Create figure with two plots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))

# Plot of RGB composite image with polygon boundary
bound_utm13N.boundary.plot(ax=ax1, color="black", zorder=10)
ax1 = ep.plot_rgb(
    arr_st,
    rgb=(3, 2, 1),
    ax=ax1,
    stretch=True,
    extent=extent,
    str_clip=0.5,
    title="Landsat 8 RGB Image with Polygon Boundary",
)

# Plot of CIR composite image with polygon boundary
bound_utm13N.boundary.plot(ax=ax2, color="black", zorder=10)
ax2 = ep.plot_rgb(
    arr_st,
    rgb=(4, 3, 2),
    ax=ax2,
    stretch=True,

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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