How to use the earthpy.plot.hist function in earthpy

arr_st, meta = es.stack(landsat_paths_pre)

# Import the landsat qa layer
with rio.open(
    "data/cold-springs-fire/landsat_collect/LC080340322016070701T1-SC20180214145604/crop/LC08_L1TP_034032_20160707_20170221_01_T1_pixel_qa_crop.tif"
) as landsat_pre_cl:
    landsat_qa = landsat_pre_cl.read(1)
    landsat_ext = plotting_extent(landsat_pre_cl)

###############################################################################
# Plot Histogram of Each Band in Your Data
# ----------------------------------------
# You can view a histogram for each band in your dataset by using the
# ``hist()`` function from the ``earthpy.plot`` module.

ep.hist(arr_st)
plt.show()

###############################################################################
# Customize Histogram Plot with Titles and Colors
# -----------------------------------------------

ep.hist(
    arr_st,
    colors=["blue"],
    title=[
        "Band 1",
        "Band 2",
        "Band 3",
        "Band 4",
        "Band 5",
        "Band 6",

# sphinx_gallery_thumbnail_number = 1
ep.hist(array_stack, colors=colors_list, title=titles)
plt.show()

###############################################################################
# Customize Bin Size and Arrangement of Histograms
# -------------------------------------------------
#
# You can customize the number of bins each histogram plot uses to group the
# data it is plotting. The default number is 20. This can be adjusted to match
# the data you are trying to display. Additionally, you can change the
# arrangement of the image overall by modifying the number of columns used
# to plot the data.

# Plot each histogram with 50 bins, arranged across three columns
ep.hist(array_stack, bins=50, cols=3)
plt.show()

"midnightblue",
    "Blue",
    "Green",
    "Red",
    "Maroon",
    "Purple",
    "Violet",
]

# Create the list of titles for each band. The titles and colors listed
# in this example reflect the order and wavelengths of the Landsat 8 bands
titles = ["Ultra Blue", "Blue", "Green", "Red", "NIR", "SWIR 1", "SWIR 2"]

# Plot the histograms with the color and title lists you just created
# sphinx_gallery_thumbnail_number = 1
ep.hist(array_stack, colors=colors_list, title=titles)
plt.show()

###############################################################################
# Customize Bin Size and Arrangement of Histograms
# -------------------------------------------------
#
# You can customize the number of bins each histogram plot uses to group the
# data it is plotting. The default number is 20. This can be adjusted to match
# the data you are trying to display. Additionally, you can change the
# arrangement of the image overall by modifying the number of columns used
# to plot the data.

# Plot each histogram with 50 bins, arranged across three columns
ep.hist(array_stack, bins=50, cols=3)
plt.show()

ep.hist(array, title=["Band 1", "Band 2", "Band 3"])
plt.show()

###########################################################################
# No Data Option
# ---------------
# ``es.stack()`` can handle ``nodata`` values in a raster. To use this
# parameter, specify ``nodata=``. This will mask every pixel that contains
# the specified ``nodata`` value. The output will be a numpy masked array.

os.chdir(os.path.join(et.io.HOME, "earth-analytics"))
array_nodata, raster_prof_nodata = es.stack(stack_band_paths, nodata=-9999)

# View hist of data with nodata values removed
ep.hist(
    array_nodata,
    title=[
        "Band 1 - No Data Values Removed",
        "Band 2 - No Data Values Removed",
        "Band 3 - No Data Values Removed",
    ],
)
plt.show()

# Recreate extent object for the No Data array

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

################################################################################

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?

ep.hist(array, title=["Band 1", "Band 2", "Band 3"])
plt.show()

###########################################################################
# No Data Option
# ---------------
# ``es.stack()`` can handle ``nodata`` values in a raster. To use this
# parameter, specify ``nodata=``. This will mask every pixel that contains
# the specified ``nodata`` value. The output will be a numpy masked array.

os.chdir(os.path.join(et.io.HOME, "earth-analytics"))
array_nodata, raster_prof_nodata = es.stack(stack_band_paths, nodata=-9999)

# View hist of data with nodata values removed
ep.hist(
    array_nodata,
    title=[

landsat_ext = plotting_extent(landsat_pre_cl)

###############################################################################
# Plot Histogram of Each Band in Your Data
# ----------------------------------------
# You can view a histogram for each band in your dataset by using the
# ``hist()`` function from the ``earthpy.plot`` module.

ep.hist(arr_st)
plt.show()

###############################################################################
# Customize Histogram Plot with Titles and Colors
# -----------------------------------------------

ep.hist(
    arr_st,
    colors=["blue"],
    title=[
        "Band 1",
        "Band 2",
        "Band 3",
        "Band 4",
        "Band 5",
        "Band 6",
        "Band 7",
    ],
)
plt.show()

###############################################################################
# View Single Band Plots