Analyze Spectral Values

This function calculates the reflectance frequencies associated with a hyperspectral datacube and writes the values out as observations to get saved out. Can also print out a histogram of average reflectance intensity.

plantcv.hyperspectral.analyze_spectral(array, mask, histplot=False, label="default")

returns reflectance histogram (if histplot=True, otherwise returns None object)

  • Parameters:
    • array - A hyperspectral datacube object, an instance of the Spectral_data class (read in with pcv.readimage with mode='envi')
    • mask - Binary mask made from selected contours
    • histplot - If True plots histogram of reflectance intensity values (default histplot = False)
    • label - Optional label parameter, modifies the variable name of observations recorded. (default label="default")
  • Example use:
    • Below
  • Output data stored: Data ('global_mean_reflectance', 'global_median_reflectance', 'global_spectral_std', 'wavelength_means', 'max_reflectance', 'min_reflectance', 'spectral_std', 'spectral_frequencies') automatically gets stored to the Outputs class when this function is ran. These data can always get accessed during a workflow (example below). For more detail about data output see Summary of Output Observations

from plantcv import plantcv as pcv

# Set global debug behavior to None (default), "print" (to file), 
# or "plot" (Jupyter Notebooks or X11)

pcv.params.debug = "print"

# Calculates reflectance frequencies and writes the values as observations. Also provides a histogram of this data
spectral_hist  = pcv.hyperspectral.analyze_spectral(array=spectral_data, mask=mask, histplot=True, label="default")

# Access data stored 
reflectance_range = max(pcv.outputs.observations['default']['max_reflectance']['value']) - min(pcv.outputs.observations['default']['min_reflectance']['value'])

Spectral Reflectance Intensity Histogram

Screenshot

Source Code: Here