Source code for geoips.plugins.modules.algorithms.visir.Day_Solar

# # # This source code is subject to the license referenced at
# # # https://github.com/NRLMMD-GEOIPS.

"""Data manipulation steps for "Day_Solar" EUMETSAT RGB product.

This algorithm expects three Infrared/Visible channels for an RGB image:
* Red SEVIRI B02Ref
* Green SEVIRI B03Ref
* Blue SEVIRI B04BT
"""
import logging

LOG = logging.getLogger(__name__)

interface = "algorithms"
family = "xarray_to_numpy"
name = "Day_Solar"


def call(xobj):
    """Dust RGB product algorithm data manipulation steps.

    This algorithm expects TBs from three SEVIRI channels:

    * Red: B02Ref
    * Green: B03Ref
    * Blue: B04BT

    Parameters
    ----------
    arrays : list of numpy.ndarray
        * list of numpy.ndarray or numpy.MaskedArray of channel data,
            in order of sensor "channels" list
        * Unit in Degrees Kelvin and reflectance

    Returns
    -------
    numpy.ndarray
        numpy.ndarray or numpy.MaskedArray of qualitative RGBA image output
    """
    bt04 = xobj["B04BT"].to_masked_array()  # 3.9 um TBs, B04 SEVIRI
    bt02 = xobj["B02Ref"].to_masked_array()  # 0.86 um reflectance, B02 SEVIRI
    bt03 = xobj["B03Ref"].to_masked_array()  # 1.6 um reflectance, B03 SEVIRI

    # Convert TB from Kevin to Celsius
    from geoips.data_manipulations.conversions import unit_conversion

    bt04 = unit_conversion(bt04, input_units="Kelvin", output_units="celsius")

    from geoips.data_manipulations.corrections import apply_data_range, apply_gamma

    data_range = [0, 1]
    red = apply_data_range(
        bt02,
        min_val=data_range[0],
        max_val=data_range[1],
        min_outbounds="crop",
        max_outbounds="crop",
        norm=True,
        inverse=False,
    )  # need inverse option?
    red = apply_gamma(red, 1.7)
    red = apply_gamma(red, 1.0)
    # Note: may need additional solar correction

    data_range = [0, 0.7]
    grn = apply_data_range(
        bt03,
        min_val=data_range[0],
        max_val=data_range[1],
        min_outbounds="crop",
        max_outbounds="crop",
        norm=True,
        inverse=False,
    )
    grn = apply_gamma(grn, 1.7)
    grn = apply_gamma(grn, 1.0)
    # Note: may need additional solar correction

    data_range = [0, 30]
    blu = apply_data_range(
        bt04,
        min_val=data_range[0],
        max_val=data_range[1],
        min_outbounds="crop",
        max_outbounds="crop",
        norm=True,
        inverse=False,
    )  # create image of deep clouds in blueish color
    # norm=True, inverse=False)    #create image of deep clouds in yellowish color
    blu = apply_gamma(blu, 1.7)
    blu = apply_gamma(blu, 1.0)

    from geoips.image_utils.mpl_utils import alpha_from_masked_arrays, rgba_from_arrays

    alp = alpha_from_masked_arrays([red, grn, blu])
    rgba = rgba_from_arrays(red, grn, blu, alp)

    return rgba