geoips.plugins.modules.algorithms.visir package#

Submodules#

geoips.plugins.modules.algorithms.visir.Night_Vis module#

Data manipulation steps for “Night_Vis” product, standard Version.

This algorithm expects one VIIRS channel (DNBRad) for a single channel image.

geoips.plugins.modules.algorithms.visir.Night_Vis.call(arrays, output_data_range=None, scale_factor=None, gamma_list=None, input_units=None, output_units=None, min_outbounds=None, max_outbounds=None, max_night_zen=None, norm=None, inverse=None)[source]#

Night-Vis algorithm data manipulation steps, standard version.

DNB obs for visible product.

This algorithm expects radaiance, between 0 and 2.5*10^-8

This is only for nighttime product.

Parameters:

arrays (list of numpy.ndarray) –

  • list of numpy.ndarray or numpy.MaskedArray of channel data

  • Channel data: Radiance, between 0 and 2.5*10^-8

Returns:

numpy.ndarray or numpy.MaskedArray of appropriately scaled channel data

Return type:

numpy.ndarray

Notes

Due to a relative maximum value of the DNBRad is much larger than that of the majority pixels in moonlight/lighting situation, it could lead to a black image if the original maximum is used to normalize the data (i.e., the normlized value is close to 0). Thus, we need to setup an tuning factor to normalize the DNBRad.

We start to use 0.05 to tune the val_max in moonlight/other lighing source, 0.5 for no lighting source.

We might have to generate night-vis product only when moonlight is present (TBD).

geoips.plugins.modules.algorithms.visir.Night_Vis_GeoIPS1 module#

Data manipulation steps for “Night_Vis” product, GeoIPS 1 Version.

This algorithm expects one VIIRS channel (DNBRad) for a single channel image.

geoips.plugins.modules.algorithms.visir.Night_Vis_GeoIPS1.call(arrays, min_outbounds='crop', max_outbounds='crop', max_night_zen=90)[source]#

Night Vis product algorithm data manipulation steps, GeoIPS 1 version.

This algorithm expects DNBRad in reflectance, and returns the adjusted array.

Parameters:

arrays (list of numpy.ndarray) –

  • list of numpy.ndarray or numpy.MaskedArray of channel data,

    in order of sensor “channels” list

  • Degrees Kelvin

Returns:

numpy.ndarray or numpy.MaskedArray of adjusted DNB output.

Return type:

numpy.ndarray

Notes

It will generate a product in daytime if we do not apply the daytime check. For now, it is for both day/night.

We will decide whether this product is only for nighttime. If so, a daytime check will be required.

We may focus only on nighttime product with moonlight after additional validation (TBD).

geoips.plugins.modules.algorithms.visir.Night_Vis_IR module#

Data manipulation steps for “Night_Vis_IR” product.

This algorithm expects two VIIRS channels (DNBRad and M16BT) for a RGB image

geoips.plugins.modules.algorithms.visir.Night_Vis_IR.call(arrays)[source]#

Night_Vis_IR RGB product algorithm data manipulation steps.

This algorithm expects DNBRad in reflectance and M16BT Brightness Temperatures in units of degrees Kelvin, and returns red green and blue gun arrays.

Parameters:

arrays (list of numpy.ndarray) –

  • list of numpy.ndarray or numpy.MaskedArray of channel data,

    in order of sensor “channels” list

  • Degrees Kelvin

Returns:

numpy.ndarray or numpy.MaskedArray of qualitative RGBA image output

Return type:

numpy.ndarray

Notes

It will generate a product in daytime if we do not apply the daytime check. For now, it is for both day/night.

We will decide whether this product is only for nighttime. If so, a daytime check will be required.

We may focus only on nighttime product with moonlight after additional validation (TBD).

geoips.plugins.modules.algorithms.visir.Night_Vis_IR_GeoIPS1 module#

Data manipulation steps for “Night_Vis_IR” product, GeoIPS 1 Version.

This algorithm expects two VIIRS channels (DNBRad and M16BT) for a RGB image

geoips.plugins.modules.algorithms.visir.Night_Vis_IR_GeoIPS1.call(arrays, max_night_zen=90)[source]#

Night Vis IR RGB product algorithm data manipulation steps.

This algorithm expects DNBRad in reflectance and M16BT Brightness Temperatures in units of degrees Kelvin, and returns red green and blue gun arrays.

Parameters:

arrays (list of numpy.ndarray) –

  • list of numpy.ndarray or numpy.MaskedArray of channel data,

    in order of sensor “channels” list

  • Degrees Kelvin

Returns:

numpy.ndarray or numpy.MaskedArray of qualitative RGBA image output

Return type:

numpy.ndarray

Notes

It will generate a product in daytime if we do not apply the daytime check. For now, it is for both day/night.

We will decide whether this product is only for nighttime. If so, a daytime check will be required.

We may focus only on nighttime product with moonlight after additional validation (TBD).

Module contents#

geoips visir algorithm init file.