geoips.plugins.modules.algorithms.visir package#

Submodules#

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

Data manipulation steps for “Convective_Storms” EUMETSAT RGB product.

This algorithm expects five Infrared/Visible channels for an RGB image: * Red SEVIRI B05BT - B06BT * Green SEVIRI B04BT - B09BT * Blue SEVIRI B03Ref - B01Ref

geoips.plugins.modules.algorithms.visir.Convective_Storms.call(xobj)[source]#

Dust RGB product algorithm data manipulation steps.

This algorithm expects TBs from five SEVIRI channels:

  • Red: B05BT - B06BT

  • Green: B04BT - B09BT

  • Blue: B03Ref - B01Ref

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

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

Data manipulation steps for “Day_Microphys_Summer” EUMETSAT RGB product.

This algorithm expects three Infrared/Visible channels for an RGB image: * Red SEVIRI B02Ref * Green SEVIRI B04BT * Blue SEVIRI B09BT

geoips.plugins.modules.algorithms.visir.Day_Microphys_Summer.call(xobj)[source]#

Dust RGB product algorithm data manipulation steps.

This algorithm expects TBs from five SEVIRI channels:

  • Red: B02Ref

  • Green: B04BT

  • Blue: B09BT

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 or numpy.MaskedArray of qualitative RGBA image output

Return type:

numpy.ndarray

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

Data manipulation steps for “Day_Microphys_Winter” EUMETSAT RGB product.

This algorithm expects three Infrared/Visible channels for an RGB image: * Red SEVIRI B02Ref * Green SEVIRI B04BT * Blue SEVIRI B09BT

geoips.plugins.modules.algorithms.visir.Day_Microphys_Winter.call(xobj)[source]#

Dust RGB product algorithm data manipulation steps.

This algorithm expects TBs from five SEVIRI channels:

  • Red: B02Ref

  • Green: B04BT

  • Blue: B09BT

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 or numpy.MaskedArray of qualitative RGBA image output

Return type:

numpy.ndarray

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

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

geoips.plugins.modules.algorithms.visir.Day_Solar.call(xobj)[source]#

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 or numpy.MaskedArray of qualitative RGBA image output

Return type:

numpy.ndarray

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

Data manipulation steps for “Dust” EUMETSAT RGB product.

This algorithm expects three visible reflectances for an RGB image: * Red SEVIRI B10BT - B09BT * Green SEVIRI B09BT - B07BT * Blue SEVIRI B09BT

geoips.plugins.modules.algorithms.visir.Dust_RGB.call(xobj)[source]#

Dust RGB product algorithm data manipulation steps.

This algorithm expects reflectance values for

  • Red SEVIRI B10BT - B09BT

  • Green SEVIRI B09BT - B07BT

  • Blue SEVIRI B09BT

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

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

Data manipulation steps for “Natural Color” EUMETSAT RGB product.

This algorithm expects three visible reflectances for an RGB image: * 0.6 um * 0.8 um * 1.6 um

geoips.plugins.modules.algorithms.visir.Natural_Color.call(xobj)[source]#

Natural Color RGB product algorithm data manipulation steps.

This algorithm expects reflectance values for

  • Blue: 0.6 um, SEVIRI B03 Reflectances

  • Green: 0.8 um, SEVIRI B02 Reflectances

  • Red: 1.6 um, SEVIRI B01 Reflectances

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

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

Data manipulation steps for “Night_Microphy” EUMETSAT RGB product.

This algorithm expects three Infrared/Visible channels for an RGB image: * Red SEVIRI B10BT - B09BT * Green SEVIRI B09Bt - B04BT * Blue SEVIRI B09BT

geoips.plugins.modules.algorithms.visir.Night_Microphys.call(xobj)[source]#

Night_Microphys product algorithm data manipulation steps.

This algorithm expects TBs from three SEVIRI channels:

  • Red: B10BT - B09BT

  • Green: B09Bt - B04BT

  • Blue: B09BT

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 or numpy.MaskedArray of qualitative RGBA image output

Return type:

numpy.ndarray

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).

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

Data manipulation steps for “Volcanic-Ash” EUMETSAT RGB product.

This algorithm expects three Infrared/Visible channels for an RGB image: * Red SEVIRI B10BT - B09BT * Green SEVIRI B09BT - B07BT * Blue SEVIRI B09BT

geoips.plugins.modules.algorithms.visir.Volcanic_Ash.call(xobj)[source]#

Night_Microphys product algorithm data manipulation steps.

This algorithm expects TBs from three SEVIRI channels:

  • Red: B10BT - B09BT

  • Green: B09BT - B07BT

  • Blue: B07BT

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 or numpy.MaskedArray of qualitative RGBA image output

Return type:

numpy.ndarray

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

Data manipulation steps for “airmass” EUMETSAT RGB product.

This algorithm expects four Infrared channels for an RGB image: * Red SEVIRI B05BT - B06BT * Green SEVIRI B08BT - B09BT * Blue SEVIRI B05BT

geoips.plugins.modules.algorithms.visir.airmass.call(xobj)[source]#

Airmass product algorithm data manipulation steps.

This algorithm expects TBs from four SEVIRI channels:

  • Red: B05BT - B06BT

  • Green: B08BT - B09BT

  • Blue: B05BT

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

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

NASA SPoRT Dust RGB product.

Data manipulation steps for the “nasa_dust_rgb” product. This algorithm expects Brightness Temperatures in units of degrees Kelvin

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

nasa_dust_rgb product algorithm data manipulation steps.

This algorithm expects Brightness Temperatures in units of Kelvins, and returns red green and blue gun arrays.

Parameters:

data (list of numpy.ndarray) –

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

    in order of channels list above

  • Kelvin

Returns:

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

  • Channels (RED: 15-13 (-6.7,2.6,G1);) – GRN: 14-11 (-0.5,20.0,G2.5) BLU: 13 (-11.95,15.55,G1)

Module contents#

geoips visir algorithm init file.