geoips.plugins.modules.algorithms package#

Subpackages#

Submodules#

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

Apply default RGB algorithm.

Plots RGB with the red gun being the first variable specified in the product YAML, green second, and blue third.

Note this is currently entirely unused, and is included here for reference/completeness. Eventually we may want to fully support this generalized RGB default algorithm, where you can pass in ranges, channel combinations, etc for RGB output, but for now it will ONLY plot arrays 0, 1, 2 as red, green, blue respectively, with no adjustments/combinations.

Parameters:

arrays (list of numpy.ndarray) –

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

    in order of sensor “channels” list

Returns:

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

Return type:

numpy.ndarray

geoips.plugins.modules.algorithms.absdiff_mst module#

Data manipulation steps for standard absolute difference algorithm.

Generalized algorithm to calculate the difference between 1 or 2 variables taken from multiple scan times over the same sector.

geoips.plugins.modules.algorithms.absdiff_mst.call(xobj, variables, product_name, output_data_range=None, input_units=None, output_units=None, min_outbounds='crop', max_outbounds='crop', norm=False, inverse=False, sun_zen_correction=False, mask_night=False, max_day_zen=None, mask_day=False, min_night_zen=None, gamma_list=None, scale_factor=None)[source]#

Apply data range and requested corrections to an absdiff product.

Data manipulation steps for applying a data range and requested corrections to a single channel productn absdiff product.

Parameters:

  • xobj (xarray.Dataset) –

    • input xarray.Dataset used for data manipulation.

  • variables (list of str) –

    • list of strings representing the variables used to produce ‘product_name’

  • product_name (str) –

    • String representing the product produced by this algorithm, using variables from ‘xobj’

  • output_data_range (list of float, default=None) –

    • list of min and max value for output data product.

    • This is applied LAST after all other corrections/adjustments

    • If None, use data min and max.

  • input_units (str, default=None) –

    • Units of input data, for applying necessary conversions

    • If None, no conversion

  • output_units (str, default=None) –

    • Units of output data, for applying necessary conversions

    • If None, no conversion

  • min_outbounds (str, default='crop') –

    • Method to use when applying bounds. Valid values are:

      • retain: keep all pixels as is

      • mask: mask all pixels that are out of range

      • crop: set all out of range values to either min_val or max_val as appropriate

  • max_outbounds (str, default='crop') –

    • Method to use when applying bounds. Valid values are:

      • retain: keep all pixels as is

      • mask: mask all pixels that are out of range

      • crop: set all out of range values to either min_val or max_val as appropriate

  • norm (bool, default=False) –

    • Boolean flag indicating whether to normalize (True) or not (False)

      • If True, returned data will be in the range from 0 to 1

      • If False, returned data will be in the range from min_val to max_val

  • inverse (bool, default=False) –

    • Boolean flag indicating whether to inverse (True) or not (False)

      • If True, returned data will be inverted

      • If False, returned data will not be inverted

  • sun_zenith_correction (bool, default=False) –

    • Boolean flag indicating whether to apply solar zenith correction (True) or not (False)

      • If True, returned data will have solar zenith correction applied (see data_manipulations.corrections.apply_solar_zenith_correction)

      • If False, returned data will not be modified based on solar zenith angle

Notes

Order of operations, based on the passed arguments, is:

  1. Mask night

  2. Mask day

  3. Apply solar zenith correction

  4. Apply gamma values

  5. Apply scale factor

  6. Convert units

  7. Apply data range.

NOTE: If “norm=True” is specified, the “output_data_range” will NOT match the actual range of the returned data, since the normalized data will be returned between 0 and 1.

If you require a different order of operations than that specified within “single_channel” algorithm, please create a new algorithm for your desired order of operations.

Returns:

numpy.ndarray or numpy.MaskedArray of appropriately scaled channel data, in units “output_units”.

Return type:

numpy.ndarray

geoips.plugins.modules.algorithms.single_channel module#

Data manipulation steps for standard “single_channel” algorithm.

Generalized algorithm to apply data manipulation steps in a standard order to apply corrections to a single channel output product.

geoips.plugins.modules.algorithms.single_channel.call(arrays, output_data_range=None, input_units=None, output_units=None, min_outbounds='crop', max_outbounds='crop', norm=False, inverse=False, sun_zen_correction=False, mask_night=False, max_day_zen=None, mask_day=False, min_night_zen=None, gamma_list=None, scale_factor=None, satellite_zenith_angle_cutoff=None)[source]#

Apply data range and requested corrections to a single channel product.

Data manipulation steps for applying a data range and requested corrections to a single channel product

Parameters:

  • arrays (list of numpy.ndarray) –

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

    • MUST be length one for single_channel algorithm.

    • If applying solar zenith corrections and diurnal masking, the second index in the arrays list must be the solar zenith angle array. This is controlled in product YAML.

    • If applying a satellite zenith angle cutoff, the final index in the arrays list must be the satellite zenith angle array. This is controlled in the product YAML.

  • output_data_range (list of float, default=None) –

    • list of min and max value for output data product.

    • This is applied LAST after all other corrections/adjustments

    • If None, use data min and max.

  • input_units (str, default=None) –

    • Units of input data, for applying necessary conversions

    • If None, no conversion

  • output_units (str, default=None) –

    • Units of output data, for applying necessary conversions

    • If None, no conversion

  • min_outbounds (str, default='crop') –

    • Method to use when applying bounds. Valid values are:

      • retain: keep all pixels as is

      • mask: mask all pixels that are out of range

      • crop: set all out of range values to either min_val or max_val as appropriate

  • max_outbounds (str, default='crop') –

    • Method to use when applying bounds. Valid values are:

      • retain: keep all pixels as is

      • mask: mask all pixels that are out of range

      • crop: set all out of range values to either min_val or max_val as appropriate

  • norm (bool, default=False) –

    • Boolean flag indicating whether to normalize (True) or not (False)

      • If True, returned data will be in the range from 0 to 1

      • If False, returned data will be in the range from min_val to max_val

  • inverse (bool, default=False) –

    • Boolean flag indicating whether to inverse (True) or not (False)

      • If True, returned data will be inverted

      • If False, returned data will not be inverted

  • sun_zenith_correction (bool, default=False) –

    • Boolean flag indicating whether to apply solar zenith correction (True) or not (False)

      • If True, returned data will have solar zenith correction applied (see data_manipulations.corrections.apply_solar_zenith_correction)

      • If False, returned data will not be modified based on solar zenith angle

  • satellite_zenith_angle_cutoff (float, default=None) –

    • Cutoff for masking data where satellite zenith angle exceeds threshold

    • If None, no masking

Notes

Order of operations, based on the passed arguments, is:

  1. Mask night

  2. Mask day

  3. Apply satellite zenith angle cutoff

  4. Apply solar zenith correction

  5. Apply gamma values

  6. Apply scale factor

  7. Convert units

  8. Apply data range.

NOTE: If “norm=True” is specified, the “output_data_range” will NOT match the actual range of the returned data, since the normalized data will be returned between 0 and 1.

If you require a different order of operations than that specified within “single_channel” algorithm, please create a new algorithm for your desired order of operations.

Returns:

numpy.ndarray or numpy.MaskedArray of appropriately scaled channel data, in units “output_units”.

Return type:

numpy.ndarray

Module contents#

geoips algorithms init file.