eolearn.mask.masking

Module for creating mask features

class eolearn.mask.masking.JoinMasksTask(input_features, output_feature, join_operation='and')[source]

Joins together masks with the provided logical operation.

Parameters:

input_features (FeaturesSpecification) – Mask features to be joined together.
output_feature (Feature) – Feature to which to save the joined mask.
join_operation (Literal['and', 'or', 'xor'] | Callable) – How to join masks. Supports ‘and’, ‘or’, ‘xor’, or a Callable object.

zip_method(*masks)[source]

Joins masks using the provided operation

class eolearn.mask.masking.MaskFeatureTask(feature, mask_feature, mask_values, no_data_value=nan)[source]

Masks out values of a feature using defined values of a given mask feature.

As an example, it can be used to mask the data feature using values from the Sen2cor Scene Classification Layer (SCL).

Contributor: Johannes Schmid, GeoVille Information Systems GmbH, 2018

Parameters:

feature (SingleFeatureSpec) – A feature to be masked with optional new feature name
mask_feature (Feature) – Masking feature. Values of this mask will be used to mask values of feature
mask_values (Iterable[int]) – List of values of mask_feature to be used for masking feature
no_data_value (float) – Value that replaces masked values in feature. Default is NaN

Returns:

The same eopatch instance with a masked array

execute(eopatch)[source]

Mask values of feature according to the mask_values in mask_feature

eolearn.mask.masking.apply_mask(data, mask, old_value, new_value, data_type, mask_type)[source]

A general masking function

Parameters:

Return type:

ndarray