Explicit arguments in tasks instead of **kwargs (#738)

* Inline **kwargs in sampling

* Inline kwargs for tiff tasks

eolearn/io/raster_io.py

@@ -13,7 +13,7 @@
 import logging
 import warnings
 from abc import ABCMeta
-from typing import Any, BinaryIO
+from typing import BinaryIO
 
 import fs
 import numpy as np
@@ -155,7 +155,10 @@ def __init__(
         crs: CRS | int | str | None = None,
         fail_on_missing: bool = True,
         compress: str | None = None,
-        **kwargs: Any,
+        filesystem: FS | None = None,
+        image_dtype: np.dtype | type | None = None,
+        no_data_value: float | None = None,
+        config: SHConfig | None = None,
     ):
         """
         :param feature: A feature to be exported.
@@ -173,9 +176,22 @@ def __init__(
         :param fail_on_missing: A flag to specify if the task should fail if a feature is missing or if it should
             just show a warning.
         :param compress: A type of compression that rasterio should apply to an exported image.
-        :param kwargs: Keyword arguments to be propagated to `BaseRasterIoTask`.
+        :param filesystem: A filesystem object. If not given it will be initialized according to `folder` parameter.
+        :param image_dtype: A data type of data in exported images or data imported from images.
+        :param no_data_value: When exporting this is the NoData value of pixels in exported images.
+            When importing this value is assigned to the pixels with NoData.
+        :param config: A configuration object with AWS credentials. By default, is set to None and in this case the
+            default configuration will be taken.
         """
-        super().__init__(feature, folder=folder, create=True, **kwargs)
+        super().__init__(
+            feature,
+            folder=folder,
+            create=True,
+            filesystem=filesystem,
+            image_dtype=image_dtype,
+            no_data_value=no_data_value,
+            config=config,
+        )
 
         self.date_indices = date_indices
         self.band_indices = band_indices
@@ -397,7 +413,10 @@ def __init__(
         *,
         use_vsi: bool = False,
         timestamp_size: int | None = None,
-        **kwargs: Any,
+        filesystem: FS | None = None,
+        image_dtype: np.dtype | type | None = None,
+        no_data_value: float | None = None,
+        config: SHConfig | None = None,
     ):
         """
         :param feature: EOPatch feature into which data will be imported
@@ -413,9 +432,21 @@ def __init__(
             channels of given tiff image should be in order
             T(1)B(1), T(1)B(2), ..., T(1)B(N), T(2)B(1), T(2)B(2), ..., T(2)B(N), ..., ..., T(M)B(N)
             where T and B are the time and band indices.
-        :param kwargs: Keyword arguments to be propagated to `BaseRasterIoTask`.
+        :param filesystem: A filesystem object. If not given it will be initialized according to `folder` parameter.
+        :param image_dtype: A data type of data in exported images or data imported from images.
+        :param no_data_value: When exporting this is the NoData value of pixels in exported images.
+            When importing this value is assigned to the pixels with NoData.
+        :param config: A configuration object with AWS credentials. By default, is set to None and in this case the
+            default configuration will be taken.
         """
-        super().__init__(feature, folder=folder, **kwargs)
+        super().__init__(
+            feature,
+            folder=folder,
+            filesystem=filesystem,
+            image_dtype=image_dtype,
+            no_data_value=no_data_value,
+            config=config,
+        )
 
         self.use_vsi = use_vsi
         self.timestamp_size = timestamp_size

eolearn/ml_tools/sampling.py

@@ -10,7 +10,7 @@
 
 from abc import ABCMeta
 from math import sqrt
-from typing import Any, Dict, Union
+from typing import Dict, Union
 
 import numpy as np
 from shapely.geometry import Point, Polygon
@@ -182,17 +182,17 @@ def __init__(
         fraction: _FractionType,
         exclude_values: list[int] | None = None,
         replace: bool = False,
-        **kwargs: Any,
+        mask_of_samples: Feature | None = None,
     ):
         """
         :param features_to_sample: Features that will be spatially sampled according to given sampling parameters.
         :param sampling_feature: A timeless mask feature according to which points will be sampled.
         :param fraction: Fraction of points to sample. Can be dictionary mapping values of mask to fractions.
         :param exclude_values: Skips points that have these values in `sampling_mask`
         :param replace: Whether to sample with replacement. False means each value can only be chosen once.
-        :param kwargs: Arguments for :class:`BaseSamplingTask<eolearn.geometry.sampling.BaseSamplingTask>`
+        :param mask_of_samples: An output mask timeless feature of counts how many times each pixel has been sampled.
         """
-        super().__init__(features_to_sample, **kwargs)
+        super().__init__(features_to_sample, mask_of_samples=mask_of_samples)
 
         self.sampling_feature = self.parse_feature(sampling_feature, allowed_feature_types={FeatureType.MASK_TIMELESS})
 
@@ -267,17 +267,17 @@ def __init__(
         amount: float,
         sample_size: tuple[int, int] = (1, 1),
         replace: bool = False,
-        **kwargs: Any,
+        mask_of_samples: Feature | None = None,
     ):
         """
         :param features_to_sample: Features that will be spatially sampled according to given sampling parameters.
         :param amount: The number of points to sample if integer valued and the fraction of all points if `float`
         :param sample_size: A tuple describing a size of sampled blocks. The size is defined as a tuple of number of
             rows and number of columns.
         :param replace: Whether to sample with replacement. False means each value can only be chosen once.
-        :param kwargs: Arguments for :class:`BaseSamplingTask<eolearn.geometry.sampling.BaseSamplingTask>`
+        :param mask_of_samples: An output mask timeless feature of counts how many times each pixel has been sampled.
         """
-        super().__init__(features_to_sample, **kwargs)
+        super().__init__(features_to_sample, mask_of_samples=mask_of_samples)
 
         self.amount = amount
         self.sample_size = tuple(sample_size)
@@ -330,7 +330,7 @@ def __init__(
         features_to_sample: FeaturesSpecification,
         sample_size: tuple[int, int] = (1, 1),
         stride: tuple[int, int] = (1, 1),
-        **kwargs: Any,
+        mask_of_samples: Feature | None = None,
     ):
         """
         :param features_to_sample: Features that will be spatially sampled according to given sampling parameters.
@@ -339,9 +339,9 @@ def __init__(
         :param stride: A tuple describing a distance between upper left corners of two consecutive sampled blocks.
             The first number is the vertical distance and the second number the horizontal distance. If stride in
             smaller than sample_size in any dimensions then sampled blocks will overlap.
-        :param kwargs: Arguments for :class:`BaseSamplingTask<eolearn.geometry.sampling.BaseSamplingTask>`
+        :param mask_of_samples: An output mask timeless feature of counts how many times each pixel has been sampled.
         """
-        super().__init__(features_to_sample, **kwargs)
+        super().__init__(features_to_sample, mask_of_samples=mask_of_samples)
 
         self.sample_size = tuple(sample_size)
         self.stride = tuple(stride)