Fix some linting issues #869

Author: soxofaan

docs/conf.py

@@ -13,13 +13,13 @@
 # All configuration values have a default; values that are commented out
 # serve to show the default.
 
+import datetime
+import os
+import sys
+
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import sys
-import datetime
 
 sys.path.insert(0, os.path.abspath('.'))
 sys.path.insert(0, os.path.abspath('../'))
@@ -45,6 +45,7 @@
 ]
 
 import sphinx_autodoc_typehints
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
 

docs/cookbook/sampling.md

@@ -6,10 +6,10 @@ but rather want to extract a result at specific locations.
 Examples include extracting training data for model calibration, or computing the result for
 areas where validation data is available.
 
-An important constraint is that most implementations assume that sampling is an operation 
-on relatively small areas, of for instance up to 512x512 pixels (but often much smaller). 
+An important constraint is that most implementations assume that sampling is an operation
+on relatively small areas, of for instance up to 512x512 pixels (but often much smaller).
 When extracting polygons with larger areas, it is recommended to look into running a separate job per 'sample'.
-Some more important performance notices are mentioned later in the chapter, please read them carefully 
+Some more important performance notices are mentioned later in the chapter, please read them carefully
 to get best results.
 
 Sampling can be done for points or polygons:
@@ -23,9 +23,9 @@ public url, and to load it in openEO using {py:meth}`openeo.rest.connection.Conn
 
 ## Sampling at point locations
 
-To sample point locations, the `openeo.rest.datacube.DataCube.aggregate_spatial` method can be used. The reducer can be a 
+To sample point locations, the `openeo.rest.datacube.DataCube.aggregate_spatial` method can be used. The reducer can be a
 commonly supported reducer like `min`, `max` or `mean` and will receive only one value as input in most cases. Note that
-in edge cases, a point can intersect with up to 4 pixels. If this is not desirable, it might be worth trying to align 
+in edge cases, a point can intersect with up to 4 pixels. If this is not desirable, it might be worth trying to align
 points with pixel centers, which does require more advanced knowledge of the pixel grid of your data cube.
 
 More information on `aggregate_spatial` is available [here](_aggregate-spatial-evi).

examples/archive/udf/udf_modify_spatial.py

@@ -1,8 +1,10 @@
+import numpy as np
 import xarray
+
+from openeo.metadata import CollectionMetadata
 from openeo.udf import XarrayDataCube
 from openeo.udf.debug import inspect
-from openeo.metadata import CollectionMetadata
-import numpy as np
+
 
 def apply_metadata(input_metadata:CollectionMetadata, context:dict) -> CollectionMetadata:
 
@@ -41,4 +43,4 @@ def apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:
     predicted_cube = xarray.DataArray(predicted_array, dims=['bands', 'x', 'y'], coords=dict(x=coord_x, y=coord_y))
 
 
-    return XarrayDataCube(predicted_cube)
+    return XarrayDataCube(predicted_cube)

openeo/udf/udf_signatures.py

@@ -6,8 +6,8 @@
 """
 # Note: this module was initially developed under the ``openeo-udf`` project (https://github.com/Open-EO/openeo-udf)
 
-from deprecated import deprecated
 import xarray
+from deprecated import deprecated
 from pandas import Series
 
 from openeo.metadata import CollectionMetadata