fix linting

Author: cbur24

odc/stats/plugins/cloud_proba_percentiles.py

@@ -8,22 +8,14 @@
 """
 
 from functools import partial
-from typing import Any, Dict, Iterable, Optional, Sequence, Tuple, Mapping
+from typing import Sequence, Tuple, Mapping
 
-import datacube
 import numpy as np
-import pandas as pd
 import xarray as xr
-from datacube.model import Dataset
-from odc.geo.xr import assign_crs
-from odc.geo.geobox import GeoBox
-from odc.algo.io import load_with_native_transform
 from odc.algo._percentile import xr_quantile_bands
 from odc.stats.plugins._registry import register, StatsPluginInterface
-from odc.algo._masking import (
-    erase_bad,
-    enum_to_bool
-)
+from odc.algo._masking import erase_bad, enum_to_bool
+
 
 class S2Cloudless_percentiles(StatsPluginInterface):
     NAME = "S2Cloudless_percentiles"
@@ -35,36 +27,37 @@ def __init__(
         self,
         resampling: str = "cubic",
         bands: Sequence[str] = ["oa_s2cloudless_prob"],
-        output_bands: Sequence[str] = ['oa_s2cloudless_prob_pc_5', 'oa_s2cloudless_prob_pc_10','oa_s2cloudless_prob_pc_25'],
+        output_bands: Sequence[str] = [
+            "oa_s2cloudless_prob_pc_5",
+            "oa_s2cloudless_prob_pc_10",
+            "oa_s2cloudless_prob_pc_25",
+        ],
         mask_band: str = "oa_s2cloudless_mask",
         chunks: Mapping[str, int] = {"y": 512, "x": 512},
         group_by: str = "solar_day",
-        nodata_classes: Sequence[str] = ["nodata"], 
+        nodata_classes: Sequence[str] = ["nodata"],
         output_dtype: str = "float32",
         **kwargs,
     ):
-        
-        self.resampling=resampling
+
+        self.resampling = resampling
         self.bands = bands
         self.output_bands = output_bands
         self.mask_band = mask_band
         self.chunks = chunks
         self.group_by = group_by
         self.resampling = resampling
-        self.nodata_classes= nodata_classes
+        self.nodata_classes = nodata_classes
         self.output_dtype = np.dtype(output_dtype)
         self.output_nodata = np.nan
 
         super().__init__(
-            input_bands=tuple(bands),
-            resampling=resampling,
-            chunks=chunks,
-            **kwargs
+            input_bands=tuple(bands), resampling=resampling, chunks=chunks, **kwargs
         )
 
     @property
     def measurements(self) -> Tuple[str, ...]:
-        return (self.output_bands)
+        return self.output_bands
 
     def native_transform(self, xx: xr.Dataset) -> xr.Dataset:
         """
@@ -79,14 +72,13 @@ def native_transform(self, xx: xr.Dataset) -> xr.Dataset:
         mask = xx[self.mask_band]
         bad = enum_to_bool(mask, self.nodata_classes)
 
-        #drop mask band
+        # drop mask band
         xx = xx.drop_vars([self.mask_band])
 
         # apply the masks
         xx = erase_bad(xx, bad)
 
         return xx
-         
 
     def reduce(self, xx: xr.Dataset) -> xr.Dataset:
         """
@@ -99,4 +91,5 @@ def reduce(self, xx: xr.Dataset) -> xr.Dataset:
 
         return yy
 
+
 register("s2_gm_tools.S2Cloudless_percentiles", S2Cloudless_percentiles)