[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

odc/stats/plugins/l34_utils/l4_natural_aquatic.py

@@ -6,6 +6,7 @@
 
 NODATA = 255
 
+
 def natural_auquatic_veg(l4, veg_cover, water_season):
 
     # mark woody/herbaceous
@@ -19,9 +20,9 @@ def natural_auquatic_veg(l4, veg_cover, water_season):
         },
         name="mark_water_season",
         dtype="float32",
-         **{"nodata": NODATA},
+        **{"nodata": NODATA},
     )
-  
+
     res = expr_eval(
         "where((a==124), 56, a)",
         {
@@ -283,7 +284,7 @@ def natural_auquatic_veg(l4, veg_cover, water_season):
         name="mark_final",
         dtype="uint8",
     )
-    
+
     res = expr_eval(
         "where((a==251)&(b==16), 92, a)",
         {
@@ -294,7 +295,7 @@ def natural_auquatic_veg(l4, veg_cover, water_season):
         dtype="uint8",
     )
 
-    # There are cases where a tile falls over water. 
+    # There are cases where a tile falls over water.
     # In these cases, the PC will have no data so we map back 251-254 to their corresponding classes
     res = expr_eval(
         "where((a>=251)&(a<=252), 57, a)",
@@ -312,6 +313,5 @@ def natural_auquatic_veg(l4, veg_cover, water_season):
         name="mark_final",
         dtype="uint8",
     )
-    
 
     return res

odc/stats/plugins/l34_utils/l4_veg_cover.py

@@ -7,7 +7,6 @@
 
 def canopyco_veg_con(xx: xr.Dataset, veg_threshold):
 
-    
     # Mask NODATA
     pv_pc_50 = expr_eval(
         "where(a==a, a, nodata)",

odc/stats/plugins/l34_utils/l4_water.py

@@ -7,27 +7,24 @@ def water_classification(xx, intertidal_mask, water_persistence):
 
     # Replace nan with nodata
     l4 = expr_eval(
-        "where((a==a), a, nodata)", 
-         {"a": xx.level_3_4.data}, 
-         name="mark_water", 
-         dtype="uint8",
-         **{"nodata": NODATA},
+        "where((a==a), a, nodata)",
+        {"a": xx.level_3_4.data},
+        name="mark_water",
+        dtype="uint8",
+        **{"nodata": NODATA},
     )
 
     l4 = expr_eval(
-        "where((a==223)|(a==221), 98, a)", 
-         {"a": l4}, 
-         name="mark_water", 
-         dtype="uint8"
+        "where((a==223)|(a==221), 98, a)", {"a": l4}, name="mark_water", dtype="uint8"
     )
-    
+
     l4 = expr_eval(
         "where((a==98)&(b!=1), 99, a)",
         {"a": l4, "b": intertidal_mask},
         name="mark_water",
         dtype="uint8",
     )
-    
+
     l4 = expr_eval(
         "where((a==98)&(b==1), 100, a)",
         {"a": l4, "b": intertidal_mask},

odc/stats/plugins/lc_level34.py

@@ -26,6 +26,7 @@
 
 NODATA = 255
 
+
 class StatsLccsLevel4(StatsPluginInterface):
     NAME = "ga_ls_lccs_Level34"
     SHORT_NAME = NAME
@@ -48,7 +49,6 @@ def __init__(
         self.watper_threshold = (
             watper_threshold if watper_threshold is not None else [1, 4, 7, 10]
         )
-        
 
     def fuser(self, xx):
         return xx
@@ -59,15 +59,15 @@ def reduce(self, xx: xr.Dataset) -> xr.Dataset:
         water_persistence = l4_water_persistence.water_persistence(
             xx, self.watper_threshold
         )
-   
+
         intertidal_mask = lc_intertidal_mask.intertidal_mask(xx)
 
         # #TODO WATER (99-104)
         l4 = l4_water.water_classification(xx, intertidal_mask, water_persistence)
 
         # Generate Level3 classes
         level3 = lc_level3.lc_level3(xx)
-        
+
         # Vegetation cover
         veg_cover = l4_veg_cover.canopyco_veg_con(xx, self.veg_threshold)
 
@@ -79,7 +79,7 @@ def reduce(self, xx: xr.Dataset) -> xr.Dataset:
 
         # Apply terrestrial vegetation classes [19-36]
         l4 = l4_natural_veg.lc_l4_natural_veg(l4, level3, lifeform, veg_cover)
- 
+
         # Bare gradation
         bare_gradation = l4_bare_gradation.bare_gradation(
             xx, self.bare_threshold, veg_cover
@@ -91,7 +91,7 @@ def reduce(self, xx: xr.Dataset) -> xr.Dataset:
 
         level3 = level3.astype(np.uint8)
         level4 = level4.astype(np.uint8)
-   
+
         attrs = xx.attrs.copy()
         attrs["nodata"] = NODATA
         dims = xx.level_3_4.dims[1:]
@@ -105,4 +105,4 @@ def reduce(self, xx: xr.Dataset) -> xr.Dataset:
         return leve34
 
 
-register("lc_l3_l4", StatsLccsLevel4)
+register("lc_l3_l4", StatsLccsLevel4)

tests/test_lc_l34.py

@@ -168,7 +168,7 @@ def image_groups():
 def test_l4_classes(image_groups):
     expected_l3 = [[216, 216, 215], [216, 216, 216], [220, 215, 215], [220, 220, 220]]
 
-    expected_l4 = [[ 95, 97, 93], [97, 96, 96], [100, 93, 93], [101, 101, 101]]
+    expected_l4 = [[95, 97, 93], [97, 96, 96], [100, 93, 93], [101, 101, 101]]
     stats_l4 = StatsLccsLevel4(measurements=["level3", "level4"])
     ds = stats_l4.reduce(image_groups)
 

tests/test_lc_l4_natural_surface.py

@@ -23,7 +23,9 @@
 NODATA = 255
 
 
-def image_groups(l34, urban, woody, bs_pc_50, pv_pc_50, cultivated, water_frequency, water_season):
+def image_groups(
+    l34, urban, woody, bs_pc_50, pv_pc_50, cultivated, water_frequency, water_season
+):
 
     tuples = [
         (np.datetime64("2000-01-01T00"), np.datetime64("2000-01-01")),
@@ -70,7 +72,7 @@ def image_groups(l34, urban, woody, bs_pc_50, pv_pc_50, cultivated, water_freque
             dims=("spec", "y", "x"),
             attrs={"nodata": 255},
         ),
-         "water_season": xr.DataArray(
+        "water_season": xr.DataArray(
             da.from_array(water_season, chunks=(1, -1, -1)),
             dims=("spec", "y", "x"),
             attrs={"nodata": 255},

tests/test_lc_l4_nav.py

@@ -21,7 +21,9 @@
 NODATA = 255
 
 
-def image_groups(l34, urban, cultivated, woody, pv_pc_50, water_frequency, water_season):
+def image_groups(
+    l34, urban, cultivated, woody, pv_pc_50, water_frequency, water_season
+):
 
     tuples = [
         (np.datetime64("2000-01-01T00"), np.datetime64("2000-01-01")),
@@ -63,7 +65,7 @@ def image_groups(l34, urban, cultivated, woody, pv_pc_50, water_frequency, water
             dims=("spec", "y", "x"),
             attrs={"nodata": 255},
         ),
-        "water_season":xr.DataArray(
+        "water_season": xr.DataArray(
             da.from_array(water_season, chunks=(1, -1, -1)),
             dims=("spec", "y", "x"),
             attrs={"nodata": 255},
@@ -148,7 +150,7 @@ def test_ntv_classes_woody_herbaceous():
         ],
         dtype="uint8",
     )
- 
+
     water_season = np.array(
         [
             [
@@ -161,7 +163,9 @@ def test_ntv_classes_woody_herbaceous():
         dtype="uint8",
     )
 
-    xx = image_groups(l34, urban, cultivated, woody, pv_pc_50, water_frequency, water_season)
+    xx = image_groups(
+        l34, urban, cultivated, woody, pv_pc_50, water_frequency, water_season
+    )
 
     stats_l4 = StatsLccsLevel4()
     level3 = lc_level3.lc_level3(xx)
@@ -245,7 +249,6 @@ def test_ntv_herbaceous_seasonal_water_veg_cover():
             ]
         ],
         dtype="uint8",
-    
     )
     water_frequency = np.array(
         [
@@ -258,7 +261,7 @@ def test_ntv_herbaceous_seasonal_water_veg_cover():
         ],
         dtype="uint8",
     )
-  
+
     water_season = np.array(
         [
             [
@@ -271,7 +274,9 @@ def test_ntv_herbaceous_seasonal_water_veg_cover():
         dtype="uint8",
     )
 
-    xx = image_groups(l34, urban, cultivated, woody, pv_pc_50, water_frequency, water_season)
+    xx = image_groups(
+        l34, urban, cultivated, woody, pv_pc_50, water_frequency, water_season
+    )
 
     stats_l4 = StatsLccsLevel4()
     level3 = lc_level3.lc_level3(xx)
@@ -285,7 +290,7 @@ def test_ntv_herbaceous_seasonal_water_veg_cover():
     l4_ctv_ntv_nav = l4_natural_aquatic.natural_auquatic_veg(
         l4_ctv_ntv, veg_cover, xx.water_season
     )
- 
+
     assert (l4_ctv_ntv_nav.compute() == expected_l4_ntv_classes).all()
 
 
@@ -356,7 +361,7 @@ def test_ntv_woody_seasonal_water_veg_cover():
         ],
         dtype="uint8",
     )
-    
+
     water_frequency = np.array(
         [
             [
@@ -368,7 +373,7 @@ def test_ntv_woody_seasonal_water_veg_cover():
         ],
         dtype="uint8",
     )
-   
+
     water_season = np.array(
         [
             [
@@ -380,7 +385,9 @@ def test_ntv_woody_seasonal_water_veg_cover():
         ],
         dtype="uint8",
     )
-    xx = image_groups(l34, urban, cultivated, woody, pv_pc_50, water_frequency, water_season)
+    xx = image_groups(
+        l34, urban, cultivated, woody, pv_pc_50, water_frequency, water_season
+    )
 
     stats_l4 = StatsLccsLevel4()
     level3 = lc_level3.lc_level3(xx)

tests/test_lc_l4_water.py

@@ -74,12 +74,7 @@ def image_groups(l34, urban, cultivated, woody, bs_pc_50, pv_pc_50, water_freque
 
 def test_water_classes():
     expected_water_classes = [
-        [
-            [104, 104, 104],
-            [103, 103, 103],
-            [102, 102, 101],
-            [99, 101, 101]
-        ],
+        [[104, 104, 104], [103, 103, 103], [102, 102, 101], [99, 101, 101]],
     ]
 
     l34 = np.array(
@@ -185,10 +180,10 @@ def test_water_classes():
 def test_water_intertidal():
 
     expected_water_classes = [
-          [100, 100, 100],
-          [100, 100, 100],
-          [102, 102, 101],
-          [101,  99, 100],
+        [100, 100, 100],
+        [100, 100, 100],
+        [102, 102, 101],
+        [101, 99, 100],
     ]
 
     l34 = np.array(