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

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

datacube.py

@@ -22,20 +22,14 @@
 
 import random
 from datetime import datetime, timedelta
+
 import geopandas as gpd
-import pystac
-import pystac_client
+import numpy as np
 import planetary_computer as pc
+import pystac_client
 import stackstac
 import xarray as xr
-import numpy as np
-from shapely.geometry import box, shape, Point
-import leafmap
-from rasterio.errors import RasterioIOError
-from rasterio.warp import transform_bounds
-from getpass import getpass
-import os
-import json
+from shapely.geometry import box
 
 STAC_API = "https://planetarycomputer.microsoft.com/api/stac/v1"
 S2_BANDS = ["B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12", "SCL"]
@@ -75,8 +69,8 @@ def get_week(year, month, day):
     date = datetime(year, month, day)
     start_of_week = date - timedelta(days=date.weekday())
     end_of_week = start_of_week + timedelta(days=6)
-    start_date_str = start_of_week.strftime('%Y-%m-%d')
-    end_date_str = end_of_week.strftime('%Y-%m-%d')
+    start_date_str = start_of_week.strftime("%Y-%m-%d")
+    end_date_str = end_of_week.strftime("%Y-%m-%d")
     return f"{start_date_str}/{end_date_str}"
 
 
@@ -241,9 +235,7 @@ def search_dem(BBOX, catalog, week, s2_items):
     Returns:
     - tuple: A tuple containing DEM items and DEM GeoDataFrame.
     """
-    search = catalog.search(
-        collections=["cop-dem-glo-30"], bbox=BBOX
-    )
+    search = catalog.search(collections=["cop-dem-glo-30"], bbox=BBOX)
     dem_items = search.item_collection()
     print(f"Found {len(dem_items)} items")
 
@@ -281,7 +273,9 @@ def make_dataarrays(s2_items, s1_items, dem_items, BBOX, resolution, epsg):
     )
 
     da_sen1: xr.DataArray = stackstac.stack(
-        items=s1_items[1:],  # To only accept the same orbit state and date. Need better way to do this.
+        items=s1_items[
+            1:
+        ],  # To only accept the same orbit state and date. Need better way to do this.
         assets=["vh", "vv"],  # SAR polarizations
         epsg=epsg,
         bounds_latlon=BBOX,  # W, S, E, N
@@ -318,7 +312,7 @@ def make_dataarrays(s2_items, s1_items, dem_items, BBOX, resolution, epsg):
         fill_value=np.nan,
     )
 
-    _, index = np.unique(da_dem['time'], return_index=True)  # Remove redundant time
+    _, index = np.unique(da_dem["time"], return_index=True)  # Remove redundant time
     da_dem = da_dem.isel(time=index)
 
     return da_sen2, da_sen1, da_dem
@@ -336,7 +330,7 @@ def merge_datarrays(da_sen2, da_sen1, da_dem):
     Returns:
     - xr.DataArray: Merged xarray DataArray.
     """
-    da_merge = xr.merge([da_sen2, da_sen1, da_dem], compat='override')
+    da_merge = xr.merge([da_sen2, da_sen1, da_dem], compat="override")
     print(da_merge)
     return da_merge
 
@@ -365,16 +359,18 @@ def process(year1, year2, aoi, resolution, epsg):
     s1_items, s1_gdf = search_sentinel1(BBOX, catalog, week)
     # s1_items, s1_gdf = search_sentinel1_calc_max_area(BBOX, catalog, week) # WIP
 
-
     dem_items, dem_gdf = search_dem(BBOX, catalog, week, s2_items)
 
-    da_sen2, da_sen1, da_dem = make_dataarrays(s2_items, s1_items, dem_items, BBOX, resolution, epsg)
+    da_sen2, da_sen1, da_dem = make_dataarrays(
+        s2_items, s1_items, dem_items, BBOX, resolution, epsg
+    )
 
     da_merge = merge_datarrays(da_sen2, da_sen1, da_dem)
     return da_merge
 
+
 # EXAMPLE
-california_tile = gpd.read_file("ca.geojson") 
+california_tile = gpd.read_file("ca.geojson")
 sample = california_tile.sample(1)
 aoi = sample.iloc[0].geometry
-process(2017, 2023,  aoi, 10, 26910) # UTM Zone 10N and spatial resolution of 10 metres
+process(2017, 2023, aoi, 10, 26910)  # UTM Zone 10N and spatial resolution of 10 metres