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

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

carbonplan_trace/fire/grid_viirs_workflow.py

@@ -35,7 +35,6 @@ def calc_buffer_distance(lats, buffer_m=375):
 
 
 def rasterize_geom(geoms, transform, shape):
-
     r = rasterize(
         [(geom, 1) for geom in geoms],
         out_shape=shape,
@@ -85,7 +84,6 @@ def open_target_grid(tile_id):
 
 
 def process_one_year(year):
-
     print(year)
     gdf = geopandas.read_parquet(
         f's3://carbonplan-climatetrace/inputs/processed/viirs/{year}.parquet'

carbonplan_trace/metadata.py

@@ -8,7 +8,6 @@
 
 
 def get_cf_global_attrs(**attrs):
-
     if 'history' not in attrs:
         attrs['history'] = 'Created: {}'.format(time.ctime(time.time()))
 

carbonplan_trace/v0/workflow.py

@@ -78,7 +78,6 @@ def process_one_tile(tile_id):
 
     # calc emissions
     if not (skip_existing and zarr_is_complete(tot_mapper) and zarr_is_complete(split_mapper)):
-
         lat, lon = tile_id.split('_')
         fire_da = open_fire_mask(tile_id).fillna(0)
         change_ds = open_hansen_change_tile(lat, lon)
@@ -112,7 +111,6 @@ def process_one_tile(tile_id):
         (tot_mapper, coarse_tot_mapper, tot_encoding),
         (split_mapper, coarse_split_mapper, split_encoding),
     ]:
-
         if not (skip_existing and zarr_is_complete(out_mapper)):
             ds = xr.open_zarr(in_mapper, consolidated=True)
 
@@ -134,7 +132,6 @@ def combine_all_tiles(encoding_kinds=None):
         encoding_kinds = [('tot', tot_encoding), ('split', split_encoding)]
     print('combining all tiles')
     for kind, encoding in encoding_kinds:
-
         mapper = fsspec.get_mapper(coarse_full_template.format(kind=kind))
 
         if not (skip_existing and zarr_is_complete(mapper)):
@@ -169,13 +166,11 @@ def rollup_shapes():
         ('tot', ['emissions']),
         ('split', ['emissions_from_clearing', 'emissions_from_fire']),
     ]:
-
         ds = xr.open_zarr(coarse_full_template.format(kind=kind), consolidated=True)
 
         mask = regionmask.mask_geopandas(shapes_df, ds['lon'], ds['lat'], numbers='numbers')
 
         for var in var_names:
-
             # this will trigger dask compute
             df = ds[var].groupby(mask).sum().to_pandas()
 

carbonplan_trace/v1/emissions_workflow.py

@@ -207,7 +207,6 @@ def coarsen_tile(tile_id):
     for in_mapper, out_mapper, encoding in [
         (split_mapper, coarse_split_mapper, split_encoding),
     ]:
-
         if not (skip_existing and zarr_is_complete(out_mapper)):
             ds = utils.open_result_tile(
                 tile_id, variable='emissions', version='v1.2', resolution='30m', apply_masks=True
@@ -233,7 +232,6 @@ def combine_all_tiles(encoding_kinds=None):
     print('combining all tiles')
 
     for kind, encoding in encoding_kinds:
-
         mapper = fsspec.get_mapper(coarse_full_template.format(kind=kind))
 
         if not (skip_existing and zarr_is_complete(mapper)):
@@ -270,7 +268,6 @@ def rollup_shapes():
     var_names = ['emissions_from_clearing', 'emissions_from_fire', 'sinks', 'net']
 
     for var in var_names:
-
         # this will trigger dask compute
         df = ds[var].groupby(mask).sum().to_pandas()
 

carbonplan_trace/v1/landsat_preprocess.py

@@ -265,7 +265,6 @@ def grab_scene_coord_info(metadata):
 
 
 def get_scene_utm_info(url, json_client):
-
     '''
     Get the USGS-provided UTM zone and letter for the specific landsat scene
 
@@ -330,7 +329,6 @@ def calc_NDVI(ds):
 
 
 def calc_NDII(ds):
-
     '''
     Calculate NDII (Hardisky et al, 1984) based upon bands 4 and 5. *Note* only valid
     for landsat 5 and 7 right now.

carbonplan_trace/v1/postprocess.py

@@ -413,7 +413,6 @@ def postprocess_subtile(parameters_dict):
 
 
 def test_to_zarr(parameters_dict):
-
     lat_increment = parameters_dict['LAT_INCREMENT']
     lon_increment = parameters_dict['LON_INCREMENT']
     year0 = parameters_dict['YEAR_0']
@@ -438,7 +437,6 @@ def test_to_zarr(parameters_dict):
     data_mapper = fs.get_mapper(data_path)
 
     with rio.Env(aws_session):
-
         da = xr.DataArray(
             np.ones((8000, 8000, 2)),
             coords={

carbonplan_trace/v1/training_prep.py

@@ -203,7 +203,6 @@ def add_parquet_urls(df):
 
 
 def find_pertinent_scenes_for_shots(lat_tag, lon_tag, scenes_in_tile_gdf):
-
     file_mapper = fs.get_mapper(
         "carbonplan-climatetrace/v2/data/intermediates/biomass/{}_{}.zarr".format(lat_tag, lon_tag)
     )
@@ -235,7 +234,6 @@ def find_pertinent_scenes_for_shots(lat_tag, lon_tag, scenes_in_tile_gdf):
 def aggregate_parquet_files(
     lat_tag, lon_tag, all_parquet_files, write=True, access_key_id=None, secret_access_key=None
 ):
-
     full_df = None
     for url in all_parquet_files:
         df = pd.read_parquet(url)

notebooks/analysis/validate_biomass.ipynb

@@ -364,7 +364,6 @@
     "    cbar_label=\"\",\n",
     "    cmap=\"viridis\",\n",
     "):\n",
-    "\n",
     "    cax = fig.add_axes([x_location, y_location, width, height])\n",
     "    cax.text(\n",
     "        0.5,\n",

notebooks/analysis/validate_biomass_training_data.ipynb

@@ -889,7 +889,6 @@
     "    cbar_label=\"\",\n",
     "    cmap=\"viridis\",\n",
     "):\n",
-    "\n",
     "    cax = fig.add_axes([x_location, y_location, width, height])\n",
     "    cax.text(\n",
     "        0.5,\n",

notebooks/analysis/validate_height_metric.ipynb

@@ -236,7 +236,6 @@
    "outputs": [],
    "source": [
     "def merge_ds(study, ref, var_name, precision=3):\n",
-    "\n",
     "    variables_study = [\n",
     "        var for var in list(study.variables.keys()) if var_name.lower() in var.lower()\n",
     "    ]\n",
@@ -526,7 +525,6 @@
     "}\n",
     "\n",
     "for var in [\"QMCH\", \"MeanH\"]:\n",
-    "\n",
     "    joined = merge_ds(study=sub, ref=margolis, var_name=var, precision=3)\n",
     "\n",
     "    plt.figure(figsize=(13, 4.5))\n",

notebooks/processing/change_point_detection.ipynb

@@ -235,7 +235,6 @@
     "                # if this subtile has already been ran, continue\n",
     "                continue\n",
     "            else:\n",
-    "\n",
     "                increment_parameters = prefect_parameters.copy()\n",
     "                increment_parameters[\"LAT_INCREMENT\"] = lat_increment\n",
     "                increment_parameters[\"LON_INCREMENT\"] = lon_increment\n",

notebooks/processing/model.ipynb

@@ -110,7 +110,6 @@
     "\n",
     "\n",
     "def get_all_prediction_result(model, df_train, df_test, df_val):\n",
-    "\n",
     "    df_train[\"biomass_pred\"] = model._predict(df_train)\n",
     "    df_test[\"biomass_pred\"] = model._predict(df_test)\n",
     "    df_val[\"biomass_pred\"] = model._predict(df_val)\n",
@@ -687,7 +686,6 @@
     "\n",
     "            # this for loop is for running different parameter sets in HPO\n",
     "            for params in [{}]:\n",
-    "\n",
     "                # instantiating the model also does .fit\n",
     "                # this will load the model if it already exist and overwrite=False, and fit the model if overwrite=True or the model does not exist\n",
     "                model = model_class(\n",

scripts/cache_glas.py

@@ -295,7 +295,6 @@ def cmr_download(urls, cache_location, credentials=None):
 
 
 def main():
-
     url_list = cmr_search(
         short_name,
         version,

scripts/preprocess_glas.py

@@ -145,7 +145,6 @@ def main(products=['GLAH01', 'GLAH14']):
 
 
 if __name__ == '__main__':
-
     client = Client(n_workers=12)
     print(client)
     print(client.dashboard_link)