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

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

Author: pre-commit-ci[bot]

notebooks/2021-08/2021-08-25/video.py

@@ -3,7 +3,6 @@
 import cv2
 import numpy as np
 import pandas as pd
-import plotly
 import plotly.graph_objects as go
 
 from nowcasting_dataset.dataset import SAT_MEAN, SAT_STD, NetCDFDataset
@@ -69,7 +68,6 @@
 channel_indexes = [1, 8, 9]
 satellite_data = []
 for channel_index in channel_indexes:
-
     # renormalize
     satellite_data.append(
         data["sat_data"][batch_index, :, :, :, channel_index] * SAT_STD.values[channel_index]

notebooks/2021-08/2021-08-26/video.py

@@ -3,8 +3,6 @@
 import cv2
 import numpy as np
 import pandas as pd
-import plotly
-import plotly.express as px
 import plotly.graph_objects as go
 
 # get satellite image, currently from https://github.com/openclimatefix/py-staticmaps
@@ -90,7 +88,6 @@
 channel_indexes = [1, 9, 8]
 satellite_data = []
 for channel_index in channel_indexes:
-
     # renormalize
     satellite_data.append(
         data["sat_data"][batch_index, :, :, :, channel_index] * SAT_STD.values[channel_index]

notebooks/2021-09/2021-09-06/gsp.py

@@ -36,7 +36,7 @@
 
 
 # find out if point is in gsp
-from shapely.geometry import Point, Polygon
+from shapely.geometry import Point
 
 _pnts = [Point(3, 3), Point(8, 8), Point(0, 51.38)]
 pnts = gpd.GeoDataFrame(geometry=_pnts, index=["A", "B", "C"])

notebooks/2021-09/2021-09-07/gsp.py

@@ -38,7 +38,7 @@
 
 
 # find out if point is in gsp
-from shapely.geometry import Point, Polygon
+from shapely.geometry import Point
 
 _pnts = [Point(3, 3), Point(8, 8), Point(0, 51.38)]
 pnts = gpd.GeoDataFrame(geometry=_pnts, index=["A", "B", "C"])

notebooks/2021-09/2021-09-08/dan.py

@@ -1,4 +1,3 @@
-import gcsfs
 import xarray as xr
 
 filename = "gs://solar-pv-nowcasting-data/satellite/EUMETSAT/SEVIRI_RSS/OSGB36/all_zarr_int16_single_timestep.zarr/"

notebooks/2021-09/2021-09-13/remove_hash.py

@@ -20,7 +20,6 @@
 
 for filenames in [train_filenames, validation_filenames]:
     for file in train_filenames:
-
         print(file)
 
         filename = file.split("/")[-1]
@@ -30,7 +29,7 @@
 
             try:
                 rename_file(remote_file=file, new_filename=new_filename)
-            except Exception as e:
+            except Exception:
                 pass
         else:
             print(f"Skipping {filename}")

notebooks/2021-09/2021-09-14/gsp_centroid.py

@@ -4,7 +4,6 @@
 
 from nowcasting_dataset.data_sources.gsp.eso import (
     get_gsp_metadata_from_eso,
-    get_gsp_shape_from_eso,
 )
 from nowcasting_dataset.geospatial import WGS84_CRS
 
@@ -16,7 +15,6 @@
 
 # for index in range(0, len(shape_data_raw)):
 for index in range(140, 150):
-
     # just select the first one
     shape_data = shape_data_raw.iloc[index : index + 1]
     shapes_dict = json.loads(shape_data["geometry"].to_json())

notebooks/2021-09/2021-09-14/gsp_duplicated.py

@@ -3,7 +3,6 @@
 import plotly.graph_objects as go
 
 from nowcasting_dataset.data_sources.gsp.eso import (
-    get_gsp_metadata_from_eso,
     get_gsp_shape_from_eso,
 )
 
@@ -13,7 +12,6 @@
 duplicated_raw["Amount"] = range(0, len(duplicated_raw))
 
 for i in range(0, 8, 2):
-
     # just select the first one
     duplicated = duplicated_raw.iloc[i : i + 2]
     shapes_dict = json.loads(duplicated["geometry"].to_json())

notebooks/2021-09/2021-09-28/get_raw_sun_data.py

@@ -23,7 +23,6 @@
 from nowcasting_dataset.data_sources.gsp.eso import get_gsp_metadata_from_eso
 from nowcasting_dataset.data_sources.sun.raw_data_load_save import (
     get_azimuth_and_elevation,
-    save_to_zarr,
 )
 
 # set up

notebooks/2021-09/2021-09-29/gsp_duplicated.py

@@ -3,7 +3,6 @@
 import plotly.graph_objects as go
 
 from nowcasting_dataset.data_sources.gsp.eso import (
-    get_gsp_metadata_from_eso,
     get_gsp_shape_from_eso,
 )
 from nowcasting_dataset.geospatial import WGS84_CRS
@@ -15,7 +14,6 @@
 duplicated_raw["Amount"] = range(0, len(duplicated_raw))
 
 for i in range(0, 8, 2):
-
     # just select the first one
     duplicated = duplicated_raw.iloc[i : i + 2]
     shapes_dict = json.loads(duplicated["geometry"].to_json())