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

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

examples/india_box_plots.py

@@ -1,8 +1,8 @@
 """Box plots of max cumulative energy generation per system"""
-import pandas as pd
+import h5py
 import numpy as np
+import pandas as pd
 import plotly.graph_objects as go
-import h5py
 
 # load hdf file with the generation data for each system
 pv_systems_hdf = os.environ.get("PV_DATA_HDF")
@@ -45,9 +45,7 @@
         df_pv_system["index"] = pd.to_datetime(df_pv_system["index"], unit="ns")
         df_pv_system = df_pv_system.groupby(pd.Grouper(key="index", freq="D")).max()
         df_pv_system["System ID"] = system_id
-        df_pv_system = pd.DataFrame(
-            df_pv_system, columns=["cumulative_energy_gen_Wh", "System ID"]
-        )
+        df_pv_system = pd.DataFrame(df_pv_system, columns=["cumulative_energy_gen_Wh", "System ID"])
         df_pv_system["cumulative_energy_gen_kWh"] = (
             df_pv_system["cumulative_energy_gen_Wh"] / 1000
         ).astype(float)

examples/india_chart_per_system.py

@@ -1,10 +1,12 @@
 """Chart generation for a single system. This shows the cumulative energy
-generation or instantaneous power generation over time. Some of the commented out code can be used to filter the data by date or plot max, mean, min, median values, etc."""
+generation or instantaneous power generation over time. Some of the commented out code can be used to filter the data by date or plot max, mean, min, median values, etc.
+"""
 import os
-import pandas as pd
+
+import h5py
 import numpy as np
+import pandas as pd
 import plotly.express as px
-import h5py
 
 # load hdf file with the generation data for each system
 pv_systems_hdf = os.environ.get("PV_DATA_HDF")

examples/india_gantt_chart.py

@@ -1,9 +1,10 @@
 """Gantt chart for India PV systems. This shows where there are gaps in the data."""
 import os
-import pandas as pd
+
+import h5py
 import numpy as np
+import pandas as pd
 import plotly.express as px
-import h5py
 
 # load hdf file with the generation data for each system
 pv_data_hdf = os.environ.get("PV_DATA_HDF")

examples/india_map.py

@@ -1,7 +1,6 @@
 """ Example of plotting PVOutput India system locations on a map."""
-import os
-import plotly.express as px
 import pandas as pd
+import plotly.express as px
 
 # load csv file with system metadata
 pv_system_metadata = "./examples/pv_data/PVOutput_India_systems.csv"

examples/india_mean_production.py

@@ -3,11 +3,12 @@
 This makes a plot with 2 columns and half as many rows as there are systems with data.
 """
 import os
-import pandas as pd
-from plotly.subplots import make_subplots
+
+import h5py
 import numpy as np
+import pandas as pd
 import plotly.graph_objects as go
-import h5py
+from plotly.subplots import make_subplots
 
 # load hdf file with the generation data for each system
 pv_data_hdf_file = os.environ.get("SYSTEM_DATA")
@@ -66,9 +67,7 @@ def row(row):
         # df["index"] = df[df["index"] > pd.Timestamp("2019-01-01")]
         df_pv_system = df.groupby(pd.Grouper(key="index", freq="M")).mean()
         df_pv_system["System ID"] = system_id
-        df_pv_system = pd.DataFrame(
-            df_pv_system, columns=["cumulative_energy_gen_Wh", "System ID"]
-        )
+        df_pv_system = pd.DataFrame(df_pv_system, columns=["cumulative_energy_gen_Wh", "System ID"])
         # convert Wh to kWh
         df_pv_system["cumulative_energy_gen_kWh"] = (
             df_pv_system["cumulative_energy_gen_Wh"] / 1000
@@ -99,8 +98,6 @@ def row(row):
             )
         i += 1
     fig.update_yaxes(title_text="kWh")
-    fig.update_layout(
-        height=3000, width=750, title_text="Mean Monthly Production per System"
-    )
+    fig.update_layout(height=3000, width=750, title_text="Mean Monthly Production per System")
     fig.update_annotations(font_size=12)
     fig.show()

examples/india_system_capacity_histogram.py

@@ -1,5 +1,6 @@
 """Histogram of system capacities for India."""
 import os
+
 import pandas as pd
 import plotly.express as px
 

pvoutput/mapscraper.py

@@ -350,6 +350,7 @@ def clean_soup(soup):
     """Function to clean scraped soup object.
 
     Note that the downloaded soup could change over time.
+
     Args:
         soup: bs4.BeautifulSoup
 

pvoutput/pvoutput.py

@@ -924,7 +924,7 @@ def _download_multiple_worker(
                     )
             else:
                 total_rows += len(timeseries)
-                _LOG.info(f'Adding timezone {timezone} to {total_rows} rows')
+                _LOG.info(f"Adding timezone {timezone} to {total_rows} rows")
                 timeseries = timeseries.tz_localize(timezone)
                 _LOG.info(
                     "system_id: %d: %d rows retrieved: %s to %s",

scripts/fetch_pv_timeseries.py

@@ -21,14 +21,15 @@
 or create and use a ~/.pvoutput.yml file as described in the PVOutput library documentation
 """
 
-from pvoutput import *
-
-import click as cl
 import datetime as dt
+import logging
+import pathlib
 import sys
+
+import click as cl
 import pandas as pd
-import pathlib
-import logging
+
+from pvoutput import *
 
 
 @cl.command()