lint

india_forecast_app/model.py

@@ -1,7 +1,6 @@
 """
 Model classes (currently just allows for loading a dummy model)
 """
-
 import datetime as dt
 import math
 import random
@@ -52,7 +51,9 @@ def _generate_dummy_forecast(self, timestamp: dt.datetime):
             values_df = pd.DataFrame(values)
 
             # smooth the wind forecast
-            values_df["forecast_power_kw"] = values_df["forecast_power_kw"].rolling(6, min_periods=1).mean()
+            values_df["forecast_power_kw"] = (
+                values_df["forecast_power_kw"].rolling(6, min_periods=1).mean()
+            )
 
             # turn back to list of dicts
             values = values_df.to_dict(orient="records")
@@ -94,14 +95,12 @@ def _basicSolarYieldFunc(timeUnix: int, scaleFactor: int = 4000) -> float:
     # Remove negative values
     basefunc = max(0, basefunc)
     # Steepen the curve. The divisor is based on the max value
-    basefunc = basefunc**4 / 1.01**4
+    basefunc = basefunc ** 4 / 1.01 ** 4
 
     # Instead of completely random noise, apply based on the following process:
     # * A base noise function which is the product of long and short sines
     # * The resultant function modulates with very small amplitude around 1
-    noise = (math.sin(math.pi * time.hour) / 20) * (
-        math.sin(math.pi * time.hour / 3)
-    ) + 1
+    noise = (math.sin(math.pi * time.hour) / 20) * (math.sin(math.pi * time.hour / 3)) + 1
     noise = noise * random.random() / 20 + 0.97
 
     # Create the output value from the base function, noise, and scale factor