formatting

rail/control.py

@@ -7,30 +7,37 @@
 import numpy as np
 from scipy.stats import lognorm
 
+
 class Control(UserDict):
     """
     A class to represent Controls
     """
-    def __init__(self, name: str, cost: float, reduction: float, implemented: bool=True) -> None:
+
+    def __init__(
+        self, name: str, cost: float, reduction: float, implemented: bool = True
+    ) -> None:
         self.data = {}
-        self.data['name'] = name
-        self.data['cost'] = cost
-        self.data['reduction'] = reduction
-        self.data['implemented'] = implemented
+        self.data["name"] = name
+        self.data["cost"] = cost
+        self.data["reduction"] = reduction
+        self.data["implemented"] = implemented
 
     def evaluate_lognormal(self, iterations=1):
         return Control(
-            name = self.data['name'],
-            cost = lognorm.ppf(np.random.rand(iterations), s=np.log(self.data['cost'])),
-            reduction = lognorm.ppf(np.random.rand(iterations), s=np.log(self.data['reduction'])),
-            implemented = self.data['implemented']
+            name=self.data["name"],
+            cost=lognorm.ppf(np.random.rand(iterations), s=np.log(self.data["cost"])),
+            reduction=lognorm.ppf(
+                np.random.rand(iterations), s=np.log(self.data["reduction"])
+            ),
+            implemented=self.data["implemented"],
         )
 
 
 class Controls(UserDict):
     """
     A class to hold multiple Controls
     """
+
     def __init__(self) -> None:
         self.data = {}
 
@@ -45,11 +52,26 @@ def costs(self):
         """
         A method to compute the deterministic costs of implemented controls in a Controls class
         """
-        return np.sum(list(map(lambda x: x['cost'] if x['implemented'] is True else 0, self.data.values())))
+        return np.sum(
+            list(
+                map(
+                    lambda x: x["cost"] if x["implemented"] is True else 0,
+                    self.data.values(),
+                )
+            )
+        )
 
     def costs_lognormal(self):
         """
         A method to compute the stochastic costs of implemented controls in a Controls class
         """
-        return np.sum(list(map(lambda x: x.evaluate_lognormal().data['cost'] if x.data['implemented'] is True else 0, self)))
-
+        return np.sum(
+            list(
+                map(
+                    lambda x: x.evaluate_lognormal().data["cost"]
+                    if x.data["implemented"] is True
+                    else 0,
+                    self,
+                )
+            )
+        )