Fix some warnings from LGTM

ciphers/enigma_machine2.py

@@ -124,7 +124,8 @@ def _plugboard(pbstring: str) -> dict:
     # a) is type string
     # b) has even length (so pairs can be made)
     if not isinstance(pbstring, str):
-        raise TypeError(f"Plugboard setting isn't type string ({type(pbstring)})")
+        raise TypeError(
+            f"Plugboard setting isn't type string ({type(pbstring)})")
     elif len(pbstring) % 2 != 0:
         raise Exception(f"Odd number of symbols ({len(pbstring)})")
     elif pbstring == "":
@@ -218,7 +219,6 @@ def enigma(
     rotorpos1 -= 1
     rotorpos2 -= 1
     rotorpos3 -= 1
-    plugboard = plugboard
 
     result = []
 

graphs/directed_and_undirected_(weighted)_graph.py

@@ -226,7 +226,9 @@ def has_cycle(self):
                                 break
                             else:
                                 return True
-                                anticipating_nodes.add(stack[len_stack_minus_one])
+                                # TODO:The following code is unreachable.
+                                anticipating_nodes.add(
+                                    stack[len_stack_minus_one])
                                 len_stack_minus_one -= 1
                     if visited.count(node[1]) < 1:
                         stack.append(node[1])
@@ -453,7 +455,10 @@ def has_cycle(self):
                                 break
                             else:
                                 return True
-                                anticipating_nodes.add(stack[len_stack_minus_one])
+                                # TODO: the following code is unreachable
+                                # is this meant to be called in the else ?
+                                anticipating_nodes.add(
+                                    stack[len_stack_minus_one])
                                 len_stack_minus_one -= 1
                     if visited.count(node[1]) < 1:
                         stack.append(node[1])

graphs/minimum_spanning_tree_boruvka.py

@@ -146,6 +146,7 @@ def union(self, item1, item2):
                 self.parent[root2] = root1
                 return root1
 
+    @staticmethod
     def boruvka_mst(graph):
         """
         Implementation of Boruvka's algorithm

maths/chudnovsky_algorithm.py

@@ -44,15 +44,16 @@ def pi(precision: int) -> str:
     getcontext().prec = precision
     num_iterations = ceil(precision / 14)
     constant_term = 426880 * Decimal(10005).sqrt()
-    multinomial_term = 1
     exponential_term = 1
     linear_term = 13591409
     partial_sum = Decimal(linear_term)
     for k in range(1, num_iterations):
-        multinomial_term = factorial(6 * k) // (factorial(3 * k) * factorial(k) ** 3)
+        multinomial_term = factorial(
+            6 * k) // (factorial(3 * k) * factorial(k) ** 3)
         linear_term += 545140134
         exponential_term *= -262537412640768000
-        partial_sum += Decimal(multinomial_term * linear_term) / exponential_term
+        partial_sum += Decimal(multinomial_term *
+                               linear_term) / exponential_term
     return str(constant_term / partial_sum)[:-1]
 
 

other/triplet_sum.py

@@ -61,8 +61,7 @@ def triplet_sum2(arr: List[int], target: int) -> Tuple[int, int, int]:
                 left += 1
             elif arr[i] + arr[left] + arr[right] > target:
                 right -= 1
-    else:
-        return (0, 0, 0)
+    return (0, 0, 0)
 
 
 def solution_times() -> Tuple[float, float]:

scheduling/shortest_job_first.py

@@ -11,7 +11,6 @@
 def calculate_waitingtime(
     arrival_time: List[int], burst_time: List[int], no_of_processes: int
 ) -> List[int]:
-
     """
     Calculate the waiting time of each processes
     Return: list of waiting times.
@@ -112,7 +111,8 @@ def calculate_average_times(
     for i in range(no_of_processes):
         total_waiting_time = total_waiting_time + waiting_time[i]
         total_turn_around_time = total_turn_around_time + turn_around_time[i]
-    print("Average waiting time = %.5f" % (total_waiting_time / no_of_processes))
+    print("Average waiting time = %.5f" %
+          (total_waiting_time / no_of_processes))
     print("Average turn around time =", total_turn_around_time / no_of_processes)
 
 
@@ -126,15 +126,20 @@ def calculate_average_times(
     for i in range(no_of_processes):
         print("Enter the arrival time and brust time for process:--" + str(i + 1))
         arrival_time[i], burst_time[i] = map(int, input().split())
-    waiting_time = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
+
+    waiting_time = calculate_waitingtime(
+        arrival_time, burst_time, no_of_processes)
+
     bt = burst_time
     n = no_of_processes
     wt = waiting_time
     turn_around_time = calculate_turnaroundtime(bt, n, wt)
+
     calculate_average_times(waiting_time, turn_around_time, no_of_processes)
-    processes = list(range(1, no_of_processes + 1))
+
     fcfs = pd.DataFrame(
-        list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
+        list(zip(processes, burst_time, arrival_time,
+                 waiting_time, turn_around_time)),
         columns=[
             "Process",
             "BurstTime",