From e6e8f49816f4b6756b176933d9b793ed4b49718e Mon Sep 17 00:00:00 2001
From: Joshua Burman <jburman@meazurelearning.com>
Date: Tue, 19 Apr 2022 14:49:31 -0400
Subject: [PATCH] formatting and print removal

---
 app/test/test_example.py | 36 +++++++++++++++---------------------
 1 file changed, 15 insertions(+), 21 deletions(-)

diff --git a/app/test/test_example.py b/app/test/test_example.py
index 51198ee..acdc04e 100644
--- a/app/test/test_example.py
+++ b/app/test/test_example.py
@@ -1,6 +1,7 @@
 # content of example.py
 from pulp import LpProblem, LpVariable, LpMinimize, LpStatus, lpSum
 
+
 def func(x):
     return x + 1
 
@@ -12,22 +13,11 @@ def test_pass():
 def test_failure():
     assert func(3) == 5
 
+
 def yosh_loop():
-    Items = [1,2,3,4,5]
-    tif = {
-        1: 0.2,
-        2: 0.5,
-        3: 0.3,
-        4: 0.8,
-        5: 0.1
-    }
-    iif = {
-        1: 0.09,
-        2: 0.2,
-        3: 0.113,
-        4: 0.3,
-        5: 0.1
-    }
+    Items = [1, 2, 3, 4, 5]
+    tif = {1: 0.2, 2: 0.5, 3: 0.3, 4: 0.8, 5: 0.1}
+    iif = {1: 0.09, 2: 0.2, 3: 0.113, 4: 0.3, 5: 0.1}
     drift = 0.0
     drift_limit = 0.2
     iif_target = 0.5
@@ -37,12 +27,17 @@ def yosh_loop():
 
     while drift <= drift_limit:
         prob = LpProblem("tif_tcc_test", LpMinimize)
-        prob += lpSum([(tif[i] + iif[i]) * item_vars[i] for i in Items]), "TifTccSum"
+        prob += lpSum([(tif[i] + iif[i]) * item_vars[i]
+                       for i in Items]), "TifTccSum"
         prob += lpSum([item_vars[i] for i in Items]) == 3, "TotalItems"
-        prob += lpSum([tif[i] * item_vars[i] for i in Items]) >= tif_target - (tif_target * drift), 'TifMin'
-        prob += lpSum([tif[i] * item_vars[i] for i in Items]) <= tif_target  + (tif_target * drift), 'TifMax'
-        prob += lpSum([iif[i] * item_vars[i] for i in Items]) >= iif_target - (iif_target * drift), 'TccMin'
-        prob += lpSum([iif[i] * item_vars[i] for i in Items]) <= iif_target + (iif_target * drift), 'TccMax'
+        prob += lpSum([tif[i] * item_vars[i] for i in Items
+                       ]) >= tif_target - (tif_target * drift), 'TifMin'
+        prob += lpSum([tif[i] * item_vars[i] for i in Items
+                       ]) <= tif_target + (tif_target * drift), 'TifMax'
+        prob += lpSum([iif[i] * item_vars[i] for i in Items
+                       ]) >= iif_target - (iif_target * drift), 'TccMin'
+        prob += lpSum([iif[i] * item_vars[i] for i in Items
+                       ]) <= iif_target + (iif_target * drift), 'TccMax'
 
         prob.solve()
 
@@ -62,5 +57,4 @@ def yosh_loop():
 
 
 def test_solver():
-    print(yosh_loop())
     assert yosh_loop() == 'Optimal'