Merge pull request #16 from cmccomb/master

Fixing `parthworth` typo

sae/__init__.py

@@ -228,7 +228,7 @@ def objectives(
         else:
             return global_obj
 
-    def parthworth_objectives(
+    def partworth_objectives(
         self,
         weights: NDArray = weights_pw_null,
         with_subobjs: bool = True,
@@ -991,13 +991,13 @@ def objectives(
             tominimize_and_scaled=tominimize_and_scaled,
         )
 
-    def parthworth_objectives(
+    def partworth_objectives(
         self,
         weights: NDArray = weights_pw_null,
         with_subobjs: bool = True,
         tominimize_and_scaled: bool = True,
     ) -> Union[float, tuple[float, NDArray]]:
-        return self.car.parthworth_objectives(
+        return self.car.partworth_objectives(
             weights=weights,
             with_subobjs=with_subobjs,
             tominimize_and_scaled=tominimize_and_scaled,

setup.py

@@ -3,13 +3,13 @@
 from setuptools import setup, find_packages
 
 setup(
-    name='sae',
-    version='0.1.0',
-    description='An implementation of an SAE systems design problem',
-    author='Akash Agrawal',
-    author_email='ask-drc@andrew.cmu.edu',
-    url='',
+    name="sae",
+    version="0.1.0",
+    description="An implementation of an SAE systems design problem",
+    author="Akash Agrawal",
+    author_email="ask-drc@andrew.cmu.edu",
+    url="",
     install_requires=["numpy", "scipy", "pandas", "openpyxl"],
     packages=find_packages(),
     include_package_data=True,
-)
+)

tests/test.py

@@ -3,12 +3,14 @@
 from sae import COTSCar, Car, weightsNull, weights1, generate_feasible
 from numpy import clip
 
-class TestFullProblem(unittest.TestCase):
 
+class TestFullProblem(unittest.TestCase):
     def test_random_generation(self):
         # generate a random car that always satisfies constraints_bound and constraints_lin_ineq
         test_car = Car()
-        test_car.objectives(weights=weights1, with_subobjs=True, tominimize_and_scaled=True)
+        test_car.objectives(
+            weights=weights1, with_subobjs=True, tominimize_and_scaled=True
+        )
 
         # evaluate constraint violation penalites (square penalty)
         test_car.constraints_bound()
@@ -19,7 +21,9 @@ def test_random_generation(self):
     def test_feasible_generation(self):
         # generate a random car that always satisfies constraints_bound and constraints_lin_ineq
         test_car = generate_feasible()
-        test_car.objectives(weights=weights1, with_subobjs=True, tominimize_and_scaled=True)
+        test_car.objectives(
+            weights=weights1, with_subobjs=True, tominimize_and_scaled=True
+        )
 
         # evaluate constraint violation penalites (square penalty)
         test_car.constraints_bound()
@@ -29,7 +33,7 @@ def test_feasible_generation(self):
 
     def test_pw(self):
         test_car = generate_feasible()
-        test_car.parthworth_objectives()
+        test_car.partworth_objectives()
 
     def test_minimize(self):
         def round_x(x):
@@ -61,18 +65,17 @@ def penalty_3(x):
         res = scipy.optimize.minimize(
             objective,
             generate_feasible().vector,
-            method='trust-constr',
+            method="trust-constr",
             constraints=(
-                {'type': 'eq', 'fun': penalty_1},
-                {'type': 'eq', 'fun': penalty_2},
-                {'type': 'eq', 'fun': penalty_3}
+                {"type": "eq", "fun": penalty_1},
+                {"type": "eq", "fun": penalty_2},
+                {"type": "eq", "fun": penalty_3},
             ),
         )
         print(res)
 
 
 class TestCOTSProblem(unittest.TestCase):
-
     def test_random_generation_cots(self):
         # generate a random car that always satisfies constraints_bound and constraints_lin_ineq
         test_car = COTSCar()
@@ -96,12 +99,15 @@ def test_feasible_generation_cots(self):
         test_car.cost()
 
     def test_minimize_cots(self):
-
         def round_x(x):
             for i in range(len(x)):
                 rounded = round(x[i])
                 x[i] = rounded
-            return clip(x, 0, [12, 12, 12, 12, 12, 6, 6, 20, 33, 4, 63, 191, 4, 4, 215, 215, 215])
+            return clip(
+                x,
+                0,
+                [12, 12, 12, 12, 12, 6, 6, 20, 33, 4, 63, 191, 4, 4, 215, 215, 215],
+            )
 
         def objective(x):
             c = COTSCar()
@@ -126,14 +132,11 @@ def penalty_3(x):
         res = scipy.optimize.minimize(
             objective,
             generate_feasible(cots=True).vector,
-            method='trust-constr',
+            method="trust-constr",
             constraints=(
-                {'type': 'eq', 'fun': penalty_1},
-                {'type': 'eq', 'fun': penalty_2},
-                {'type': 'eq', 'fun': penalty_3}
+                {"type": "eq", "fun": penalty_1},
+                {"type": "eq", "fun": penalty_2},
+                {"type": "eq", "fun": penalty_3},
             ),
         )
         print(res)
-
-
-