Merge pull request #470 from rht/F401

Fix flake8 errors

Author: mmcky

.travis.yml

@@ -46,7 +46,7 @@ install:
   - python setup.py install
 
 script:
-  - flake8 --select F401 quantecon
+  - flake8 --select F401, F405,E231 quantecon
   - nosetests --with-coverage -a "!slow" --cover-package=quantecon
 
 after_success:

quantecon/__init__.py

@@ -6,7 +6,8 @@
 try:
 	import numba
 except:
-	raise ImportError("Cannot import numba from current anaconda distribution. Please run `conda install numba` to install the latest version.")
+	raise ImportError(
+		"Cannot import numba from current anaconda distribution. Please run `conda install numba` to install the latest version.")
 
 #-Modules-#
 from . import distributions
@@ -35,8 +36,10 @@
 from .matrix_eqn import solve_discrete_lyapunov, solve_discrete_riccati
 from .quadsums import var_quadratic_sum, m_quadratic_sum
 #->Propose Delete From Top Level
-from .markov import MarkovChain, random_markov_chain, random_stochastic_matrix, gth_solve, tauchen, rouwenhorst 	 	#Promote to keep current examples working
-from .markov import mc_compute_stationary, mc_sample_path 												#Imports that Should be Deprecated with markov package
+#Promote to keep current examples working
+from .markov import MarkovChain, random_markov_chain, random_stochastic_matrix, gth_solve, tauchen, rouwenhorst
+#Imports that Should be Deprecated with markov package
+from .markov import mc_compute_stationary, mc_sample_path
 #<-
 from .rank_nullspace import rank_est, nullspace
 from .robustlq import RBLQ

quantecon/game_theory/game_generators/tests/test_bimatrix_generators.py

@@ -112,7 +112,7 @@ def test_seed(self):
     @raises(ValueError)
     def test_raises_value_error_too_large_inputs(self):
         n, k = 100, 50
-        g = tournament_game(n, k)
+        tournament_game(n, k)
 
 
 class TestUnitVectorGame:
@@ -149,7 +149,7 @@ def test_redraw(self):
     @raises(ValueError)
     def test_raises_value_error_avoid_pure_nash_n_1(self):
         n = 1
-        g = unit_vector_game(n, avoid_pure_nash=True)
+        unit_vector_game(n, avoid_pure_nash=True)
 
 
 def test_payoff_range():

quantecon/game_theory/tests/test_mclennan_tourky.py

@@ -147,7 +147,7 @@ def test_best_response_selection_no_indptr():
                 [(0, 3), (6, 1)]]
     g = NormalFormGame(bimatrix)
 
-    test_obj = _best_response_selection([1/3, 1/3, 1/3, 1/2,1/2], g)
+    test_obj = _best_response_selection([1/3, 1/3, 1/3, 1/2, 1/2], g)
     expected_output = np.array([0., 1., 0., 0., 1.])
 
     assert_array_equal(test_obj, expected_output)

quantecon/game_theory/tests/test_normal_form_game.py

@@ -482,43 +482,43 @@ def test_player_repr():
 
 @raises(ValueError)
 def test_player_zero_actions():
-    p = Player([[]])
+    Player([[]])
 
 
 @raises(ValueError)
 def test_normalformgame_invalid_input_players_shape_inconsistent():
     p0 = Player(np.zeros((2, 3)))
     p1 = Player(np.zeros((2, 3)))
-    g = NormalFormGame([p0, p1])
+    NormalFormGame([p0, p1])
 
 
 @raises(ValueError)
 def test_normalformgame_invalid_input_players_num_inconsistent():
     p0 = Player(np.zeros((2, 2, 2)))
     p1 = Player(np.zeros((2, 2, 2)))
-    g = NormalFormGame([p0, p1])
+    NormalFormGame([p0, p1])
 
 
 @raises(ValueError)
 def test_normalformgame_invalid_input_players_dtype_inconsistent():
     p0 = Player(np.zeros((2, 2), dtype=int))
     p1 = Player(np.zeros((2, 2), dtype=float))
-    g = NormalFormGame([p0, p1])
+    NormalFormGame([p0, p1])
 
 
 @raises(ValueError)
 def test_normalformgame_invalid_input_nosquare_matrix():
-    g = NormalFormGame(np.zeros((2, 3)))
+    NormalFormGame(np.zeros((2, 3)))
 
 
 @raises(ValueError)
 def test_normalformgame_invalid_input_payoff_profiles():
-    g = NormalFormGame(np.zeros((2, 2, 1)))
+    NormalFormGame(np.zeros((2, 2, 1)))
 
 
 @raises(ValueError)
 def test_normalformgame_zero_actions():
-    g = NormalFormGame((2, 0))
+    NormalFormGame((2, 0))
 
 
 # Utility functions #

quantecon/game_theory/tests/test_support_enumeration.py

@@ -66,7 +66,7 @@ def test_no_error_skew_sym(self):
         n, m = 3, 2
         seed = 7028
         g = random_skew_sym(n, m, random_state=seed)
-        NEs = support_enumeration(g)
+        support_enumeration(g)
 
 
 @raises(TypeError)

quantecon/game_theory/vertex_enumeration.py

@@ -225,7 +225,6 @@ def __init__(self, opponent_player, idx=0, qhull_options=None):
         # Shift the payoffs to be nonnegative and have no zero column
         col_mins = B.min(axis=0)
         col_maxs = B.max(axis=0)
-        neg_cols = (col_mins < 0)
         nonpos_const_cols = (col_maxs == col_mins) * (col_mins <= 0)
         shifts = np.zeros(m)
         shifts[col_mins < 0] = -col_mins[col_mins < 0]

quantecon/lss.py

@@ -109,13 +109,17 @@ def __init__(self, A, C, G, H=None, mu_0=None, Sigma_0=None):
         # = Check Input Shapes = #
         ni, nj = self.A.shape
         if ni != nj:
-            raise ValueError("Matrix A (shape: %s) needs to be square" % (self.A.shape))
+            raise ValueError(
+                "Matrix A (shape: %s) needs to be square" % (self.A.shape))
         if ni != self.C.shape[0]:
-            raise ValueError("Matrix C (shape: %s) does not have compatible dimensions with A. It should be shape: %s" % (self.C.shape, (ni,1)))
+            raise ValueError(
+                "Matrix C (shape: %s) does not have compatible dimensions with A. "
+                "It should be shape: %s" % (self.C.shape, (ni, 1)))
         self.m = self.C.shape[1]
         self.k, self.n = self.G.shape
         if self.n != ni:
-            raise ValueError("Matrix G (shape: %s) does not have compatible dimensions with A (%s)"%(self.G.shape, self.A.shape))
+            raise ValueError("Matrix G (shape: %s) does not have compatible dimensions with A (%s)"%(
+                self.G.shape, self.A.shape))
         if H is None:
             self.H = None
             self.l = None

quantecon/markov/core.py

@@ -429,7 +429,6 @@ def cdfs(self):
     def cdfs1d(self):
         if (self._cdfs1d is None) and self.is_sparse:
             data = self.P.data
-            indices = self.P.indices
             indptr = self.P.indptr
 
             cdfs1d = np.empty(self.P.nnz, order='C')

quantecon/markov/ddp.py

@@ -511,7 +511,8 @@ def to_product_form(self):
             R[self.s_indices, self.a_indices] = self.R
             Q = np.zeros((ns, na, ns))
             if self._sparse:
-                _fill_dense_Q(self.s_indices, self.a_indices, self.Q.toarray(), Q)
+                _fill_dense_Q(self.s_indices, self.a_indices,
+                              self.Q.toarray(), Q)
             else:
                 _fill_dense_Q(self.s_indices, self.a_indices, self.Q, Q)
             return DiscreteDP(R, Q, self.beta)