CLN: Remove small bug bears

Remove unused iteration variables and related bug bears

arch/tests/bootstrap/test_bootstrap.py

@@ -147,25 +147,25 @@ def test_state(self):
         bs = IIDBootstrap(np.arange(100))
         bs.seed(23456)
         state = bs.get_state()
-        for data, kwdata in bs.bootstrap(10):
+        for data, _ in bs.bootstrap(10):
             final = data[0]
         bs.seed(23456)
-        for data, kwdata in bs.bootstrap(10):
+        for data, _ in bs.bootstrap(10):
             final_seed = data[0]
         bs.set_state(state)
-        for data, kwdata in bs.bootstrap(10):
+        for data, _ in bs.bootstrap(10):
             final_state = data[0]
         assert_equal(final, final_seed)
         assert_equal(final, final_state)
 
     def test_reset(self):
         bs = IIDBootstrap(np.arange(100))
         state = bs.get_state()
-        for data, kwdata in bs.bootstrap(10):
+        for data, _ in bs.bootstrap(10):
             final = data[0]
         bs.reset()
         state_reset = bs.get_state()
-        for data, kwdata in bs.bootstrap(10):
+        for data, _ in bs.bootstrap(10):
             final_reset = data[0]
         assert_equal(final, final_reset)
         assert_equal(state, state_reset)
@@ -248,7 +248,7 @@ def func(y):
         bs.reset()
         results = np.zeros((num_bootstrap, 2))
         count = 0
-        for pos, kw in bs.bootstrap(num_bootstrap):
+        for pos, _ in bs.bootstrap(num_bootstrap):
             results[count] = func(*pos)
             count += 1
         mu = func(self.x)
@@ -283,7 +283,7 @@ def func(y):
         bs.reset()
         results = np.zeros((num_bootstrap, 2))
         count = 0
-        for pos, kw in bs.bootstrap(num_bootstrap):
+        for pos, _ in bs.bootstrap(num_bootstrap):
             results[count] = func(*pos)
             count += 1
 
@@ -371,7 +371,7 @@ def std_err_func(mu, y):
         results = np.zeros((num_bootstrap, 2))
         stud_results = np.zeros((num_bootstrap, 2))
         count = 0
-        for pos, kwdata in bs.bootstrap(reps=num_bootstrap):
+        for pos, _ in bs.bootstrap(reps=num_bootstrap):
             results[count] = func(*pos)
             std_err = std_err_func(results[count], *pos)
             stud_results[count] = (results[count] - base) / std_err
@@ -398,7 +398,7 @@ def std_err_func(mu, y):
         results = np.zeros((num_bootstrap, 2))
         stud_results = np.zeros((num_bootstrap, 2))
         count = 0
-        for pos, kwdata in bs.bootstrap(reps=num_bootstrap):
+        for pos, _ in bs.bootstrap(reps=num_bootstrap):
             results[count] = func(*pos)
             inner_bs = IIDBootstrap(*pos)
             seed = bs.random_state.randint(2 ** 31 - 1)
@@ -495,7 +495,7 @@ def semi_func(x, params=None):
         results = np.zeros((reps, 2))
         count = 0
         mu = self.x.mean(0)
-        for pos, kw in bs.bootstrap(100):
+        for pos, _ in bs.bootstrap(100):
             results[count] = param_func(*pos, params=mu,
                                         state=bs.random_state)
             count += 1
@@ -508,7 +508,7 @@ def semi_func(x, params=None):
         bs.reset()
         results = np.zeros((reps, 2))
         count = 0
-        for pos, kw in bs.bootstrap(100):
+        for pos, _ in bs.bootstrap(100):
             results[count] = semi_func(*pos, params=mu)
             count += 1
         assert_allclose(bs._results, results)
@@ -626,7 +626,7 @@ def func(y):
         results = bs.apply(func, 1000)
         bs.reset(23456)
         direct_results = []
-        for pos, kw in bs.bootstrap(1000):
+        for pos, _ in bs.bootstrap(1000):
             direct_results.append(func(*pos))
         direct_results = np.array(direct_results)
         assert_equal(results, direct_results)
@@ -641,7 +641,7 @@ def func(y):
         results = bs.apply(func, 1000)
         bs.reset(23456)
         direct_results = []
-        for pos, kw in bs.bootstrap(1000):
+        for pos, _ in bs.bootstrap(1000):
             direct_results.append(func(*pos))
         direct_results = np.array(direct_results)
         direct_results = direct_results[:, None]

arch/tests/univariate/test_variance_forecasting.py

@@ -1467,7 +1467,7 @@ def test_bs_rng_errors(self):
         bs_rng = BootstrapRng(y, 100)
         rng = bs_rng.rng()
         with pytest.raises(IndexError):
-            for i in range(100, 1001):
+            for _ in range(100, 1001):
                 rng(1)
 
         y = self.rng.rand(1000)

arch/unitroot/critical_values/dickey_fuller.py

@@ -6,8 +6,6 @@
 did not appear in the original paper and have been computed using an identical
 simulation.
 """
-from arch.compat.python import iteritems
-
 from numpy import asarray, inf
 
 small_scaling = asarray([1, 1, 1e-2])
@@ -247,7 +245,7 @@
                  'ctt': [3.4216, 0.4170, 1.6939, 0.4203, 0.4153]}
 
 adf_z_large_p_scale = asarray([1.0, 1.0, 1e-2, 1e-3, 1e-5])
-for k, v in iteritems(adf_z_large_p):
+for k in adf_z_large_p.keys():
     adf_z_large_p[k] = asarray(adf_z_large_p[k]) * adf_z_large_p_scale
 
 adf_z_cv_approx = {

arch/univariate/volatility.py

@@ -125,7 +125,7 @@ class VolatilityProcess(object):
     separately from the conditional variance, even though parameters are estimated jointly.
     """
 
-    __metaclass__ = DocStringInheritor
+    __metaclass__ = DocStringInheritor  # noqa
 
     def __init__(self):
         self.num_params = 0
@@ -1451,13 +1451,13 @@ class RiskMetrics2006(VolatilityProcess):
     Parameters
     ----------
     tau0 : int, optional
-        Length of long cycle
+        Length of long cycle. Default is 1560.
     tau1 : int, optional
-        Length of short cycle
+        Length of short cycle. Default is 4.
     kmax : int, optional
-        Number of components
+        Number of components. Default is 14.
     rho : float, optional
-        Relative scale of adjacent cycles
+        Relative scale of adjacent cycles. Default is sqrt(2)
 
     Attributes
     ----------
@@ -1479,7 +1479,7 @@ class RiskMetrics2006(VolatilityProcess):
     This model has no parameters since the smoothing parameter is fixed.
     """
 
-    def __init__(self, tau0=1560, tau1=4, kmax=14, rho=sqrt(2)):
+    def __init__(self, tau0=1560, tau1=4, kmax=14, rho=1.4142135623730951):
         super(RiskMetrics2006, self).__init__()
         self.tau0 = tau0
         self.tau1 = tau1