Merge pull request #5836 from jbrockmendel/over

CLN: over-indentation E117

setup.cfg

@@ -59,6 +59,8 @@ select=
     # E111: Indentation is not a multiple of four
     E114,
     # E114: Indentation is not a multiple of four (comment)
+    E117,
+    # E117: over-indented
     E227,
     # E227: missing whitespace around bitwise or shift operator
     E228,

statsmodels/base/_constraints.py

@@ -299,14 +299,14 @@ def fit_constrained_wrap(model, constraints, start_params=None, **fit_kwds):
                                               fit_kwds=fit_kwds)
     #create dummy results Instance, TODO: wire up properly
     res = self.fit(start_params=params, maxiter=0,
-                   warn_convergence=False) # we get a wrapper back
+                   warn_convergence=False)  # we get a wrapper back
     res._results.params = params
     res._results.cov_params_default = cov
     cov_type = fit_kwds.get('cov_type', 'nonrobust')
     if cov_type == 'nonrobust':
-            res._results.normalized_cov_params = cov / res_constr.scale
+        res._results.normalized_cov_params = cov / res_constr.scale
     else:
-            res._results.normalized_cov_params = None
+        res._results.normalized_cov_params = None
 
     k_constr = len(q)
     res._results.df_resid += k_constr

statsmodels/discrete/tests/test_count_model.py

@@ -530,30 +530,30 @@ def test_predict_generic_zi(self):
 
 
 class TestZeroInflatedNegativeBinomialP_predict2(object):
-        @classmethod
-        def setup_class(cls):
-            data = sm.datasets.randhie.load(as_pandas=False)
-
-            cls.endog = data.endog
-            exog = data.exog
-            start_params = np.array(
-                [-2.83983767, -2.31595924, -3.9263248 , -4.01816431, -5.52251843,
-                -2.4351714 , -4.61636366, -4.17959785, -0.12960256, -0.05653484,
-                -0.21206673,  0.08782572, -0.02991995,  0.22901208,  0.0620983 ,
-                 0.06809681,  0.0841814 ,  0.185506  ,  1.36527888])
-            mod = sm.ZeroInflatedNegativeBinomialP(
-                cls.endog, exog, exog_infl=exog, p=2)
-            res = mod.fit(start_params=start_params, method="bfgs",
-                          maxiter=1000, disp=0)
-
-            cls.res = res
-
-        def test_mean(self):
-            assert_allclose(self.res.predict().mean(), self.endog.mean(),
-                            atol=0.02)
-
-        def test_zero_nonzero_mean(self):
-            mean1 = self.endog.mean()
-            mean2 = ((1 - self.res.predict(which='prob-zero').mean()) *
-                     self.res.predict(which='mean-nonzero').mean())
-            assert_allclose(mean1, mean2, atol=0.2)
+    @classmethod
+    def setup_class(cls):
+        data = sm.datasets.randhie.load(as_pandas=False)
+
+        cls.endog = data.endog
+        exog = data.exog
+        start_params = np.array([
+            -2.83983767, -2.31595924, -3.9263248,  -4.01816431, -5.52251843,
+            -2.4351714,  -4.61636366, -4.17959785, -0.12960256, -0.05653484,
+            -0.21206673,  0.08782572, -0.02991995,  0.22901208,  0.0620983,
+            0.06809681,  0.0841814,   0.185506,    1.36527888])
+        mod = sm.ZeroInflatedNegativeBinomialP(
+            cls.endog, exog, exog_infl=exog, p=2)
+        res = mod.fit(start_params=start_params, method="bfgs",
+                      maxiter=1000, disp=0)
+
+        cls.res = res
+
+    def test_mean(self):
+        assert_allclose(self.res.predict().mean(), self.endog.mean(),
+                        atol=0.02)
+
+    def test_zero_nonzero_mean(self):
+        mean1 = self.endog.mean()
+        mean2 = ((1 - self.res.predict(which='prob-zero').mean()) *
+                 self.res.predict(which='mean-nonzero').mean())
+        assert_allclose(mean1, mean2, atol=0.2)

statsmodels/iolib/foreign.py

@@ -544,8 +544,9 @@ def _dtype_to_stata_type(dtype):
     #TODO: expand to handle datetime to integer conversion
     if dtype.type == np.string_:
         return chr(dtype.itemsize)
-    elif dtype.type == np.object_: # try to coerce it to the biggest string
-                        # not memory efficient, what else could we do?
+    elif dtype.type == np.object_:
+        # try to coerce it to the biggest string
+        # not memory efficient, what else could we do?
         return chr(244)
     elif dtype == np.float64:
         return chr(255)

statsmodels/miscmodels/nonlinls.py

@@ -228,7 +228,7 @@ def fit_random(self, ntries=10, rvs_generator=None, nparams=None):
         '''
 
         if nparams is None:
-                nparams = self.nparams
+            nparams = self.nparams
         if rvs_generator is None:
             rvs = np.random.uniform(low=-10, high=10, size=(ntries, nparams))
         else:

statsmodels/sandbox/descstats.py

@@ -118,10 +118,16 @@ def descstats(data, cols=None, axis=0):
 #                +str(x[var].max())+')'+os.linesep}
 #        else:
         for var in range(x.shape[1]):
-                desc += "%(name)15s %(obs)9i %(mean)12.4g %(stddev)12.4g \
-%(range)20s" % {'name': var, 'obs': len(x[:,var]), 'mean': x[:,var].mean(),
-                'stddev': x[:,var].std(), 'range': '('+str(x[:,var].min())+', '+\
-                str(x[:,var].max())+')'+os.linesep}
+            xv = x[:, var]
+            kwargs = {
+                'name': var,
+                'obs': len(xv),
+                'mean': xv.mean(),
+                'stddev': xv.std(),
+                'range': '('+str(xv.min())+', '+str(xv.max())+')'+os.linesep
+                }
+            desc += ("%(name)15s %(obs)9i %(mean)12.4g %(stddev)12.4g "
+                     "%(range)20s" % kwargs)
     else:
         raise ValueError("data not understood")
 

statsmodels/sandbox/regression/penalized.py

@@ -159,7 +159,7 @@ def __init__(self, endog, exog, r_matrix=None, q_matrix=None,
         else:
             self.q_matrix = np.zeros(k_constraints)[:, None]
         if self.q_matrix.shape != (k_constraints, 1):
-                raise ValueError('q_matrix has wrong shape')
+            raise ValueError('q_matrix has wrong shape')
 
         if sigma_prior is not None:
             sigma_prior = np.asarray(sigma_prior)

statsmodels/sandbox/tools/try_mctools.py

@@ -38,7 +38,7 @@ def lb(x):
 #----------------------
 
 def randwalksim(nobs=500, drift=0.0):
-        return (drift+np.random.randn(nobs)).cumsum()
+    return (drift+np.random.randn(nobs)).cumsum()
 
 def adf20(x):
     return unitroot_adf(x, 2, trendorder=0, autolag=None)

statsmodels/tsa/arima_model.py

@@ -390,10 +390,11 @@ def _make_arma_names(data, k_trend, order, exog_names):
     if k_ma ==0 and k_ar ==0:
         if len(exog_names) != 0:
             return exog_names
-    elif (exog_names[-k_ma:] == ma_lag_names ) and \
-           exog_names[-(k_ar+k_ma):-k_ma] == ar_lag_names and \
-           (not exog_names or not trend_name or trend_name[0] == exog_names[0]):
-            return exog_names
+    elif (exog_names[-k_ma:] == ma_lag_names and
+          exog_names[-(k_ar+k_ma):-k_ma] == ar_lag_names and
+          (not exog_names or not trend_name
+           or trend_name[0] == exog_names[0])):
+        return exog_names
 
     exog_names = trend_name + exog_names + ar_lag_names + ma_lag_names
     return exog_names

statsmodels/tsa/vector_ar/irf.py

@@ -486,12 +486,12 @@ def err_band_sz3(self, orth=False, svar=False, repl=1000, signif=0.05,
 
         gamma = np.zeros((repl, periods+1, neqs, neqs))
         for p in range(repl):
-            c=0
+            c = 0
             for j in range(neqs):
                 for i in range(neqs):
-                        gamma[p,1:,i,j] = W[j,k[j],i*periods:(i+1)*periods] * irf_resim[p,1:,i,j]
-                        if i == neqs-1:
-                            gamma[p,1:,i,j] = W[j,k[j],i*periods:] * irf_resim[p,1:,i,j]
+                    gamma[p,1:,i,j] = W[j,k[j],i*periods:(i+1)*periods] * irf_resim[p,1:,i,j]
+                    if i == neqs-1:
+                        gamma[p,1:,i,j] = W[j,k[j],i*periods:] * irf_resim[p,1:,i,j]
 
         gamma_sort = np.sort(gamma, axis=0) #sort to get quantiles
         indx = round(signif/2*repl)-1,round((1-signif/2)*repl)-1

statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_var_output.py

@@ -13,17 +13,17 @@
 
 
 def print_debug_output(results, dt):
-        print("\n\n\nDETERMINISTIC TERMS: " + dt)
-        coefs = results["est"]["Lagged endogenous term"]
-        print("coefs:")
-        print(str(type(coefs)) + str(coefs.shape))
-        print(coefs)
-        print("se: ")
-        print(results["se"]["Lagged endogenous term"])
-        print("t: ")
-        print(results["t"]["Lagged endogenous term"])
-        print("p: ")
-        print(results["p"]["Lagged endogenous term"])
+    print("\n\n\nDETERMINISTIC TERMS: " + dt)
+    coefs = results["est"]["Lagged endogenous term"]
+    print("coefs:")
+    print(str(type(coefs)) + str(coefs.shape))
+    print(coefs)
+    print("se: ")
+    print(results["se"]["Lagged endogenous term"])
+    print("t: ")
+    print(results["t"]["Lagged endogenous term"])
+    print("p: ")
+    print(results["p"]["Lagged endogenous term"])
 
 
 def dt_s_tup_to_string(dt_s_tup):

statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_vecm_output.py

@@ -11,32 +11,32 @@
 
 
 def print_debug_output(results, dt):
-        print("\n\n\nDETERMINISTIC TERMS: " + dt)
-        alpha = results["est"]["alpha"]
-        print("alpha:")
-        print(str(type(alpha)) + str(alpha.shape))
-        print(alpha)
+    print("\n\n\nDETERMINISTIC TERMS: " + dt)
+    alpha = results["est"]["alpha"]
+    print("alpha:")
+    print(str(type(alpha)) + str(alpha.shape))
+    print(alpha)
+    print("se: ")
+    print(results["se"]["alpha"])
+    print("t: ")
+    print(results["t"]["alpha"])
+    print("p: ")
+    print(results["p"]["alpha"])
+    beta = results["est"]["beta"]
+    print("beta:")
+    print(str(type(beta)) + str(beta.shape))
+    print(beta)
+    gamma = results["est"]["Gamma"]
+    print("Gamma:")
+    print(str(type(gamma)) + str(gamma.shape))
+    print(gamma)
+    if "co" in dt or "s" in dt or "lo" in dt:
+        c = results["est"]["C"]
+        print("C:")
+        print(str(type(c)) + str(c.shape))
+        print(c)
         print("se: ")
-        print(results["se"]["alpha"])
-        print("t: ")
-        print(results["t"]["alpha"])
-        print("p: ")
-        print(results["p"]["alpha"])
-        beta = results["est"]["beta"]
-        print("beta:")
-        print(str(type(beta)) + str(beta.shape))
-        print(beta)
-        gamma = results["est"]["Gamma"]
-        print("Gamma:")
-        print(str(type(gamma)) + str(gamma.shape))
-        print(gamma)
-        if "co" in dt or "s" in dt or "lo" in dt:
-            c = results["est"]["C"]
-            print("C:")
-            print(str(type(c)) + str(c.shape))
-            print(c)
-            print("se: ")
-            print(results["se"]["C"])
+        print(results["se"]["C"])
 
 
 def dt_s_tup_to_string(dt_s_tup):