fix and lint for E228 missing space around modulo operator

jbrockmendel · jbrockmendel · commit 87db4a416b5b · 2018-08-14T19:08:16.000-07:00
diff --git a/examples/incomplete/wls_extended.py b/examples/incomplete/wls_extended.py
@@ -277,7 +277,7 @@ def index_trim_outlier(resid, k):
 for i in range(4):
     plt.subplot(2,2,i+1)
     plt.hist(bootres[:,i],50)
-    plt.title('var%d'%i)
+    plt.title('var%d' % i)
 #@savefig wls_bootstrap.png
 plt.figtext(0.5, 0.935,  'OLS Bootstrap',
                ha='center', color='black', weight='bold', size='large')
@@ -316,7 +316,7 @@ def index_trim_outlier(resid, k):
 for i in range(4):
     plt.subplot(2,2,i+1)
     plt.hist(bootreswls[:,i],50)
-    plt.title('var%d'%i)
+    plt.title('var%d' % i)
 #@savefig wls_bootstrap_rm2.png
 plt.figtext(0.5, 0.935,  'WLS rm2 Bootstrap',
                ha='center', color='black', weight='bold', size='large')
diff --git a/setup.cfg b/setup.cfg
@@ -19,6 +19,8 @@ select=
     # E114: Indentation is not a multiple of four (comment)
     E227,
     # E227: missing whitespace around bitwise or shift operator
+    E228,
+    # E228: missing whitespace around modulo operator
     E211,
     # E211: whitespace before '['
     E401,
diff --git a/statsmodels/examples/l1_demo/demo.py b/statsmodels/examples/l1_demo/demo.py
@@ -232,16 +232,16 @@ def get_summary_str(results, true_params, get_l1_slsqp_results,
     #### Format summaries
     # Short summary
     print_str = '\n\n=========== Short Error Summary ============'
-    print_str += '\n\n The maximum likelihood fit RMS error = %.4f'%RMSE_ML
+    print_str += '\n\n The maximum likelihood fit RMS error = %.4f' % RMSE_ML
     if get_l1_slsqp_results:
         RMSE_l1_slsqp = get_RMSE(results_l1_slsqp, true_params)
-        print_str += '\n The l1_slsqp fit RMS error = %.4f'%RMSE_l1_slsqp
+        print_str += '\n The l1_slsqp fit RMS error = %.4f' % RMSE_l1_slsqp
     if get_l1_cvxopt_results:
         RMSE_l1_cvxopt_cp = get_RMSE(results_l1_cvxopt_cp, true_params)
-        print_str += '\n The l1_cvxopt_cp fit RMS error = %.4f'%RMSE_l1_cvxopt_cp
+        print_str += '\n The l1_cvxopt_cp fit RMS error = %.4f' % RMSE_l1_cvxopt_cp
     # Parameters
     print_str += '\n\n\n============== Parameters ================='
-    print_str += "\n\nTrue parameters: \n%s"%true_params
+    print_str += "\n\nTrue parameters: \n%s" % true_params
     # Full summary
     if print_summaries:
         print_str += '\n' + results_ML.summary().as_text()
@@ -250,11 +250,11 @@ def get_summary_str(results, true_params, get_l1_slsqp_results,
         if get_l1_cvxopt_results:
             print_str += '\n' + results_l1_cvxopt_cp.summary().as_text()
     else:
-        print_str += '\n\nThe maximum likelihood params are \n%s'%results_ML.params
+        print_str += '\n\nThe maximum likelihood params are \n%s' % results_ML.params
         if get_l1_slsqp_results:
-            print_str += '\n\nThe l1_slsqp params are \n%s'%results_l1_slsqp.params
+            print_str += '\n\nThe l1_slsqp params are \n%s' % results_l1_slsqp.params
         if get_l1_cvxopt_results:
-            print_str += '\n\nThe l1_cvxopt_cp params are \n%s'%\
+            print_str += '\n\nThe l1_cvxopt_cp params are \n%s' % \
                     results_l1_cvxopt_cp.params
     # Return
     return print_str
diff --git a/statsmodels/iolib/tests/test_table_econpy.py b/statsmodels/iolib/tests/test_table_econpy.py
@@ -58,7 +58,8 @@ def custom_labeller(cell):
 class TestCell(object):
     def test_celldata(self):
         celldata = cell0data, cell1data, row1data[0], row1data[1]
-        cells = [Cell(datum, datatype=i%2) for i, datum in enumerate(celldata)]
+        cells = [Cell(datum, datatype=i % 2)
+                 for i, datum in enumerate(celldata)]
         for cell, datum in zip(cells, celldata):
             assert_equal(cell.data, datum)
 
diff --git a/statsmodels/sandbox/distributions/examples/matchdist.py b/statsmodels/sandbox/distributions/examples/matchdist.py
@@ -46,7 +46,7 @@ def plothist(x,distfn, args, loc, scale, right=1):
 
     plt.xlabel('Smarts')
     plt.ylabel('Probability')
-    plt.title(r'$\mathrm{Testing: %s :}\ \mu=%f,\ \sigma=%f$'%(distfn.name,loc,scale))
+    plt.title(r'$\mathrm{Testing: %s :}\ \mu=%f,\ \sigma=%f$' % (distfn.name,loc,scale))
 
     #plt.axis([bins[0], bins[-1], 0, 0.134+0.05])
 
diff --git a/statsmodels/sandbox/examples/ex_onewaygls.py b/statsmodels/sandbox/examples/ex_onewaygls.py
@@ -70,7 +70,7 @@ def print_results(res):
 ##        else:
 ##            print ''
     from statsmodels.iolib import SimpleTable
-    print(SimpleTable([(['%r'%(row[0],)]
+    print(SimpleTable([(['%r' % (row[0],)]
                         + list(row[1])
                         + ['*']*(row[1][1]>0.5).item() ) for row in ft[1]],
                       headers=['pair', 'F-statistic','p-value','df_denom',
@@ -148,7 +148,7 @@ def print_results2(res):
 
     from statsmodels.iolib import SimpleTable
     resvals = {}
-    resvals['tab'] = str(SimpleTable([(['%r'%(row[0],)]
+    resvals['tab'] = str(SimpleTable([(['%r' % (row[0],)]
                         + list(row[1])
                         + ['*']*(row[1][1]>0.5).item() ) for row in ft[1]],
                       headers=['pair', 'F-statistic','p-value','df_denom',
diff --git a/statsmodels/sandbox/examples/try_quantile_regression.py b/statsmodels/sandbox/examples/try_quantile_regression.py
@@ -40,7 +40,7 @@
 plt.figure()
 plt.plot(y[sortidx], 'o', alpha=0.75)
 for lab, beta in zip(['ols', 'qr 0.25', 'qr 0.5', 'qr 0.75'], params):
-    print('%-8s'%lab, np.round(beta, 4))
+    print('%-8s' % lab, np.round(beta, 4))
     fitted = np.dot(x_sorted, beta)
     lw = 2 if lab == 'ols' else 1
     plt.plot(fitted, lw=lw, label=lab)
diff --git a/statsmodels/sandbox/examples/try_quantile_regression1.py b/statsmodels/sandbox/examples/try_quantile_regression1.py
@@ -35,7 +35,7 @@
 plt.figure()
 plt.plot(x, y, '.', alpha=0.5)
 for lab, beta in zip(['ols', 'qr 0.1', 'qr 0.5', 'qr 0.75'], params):
-    print('%-8s'%lab, np.round(beta, 4))
+    print('%-8s' % lab, np.round(beta, 4))
     fitted = np.dot(exog, beta)
     lw = 2
     plt.plot(x, fitted, lw=lw, label=lab)
diff --git a/statsmodels/sandbox/regression/onewaygls.py b/statsmodels/sandbox/regression/onewaygls.py
@@ -335,7 +335,7 @@ def print_summary(self, res):
 
         from statsmodels.iolib import SimpleTable
         resvals = {}
-        resvals['tab'] = str(SimpleTable([(['%r'%(row[0],)]
+        resvals['tab'] = str(SimpleTable([(['%r' % (row[0],)]
                             + list(row[1])
                             + ['*']*(row[1][1]>0.5).item() ) for row in summtable],
                           headers=['pair', 'F-statistic','p-value','df_denom',
diff --git a/statsmodels/sandbox/regression/try_catdata.py b/statsmodels/sandbox/regression/try_catdata.py
@@ -85,7 +85,7 @@ def convertlabels(ys, indices=None):
     else:
         idx = np.array(indices)
         if idx.size > 1 and ys.ndim == 2:
-            ylabel = np.array(['@%s@'%ii[:2].tostring() for ii in ys])[:,np.newaxis]
+            ylabel = np.array(['@%s@' % ii[:2].tostring() for ii in ys])[:,np.newaxis]
             #alternative
     ##        if ys[:,idx].dtype.kind == 'S':
     ##            ylabel = nd.array([' '.join(ii[:2]) for ii in ys])[:,np.newaxis]
diff --git a/statsmodels/sandbox/stats/contrast_tools.py b/statsmodels/sandbox/stats/contrast_tools.py
@@ -639,7 +639,7 @@ def test_dummy_limits(self):
 
     y = np.arange(12)
     x1 = np.arange(12)//4
-    x2 = np.arange(12)//2%2
+    x2 = np.arange(12)//2 % 2
 
     if 'small' in examples:
         d1, d1_labels = dummy_1d(x1)
diff --git a/statsmodels/sandbox/stats/multicomp.py b/statsmodels/sandbox/stats/multicomp.py
@@ -799,7 +799,7 @@ def __init__(self, data, groups, group_order=None):
             for grp in group_order:
                 if grp not in groups:
                     raise ValueError(
-                            "group_order value '%s' not found in groups"%grp)
+                            "group_order value '%s' not found in groups" % grp)
             self.groupsunique = np.array(group_order)
             self.groupintlab = np.empty(len(data), int)
             self.groupintlab.fill(-999)  # instead of a nan
diff --git a/statsmodels/sandbox/tsa/example_arma.py b/statsmodels/sandbox/tsa/example_arma.py
@@ -340,7 +340,7 @@ def xcorr(self, x, y, normed=True, detrend=detrend_none,
 
     if maxlags >= Nx or maxlags < 1:
         raise ValueError('maxlags must be None or strictly '
-                         'positive < %d'%Nx)
+                         'positive < %d' % Nx)
 
     lags = np.arange(-maxlags,maxlags+1)
     c = c[Nx-1-maxlags:Nx+maxlags]
diff --git a/statsmodels/stats/contrast.py b/statsmodels/stats/contrast.py
@@ -146,7 +146,7 @@ def summary(self, xname=None, alpha=0.05, title=None):
             use_t = (self.distribution == 't')
             yname='constraints' # Not used in params_frame
             if xname is None:
-                xname = ['c%d'%ii for ii in range(len(self.effect))]
+                xname = ['c%d' % ii for ii in range(len(self.effect))]
             from statsmodels.iolib.summary import summary_params
             pvalues = np.atleast_1d(self.pvalue)
             summ = summary_params((self, self.effect, self.sd, self.statistic,
@@ -177,7 +177,7 @@ def summary_frame(self, xname=None, alpha=0.05):
             use_t = (self.distribution == 't')
             yname='constraints'  # Not used in params_frame
             if xname is None:
-                xname = ['c%d'%ii for ii in range(len(self.effect))]
+                xname = ['c%d' % ii for ii in range(len(self.effect))]
             from statsmodels.iolib.summary import summary_params_frame
             summ = summary_params_frame((self, self.effect, self.sd,
                                          self.statistic,self.pvalue,
diff --git a/statsmodels/stats/descriptivestats.py b/statsmodels/stats/descriptivestats.py
@@ -223,7 +223,7 @@ def summary(self, stats='basic', columns='all', orientation='auto'):
         def _fun(per):
             return lambda x: scipy.stats.scoreatpercentile(x, per)
 
-        perdict = dict(('perc_%02d'%per, [_fun(per), None, None])
+        perdict = dict(('perc_%02d' % per, [_fun(per), None, None])
                        for per in (1,5,10,25,50,75,90,95,99))
 
         if 'percentiles' in stats:
diff --git a/statsmodels/stats/weightstats.py b/statsmodels/stats/weightstats.py
@@ -807,7 +807,7 @@ def summary(self, use_t=True, alpha=0.05, usevar='pooled', value=0):
         
         title = 'Test for equality of means'
         yname = 'y' # not used in params_frame
-        xname = ['subset #%d'%(ii + 1) for ii in range(tstat.shape[0])]
+        xname = ['subset #%d' % (ii + 1) for ii in range(tstat.shape[0])]
 
         from statsmodels.iolib.summary import summary_params
         return summary_params((None, params, std_err, tstat, pvalue, conf_int),
diff --git a/statsmodels/tsa/tests/test_seasonal.py b/statsmodels/tsa/tests/test_seasonal.py
@@ -143,7 +143,7 @@ def test_pandas(self):
     def test_pandas_nofreq(self):
         # issue #3503
         nobs = 100
-        dta = pd.Series([x%3 for x in range(nobs)] + np.random.randn(nobs))
+        dta = pd.Series([x % 3 for x in range(nobs)] + np.random.randn(nobs))
         res_np = seasonal_decompose(dta.values, freq=3)
         res = seasonal_decompose(dta, freq=3)
 
