Merge pull request statsmodels#4926 from bashtage/flake8-e306

CLN: Enforce E306, blank lines in nested funcs

Author: josef-pkt

setup.cfg

@@ -35,5 +35,15 @@ select=
     # F402: import 'assert_equal' from line 7 shadowed by loop variable
     W601,
     # W601: .has_key() is deprecated, use 'in'
-    W391
+    W391,
     # W391: blank line at end of file
+
+
+
+
+
+
+
+    E306
+    # E306: expected 1 blank line before a nested definition, found 0
+

statsmodels/imputation/tests/test_mice.py

@@ -167,6 +167,7 @@ def test_phreg(self):
 
         # Save the dataset size at each iteration.
         hist = []
+
         def cb(imp):
             hist.append(imp.data.shape)
 

statsmodels/iolib/tests/test_table.py

@@ -106,6 +106,7 @@ def test_SimpleTable_4(self):
         test1header = ('header1', 'header2')
         tbl = SimpleTable(table1data, test1header, test1stubs,txt_fmt=txt_fmt1,
                           ltx_fmt=ltx_fmt1, html_fmt=html_fmt1)
+
         def test_txt_fmt1(self):
             # Limited test of custom txt_fmt
             desired = """

statsmodels/sandbox/distributions/estimators.py

@@ -265,6 +265,7 @@ def fitbinned(distfn, freq, binedges, start, fixed=None):
         raise NotImplementedError
     nobs = np.sum(freq)
     lnnobsfact = special.gammaln(nobs+1)
+
     def nloglike(params):
         '''negative loglikelihood function of binned data
 
@@ -315,6 +316,7 @@ def fitbinnedgmm(distfn, freq, binedges, start, fixed=None, weightsoptimal=True)
         weights = np.ones(len(freq))
     freqnormed = freq/float(nobs)
     # skip turning weights into matrix diag(freq/float(nobs))
+
     def gmmobjective(params):
         '''negative loglikelihood function of binned data
 

statsmodels/sandbox/regression/anova_nistcertified.py

@@ -65,6 +65,7 @@ def anova_oneway(y, x, seq=0):
     R2 = (ssbn/(sswn+ssbn))  #R-squared
     resstd = np.sqrt(msw) #residual standard deviation
     #print(f, prob
+
     def _fix2scalar(z): # return number
         if np.shape(z) == (1, 1): return z[0,0]
         else: return z

statsmodels/sandbox/stats/multicomp.py

@@ -1662,6 +1662,7 @@ def homogeneous_subsets(vals, dcrit):
     subsetsli = []
     if np.size(dcrit) == 1:
         dcrit = dcrit*np.ones((nvals, nvals))  #example numbers for experimenting
+
     def subsets(vals, indices_):
         '''recursive function for constructing homogeneous subset
 

statsmodels/sandbox/tests/test_formula.py

@@ -126,6 +126,7 @@ def test_termcolumns(self):
         t1 = formula.Term("A")
         t2 = formula.Term("B")
         f = t1 + t2 + t1 * t2
+
         def other(val):
             return np.array([3.2*val,4.342*val**2, 5.234*val**3])
         q = formula.Quantitative(['other%d' % i for i in range(1,4)], termname='other', func=t1, transform=other)

statsmodels/stats/descriptivestats.py

@@ -159,6 +159,7 @@ def string_like():
             except (TypeError, ValueError):
                 return False
             return True
+
         def number_like():
             try:
                 self.dataset[col][0] + 1.0

statsmodels/stats/power.py

@@ -232,6 +232,7 @@ def solve_power(self, **kwds):
 
 
         self._counter = 0
+
         def func(x):
             kwds[key] = x
             fval = self._power_identity(**kwds)