Merge pull request statsmodels#5988 from jbrockmendel/follow5928

CLN: follow-up to statsmodels#5928

Author: bashtage

lint.sh

@@ -97,6 +97,8 @@ if [ "$LINT" == true ]; then
         statsmodels/tools/web.py \
         statsmodels/tools/tests/test_linalg.py \
         statsmodels/tools/decorators.py \
+        statsmodels/tools/sm_exceptions.py \
+        statsmodels/tools/validation/ \
         statsmodels/tools/tests/test_decorators.py \
         statsmodels/tsa/adfvalues.py \
         statsmodels/tsa/base/tests/test_datetools.py \
@@ -111,7 +113,6 @@ if [ "$LINT" == true ]; then
         statsmodels/tsa/tests/test_stl.py \
         statsmodels/__init__.py \
         statsmodels/conftest.py \
-        statsmodels/tools/sm_exceptions.py \
         examples/ \
         tools/ \
         setup.py

statsmodels/tsa/arima_model.py

@@ -1528,7 +1528,7 @@ def forecast(self, steps=1, exog=None, alpha=.05):
             2d array of the confidence interval for the forecast
         """
         if exog is not None:
-            #TODO: make a convenience function for this. we're using the
+            # TODO: make a convenience function for this. we're using the
             # pattern elsewhere in the codebase
             exog = array_like(exog, 'exog', maxdim=2)
             if self.k_exog == 1 and exog.ndim == 1:

statsmodels/tsa/filters/filtertools.py

@@ -339,25 +339,17 @@ def miso_lfilter(ar, ma, x, useic=False):
     '''
     ma = array_like(ma, 'ma')
     ar = array_like(ar, 'ar')
-    # inp = signal.convolve(x, ma, mode='valid')
-    # inp = signal.convolve(x, ma)[:, (x.shape[1]+1)//2]
-    # Note: convolve mixes up the variable left-right flip
-    # I only want the flip in time direction
-    # this might also be a mistake or problem in other code where I
-    # switched from correlate to convolve
-    # correct convolve version, for use with fftconvolve in other cases
-    # inp2 = signal.convolve(x, ma[:,::-1])[:, (x.shape[1]+1)//2]
     inp = signal.correlate(x, ma[::-1, :])[:, (x.shape[1] + 1) // 2]
     # for testing 2d equivalence between convolve and correlate
-    # np.testing.assert_almost_equal(inp2, inp)
+    #  inp2 = signal.convolve(x, ma[:,::-1])[:, (x.shape[1]+1)//2]
+    #  np.testing.assert_almost_equal(inp2, inp)
     nobs = x.shape[0]
     # cut of extra values at end
 
-    # todo initialize also x for correlate
+    # TODO: initialize also x for correlate
     if useic:
         return signal.lfilter([1], ar, inp,
                               zi=signal.lfiltic(np.array([1., 0.]), ar,
                                                 useic))[0][:nobs], inp[:nobs]
     else:
         return signal.lfilter([1], ar, inp)[:nobs], inp[:nobs]
-    # return signal.lfilter([1], ar, inp), inp

statsmodels/tsa/stattools.py

@@ -559,13 +559,15 @@ def acf(x, unbiased=False, nlags=40, qstat=False, fft=None, alpha=None,
 
     if fft is None:
         import warnings
-        msg = 'fft=True will become the default in a future version of ' \
-              'statsmodels. To suppress this warning, explicitly set ' \
-              'fft=False.'
-        warnings.warn(msg, FutureWarning)
+        warnings.warn(
+            'fft=True will become the default in a future version of '
+            'statsmodels. To suppress this warning, explicitly set '
+            'fft=False.',
+            FutureWarning
+        )
         fft = False
     x = array_like(x, 'x')
-    nobs = len(x)  # should this shrink for missing='drop' and NaNs in x?
+    nobs = len(x)  # TODO: should this shrink for missing='drop' and NaNs in x?
     avf = acovf(x, unbiased=unbiased, demean=True, fft=fft, missing=missing)
     acf = avf[:nlags + 1] / avf[0]
     if not (qstat or alpha):
@@ -955,8 +957,6 @@ def periodogram(x):
     warnings.warn('periodogram is deprecated and will be removed after 0.11. '
                   'Use scipy.signal.periodogram instead.', FutureWarning)
     x = array_like(x, 'x')
-    # if kernel == "bartlett":
-    #    w = 1 - np.arange(M+1.)/M   #JP removed integer division
 
     pergr = 1. / len(x) * np.abs(np.fft.fft(x)) ** 2
     pergr[0] = 0.  # what are the implications of this?