DOC: fix 4 remaining doc build warnings.

2 for polyfit rankwarning, 2 for divide by zero in log10.

2 files changed, 35 insertions, 33 deletions

diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index fb40ef927..dad2e3bcd 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -377,7 +377,7 @@ def average(a, axis=None, weights=None, returned=False):
     Traceback (most recent call last):
         ...
     TypeError: Axis must be specified when shapes of a and weights differ.
-    
+
     >>> a = np.ones(5, dtype=np.float128)
     >>> w = np.ones(5, dtype=np.complex64)
     >>> avg = np.average(a, weights=w)
@@ -1431,7 +1431,7 @@ def angle(z, deg=False):
     angle : ndarray or scalar
         The counterclockwise angle from the positive real axis on
         the complex plane, with dtype as numpy.float64.
-        
+
         ..versionchanged:: 1.16.0
             This function works on subclasses of ndarray like `ma.array`.
 
@@ -1929,6 +1929,30 @@ class vectorize(object):
     vectorized : callable
         Vectorized function.
 
+    See Also
+    --------
+    frompyfunc : Takes an arbitrary Python function and returns a ufunc
+
+    Notes
+    -----
+    The `vectorize` function is provided primarily for convenience, not for
+    performance. The implementation is essentially a for loop.
+
+    If `otypes` is not specified, then a call to the function with the
+    first argument will be used to determine the number of outputs.  The
+    results of this call will be cached if `cache` is `True` to prevent
+    calling the function twice.  However, to implement the cache, the
+    original function must be wrapped which will slow down subsequent
+    calls, so only do this if your function is expensive.
+
+    The new keyword argument interface and `excluded` argument support
+    further degrades performance.
+
+    References
+    ----------
+    .. [1] NumPy Reference, section `Generalized Universal Function API
+           <https://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html>`_.
+
     Examples
     --------
     >>> def myfunc(a, b):
@@ -1988,8 +2012,8 @@ class vectorize(object):
 
     >>> import scipy.stats
     >>> pearsonr = np.vectorize(scipy.stats.pearsonr,
-    ...                 signature='(n),(n)->(),()') 
-    >>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]]) 
+    ...                 signature='(n),(n)->(),()')
+    >>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]])
     (array([ 1., -1.]), array([ 0.,  0.]))
 
     Or for a vectorized convolution:
@@ -2001,31 +2025,7 @@ class vectorize(object):
            [0., 0., 1., 2., 1., 0.],
            [0., 0., 0., 1., 2., 1.]])
 
-    See Also
-    --------
-    frompyfunc : Takes an arbitrary Python function and returns a ufunc
-
-    Notes
-    -----
-    The `vectorize` function is provided primarily for convenience, not for
-    performance. The implementation is essentially a for loop.
-
-    If `otypes` is not specified, then a call to the function with the
-    first argument will be used to determine the number of outputs.  The
-    results of this call will be cached if `cache` is `True` to prevent
-    calling the function twice.  However, to implement the cache, the
-    original function must be wrapped which will slow down subsequent
-    calls, so only do this if your function is expensive.
-
-    The new keyword argument interface and `excluded` argument support
-    further degrades performance.
-
-    References
-    ----------
-    .. [1] NumPy Reference, section `Generalized Universal Function API
-           <https://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html>`_.
     """
-
     def __init__(self, pyfunc, otypes=None, doc=None, excluded=None,
                  cache=False, signature=None):
         self.pyfunc = pyfunc
@@ -2602,7 +2602,7 @@ def blackman(M):
     Plot the window and the frequency response:
 
     >>> from numpy.fft import fft, fftshift
-    >>> window = np.blackman(51)
+    >>> window = np.blackman(51) + 1e-10  # eps shift to avoid division by zero
     >>> plt.plot(window)
     [<matplotlib.lines.Line2D object at 0x...>]
     >>> plt.title("Blackman window")
@@ -2709,7 +2709,7 @@ def bartlett(M):
     Plot the window and its frequency response (requires SciPy and matplotlib):
 
     >>> from numpy.fft import fft, fftshift
-    >>> window = np.bartlett(51)
+    >>> window = np.bartlett(51) + 1e-10  # eps shift to avoid division by zero
     >>> plt.plot(window)
     [<matplotlib.lines.Line2D object at 0x...>]
     >>> plt.title("Bartlett window")
@@ -2810,7 +2810,7 @@ def hanning(M):
 
     >>> import matplotlib.pyplot as plt
     >>> from numpy.fft import fft, fftshift
-    >>> window = np.hanning(51)
+    >>> window = np.hanning(51) + 1e-10  # eps shift to avoid division by zero
     >>> plt.plot(window)
     [<matplotlib.lines.Line2D object at 0x...>]
     >>> plt.title("Hann window")
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index 62f96f773..8c6f69cb0 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -548,6 +548,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
 
     Examples
     --------
+    >>> import warnings
     >>> x = np.array([0.0, 1.0, 2.0, 3.0,  4.0,  5.0])
     >>> y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
     >>> z = np.polyfit(x, y, 3)
@@ -566,9 +567,10 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
 
     High-order polynomials may oscillate wildly:
 
-    >>> p30 = np.poly1d(np.polyfit(x, y, 30))
+    >>> with warnings.catch_warnings():
+    ...     warnings.simplefilter('ignore', np.RankWarning)
+    ...     p30 = np.poly1d(np.polyfit(x, y, 30))
     ...
-    >>> # RankWarning: Polyfit may be poorly conditioned...
     >>> p30(4)
     -0.80000000000000204 # may vary
     >>> p30(5)