TST: run refguide-check on rst files in doc/*

doc/example.py

@@ -112,9 +112,9 @@ def foo(var1, var2, long_var_name='hi'):
     use the function.
 
     >>> a = [1, 2, 3]
-    >>> print [x + 3 for x in a]
+    >>> print([x + 3 for x in a])
     [4, 5, 6]
-    >>> print "a\n\nb"
+    >>> print("a\n\nb")
     a
     b
 

doc/source/dev/development_environment.rst

@@ -146,7 +146,7 @@ Running tests
 Besides using ``runtests.py``, there are various ways to run the tests.  Inside
 the interpreter, tests can be run like this::
 
-    >>> np.test()
+    >>> np.test()  # doctest: +SKIPBLOCK
     >>> np.test('full')   # Also run tests marked as slow
     >>> np.test('full', verbose=2)   # Additionally print test name/file
 

doc/source/reference/arrays.datetime.rst

@@ -368,7 +368,7 @@ times in UTC. By default, creating a datetime64 object from a string or
 printing it would convert from or to local time::
 
     # old behavior
-    >>>> np.datetime64('2000-01-01T00:00:00')
+    >>> np.datetime64('2000-01-01T00:00:00')
     numpy.datetime64('2000-01-01T00:00:00-0800')  # note the timezone offset -08:00
 
 A consensus of datetime64 users agreed that this behavior is undesirable
@@ -378,7 +378,7 @@ most use cases, a timezone naive datetime type is preferred, similar to the
 datetime64 no longer assumes that input is in local time, nor does it print
 local times::
 
-    >>>> np.datetime64('2000-01-01T00:00:00')
+    >>> np.datetime64('2000-01-01T00:00:00')
     numpy.datetime64('2000-01-01T00:00:00')
 
 For backwards compatibility, datetime64 still parses timezone offsets, which
@@ -393,4 +393,4 @@ As a corollary to this change, we no longer prohibit casting between datetimes
 with date units and datetimes with timeunits. With timezone naive datetimes,
 the rule for casting from dates to times is no longer ambiguous.
 
-.. _pandas: http://pandas.pydata.org
+.. _pandas: http://pandas.pydata.org

doc/source/release/1.11.0-notes.rst

@@ -85,7 +85,7 @@ times in UTC. By default, creating a datetime64 object from a string or
 printing it would convert from or to local time::
 
     # old behavior
-    >>>> np.datetime64('2000-01-01T00:00:00')
+    >>> np.datetime64('2000-01-01T00:00:00')
     numpy.datetime64('2000-01-01T00:00:00-0800')  # note the timezone offset -08:00
 
 
@@ -96,7 +96,7 @@ type is preferred, similar to the ``datetime.datetime`` type in the Python
 standard library. Accordingly, datetime64 no longer assumes that input is in
 local time, nor does it print local times::
 
-    >>>> np.datetime64('2000-01-01T00:00:00')
+    >>> np.datetime64('2000-01-01T00:00:00')
     numpy.datetime64('2000-01-01T00:00:00')
 
 For backwards compatibility, datetime64 still parses timezone offsets, which

doc/source/user/basics.io.genfromtxt.rst

@@ -98,13 +98,11 @@ This behavior can be overwritten by setting the optional argument
    >>> # Without autostrip
    >>> np.genfromtxt(StringIO(data), delimiter=",", dtype="|U5")
    array([['1', ' abc ', ' 2'],
-          ['3', ' xxx', ' 4']],
-         dtype='|U5')
+          ['3', ' xxx', ' 4']], dtype='<U5')
    >>> # With autostrip
    >>> np.genfromtxt(StringIO(data), delimiter=",", dtype="|U5", autostrip=True)
    array([['1', 'abc', '2'],
-          ['3', 'xxx', '4']],
-         dtype='|U5')
+          ['3', 'xxx', '4']], dtype='<U5')
 
 
 The ``comments`` argument
@@ -127,11 +125,11 @@ marker(s) is simply ignored::
    ... 9, 0
    ... """
    >>> np.genfromtxt(StringIO(data), comments="#", delimiter=",")
-   [[ 1.  2.]
-    [ 3.  4.]
-    [ 5.  6.]
-    [ 7.  8.]
-    [ 9.  0.]]
+   array([[1., 2.],
+          [3., 4.],
+          [5., 6.],
+          [7., 8.],
+          [9., 0.]])
 
 .. versionadded:: 1.7.0
 
@@ -376,12 +374,12 @@ single element of the wanted type.
 In the following example, the second column is converted from as string
 representing a percentage to a float between 0 and 1::
 
-   >>> convertfunc = lambda x: float(x.strip("%"))/100.
+   >>> convertfunc = lambda x: float(x.strip(b"%"))/100.
    >>> data = u"1, 2.3%, 45.\n6, 78.9%, 0"
    >>> names = ("i", "p", "n")
    >>> # General case .....
    >>> np.genfromtxt(StringIO(data), delimiter=",", names=names)
-   array([(1.0, nan, 45.0), (6.0, nan, 0.0)],
+   array([(1., nan, 45.), (6., nan, 0.)],
          dtype=[('i', '<f8'), ('p', '<f8'), ('n', '<f8')])
 
 We need to keep in mind that by default, ``dtype=float``.  A float is