Move numpydoc to a separate repository

1 files changed, 0 insertions, 767 deletions

diff --git a/doc/sphinxext/numpydoc/tests/test_docscrape.py b/doc/sphinxext/numpydoc/tests/test_docscrape.py
deleted file mode 100644
index b682504e1..000000000
--- a/doc/sphinxext/numpydoc/tests/test_docscrape.py
+++ /dev/null
@@ -1,767 +0,0 @@
-# -*- encoding:utf-8 -*-
-from __future__ import division, absolute_import, print_function
-
-import sys, textwrap
-
-from numpydoc.docscrape import NumpyDocString, FunctionDoc, ClassDoc
-from numpydoc.docscrape_sphinx import SphinxDocString, SphinxClassDoc
-from nose.tools import *
-
-if sys.version_info[0] >= 3:
-    sixu = lambda s: s
-else:
-    sixu = lambda s: unicode(s, 'unicode_escape')
-
-
-doc_txt = '''\
-  numpy.multivariate_normal(mean, cov, shape=None, spam=None)
-
-  Draw values from a multivariate normal distribution with specified
-  mean and covariance.
-
-  The multivariate normal or Gaussian distribution is a generalisation
-  of the one-dimensional normal distribution to higher dimensions.
-
-  Parameters
-  ----------
-  mean : (N,) ndarray
-      Mean of the N-dimensional distribution.
-
-      .. math::
-
-         (1+2+3)/3
-
-  cov : (N, N) ndarray
-      Covariance matrix of the distribution.
-  shape : tuple of ints
-      Given a shape of, for example, (m,n,k), m*n*k samples are
-      generated, and packed in an m-by-n-by-k arrangement.  Because
-      each sample is N-dimensional, the output shape is (m,n,k,N).
-
-  Returns
-  -------
-  out : ndarray
-      The drawn samples, arranged according to `shape`.  If the
-      shape given is (m,n,...), then the shape of `out` is is
-      (m,n,...,N).
-
-      In other words, each entry ``out[i,j,...,:]`` is an N-dimensional
-      value drawn from the distribution.
-  list of str
-      This is not a real return value.  It exists to test
-      anonymous return values.
-
-  Other Parameters
-  ----------------
-  spam : parrot
-      A parrot off its mortal coil.
-
-  Raises
-  ------
-  RuntimeError
-      Some error
-
-  Warns
-  -----
-  RuntimeWarning
-      Some warning
-
-  Warnings
-  --------
-  Certain warnings apply.
-
-  Notes
-  -----
-  Instead of specifying the full covariance matrix, popular
-  approximations include:
-
-    - Spherical covariance (`cov` is a multiple of the identity matrix)
-    - Diagonal covariance (`cov` has non-negative elements only on the diagonal)
-
-  This geometrical property can be seen in two dimensions by plotting
-  generated data-points:
-
-  >>> mean = [0,0]
-  >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis
-
-  >>> x,y = multivariate_normal(mean,cov,5000).T
-  >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()
-
-  Note that the covariance matrix must be symmetric and non-negative
-  definite.
-
-  References
-  ----------
-  .. [1] A. Papoulis, "Probability, Random Variables, and Stochastic
-         Processes," 3rd ed., McGraw-Hill Companies, 1991
-  .. [2] R.O. Duda, P.E. Hart, and D.G. Stork, "Pattern Classification,"
-         2nd ed., Wiley, 2001.
-
-  See Also
-  --------
-  some, other, funcs
-  otherfunc : relationship
-
-  Examples
-  --------
-  >>> mean = (1,2)
-  >>> cov = [[1,0],[1,0]]
-  >>> x = multivariate_normal(mean,cov,(3,3))
-  >>> print x.shape
-  (3, 3, 2)
-
-  The following is probably true, given that 0.6 is roughly twice the
-  standard deviation:
-
-  >>> print list( (x[0,0,:] - mean) < 0.6 )
-  [True, True]
-
-  .. index:: random
-     :refguide: random;distributions, random;gauss
-
-  '''
-doc = NumpyDocString(doc_txt)
-
-
-def test_signature():
-    assert doc['Signature'].startswith('numpy.multivariate_normal(')
-    assert doc['Signature'].endswith('spam=None)')
-
-def test_summary():
-    assert doc['Summary'][0].startswith('Draw values')
-    assert doc['Summary'][-1].endswith('covariance.')
-
-def test_extended_summary():
-    assert doc['Extended Summary'][0].startswith('The multivariate normal')
-
-def test_parameters():
-    assert_equal(len(doc['Parameters']), 3)
-    assert_equal([n for n,_,_ in doc['Parameters']], ['mean','cov','shape'])
-
-    arg, arg_type, desc = doc['Parameters'][1]
-    assert_equal(arg_type, '(N, N) ndarray')
-    assert desc[0].startswith('Covariance matrix')
-    assert doc['Parameters'][0][-1][-2] == '   (1+2+3)/3'
-
-def test_other_parameters():
-    assert_equal(len(doc['Other Parameters']), 1)
-    assert_equal([n for n,_,_ in doc['Other Parameters']], ['spam'])
-    arg, arg_type, desc = doc['Other Parameters'][0]
-    assert_equal(arg_type, 'parrot')
-    assert desc[0].startswith('A parrot off its mortal coil')
-
-def test_returns():
-    assert_equal(len(doc['Returns']), 2)
-    arg, arg_type, desc = doc['Returns'][0]
-    assert_equal(arg, 'out')
-    assert_equal(arg_type, 'ndarray')
-    assert desc[0].startswith('The drawn samples')
-    assert desc[-1].endswith('distribution.')
-
-    arg, arg_type, desc = doc['Returns'][1]
-    assert_equal(arg, 'list of str')
-    assert_equal(arg_type, '')
-    assert desc[0].startswith('This is not a real')
-    assert desc[-1].endswith('anonymous return values.')
-
-def test_notes():
-    assert doc['Notes'][0].startswith('Instead')
-    assert doc['Notes'][-1].endswith('definite.')
-    assert_equal(len(doc['Notes']), 17)
-
-def test_references():
-    assert doc['References'][0].startswith('..')
-    assert doc['References'][-1].endswith('2001.')
-
-def test_examples():
-    assert doc['Examples'][0].startswith('>>>')
-    assert doc['Examples'][-1].endswith('True]')
-
-def test_index():
-    assert_equal(doc['index']['default'], 'random')
-    assert_equal(len(doc['index']), 2)
-    assert_equal(len(doc['index']['refguide']), 2)
-
-def non_blank_line_by_line_compare(a,b):
-    a = textwrap.dedent(a)
-    b = textwrap.dedent(b)
-    a = [l.rstrip() for l in a.split('\n') if l.strip()]
-    b = [l.rstrip() for l in b.split('\n') if l.strip()]
-    for n,line in enumerate(a):
-        if not line == b[n]:
-            raise AssertionError("Lines %s of a and b differ: "
-                                 "\n>>> %s\n<<< %s\n" %
-                                 (n,line,b[n]))
-def test_str():
-    non_blank_line_by_line_compare(str(doc),
-"""numpy.multivariate_normal(mean, cov, shape=None, spam=None)
-
-Draw values from a multivariate normal distribution with specified
-mean and covariance.
-
-The multivariate normal or Gaussian distribution is a generalisation
-of the one-dimensional normal distribution to higher dimensions.
-
-Parameters
-----------
-mean : (N,) ndarray
-    Mean of the N-dimensional distribution.
-
-    .. math::
-
-       (1+2+3)/3
-
-cov : (N, N) ndarray
-    Covariance matrix of the distribution.
-shape : tuple of ints
-    Given a shape of, for example, (m,n,k), m*n*k samples are
-    generated, and packed in an m-by-n-by-k arrangement.  Because
-    each sample is N-dimensional, the output shape is (m,n,k,N).
-
-Returns
--------
-out : ndarray
-    The drawn samples, arranged according to `shape`.  If the
-    shape given is (m,n,...), then the shape of `out` is is
-    (m,n,...,N).
-
-    In other words, each entry ``out[i,j,...,:]`` is an N-dimensional
-    value drawn from the distribution.
-list of str
-    This is not a real return value.  It exists to test
-    anonymous return values.
-
-Other Parameters
-----------------
-spam : parrot
-    A parrot off its mortal coil.
-
-Raises
-------
-RuntimeError
-    Some error
-
-Warns
------
-RuntimeWarning
-    Some warning
-
-Warnings
---------
-Certain warnings apply.
-
-See Also
---------
-`some`_, `other`_, `funcs`_
-
-`otherfunc`_
-    relationship
-
-Notes
------
-Instead of specifying the full covariance matrix, popular
-approximations include:
-
-  - Spherical covariance (`cov` is a multiple of the identity matrix)
-  - Diagonal covariance (`cov` has non-negative elements only on the diagonal)
-
-This geometrical property can be seen in two dimensions by plotting
-generated data-points:
-
->>> mean = [0,0]
->>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis
-
->>> x,y = multivariate_normal(mean,cov,5000).T
->>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()
-
-Note that the covariance matrix must be symmetric and non-negative
-definite.
-
-References
-----------
-.. [1] A. Papoulis, "Probability, Random Variables, and Stochastic
-       Processes," 3rd ed., McGraw-Hill Companies, 1991
-.. [2] R.O. Duda, P.E. Hart, and D.G. Stork, "Pattern Classification,"
-       2nd ed., Wiley, 2001.
-
-Examples
---------
->>> mean = (1,2)
->>> cov = [[1,0],[1,0]]
->>> x = multivariate_normal(mean,cov,(3,3))
->>> print x.shape
-(3, 3, 2)
-
-The following is probably true, given that 0.6 is roughly twice the
-standard deviation:
-
->>> print list( (x[0,0,:] - mean) < 0.6 )
-[True, True]
-
-.. index:: random
-   :refguide: random;distributions, random;gauss""")
-
-
-def test_sphinx_str():
-    sphinx_doc = SphinxDocString(doc_txt)
-    non_blank_line_by_line_compare(str(sphinx_doc),
-"""
-.. index:: random
-   single: random;distributions, random;gauss
-
-Draw values from a multivariate normal distribution with specified
-mean and covariance.
-
-The multivariate normal or Gaussian distribution is a generalisation
-of the one-dimensional normal distribution to higher dimensions.
-
-:Parameters:
-
-    **mean** : (N,) ndarray
-
-        Mean of the N-dimensional distribution.
-
-        .. math::
-
-           (1+2+3)/3
-
-    **cov** : (N, N) ndarray
-
-        Covariance matrix of the distribution.
-
-    **shape** : tuple of ints
-
-        Given a shape of, for example, (m,n,k), m*n*k samples are
-        generated, and packed in an m-by-n-by-k arrangement.  Because
-        each sample is N-dimensional, the output shape is (m,n,k,N).
-
-:Returns:
-
-    **out** : ndarray
-
-        The drawn samples, arranged according to `shape`.  If the
-        shape given is (m,n,...), then the shape of `out` is is
-        (m,n,...,N).
-
-        In other words, each entry ``out[i,j,...,:]`` is an N-dimensional
-        value drawn from the distribution.
-
-    list of str
-
-        This is not a real return value.  It exists to test
-        anonymous return values.
-
-:Other Parameters:
-
-    **spam** : parrot
-
-        A parrot off its mortal coil.
-
-:Raises:
-
-    **RuntimeError**
-
-        Some error
-
-:Warns:
-
-    **RuntimeWarning**
-
-        Some warning
-
-.. warning::
-
-    Certain warnings apply.
-
-.. seealso::
-
-    :obj:`some`, :obj:`other`, :obj:`funcs`
-
-    :obj:`otherfunc`
-        relationship
-
-.. rubric:: Notes
-
-Instead of specifying the full covariance matrix, popular
-approximations include:
-
-  - Spherical covariance (`cov` is a multiple of the identity matrix)
-  - Diagonal covariance (`cov` has non-negative elements only on the diagonal)
-
-This geometrical property can be seen in two dimensions by plotting
-generated data-points:
-
->>> mean = [0,0]
->>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis
-
->>> x,y = multivariate_normal(mean,cov,5000).T
->>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()
-
-Note that the covariance matrix must be symmetric and non-negative
-definite.
-
-.. rubric:: References
-
-.. [1] A. Papoulis, "Probability, Random Variables, and Stochastic
-       Processes," 3rd ed., McGraw-Hill Companies, 1991
-.. [2] R.O. Duda, P.E. Hart, and D.G. Stork, "Pattern Classification,"
-       2nd ed., Wiley, 2001.
-
-.. only:: latex
-
-   [1]_, [2]_
-
-.. rubric:: Examples
-
->>> mean = (1,2)
->>> cov = [[1,0],[1,0]]
->>> x = multivariate_normal(mean,cov,(3,3))
->>> print x.shape
-(3, 3, 2)
-
-The following is probably true, given that 0.6 is roughly twice the
-standard deviation:
-
->>> print list( (x[0,0,:] - mean) < 0.6 )
-[True, True]
-""")
-
-
-doc2 = NumpyDocString("""
-    Returns array of indices of the maximum values of along the given axis.
-
-    Parameters
-    ----------
-    a : {array_like}
-        Array to look in.
-    axis : {None, integer}
-        If None, the index is into the flattened array, otherwise along
-        the specified axis""")
-
-def test_parameters_without_extended_description():
-    assert_equal(len(doc2['Parameters']), 2)
-
-doc3 = NumpyDocString("""
-    my_signature(*params, **kwds)
-
-    Return this and that.
-    """)
-
-def test_escape_stars():
-    signature = str(doc3).split('\n')[0]
-    assert_equal(signature, 'my_signature(\*params, \*\*kwds)')
-
-doc4 = NumpyDocString(
-    """a.conj()
-
-    Return an array with all complex-valued elements conjugated.""")
-
-def test_empty_extended_summary():
-    assert_equal(doc4['Extended Summary'], [])
-
-doc5 = NumpyDocString(
-    """
-    a.something()
-
-    Raises
-    ------
-    LinAlgException
-        If array is singular.
-
-    Warns
-    -----
-    SomeWarning
-        If needed
-    """)
-
-def test_raises():
-    assert_equal(len(doc5['Raises']), 1)
-    name,_,desc = doc5['Raises'][0]
-    assert_equal(name,'LinAlgException')
-    assert_equal(desc,['If array is singular.'])
-
-def test_warns():
-    assert_equal(len(doc5['Warns']), 1)
-    name,_,desc = doc5['Warns'][0]
-    assert_equal(name,'SomeWarning')
-    assert_equal(desc,['If needed'])
-
-def test_see_also():
-    doc6 = NumpyDocString(
-    """
-    z(x,theta)
-
-    See Also
-    --------
-    func_a, func_b, func_c
-    func_d : some equivalent func
-    foo.func_e : some other func over
-             multiple lines
-    func_f, func_g, :meth:`func_h`, func_j,
-    func_k
-    :obj:`baz.obj_q`
-    :class:`class_j`: fubar
-        foobar
-    """)
-
-    assert len(doc6['See Also']) == 12
-    for func, desc, role in doc6['See Also']:
-        if func in ('func_a', 'func_b', 'func_c', 'func_f',
-                    'func_g', 'func_h', 'func_j', 'func_k', 'baz.obj_q'):
-            assert(not desc)
-        else:
-            assert(desc)
-
-        if func == 'func_h':
-            assert role == 'meth'
-        elif func == 'baz.obj_q':
-            assert role == 'obj'
-        elif func == 'class_j':
-            assert role == 'class'
-        else:
-            assert role is None
-
-        if func == 'func_d':
-            assert desc == ['some equivalent func']
-        elif func == 'foo.func_e':
-            assert desc == ['some other func over', 'multiple lines']
-        elif func == 'class_j':
-            assert desc == ['fubar', 'foobar']
-
-def test_see_also_print():
-    class Dummy(object):
-        """
-        See Also
-        --------
-        func_a, func_b
-        func_c : some relationship
-                 goes here
-        func_d
-        """
-        pass
-
-    obj = Dummy()
-    s = str(FunctionDoc(obj, role='func'))
-    assert(':func:`func_a`, :func:`func_b`' in s)
-    assert('    some relationship' in s)
-    assert(':func:`func_d`' in s)
-
-doc7 = NumpyDocString("""
-
-        Doc starts on second line.
-
-        """)
-
-def test_empty_first_line():
-    assert doc7['Summary'][0].startswith('Doc starts')
-
-
-def test_no_summary():
-    str(SphinxDocString("""
-    Parameters
-    ----------"""))
-
-
-def test_unicode():
-    doc = SphinxDocString("""
-    öäöäöäöäöåååå
-
-    öäöäöäööäååå
-
-    Parameters
-    ----------
-    ååå : äää
-        ööö
-
-    Returns
-    -------
-    ååå : ööö
-        äää
-
-    """)
-    assert isinstance(doc['Summary'][0], str)
-    assert doc['Summary'][0] == 'öäöäöäöäöåååå'
-
-def test_plot_examples():
-    cfg = dict(use_plots=True)
-
-    doc = SphinxDocString("""
-    Examples
-    --------
-    >>> import matplotlib.pyplot as plt
-    >>> plt.plot([1,2,3],[4,5,6])
-    >>> plt.show()
-    """, config=cfg)
-    assert 'plot::' in str(doc), str(doc)
-
-    doc = SphinxDocString("""
-    Examples
-    --------
-    .. plot::
-
-       import matplotlib.pyplot as plt
-       plt.plot([1,2,3],[4,5,6])
-       plt.show()
-    """, config=cfg)
-    assert str(doc).count('plot::') == 1, str(doc)
-
-def test_class_members():
-
-    class Dummy(object):
-        """
-        Dummy class.
-
-        """
-        def spam(self, a, b):
-            """Spam\n\nSpam spam."""
-            pass
-        def ham(self, c, d):
-            """Cheese\n\nNo cheese."""
-            pass
-        @property
-        def spammity(self):
-            """Spammity index"""
-            return 0.95
-
-        class Ignorable(object):
-            """local class, to be ignored"""
-            pass
-
-    for cls in (ClassDoc, SphinxClassDoc):
-        doc = cls(Dummy, config=dict(show_class_members=False))
-        assert 'Methods' not in str(doc), (cls, str(doc))
-        assert 'spam' not in str(doc), (cls, str(doc))
-        assert 'ham' not in str(doc), (cls, str(doc))
-        assert 'spammity' not in str(doc), (cls, str(doc))
-        assert 'Spammity index' not in str(doc), (cls, str(doc))
-
-        doc = cls(Dummy, config=dict(show_class_members=True))
-        assert 'Methods' in str(doc), (cls, str(doc))
-        assert 'spam' in str(doc), (cls, str(doc))
-        assert 'ham' in str(doc), (cls, str(doc))
-        assert 'spammity' in str(doc), (cls, str(doc))
-
-        if cls is SphinxClassDoc:
-            assert '.. autosummary::' in str(doc), str(doc)
-        else:
-            assert 'Spammity index' in str(doc), str(doc)
-
-def test_duplicate_signature():
-    # Duplicate function signatures occur e.g. in ufuncs, when the
-    # automatic mechanism adds one, and a more detailed comes from the
-    # docstring itself.
-
-    doc = NumpyDocString(
-    """
-    z(x1, x2)
-
-    z(a, theta)
-    """)
-
-    assert doc['Signature'].strip() == 'z(a, theta)'
-
-
-class_doc_txt = """
-    Foo
-
-    Parameters
-    ----------
-    f : callable ``f(t, y, *f_args)``
-        Aaa.
-    jac : callable ``jac(t, y, *jac_args)``
-        Bbb.
-
-    Attributes
-    ----------
-    t : float
-        Current time.
-    y : ndarray
-        Current variable values.
-
-    Methods
-    -------
-    a
-    b
-    c
-
-    Examples
-    --------
-    For usage examples, see `ode`.
-"""
-
-def test_class_members_doc():
-    doc = ClassDoc(None, class_doc_txt)
-    non_blank_line_by_line_compare(str(doc),
-    """
-    Foo
-
-    Parameters
-    ----------
-    f : callable ``f(t, y, *f_args)``
-        Aaa.
-    jac : callable ``jac(t, y, *jac_args)``
-        Bbb.
-
-    Examples
-    --------
-    For usage examples, see `ode`.
-
-    Attributes
-    ----------
-    t : float
-        Current time.
-    y : ndarray
-        Current variable values.
-
-    Methods
-    -------
-    a
-
-    b
-
-    c
-
-    .. index::
-
-    """)
-
-def test_class_members_doc_sphinx():
-    doc = SphinxClassDoc(None, class_doc_txt)
-    non_blank_line_by_line_compare(str(doc),
-    """
-    Foo
-
-    :Parameters:
-
-        **f** : callable ``f(t, y, *f_args)``
-
-            Aaa.
-
-        **jac** : callable ``jac(t, y, *jac_args)``
-
-            Bbb.
-
-    .. rubric:: Examples
-
-    For usage examples, see `ode`.
-
-    .. rubric:: Attributes
-
-    ===  ==========
-      t  (float) Current time.
-      y  (ndarray) Current variable values.
-    ===  ==========
-
-    .. rubric:: Methods
-
-    ===  ==========
-      a
-      b
-      c
-    ===  ==========
-
-    """)
-
-if __name__ == "__main__":
-    import nose
-    nose.run()