# -*- encoding:utf-8 -*-
from __future__ import division, absolute_import, print_function

import re
import sys
import textwrap
import warnings

import jinja2

from numpydoc.docscrape import (
    NumpyDocString,
    FunctionDoc,
    ClassDoc,
    ParseError
)
from numpydoc.docscrape_sphinx import (SphinxDocString, SphinxClassDoc,
                                       SphinxFunctionDoc, get_doc_object)
from pytest import raises as assert_raises


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
      (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.
  no_description

  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)

doc_yields_txt = """
Test generator

Yields
------
a : int
    The number of apples.
b : int
    The number of bananas.
int
    The number of unknowns.
"""
doc_yields = NumpyDocString(doc_yields_txt)


doc_sent_txt = """
Test generator

Yields
------
a : int
    The number of apples.

Receives
--------
b : int
    The number of bananas.
c : int
    The number of oranges.

"""
doc_sent = NumpyDocString(doc_sent_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 len(doc['Parameters']) == 3
    names = [n for n, _, _ in doc['Parameters']]
    assert all(a == b for a, b in zip(names, ['mean', 'cov', 'shape']))

    arg, arg_type, desc = doc['Parameters'][1]
    assert arg_type == '(N, N) ndarray'
    assert desc[0].startswith('Covariance matrix')
    assert doc['Parameters'][0][-1][-1] == '   (1+2+3)/3'


def test_other_parameters():
    assert len(doc['Other Parameters']) == 1
    assert [n for n, _, _ in doc['Other Parameters']] == ['spam']
    arg, arg_type, desc = doc['Other Parameters'][0]
    assert arg_type == 'parrot'
    assert desc[0].startswith('A parrot off its mortal coil')


def test_returns():
    assert len(doc['Returns']) == 3
    arg, arg_type, desc = doc['Returns'][0]
    assert arg == 'out'
    assert arg_type == 'ndarray'
    assert desc[0].startswith('The drawn samples')
    assert desc[-1].endswith('distribution.')

    arg, arg_type, desc = doc['Returns'][1]
    assert arg == 'list of str'
    assert arg_type == ''
    assert desc[0].startswith('This is not a real')
    assert desc[-1].endswith('anonymous return values.')

    arg, arg_type, desc = doc['Returns'][2]
    assert arg == 'no_description'
    assert arg_type == ''
    assert not ''.join(desc).strip()


def test_yields():
    section = doc_yields['Yields']
    assert len(section) == 3
    truth = [('a', 'int', 'apples.'),
             ('b', 'int', 'bananas.'),
             ('int', '', 'unknowns.')]
    for (arg, arg_type, desc), (arg_, arg_type_, end) in zip(section, truth):
        assert arg == arg_
        assert arg_type == arg_type_
        assert desc[0].startswith('The number of')
        assert desc[0].endswith(end)


def test_sent():
    section = doc_sent['Receives']
    assert len(section) == 2
    truth = [('b', 'int', 'bananas.'),
             ('c', 'int', 'oranges.')]
    for (arg, arg_type, desc), (arg_, arg_type_, end) in zip(section, truth):
        assert arg == arg_
        assert arg_type == arg_type_
        assert desc[0].startswith('The number of')
        assert desc[0].endswith(end)


def test_returnyield():
    doc_text = """
Test having returns and yields.

Returns
-------
int
    The number of apples.

Yields
------
a : int
    The number of apples.
b : int
    The number of bananas.

"""
    assert_raises(ValueError, NumpyDocString, doc_text)


def test_returnyield():
    doc_text = """
Test having returns and yields.

Returns
-------
int
    The number of apples.

Yields
------
a : int
    The number of apples.
b : int
    The number of bananas.

"""
    assert_raises(ValueError, NumpyDocString, doc_text)


def test_section_twice():
    doc_text = """
Test having a section Notes twice

Notes
-----
See the next note for more information

Notes
-----
That should break...
"""
    assert_raises(ValueError, NumpyDocString, doc_text)

    # if we have a numpydoc object, we know where the error came from
    class Dummy(object):
        """
        Dummy class.

        Notes
        -----
        First note.

        Notes
        -----
        Second note.

        """
        def spam(self, a, b):
            """Spam\n\nSpam spam."""
            pass

        def ham(self, c, d):
            """Cheese\n\nNo cheese."""
            pass

    def dummy_func(arg):
        """
        Dummy function.

        Notes
        -----
        First note.

        Notes
        -----
        Second note.
        """

    try:
        SphinxClassDoc(Dummy)
    except ValueError as e:
        # python 3 version or python 2 version
        assert ("test_section_twice.<locals>.Dummy" in str(e)
                or 'test_docscrape.Dummy' in str(e))

    try:
        SphinxFunctionDoc(dummy_func)
    except ValueError as e:
        # python 3 version or python 2 version
        assert ("test_section_twice.<locals>.dummy_func" in str(e)
                or 'function dummy_func' in str(e))


def test_notes():
    assert doc['Notes'][0].startswith('Instead')
    assert doc['Notes'][-1].endswith('definite.')
    assert 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 doc['index']['default'] == 'random'
    assert len(doc['index']) == 2
    assert len(doc['index']['refguide']) == 2


def _strip_blank_lines(s):
    "Remove leading, trailing and multiple blank lines"
    s = re.sub(r'^\s*\n', '', s)
    s = re.sub(r'\n\s*$', '', s)
    s = re.sub(r'\n\s*\n', r'\n\n', s)
    return s


def line_by_line_compare(a, b):
    a = textwrap.dedent(a)
    b = textwrap.dedent(b)
    a = [l.rstrip() for l in _strip_blank_lines(a).split('\n')]
    b = [l.rstrip() for l in _strip_blank_lines(b).split('\n')]
    assert all(x == y for x, y in zip(a, b))


def test_str():
    # doc_txt has the order of Notes and See Also sections flipped.
    # This should be handled automatically, and so, one thing this test does
    # is to make sure that See Also precedes Notes in the output.
    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
    (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.
no_description

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_yield_str():
    line_by_line_compare(str(doc_yields),
"""Test generator

Yields
------
a : int
    The number of apples.
b : int
    The number of bananas.
int
    The number of unknowns.

.. index:: """)


def test_receives_str():
    line_by_line_compare(str(doc_sent),
"""Test generator

Yields
------
a : int
    The number of apples.

Receives
--------
b : int
    The number of bananas.
c : int
    The number of oranges.

.. index:: """)


def test_no_index_in_str():
    assert "index" not in str(NumpyDocString("""Test idx

    """))

    assert "index" in str(NumpyDocString("""Test idx

    .. index :: random
    """))

    assert "index" in str(NumpyDocString("""Test idx

    .. index ::
        foo
    """))

def test_sphinx_str():
    sphinx_doc = SphinxDocString(doc_txt)
    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
        (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.

    **no_description**
        ..

: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]
""")


def test_sphinx_yields_str():
    sphinx_doc = SphinxDocString(doc_yields_txt)
    line_by_line_compare(str(sphinx_doc),
"""Test generator

:Yields:

    **a** : int
        The number of apples.

    **b** : int
        The number of bananas.

    **int**
        The number of unknowns.
""")


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 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 signature == r'my_signature(\*params, \*\*kwds)'

    def my_func(a, b, **kwargs):
        pass

    fdoc = FunctionDoc(func=my_func)
    assert fdoc['Signature'] == r'my_func(a, b, \*\*kwargs)'


doc4 = NumpyDocString(
    """a.conj()

    Return an array with all complex-valued elements conjugated.""")


def test_empty_extended_summary():
    assert doc4['Extended Summary'] == []


doc5 = NumpyDocString(
    """
    a.something()

    Raises
    ------
    LinAlgException
        If array is singular.

    Warns
    -----
    SomeWarning
        If needed
    """)


def test_raises():
    assert len(doc5['Raises']) == 1
    name, _, desc = doc5['Raises'][0]
    assert name == 'LinAlgException'
    assert desc == ['If array is singular.']


def test_warns():
    assert len(doc5['Warns']) == 1
    name, _, desc = doc5['Warns'][0]
    assert name == 'SomeWarning'
    assert 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`
    :obj:`~baz.obj_r`
    :class:`class_j`: fubar
        foobar
    """)

    assert len(doc6['See Also']) == 13
    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',
                    '~baz.obj_r'):
            assert(not desc)
        else:
            assert(desc)

        if func == 'func_h':
            assert role == 'meth'
        elif func == 'baz.obj_q' or func == '~baz.obj_r':
            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_parse_error():
    text = (
    """
    z(x,theta)

    See Also
    --------
    :func:`~foo`
    """)
    with assert_raises(ParseError) as err:
        NumpyDocString(text)

    s1 = str(r":func:`~foo` is not a item name in '\n    z(x,theta)\n\n    See Also\n    --------\n    :func:`~foo`\n    '")
    s2 = str(err.value)
    assert s1 == s2


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)


def test_unknown_section():
    doc_text = """
Test having an unknown section

Mope
----
This should be ignored and warned about
"""

    class BadSection(object):
        """Class with bad section.

        Nope
        ----
        This class has a nope section.
        """
        pass

    with warnings.catch_warnings(record=True) as w:
        warnings.filterwarnings('always', '', UserWarning)
        NumpyDocString(doc_text)
        assert len(w) == 1
        assert "Unknown section Mope" == str(w[0].message)

    with warnings.catch_warnings(record=True) as w:
        warnings.filterwarnings('always', '', UserWarning)
        SphinxClassDoc(BadSection)
        assert len(w) == 1
        assert('test_docscrape.test_unknown_section.<locals>.BadSection'
               in str(w[0].message)
               or 'test_docscrape.BadSection' in str(w[0].message))


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
    --------
    >>> from matplotlib import 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_use_blockquotes():
    cfg = dict(use_blockquotes=True)
    doc = SphinxDocString("""
    Parameters
    ----------
    abc : def
        ghi
    jkl
        mno

    Returns
    -------
    ABC : DEF
        GHI
    JKL
        MNO
    """, config=cfg)
    line_by_line_compare(str(doc), '''
    :Parameters:

        **abc** : def

            ghi

        **jkl**

            mno

    :Returns:

        **ABC** : DEF

            GHI

        **JKL**

            MNO
    ''')


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)

    class SubDummy(Dummy):
        """
        Subclass of Dummy class.

        """
        def ham(self, c, d):
            """Cheese\n\nNo cheese.\nOverloaded Dummy.ham"""
            pass

        def bar(self, a, b):
            """Bar\n\nNo bar"""
            pass

    for cls in (ClassDoc, SphinxClassDoc):
        doc = cls(SubDummy, config=dict(show_class_members=True,
                                        show_inherited_class_members=False))
        assert 'Methods' in str(doc), (cls, str(doc))
        assert 'spam' not in str(doc), (cls, str(doc))
        assert 'ham' in str(doc), (cls, str(doc))
        assert 'bar' in str(doc), (cls, str(doc))
        assert 'spammity' not in str(doc), (cls, str(doc))

        if cls is SphinxClassDoc:
            assert '.. autosummary::' in str(doc), str(doc)
        else:
            assert 'Spammity index' not in str(doc), str(doc)

        doc = cls(SubDummy, config=dict(show_class_members=True,
                                        show_inherited_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 'bar' 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.

        * hello
        * world
    an_attribute : float
        The docstring is printed instead
    no_docstring : str
        But a description
    no_docstring2 : str
    multiline_sentence
    midword_period
    no_period

    Methods
    -------
    a
    b
    c

    Examples
    --------
    For usage examples, see `ode`.
"""


def test_class_members_doc():
    doc = ClassDoc(None, class_doc_txt)
    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.

        * hello
        * world
    an_attribute : float
        The docstring is printed instead
    no_docstring : str
        But a description
    no_docstring2 : str
    multiline_sentence
    midword_period
    no_period

    Methods
    -------
    a
    b
    c

    .. index::

    """)


def test_class_members_doc_sphinx():
    class Foo:
        @property
        def an_attribute(self):
            """Test attribute"""
            return None

        @property
        def no_docstring(self):
            return None

        @property
        def no_docstring2(self):
            return None

        @property
        def multiline_sentence(self):
            """This is a
            sentence. It spans multiple lines."""
            return None

        @property
        def midword_period(self):
            """The sentence for numpy.org."""
            return None

        @property
        def no_period(self):
            """This does not have a period
            so we truncate its summary to the first linebreak

            Apparently.
            """
            return None

    doc = SphinxClassDoc(Foo, class_doc_txt)
    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`.

    :Attributes:

        **t** : float
            Current time.

        **y** : ndarray
            Current variable values.

            * hello
            * world

        :obj:`an_attribute <an_attribute>` : float
            Test attribute

        **no_docstring** : str
            But a description

        **no_docstring2** : str
            ..

        :obj:`multiline_sentence <multiline_sentence>`
            This is a sentence.

        :obj:`midword_period <midword_period>`
            The sentence for numpy.org.

        :obj:`no_period <no_period>`
            This does not have a period

    .. rubric:: Methods

    =====  ==========
    **a**
    **b**
    **c**
    =====  ==========

    """)


def test_templated_sections():
    doc = SphinxClassDoc(None, class_doc_txt,
                         config={'template': jinja2.Template('{{examples}}\n{{parameters}}')})
    line_by_line_compare(str(doc),
    """
    .. rubric:: Examples

    For usage examples, see `ode`.

    :Parameters:

        **f** : callable ``f(t, y, *f_args)``
            Aaa.

        **jac** : callable ``jac(t, y, *jac_args)``
            Bbb.

    """)


def test_nonstandard_property():
    # test discovery of a property that does not satisfy isinstace(.., property)

    class SpecialProperty(object):

        def __init__(self, axis=0, doc=""):
            self.axis = axis
            self.__doc__ = doc

        def __get__(self, obj, type):
            if obj is None:
                # Only instances have actual _data, not classes
                return self
            else:
                return obj._data.axes[self.axis]

        def __set__(self, obj, value):
            obj._set_axis(self.axis, value)

    class Dummy:

        attr = SpecialProperty(doc="test attribute")

    doc = get_doc_object(Dummy)
    assert "test attribute" in str(doc)


def test_args_and_kwargs():
    cfg = dict()
    doc = SphinxDocString("""
    Parameters
    ----------
    param1 : int
        First parameter
    *args : tuple
        Arguments
    **kwargs : dict
        Keyword arguments
    """, config=cfg)
    line_by_line_compare(str(doc), r"""
:Parameters:

    **param1** : int
        First parameter

    **\*args** : tuple
        Arguments

    **\*\*kwargs** : dict
        Keyword arguments
    """)

def test_autoclass():
    cfg=dict(show_class_members=True,
             show_inherited_class_members=True)
    doc = SphinxClassDoc(str, '''
A top section before

.. autoclass:: str
    ''', config=cfg)
    line_by_line_compare(str(doc), r'''
A top section before

.. autoclass:: str

.. rubric:: Methods


    ''')


if __name__ == "__main__":
    import pytest
    pytest.main()
