Move Sphinx extensions under Numpy's SVN trunk

1 files changed, 490 insertions, 0 deletions

diff --git a/doc/sphinxext/tests/test_docscrape.py b/doc/sphinxext/tests/test_docscrape.py
new file mode 100644
index 000000000..15c9b17f4
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+++ b/doc/sphinxext/tests/test_docscrape.py
@@ -0,0 +1,490 @@
+# -*- encoding:utf-8 -*-
+
+import sys, os
+sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+
+from docscrape import NumpyDocString, FunctionDoc
+from docscrape_sphinx import SphinxDocString
+from nose.tools import *
+
+doc_txt = '''\
+  numpy.multivariate_normal(mean, cov, shape=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.
+
+  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('shape=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_returns():
+    assert_equal(len(doc['Returns']), 1)
+    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.')
+
+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')
+    print doc['index']
+    assert_equal(len(doc['index']), 2)
+    assert_equal(len(doc['index']['refguide']), 2)
+
+def non_blank_line_by_line_compare(a,b):
+    a = [l for l in a.split('\n') if l.strip()]
+    b = [l 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)
+
+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.
+
+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.
+
+.. 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.
+
+.. 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.
+
+    """)
+
+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_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 doc['Summary'][0] == u'öäöäöäöäöåååå'.encode('utf-8')