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_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_xref():
xref_aliases = {
'sequence': ':obj:`python:sequence`',
}
class Config():
def __init__(self, a, b):
self.numpydoc_xref_aliases = a
self.numpydoc_xref_aliases_complete = b
xref_aliases_complete = deepcopy(DEFAULT_LINKS)
for key in xref_aliases:
xref_aliases_complete[key] = xref_aliases[key]
config = Config(xref_aliases, xref_aliases_complete)
app = namedtuple('config', 'config')(config)
update_config(app)
xref_ignore = {'of', 'default', 'optional'}
doc = SphinxDocString(xref_doc_txt,
config=dict(xref_param_type=True,
xref_aliases=xref_aliases_complete,
xref_ignore=xref_ignore))
line_by_line_compare(str(doc), xref_doc_txt_expected)
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.
""")
def test_xref():
xref_aliases = {
'sequence': ':obj:`python:sequence`',
}
config = namedtuple('numpydoc_xref_aliases',
'numpydoc_xref_aliases')(xref_aliases)
app = namedtuple('config', 'config')(config)
update_config(app)
xref_ignore = {'of', 'default', 'optional'}
doc = SphinxDocString(xref_doc_txt,
config=dict(xref_param_type=True,
xref_aliases=xref_aliases,
xref_ignore=xref_ignore))
line_by_line_compare(str(doc), xref_doc_txt_expected)
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_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_unicode():
doc = SphinxDocString("""
öäöäöäöäöåååå
öäöäöäööäååå
Parameters
----------
ååå : äää
ööö
Returns
-------
ååå : ööö
äää
""")
assert isinstance(doc['Summary'][0], str)
if sys.version_info[0] >= 3:
assert doc['Summary'][0] == u'öäöäöäöäöåååå'
else:
assert doc['Summary'][0] == u'öäöäöäöäöåååå'.encode('utf-8')
def test_no_summary():
str(SphinxDocString("""
Parameters
----------"""))
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]
""")