From e4ce5860796e9edef3f9d7762d92da23b5f512d1 Mon Sep 17 00:00:00 2001
From: Christoph Groth <christoph.groth@cea.fr>
Date: Mon, 26 Aug 2013 14:22:47 +0200
Subject: [PATCH] remove unneeded files from sphinxext
---
doc/sphinxext/MANIFEST.in | 2 -
doc/sphinxext/setup.py | 31 --
doc/sphinxext/tests/test_docscrape.py | 615 --------------------------
3 files changed, 648 deletions(-)
delete mode 100644 doc/sphinxext/MANIFEST.in
delete mode 100644 doc/sphinxext/setup.py
delete mode 100644 doc/sphinxext/tests/test_docscrape.py
diff --git a/doc/sphinxext/MANIFEST.in b/doc/sphinxext/MANIFEST.in
deleted file mode 100644
index f88ed785..00000000
--- a/doc/sphinxext/MANIFEST.in
+++ /dev/null
@@ -1,2 +0,0 @@
-recursive-include tests *.py
-include *.txt
diff --git a/doc/sphinxext/setup.py b/doc/sphinxext/setup.py
deleted file mode 100644
index 76e3fd81..00000000
--- a/doc/sphinxext/setup.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from distutils.core import setup
-import setuptools
-import sys, os
-
-version = "0.4"
-
-setup(
- name="numpydoc",
- packages=["numpydoc"],
- package_dir={"numpydoc": ""},
- version=version,
- description="Sphinx extension to support docstrings in Numpy format",
- # classifiers from http://pypi.python.org/pypi?%3Aaction=list_classifiers
- classifiers=["Development Status :: 3 - Alpha",
- "Environment :: Plugins",
- "License :: OSI Approved :: BSD License",
- "Topic :: Documentation"],
- keywords="sphinx numpy",
- author="Pauli Virtanen and others",
- author_email="pav@iki.fi",
- url="http://github.com/numpy/numpy/tree/master/doc/sphinxext",
- license="BSD",
- zip_safe=False,
- install_requires=["Sphinx >= 1.0.1"],
- package_data={'numpydoc': 'tests', '': ''},
- entry_points={
- "console_scripts": [
- "autosummary_generate = numpydoc.autosummary_generate:main",
- ],
- },
-)
diff --git a/doc/sphinxext/tests/test_docscrape.py b/doc/sphinxext/tests/test_docscrape.py
deleted file mode 100644
index 6fab7983..00000000
--- a/doc/sphinxext/tests/test_docscrape.py
+++ /dev/null
@@ -1,615 +0,0 @@
-# -*- encoding:utf-8 -*-
-
-import sys, os
-sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
-
-from docscrape import NumpyDocString, FunctionDoc, ClassDoc
-from docscrape_sphinx import SphinxDocString, SphinxClassDoc
-from nose.tools import *
-
-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.
-
- 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']), 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, 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.
-
-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.
-
-: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 doc['Summary'][0] == u'öäöäöäöäöåååå'.encode('utf-8')
-
-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
-
- 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))
-
- 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))
-
- if cls is SphinxClassDoc:
- assert '.. autosummary::' in str(doc), str(doc)
-
-if __name__ == "__main__":
- import nose
- nose.run()
-
--
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