Fix Sphinx and unitary tests issues

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/ST_cythonize@2354 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 7852df8e3fcea5b0b7f37f79f2b8b34cfd1db62c

5 files changed, 153 insertions, 147 deletions

diff --git a/src/cython/doc/alpha_complex_user.rst b/src/cython/doc/alpha_complex_user.rst
index 2356944d..9aa6b13b 100644
--- a/src/cython/doc/alpha_complex_user.rst
+++ b/src/cython/doc/alpha_complex_user.rst
@@ -30,39 +30,40 @@ This example builds the Delaunay triangulation from the given points, and initia
         repr(simplex_tree.num_simplices()) + ' simplices - ' + \
         repr(simplex_tree.num_vertices()) + ' vertices.'
     print(result_str)
+    fmt = '%s -> %.2f'
     for filtered_value in simplex_tree.get_filtration():
-        print(filtered_value)
+        print(fmt % tuple(filtered_value))
 
 The output is:
 
 .. testoutput::
 
    Alpha complex is of dimension 2 - 25 simplices - 7 vertices.
-   ([0], 0.0)
-   ([1], 0.0)
-   ([2], 0.0)
-   ([3], 0.0)
-   ([4], 0.0)
-   ([5], 0.0)
-   ([6], 0.0)
-   ([2, 3], 6.25)
-   ([4, 5], 7.25)
-   ([0, 2], 8.5)
-   ([0, 1], 9.25)
-   ([1, 3], 10.0)
-   ([1, 2], 11.25)
-   ([1, 2, 3], 12.5)
-   ([0, 1, 2], 12.995867768595042)
-   ([5, 6], 13.25)
-   ([2, 4], 20.0)
-   ([4, 6], 22.736686390532547)
-   ([4, 5, 6], 22.736686390532547)
-   ([3, 6], 30.25)
-   ([2, 6], 36.5)
-   ([2, 3, 6], 36.5)
-   ([2, 4, 6], 37.24489795918368)
-   ([0, 4], 59.710743801652896)
-   ([0, 2, 4], 59.710743801652896)
+   [0] -> 0.00
+   [1] -> 0.00
+   [2] -> 0.00
+   [3] -> 0.00
+   [4] -> 0.00
+   [5] -> 0.00
+   [6] -> 0.00
+   [2, 3] -> 6.25
+   [4, 5] -> 7.25
+   [0, 2] -> 8.50
+   [0, 1] -> 9.25
+   [1, 3] -> 10.00
+   [1, 2] -> 11.25
+   [1, 2, 3] -> 12.50
+   [0, 1, 2] -> 13.00
+   [5, 6] -> 13.25
+   [2, 4] -> 20.00
+   [4, 6] -> 22.74
+   [4, 5, 6] -> 22.74
+   [3, 6] -> 30.25
+   [2, 6] -> 36.50
+   [2, 3, 6] -> 36.50
+   [2, 4, 6] -> 37.24
+   [0, 4] -> 59.71
+   [0, 2, 4] -> 59.71
 
 
 Algorithm
@@ -164,37 +165,38 @@ Then, it is asked to display information about the alpha complex:
         repr(simplex_tree.num_simplices()) + ' simplices - ' + \
         repr(simplex_tree.num_vertices()) + ' vertices.'
     print(result_str)
+    fmt = '%s -> %.2f'
     for filtered_value in simplex_tree.get_filtration():
-        print(filtered_value)
+        print(fmt % tuple(filtered_value))
 
 the program output is:
 
 .. testoutput::
 
    Alpha complex is of dimension 2 - 23 simplices - 7 vertices.
-   ([0], 0.0)
-   ([1], 0.0)
-   ([2], 0.0)
-   ([3], 0.0)
-   ([4], 0.0)
-   ([5], 0.0)
-   ([6], 0.0)
-   ([2, 3], 6.25)
-   ([4, 5], 7.25)
-   ([0, 2], 8.5)
-   ([0, 1], 9.25)
-   ([1, 3], 10.0)
-   ([1, 2], 11.25)
-   ([1, 2, 3], 12.5)
-   ([0, 1, 2], 12.995867768595042)
-   ([5, 6], 13.25)
-   ([2, 4], 20.0)
-   ([4, 6], 22.736686390532547)
-   ([4, 5, 6], 22.736686390532547)
-   ([3, 6], 30.25)
-   ([2, 6], 36.5)
-   ([2, 3, 6], 36.5)
-   ([2, 4, 6], 37.24489795918368)
+   [0] -> 0.00
+   [1] -> 0.00
+   [2] -> 0.00
+   [3] -> 0.00
+   [4] -> 0.00
+   [5] -> 0.00
+   [6] -> 0.00
+   [2, 3] -> 6.25
+   [4, 5] -> 7.25
+   [0, 2] -> 8.50
+   [0, 1] -> 9.25
+   [1, 3] -> 10.00
+   [1, 2] -> 11.25
+   [1, 2, 3] -> 12.50
+   [0, 1, 2] -> 13.00
+   [5, 6] -> 13.25
+   [2, 4] -> 20.00
+   [4, 6] -> 22.74
+   [4, 5, 6] -> 22.74
+   [3, 6] -> 30.25
+   [2, 6] -> 36.50
+   [2, 3, 6] -> 36.50
+   [2, 4, 6] -> 37.24
 
 ==============
 CGAL citations
diff --git a/src/cython/doc/bottleneck_distance_user.rst b/src/cython/doc/bottleneck_distance_user.rst
index 8c29d069..546a15bb 100644
--- a/src/cython/doc/bottleneck_distance_user.rst
+++ b/src/cython/doc/bottleneck_distance_user.rst
@@ -23,15 +23,15 @@ This example computes the bottleneck distance from 2 persistence diagrams:
     diag1 = [[2.7, 3.7],[9.6, 14.],[34.2, 34.974], [3.,float('Inf')]]
     diag2 = [[2.8, 4.45],[9.5, 14.1],[3.2,float('Inf')]]
 
-    message = "Bottleneck distance approximation=" + repr(gudhi.bottleneck_distance(diag1, diag2, 0.1))
+    message = "Bottleneck distance approximation=" + '%.2f' % gudhi.bottleneck_distance(diag1, diag2, 0.1)
     print(message)
 
-    message = "Bottleneck distance exact value=" + repr(gudhi.bottleneck_distance(diag1, diag2, 0))
+    message = "Bottleneck distance exact value=" + '%.2f' % gudhi.bottleneck_distance(diag1, diag2, 0)
     print(message)
 
 The output is:
 
 .. testoutput::
 
-    Bottleneck distance approximation=0.8081763781405569
+    Bottleneck distance approximation=0.81
     Bottleneck distance exact value=0.75
diff --git a/src/cython/doc/installation.rst b/src/cython/doc/installation.rst
index 373e0717..f98a5039 100644
--- a/src/cython/doc/installation.rst
+++ b/src/cython/doc/installation.rst
@@ -33,7 +33,7 @@ To build the GUDHI cython module, run the following commands in a terminal:
 Test suites
 ===========
 
-To test your build, `py.test <http://doc.pytest.org>`_ is required. Run the
+To test your build, `py.test <http://doc.pytest.org>`_ is optional. Run the
 following command in a terminal:
 
 .. code-block:: bash
@@ -41,7 +41,7 @@ following command in a terminal:
     cd /path-to-gudhi/build/cython
     # For windows, you have to set PYTHONPATH environment variable
     export PYTHONPATH='$PYTHONPATH:/path-to-gudhi/build/cython'
-    py.test
+    ctest -R py_test
 
 Documentation
 =============
diff --git a/src/cython/doc/rips_complex_user.rst b/src/cython/doc/rips_complex_user.rst
index c89409a0..65d10304 100644
--- a/src/cython/doc/rips_complex_user.rst
+++ b/src/cython/doc/rips_complex_user.rst
@@ -60,8 +60,9 @@ Finally, it is asked to display information about the simplicial complex.
         repr(simplex_tree.num_simplices()) + ' simplices - ' + \
         repr(simplex_tree.num_vertices()) + ' vertices.'
     print(result_str)
+    fmt = '%s -> %.2f'
     for filtered_value in simplex_tree.get_filtration():
-        print(filtered_value)
+        print(fmt % tuple(filtered_value))
 
 When launching (Rips maximal distance between 2 points is 12.0, is expanded
 until dimension 1 - one skeleton graph in other words), the output is:
@@ -69,24 +70,24 @@ until dimension 1 - one skeleton graph in other words), the output is:
 .. testoutput::
 
     Rips complex is of dimension 1 - 18 simplices - 7 vertices.
-    ([0], 0.0)
-    ([1], 0.0)
-    ([2], 0.0)
-    ([3], 0.0)
-    ([4], 0.0)
-    ([5], 0.0)
-    ([6], 0.0)
-    ([2, 3], 5.0)
-    ([4, 5], 5.385164807134504)
-    ([0, 2], 5.830951894845301)
-    ([0, 1], 6.082762530298219)
-    ([1, 3], 6.324555320336759)
-    ([1, 2], 6.708203932499369)
-    ([5, 6], 7.280109889280518)
-    ([2, 4], 8.94427190999916)
-    ([0, 3], 9.433981132056603)
-    ([4, 6], 9.486832980505138)
-    ([3, 6], 11.0)
+    [0] -> 0.00
+    [1] -> 0.00
+    [2] -> 0.00
+    [3] -> 0.00
+    [4] -> 0.00
+    [5] -> 0.00
+    [6] -> 0.00
+    [2, 3] -> 5.00
+    [4, 5] -> 5.39
+    [0, 2] -> 5.83
+    [0, 1] -> 6.08
+    [1, 3] -> 6.32
+    [1, 2] -> 6.71
+    [5, 6] -> 7.28
+    [2, 4] -> 8.94
+    [0, 3] -> 9.43
+    [4, 6] -> 9.49
+    [3, 6] -> 11.00
 
 Example from OFF file
 ^^^^^^^^^^^^^^^^^^^^^
@@ -107,32 +108,33 @@ Finally, it is asked to display information about the Rips complex.
         repr(simplex_tree.num_simplices()) + ' simplices - ' + \
         repr(simplex_tree.num_vertices()) + ' vertices.'
     print(result_str)
+    fmt = '%s -> %.2f'
     for filtered_value in simplex_tree.get_filtration():
-        print(filtered_value)
+        print(fmt % tuple(filtered_value))
 
 the program output is:
 
 .. testoutput::
 
     Rips complex is of dimension 1 - 18 simplices - 7 vertices.
-    ([0], 0.0)
-    ([1], 0.0)
-    ([2], 0.0)
-    ([3], 0.0)
-    ([4], 0.0)
-    ([5], 0.0)
-    ([6], 0.0)
-    ([2, 3], 5.0)
-    ([4, 5], 5.385164807134504)
-    ([0, 2], 5.830951894845301)
-    ([0, 1], 6.082762530298219)
-    ([1, 3], 6.324555320336759)
-    ([1, 2], 6.708203932499369)
-    ([5, 6], 7.280109889280518)
-    ([2, 4], 8.94427190999916)
-    ([0, 3], 9.433981132056603)
-    ([4, 6], 9.486832980505138)
-    ([3, 6], 11.0)
+    [0] -> 0.00
+    [1] -> 0.00
+    [2] -> 0.00
+    [3] -> 0.00
+    [4] -> 0.00
+    [5] -> 0.00
+    [6] -> 0.00
+    [2, 3] -> 5.00
+    [4, 5] -> 5.39
+    [0, 2] -> 5.83
+    [0, 1] -> 6.08
+    [1, 3] -> 6.32
+    [1, 2] -> 6.71
+    [5, 6] -> 7.28
+    [2, 4] -> 8.94
+    [0, 3] -> 9.43
+    [4, 6] -> 9.49
+    [3, 6] -> 11.00
 
 Distance matrix
 ---------------
@@ -162,8 +164,9 @@ Finally, it is asked to display information about the simplicial complex.
         repr(simplex_tree.num_simplices()) + ' simplices - ' + \
         repr(simplex_tree.num_vertices()) + ' vertices.'
     print(result_str)
+    fmt = '%s -> %.2f'
     for filtered_value in simplex_tree.get_filtration():
-        print(filtered_value)
+        print(fmt % tuple(filtered_value))
 
 When launching (Rips maximal distance between 2 points is 12.0, is expanded
 until dimension 1 - one skeleton graph in other words), the output is:
@@ -171,24 +174,24 @@ until dimension 1 - one skeleton graph in other words), the output is:
 .. testoutput::
 
     Rips complex is of dimension 1 - 18 simplices - 7 vertices.
-    ([0], 0.0)
-    ([1], 0.0)
-    ([2], 0.0)
-    ([3], 0.0)
-    ([4], 0.0)
-    ([5], 0.0)
-    ([6], 0.0)
-    ([2, 3], 5.0)
-    ([4, 5], 5.3851648071)
-    ([0, 2], 5.8309518948)
-    ([0, 1], 6.0827625303)
-    ([1, 3], 6.3245553203)
-    ([1, 2], 6.7082039325)
-    ([5, 6], 7.2801098893)
-    ([2, 4], 8.94427191)
-    ([0, 3], 9.4339811321)
-    ([4, 6], 9.4868329805)
-    ([3, 6], 11.0)
+    [0] -> 0.00
+    [1] -> 0.00
+    [2] -> 0.00
+    [3] -> 0.00
+    [4] -> 0.00
+    [5] -> 0.00
+    [6] -> 0.00
+    [2, 3] -> 5.00
+    [4, 5] -> 5.39
+    [0, 2] -> 5.83
+    [0, 1] -> 6.08
+    [1, 3] -> 6.32
+    [1, 2] -> 6.71
+    [5, 6] -> 7.28
+    [2, 4] -> 8.94
+    [0, 3] -> 9.43
+    [4, 6] -> 9.49
+    [3, 6] -> 11.00
 
 Example from csv file
 ^^^^^^^^^^^^^^^^^^^^^
@@ -209,29 +212,30 @@ Finally, it is asked to display information about the Rips complex.
         repr(simplex_tree.num_simplices()) + ' simplices - ' + \
         repr(simplex_tree.num_vertices()) + ' vertices.'
     print(result_str)
+    fmt = '%s -> %.2f'
     for filtered_value in simplex_tree.get_filtration():
-        print(filtered_value)
+        print(fmt % tuple(filtered_value))
 
 the program output is:
 
 .. testoutput::
 
     Rips complex is of dimension 1 - 18 simplices - 7 vertices.
-    ([0], 0.0)
-    ([1], 0.0)
-    ([2], 0.0)
-    ([3], 0.0)
-    ([4], 0.0)
-    ([5], 0.0)
-    ([6], 0.0)
-    ([2, 3], 5.0)
-    ([4, 5], 5.3851648071)
-    ([0, 2], 5.8309518948)
-    ([0, 1], 6.0827625303)
-    ([1, 3], 6.3245553203)
-    ([1, 2], 6.7082039325)
-    ([5, 6], 7.2801098893)
-    ([2, 4], 8.94427191)
-    ([0, 3], 9.4339811321)
-    ([4, 6], 9.4868329805)
-    ([3, 6], 11.0)
+    [0] -> 0.00
+    [1] -> 0.00
+    [2] -> 0.00
+    [3] -> 0.00
+    [4] -> 0.00
+    [5] -> 0.00
+    [6] -> 0.00
+    [2, 3] -> 5.00
+    [4, 5] -> 5.39
+    [0, 2] -> 5.83
+    [0, 1] -> 6.08
+    [1, 3] -> 6.32
+    [1, 2] -> 6.71
+    [5, 6] -> 7.28
+    [2, 4] -> 8.94
+    [0, 3] -> 9.43
+    [4, 6] -> 9.49
+    [3, 6] -> 11.00
diff --git a/src/cython/doc/tangential_complex_user.rst b/src/cython/doc/tangential_complex_user.rst
index 24f68f85..b2f40cce 100644
--- a/src/cython/doc/tangential_complex_user.rst
+++ b/src/cython/doc/tangential_complex_user.rst
@@ -134,7 +134,7 @@ This example builds the Tangential complex of point set read in an OFF file.
         repr(st.num_vertices()) + ' vertices.'
     print(result_str)
     for filtered_value in st.get_filtration():
-        print(filtered_value)
+        print(filtered_value[0])
 
 The output is:
 
@@ -142,21 +142,21 @@ The output is:
 
     Tangential contains 12 simplices - 7 vertices.
     Simplex tree is of dimension 1 - 15 simplices - 7 vertices.
-    ([0], 0.0)
-    ([1], 0.0)
-    ([0, 1], 0.0)
-    ([2], 0.0)
-    ([0, 2], 0.0)
-    ([1, 2], 0.0)
-    ([3], 0.0)
-    ([1, 3], 0.0)
-    ([4], 0.0)
-    ([2, 4], 0.0)
-    ([5], 0.0)
-    ([4, 5], 0.0)
-    ([6], 0.0)
-    ([3, 6], 0.0)
-    ([5, 6], 0.0)
+    [0]
+    [1]
+    [0, 1]
+    [2]
+    [0, 2]
+    [1, 2]
+    [3]
+    [1, 3]
+    [4]
+    [2, 4]
+    [5]
+    [4, 5]
+    [6]
+    [3, 6]
+    [5, 6]
 
 
 Example with perturbation