Spell out types as in PEP 484

but don't actually use annotations. A quick try with
sphinx-autodoc-typehints failed, I didn't investigate much, it can wait.

3 files changed, 10 insertions, 10 deletions

diff --git a/src/python/gudhi/off_reader.pyx b/src/python/gudhi/off_reader.pyx
index e45356be..3a31eba6 100644
--- a/src/python/gudhi/off_reader.pyx
+++ b/src/python/gudhi/off_reader.pyx
@@ -26,7 +26,7 @@ def read_off(off_file=''):
     :type off_file: string
 
     :returns:  The point set.
-    :rtype: list(list(float))
+    :rtype: List[List[float]]
     """
     if off_file:
         if os.path.isfile(off_file):
diff --git a/src/python/gudhi/reader_utils.pyx b/src/python/gudhi/reader_utils.pyx
index 5a756e4b..9ced6bca 100644
--- a/src/python/gudhi/reader_utils.pyx
+++ b/src/python/gudhi/reader_utils.pyx
@@ -34,7 +34,7 @@ def read_lower_triangular_matrix_from_csv_file(csv_file='', separator=';'):
     :type separator: char
 
     :returns:  The lower triangular matrix.
-    :rtype: list(list(float))
+    :rtype: List[List[float]]
     """
     if csv_file:
         if path.isfile(csv_file):
@@ -46,14 +46,14 @@ def read_persistence_intervals_grouped_by_dimension(persistence_file=''):
     """Reads a file containing persistence intervals.
     Each line might contain 2, 3 or 4 values: [[field] dimension] birth death
     The return value is a `dict(dim, list(tuple(birth, death)))`
-    where `dim` is an `int`, `birth` a `double`, and `death` a `double`.
+    where `dim` is an `int`, `birth` a `float`, and `death` a `float`.
     Note: the function does not check that birth <= death.
 
     :param persistence_file: A persistence file style name.
     :type persistence_file: string
 
     :returns:  The persistence pairs grouped by dimension.
-    :rtype: dict(int, list(tuple(float, float)))
+    :rtype: Dict[int, List[Tuple[float, float]]]
     """
     if persistence_file:
         if path.isfile(persistence_file):
diff --git a/src/python/gudhi/subsampling.pyx b/src/python/gudhi/subsampling.pyx
index 04f66219..f50f4b0c 100644
--- a/src/python/gudhi/subsampling.pyx
+++ b/src/python/gudhi/subsampling.pyx
@@ -33,7 +33,7 @@ def choose_n_farthest_points(points=None, off_file='', nb_points=0, starting_poi
     The iteration starts with the landmark `starting point`.
 
     :param points: The input point set.
-    :type points: list(list(float)).
+    :type points: Iterable[Iterable[float]].
 
     Or
 
@@ -47,7 +47,7 @@ def choose_n_farthest_points(points=None, off_file='', nb_points=0, starting_poi
     index is chosen randomly.
     :type starting_point: unsigned.
     :returns:  The subsample point set.
-    :rtype: list(list(float)).
+    :rtype: List[List[float]].
     """
     if off_file:
         if os.path.isfile(off_file):
@@ -74,7 +74,7 @@ def pick_n_random_points(points=None, off_file='', nb_points=0):
     """Subsample a point set by picking random vertices.
 
     :param points: The input point set.
-    :type points: list(list(float)).
+    :type points: Iterable[Iterable[float]].
 
     Or
 
@@ -84,7 +84,7 @@ def pick_n_random_points(points=None, off_file='', nb_points=0):
     :param nb_points: Number of points of the subsample.
     :type nb_points: unsigned.
     :returns:  The subsample point set.
-    :rtype: list(list(float))
+    :rtype: List[List[float]]
     """
     if off_file:
         if os.path.isfile(off_file):
@@ -103,7 +103,7 @@ def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0):
     between any two points is greater than or equal to min_squared_dist.
 
     :param points: The input point set.
-    :type points: list(list(float)).
+    :type points: Iterable[Iterable[float]].
 
     Or
 
@@ -114,7 +114,7 @@ def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0):
     points.
     :type min_squared_dist: float.
     :returns:  The subsample point set.
-    :rtype: list(list(float))
+    :rtype: List[List[float]]
     """
     if off_file:
         if os.path.isfile(off_file):