Merge branch 'master' into endpoints

17 files changed, 243 insertions, 123 deletions

diff --git a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h
index 4a6af3a4..29fabc6c 100644
--- a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h
+++ b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h
@@ -241,10 +241,16 @@ class Bitmap_cubical_complex : public T {
    **/
   class Filtration_simplex_range;
 
-  class Filtration_simplex_iterator : std::iterator<std::input_iterator_tag, Simplex_handle> {
+  class Filtration_simplex_iterator {
     // Iterator over all simplices of the complex in the order of the indexing scheme.
     // 'value_type' must be 'Simplex_handle'.
    public:
+    typedef std::input_iterator_tag iterator_category;
+    typedef Simplex_handle value_type;
+    typedef std::ptrdiff_t difference_type;
+    typedef value_type* pointer;
+    typedef value_type reference;
+
     Filtration_simplex_iterator(Bitmap_cubical_complex* b) : b(b), position(0) {}
 
     Filtration_simplex_iterator() : b(NULL), position(0) {}
@@ -386,10 +392,16 @@ class Bitmap_cubical_complex : public T {
    **/
   class Skeleton_simplex_range;
 
-  class Skeleton_simplex_iterator : std::iterator<std::input_iterator_tag, Simplex_handle> {
+  class Skeleton_simplex_iterator {
     // Iterator over all simplices of the complex in the order of the indexing scheme.
     // 'value_type' must be 'Simplex_handle'.
    public:
+    typedef std::input_iterator_tag iterator_category;
+    typedef Simplex_handle value_type;
+    typedef std::ptrdiff_t difference_type;
+    typedef value_type* pointer;
+    typedef value_type reference;
+
     Skeleton_simplex_iterator(Bitmap_cubical_complex* b, std::size_t d) : b(b), dimension(d) {
       if (globalDbg) {
         std::clog << "Skeleton_simplex_iterator ( Bitmap_cubical_complex* b , std::size_t d )\n";
diff --git a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h
index bafe7981..2bf62f9b 100644
--- a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h
+++ b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h
@@ -251,8 +251,14 @@ class Bitmap_cubical_complex_base {
    * @brief Iterator through all cells in the complex (in order they appear in the structure -- i.e.
    * in lexicographical order).
    **/
-  class All_cells_iterator : std::iterator<std::input_iterator_tag, T> {
+  class All_cells_iterator {
    public:
+    typedef std::input_iterator_tag iterator_category;
+    typedef std::size_t value_type;
+    typedef std::ptrdiff_t difference_type;
+    typedef value_type* pointer;
+    typedef value_type reference;
+
     All_cells_iterator() { this->counter = 0; }
 
     All_cells_iterator operator++() {
@@ -355,8 +361,14 @@ class Bitmap_cubical_complex_base {
    * @brief Iterator through top dimensional cells of the complex. The cells appear in order they are stored
    * in the structure (i.e. in lexicographical order)
    **/
-  class Top_dimensional_cells_iterator : std::iterator<std::input_iterator_tag, T> {
+  class Top_dimensional_cells_iterator {
    public:
+    typedef std::input_iterator_tag iterator_category;
+    typedef std::size_t value_type;
+    typedef std::ptrdiff_t difference_type;
+    typedef value_type* pointer;
+    typedef value_type reference;
+
     Top_dimensional_cells_iterator(Bitmap_cubical_complex_base& b) : b(b) {
       this->counter = std::vector<std::size_t>(b.dimension());
       // std::fill( this->counter.begin() , this->counter.end() , 0 );
diff --git a/src/Bottleneck_distance/include/gudhi/Persistence_graph.h b/src/Bottleneck_distance/include/gudhi/Persistence_graph.h
index 33f03b9c..c1e10f8e 100644
--- a/src/Bottleneck_distance/include/gudhi/Persistence_graph.h
+++ b/src/Bottleneck_distance/include/gudhi/Persistence_graph.h
@@ -20,6 +20,7 @@
 #include <vector>
 #include <algorithm>
 #include <limits>  // for numeric_limits
+#include <cmath>
 
 namespace Gudhi {
 
@@ -31,7 +32,7 @@ namespace persistence_diagram {
  * \ingroup bottleneck_distance
  */
 class Persistence_graph {
- public:
+public:
   /** \internal \brief Constructor taking 2 PersistenceDiagrams (concept) as parameters. */
   template<typename Persistence_diagram1, typename Persistence_diagram2>
   Persistence_graph(const Persistence_diagram1& diag1, const Persistence_diagram2& diag2, double e);
@@ -58,7 +59,7 @@ class Persistence_graph {
   /** \internal \brief Returns the corresponding internal point */
   Internal_point get_v_point(int v_point_index) const;
 
- private:
+private:
   std::vector<Internal_point> u;
   std::vector<Internal_point> v;
   double b_alive;
@@ -67,30 +68,54 @@ class Persistence_graph {
 template<typename Persistence_diagram1, typename Persistence_diagram2>
 Persistence_graph::Persistence_graph(const Persistence_diagram1 &diag1,
                                      const Persistence_diagram2 &diag2, double e)
-    : u(), v(), b_alive(0.) {
+  : u(), v(), b_alive(0.) {
   std::vector<double> u_alive;
   std::vector<double> v_alive;
+  std::vector<double> u_nalive;
+  std::vector<double> v_nalive;
+  int u_inf = 0;
+  int v_inf = 0;
+  double inf = std::numeric_limits<double>::infinity();
+  double neginf = -inf;
+
   for (auto it = std::begin(diag1); it != std::end(diag1); ++it) {
-    if (std::get<1>(*it) == std::numeric_limits<double>::infinity())
-      u_alive.push_back(std::get<0>(*it));
-    else if (std::get<1>(*it) - std::get<0>(*it) > e)
-      u.push_back(Internal_point(std::get<0>(*it), std::get<1>(*it), u.size()));
+    if (std::get<0>(*it) != inf && std::get<1>(*it) != neginf){
+      if (std::get<0>(*it) == neginf && std::get<1>(*it) == inf)
+        u_inf++;
+      else if (std::get<0>(*it) == neginf)
+        u_nalive.push_back(std::get<1>(*it));
+      else if (std::get<1>(*it) == inf)
+        u_alive.push_back(std::get<0>(*it));
+      else if (std::get<1>(*it) - std::get<0>(*it) > e)
+        u.push_back(Internal_point(std::get<0>(*it), std::get<1>(*it), u.size()));
+    }
   }
   for (auto it = std::begin(diag2); it != std::end(diag2); ++it) {
-    if (std::get<1>(*it) == std::numeric_limits<double>::infinity())
-      v_alive.push_back(std::get<0>(*it));
-    else if (std::get<1>(*it) - std::get<0>(*it) > e)
-      v.push_back(Internal_point(std::get<0>(*it), std::get<1>(*it), v.size()));
+    if (std::get<0>(*it) != inf && std::get<1>(*it) != neginf){
+      if (std::get<0>(*it) == neginf && std::get<1>(*it) == inf)
+        v_inf++;
+      else if (std::get<0>(*it) == neginf)
+        v_nalive.push_back(std::get<1>(*it));
+      else if (std::get<1>(*it) == inf)
+        v_alive.push_back(std::get<0>(*it));
+      else if (std::get<1>(*it) - std::get<0>(*it) > e)
+        v.push_back(Internal_point(std::get<0>(*it), std::get<1>(*it), v.size()));
+    }
   }
   if (u.size() < v.size())
     swap(u, v);
-  std::sort(u_alive.begin(), u_alive.end());
-  std::sort(v_alive.begin(), v_alive.end());
-  if (u_alive.size() != v_alive.size()) {
+
+  if (u_alive.size() != v_alive.size() || u_nalive.size() != v_nalive.size() || u_inf != v_inf) {
     b_alive = std::numeric_limits<double>::infinity();
   } else {
+    std::sort(u_alive.begin(), u_alive.end());
+    std::sort(v_alive.begin(), v_alive.end());
+    std::sort(u_nalive.begin(), u_nalive.end());
+    std::sort(v_nalive.begin(), v_nalive.end());
     for (auto it_u = u_alive.cbegin(), it_v = v_alive.cbegin(); it_u != u_alive.cend(); ++it_u, ++it_v)
       b_alive = (std::max)(b_alive, std::fabs(*it_u - *it_v));
+    for (auto it_u = u_nalive.cbegin(), it_v = v_nalive.cbegin(); it_u != u_nalive.cend(); ++it_u, ++it_v)
+      b_alive = (std::max)(b_alive, std::fabs(*it_u - *it_v));
   }
 }
 
@@ -104,12 +129,12 @@ inline bool Persistence_graph::on_the_v_diagonal(int v_point_index) const {
 
 inline int Persistence_graph::corresponding_point_in_u(int v_point_index) const {
   return on_the_v_diagonal(v_point_index) ?
-      v_point_index - static_cast<int> (v.size()) : v_point_index + static_cast<int> (u.size());
+        v_point_index - static_cast<int> (v.size()) : v_point_index + static_cast<int> (u.size());
 }
 
 inline int Persistence_graph::corresponding_point_in_v(int u_point_index) const {
   return on_the_u_diagonal(u_point_index) ?
-      u_point_index - static_cast<int> (u.size()) : u_point_index + static_cast<int> (v.size());
+        u_point_index - static_cast<int> (u.size()) : u_point_index + static_cast<int> (v.size());
 }
 
 inline double Persistence_graph::distance(int u_point_index, int v_point_index) const {
diff --git a/src/Bottleneck_distance/test/bottleneck_unit_test.cpp b/src/Bottleneck_distance/test/bottleneck_unit_test.cpp
index 44141baa..9872f20c 100644
--- a/src/Bottleneck_distance/test/bottleneck_unit_test.cpp
+++ b/src/Bottleneck_distance/test/bottleneck_unit_test.cpp
@@ -159,3 +159,81 @@ BOOST_AUTO_TEST_CASE(global) {
   BOOST_CHECK(bottleneck_distance(empty, empty) == 0);
   BOOST_CHECK(bottleneck_distance(empty, one) == 1);
 }
+
+BOOST_AUTO_TEST_CASE(neg_global) {
+  std::uniform_real_distribution<double> unif1(0., upper_bound);
+  std::uniform_real_distribution<double> unif2(upper_bound / 10000., upper_bound / 100.);
+  std::default_random_engine re;
+  std::vector< std::pair<double, double> > v1, v2;
+  for (int i = 0; i < n1; i++) {
+    double a = std::log(unif1(re));
+    double b = std::log(unif1(re));
+    double x = std::log(unif2(re));
+    double y = std::log(unif2(re));
+    v1.emplace_back(std::min(a, b), std::max(a, b));
+    v2.emplace_back(std::min(a, b) + std::min(x, y), std::max(a, b) + std::max(x, y));
+    if (i % 5 == 0)
+      v1.emplace_back(std::min(a, b), std::min(a, b) + x);
+    if (i % 3 == 0)
+      v2.emplace_back(std::max(a, b), std::max(a, b) + y);
+  }
+  BOOST_CHECK(bottleneck_distance(v1, v2, 0.) <= upper_bound / 100.);
+  BOOST_CHECK(bottleneck_distance(v1, v2, upper_bound / 10000.) <= upper_bound / 100. + upper_bound / 10000.);
+  BOOST_CHECK(std::abs(bottleneck_distance(v1, v2, 0.) - bottleneck_distance(v1, v2, upper_bound / 10000.)) <= upper_bound / 10000.);
+
+  std::vector< std::pair<double, double> > empty;
+  std::vector< std::pair<double, double> > one = {{8, 10}};
+  BOOST_CHECK(bottleneck_distance(empty, empty) == 0);
+  BOOST_CHECK(bottleneck_distance(empty, one) == 1);
+}
+
+BOOST_AUTO_TEST_CASE(bottleneck_simple_test) {
+  std::vector< std::pair<double, double> > v1, v2;
+  double inf = std::numeric_limits<double>::infinity();
+  double neginf = -inf;
+  double b;
+
+  v1.emplace_back(9.6, 14.);
+  v2.emplace_back(9.5, 14.1);
+
+  b = Gudhi::persistence_diagram::bottleneck_distance(v1, v2, 0.);
+  BOOST_CHECK(b > 0.09 && b < 0.11);
+
+  v1.emplace_back(-34.974, -34.2);
+
+  b = Gudhi::persistence_diagram::bottleneck_distance(v1, v2, 0.);
+  BOOST_CHECK(b > 0.386 && b < 0.388);
+
+  v1.emplace_back(neginf, 3.7);
+
+  b = Gudhi::persistence_diagram::bottleneck_distance(v1, v2, 0.);
+  BOOST_CHECK_EQUAL(b, inf);
+
+  v2.emplace_back(neginf, 4.45);
+
+  b = Gudhi::persistence_diagram::bottleneck_distance(v1, v2, 0.);
+  BOOST_CHECK(b > 0.74 && b < 0.76);
+
+  v1.emplace_back(-60.6, 52.1);
+  v2.emplace_back(-61.5, 53.);
+
+  b = Gudhi::persistence_diagram::bottleneck_distance(v1, v2, 0.);
+  BOOST_CHECK(b > 0.89 && b < 0.91);
+
+  v1.emplace_back(3., inf);
+  v2.emplace_back(3.2, inf);
+
+  b = Gudhi::persistence_diagram::bottleneck_distance(v1, v2, 0.);
+  BOOST_CHECK(b > 0.89 && b < 0.91);
+
+  v1.emplace_back(neginf, inf);
+  v2.emplace_back(neginf, inf);
+
+  b = Gudhi::persistence_diagram::bottleneck_distance(v1, v2, 0.);
+  BOOST_CHECK(b > 0.89 && b < 0.91);
+
+  v2.emplace_back(6, inf);
+
+  b = Gudhi::persistence_diagram::bottleneck_distance(v1, v2, 0.);
+  BOOST_CHECK_EQUAL(b, inf);
+}
diff --git a/src/GudhUI/view/Viewer.cpp b/src/GudhUI/view/Viewer.cpp
index 6b17c833..2c00f86f 100644
--- a/src/GudhUI/view/Viewer.cpp
+++ b/src/GudhUI/view/Viewer.cpp
@@ -31,7 +31,11 @@ void Viewer::set_bounding_box(const Point_3 & lower_left, const Point_3 & upper_
 }
 
 void Viewer::update_GL() {
+#if QGLVIEWER_VERSION >= 0x020700
+  this->update();
+#else
   this->updateGL();
+#endif
 }
 
 void Viewer::init_scene() {
diff --git a/src/Spatial_searching/example/example_spatial_searching.cpp b/src/Spatial_searching/example/example_spatial_searching.cpp
index 8f9151fc..09c2dabf 100644
--- a/src/Spatial_searching/example/example_spatial_searching.cpp
+++ b/src/Spatial_searching/example/example_spatial_searching.cpp
@@ -25,7 +25,7 @@ int main(void) {
   // 10-nearest neighbor query
   std::clog << "10 nearest neighbors from points[20]:\n";
   auto knn_range = points_ds.k_nearest_neighbors(points[20], 10, true);
-  for (auto const& nghb : knn_range)
+  for (auto const nghb : knn_range)
     std::clog << nghb.first << " (sq. dist. = " << nghb.second << ")\n";
 
   // Incremental nearest neighbor query
@@ -38,7 +38,7 @@ int main(void) {
   // 10-furthest neighbor query
   std::clog << "10 furthest neighbors from points[20]:\n";
   auto kfn_range = points_ds.k_furthest_neighbors(points[20], 10, true);
-  for (auto const& nghb : kfn_range)
+  for (auto const nghb : kfn_range)
     std::clog << nghb.first << " (sq. dist. = " << nghb.second << ")\n";
 
   // Incremental furthest neighbor query
diff --git a/src/Spatial_searching/test/test_Kd_tree_search.cpp b/src/Spatial_searching/test/test_Kd_tree_search.cpp
index d6c6fba3..e9acfaa7 100644
--- a/src/Spatial_searching/test/test_Kd_tree_search.cpp
+++ b/src/Spatial_searching/test/test_Kd_tree_search.cpp
@@ -45,7 +45,7 @@ BOOST_AUTO_TEST_CASE(test_Kd_tree_search) {
 
   std::vector<std::size_t> knn_result;
   FT last_dist = -1.;
-  for (auto const& nghb : kns_range) {
+  for (auto const nghb : kns_range) {
     BOOST_CHECK(nghb.second > last_dist);
     knn_result.push_back(nghb.second);
     last_dist = nghb.second;
@@ -76,7 +76,7 @@ BOOST_AUTO_TEST_CASE(test_Kd_tree_search) {
 
   std::vector<std::size_t> kfn_result;
   last_dist = kfn_range.begin()->second;
-  for (auto const& nghb : kfn_range) {
+  for (auto const nghb : kfn_range) {
     BOOST_CHECK(nghb.second <= last_dist);
     kfn_result.push_back(nghb.second);
     last_dist = nghb.second;
diff --git a/src/python/CMakeLists.txt b/src/python/CMakeLists.txt
index 8f8df138..32ec13bd 100644
--- a/src/python/CMakeLists.txt
+++ b/src/python/CMakeLists.txt
@@ -53,7 +53,7 @@ if(PYTHONINTERP_FOUND)
     set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'datasets', ")
 
     # Cython modules
-    set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'off_reader', ")
+    set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'off_utils', ")
     set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'simplex_tree', ")
     set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'rips_complex', ")
     set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'cubical_complex', ")
@@ -152,7 +152,7 @@ if(PYTHONINTERP_FOUND)
       set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-DCGAL_EIGEN3_ENABLED', ")
     endif (EIGEN3_FOUND)
 
-    set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'off_reader', ")
+    set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'off_utils', ")
     set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'simplex_tree', ")
     set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'rips_complex', ")
     set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'cubical_complex', ")
@@ -546,6 +546,7 @@ if(PYTHONINTERP_FOUND)
 
     # Reader utils
     add_gudhi_py_test(test_reader_utils)
+    add_gudhi_py_test(test_off)
 
     # Wasserstein
     if(OT_FOUND)
diff --git a/src/python/doc/clustering.rst b/src/python/doc/clustering.rst
index c5a57d3c..62422682 100644
--- a/src/python/doc/clustering.rst
+++ b/src/python/doc/clustering.rst
@@ -17,9 +17,8 @@ As a by-product, we produce the persistence diagram of the merge tree of the ini
    :include-source:
 
    import gudhi
-   f = open(gudhi.__root_source_dir__ + '/data/points/spiral_2d.csv', 'r')
-   import numpy as np
-   data = np.loadtxt(f)
+   from gudhi.datasets.remote import fetch_spiral_2d
+   data = fetch_spiral_2d()
    import matplotlib.pyplot as plt
    plt.scatter(data[:,0],data[:,1],marker='.',s=1)
    plt.show()
diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst
index b704f778..5491542f 100644
--- a/src/python/doc/installation.rst
+++ b/src/python/doc/installation.rst
@@ -150,7 +150,7 @@ You shall have something like:
     Cython version 0.29.25 
     Numpy version 1.21.4 
     Boost version 1.77.0
-    + Installed modules are: off_reader;simplex_tree;rips_complex;cubical_complex;periodic_cubical_complex;
+    + Installed modules are: off_utils;simplex_tree;rips_complex;cubical_complex;periodic_cubical_complex;
         persistence_graphical_tools;reader_utils;witness_complex;strong_witness_complex;
     + Missing modules are: bottleneck;nerve_gic;subsampling;tangential_complex;alpha_complex;euclidean_witness_complex;
         euclidean_strong_witness_complex;
@@ -188,7 +188,7 @@ A complete configuration would be :
     GMPXX_LIBRARIES = /usr/lib/x86_64-linux-gnu/libgmpxx.so
     MPFR_LIBRARIES = /usr/lib/x86_64-linux-gnu/libmpfr.so
     TBB version 9107 found and used
-    + Installed modules are: bottleneck;off_reader;simplex_tree;rips_complex;cubical_complex;periodic_cubical_complex;
+    + Installed modules are: bottleneck;off_utils;simplex_tree;rips_complex;cubical_complex;periodic_cubical_complex;
         persistence_graphical_tools;reader_utils;witness_complex;strong_witness_complex;nerve_gic;subsampling;
         tangential_complex;alpha_complex;euclidean_witness_complex;euclidean_strong_witness_complex;
     + Missing modules are: 
@@ -391,7 +391,7 @@ The :doc:`persistence graphical tools </persistence_graphical_tools_user>` and
 mathematics, science, and engineering.
 
 :class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package
-`SciPy <http://scipy.org>`_ as a backend if explicitly requested.
+`SciPy <http://scipy.org>`_ :math:`\geq` 1.6.0 as a backend if explicitly requested.
 
 TensorFlow
 ----------
diff --git a/src/python/doc/point_cloud.rst b/src/python/doc/point_cloud.rst
index ffd8f85b..473b303f 100644
--- a/src/python/doc/point_cloud.rst
+++ b/src/python/doc/point_cloud.rst
@@ -13,6 +13,11 @@ File Readers
 
 .. autofunction:: gudhi.read_lower_triangular_matrix_from_csv_file
 
+File Writers
+------------
+
+.. autofunction:: gudhi.write_points_to_off_file
+
 Subsampling
 -----------
 
diff --git a/src/python/gudhi/off_reader.pyx b/src/python/gudhi/off_utils.pyx
index a3200704..9276c7b0 100644
--- a/src/python/gudhi/off_reader.pyx
+++ b/src/python/gudhi/off_utils.pyx
@@ -13,8 +13,10 @@ from __future__ import print_function
 from cython cimport numeric
 from libcpp.vector cimport vector
 from libcpp.string cimport string
+cimport cython
 import errno
 import os
+import numpy as np
 
 __author__ = "Vincent Rouvreau"
 __copyright__ = "Copyright (C) 2016 Inria"
@@ -24,7 +26,7 @@ cdef extern from "Off_reader_interface.h" namespace "Gudhi":
     vector[vector[double]] read_points_from_OFF_file(string off_file)
 
 def read_points_from_off_file(off_file=''):
-    """Read points from OFF file.
+    """Read points from an `OFF file <fileformats.html#off-file-format>`_.
 
     :param off_file: An OFF file style name.
     :type off_file: string
@@ -39,3 +41,22 @@ def read_points_from_off_file(off_file=''):
             raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT),
                                     off_file)
 
+@cython.embedsignature(True)
+def write_points_to_off_file(fname, points):
+    """Write points to an `OFF file <fileformats.html#off-file-format>`_.
+
+    A simple wrapper for `numpy.savetxt`.
+
+    :param fname: Name of the OFF file.
+    :type fname: str or file handle
+    :param points: Point coordinates.
+    :type points: numpy array of shape (n, dim)
+    """
+    points = np.array(points, copy=False)
+    assert len(points.shape) == 2
+    dim = points.shape[1]
+    if dim == 3:
+        head = 'OFF\n{} 0 0'.format(points.shape[0])
+    else:
+        head = 'nOFF\n{} {} 0 0'.format(dim, points.shape[0])
+    np.savetxt(fname, points, header=head, comments='')
diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py
index de5844f9..7dc83817 100644
--- a/src/python/gudhi/point_cloud/knn.py
+++ b/src/python/gudhi/point_cloud/knn.py
@@ -314,7 +314,9 @@ class KNearestNeighbors:
             return None
 
         if self.params["implementation"] == "ckdtree":
-            qargs = {key: val for key, val in self.params.items() if key in {"p", "eps", "n_jobs"}}
+            qargs = {key: val for key, val in self.params.items() if key in {"p", "eps"}}
+            # SciPy renamed n_jobs to workers
+            qargs["workers"] = self.params.get("workers") or self.params.get("n_jobs") or 1
             distances, neighbors = self.kdtree.query(X, k=self.k, **qargs)
             if k == 1:
                 # SciPy decided to squeeze the last dimension for k=1
diff --git a/src/python/gudhi/rips_complex.pyx b/src/python/gudhi/rips_complex.pyx
index c3470292..d748f91e 100644
--- a/src/python/gudhi/rips_complex.pyx
+++ b/src/python/gudhi/rips_complex.pyx
@@ -41,31 +41,30 @@ cdef class RipsComplex:
     cdef Rips_complex_interface thisref
 
     # Fake constructor that does nothing but documenting the constructor
-    def __init__(self, points=None, distance_matrix=None,
+    def __init__(self, *, points=None, distance_matrix=None,
                  max_edge_length=float('inf'), sparse=None):
         """RipsComplex constructor.
 
-        :param max_edge_length: Rips value.
-        :type max_edge_length: float
-
         :param points: A list of points in d-Dimension.
-        :type points: list of list of float
+        :type points: List[List[float]]
 
         Or
 
         :param distance_matrix: A distance matrix (full square or lower
             triangular).
-        :type points: list of list of float
+        :type distance_matrix: List[List[float]]
 
         And in both cases
 
+        :param max_edge_length: Rips value.
+        :type max_edge_length: float
         :param sparse: If this is not None, it switches to building a sparse
             Rips and represents the approximation parameter epsilon.
         :type sparse: float
         """
 
     # The real cython constructor
-    def __cinit__(self, points=None, distance_matrix=None,
+    def __cinit__(self, *, points=None, distance_matrix=None,
                   max_edge_length=float('inf'), sparse=None):
         if sparse is not None:
           if distance_matrix is not None:
diff --git a/src/python/test/test_off.py b/src/python/test/test_off.py
new file mode 100644
index 00000000..aea1941b
--- /dev/null
+++ b/src/python/test/test_off.py
@@ -0,0 +1,21 @@
+""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
+    See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
+    Author(s):       Marc Glisse
+
+    Copyright (C) 2022 Inria
+
+    Modification(s):
+      - YYYY/MM Author: Description of the modification
+"""
+
+import gudhi as gd
+import numpy as np
+import pytest
+
+
+def test_off_rw():
+    for dim in range(2, 6):
+        X = np.random.rand(123, dim)
+        gd.write_points_to_off_file("rand.off", X)
+        Y = gd.read_points_from_off_file("rand.off")
+        assert Y == pytest.approx(X)
diff --git a/src/python/test/test_simplex_generators.py b/src/python/test/test_simplex_generators.py
index 8a9b4844..c567d4c1 100755
--- a/src/python/test/test_simplex_generators.py
+++ b/src/python/test/test_simplex_generators.py
@@ -14,7 +14,7 @@ import numpy as np
 
 def test_flag_generators():
     pts = np.array([[0, 0], [0, 1.01], [1, 0], [1.02, 1.03], [100, 0], [100, 3.01], [103, 0], [103.02, 3.03]])
-    r = gudhi.RipsComplex(pts, max_edge_length=4)
+    r = gudhi.RipsComplex(points=pts, max_edge_length=4)
     st = r.create_simplex_tree(max_dimension=50)
     st.persistence()
     g = st.flag_persistence_generators()
diff --git a/src/python/test/test_subsampling.py b/src/python/test/test_subsampling.py
index 3431f372..c1cb4e3f 100755
--- a/src/python/test/test_subsampling.py
+++ b/src/python/test/test_subsampling.py
@@ -16,17 +16,9 @@ __license__ = "MIT"
 
 
 def test_write_off_file_for_tests():
-    file = open("subsample.off", "w")
-    file.write("nOFF\n")
-    file.write("2 7 0 0\n")
-    file.write("1.0 1.0\n")
-    file.write("7.0 0.0\n")
-    file.write("4.0 6.0\n")
-    file.write("9.0 6.0\n")
-    file.write("0.0 14.0\n")
-    file.write("2.0 19.0\n")
-    file.write("9.0 17.0\n")
-    file.close()
+    gudhi.write_points_to_off_file(
+        "subsample.off", [[1.0, 1.0], [7.0, 0.0], [4.0, 6.0], [9.0, 6.0], [0.0, 14.0], [2.0, 19.0], [9.0, 17.0]]
+    )
 
 
 def test_simple_choose_n_farthest_points_with_a_starting_point():
@@ -34,54 +26,29 @@ def test_simple_choose_n_farthest_points_with_a_starting_point():
     i = 0
     for point in point_set:
         # The iteration starts with the given starting point
-        sub_set = gudhi.choose_n_farthest_points(
-            points=point_set, nb_points=1, starting_point=i
-        )
+        sub_set = gudhi.choose_n_farthest_points(points=point_set, nb_points=1, starting_point=i)
         assert sub_set[0] == point_set[i]
         i = i + 1
 
     # The iteration finds then the farthest
-    sub_set = gudhi.choose_n_farthest_points(
-        points=point_set, nb_points=2, starting_point=1
-    )
+    sub_set = gudhi.choose_n_farthest_points(points=point_set, nb_points=2, starting_point=1)
     assert sub_set[1] == point_set[3]
-    sub_set = gudhi.choose_n_farthest_points(
-        points=point_set, nb_points=2, starting_point=3
-    )
+    sub_set = gudhi.choose_n_farthest_points(points=point_set, nb_points=2, starting_point=3)
     assert sub_set[1] == point_set[1]
-    sub_set = gudhi.choose_n_farthest_points(
-        points=point_set, nb_points=2, starting_point=0
-    )
+    sub_set = gudhi.choose_n_farthest_points(points=point_set, nb_points=2, starting_point=0)
     assert sub_set[1] == point_set[2]
-    sub_set = gudhi.choose_n_farthest_points(
-        points=point_set, nb_points=2, starting_point=2
-    )
+    sub_set = gudhi.choose_n_farthest_points(points=point_set, nb_points=2, starting_point=2)
     assert sub_set[1] == point_set[0]
 
     # Test the limits
-    assert (
-        gudhi.choose_n_farthest_points(points=[], nb_points=0, starting_point=0) == []
-    )
-    assert (
-        gudhi.choose_n_farthest_points(points=[], nb_points=1, starting_point=0) == []
-    )
-    assert (
-        gudhi.choose_n_farthest_points(points=[], nb_points=0, starting_point=1) == []
-    )
-    assert (
-        gudhi.choose_n_farthest_points(points=[], nb_points=1, starting_point=1) == []
-    )
+    assert gudhi.choose_n_farthest_points(points=[], nb_points=0, starting_point=0) == []
+    assert gudhi.choose_n_farthest_points(points=[], nb_points=1, starting_point=0) == []
+    assert gudhi.choose_n_farthest_points(points=[], nb_points=0, starting_point=1) == []
+    assert gudhi.choose_n_farthest_points(points=[], nb_points=1, starting_point=1) == []
 
     # From off file test
     for i in range(0, 7):
-        assert (
-            len(
-                gudhi.choose_n_farthest_points(
-                    off_file="subsample.off", nb_points=i, starting_point=i
-                )
-            )
-            == i
-        )
+        assert len(gudhi.choose_n_farthest_points(off_file="subsample.off", nb_points=i, starting_point=i)) == i
 
 
 def test_simple_choose_n_farthest_points_randomed():
@@ -104,10 +71,7 @@ def test_simple_choose_n_farthest_points_randomed():
 
     # From off file test
     for i in range(0, 7):
-        assert (
-            len(gudhi.choose_n_farthest_points(off_file="subsample.off", nb_points=i))
-            == i
-        )
+        assert len(gudhi.choose_n_farthest_points(off_file="subsample.off", nb_points=i)) == i
 
 
 def test_simple_pick_n_random_points():
@@ -130,9 +94,7 @@ def test_simple_pick_n_random_points():
 
     # From off file test
     for i in range(0, 7):
-        assert (
-            len(gudhi.pick_n_random_points(off_file="subsample.off", nb_points=i)) == i
-        )
+        assert len(gudhi.pick_n_random_points(off_file="subsample.off", nb_points=i)) == i
 
 
 def test_simple_sparsify_points():
@@ -152,31 +114,10 @@ def test_simple_sparsify_points():
     ]
     assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.001) == [[0, 1]]
 
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=0.0))
-        == 7
-    )
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=30.0))
-        == 5
-    )
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=40.1))
-        == 4
-    )
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=89.9))
-        == 3
-    )
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=100.0))
-        == 2
-    )
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=324.9))
-        == 2
-    )
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=325.01))
-        == 1
-    )
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=0.0)) == 7
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=30.0)) == 5
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=40.1)) == 4
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=89.9)) == 3
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=100.0)) == 2
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=324.9)) == 2
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=325.01)) == 1