From 74155081bb8b3330c562d5c40d7f0a32fc188012 Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Mon, 30 Mar 2020 18:02:43 +0200
Subject: Add density estimator

---
 src/python/gudhi/point_cloud/dtm.py | 66 +++++++++++++++++++++++++++++++++++++
 1 file changed, 66 insertions(+)

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index 23c36b88..e12eefa1 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -8,6 +8,7 @@
 #   - YYYY/MM Author: Description of the modification
 
 from .knn import KNN
+import numpy as np
 
 
 class DTM:
@@ -54,3 +55,68 @@ class DTM:
         # We compute too many powers, 1/p in knn then q in dtm, 1/q in dtm then q or some log in the caller.
         # Add option to skip the final root?
         return dtm
+
+
+class DTMDensity:
+    """
+    Density estimator based on the distance to the empirical measure defined by a point set, as defined in :cite:`dtmdensity`. Note that this implementation does not renormalize so the total measure is not 1, see the reference for suitable normalization factors in the Euclidean case.
+    """
+
+    def __init__(self, k=None, weights=None, q=None, dim=None, **kwargs):
+        """
+        Args:
+            k (int): number of neighbors (possibly including the point itself).
+            weights (numpy.array): weights of each of the k neighbors, optional.
+            q (float): order used to compute the distance to measure. Defaults to dim.
+            dim (float): final exponent representing the dimension. Defaults to the dimension, and must be specified when the dimension cannot be read from the input (metric="neighbors" or metric="precomputed").
+            kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNN`, except that metric="neighbors" means that :func:`transform` expects an array with the distances to the k nearest neighbors.
+        """
+        if weights is None:
+            assert k is not None, "Must specify k or weights"
+            self.k = k
+            self.weights = np.full(k, 1.0 / k)
+        else:
+            self.weights = weights
+            self.k = len(weights)
+            assert k is None or k == self.k, "k differs from the length of weights"
+        self.q = q
+        self.dim = dim
+        self.params = kwargs
+
+    def fit_transform(self, X, y=None):
+        return self.fit(X).transform(X)
+
+    def fit(self, X, y=None):
+        """
+        Args:
+            X (numpy.array): coordinates for mass points.
+        """
+        if self.params.setdefault("metric", "euclidean") != "neighbors":
+            self.knn = KNN(self.k, return_index=False, return_distance=True, sort_results=False, **self.params)
+            self.knn.fit(X)
+        return self
+
+    def transform(self, X):
+        """
+        Args:
+            X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed", or distances to the k nearest neighbors if metric is "neighbors" (if the array has more than k columns, the remaining ones are ignored).
+        """
+        q = self.q
+        dim = self.dim
+        if dim is None:
+            assert self.params["metric"] not in {
+                "neighbors",
+                "precomputed",
+            }, "dim not specified and cannot guess the dimension"
+            dim = len(X[0])
+        if q is None:
+            q = dim
+        if self.params["metric"] == "neighbors":
+            distances = X[:, : self.k]
+        else:
+            distances = self.knn.transform(X)
+        distances = distances ** q
+        dtm = (distances * weights).sum(-1)
+        return dtm ** (-dim / q)
+        # We compute too many powers, 1/p in knn then q in dtm, d/q in dtm then whatever in the caller.
+        # Add option to skip the final root?
-- 
cgit v1.2.3


From 9b66423fefca29e9e18f08d524b1fa0ce4db85a1 Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Mon, 11 May 2020 19:13:44 +0200
Subject: Reformat doc

---
 src/python/gudhi/point_cloud/dtm.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index c5405526..ef6eef05 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -73,7 +73,9 @@ class DistanceToMeasure:
 
 class DTMDensity:
     """
-    Density estimator based on the distance to the empirical measure defined by a point set, as defined in :cite:`dtmdensity`. Note that this implementation does not renormalize so the total measure is not 1, see the reference for suitable normalization factors in the Euclidean case.
+    Density estimator based on the distance to the empirical measure defined by a point set, as defined
+    in :cite:`dtmdensity`. Note that this implementation does not renormalize so the total measure is not 1,
+    see the reference for suitable normalization factors in the Euclidean case.
     """
 
     def __init__(self, k=None, weights=None, q=None, dim=None, **kwargs):
@@ -82,8 +84,10 @@ class DTMDensity:
             k (int): number of neighbors (possibly including the point itself).
             weights (numpy.array): weights of each of the k neighbors, optional.
             q (float): order used to compute the distance to measure. Defaults to dim.
-            dim (float): final exponent representing the dimension. Defaults to the dimension, and must be specified when the dimension cannot be read from the input (metric="neighbors" or metric="precomputed").
-            kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNN`, except that metric="neighbors" means that :func:`transform` expects an array with the distances to the k nearest neighbors.
+            dim (float): final exponent representing the dimension. Defaults to the dimension, and must be specified
+                when the dimension cannot be read from the input (metric="neighbors" or metric="precomputed").
+            kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNN`, except that metric="neighbors" means that
+                :func:`transform` expects an array with the distances to the k nearest neighbors.
         """
         if weights is None:
             assert k is not None, "Must specify k or weights"
@@ -113,7 +117,9 @@ class DTMDensity:
     def transform(self, X):
         """
         Args:
-            X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed", or distances to the k nearest neighbors if metric is "neighbors" (if the array has more than k columns, the remaining ones are ignored).
+            X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed",
+                or distances to the k nearest neighbors if metric is "neighbors" (if the array has more
+                than k columns, the remaining ones are ignored).
         """
         q = self.q
         dim = self.dim
-- 
cgit v1.2.3


From d86676e247bfa6f29b625a9a5752bf2a2fab438f Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Mon, 11 May 2020 21:31:34 +0200
Subject: Normalize the density if asked

---
 src/python/gudhi/point_cloud/dtm.py | 38 +++++++++++++++++++++++++++++--------
 1 file changed, 30 insertions(+), 8 deletions(-)

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index ef6eef05..2f30908d 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -74,19 +74,27 @@ class DistanceToMeasure:
 class DTMDensity:
     """
     Density estimator based on the distance to the empirical measure defined by a point set, as defined
-    in :cite:`dtmdensity`. Note that this implementation does not renormalize so the total measure is not 1,
-    see the reference for suitable normalization factors in the Euclidean case.
+    in :cite:`dtmdensity`. Note that this implementation only renormalizes when asked, and the renormalization
+    only works for a Euclidean metric, so in other cases the total measure may not be 1.
     """
 
-    def __init__(self, k=None, weights=None, q=None, dim=None, **kwargs):
+    def __init__(self, k=None, weights=None, q=None, dim=None, normalize=False, n_samples=None, **kwargs):
         """
         Args:
             k (int): number of neighbors (possibly including the point itself).
-            weights (numpy.array): weights of each of the k neighbors, optional.
+            weights (numpy.array): weights of each of the k neighbors, optional. They are supposed to sum to 1.
             q (float): order used to compute the distance to measure. Defaults to dim.
             dim (float): final exponent representing the dimension. Defaults to the dimension, and must be specified
                 when the dimension cannot be read from the input (metric="neighbors" or metric="precomputed").
-            kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNN`, except that metric="neighbors" means that
+
+                .. note:: when the dimension is high, using it as an exponent can quickly lead to under- or overflows.
+                    We recommend using a small fixed value instead in those cases, even if it won't have the same nice
+                    theoretical properties as the dimension.
+            normalize (bool): normalize the density so it corresponds to a probability measure on ℝᵈ.
+                Only available for the Euclidean metric, defaults to False.
+            n_samples (int): number of sample points used for fitting. Only needed if `normalize` is True and
+                metric is "neighbors".
+            kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNearestNeighbors`, except that metric="neighbors" means that
                 :func:`transform` expects an array with the distances to the k nearest neighbors.
         """
         if weights is None:
@@ -100,6 +108,8 @@ class DTMDensity:
         self.q = q
         self.dim = dim
         self.params = kwargs
+        self.normalize=normalize
+        self.n_samples=n_samples
 
     def fit_transform(self, X, y=None):
         return self.fit(X).transform(X)
@@ -110,8 +120,10 @@ class DTMDensity:
             X (numpy.array): coordinates for mass points.
         """
         if self.params.setdefault("metric", "euclidean") != "neighbors":
-            self.knn = KNN(self.k, return_index=False, return_distance=True, sort_results=False, **self.params)
+            self.knn = KNearestNeighbors(self.k, return_index=False, return_distance=True, sort_results=False, **self.params)
             self.knn.fit(X)
+            if self.params["metric"] != "precomputed":
+                self.n_samples = len(X)
         return self
 
     def transform(self, X):
@@ -136,7 +148,17 @@ class DTMDensity:
         else:
             distances = self.knn.transform(X)
         distances = distances ** q
-        dtm = (distances * weights).sum(-1)
-        return dtm ** (-dim / q)
+        dtm = (distances * self.weights).sum(-1)
+        if self.normalize:
+            dtm /= (np.arange(1, self.k + 1) ** (q / dim) * self.weights).sum()
+        density = dtm ** (-dim / q)
+        if self.normalize:
+            import math
+            if self.params["metric"] == "precomputed":
+                self.n_samples = len(X[0])
+            # Volume of d-ball
+            Vd = math.pi ** (dim / 2) / math.gamma(dim / 2 + 1)
+            density /= self.n_samples * Vd
+        return density
         # We compute too many powers, 1/p in knn then q in dtm, d/q in dtm then whatever in the caller.
         # Add option to skip the final root?
-- 
cgit v1.2.3


From 8c9a1c674dcacc8b66e88897b6116561bb811ffa Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Mon, 11 May 2020 21:55:21 +0200
Subject: Handle k=1 in KNearestNeighbors with SciPy

---
 src/python/gudhi/point_cloud/knn.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py
index 34e80b5d..65896847 100644
--- a/src/python/gudhi/point_cloud/knn.py
+++ b/src/python/gudhi/point_cloud/knn.py
@@ -302,6 +302,10 @@ class KNearestNeighbors:
         if self.params["implementation"] == "ckdtree":
             qargs = {key: val for key, val in self.params.items() if key in {"p", "eps", "n_jobs"}}
             distances, neighbors = self.kdtree.query(X, k=self.k, **qargs)
+            if k == 1:
+                # SciPy decided to squeeze the last dimension for k=1
+                distances = distances[:, None]
+                neighbors = neighbors[:, None]
             if self.return_index:
                 if self.return_distance:
                     return neighbors, distances
-- 
cgit v1.2.3


From 7bbbe63ffa2a812dc49c37c77b4f4a4be46b2a49 Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Mon, 11 May 2020 23:34:23 +0200
Subject: move note

---
 src/python/gudhi/point_cloud/dtm.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index 2f30908d..f8cca2c1 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -76,6 +76,10 @@ class DTMDensity:
     Density estimator based on the distance to the empirical measure defined by a point set, as defined
     in :cite:`dtmdensity`. Note that this implementation only renormalizes when asked, and the renormalization
     only works for a Euclidean metric, so in other cases the total measure may not be 1.
+
+    .. note:: When the dimension is high, using it as an exponent can quickly lead to under- or overflows.
+        We recommend using a small fixed value instead in those cases, even if it won't have the same nice
+        theoretical properties as the dimension.
     """
 
     def __init__(self, k=None, weights=None, q=None, dim=None, normalize=False, n_samples=None, **kwargs):
@@ -86,10 +90,6 @@ class DTMDensity:
             q (float): order used to compute the distance to measure. Defaults to dim.
             dim (float): final exponent representing the dimension. Defaults to the dimension, and must be specified
                 when the dimension cannot be read from the input (metric="neighbors" or metric="precomputed").
-
-                .. note:: when the dimension is high, using it as an exponent can quickly lead to under- or overflows.
-                    We recommend using a small fixed value instead in those cases, even if it won't have the same nice
-                    theoretical properties as the dimension.
             normalize (bool): normalize the density so it corresponds to a probability measure on ℝᵈ.
                 Only available for the Euclidean metric, defaults to False.
             n_samples (int): number of sample points used for fitting. Only needed if `normalize` is True and
-- 
cgit v1.2.3


From c87a1f10e048477d210ae0abd657da87bba1102a Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Tue, 12 May 2020 20:36:38 +0200
Subject: test + reformat

---
 src/python/gudhi/point_cloud/dtm.py |  9 ++++++---
 src/python/test/test_dtm.py         | 11 ++++++++++-
 2 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index f8cca2c1..4454d8a2 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -108,8 +108,8 @@ class DTMDensity:
         self.q = q
         self.dim = dim
         self.params = kwargs
-        self.normalize=normalize
-        self.n_samples=n_samples
+        self.normalize = normalize
+        self.n_samples = n_samples
 
     def fit_transform(self, X, y=None):
         return self.fit(X).transform(X)
@@ -120,7 +120,9 @@ class DTMDensity:
             X (numpy.array): coordinates for mass points.
         """
         if self.params.setdefault("metric", "euclidean") != "neighbors":
-            self.knn = KNearestNeighbors(self.k, return_index=False, return_distance=True, sort_results=False, **self.params)
+            self.knn = KNearestNeighbors(
+                self.k, return_index=False, return_distance=True, sort_results=False, **self.params
+            )
             self.knn.fit(X)
             if self.params["metric"] != "precomputed":
                 self.n_samples = len(X)
@@ -154,6 +156,7 @@ class DTMDensity:
         density = dtm ** (-dim / q)
         if self.normalize:
             import math
+
             if self.params["metric"] == "precomputed":
                 self.n_samples = len(X[0])
             # Volume of d-ball
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py
index bff4c267..34d28d4d 100755
--- a/src/python/test/test_dtm.py
+++ b/src/python/test/test_dtm.py
@@ -8,10 +8,11 @@
       - YYYY/MM Author: Description of the modification
 """
 
-from gudhi.point_cloud.dtm import DistanceToMeasure
+from gudhi.point_cloud.dtm import DistanceToMeasure, DTMDensity
 import numpy
 import pytest
 import torch
+import math
 
 
 def test_dtm_compare_euclidean():
@@ -66,3 +67,11 @@ def test_dtm_precomputed():
     dtm = DistanceToMeasure(2, q=2, metric="neighbors")
     r = dtm.fit_transform(dist)
     assert r == pytest.approx([2.0, 0.707, 3.5355], rel=0.01)
+
+
+def test_density_normalized():
+    sample = numpy.random.normal(0, 1, (1000000, 2))
+    queries = numpy.array([[0.0, 0.0], [-0.5, 0.7], [0.4, 1.7]])
+    expected = numpy.exp(-(queries ** 2).sum(-1) / 2) / (2 * math.pi)
+    estimated = DTMDensity(k=150, normalize=True).fit(sample).transform(queries)
+    assert estimated == pytest.approx(expected, rel=0.4)
-- 
cgit v1.2.3


From c5fca5477cc6fff77acedf7b5324eb5f8b417ed3 Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Tue, 12 May 2020 22:31:42 +0200
Subject: More test

---
 src/python/doc/point_cloud_sum.inc  | 4 ++--
 src/python/gudhi/point_cloud/dtm.py | 4 ++--
 src/python/test/test_dtm.py         | 7 +++++++
 3 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/src/python/doc/point_cloud_sum.inc b/src/python/doc/point_cloud_sum.inc
index d4761aba..d28f387a 100644
--- a/src/python/doc/point_cloud_sum.inc
+++ b/src/python/doc/point_cloud_sum.inc
@@ -3,8 +3,8 @@
 
    +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+
    | | :math:`(x_1, x_2, \ldots, x_d)`                              | Utilities to process point clouds: read from file, subsample,          | :Authors: Vincent Rouvreau, Marc Glisse, Masatoshi Takenouchi                                                               |
-   | | :math:`(y_1, y_2, \ldots, y_d)`                              | find neighbors, embed time series in higher dimension, etc.            |                                                                                                                             |
-   |                                                                |                                                                        | :Since: GUDHI 2.0.0                                                                                                         |
+   | | :math:`(y_1, y_2, \ldots, y_d)`                              | find neighbors, embed time series in higher dimension, estimate        |                                                                                                                             |
+   |                                                                | a density, etc.                                                        | :Since: GUDHI 2.0.0                                                                                                         |
    |                                                                |                                                                        |                                                                                                                             |
    |                                                                |                                                                        | :License: MIT (`GPL v3 </licensing/>`_, BSD-3-Clause, Apache-2.0)                                                           |
    |                                                                | Parts of this package require CGAL.                                    |                                                                                                                             |
diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index 4454d8a2..88f197e7 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -89,7 +89,7 @@ class DTMDensity:
             weights (numpy.array): weights of each of the k neighbors, optional. They are supposed to sum to 1.
             q (float): order used to compute the distance to measure. Defaults to dim.
             dim (float): final exponent representing the dimension. Defaults to the dimension, and must be specified
-                when the dimension cannot be read from the input (metric="neighbors" or metric="precomputed").
+                when the dimension cannot be read from the input (metric is "neighbors" or "precomputed").
             normalize (bool): normalize the density so it corresponds to a probability measure on ℝᵈ.
                 Only available for the Euclidean metric, defaults to False.
             n_samples (int): number of sample points used for fitting. Only needed if `normalize` is True and
@@ -146,7 +146,7 @@ class DTMDensity:
         if q is None:
             q = dim
         if self.params["metric"] == "neighbors":
-            distances = X[:, : self.k]
+            distances = np.asarray(X)[:, : self.k]
         else:
             distances = self.knn.transform(X)
         distances = distances ** q
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py
index 34d28d4d..8ab0cc44 100755
--- a/src/python/test/test_dtm.py
+++ b/src/python/test/test_dtm.py
@@ -75,3 +75,10 @@ def test_density_normalized():
     expected = numpy.exp(-(queries ** 2).sum(-1) / 2) / (2 * math.pi)
     estimated = DTMDensity(k=150, normalize=True).fit(sample).transform(queries)
     assert estimated == pytest.approx(expected, rel=0.4)
+
+
+def test_density():
+    distances = [[0, 1, 10], [2, 0, 30], [1, 3, 5]]
+    density = DTMDensity(k=2, metric="neighbors", dim=1).fit_transform(distances)
+    expected = numpy.array([2.0, 1.0, 0.5])
+    assert density == pytest.approx(expected)
-- 
cgit v1.2.3


From 84b823b6436746a06cb8323fecd7b1f38d7ba244 Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Sat, 16 May 2020 13:30:19 +0200
Subject: Minimal nogil support for cubical complexes

---
 src/python/gudhi/cubical_complex.pyx          | 29 +++++++++++++++------------
 src/python/gudhi/periodic_cubical_complex.pyx | 29 +++++++++++++++------------
 2 files changed, 32 insertions(+), 26 deletions(-)

diff --git a/src/python/gudhi/cubical_complex.pyx b/src/python/gudhi/cubical_complex.pyx
index ca979eda..308b5099 100644
--- a/src/python/gudhi/cubical_complex.pyx
+++ b/src/python/gudhi/cubical_complex.pyx
@@ -27,20 +27,20 @@ __license__ = "MIT"
 
 cdef extern from "Cubical_complex_interface.h" namespace "Gudhi":
     cdef cppclass Bitmap_cubical_complex_base_interface "Gudhi::Cubical_complex::Cubical_complex_interface<>":
-        Bitmap_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells)
-        Bitmap_cubical_complex_base_interface(string perseus_file)
-        int num_simplices()
-        int dimension()
+        Bitmap_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells) nogil
+        Bitmap_cubical_complex_base_interface(string perseus_file) nogil
+        int num_simplices() nogil
+        int dimension() nogil
 
 cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi":
     cdef cppclass Cubical_complex_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Cubical_complex::Cubical_complex_interface<>>":
-        Cubical_complex_persistence_interface(Bitmap_cubical_complex_base_interface * st, bool persistence_dim_max)
-        void compute_persistence(int homology_coeff_field, double min_persistence)
-        vector[pair[int, pair[double, double]]] get_persistence()
-        vector[vector[int]] cofaces_of_cubical_persistence_pairs()
-        vector[int] betti_numbers()
-        vector[int] persistent_betti_numbers(double from_value, double to_value)
-        vector[pair[double,double]] intervals_in_dimension(int dimension)
+        Cubical_complex_persistence_interface(Bitmap_cubical_complex_base_interface * st, bool persistence_dim_max) nogil
+        void compute_persistence(int homology_coeff_field, double min_persistence) nogil
+        vector[pair[int, pair[double, double]]] get_persistence() nogil
+        vector[vector[int]] cofaces_of_cubical_persistence_pairs() nogil
+        vector[int] betti_numbers() nogil
+        vector[int] persistent_betti_numbers(double from_value, double to_value) nogil
+        vector[pair[double,double]] intervals_in_dimension(int dimension) nogil
 
 # CubicalComplex python interface
 cdef class CubicalComplex:
@@ -151,8 +151,11 @@ cdef class CubicalComplex:
         if self.pcohptr != NULL:
             del self.pcohptr
         assert self.__is_defined()
-        self.pcohptr = new Cubical_complex_persistence_interface(self.thisptr, True)
-        self.pcohptr.compute_persistence(homology_coeff_field, min_persistence)
+        cdef int field = homology_coeff_field
+        cdef double minp = min_persistence
+        with nogil:
+            self.pcohptr = new Cubical_complex_persistence_interface(self.thisptr, 1)
+            self.pcohptr.compute_persistence(field, minp)
 
     def persistence(self, homology_coeff_field=11, min_persistence=0):
         """This function computes and returns the persistence of the complex.
diff --git a/src/python/gudhi/periodic_cubical_complex.pyx b/src/python/gudhi/periodic_cubical_complex.pyx
index 06309772..dcca7b63 100644
--- a/src/python/gudhi/periodic_cubical_complex.pyx
+++ b/src/python/gudhi/periodic_cubical_complex.pyx
@@ -24,20 +24,20 @@ __license__ = "MIT"
 
 cdef extern from "Cubical_complex_interface.h" namespace "Gudhi":
     cdef cppclass Periodic_cubical_complex_base_interface "Gudhi::Cubical_complex::Cubical_complex_interface<Gudhi::cubical_complex::Bitmap_cubical_complex_periodic_boundary_conditions_base<double>>":
-        Periodic_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells, vector[bool] periodic_dimensions)
-        Periodic_cubical_complex_base_interface(string perseus_file)
-        int num_simplices()
-        int dimension()
+        Periodic_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells, vector[bool] periodic_dimensions) nogil
+        Periodic_cubical_complex_base_interface(string perseus_file) nogil
+        int num_simplices() nogil
+        int dimension() nogil
 
 cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi":
     cdef cppclass Periodic_cubical_complex_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Cubical_complex::Cubical_complex_interface<Gudhi::cubical_complex::Bitmap_cubical_complex_periodic_boundary_conditions_base<double>>>":
-        Periodic_cubical_complex_persistence_interface(Periodic_cubical_complex_base_interface * st, bool persistence_dim_max)
-        void compute_persistence(int homology_coeff_field, double min_persistence)
-        vector[pair[int, pair[double, double]]] get_persistence()
-        vector[vector[int]] cofaces_of_cubical_persistence_pairs()
-        vector[int] betti_numbers()
-        vector[int] persistent_betti_numbers(double from_value, double to_value)
-        vector[pair[double,double]] intervals_in_dimension(int dimension)
+        Periodic_cubical_complex_persistence_interface(Periodic_cubical_complex_base_interface * st, bool persistence_dim_max) nogil
+        void compute_persistence(int homology_coeff_field, double min_persistence) nogil
+        vector[pair[int, pair[double, double]]] get_persistence() nogil
+        vector[vector[int]] cofaces_of_cubical_persistence_pairs() nogil
+        vector[int] betti_numbers() nogil
+        vector[int] persistent_betti_numbers(double from_value, double to_value) nogil
+        vector[pair[double,double]] intervals_in_dimension(int dimension) nogil
 
 # PeriodicCubicalComplex python interface
 cdef class PeriodicCubicalComplex:
@@ -156,8 +156,11 @@ cdef class PeriodicCubicalComplex:
         if self.pcohptr != NULL:
             del self.pcohptr
         assert self.__is_defined()
-        self.pcohptr = new Periodic_cubical_complex_persistence_interface(self.thisptr, True)
-        self.pcohptr.compute_persistence(homology_coeff_field, min_persistence)
+        cdef int field = homology_coeff_field
+        cdef double minp = min_persistence
+        with nogil:
+            self.pcohptr = new Periodic_cubical_complex_persistence_interface(self.thisptr, 1)
+            self.pcohptr.compute_persistence(field, minp)
 
     def persistence(self, homology_coeff_field=11, min_persistence=0):
         """This function computes and returns the persistence of the complex.
-- 
cgit v1.2.3


From 207050fb1f5af375a98c70dbd5fc22149d6f6e22 Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Sat, 16 May 2020 14:08:23 +0200
Subject: nogil for cubical constructor

There may be some extra copying until cython3, but it is probably not
that bad.
---
 src/python/gudhi/cubical_complex.pyx          | 14 +++++++++++---
 src/python/gudhi/periodic_cubical_complex.pyx | 18 +++++++++++-------
 2 files changed, 22 insertions(+), 10 deletions(-)

diff --git a/src/python/gudhi/cubical_complex.pyx b/src/python/gudhi/cubical_complex.pyx
index 308b5099..0068f2ff 100644
--- a/src/python/gudhi/cubical_complex.pyx
+++ b/src/python/gudhi/cubical_complex.pyx
@@ -80,7 +80,7 @@ cdef class CubicalComplex:
                   perseus_file=''):
         if ((dimensions is not None) and (top_dimensional_cells is not None)
             and (perseus_file == '')):
-            self.thisptr = new Bitmap_cubical_complex_base_interface(dimensions, top_dimensional_cells)
+            self._construct_from_cells(dimensions, top_dimensional_cells)
         elif ((dimensions is None) and (top_dimensional_cells is not None)
             and (perseus_file == '')):
             top_dimensional_cells = np.array(top_dimensional_cells,
@@ -88,11 +88,11 @@ cdef class CubicalComplex:
                                              order = 'F')
             dimensions = top_dimensional_cells.shape
             top_dimensional_cells = top_dimensional_cells.ravel(order='F')
-            self.thisptr = new Bitmap_cubical_complex_base_interface(dimensions, top_dimensional_cells)
+            self._construct_from_cells(dimensions, top_dimensional_cells)
         elif ((dimensions is None) and (top_dimensional_cells is None)
             and (perseus_file != '')):
             if os.path.isfile(perseus_file):
-                self.thisptr = new Bitmap_cubical_complex_base_interface(perseus_file.encode('utf-8'))
+                self._construct_from_file(perseus_file.encode('utf-8'))
             else:
                 raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT),
                                         perseus_file)
@@ -101,6 +101,14 @@ cdef class CubicalComplex:
                 "top_dimensional_cells or from a Perseus-style file name.",
                 file=sys.stderr)
 
+    def _construct_from_cells(self, vector[unsigned] dimensions, vector[double] top_dimensional_cells):
+        with nogil:
+            self.thisptr = new Bitmap_cubical_complex_base_interface(dimensions, top_dimensional_cells)
+
+    def _construct_from_file(self, string filename):
+        with nogil:
+            self.thisptr = new Bitmap_cubical_complex_base_interface(filename)
+
     def __dealloc__(self):
         if self.thisptr != NULL:
             del self.thisptr
diff --git a/src/python/gudhi/periodic_cubical_complex.pyx b/src/python/gudhi/periodic_cubical_complex.pyx
index dcca7b63..11e1766c 100644
--- a/src/python/gudhi/periodic_cubical_complex.pyx
+++ b/src/python/gudhi/periodic_cubical_complex.pyx
@@ -81,9 +81,7 @@ cdef class PeriodicCubicalComplex:
                   periodic_dimensions=None, perseus_file=''):
         if ((dimensions is not None) and (top_dimensional_cells is not None)
             and (periodic_dimensions is not None) and (perseus_file == '')):
-            self.thisptr = new Periodic_cubical_complex_base_interface(dimensions,
-                                                                       top_dimensional_cells,
-                                                                       periodic_dimensions)
+            self._construct_from_cells(dimensions, top_dimensional_cells, periodic_dimensions)
         elif ((dimensions is None) and (top_dimensional_cells is not None)
             and (periodic_dimensions is not None) and (perseus_file == '')):
             top_dimensional_cells = np.array(top_dimensional_cells,
@@ -91,13 +89,11 @@ cdef class PeriodicCubicalComplex:
                                              order = 'F')
             dimensions = top_dimensional_cells.shape
             top_dimensional_cells = top_dimensional_cells.ravel(order='F')
-            self.thisptr = new Periodic_cubical_complex_base_interface(dimensions,
-                                                                       top_dimensional_cells,
-                                                                       periodic_dimensions)
+            self._construct_from_cells(dimensions, top_dimensional_cells, periodic_dimensions)
         elif ((dimensions is None) and (top_dimensional_cells is None)
             and (periodic_dimensions is None) and (perseus_file != '')):
             if os.path.isfile(perseus_file):
-                self.thisptr = new Periodic_cubical_complex_base_interface(perseus_file.encode('utf-8'))
+                self._construct_from_file(perseus_file.encode('utf-8'))
             else:
                 print("file " + perseus_file + " not found.", file=sys.stderr)
         else:
@@ -106,6 +102,14 @@ cdef class PeriodicCubicalComplex:
               "top_dimensional_cells and periodic_dimensions or from "
               "a Perseus-style file name.", file=sys.stderr)
 
+    def _construct_from_cells(self, vector[unsigned] dimensions, vector[double] top_dimensional_cells, vector[bool] periodic_dimensions):
+        with nogil:
+            self.thisptr = new Periodic_cubical_complex_base_interface(dimensions, top_dimensional_cells, periodic_dimensions)
+
+    def _construct_from_file(self, string filename):
+        with nogil:
+            self.thisptr = new Periodic_cubical_complex_base_interface(filename)
+
     def __dealloc__(self):
         if self.thisptr != NULL:
             del self.thisptr
-- 
cgit v1.2.3


From c156309dfd00c6180f2fd2dc03be159fd21c2626 Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Sun, 17 May 2020 23:32:21 +0200
Subject: One more nogil in cubical

---
 src/python/gudhi/cubical_complex.pyx          | 3 ++-
 src/python/gudhi/periodic_cubical_complex.pyx | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/python/gudhi/cubical_complex.pyx b/src/python/gudhi/cubical_complex.pyx
index 0068f2ff..3ace2517 100644
--- a/src/python/gudhi/cubical_complex.pyx
+++ b/src/python/gudhi/cubical_complex.pyx
@@ -215,7 +215,8 @@ cdef class CubicalComplex:
 
         cdef vector[vector[int]] persistence_result
         output = [[],[]]
-        persistence_result = self.pcohptr.cofaces_of_cubical_persistence_pairs()
+        with nogil:
+            persistence_result = self.pcohptr.cofaces_of_cubical_persistence_pairs()
         pr = np.array(persistence_result)
 
         ess_ind = np.argwhere(pr[:,2] == -1)[:,0]
diff --git a/src/python/gudhi/periodic_cubical_complex.pyx b/src/python/gudhi/periodic_cubical_complex.pyx
index 11e1766c..bed55101 100644
--- a/src/python/gudhi/periodic_cubical_complex.pyx
+++ b/src/python/gudhi/periodic_cubical_complex.pyx
@@ -214,7 +214,8 @@ cdef class PeriodicCubicalComplex:
         cdef vector[vector[int]] persistence_result
         if self.pcohptr != NULL:
             output = [[],[]]
-            persistence_result = self.pcohptr.cofaces_of_cubical_persistence_pairs()
+            with nogil:
+                persistence_result = self.pcohptr.cofaces_of_cubical_persistence_pairs()
             pr = np.array(persistence_result)
 
             ess_ind = np.argwhere(pr[:,2] == -1)[:,0]
-- 
cgit v1.2.3


From 2287b727126ffb9fc47869ac9ed6b6bd61c6605a Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Mon, 18 May 2020 23:54:02 +0200
Subject: Infer k when we pass the distances to the nearest neighbors

---
 src/python/gudhi/point_cloud/dtm.py | 23 +++++++++++++++++------
 src/python/test/test_dtm.py         |  4 ++++
 2 files changed, 21 insertions(+), 6 deletions(-)

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index 88f197e7..d836c28d 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -85,7 +85,8 @@ class DTMDensity:
     def __init__(self, k=None, weights=None, q=None, dim=None, normalize=False, n_samples=None, **kwargs):
         """
         Args:
-            k (int): number of neighbors (possibly including the point itself).
+            k (int): number of neighbors (possibly including the point itself). Optional if it can be guessed
+                from weights or metric="neighbors".
             weights (numpy.array): weights of each of the k neighbors, optional. They are supposed to sum to 1.
             q (float): order used to compute the distance to measure. Defaults to dim.
             dim (float): final exponent representing the dimension. Defaults to the dimension, and must be specified
@@ -98,9 +99,12 @@ class DTMDensity:
                 :func:`transform` expects an array with the distances to the k nearest neighbors.
         """
         if weights is None:
-            assert k is not None, "Must specify k or weights"
             self.k = k
-            self.weights = np.full(k, 1.0 / k)
+            if k is None:
+                assert kwargs.get("metric") == "neighbors", 'Must specify k or weights, unless metric is "neighbors"'
+                self.weights = None
+            else:
+                self.weights = np.full(k, 1.0 / k)
         else:
             self.weights = weights
             self.k = len(weights)
@@ -145,14 +149,21 @@ class DTMDensity:
             dim = len(X[0])
         if q is None:
             q = dim
+        k = self.k
+        weights = self.weights
         if self.params["metric"] == "neighbors":
-            distances = np.asarray(X)[:, : self.k]
+            distances = np.asarray(X)
+            if weights is None:
+                k = distances.shape[1]
+                weights = np.full(k, 1.0 / k)
+            else:
+                distances = distances[:, :k]
         else:
             distances = self.knn.transform(X)
         distances = distances ** q
-        dtm = (distances * self.weights).sum(-1)
+        dtm = (distances * weights).sum(-1)
         if self.normalize:
-            dtm /= (np.arange(1, self.k + 1) ** (q / dim) * self.weights).sum()
+            dtm /= (np.arange(1, k + 1) ** (q / dim) * weights).sum()
         density = dtm ** (-dim / q)
         if self.normalize:
             import math
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py
index 8ab0cc44..8d400c7e 100755
--- a/src/python/test/test_dtm.py
+++ b/src/python/test/test_dtm.py
@@ -82,3 +82,7 @@ def test_density():
     density = DTMDensity(k=2, metric="neighbors", dim=1).fit_transform(distances)
     expected = numpy.array([2.0, 1.0, 0.5])
     assert density == pytest.approx(expected)
+    distances = [[0, 1], [2, 0], [1, 3]]
+    density = DTMDensity(metric="neighbors", dim=1).fit_transform(distances)
+    expected = numpy.array([2.0, 1.0, 0.5])
+    assert density == pytest.approx(expected)
-- 
cgit v1.2.3


From 35d5ac4e6bb79ec41b35c0df611207b9cd578f49 Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Tue, 19 May 2020 18:10:16 +0200
Subject: Test with explicit weights

and remove duplicated assignment
---
 src/python/test/test_dtm.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py
index 8d400c7e..0a52279e 100755
--- a/src/python/test/test_dtm.py
+++ b/src/python/test/test_dtm.py
@@ -84,5 +84,6 @@ def test_density():
     assert density == pytest.approx(expected)
     distances = [[0, 1], [2, 0], [1, 3]]
     density = DTMDensity(metric="neighbors", dim=1).fit_transform(distances)
-    expected = numpy.array([2.0, 1.0, 0.5])
+    assert density == pytest.approx(expected)
+    density = DTMDensity(weights=[0.5, 0.5], metric="neighbors", dim=1).fit_transform(distances)
     assert density == pytest.approx(expected)
-- 
cgit v1.2.3


From bb9b6b2a58d3b31a0e25d473339f2bde6430a52d Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Tue, 19 May 2020 20:16:32 +0200
Subject: long line

---
 src/python/gudhi/point_cloud/dtm.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index d836c28d..55ac58e6 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -95,8 +95,9 @@ class DTMDensity:
                 Only available for the Euclidean metric, defaults to False.
             n_samples (int): number of sample points used for fitting. Only needed if `normalize` is True and
                 metric is "neighbors".
-            kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNearestNeighbors`, except that metric="neighbors" means that
-                :func:`transform` expects an array with the distances to the k nearest neighbors.
+            kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNearestNeighbors`, except that
+                metric="neighbors" means that :func:`transform` expects an array with the distances to
+                the k nearest neighbors.
         """
         if weights is None:
             self.k = k
-- 
cgit v1.2.3