From ec06a9b9ae0a9ff1897249dcbc2b497764f54aaf Mon Sep 17 00:00:00 2001
From: Vincent Rouvreau <vincent.rouvreau@inria.fr>
Date: Mon, 18 Oct 2021 17:01:02 +0200
Subject: First part of the fix

---
 src/python/gudhi/cubical_complex.pyx               |  7 ++-
 src/python/gudhi/periodic_cubical_complex.pyx      |  7 ++-
 src/python/gudhi/representations/vector_methods.py | 60 ++++++++++++++--------
 src/python/gudhi/simplex_tree.pyx                  | 26 ++++++----
 src/python/test/test_cubical_complex.py            | 25 +++++++++
 src/python/test/test_representations.py            | 37 +++++++++++++
 6 files changed, 129 insertions(+), 33 deletions(-)

diff --git a/src/python/gudhi/cubical_complex.pyx b/src/python/gudhi/cubical_complex.pyx
index 97c69a2d..04569bd8 100644
--- a/src/python/gudhi/cubical_complex.pyx
+++ b/src/python/gudhi/cubical_complex.pyx
@@ -281,4 +281,9 @@ cdef class CubicalComplex:
             launched first.
         """
         assert self.pcohptr != NULL, "compute_persistence() must be called before persistence_intervals_in_dimension()"
-        return np.array(self.pcohptr.intervals_in_dimension(dimension))
+        piid = np.array(self.pcohptr.intervals_in_dimension(dimension))
+        # Workaround https://github.com/GUDHI/gudhi-devel/issues/507
+        if piid.shape[0] == 0:
+            return np.empty(shape = [0, 2])
+        else:
+            return piid
diff --git a/src/python/gudhi/periodic_cubical_complex.pyx b/src/python/gudhi/periodic_cubical_complex.pyx
index ef1d3080..bd91ccde 100644
--- a/src/python/gudhi/periodic_cubical_complex.pyx
+++ b/src/python/gudhi/periodic_cubical_complex.pyx
@@ -280,4 +280,9 @@ cdef class PeriodicCubicalComplex:
             launched first.
         """
         assert self.pcohptr != NULL, "compute_persistence() must be called before persistence_intervals_in_dimension()"
-        return np.array(self.pcohptr.intervals_in_dimension(dimension))
+        piid = np.array(self.pcohptr.intervals_in_dimension(dimension))
+        # Workaround https://github.com/GUDHI/gudhi-devel/issues/507
+        if piid.shape[0] == 0:
+            return np.empty(shape = [0, 2])
+        else:
+            return piid
diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index 84bc99a2..711c32a7 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -44,11 +44,15 @@ class PersistenceImage(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        if np.isnan(np.array(self.im_range)).any():
-            new_X = BirthPersistenceTransform().fit_transform(X)
-            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(new_X,y)
-            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-            self.im_range = np.where(np.isnan(np.array(self.im_range)), np.array([mx, Mx, my, My]), np.array(self.im_range))
+        try:
+            if np.isnan(np.array(self.im_range)).any():
+                new_X = BirthPersistenceTransform().fit_transform(X)
+                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(new_X,y)
+                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+                self.im_range = np.where(np.isnan(np.array(self.im_range)), np.array([mx, Mx, my, My]), np.array(self.im_range))
+        except ValueError:
+            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+            pass
         return self
 
     def transform(self, X):
@@ -120,9 +124,13 @@ class Landscape(BaseEstimator, TransformerMixin):
             y (n x 1 array): persistence diagram labels (unused).
         """
         if self.nan_in_range.any():
-            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
-            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-            self.sample_range = np.where(self.nan_in_range, np.array([mx, My]), np.array(self.sample_range))
+            try:
+                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+                self.sample_range = np.where(self.nan_in_range, np.array([mx, My]), np.array(self.sample_range))
+            except ValueError:
+                # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+                pass
         return self
 
     def transform(self, X):
@@ -218,10 +226,14 @@ class Silhouette(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        if np.isnan(np.array(self.sample_range)).any():
-            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
-            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-            self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        try:
+            if np.isnan(np.array(self.sample_range)).any():
+                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+                self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        except ValueError:
+            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+            pass
         return self
 
     def transform(self, X):
@@ -307,10 +319,14 @@ class BettiCurve(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        if np.isnan(np.array(self.sample_range)).any():
-            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
-            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-            self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        try:
+            if np.isnan(np.array(self.sample_range)).any():
+                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+                self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        except ValueError:
+            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+            pass
         return self
 
     def transform(self, X):
@@ -374,10 +390,14 @@ class Entropy(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        if np.isnan(np.array(self.sample_range)).any():
-            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
-            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-            self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        try:
+            if np.isnan(np.array(self.sample_range)).any():
+                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+                self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        except ValueError:
+            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+            pass
         return self
 
     def transform(self, X):
diff --git a/src/python/gudhi/simplex_tree.pyx b/src/python/gudhi/simplex_tree.pyx
index 9c51cb46..e9bac036 100644
--- a/src/python/gudhi/simplex_tree.pyx
+++ b/src/python/gudhi/simplex_tree.pyx
@@ -9,8 +9,7 @@
 
 from cython.operator import dereference, preincrement
 from libc.stdint cimport intptr_t
-import numpy
-from numpy import array as np_array
+import numpy as np
 cimport gudhi.simplex_tree
 
 __author__ = "Vincent Rouvreau"
@@ -542,7 +541,12 @@ cdef class SimplexTree:
             function to be launched first.
         """
         assert self.pcohptr != NULL, "compute_persistence() must be called before persistence_intervals_in_dimension()"
-        return np_array(self.pcohptr.intervals_in_dimension(dimension))
+        piid = np.array(self.pcohptr.intervals_in_dimension(dimension))
+        # Workaround https://github.com/GUDHI/gudhi-devel/issues/507
+        if piid.shape[0] == 0:
+            return np.empty(shape = [0, 2])
+        else:
+            return piid
 
     def persistence_pairs(self):
         """This function returns a list of persistence birth and death simplices pairs.
@@ -583,8 +587,8 @@ cdef class SimplexTree:
         """
         assert self.pcohptr != NULL, "lower_star_persistence_generators() requires that persistence() be called first."
         gen = self.pcohptr.lower_star_generators()
-        normal = [np_array(d).reshape(-1,2) for d in gen.first]
-        infinite = [np_array(d) for d in gen.second]
+        normal = [np.array(d).reshape(-1,2) for d in gen.first]
+        infinite = [np.array(d) for d in gen.second]
         return (normal, infinite)
 
     def flag_persistence_generators(self):
@@ -602,19 +606,19 @@ cdef class SimplexTree:
         assert self.pcohptr != NULL, "flag_persistence_generators() requires that persistence() be called first."
         gen = self.pcohptr.flag_generators()
         if len(gen.first) == 0:
-            normal0 = numpy.empty((0,3))
+            normal0 = np.empty((0,3))
             normals = []
         else:
             l = iter(gen.first)
-            normal0 = np_array(next(l)).reshape(-1,3)
-            normals = [np_array(d).reshape(-1,4) for d in l]
+            normal0 = np.array(next(l)).reshape(-1,3)
+            normals = [np.array(d).reshape(-1,4) for d in l]
         if len(gen.second) == 0:
-            infinite0 = numpy.empty(0)
+            infinite0 = np.empty(0)
             infinites = []
         else:
             l = iter(gen.second)
-            infinite0 = np_array(next(l))
-            infinites = [np_array(d).reshape(-1,2) for d in l]
+            infinite0 = np.array(next(l))
+            infinites = [np.array(d).reshape(-1,2) for d in l]
         return (normal0, normals, infinite0, infinites)
 
     def collapse_edges(self, nb_iterations = 1):
diff --git a/src/python/test/test_cubical_complex.py b/src/python/test/test_cubical_complex.py
index d0e4e9e8..29d559b3 100755
--- a/src/python/test/test_cubical_complex.py
+++ b/src/python/test/test_cubical_complex.py
@@ -174,3 +174,28 @@ def test_periodic_cofaces_of_persistence_pairs_when_pd_has_no_paired_birth_and_d
     assert np.array_equal(pairs[1][0], np.array([0]))
     assert np.array_equal(pairs[1][1], np.array([0, 1]))
     assert np.array_equal(pairs[1][2], np.array([1]))
+
+def test_cubical_persistence_intervals_in_dimension():
+    cub = CubicalComplex(
+        dimensions=[3, 3],
+        top_dimensional_cells=[1, 2, 3, 4, 5, 6, 7, 8, 9],
+    )
+    cub.compute_persistence()
+    H0 = cub.persistence_intervals_in_dimension(0)
+    assert np.array_equal(H0, np.array([[ 1., float("inf")]]))
+    assert cub.persistence_intervals_in_dimension(1).shape == (0, 2)
+
+def test_periodic_cubical_persistence_intervals_in_dimension():
+    cub = PeriodicCubicalComplex(
+        dimensions=[3, 3],
+        top_dimensional_cells=[1, 2, 3, 4, 5, 6, 7, 8, 9],
+        periodic_dimensions = [True, True]
+    )
+    cub.compute_persistence()
+    H0 = cub.persistence_intervals_in_dimension(0)
+    assert np.array_equal(H0, np.array([[ 1., float("inf")]]))
+    H1 = cub.persistence_intervals_in_dimension(1)
+    assert np.array_equal(H1, np.array([[ 3., float("inf")], [ 7., float("inf")]]))
+    H2 = cub.persistence_intervals_in_dimension(2)
+    assert np.array_equal(H2, np.array([[ 9., float("inf")]]))
+    assert cub.persistence_intervals_in_dimension(3).shape == (0, 2)
diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py
index cda1a15b..c1f4df12 100755
--- a/src/python/test/test_representations.py
+++ b/src/python/test/test_representations.py
@@ -6,6 +6,12 @@ import pytest
 
 from sklearn.cluster import KMeans
 
+from gudhi.representations import (DiagramSelector, Clamping, Landscape, Silhouette, BettiCurve, ComplexPolynomial,\
+  TopologicalVector, DiagramScaler, BirthPersistenceTransform,\
+  PersistenceImage, PersistenceWeightedGaussianKernel, Entropy, \
+  PersistenceScaleSpaceKernel, SlicedWassersteinDistance,\
+  SlicedWassersteinKernel, PersistenceFisherKernel, WassersteinDistance)
+
 
 def test_representations_examples():
     # Disable graphics for testing purposes
@@ -98,3 +104,34 @@ def test_infinity():
     assert c[1] == 0
     assert c[7] == 3
     assert c[9] == 2
+
+def pow(n):
+  return lambda x: np.power(x[1]-x[0],n)
+
+def test_vectorization_empty_diagrams():
+    empty_diag = np.empty(shape = [0, 2])
+    Landscape(resolution=1000)(empty_diag)
+    Silhouette(resolution=1000, weight=pow(2))(empty_diag)
+    BettiCurve(resolution=1000)(empty_diag)
+    ComplexPolynomial(threshold=-1, polynomial_type="T")(empty_diag)
+    TopologicalVector(threshold=-1)(empty_diag)
+    PersistenceImage(bandwidth=.1, weight=lambda x: x[1], im_range=[0,1,0,1], resolution=[100,100])(empty_diag)
+    #Entropy(mode="scalar")(empty_diag)
+    #Entropy(mode="vector", normalized=False)(empty_diag)
+
+#def arctan(C,p):
+#  return lambda x: C*np.arctan(np.power(x[1], p))
+#
+#def test_kernel_empty_diagrams():
+#    empty_diag = np.empty(shape = [0, 2])
+#    PersistenceWeightedGaussianKernel(bandwidth=1., kernel_approx=None, weight=arctan(1.,1.))(empty_diag, empty_diag)
+#    PersistenceWeightedGaussianKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])), weight=arctan(1.,1.))(empty_diag, empty_diag)
+#    PersistenceScaleSpaceKernel(bandwidth=1.)(empty_diag, empty_diag)
+#    PersistenceScaleSpaceKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))(empty_diag, empty_diag)
+#    SlicedWassersteinDistance(num_directions=100)(empty_diag, empty_diag)
+#    SlicedWassersteinKernel(num_directions=100, bandwidth=1.)(empty_diag, empty_diag)
+#    WassersteinDistance(order=2, internal_p=2, mode="pot")(empty_diag, empty_diag)
+#    WassersteinDistance(order=2, internal_p=2, mode="hera", delta=0.0001)(empty_diag, empty_diag)
+#    BottleneckDistance(epsilon=.001)(empty_diag, empty_diag)
+#    PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.)(empty_diag, empty_diag)
+#    PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1., kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))(empty_diag, empty_diag)
-- 
cgit v1.2.3


From 0c8e1e2b69c7658c153df99931e3407ec18c1332 Mon Sep 17 00:00:00 2001
From: Vincent Rouvreau <vincent.rouvreau@inria.fr>
Date: Tue, 19 Oct 2021 21:20:01 +0200
Subject: Add empty diags tests

---
 src/python/test/test_representations.py | 60 +++++++++++++++++++++------------
 1 file changed, 39 insertions(+), 21 deletions(-)

diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py
index c1f4df12..b888b7f1 100755
--- a/src/python/test/test_representations.py
+++ b/src/python/test/test_representations.py
@@ -6,9 +6,16 @@ import pytest
 
 from sklearn.cluster import KMeans
 
-from gudhi.representations import (DiagramSelector, Clamping, Landscape, Silhouette, BettiCurve, ComplexPolynomial,\
-  TopologicalVector, DiagramScaler, BirthPersistenceTransform,\
-  PersistenceImage, PersistenceWeightedGaussianKernel, Entropy, \
+# Vectorization
+from gudhi.representations import (Landscape, Silhouette, BettiCurve, ComplexPolynomial,\
+  TopologicalVector, PersistenceImage, Entropy)
+
+# Preprocessing
+from gudhi.representations import (BirthPersistenceTransform, Clamping, DiagramScaler, Padding, ProminentPoints, \
+  DiagramSelector)
+
+# Kernel
+from gudhi.representations import (PersistenceWeightedGaussianKernel, \
   PersistenceScaleSpaceKernel, SlicedWassersteinDistance,\
   SlicedWassersteinKernel, PersistenceFisherKernel, WassersteinDistance)
 
@@ -105,33 +112,44 @@ def test_infinity():
     assert c[7] == 3
     assert c[9] == 2
 
+
+def test_preprocessing_empty_diagrams():
+    empty_diag = np.empty(shape = [0, 2])
+    assert not np.any(BirthPersistenceTransform()(empty_diag))
+    assert not np.any(Clamping().fit_transform(empty_diag))
+    assert not np.any(DiagramScaler()(empty_diag))
+    assert not np.any(Padding()(empty_diag))
+    assert not np.any(ProminentPoints()(empty_diag))
+    assert not np.any(DiagramSelector()(empty_diag))
+
 def pow(n):
   return lambda x: np.power(x[1]-x[0],n)
 
 def test_vectorization_empty_diagrams():
     empty_diag = np.empty(shape = [0, 2])
-    Landscape(resolution=1000)(empty_diag)
-    Silhouette(resolution=1000, weight=pow(2))(empty_diag)
-    BettiCurve(resolution=1000)(empty_diag)
-    ComplexPolynomial(threshold=-1, polynomial_type="T")(empty_diag)
-    TopologicalVector(threshold=-1)(empty_diag)
-    PersistenceImage(bandwidth=.1, weight=lambda x: x[1], im_range=[0,1,0,1], resolution=[100,100])(empty_diag)
-    #Entropy(mode="scalar")(empty_diag)
-    #Entropy(mode="vector", normalized=False)(empty_diag)
-
-#def arctan(C,p):
-#  return lambda x: C*np.arctan(np.power(x[1], p))
+    assert not np.any(Landscape(resolution=1000)(empty_diag))
+    assert not np.any(Silhouette(resolution=1000, weight=pow(2))(empty_diag))
+    assert not np.any(BettiCurve(resolution=1000)(empty_diag))
+    assert not np.any(ComplexPolynomial(threshold=-1, polynomial_type="T")(empty_diag))
+    assert not np.any(TopologicalVector(threshold=-1)(empty_diag))
+    assert not np.any(PersistenceImage(bandwidth=.1, weight=lambda x: x[1], im_range=[0,1,0,1], resolution=[100,100])(empty_diag))
+    assert not np.any(Entropy(mode="scalar")(empty_diag))
+    assert not np.any(Entropy(mode="vector", normalized=False)(empty_diag))
+
+def arctan(C,p):
+  return lambda x: C*np.arctan(np.power(x[1], p))
 #
-#def test_kernel_empty_diagrams():
-#    empty_diag = np.empty(shape = [0, 2])
+def test_kernel_empty_diagrams():
+    empty_diag = np.empty(shape = [0, 2])
 #    PersistenceWeightedGaussianKernel(bandwidth=1., kernel_approx=None, weight=arctan(1.,1.))(empty_diag, empty_diag)
 #    PersistenceWeightedGaussianKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])), weight=arctan(1.,1.))(empty_diag, empty_diag)
 #    PersistenceScaleSpaceKernel(bandwidth=1.)(empty_diag, empty_diag)
 #    PersistenceScaleSpaceKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))(empty_diag, empty_diag)
-#    SlicedWassersteinDistance(num_directions=100)(empty_diag, empty_diag)
-#    SlicedWassersteinKernel(num_directions=100, bandwidth=1.)(empty_diag, empty_diag)
-#    WassersteinDistance(order=2, internal_p=2, mode="pot")(empty_diag, empty_diag)
-#    WassersteinDistance(order=2, internal_p=2, mode="hera", delta=0.0001)(empty_diag, empty_diag)
-#    BottleneckDistance(epsilon=.001)(empty_diag, empty_diag)
+    assert SlicedWassersteinDistance(num_directions=100)(empty_diag, empty_diag) == 0.
+    assert SlicedWassersteinKernel(num_directions=100, bandwidth=1.)(empty_diag, empty_diag) == 1.
+    assert WassersteinDistance(mode="hera", delta=0.0001)(empty_diag, empty_diag) == 0.
+    assert WassersteinDistance(mode="pot")(empty_diag, empty_diag) == 0.
+    assert BottleneckDistance(epsilon=.001)(empty_diag, empty_diag) == 0.
+    assert BottleneckDistance()(empty_diag, empty_diag) == 0.
 #    PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.)(empty_diag, empty_diag)
 #    PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1., kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))(empty_diag, empty_diag)
-- 
cgit v1.2.3


From 74e6bad5e4bb848ad7d15afd5d6302ec4c698ac9 Mon Sep 17 00:00:00 2001
From: Vincent Rouvreau <vincent.rouvreau@inria.fr>
Date: Wed, 20 Oct 2021 09:57:43 +0200
Subject: Improve tests for empty representations

---
 src/python/test/test_representations.py | 48 ++++++++++++++++++++++-----------
 1 file changed, 32 insertions(+), 16 deletions(-)

diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py
index b888b7f1..93461f1e 100755
--- a/src/python/test/test_representations.py
+++ b/src/python/test/test_representations.py
@@ -3,6 +3,7 @@ import sys
 import matplotlib.pyplot as plt
 import numpy as np
 import pytest
+import random
 
 from sklearn.cluster import KMeans
 
@@ -127,29 +128,44 @@ def pow(n):
 
 def test_vectorization_empty_diagrams():
     empty_diag = np.empty(shape = [0, 2])
-    assert not np.any(Landscape(resolution=1000)(empty_diag))
-    assert not np.any(Silhouette(resolution=1000, weight=pow(2))(empty_diag))
-    assert not np.any(BettiCurve(resolution=1000)(empty_diag))
-    assert not np.any(ComplexPolynomial(threshold=-1, polynomial_type="T")(empty_diag))
-    assert not np.any(TopologicalVector(threshold=-1)(empty_diag))
-    assert not np.any(PersistenceImage(bandwidth=.1, weight=lambda x: x[1], im_range=[0,1,0,1], resolution=[100,100])(empty_diag))
-    assert not np.any(Entropy(mode="scalar")(empty_diag))
-    assert not np.any(Entropy(mode="vector", normalized=False)(empty_diag))
-
-def arctan(C,p):
-  return lambda x: C*np.arctan(np.power(x[1], p))
-#
+    random_resolution = random.randint(50,100)*10 # between 500 and 1000
+    print("resolution = ", random_resolution)
+    lsc = Landscape(resolution=random_resolution)(empty_diag)
+    assert not np.any(lsc)
+    assert lsc.shape[0]%random_resolution == 0
+    slt = Silhouette(resolution=random_resolution, weight=pow(2))(empty_diag)
+    assert not np.any(slt)
+    assert slt.shape[0] == random_resolution
+    btc = BettiCurve(resolution=random_resolution)(empty_diag)
+    assert not np.any(btc)
+    assert btc.shape[0] == random_resolution
+    cpp = ComplexPolynomial(threshold=random_resolution, polynomial_type="T")(empty_diag)
+    assert not np.any(cpp)
+    assert cpp.shape[0] == random_resolution
+    tpv = TopologicalVector(threshold=random_resolution)(empty_diag)
+    assert tpv.shape[0] == random_resolution
+    assert not np.any(tpv)
+    prmg = PersistenceImage(resolution=[random_resolution,random_resolution])(empty_diag)
+    assert not np.any(prmg)
+    assert prmg.shape[0] == random_resolution * random_resolution
+    sce = Entropy(mode="scalar", resolution=random_resolution)(empty_diag)
+    assert not np.any(sce)
+    assert sce.shape[0] == 1
+    scv = Entropy(mode="vector", normalized=False, resolution=random_resolution)(empty_diag)
+    assert not np.any(scv)
+    assert scv.shape[0] == random_resolution
+
 def test_kernel_empty_diagrams():
     empty_diag = np.empty(shape = [0, 2])
-#    PersistenceWeightedGaussianKernel(bandwidth=1., kernel_approx=None, weight=arctan(1.,1.))(empty_diag, empty_diag)
-#    PersistenceWeightedGaussianKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])), weight=arctan(1.,1.))(empty_diag, empty_diag)
-#    PersistenceScaleSpaceKernel(bandwidth=1.)(empty_diag, empty_diag)
-#    PersistenceScaleSpaceKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))(empty_diag, empty_diag)
     assert SlicedWassersteinDistance(num_directions=100)(empty_diag, empty_diag) == 0.
     assert SlicedWassersteinKernel(num_directions=100, bandwidth=1.)(empty_diag, empty_diag) == 1.
     assert WassersteinDistance(mode="hera", delta=0.0001)(empty_diag, empty_diag) == 0.
     assert WassersteinDistance(mode="pot")(empty_diag, empty_diag) == 0.
     assert BottleneckDistance(epsilon=.001)(empty_diag, empty_diag) == 0.
     assert BottleneckDistance()(empty_diag, empty_diag) == 0.
+#    PersistenceWeightedGaussianKernel(bandwidth=1., kernel_approx=None, weight=arctan(1.,1.))(empty_diag, empty_diag)
+#    PersistenceWeightedGaussianKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])), weight=arctan(1.,1.))(empty_diag, empty_diag)
+#    PersistenceScaleSpaceKernel(bandwidth=1.)(empty_diag, empty_diag)
+#    PersistenceScaleSpaceKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))(empty_diag, empty_diag)
 #    PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.)(empty_diag, empty_diag)
 #    PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1., kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))(empty_diag, empty_diag)
-- 
cgit v1.2.3


From 44946b900ea13b2d6bb8d285c18cf0d37d515215 Mon Sep 17 00:00:00 2001
From: Vincent Rouvreau <vincent.rouvreau@inria.fr>
Date: Wed, 20 Oct 2021 11:30:29 +0200
Subject: Add simplex tree test for persistence_intervals_in_dimension

---
 src/python/test/test_simplex_tree.py | 44 ++++++++++++++++++++++++++++++++++++
 1 file changed, 44 insertions(+)

diff --git a/src/python/test/test_simplex_tree.py b/src/python/test/test_simplex_tree.py
index a3eacaa9..31c46213 100755
--- a/src/python/test/test_simplex_tree.py
+++ b/src/python/test/test_simplex_tree.py
@@ -9,6 +9,7 @@
 """
 
 from gudhi import SimplexTree, __GUDHI_USE_EIGEN3
+import numpy as np
 import pytest
 
 __author__ = "Vincent Rouvreau"
@@ -404,3 +405,46 @@ def test_boundaries_iterator():
 
     with pytest.raises(RuntimeError):
         list(st.get_boundaries([6])) # (6) does not exist
+
+def test_persistence_intervals_in_dimension():
+    # Here is our triangulation of a 2-torus - taken from https://dioscuri-tda.org/Paris_TDA_Tutorial_2021.html
+    #   0-----3-----4-----0
+    #   | \   | \   | \   | \   |
+    #   |   \ |   \ |    \|   \ | 
+    #   1-----8-----7-----1
+    #   | \   | \   | \   | \   |
+    #   |   \ |   \ |   \ |   \ |
+    #   2-----5-----6-----2
+    #   | \   | \   | \   | \   |
+    #   |   \ |   \ |   \ |   \ |
+    #   0-----3-----4-----0
+    st = SimplexTree()
+    st.insert([0,1,8])
+    st.insert([0,3,8])
+    st.insert([3,7,8])
+    st.insert([3,4,7])
+    st.insert([1,4,7])
+    st.insert([0,1,4])
+    st.insert([1,2,5])
+    st.insert([1,5,8])
+    st.insert([5,6,8])
+    st.insert([6,7,8])
+    st.insert([2,6,7])
+    st.insert([1,2,7])
+    st.insert([0,2,3])
+    st.insert([2,3,5])
+    st.insert([3,4,5])
+    st.insert([4,5,6])
+    st.insert([0,4,6])
+    st.insert([0,2,6])
+    st.compute_persistence(persistence_dim_max=True)
+    
+    H0 = st.persistence_intervals_in_dimension(0)
+    assert np.array_equal(H0, np.array([[ 0., float("inf")]]))
+    H1 = st.persistence_intervals_in_dimension(1)
+    assert np.array_equal(H1, np.array([[ 0., float("inf")], [ 0., float("inf")]]))
+    H2 = st.persistence_intervals_in_dimension(2)
+    assert np.array_equal(H2, np.array([[ 0., float("inf")]]))
+    # Test empty case
+    assert st.persistence_intervals_in_dimension(3).shape == (0, 2)
+    
\ No newline at end of file
-- 
cgit v1.2.3


From e4122147ee4643dbca6c65efebf83eb2adad6aec Mon Sep 17 00:00:00 2001
From: Vincent Rouvreau <vincent.rouvreau@inria.fr>
Date: Wed, 20 Oct 2021 11:31:00 +0200
Subject: Make Entropy work and also fix a bug in the loop

---
 src/python/gudhi/representations/vector_methods.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index 711c32a7..47c5224c 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -416,9 +416,12 @@ class Entropy(BaseEstimator, TransformerMixin):
         new_X = BirthPersistenceTransform().fit_transform(X)        
 
         for i in range(num_diag):
-
             orig_diagram, diagram, num_pts_in_diag = X[i], new_X[i], X[i].shape[0]
-            new_diagram = DiagramScaler(use=True, scalers=[([1], MaxAbsScaler())]).fit_transform([diagram])[0]
+            try:
+                new_diagram = DiagramScaler(use=True, scalers=[([1], MaxAbsScaler())]).fit_transform([diagram])[0]
+            except ValueError:
+                # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+                new_diagram = np.empty(shape = [0, 2])
 
             if self.mode == "scalar":
                 ent = - np.sum( np.multiply(new_diagram[:,1], np.log(new_diagram[:,1])) )
@@ -432,12 +435,11 @@ class Entropy(BaseEstimator, TransformerMixin):
                     max_idx = np.clip(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
                     for k in range(min_idx, max_idx):
                         ent[k] += (-1) * new_diagram[j,1] * np.log(new_diagram[j,1])
-                    if self.normalized:
-                        ent = ent / np.linalg.norm(ent, ord=1)
-                    Xfit.append(np.reshape(ent,[1,-1]))
-
-        Xfit = np.concatenate(Xfit, 0)
+                if self.normalized:
+                    ent = ent / np.linalg.norm(ent, ord=1)
+                Xfit.append(np.reshape(ent,[1,-1]))
 
+        Xfit = np.concatenate(Xfit, axis=0)
         return Xfit
 
     def __call__(self, diag):
-- 
cgit v1.2.3


From 4a0bc0fe1d6424da9bf979cfc322067a62f41cc9 Mon Sep 17 00:00:00 2001
From: Vincent Rouvreau <vincent.rouvreau@inria.fr>
Date: Fri, 22 Oct 2021 12:44:07 +0200
Subject: Fix exception management when sklearn version < 1.0

---
 src/python/gudhi/representations/vector_methods.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index 47c5224c..b83c2a87 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -500,7 +500,11 @@ class TopologicalVector(BaseEstimator, TransformerMixin):
             diagram, num_pts_in_diag = X[i], X[i].shape[0]
             pers = 0.5 * (diagram[:,1]-diagram[:,0])
             min_pers = np.minimum(pers,np.transpose(pers))
-            distances = DistanceMetric.get_metric("chebyshev").pairwise(diagram)
+            # Works fine with sklearn 1.0, but an ValueError exception is thrown on past versions
+            try:
+                distances = DistanceMetric.get_metric("chebyshev").pairwise(diagram)
+            except ValueError:
+                distances = np.empty(shape = [0, 0])
             vect = np.flip(np.sort(np.triu(np.minimum(distances, min_pers)), axis=None), 0)
             dim = min(len(vect), thresh)
             Xfit[i, :dim] = vect[:dim]
-- 
cgit v1.2.3


From a2761c01ceb26a057b94be1d45433335704c1581 Mon Sep 17 00:00:00 2001
From: Vincent Rouvreau <vincent.rouvreau@inria.fr>
Date: Thu, 4 Nov 2021 17:24:15 +0100
Subject: code review: try-except inside the if

---
 src/python/gudhi/representations/vector_methods.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index b83c2a87..e7ee57a4 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -44,15 +44,15 @@ class PersistenceImage(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        try:
-            if np.isnan(np.array(self.im_range)).any():
+        if np.isnan(np.array(self.im_range)).any():
+            try:
                 new_X = BirthPersistenceTransform().fit_transform(X)
                 pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(new_X,y)
                 [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
                 self.im_range = np.where(np.isnan(np.array(self.im_range)), np.array([mx, Mx, my, My]), np.array(self.im_range))
-        except ValueError:
-            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
-            pass
+            except ValueError:
+                # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+                pass
         return self
 
     def transform(self, X):
-- 
cgit v1.2.3


From 7c26436a703a476d28cf568949275d26d1827c36 Mon Sep 17 00:00:00 2001
From: Vincent Rouvreau <vincent.rouvreau@inria.fr>
Date: Thu, 4 Nov 2021 17:26:04 +0100
Subject: code review: use len instead of .shape[0]

---
 src/python/gudhi/cubical_complex.pyx          | 5 ++---
 src/python/gudhi/periodic_cubical_complex.pyx | 5 ++---
 src/python/gudhi/simplex_tree.pyx             | 5 ++---
 3 files changed, 6 insertions(+), 9 deletions(-)

diff --git a/src/python/gudhi/cubical_complex.pyx b/src/python/gudhi/cubical_complex.pyx
index 04569bd8..8e244bb8 100644
--- a/src/python/gudhi/cubical_complex.pyx
+++ b/src/python/gudhi/cubical_complex.pyx
@@ -283,7 +283,6 @@ cdef class CubicalComplex:
         assert self.pcohptr != NULL, "compute_persistence() must be called before persistence_intervals_in_dimension()"
         piid = np.array(self.pcohptr.intervals_in_dimension(dimension))
         # Workaround https://github.com/GUDHI/gudhi-devel/issues/507
-        if piid.shape[0] == 0:
+        if len(piid) == 0:
             return np.empty(shape = [0, 2])
-        else:
-            return piid
+        return piid
diff --git a/src/python/gudhi/periodic_cubical_complex.pyx b/src/python/gudhi/periodic_cubical_complex.pyx
index bd91ccde..6c21e902 100644
--- a/src/python/gudhi/periodic_cubical_complex.pyx
+++ b/src/python/gudhi/periodic_cubical_complex.pyx
@@ -282,7 +282,6 @@ cdef class PeriodicCubicalComplex:
         assert self.pcohptr != NULL, "compute_persistence() must be called before persistence_intervals_in_dimension()"
         piid = np.array(self.pcohptr.intervals_in_dimension(dimension))
         # Workaround https://github.com/GUDHI/gudhi-devel/issues/507
-        if piid.shape[0] == 0:
+        if len(piid) == 0:
             return np.empty(shape = [0, 2])
-        else:
-            return piid
+        return piid
diff --git a/src/python/gudhi/simplex_tree.pyx b/src/python/gudhi/simplex_tree.pyx
index e9bac036..c3720936 100644
--- a/src/python/gudhi/simplex_tree.pyx
+++ b/src/python/gudhi/simplex_tree.pyx
@@ -543,10 +543,9 @@ cdef class SimplexTree:
         assert self.pcohptr != NULL, "compute_persistence() must be called before persistence_intervals_in_dimension()"
         piid = np.array(self.pcohptr.intervals_in_dimension(dimension))
         # Workaround https://github.com/GUDHI/gudhi-devel/issues/507
-        if piid.shape[0] == 0:
+        if len(piid) == 0:
             return np.empty(shape = [0, 2])
-        else:
-            return piid
+        return piid
 
     def persistence_pairs(self):
         """This function returns a list of persistence birth and death simplices pairs.
-- 
cgit v1.2.3


From 3094e1fe51acc49e4ea7e4f38648bb25d96784a4 Mon Sep 17 00:00:00 2001
From: Vincent Rouvreau <vincent.rouvreau@inria.fr>
Date: Fri, 5 Nov 2021 10:27:46 +0100
Subject: code review: factorize sample range computation

---
 src/python/gudhi/representations/vector_methods.py | 46 ++++++++++++----------
 1 file changed, 26 insertions(+), 20 deletions(-)

diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index e7ee57a4..140162af 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -6,6 +6,7 @@
 #
 # Modification(s):
 #   - 2020/06 Martin: ATOL integration
+#   - 2021/11 Vincent Rouvreau: factorize _automatic_sample_range
 
 import numpy as np
 from sklearn.base          import BaseEstimator, TransformerMixin
@@ -98,6 +99,23 @@ class PersistenceImage(BaseEstimator, TransformerMixin):
         """
         return self.fit_transform([diag])[0,:]
 
+def _automatic_sample_range(sample_range, X, y):
+        """
+        Compute and returns sample range from the persistence diagrams if one of the sample_range values is numpy.nan.
+
+        Parameters:
+            sample_range (a numpy array of 2 float): minimum and maximum of all piecewise-linear function domains, of
+                the form [x_min, x_max].
+            X (list of n x 2 numpy arrays): input persistence diagrams.
+            y (n x 1 array): persistence diagram labels (unused).
+        """
+        nan_in_range = np.isnan(sample_range)
+        if nan_in_range.any():
+            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+            return np.where(nan_in_range, np.array([mx, My]), sample_range)
+        return sample_range
+
 class Landscape(BaseEstimator, TransformerMixin):
     """
     This is a class for computing persistence landscapes from a list of persistence diagrams. A persistence landscape is a collection of 1D piecewise-linear functions computed from the rank function associated to the persistence diagram. These piecewise-linear functions are then sampled evenly on a given range and the corresponding vectors of samples are concatenated and returned. See http://jmlr.org/papers/v16/bubenik15a.html for more details.
@@ -123,14 +141,11 @@ class Landscape(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        if self.nan_in_range.any():
-            try:
-                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
-                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-                self.sample_range = np.where(self.nan_in_range, np.array([mx, My]), np.array(self.sample_range))
-            except ValueError:
-                # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
-                pass
+        try:
+            self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y)
+        except ValueError:
+            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+            pass
         return self
 
     def transform(self, X):
@@ -227,10 +242,7 @@ class Silhouette(BaseEstimator, TransformerMixin):
             y (n x 1 array): persistence diagram labels (unused).
         """
         try:
-            if np.isnan(np.array(self.sample_range)).any():
-                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
-                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-                self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+            self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y)
         except ValueError:
             # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
             pass
@@ -320,10 +332,7 @@ class BettiCurve(BaseEstimator, TransformerMixin):
             y (n x 1 array): persistence diagram labels (unused).
         """
         try:
-            if np.isnan(np.array(self.sample_range)).any():
-                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
-                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-                self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+            self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y)
         except ValueError:
             # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
             pass
@@ -391,10 +400,7 @@ class Entropy(BaseEstimator, TransformerMixin):
             y (n x 1 array): persistence diagram labels (unused).
         """
         try:
-            if np.isnan(np.array(self.sample_range)).any():
-                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
-                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-                self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+            self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y)
         except ValueError:
             # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
             pass
-- 
cgit v1.2.3


From 37d7743a91f7fb970425a06798ac6cb61b0be109 Mon Sep 17 00:00:00 2001
From: Vincent Rouvreau <vincent.rouvreau@inria.fr>
Date: Fri, 5 Nov 2021 12:05:45 +0100
Subject: code review: try/except in function and assert on length of diagrams
 for error menagement

---
 src/python/gudhi/representations/vector_methods.py | 38 +++++++++-------------
 1 file changed, 15 insertions(+), 23 deletions(-)

diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index 140162af..e883b5dd 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -111,9 +111,14 @@ def _automatic_sample_range(sample_range, X, y):
         """
         nan_in_range = np.isnan(sample_range)
         if nan_in_range.any():
-            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
-            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-            return np.where(nan_in_range, np.array([mx, My]), sample_range)
+            try:
+                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+                [mx,my] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]]
+                [Mx,My] = [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+                return np.where(nan_in_range, np.array([mx, My]), sample_range)
+            except ValueError:
+                # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+                pass
         return sample_range
 
 class Landscape(BaseEstimator, TransformerMixin):
@@ -141,11 +146,7 @@ class Landscape(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        try:
-            self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y)
-        except ValueError:
-            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
-            pass
+        self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y)
         return self
 
     def transform(self, X):
@@ -241,11 +242,7 @@ class Silhouette(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        try:
-            self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y)
-        except ValueError:
-            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
-            pass
+        self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y)
         return self
 
     def transform(self, X):
@@ -331,11 +328,7 @@ class BettiCurve(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        try:
-            self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y)
-        except ValueError:
-            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
-            pass
+        self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y)
         return self
 
     def transform(self, X):
@@ -399,11 +392,7 @@ class Entropy(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        try:
-            self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y)
-        except ValueError:
-            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
-            pass
+        self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y)
         return self
 
     def transform(self, X):
@@ -427,6 +416,7 @@ class Entropy(BaseEstimator, TransformerMixin):
                 new_diagram = DiagramScaler(use=True, scalers=[([1], MaxAbsScaler())]).fit_transform([diagram])[0]
             except ValueError:
                 # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+                assert len(diagram) == 0
                 new_diagram = np.empty(shape = [0, 2])
 
             if self.mode == "scalar":
@@ -510,6 +500,8 @@ class TopologicalVector(BaseEstimator, TransformerMixin):
             try:
                 distances = DistanceMetric.get_metric("chebyshev").pairwise(diagram)
             except ValueError:
+                # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+                assert len(diagram) == 0
                 distances = np.empty(shape = [0, 0])
             vect = np.flip(np.sort(np.triu(np.minimum(distances, min_pers)), axis=None), 0)
             dim = min(len(vect), thresh)
-- 
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