First part of the fix

4 files changed, 67 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):