fixed useless coordinates in Landscape if min and max are computed from data

1 files changed, 9 insertions, 3 deletions

diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index 61c4fb84..083551a4 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -104,10 +104,11 @@ class Landscape(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
+        self.nan_in_range = np.isnan(np.array(self.sample_range))
         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 = np.where(self.nan_in_range, np.array([mx, My]), np.array(self.sample_range))
         return self
 
     def transform(self, X):
@@ -121,7 +122,7 @@ class Landscape(BaseEstimator, TransformerMixin):
             numpy array with shape (number of diagrams) x (number of samples = **num_landscapes** x **resolution**): output persistence landscapes.
         """
         num_diag, Xfit = len(X), []
-        x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution)
+        x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution + self.nan_in_range.sum())
         step_x = x_values[1] - x_values[0]
 
         for i in range(num_diag):
@@ -157,7 +158,12 @@ class Landscape(BaseEstimator, TransformerMixin):
                 for k in range( min(self.num_landscapes, len(events[j])) ):
                     ls[k,j] = events[j][k]
 
-            Xfit.append(np.sqrt(2)*np.reshape(ls,[1,-1]))
+            if self.nan_in_range[0]:
+                ls = ls[:,1:]
+            if self.nan_in_range[1]:
+                ls = ls[:,:-1]
+            ls = np.sqrt(2)*np.reshape(ls,[1,-1])
+            Xfit.append(ls)
 
         Xfit = np.concatenate(Xfit,0)