Merge branch 'master' into insert

4 files changed, 108 insertions, 82 deletions

diff --git a/src/python/test/test_off.py b/src/python/test/test_off.py
new file mode 100644
index 00000000..aea1941b
--- /dev/null
+++ b/src/python/test/test_off.py
@@ -0,0 +1,21 @@
+""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
+    See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
+    Author(s):       Marc Glisse
+
+    Copyright (C) 2022 Inria
+
+    Modification(s):
+      - YYYY/MM Author: Description of the modification
+"""
+
+import gudhi as gd
+import numpy as np
+import pytest
+
+
+def test_off_rw():
+    for dim in range(2, 6):
+        X = np.random.rand(123, dim)
+        gd.write_points_to_off_file("rand.off", X)
+        Y = gd.read_points_from_off_file("rand.off")
+        assert Y == pytest.approx(X)
diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py
index 4a455bb6..58caab21 100755
--- a/src/python/test/test_representations.py
+++ b/src/python/test/test_representations.py
@@ -187,3 +187,67 @@ def test_kernel_empty_diagrams():
 #    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)
 
+
+def test_silhouette_permutation_invariance():
+    dgm = _n_diags(1)[0]
+    dgm_permuted = dgm[np.random.permutation(dgm.shape[0]).astype(int)]
+    random_resolution = random.randint(50, 100) * 10
+    slt = Silhouette(resolution=random_resolution, weight=pow(2))
+
+    assert np.all(np.isclose(slt(dgm), slt(dgm_permuted)))
+
+
+def test_silhouette_multiplication_invariance():
+    dgm = _n_diags(1)[0]
+    n_repetitions = np.random.randint(2, high=10)
+    dgm_augmented = np.repeat(dgm, repeats=n_repetitions, axis=0)
+
+    random_resolution = random.randint(50, 100) * 10
+    slt = Silhouette(resolution=random_resolution, weight=pow(2))
+    assert np.all(np.isclose(slt(dgm), slt(dgm_augmented)))
+
+
+def test_silhouette_numeric():
+    dgm = np.array([[2., 3.], [5., 6.]])
+    slt = Silhouette(resolution=9, weight=pow(1), sample_range=[2., 6.])
+    #slt.fit([dgm])
+    # x_values = array([2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6.])
+
+    expected_silhouette = np.array([0., 0.5, 0., 0., 0., 0., 0., 0.5, 0.])/np.sqrt(2)
+    output_silhouette = slt(dgm)
+    assert np.all(np.isclose(output_silhouette, expected_silhouette))
+
+
+def test_landscape_small_persistence_invariance():
+    dgm = np.array([[2., 6.], [2., 5.], [3., 7.]])
+    small_persistence_pts = np.random.rand(10, 2)
+    small_persistence_pts[:, 1] += small_persistence_pts[:, 0]
+    small_persistence_pts += np.min(dgm)
+    dgm_augmented = np.concatenate([dgm, small_persistence_pts], axis=0)
+
+    lds = Landscape(num_landscapes=2, resolution=5)
+    lds_dgm, lds_dgm_augmented = lds(dgm), lds(dgm_augmented)
+
+    assert np.all(np.isclose(lds_dgm, lds_dgm_augmented))
+
+
+def test_landscape_numeric():
+    dgm = np.array([[2., 6.], [3., 5.]])
+    lds_ref = np.array([
+        0., 0.5, 1., 1.5, 2., 1.5, 1., 0.5, 0.,  # tent of [2, 6]
+        0., 0., 0., 0.5, 1., 0.5, 0., 0., 0.,
+        0., 0., 0., 0., 0., 0., 0., 0., 0.,
+        0., 0., 0., 0., 0., 0., 0., 0., 0.,
+    ])
+    lds_ref *= np.sqrt(2)
+    lds = Landscape(num_landscapes=4, resolution=9, sample_range=[2., 6.])
+    lds_dgm = lds(dgm)
+    assert np.all(np.isclose(lds_dgm, lds_ref))
+
+
+def test_landscape_nan_range():
+    dgm = np.array([[2., 6.], [3., 5.]])
+    lds = Landscape(num_landscapes=2, resolution=9, sample_range=[np.nan, 6.])
+    lds_dgm = lds(dgm)
+    assert (lds.sample_range[0] == 2) & (lds.sample_range[1] == 6)
+    assert lds.new_resolution == 10
diff --git a/src/python/test/test_simplex_generators.py b/src/python/test/test_simplex_generators.py
index 8a9b4844..c567d4c1 100755
--- a/src/python/test/test_simplex_generators.py
+++ b/src/python/test/test_simplex_generators.py
@@ -14,7 +14,7 @@ import numpy as np
 
 def test_flag_generators():
     pts = np.array([[0, 0], [0, 1.01], [1, 0], [1.02, 1.03], [100, 0], [100, 3.01], [103, 0], [103.02, 3.03]])
-    r = gudhi.RipsComplex(pts, max_edge_length=4)
+    r = gudhi.RipsComplex(points=pts, max_edge_length=4)
     st = r.create_simplex_tree(max_dimension=50)
     st.persistence()
     g = st.flag_persistence_generators()
diff --git a/src/python/test/test_subsampling.py b/src/python/test/test_subsampling.py
index 3431f372..c1cb4e3f 100755
--- a/src/python/test/test_subsampling.py
+++ b/src/python/test/test_subsampling.py
@@ -16,17 +16,9 @@ __license__ = "MIT"
 
 
 def test_write_off_file_for_tests():
-    file = open("subsample.off", "w")
-    file.write("nOFF\n")
-    file.write("2 7 0 0\n")
-    file.write("1.0 1.0\n")
-    file.write("7.0 0.0\n")
-    file.write("4.0 6.0\n")
-    file.write("9.0 6.0\n")
-    file.write("0.0 14.0\n")
-    file.write("2.0 19.0\n")
-    file.write("9.0 17.0\n")
-    file.close()
+    gudhi.write_points_to_off_file(
+        "subsample.off", [[1.0, 1.0], [7.0, 0.0], [4.0, 6.0], [9.0, 6.0], [0.0, 14.0], [2.0, 19.0], [9.0, 17.0]]
+    )
 
 
 def test_simple_choose_n_farthest_points_with_a_starting_point():
@@ -34,54 +26,29 @@ def test_simple_choose_n_farthest_points_with_a_starting_point():
     i = 0
     for point in point_set:
         # The iteration starts with the given starting point
-        sub_set = gudhi.choose_n_farthest_points(
-            points=point_set, nb_points=1, starting_point=i
-        )
+        sub_set = gudhi.choose_n_farthest_points(points=point_set, nb_points=1, starting_point=i)
         assert sub_set[0] == point_set[i]
         i = i + 1
 
     # The iteration finds then the farthest
-    sub_set = gudhi.choose_n_farthest_points(
-        points=point_set, nb_points=2, starting_point=1
-    )
+    sub_set = gudhi.choose_n_farthest_points(points=point_set, nb_points=2, starting_point=1)
     assert sub_set[1] == point_set[3]
-    sub_set = gudhi.choose_n_farthest_points(
-        points=point_set, nb_points=2, starting_point=3
-    )
+    sub_set = gudhi.choose_n_farthest_points(points=point_set, nb_points=2, starting_point=3)
     assert sub_set[1] == point_set[1]
-    sub_set = gudhi.choose_n_farthest_points(
-        points=point_set, nb_points=2, starting_point=0
-    )
+    sub_set = gudhi.choose_n_farthest_points(points=point_set, nb_points=2, starting_point=0)
     assert sub_set[1] == point_set[2]
-    sub_set = gudhi.choose_n_farthest_points(
-        points=point_set, nb_points=2, starting_point=2
-    )
+    sub_set = gudhi.choose_n_farthest_points(points=point_set, nb_points=2, starting_point=2)
     assert sub_set[1] == point_set[0]
 
     # Test the limits
-    assert (
-        gudhi.choose_n_farthest_points(points=[], nb_points=0, starting_point=0) == []
-    )
-    assert (
-        gudhi.choose_n_farthest_points(points=[], nb_points=1, starting_point=0) == []
-    )
-    assert (
-        gudhi.choose_n_farthest_points(points=[], nb_points=0, starting_point=1) == []
-    )
-    assert (
-        gudhi.choose_n_farthest_points(points=[], nb_points=1, starting_point=1) == []
-    )
+    assert gudhi.choose_n_farthest_points(points=[], nb_points=0, starting_point=0) == []
+    assert gudhi.choose_n_farthest_points(points=[], nb_points=1, starting_point=0) == []
+    assert gudhi.choose_n_farthest_points(points=[], nb_points=0, starting_point=1) == []
+    assert gudhi.choose_n_farthest_points(points=[], nb_points=1, starting_point=1) == []
 
     # From off file test
     for i in range(0, 7):
-        assert (
-            len(
-                gudhi.choose_n_farthest_points(
-                    off_file="subsample.off", nb_points=i, starting_point=i
-                )
-            )
-            == i
-        )
+        assert len(gudhi.choose_n_farthest_points(off_file="subsample.off", nb_points=i, starting_point=i)) == i
 
 
 def test_simple_choose_n_farthest_points_randomed():
@@ -104,10 +71,7 @@ def test_simple_choose_n_farthest_points_randomed():
 
     # From off file test
     for i in range(0, 7):
-        assert (
-            len(gudhi.choose_n_farthest_points(off_file="subsample.off", nb_points=i))
-            == i
-        )
+        assert len(gudhi.choose_n_farthest_points(off_file="subsample.off", nb_points=i)) == i
 
 
 def test_simple_pick_n_random_points():
@@ -130,9 +94,7 @@ def test_simple_pick_n_random_points():
 
     # From off file test
     for i in range(0, 7):
-        assert (
-            len(gudhi.pick_n_random_points(off_file="subsample.off", nb_points=i)) == i
-        )
+        assert len(gudhi.pick_n_random_points(off_file="subsample.off", nb_points=i)) == i
 
 
 def test_simple_sparsify_points():
@@ -152,31 +114,10 @@ def test_simple_sparsify_points():
     ]
     assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.001) == [[0, 1]]
 
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=0.0))
-        == 7
-    )
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=30.0))
-        == 5
-    )
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=40.1))
-        == 4
-    )
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=89.9))
-        == 3
-    )
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=100.0))
-        == 2
-    )
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=324.9))
-        == 2
-    )
-    assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=325.01))
-        == 1
-    )
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=0.0)) == 7
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=30.0)) == 5
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=40.1)) == 4
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=89.9)) == 3
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=100.0)) == 2
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=324.9)) == 2
+    assert len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=325.01)) == 1