def test_count_children_multiple(s):
"""As previous test, but for the case with multiple children.
This test is for the case with only one child.
"""
clust = Depression.create(*cont5())[0]
s.assertEqual(clust.n_children(), 3)
def test_bcc_100(s):
"""For bcc_frac=1.0, a single Depression object containing all
contours is created."""
depr = Depression.create(s.cont, bcc_frac=1.0)
s.assertEqual(len(depr), 1)
res = depr[0].contours()
s.assertCountEqual(res, s.cont)
def test_path(s):
"""Return the path of the contour as a list of coordinate tuples."""
outer_path = [(1, 1), (4, 1), (4, 5), (1, 5), (1, 1)]
inner_path = [(2, 2), (3, 2), (3, 3), (2, 3), (2, 2)]
cont = [ContourSimple(outer_path, 2), ContourSimple(inner_path, 1)]
depr = Depression.create(cont)[0]
s.assertCountEqual(depr.path(), outer_path)
def setUp(s):
s.cont, s.min = cont1()
s.enclosing = s.cont[-1] # sorted ascendingly by contour value
assert contours_are_sorted(s.cont)
shuffle_contours_assert(s.cont)
assert not contours_are_sorted(s.cont)
s.clust = Depression.create(s.cont, s.min)[0]
def test_depression_bcc_065(s):
"""For bcc-frac=0.65, all but 1 BCCs are accepted."""
dep = Depression.create(s.cont, [], bcc_frac=0.65)
s.assertEqual(len(dep), 1)
res = dep[0].contours()
sol = s.cont[: 4 + 7]
s.assertCountEqual(res, sol)
def test_depression_bcc_100(s):
"""For bcc-frac=1.0, all 8 BCCs are accepted."""
dep = Depression.create(s.cont, [], bcc_frac=1.0)
s.assertEqual(len(dep), 1)
res = dep[0].contours()
sol = s.cont[: 4 + 8]
s.assertCountEqual(res, sol)
def test_depression_bcc_050(s):
"""For bcc-frac=0.5, only 4 BCCs are accepted."""
dep = Depression.create(s.cont, [], bcc_frac=0.5)
s.assertEqual(len(dep), 1)
res = dep[0].contours()
sol = s.cont[: 4 + 4]
s.assertCountEqual(res, sol)
def test_depression_bcc_020(s):
"""For bcc-frac=0.2, only 1 BCC is accepted."""
dep = Depression.create(s.cont, [], bcc_frac=0.2)
s.assertEqual(len(dep), 1)
res = dep[0].contours()
sol = s.cont[: 4 + 1]
s.assertCountEqual(res, sol)
def test_assign_minima_complete_list(s):
"""Assign a list of points that contains all minima (i.e. at least
one for every innermost contour). The deepest minimum in each inner-
most contour is retained, all other minima are discarded.
"""
clust = Depression.create(s.cont, s.pt_all)[0]
s.assertSetEqual(set(clust.minima()), set(s.min_complete))
def test_separate_toplevel_clusters_different_levels(s):
"""Contours belonging to two separate top-level clusters are
clustered correctly. In this case, the enclosing contours
of the two clusters have a different value.
"""
clust = Depression.create(*cont4())
s.assertEqual(len(clust), 2)
def setUp(s):
"""The cluster contains more than 3 minima. Thus, it is split up
into 2 clusters which both contain 2 minima. They correspond
to cont3 and cont5, with shared contour ratios of 0.28 and 0.57,
respectively.
"""
s.kwargs = KWARGS
s.depr = Depression.create(*cont6())[0]
def setUp(s):
"""The maximum contour depth of the cluster is 11.
The outermost 8 contours are shared by all three subclusters.
This results in a shared contour ratio of 8/11=0.73, which is
above the threshold for TCCs.
"""
s.kwargs = KWARGS
s.depr = Depression.create(*cont4())[0]
def setUp(s):
"""The maximum contour depth of the cluster is 7.
The outermost 2 contours are shared by both subclusters.
This results in a shared contour ratio of 2/7=0.28, which is
below the threshold for DCCs.
"""
s.kwargs = KWARGS
s.depr = Depression.create(*cont3())[0]
def test_bcc_045(s):
"""For bcc_frac=0.45, the initial Depression object is split up into
two because all enclosing contours are discarded.
"""
depr = Depression.create(s.cont, bcc_frac=0.45)
s.assertEqual(len(depr), 2)
res = depr[0].contours() + depr[1].contours()
s.assertSetEqual(set(res), set(s.cont[:-5]))
def test_depression_bcc_fraction_double_center(s):
"""Check the method Depression.bcc_fraction for a cluster with
two centers (i.e. sub-clusters).
"""
# cont2: 3 CCs + (2+5) BCCs = 10 Cs (deepest sub-cluster only)
cont = cont2(cont=True)
res = Depression.create(cont, bcc_frac=1.0)[0].bcc_fraction()
s.assertAlmostEqual(res, 7.0 / 10.0)
def test_count_children_single(s):
"""Check whether the number of children is correctly returned.
This test is for the case with only one child.
"""
cont, min = cncc(3, (3.0, 3.0), 0.2, (1, 1), no_min=True)
clust = Depression.create(cont, min)[0]
s.assertEqual(clust.n_children(), 1)
def test_contours_complex(s):
"""As ~_simple, but for a complex cluster (imperfectly nested).
All contours of the cluster and all subclusters are collected.
"""
cont, min = cont3()
clust = Depression.create(cont, min)[0]
s.assertSetEqual(set(clust.contours()), set(cont))
def test_perfectly_nested_true(s):
"""Test Depression.is_perfectly_nested(), which returns True if all
contours are perfectly nested, and False if the cluster branches at
some point into separate sub-clusters.
This test is for a perfectly nested cluster.
"""
s.assertTrue(Depression.create(*cont1())[0].is_perfectly_nested())
def test_depression_bcc_fraction_single_center(s):
"""Check the method Depression.bcc_fraction for a simple cluster with
only one center (i.e. a perfectly nested one).
"""
# cont1: 4 CCs + 8 BCCs = 12 Cs
cont = cont1(cont=True)
# res = Depression.create(cont,bcc_frac=1.0)[0].bcc_fraction()
d = Depression.create(cont, bcc_frac=1.0)[0]
res = d.bcc_fraction()
s.assertAlmostEqual(res, 8.0 / 12.0)
def test_bcc_055(s):
"""For bcc_frac=0.55, a single Depression object containing all
but the 4 outermost contours is created.
"""
# For bcc_frac=0.55, 4/5 enclosing contours should be discarded,
# but the Depression should not yet be split up!
depr = Depression.create(s.cont, bcc_frac=0.55)
s.assertEqual(len(depr), 1)
res = depr[0].contours()
s.assertCountEqual(res, s.cont[:-4])
def test_bcc_020(s):
"""For bcc_frac=0.2, two Depression objects are created, but neither
contains any BCCs.
"""
depr = Depression.create(s.cont, bcc_frac=0.20)
s.assertEqual(len(depr), 2)
res = depr[0].contours() + depr[1].contours()
# This is quite messy...
sol = s.cont[:3] + s.cont[5:9]
s.assertSetEqual(set(res), set(sol))
def test_depression_bcc_fraction_double_center_equal_depth(s):
"""Check the method Depression.bcc_fraction for a cluster with
two centers, which have equal depth but different number of BCCs.
The sub-cluster with more BCCs is used for the computation.
"""
# Remove the innermost contour from the deeper cluster so both
# have equal depth, but different numbers of BCCs (0/4 and 2/4).
# The resulting total depth is 4+5=9.
cont = cont2(cont=True)[1:]
res = Depression.create(cont, bcc_frac=1.0)[0].bcc_fraction()
s.assertAlmostEqual(res, 7.0 / 9.0)
def setUp(s):
"""The maximum contour depth of the cluster is 10.
The outermost 3 contours are shared by both subclusters.
This results in a shared contour ratio of 3/10=0.30, which is
below the threshold for TCCs. The cluster is therefore split up.
The maximum depth of the subcluster which contains two minimums
is 7, which, given 4 shared contours, results in a shared contour
ratio of 4/7=0.57, which is above the threshold for DCCs.
"""
s.kwargs = KWARGS
s.depr = Depression.create(*cont5())[0]
def test_two_minima(s):
"""Two centers of different depths.
- Enclosing contour: level 6
- Minimum 1 @ (6,3): level 0 -> rel. depth 6 -> weight 0.66
- Minimum 2 @ (7,4): level 3 -> rel. depth 3 -> weight 0.33
-> Center: 2/3*(6,3) + 1/3*(7,4) = (4+2.33,2+1.33) = (6.33,3.33)
"""
clust = Depression.create(*cont3())[0]
res = clust.center()
sol = (6.33, 3.33)
assert_almost_equal(sol, res, 2)
def test_three_minima(s):
"""Three centers of different depths.
- Enclosing contour: level 8
- Minimum 1 @ (3,3): level 2 -> rel. depth 6 -> weight 0.32
- Minimum 2 @ (6,3): level 0 -> rel. depth 8 -> weight 0.42
- Minimum 3 @ (7,4): level 3 -> rel. depth 5 -> weight 0.26
-> Center: 0.32*(3,3) + 0.42*(6,5) + 0.26*(7,4) = (5.32,3.26)
"""
clust = Depression.create(*cont6())[0]
res = clust.center()
sol = (5.32, 3.26)
assert_almost_equal(sol, res, 2)
def setUp(s):
"""The cluster contains 1 subclusters, one of which contains
1 minimum, the other 2.
"""
cont, min = cont6()
s.depr = Depression.create(cont, min)[0]
def setUp(s):
"""
2 + 2|5 contours = 4|7 contours
2 + 2|5 contours + minimum = 4.0|7.0
"""
s.clust = Depression.create(*cont3())[0]
def setUp(s):
"""The cluster consists of 5 contours and one minimum, which amounts
to a total depth of 5.0.
"""
s.clust = Depression.create(*cont1())[0]
def test_innermost_contours(s):
"""Return a list containing the innermost contours."""
clust = Depression.create(s.cont, s.pt_all)[0]
foo = clust.innermost_contours()
s.assertSetEqual(set(foo), set(s.cont_inner))
def test_contours_simple(s):
"""Test the contours method for a simple cluster (perfectly nested)."""
cont, min = cont1()
clust = Depression.create(cont, min)[0]
s.assertSetEqual(set(clust.contours()), set(cont))