def test_rops2(self):
"""Checking k-close pairs pair score"""
IMP.set_log_level(IMP.VERBOSE)
m = IMP.Model()
threshold = 5.0
r0 = self._random_hierarchy(m)
r1 = self._random_hierarchy(m)
ls0 = IMP.core.get_leaves(r0)
ls1 = IMP.core.get_leaves(r1)
print(ls0)
print(ls1)
cpr = IMP.core.LeavesRefiner(r0.get_traits())
lps = IMP._LogPairScore()
k = 1
cpps = IMP.core.KClosePairsPairScore(lps, cpr, k)
cpps.evaluate_index(m, (r0, r1), None)
print(str(len(lps.get_particle_pairs())) + " pairs", "in", k)
for pp in lps.get_particle_pairs():
print(pp[0].get_name(), pp[1].get_name())
apd = [(IMP.core.get_distance(IMP.core.XYZR(p0),
IMP.core.XYZR(p1)), (p0, p1))
for p0 in ls0 for p1 in ls1]
apd.sort(key=lambda a: a[0])
# print apd
for p in lps.get_particle_pairs():
print(IMP.core.get_distance(IMP.core.XYZR(p[0]), IMP.core.XYZR(p[1])),\
p[0].get_name(), p[1].get_name())
for i in range(0, k):
print("looking for", apd[i][0], apd[i][1][0].get_name(),
apd[i][1][1].get_name())
self.assertTrue(lps.get_contains(apd[i][1]))
for i in range(k, len(apd)):
print("not looking for", apd[i][0], apd[i][1][0].get_name(),
apd[i][1][1].get_name())
self.assertFalse(lps.get_contains(apd[i][1]))
def test_rops2(self):
"""Checking k-close pairs pair score"""
IMP.set_log_level(IMP.VERBOSE)
m = IMP.Model()
threshold = 5.0
r0 = self._random_hierarchy(m)
r1 = self._random_hierarchy(m)
ls0 = IMP.core.get_leaves(r0)
ls1 = IMP.core.get_leaves(r1)
print(ls0)
print(ls1)
cpr = IMP.core.LeavesRefiner(r0.get_traits())
lps = IMP._LogPairScore()
k = 1
cpps = IMP.core.KClosePairsPairScore(lps, cpr, k)
cpps.evaluate_index(m, (r0, r1), None)
print(str(len(lps.get_particle_pairs())) + " pairs", "in", k)
for pp in lps.get_particle_pairs():
print(pp[0].get_name(), pp[1].get_name())
apd = [(IMP.core.get_distance(IMP.core.XYZR(p0),
IMP.core.XYZR(p1)),
(p0, p1)) for p0 in ls0 for p1 in ls1]
apd.sort(key=lambda a: a[0])
# print apd
for p in lps.get_particle_pairs():
print(IMP.core.get_distance(IMP.core.XYZR(p[0]), IMP.core.XYZR(p[1])),\
p[0].get_name(), p[1].get_name())
for i in range(0, k):
print("looking for", apd[i][0], apd[i][1][0].get_name(), apd[i][1][1].get_name())
self.assertTrue(lps.get_contains(apd[i][1]))
for i in range(k, len(apd)):
print("not looking for", apd[i][0], apd[i][1][0].get_name(), apd[i][1][1].get_name())
self.assertFalse(lps.get_contains(apd[i][1]))
def test_rops(self):
"""Checking close pairs pair score"""
IMP.set_log_level(IMP.VERBOSE)
m = IMP.Model()
threshold = 5.0
r0 = self._random_hierarchy(m)
r1 = self._random_hierarchy(m)
ls0 = IMP.core.get_leaves(r0)
ls1 = IMP.core.get_leaves(r1)
print(ls0)
print(ls1)
cpr = IMP.core.LeavesRefiner(r0.get_traits())
lps = IMP._LogPairScore()
cpps = IMP.core.ClosePairsPairScore(lps, cpr, threshold)
cpps.evaluate_index(m, (r0, r1), None)
print(str(len(lps.get_particle_pairs())) + " pairs", "in", threshold)
for pp in lps.get_particle_pairs():
print(pp[0].get_name(), pp[1].get_name())
for l0 in ls0:
for l1 in ls1:
d0 = IMP.core.XYZR(l0.get_particle())
d1 = IMP.core.XYZR(l1.get_particle())
if (IMP.core.get_distance(d0, d1) < .95 * threshold):
print("looking for", l0.get_particle().get_name() + " " \
+ \
l1.get_particle().get_name(), IMP.core.get_distance(
d0, d1))
self.assertTrue(
lps.get_contains(
(l0.get_particle(), l1.get_particle())))
else:
self.assertFalse(
lps.get_contains(
(l0.get_particle(), l1.get_particle())))
def test_rops(self):
"""Checking close pairs pair score"""
IMP.set_log_level(IMP.VERBOSE)
m = IMP.Model()
threshold = 5.0
r0 = self._random_hierarchy(m)
r1 = self._random_hierarchy(m)
ls0 = IMP.core.get_leaves(r0)
ls1 = IMP.core.get_leaves(r1)
print(ls0)
print(ls1)
cpr = IMP.core.LeavesRefiner(r0.get_traits())
lps = IMP._LogPairScore()
cpps = IMP.core.ClosePairsPairScore(lps, cpr, threshold)
cpps.evaluate_index(m, (r0, r1), None)
print(str(len(lps.get_particle_pairs())) + " pairs", "in", threshold)
for pp in lps.get_particle_pairs():
print(pp[0].get_name(), pp[1].get_name())
for l0 in ls0:
for l1 in ls1:
d0 = IMP.core.XYZR(l0.get_particle())
d1 = IMP.core.XYZR(l1.get_particle())
if (IMP.core.get_distance(d0, d1) < .95 * threshold):
print("looking for", l0.get_particle().get_name() + " " \
+ \
l1.get_particle().get_name(), IMP.core.get_distance(
d0, d1))
self.assertTrue(lps.get_contains((l0.get_particle(),
l1.get_particle())))
else:
self.assertFalse(lps.get_contains((l0.get_particle(),
l1.get_particle())))
