def setUp(self):
IMP.test.TestCase.setUp(self)
#IMP.base.set_log_level(IMP.MEMORY)
IMP.base.set_log_level(0)
self.m = IMP.Model()
self.n_sigmas = 20
self.lower=0.1
self.upper=100
self.sigmas = [Scale.setup_particle(IMP.Particle(self.m), 1.0+i) \
for i in xrange(self.n_sigmas)]
for s in self.sigmas:
s.set_lower(self.lower)
s.set_upper(self.upper)
self.J = [IMP.isd.JeffreysRestraint(s) for s in self.sigmas]
for j in self.J:
self.m.add_restraint(j)
self.so=MaintainScaleOrderConstraint(self.sigmas)
self.m.add_score_state(self.so)
class Tests(IMP.test.TestCase):
def setUp(self):
IMP.test.TestCase.setUp(self)
#IMP.base.set_log_level(IMP.MEMORY)
IMP.base.set_log_level(0)
self.m = IMP.Model()
self.n_sigmas = 20
self.lower=0.1
self.upper=100
self.sigmas = [Scale.setup_particle(IMP.Particle(self.m), 1.0+i) \
for i in xrange(self.n_sigmas)]
for s in self.sigmas:
s.set_lower(self.lower)
s.set_upper(self.upper)
self.J = [IMP.isd.JeffreysRestraint(s) for s in self.sigmas]
for j in self.J:
self.m.add_restraint(j)
self.so=MaintainScaleOrderConstraint(self.sigmas)
self.m.add_score_state(self.so)
def testParticles(self):
"Test MaintainScaleOrderConstraint.get_input_particles()"
self.assertEqual(self.so.get_input_particles(),self.sigmas)
def testContainers(self):
"Test MaintainScaleOrderConstraint.get_input_containers()"
self.assertEqual(self.so.get_input_containers(),[])
def testOrdering(self):
"Test if randomly shuffled scales are nicely sorted"
for rep in xrange(100):
expected = [uniform(self.lower, self.upper) \
for i in xrange(self.n_sigmas)]
for (i,j) in zip(self.sigmas, expected):
i.set_scale(j)
expected.sort()
self.m.evaluate(None)
observed = [ i.get_scale() for i in self.sigmas ]
for (i,j) in zip(expected,observed):
if j != 0:
self.assertAlmostEqual(i/j,1,delta=1e-4)
else:
self.assertEqual(i,0)
def contain(self, num):
if num < self.lower:
return self.lower
if num > self.upper:
return self.upper
return num
def testOrdering2(self):
"""Test sorting and bounds of scales"""
for rep in xrange(100):
scales = [uniform(-10,2*self.upper) for i in xrange(self.n_sigmas)]
for (i,j) in zip(self.sigmas,scales):
i.get_particle().set_value(IMP.FloatKey("nuisance"), j)
expected = [self.contain(i) for i in scales]
expected.sort()
self.m.evaluate(None)
observed = [ i.get_scale() for i in self.sigmas ]
for (i,j) in zip(expected,observed):
if j != 0:
self.assertAlmostEqual(i/j,1,delta=1e-4)
else:
self.assertEqual(i,0)
