def testMultigenic(self):
class Foo(object):
a = b = c = d = e = f = 1
# Representation
g1 = KarvaGene(['a', 'b', 'c'], 1)
g2 = KarvaGene(['d', 'e', 'f'], 1)
c = Computation([g1, g2], 1)
self.assertEqual('abcdef', repr(c))
# Evaluation
self.assertEqual((1, 1), c(Foo()))
def testDerivation(self):
alleles = [subtract_op, '?', '?', 1, 0]
gene = KarvaGene(alleles, 1, [2, 5])
self.assertEqual(gene._evaluation, [subtract_op, 5, 2])
self.assertEqual(gene.alleles, alleles)
# Make sure the RNCs are evaluated correctly
o = object()
self.assertEqual(3, gene(o))
# Make sure all caching is done
self.assertTrue(o in getattr(gene, '___call___memo'))
self.assertEqual(gene._evaluation, [3, 5, 2])
# And that changes to the used RNCs eliminate that cache
gene2 = gene.derive([(4, [1])])
self.assertNotEqual(gene2._evaluation, gene._evaluation)
self.assertRaises(AttributeError, getattr, gene2, '___call___memo')
def setUp(self):
self.gene = KarvaGene([add_op, subtract_op, 'a', 1, 'a'], 2)
def setUp(self):
self.gene = KarvaGene([z,z,'y',3,4], 1)
self.chromosome = Computation([self.gene], 2)
