class Test_Model(Model):
def __init__(self, args, rng):
super(Test_Model, self).__init__(args, rng)
def get_summary(self):
print "Running test model"
def get_output(self):
print self.evolver.current_code
print "Model done"
def run(self):
trnas = 8
aarss = 8
phi = 0.9
mu = .01
msg_mu = .1
pop = 100
trna_names = ["tra_" + str(i) for i in xrange(trnas)]
codon_names = ["codon_" + str(i) for i in xrange(trnas)]
aars_names = ["aars_" + str(i) for i in xrange(aarss)]
aa_names = ["aa_" + str(i) for i in xrange(aarss)]
site_names = ["site_" + str(i) for i in xrange(aarss)]
trna_space = Bit_TRNA_Space(trna_names, codon_names)
aars_space = Id_AARS_Space(aars_names, aa_names)
trna = range(trnas)
trna[1] = 0
trna[2] = 0
code = Code(trna, range(aarss), trna_space, aars_space)
site_types = Ring_Site_Types(phi, zip(aa_names, np.arange(0, 1, .125)),
zip(site_names, np.arange(0, 1, .125)),
[1] * len(site_names))
self.evolver = Evolver(
code, site_types, mu,
get_ring_mutation_matrix(len(codon_names), msg_mu), pop, self.rng)
mut = Code_Mutator(code, mu)
trna[0] = 1
code2 = Code(trna, range(aarss), trna_space, aars_space)
#print mut.mutation_probability(code2)
#print [trna_space.mutation_probability(0, i, mu) for i in xrange(8)]
# mut = Code_Mutator(code, mu)
# code_dict = mut.get_one_gene_mutation_probabilities()
#for code in code_dict:
# print code, code_dict[code]
for i in xrange(10000):
self.evolver.step_time()
class Test_Model(Model):
def __init__(self, args, rng):
super(Test_Model, self).__init__(args, rng)
def get_summary(self):
print "Running test model"
def get_output(self):
print self.evolver.current_code
print "Model done"
def run(self):
trnas = 8
aarss = 8
phi = 0.9
mu = .01
msg_mu = .1
pop = 100
trna_names = ["tra_" + str(i) for i in xrange(trnas)]
codon_names = ["codon_" + str(i) for i in xrange(trnas)]
aars_names = ["aars_" + str(i) for i in xrange(aarss)]
aa_names = ["aa_" + str(i) for i in xrange(aarss)]
site_names = ["site_" + str(i) for i in xrange(aarss)]
trna_space = Bit_TRNA_Space(trna_names, codon_names)
aars_space = Id_AARS_Space(aars_names, aa_names)
trna = range(trnas)
trna[1] = 0
trna[2] = 0
code = Code(trna, range(aarss), trna_space, aars_space)
site_types = Ring_Site_Types(phi, zip(aa_names, np.arange(0, 1, .125)),
zip(site_names, np.arange(0, 1, .125)), [1] * len(site_names))
self.evolver = Evolver(code, site_types, mu,
get_ring_mutation_matrix(len(codon_names), msg_mu), pop, self.rng)
mut = Code_Mutator(code, mu)
trna[0] = 1
code2 = Code(trna, range(aarss), trna_space, aars_space)
#print mut.mutation_probability(code2)
#print [trna_space.mutation_probability(0, i, mu) for i in xrange(8)]
# mut = Code_Mutator(code, mu)
# code_dict = mut.get_one_gene_mutation_probabilities()
#for code in code_dict:
# print code, code_dict[code]
for i in xrange(10000):
self.evolver.step_time()
class Evolver_Test(unittest.TestCase):
def setUp(self):
self.aars_space = AARS_Space(range(2), np.diag([1] * 2),
get_uniform_mutation_matrix(2, .01))
# mu is zero so that there is only 3 potential codes available
# and so the code is forced to handle mutants with a fixation
# probability of zero.
self.trna_space = TRNA_Space(range(2), np.diag([1] * 2), np.diag([1] * 2),
get_uniform_mutation_matrix(2, .01))
pchem_vals = [.30, .70]
self.site_types = Ring_Site_Types(.05, zip(range(2), pchem_vals),
zip(range(2), pchem_vals), [1] * 2)
message_mutation_matrix = get_uniform_mutation_matrix(2, .01)
#print message_mutation_matrix.shape
#print np.matrix([[.5, .5], [.5, .5]]).shape
self.code = Code([1], [1], self.trna_space, self.aars_space)
rng = Random()
rng.seed(42)
self.evolver = Evolver(self.code, self.site_types, message_mutation_matrix,
100, rng)
def test_overall_fitness(self):
""" Check if Eq5 SellaArdell 06 is implemented correctly. """
self.assertAlmostEqual(.5 ** 2, self.evolver._overall_fitness([.5, .5]))
def test_transition_probabilities(self):
self.assertTrue(False, "Still need to implement this test.")
def test_step_time(self):
self.evolver.step_time()
self.assertEqual(self.evolver._current_code, self.code)
