def setUp(self):
"""Set up code for test fixtures."""
params = {'c': 1.0, 'e_G': 0.5, 'e_g': 0.5, 'd_A': 0.5, 'd_a': 0.5,
'PiaA': 7.0, 'PiAA': 5.0, 'Piaa': 3.0, 'PiAa': 2.0}
self.family = families.OneMaleTwoFemales(params=params,
SGA=pugh_schaffer_seabright.SGA,
Sga=pugh_schaffer_seabright.Sga)
def setUp(self):
"""Set up code for Wright-Bergstrom test case."""
# females send precise signals, but males screen almost randomly
eps = 0.5
params = {'c': 5.0, 'e': eps,
'PiaA': 9.0, 'PiAA': 5.0, 'Piaa': 3.0, 'PiAa': 2.0}
self.family = families.OneMaleTwoFemales(params=params,
SGA=wright_bergstrom.SGA,
Sga=wright_bergstrom.Sga)
e = sym.var('e')
# Female population by phenotype.
altruistic_girls = girls[0] + girls[2]
selfish_girls = girls[1] + girls[3]
# conditional phenotype matching probabilities (a la Wright/Bergstrom)
SGA = e + (1 - e) * altruistic_girls / (altruistic_girls + selfish_girls)
SGa = 1 - SGA
Sga = e + (1 - e) * selfish_girls / (altruistic_girls + selfish_girls)
SgA = 1 - Sga
# females send precise signals, but males screen almost randomly
eps = 0.5
params = {
'c': 5.0,
'e': eps,
'PiaA': 9.0,
'PiAA': 5.0,
'Piaa': 3.0,
'PiAa': 2.0
}
# define an array of initial conditions S_A = mGA = fGA
N_initial = 5
eps = 1e-3
mGA0 = np.linspace(eps, 1 - eps, N_initial)
# create an instance of the model
example = families.OneMaleTwoFemales(params=params, SGA=SGA, Sga=Sga)
# Payoff parameters (from a Prisoner's dilemma)
prisoners_dilemma_payoffs = sym.var('PiaA, PiAA, Piaa, PiAa')
PiaA, PiAA, Piaa, PiAa = prisoners_dilemma_payoffs
# Female fecundity scaling factor
fecundity_factor = sym.var('c')
# Female population by phenotype.
altruistic_girls = girls[0] + girls[2]
selfish_girls = girls[1] + girls[3]
# Conditional phenotype matching probabilities
true_altruistic_girls = d_A * altruistic_girls
false_altruistic_girls = (1 - d_a) * selfish_girls
mistaken_for_altruistic_girls = (1 - e_G) * false_altruistic_girls
altruist_adoption_pool = true_altruistic_girls + mistaken_for_altruistic_girls
true_selfish_girls = d_a * selfish_girls
false_selfish_girls = (1 - d_A) * altruistic_girls
mistaken_for_selfish_girls = (1 - e_g) * false_selfish_girls
selfish_adoption_pool = true_selfish_girls + mistaken_for_selfish_girls
SGA = true_altruistic_girls / altruist_adoption_pool
Sga = true_selfish_girls / selfish_adoption_pool
# define some parameters
hierarchy_params = {'c': 2.0, 'eG': 0.5, 'eg': 0.5, 'dA': 0.5, 'da': 0.5,
'PiaA': 9.0, 'PiAA': 5.0, 'Piaa': 3.0, 'PiAa': 2.0}
family = families.OneMaleTwoFemales(params=hierarchy_params, SGA=SGA, Sga=Sga)
import families
import simulators
import wright_bergstrom
# create an instance of the Family class
params = {
'c': 5.0,
'e': 0.5,
'PiaA': 7.0,
'PiAA': 5.0,
'Piaa': 3.0,
'PiAa': 2.0
}
family = families.OneMaleTwoFemales(params,
SGA=wright_bergstrom.SGA,
Sga=wright_bergstrom.Sga)
simulator = simulators.Simulator(family)
def test_simulate():
"""test the simulate method of the Simulator class."""
# one of either rtol or T must be specified!
simulator.initial_condition = 0.5
with nose.tools.assert_raises(ValueError):
simulator.simulate()
def test_plot_isolated_subpopulations_simulation():
"""Test return type for isolated subpopulations simulation plot."""
valid_kinds = ['genotypes', 'alpha_allele', 'gamma_allele']