def get_families_obs(n, models):
'models must be list of tuples [(mom_model, dad_model, child_model),]'
obsSet = model.ObsSet('mating obs')
for i, t in enumerate(models):
for j, var in enumerate(('mom', 'dad', 'child')):
obsSet.add_obs(t[j].rvs(n), var=var, matingID=i)
return obsSet
def get_2family_obs(modelWh, modelPu):
obsSet = model.ObsSet('mating obs')
obsSet.add_obs(modelWh.rvs(1), var='mom', matingID=0)
obsSet.add_obs(modelWh.rvs(1), var='dad', matingID=0)
obsSet.add_obs(modelWh.rvs(1), var='child', matingID=0)
obsSet.add_obs(modelPu.rvs(1), var='mom', matingID=1)
obsSet.add_obs(modelPu.rvs(1), var='dad', matingID=1)
obsSet.add_obs(modelPu.rvs(1), var='child', matingID=1)
return obsSet
def random_cross(n=100, nobs=1):
obsSet = model.ObsSet('mating obs')
c = (purple_plant, white_plant)
for i in range(n):
mom = random.choice(c)
dad = random.choice(c)
child = mom * dad
obsSet.add_obs(mom.rvs(nobs), var='mom', matingID=i)
obsSet.add_obs(dad.rvs(nobs), var='dad', matingID=i)
obsSet.add_obs(child.rvs(nobs), var='child', matingID=i)
return obsSet
def robomendel_cross_obs(n1=10, n2=10, n=1):
'construct n1 Pu x Pu crosses, and n2 Hy x Hy cross; n obs per plant'
obsSet = model.ObsSet('mating obs')
parents = [(purple_plant, purple_plant)
] * n1 #, (purple_plant, white_plant)]
parents.extend([(hybrid_plant, hybrid_plant)] * n2)
for i in range(len(parents)):
(parent_1, parent_2) = parents[i]
child = parent_1 * parent_2
obsSet.add_obs(parent_1.rvs(n), var='mom', matingID=i)
obsSet.add_obs(parent_2.rvs(n), var='dad', matingID=i)
obsSet.add_obs(child.rvs(n), var='child', matingID=i)
#i = len(parents)
#obsSet.add_obs(purple_plant.rvs(n), var='mom', matingID=i)
#obsSet.add_obs(purple_plant.rvs(n), var='dad', matingID=i)
#obsSet.add_obs(white_plant.rvs(n), var='child', matingID=i)
return obsSet
def mating_test(species, priors=None, **kwargs):
'generate 2 x 2 test of all possible mating combinations'
if not priors:
priors = (1. / len(species), ) * len(species)
scm = robomendel.SpeciesCrossModel(species, priors, **kwargs)
mstate = model.LinearState('mating', scm)
prior = model.StateGraph({'START': {mstate: 1}})
branches = model.BranchGenerator('chi', prior, iterTag='matingID')
stop = model.StopState(useObsLabel=False)
term = model.StateGraph({mstate: {stop: 1.}})
dg = model.DependencyGraph({
'START': {
branches: {}
},
'chi': {
'STOP': term
}
})
obsSet = model.ObsSet('mating obs')
obsSet.add_obs(species[0].rvs(3), matingID=0)
obsSet.add_obs((species[0].rvs(1)[0], species[1].rvs(1)[0], None),
matingID=1)
obsSet.add_obs((species[0].rvs(1)[0], species[0].rvs(1)[0], None),
matingID=2)
obsSet.add_obs(
(species[0].rvs(1)[0], species[1].rvs(1)[0], species[0].rvs(1)[0]),
matingID=3)
m = model.Model(dg, obsSet)
logPobs = m.calc_fb()
llDict = m.posterior_ll()
for matingID, t in enumerate(
((0, 0, 0), (0, 1, None), (0, 0, None), (0, 1, 0))):
obsLabel = obsSet.get_subset(matingID=matingID)
print 'mating %s:\tlogP = %1.3f, %1.3f, %1.3f' % \
tuple([str(t)] + llDict[obsLabel])
def pheno1_setup(modelWh, modelPu):
pstate = model.LinearState('Pu', modelPu)
wstate = model.LinearState('Wh', modelWh)
prior = model.StateGraph({'START': {pstate: 0.9, wstate: 0.1}})
stop = model.StopState(useObsLabel=False)
term = model.StateGraph({pstate: {stop: 1.}, wstate: {stop: 1.}})
branches = model.BranchGenerator('chi', prior, iterTag='plantID')
dg = model.DependencyGraph({
'START': {
branches: {}
},
'chi': {
'STOP': term
}
})
obsSet = model.ObsSet('plants')
for plant in range(2): # two white plants
obsSet.add_obs(modelWh.rvs(100), plantID=plant)
for plant in range(2, 20): # 18 purple plants
obsSet.add_obs(modelPu.rvs(100), plantID=plant)
m = model.Model(dg, obsSet)
return m, obsSet
def get_family_obs(mom=(0., ), dad=(1., ), child=(0.5, ), **tags):
obsSet = model.ObsSet('mating obs')
obsSet.add_obs(mom, var='mom', **tags)
obsSet.add_obs(dad, var='dad', **tags)
obsSet.add_obs(child, var='child', **tags)
return obsSet