def add_pruned_branchrate_model(self, distribution, name, tree_id):
xml.branchRateModel(
distribution,
id="PrunedRelaxedClockModel.c:%s" % name,
spec="beast.evolution.branchratemodel.PrunedRelaxedClockModel",
rates="@%s" % self.branchrate_model_id,
tree="@%s" % tree_id)
def add_branchrate_model(self, beast):
xml.branchRateModel(
beast,
attrib={"clock.rate": self.mean_rate_idref},
id="RandomLocalClock.c:%s" % self.name,
spec="beast.evolution.branchratemodel.RandomLocalClockModel",
indicators="@Indicators.c:%s" % self.name,
rates="@clockrates.c:%s" % self.name,
ratesAreMultipliers=self.correlated,
tree="@Tree.t:beastlingTree")
self.branchrate_model_id = "RandomLocalClock.c:%s" % self.name
def add_branchrate_model(self, beast):
self.branchrate = xml.branchRateModel(
beast,
id="RelaxedClockModel.c:%s" % self.name,
spec="beast.evolution.branchratemodel.UCRelaxedClockModel",
rateCategories="@rateCategories.c:%s" % self.name,
tree="@Tree.t:beastlingTree",
numberOfDiscreteRates=self.number_of_rates,
attrib={"clock.rate": self.mean_rate_idref})
self.branchrate_model_id = "RelaxedClockModel.c:%s" % self.name
def add_likelihood(self, likelihood):
"""
Add likelihood distribution corresponding to all features in the
dataset.
"""
attribs = {
"id": "sphericalGeographyLikelihood",
"tree": "@Tree.t:beastlingTree",
"logAverage": "true",
"scale": "false",
"location": "@location.geo"
}
# Use appropriate Likelihood implementation depending
# upon presence/absence of sampled locations
if self.sampling_points:
attribs[
"spec"] = "sphericalGeo.ApproxMultivariateTraitLikelihoodF2"
else:
attribs["spec"] = "sphericalGeo.ApproxMultivariateTraitLikelihood"
distribution = xml.distribution(likelihood, attrib=attribs)
# Add data first, as this may trigger the creation of additional
# sampling points (for langs with missing locations data)
self.add_data(distribution)
# Now add geopriors
if self.sampling_points:
multi = xml.multiGeoprior(distribution,
id="multiGeoPrior",
spec="sphericalGeo.MultiGeoPrior",
tree="@Tree.t:beastlingTree",
newick="")
for clade in self.sampling_points:
# Get languages in clade
if clade == "root":
langs = self.config.languages.languages
else:
langs = self.config.language_group(clade)
if not langs:
continue
# Add the geo prior, which will trigger sampling
geoprior = xml.geoprior(multi,
id="%s.geoPrior" % clade,
spec="sphericalGeo.GeoPrior",
location="@location.geo",
tree="@Tree.t:beastlingTree")
if len(langs) > 1:
self.beastxml.add_taxon_set(geoprior, "%s.geo" % clade,
langs)
else:
xml.taxon(geoprior, idref=list(langs)[0])
# Drop back to F, not F2, so singletons can be sampled
distribution.set(
"spec",
"sphericalGeo.ApproxMultivariateTraitLikelihoodF")
# Also add the KML file if we have an actual constraint
if clade in self.geo_priors:
xml.region(geoprior,
spec="sphericalGeo.region.KMLRegion",
kml=self.geo_priors[clade])
self.add_sitemodel(distribution)
xml.branchRateModel(distribution, idref=self.clock.branchrate_model_id)
def add_pruned_branchrate_model(self, distribution, name, tree_id):
# For StrictClocks, it's safe to just reuse the global branchRateModel
self.branchrate = xml.branchRateModel(distribution,
idref=self.branchrate_model_id)
def add_branchrate_model(self, beast):
xml.branchRateModel(
beast,
id="StrictClockModel.c:%s" % self.name,
spec="beast.evolution.branchratemodel.StrictClockModel",
attrib={"clock.rate": "@clockRate.c:%s" % self.name})