def apply(cls, node):
# when combine selection on multiple loci using MlSelector in
# simuPOP, the class only works if every (Map/Ma)Selectors
# have identical target population (sex and demes).
#
# This means that mulitple single locus selectors may be
# provided even if a mode of selection on the particular locus
# is neither sex- nor deme-specific.
#
# Do test what's the most general case upfront.
mode = choose_most_specific_scenario(node, 'non-neutral loci', 'selection coefficient')
selcoeff = node.getNodes('selection coefficient')
# make sure there is a single specification of selection
# coefficient per locus.
if len(selcoeff) != 1:
raise Error
else:
selcoeff = selcoeff[0]
true_mode = get_scenario(node, 'selection coefficient')
position = get_position(node)
# selection must be specified locus-by-locus.
if len(position) != 1:
raise Error
else:
position = position[0]
chromosome = get_chromosome(node)
n_demes = len(node.root().get('population size'))
return build_loci(selcoeff, chromosome, position, mode, true_mode, n_demes)
def apply(cls, node):
scenario = choose_most_specific_scenario(node, 'recombination', 'rate')
recs = node.getNodes('recombination')
loci = []
n_demes = len(node.root().get('population size'))
for r in recs:
chromosome = get_chromosome(r)
pos = get_list_of_values(r.descendent('at'))
n_pos = len(pos)
true_scenario = get_scenario(r, 'rate')
rate = r.getNodes('rate')
if len(rate) != 1:
raise Error
else:
rate = rate[0]
loci.extend(build_loci(rate, chromosome, pos, scenario, true_scenario, n_demes))
new_loci = []
for l in loci:
new_loci.extend([Locus(v, l.chrom, p, l.subPops)
for v, p in zip(l.val, l.loci)])
return new_loci
