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):
"""return information of non-neutral locus.
Initial frequency is normalized if necessary."""
position = get_position(node)
chromosome = get_chromosome(node)
init_freq = [init for c in node.children for init in c.children
if init.id == 'initial frequency']
if len(init_freq) != 1:
# make sure there is only one 'initial frequency' per
# single loci specificiation.
raise Error
else:
# get rid of list to expose an atomic element.
init_freq = init_freq[0]
test_node = init_freq.children[0]
mode = get_scenario(node, 'initial frequency')
n_demes = len(node.root().get('population size'))
return build_loci(init_freq, chromosome, position, mode, mode, n_demes)
