def _run_msprime_coalescent_stats(self, args):
print("\t msprime:", args)
runner = cli.get_mspms_runner(args.split())
sim = runner.get_simulator()
rng = msprime.RandomGenerator(random.randint(1, 2**32 - 1))
sim.set_random_generator(rng)
num_populations = sim.get_num_populations()
replicates = runner.get_num_replicates()
num_trees = [0 for j in range(replicates)]
time = [0 for j in range(replicates)]
ca_events = [0 for j in range(replicates)]
re_events = [0 for j in range(replicates)]
mig_events = [None for j in range(replicates)]
for j in range(replicates):
sim.reset()
sim.run()
num_trees[j] = sim.get_num_breakpoints() + 1
time[j] = sim.get_time() / 4 # Convert to coalescent units
ca_events[j] = sim.get_num_common_ancestor_events()
re_events[j] = sim.get_num_recombination_events()
mig_events[j] = [
r for row in sim.get_num_migration_events() for r in row
]
d = {
"t": time,
"num_trees": num_trees,
"ca_events": ca_events,
"re_events": re_events
}
for j in range(num_populations**2):
events = [mig_events[k][j] for k in range(replicates)]
d["mig_events_{}".format(j)] = events
df = pd.DataFrame(d)
return df
def _run_msprime_coalescent_stats(self, args):
print("\t msprime:", args)
runner = cli.get_mspms_runner(args.split())
sim = runner.get_simulator()
rng = msprime.RandomGenerator(random.randint(1, 2**32 - 1))
sim.set_random_generator(rng)
num_populations = sim.get_num_populations()
replicates = runner.get_num_replicates()
num_trees = [0 for j in range(replicates)]
time = [0 for j in range(replicates)]
ca_events = [0 for j in range(replicates)]
re_events = [0 for j in range(replicates)]
mig_events = [None for j in range(replicates)]
for j in range(replicates):
sim.reset()
sim.run()
num_trees[j] = sim.get_num_breakpoints() + 1
time[j] = sim.get_time() / 4 # Convert to coalescent units
ca_events[j] = sim.get_num_common_ancestor_events()
re_events[j] = sim.get_num_recombination_events()
mig_events[j] = [
r for row in sim.get_num_migration_events() for r in row]
d = {
"t": time, "num_trees": num_trees,
"ca_events": ca_events, "re_events": re_events}
for j in range(num_populations**2):
events = [mig_events[k][j] for k in range(replicates)]
d["mig_events_{}".format(j)] = events
df = pd.DataFrame(d)
return df