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Syncing sweeps implementation in algorithms.py #2247

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Syncing sweeps implementation in algorithms.py #2247

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27 changes: 19 additions & 8 deletions algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -358,7 +358,7 @@ def _reset(self):
self._times = []

def _genic_selection_stochastic_forwards(self, dt, freq, alpha):
ux = (alpha * freq * (1 - freq)) / np.tanh(alpha * freq)
ux = (alpha / 2.0 * freq * (1 - freq)) / np.tanh(alpha / 2.0 * freq)
sign = 1 if random.random() < 0.5 else -1
freq += (ux * dt) + sign * np.sqrt(freq * (1.0 - freq) * dt)
return freq
Expand Down Expand Up @@ -1022,36 +1022,47 @@ def single_sweep_simulate(self):

# main loop time
t_inc_orig = self.time_slice
t_start = self.t
e_time = 0.0
while self.ancestors_remain() and sweep_traj_step < len(times) - 1:
self.verify()
event_prob = 1.0
while event_prob > random.random() and sweep_traj_step < len(times) - 1:
sweep_traj_step += 1
x = allele_freqs[sweep_traj_step]
e_time += times[sweep_traj_step]
e_time = times[sweep_traj_step] * 2 * self.P[0].start_size
# self.t = self.t + times[sweep_traj_step]
sweep_pop_sizes = [
self.P[0].get_num_ancestors(label=0),
self.P[0].get_num_ancestors(label=1),
]
p_rec_b = self.get_total_recombination_rate(0) * t_inc_orig
p_rec_B = self.get_total_recombination_rate(1) * t_inc_orig
p_rec_b = (
self.get_total_recombination_rate(0)
* t_inc_orig
* 2
* self.P[0].start_size
)
p_rec_B = (
self.get_total_recombination_rate(1)
* t_inc_orig
* 2
* self.P[0].start_size
)

# JK NOTE: We should probably factor these pop size calculations
# into a method in Population like get_common_ancestor_waiting_time().
# That way we can handle exponentially growing populations as well?
p_coal_b = (
(sweep_pop_sizes[0] * (sweep_pop_sizes[0] - 1))
* 0.5
/ (1.0 - x)
* t_inc_orig
/ self.P[0].start_size
)
p_coal_B = (
(sweep_pop_sizes[1] * (sweep_pop_sizes[1] - 1))
* 0.5
/ x
* t_inc_orig
/ self.P[0].start_size
)
sweep_pop_tot_rate = p_rec_b + p_rec_B + p_coal_b + p_coal_B

Expand All @@ -1062,12 +1073,12 @@ def single_sweep_simulate(self):

if total_rate == 0:
break
if self.t + e_time > self.modifier_events[0][0]:
if t_start + e_time > self.modifier_events[0][0]:
t, func, args = self.modifier_events.pop(0)
self.t = t
func(*args)
else:
self.t += e_time
self.t = t_start + e_time
# choose which event happened
if random.random() < sweep_pop_tot_rate / total_rate:
# even in sweeping pop, choose which kind
Expand Down
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