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Add fwdpy11.conditional_models (draft API).
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(conditional_models)= | ||
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# Module `fwdpy11.conditional_models` | ||
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```{eval-rst} | ||
.. automodule:: fwdpy11.conditional_models | ||
:members: | ||
:undoc-members: | ||
.. autofunction:: fwdpy11.conditional_models.track_mutation | ||
.. autofunction:: fwdpy11.conditional_models.selective_sweep | ||
``` | ||
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--- | ||
jupytext: | ||
formats: md:myst | ||
text_representation: | ||
extension: .md | ||
format_name: myst | ||
kernelspec: | ||
display_name: Python 3 | ||
language: python | ||
name: python3 | ||
--- | ||
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(selective_sweeps)= | ||
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# Selective sweeps | ||
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```{code-cell} python | ||
--- | ||
tags: ['hide-input'] | ||
--- | ||
import fwdpy11 | ||
import numpy as np | ||
import msprime | ||
``` | ||
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```{code-cell} python | ||
import fwdpy11.conditional_models | ||
import fwdpy11.tskit_tools | ||
``` | ||
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```{code-cell} python | ||
--- | ||
tags: ['hide-input'] | ||
--- | ||
def setup(prune_selected=False): | ||
# Dropping mutations requires existing | ||
# ancestry, which we can get either | ||
# from a burn-in or from msprime. | ||
initial_ts = msprime.sim_ancestry( | ||
samples=500, | ||
population_size=500, | ||
recombination_rate=1e-1, | ||
random_seed=43215, | ||
sequence_length=1.0, | ||
) | ||
# Build the pop from msprime output | ||
pop = fwdpy11.DiploidPopulation.create_from_tskit(initial_ts) | ||
# Set up basic model parameters | ||
pdict = { | ||
"recregions": [fwdpy11.PoissonInterval(0, 1, 1e-1)], | ||
# Here, 2 means that fitness is multiplicative | ||
# over 1, 1+hs, 1+2s. | ||
"gvalue": fwdpy11.Multiplicative(2.0), | ||
"rates": (0, 0, None), | ||
"prune_selected": False, | ||
"simlen": 200, | ||
} | ||
params = fwdpy11.ModelParams(**pdict) | ||
return pop, params | ||
``` | ||
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## From a new mutation | ||
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```{code-cell} python | ||
ALPHA = 1000.0 | ||
``` | ||
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```{code-cell} python | ||
rng = fwdpy11.GSLrng(12345) | ||
pop, params = setup() | ||
``` | ||
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```{code-cell} python | ||
mutation_data = fwdpy11.conditional_models.NewMutationParameters( | ||
frequency=fwdpy11.conditional_models.AlleleCount(1), | ||
data=fwdpy11.NewMutationData(effect_size=ALPHA / 2 / pop.N, dominance=1), | ||
position=fwdpy11.conditional_models.PositionRange(left=0.49, right=0.51), | ||
) | ||
``` | ||
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```{code-cell} python | ||
output = fwdpy11.conditional_models.selective_sweep( | ||
rng, | ||
pop, | ||
params, | ||
mutation_data, | ||
fwdpy11.conditional_models.GlobalFixation() | ||
) | ||
``` | ||
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```{code-cell} python | ||
assert output.pop.generation == params.simlen | ||
assert pop.generation == 0 | ||
``` | ||
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```{code-cell} python | ||
print(output.pop.mutations[output.index]) | ||
``` | ||
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```{code-cell} | ||
for fixation, time in zip(output.pop.fixations, output.pop.fixation_times): | ||
print(fixation, time) | ||
``` | ||
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```{code-cell} | ||
FIXATION_TIME = output.pop.fixation_times[0] | ||
``` | ||
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### Recording the generation when fixation happened | ||
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```{code-cell} python | ||
rng = fwdpy11.GSLrng(12345) | ||
``` | ||
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```{code-cell} python | ||
output = fwdpy11.conditional_models.selective_sweep( | ||
rng, | ||
pop, | ||
params, | ||
mutation_data, | ||
fwdpy11.conditional_models.GlobalFixation(), | ||
sampling_policy=fwdpy11.conditional_models.AncientSamplePolicy.COMPLETION, | ||
) | ||
``` | ||
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```{code-cell} python | ||
assert len(output.pop.ancient_sample_nodes) == 2 * output.pop.N | ||
assert output.pop.fixation_times[output.index] == FIXATION_TIME | ||
``` | ||
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```{code-cell} python | ||
node_array = np.array(output.pop.tables.nodes, copy=False) | ||
ancient_sample_node_times = \ | ||
node_array["time"][output.pop.ancient_sample_nodes] | ||
assert np.all([ancient_sample_node_times == \ | ||
output.pop.fixation_times[output.index]]) | ||
``` | ||
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## From a standing variant | ||
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The recipes for a standing variant are identical to those show above, except that one uses {class}`fwdpy11.conditional_models.AlleleCountRange` or {class}`fwdpy11.conditional_models.FrequencyRange` to specify the starting frequencies. |
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--- | ||
jupytext: | ||
formats: md:myst | ||
text_representation: | ||
extension: .md | ||
format_name: myst | ||
kernelspec: | ||
display_name: Python 3 | ||
language: python | ||
name: python3 | ||
--- | ||
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(tracking_mutation_fates)= | ||
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# Need title | ||
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```{code-cell} python | ||
--- | ||
tags: ['hide-input'] | ||
--- | ||
import fwdpy11 | ||
import numpy as np | ||
import msprime | ||
``` | ||
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```{code-cell} python | ||
import fwdpy11.conditional_models | ||
``` | ||
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```{code-cell} python | ||
--- | ||
tags: ['hide-input'] | ||
--- | ||
def setup(prune_selected=False): | ||
# Dropping mutations requires existing | ||
# ancestry, which we can get either | ||
# from a burn-in or from msprime. | ||
initial_ts = msprime.sim_ancestry( | ||
samples=500, | ||
population_size=500, | ||
recombination_rate=1e-1, | ||
random_seed=43215, | ||
sequence_length=1.0, | ||
) | ||
# Build the pop from msprime output | ||
pop = fwdpy11.DiploidPopulation.create_from_tskit(initial_ts) | ||
# Set up basic model parameters | ||
pdict = { | ||
"recregions": [fwdpy11.PoissonInterval(0, 1, 1e-1)], | ||
# Here, 2 means that fitness is multiplicative | ||
# over 1, 1+hs, 1+2s. | ||
"gvalue": fwdpy11.Multiplicative(2.0), | ||
"rates": (0, 0, None), | ||
"prune_selected": False, | ||
"simlen": 200, | ||
} | ||
params = fwdpy11.ModelParams(**pdict) | ||
return pop, params | ||
``` | ||
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## Need title | ||
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```{code-cell} python | ||
ALPHA = -10.0 | ||
``` | ||
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```{code-cell} python | ||
rng = fwdpy11.GSLrng(12345) | ||
pop, params = setup() | ||
``` | ||
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```{code-cell} python | ||
mutation_data = fwdpy11.conditional_models.NewMutationParameters( | ||
frequency=fwdpy11.conditional_models.AlleleCount(1), | ||
data=fwdpy11.NewMutationData(effect_size=ALPHA / 2 / pop.N, dominance=1), | ||
position=fwdpy11.conditional_models.PositionRange(left=0.49, right=0.51), | ||
) | ||
``` | ||
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```{code-cell} python | ||
output = fwdpy11.conditional_models.track_mutation( | ||
rng, | ||
pop, | ||
params, | ||
mutation_data, | ||
when=3, | ||
until=7, | ||
) | ||
``` | ||
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When tracking deleterious variants, it is unlikely that they will be around at the end of the simulation: | ||
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```{code-cell} python | ||
try: | ||
print(output.pop.mutations[output.index]) | ||
except IndexError as _: | ||
print(f"mutation {output.index} is no longer in the population!") | ||
``` | ||
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### Recording all generations of the mutation's sojourn | ||
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So, we've lost all the information about this variant. | ||
That's not so useful. | ||
Let's record all generations of its existence as ancient samples: | ||
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```{code-cell} python | ||
rng = fwdpy11.GSLrng(12345) | ||
``` | ||
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```{code-cell} python | ||
output = fwdpy11.conditional_models.track_mutation( | ||
rng, | ||
pop, | ||
params, | ||
mutation_data, | ||
when=3, | ||
until=7, | ||
sampling_policy=fwdpy11.conditional_models.AncientSamplePolicy.DURATION, | ||
) | ||
``` | ||
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Now, our mutation is present in nodes in our tree sequence. | ||
Let's try to print it again: | ||
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```{code-cell} python | ||
try: | ||
print(output.pop.mutations[output.index]) | ||
except IndexError as _: | ||
output.index(f"mutation {output.index} is no longer in the population!") | ||
``` | ||
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Let's track this variant's frequency at each time point: | ||
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```{code-cell} | ||
for time, nodes, _ in output.pop.sample_timepoints(include_alive=False): | ||
print(time, len(nodes)) | ||
tree_itr = fwdpy11.TreeIterator(output.pop.tables, nodes) | ||
for tree in tree_itr: | ||
for mutation in tree.mutations(): | ||
print(time, tree.leaf_counts(mutation.node), mutation.key) | ||
``` | ||
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