Quickstart#

import numpy as np
import bdms

Birth, death, and mutation processes#

We define a simple binary process.

birth = bdms.poisson.DiscreteProcess([1.0, 2.0])
death = bdms.poisson.ConstantProcess(1.0)
mutation = bdms.poisson.ConstantProcess(1.0)
mutator = bdms.mutators.DiscreteMutator((0, 1), np.array([[0, 1], [1, 0]]))

Simulation#

Initialize random number generator

rng = np.random.default_rng(seed=0)

Initialize a tree with a root node in state 0

tree = bdms.Tree(state=0)

Simulate for a specified time

time_to_sampling = 5.0

tree.evolve(
    time_to_sampling,
    birth_process=birth,
    death_process=death,
    mutation_process=mutation,
    mutator=mutator,
    seed=rng,
)

Randomly sample survivor tips

tree.sample_survivors(p=0.5, seed=rng)

Visualization#

Define keyword arguments that we’ll use repeatedly for tree visualization

viz_kwargs = dict(
    color_map={0: "red", 1: "blue"},
    h=5,
    units="in",
)

Render the full simulated tree

tree.render("%%inline", **viz_kwargs)

../_images/8ba2fe63d146df9545ab10c5f9ac07bf16dcf09d370fb0c7044aaa14ec4a9a4d.png

Partially observed trees#

Prune the tree to only include the ancestry of the sampled leaves

tree.prune_unsampled()

tree.render("%%inline", **viz_kwargs)

../_images/0565498af253f24e3123c212ffaf153d332e3fdeb34eeedf7eb655aa8f498e59.png

Also remove the mutation event unifurcations

tree.remove_mutation_events()

tree.render("%%inline", **viz_kwargs)

../_images/a4423a562db79836e4509c40e2dc06957cb3915630793266292cff3c2cd5cce1.png

Inhomogeneous processes#

We subclass the abstract class bdms.poisson.InhomogeneousProcess. To concretize, we must override its abstract method bdms.poisson.InhomogeneousProcess.λ_inhomogeneous(). We’ll use this for a death rate that linearly increases over time, and is independent of state.

class RampingProcess(bdms.poisson.InhomogeneousProcess):
    def __init__(self, intercept, slope, *args, **kwargs):
        self.intercept = intercept
        self.slope = slope
        super().__init__(*args, **kwargs)

    def λ_inhomogeneous(self, x, t):
        return self.intercept + self.slope * t

death = RampingProcess(1.0, 0.06)

Initialize tree as before

tree = bdms.Tree()
tree.state = 0

Simulate, sample, and visualize

tree.evolve(
    time_to_sampling,
    birth_process=birth,
    death_process=death,
    mutation_process=mutation,
    mutator=mutator,
    seed=rng,
)
tree.sample_survivors(p=0.5, seed=rng)
tree.render("%%inline", **viz_kwargs)

../_images/f3fc6ca75ef1c8b66c9d92c1d32740727c880f3bd4d9093e97fa6e3dbe147bd9.png

Prune and visualize

tree.prune_unsampled()
tree.render("%%inline", **viz_kwargs)

../_images/f05c4741fd10fe5a9298c352d2a7e3de73e6b0081c307b6bce0d8114d830cd50.png

tree.remove_mutation_events()
tree.render("%%inline", **viz_kwargs)

../_images/415620d3d2aa8e693538682bc9f0c3413985ae2a5bf992ae3ef6c809c1cd8765.png

Quickstart

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