API Reference
I recommend that TACT only be used via its console commands.
While TACT can be used as a Python library, its internal interface is subject to change at any time, even for minor or patch versions. This API documentation is provided merely for the sake of completeness.
Numerical functions
Functions to handle various numerical operations, including optimization.
crown_capture_probability(n, k)
Calculate the probability that a sample of k
taxa from a clade
of n
total taxa includes a root node, under a Yule process.
This equation is taken from:
Sanderson, M. J. 1996. How many taxa must be sampled to identify the root node of a large clade? Systematic Biology 45:168-173
Parameters:
Name | Type | Description | Default |
---|---|---|---|
n |
int
|
total number of taxa |
required |
k |
int
|
sampled taxa |
required |
Returns:
Name | Type | Description |
---|---|---|
float | probability |
Source code in tact/lib.py
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get_bd(r, a)
Converts turnover and relative extinction to birth and death rates.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
r |
float
|
turnover or net diversification (birth - death) |
required |
a |
float
|
relative extinction (death / birth) |
required |
Returns:
Type | Description |
---|---|
float, float
|
birth, death |
Source code in tact/lib.py
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get_new_times(ages, birth, death, missing, told=None, tyoung=None)
Simulates new speciation events in an incomplete phylogeny assuming a constant-rate birth-death process.
Adapted from the R function TreeSim::corsim
written by Tanja Stadler.
N. Cusimano, T. Stadler, S. Renner. A new method for handling missing species in diversification analysis applicable to randomly or non-randomly sampled phylogenies. Syst. Biol., 61(5): 785-792, 2012.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
ages |
list
|
vector of waiting times |
required |
birth |
float
|
birth rate |
required |
death |
float
|
death rate |
required |
missing |
int
|
number of missing taxa to simulate |
required |
told |
float
|
maximum simulated age (default: |
None
|
tyoung |
float
|
minimum simulated age bound (default: |
None
|
Returns:
Name | Type | Description |
---|---|---|
list | vector of simulated waiting times. |
Source code in tact/lib.py
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get_ra(b, d)
Converts birth and death to turnover and relative extinction rates.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
b |
float
|
birth rate |
required |
d |
float
|
extinction rate |
required |
Returns:
Type | Description |
---|---|
float, float
|
turnover, relative extinction |
Source code in tact/lib.py
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|
intp1(t, l, m)
Source code in tact/lib.py
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intp1_exact(t, l, m)
Exact version of intp1
using Decimal math.
Source code in tact/lib.py
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lik_constant(vec, rho, t, root=1, survival=1, p1=p1)
Calculates the likelihood of a constant-rate birth-death process, conditioned on the waiting times of a phylogenetic tree and degree of incomplete sampling.
Based off of the R function TreePar::LikConstant
written by Tanja Stadler.
T. Stadler. On incomplete sampling under birth-death models and connections to the sampling-based coalescent. Jour. Theo. Biol. 261: 58-66, 2009.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
vec |
float, float
|
two element tuple of birth and death |
required |
rho |
float
|
sampling fraction |
required |
t |
list
|
vector of waiting times |
required |
root |
bool
|
include the root or not? (default: 1) |
1
|
survival |
bool
|
assume survival of the process? (default: 1) |
1
|
Returns:
Name | Type | Description |
---|---|---|
float | a likelihood |
Source code in tact/lib.py
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optim_bd(ages, sampling, min_bound=1e-09)
Optimizes birth and death parameters given a vector of splitting times and sampling fraction.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
ages |
list
|
vector of node ages |
required |
sampling |
float
|
sampling fraction (0, 1] |
required |
min_bound |
float
|
minimum birth rate |
1e-09
|
Returns:
Type | Description |
---|---|
float, float: birth and death rates |
Source code in tact/lib.py
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optim_yule(ages, sampling, min_bound=1e-09)
Optimizes birth parameter under a Yule model, given a vector of splitting times and sampling fraction.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
ages |
list
|
vector of node ages |
required |
sampling |
float
|
sampling fraction (0, 1] |
required |
min_bound |
float
|
minimum birth rate |
1e-09
|
Returns:
Type | Description |
---|---|
float, float: birth and death rates (where death is always 0) |
Source code in tact/lib.py
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p0(t, l, m, rho)
Source code in tact/lib.py
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p0_exact(t, l, m, rho)
Exact version of p0
using Decimal math.
Source code in tact/lib.py
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p1(t, l, m, rho)
Optimized version of p1_orig
using common subexpression elimination and strength reduction
from exponentiation to multiplication.
Source code in tact/lib.py
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p1_exact(t, l, m, rho)
Exact version of p1
using Decimal math.
Source code in tact/lib.py
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p1_orig(t, l, m, rho)
Original version of p1
, here for testing and comparison purposes.
Source code in tact/lib.py
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two_step_optim(func, x0, bounds, args)
Conduct a two-step function optimization, first by using the fast L-BFGS-B method, and if that fails, use simulated annealing.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
func |
callable
|
function to optimize |
required |
x0 |
tuple
|
initial conditions |
required |
bounds |
tuple
|
boundary conditions |
required |
args |
lsit
|
additional argumnets to pass to |
required |
Returns:
Name | Type | Description |
---|---|---|
tuple | optimized parameter values |
Source code in tact/lib.py
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wrapped_lik_constant(x, sampling, ages)
Wrapper for birth-death likelihood to make optimizing more convenient.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x |
float, float
|
turnover, relative extinction |
required |
sampling |
float
|
sampling fraction (0, 1] |
required |
ages |
list
|
vector of node ages |
required |
Returns:
Name | Type | Description |
---|---|---|
float | a likelihood |
Source code in tact/lib.py
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wrapped_lik_constant_yule(x, sampling, ages)
Wrapper for Yule likelihood to make optimizing more convenient.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x |
float
|
birth rate |
required |
sampling |
float
|
sampling fraction (0, 1] |
required |
ages |
list
|
vector of node ages |
required |
Returns:
Name | Type | Description |
---|---|---|
float | a likelihood |
Source code in tact/lib.py
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Tree functions
Functions specifically to handle DendroPy tree objects.
compute_node_depths(tree)
Returns a dictionary of node depths for each node with a label.
Source code in tact/tree_util.py
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count_locked(node)
How many edges under node
are locked?
Source code in tact/tree_util.py
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edge_iter(node, filter_fn=None)
Iterates over the child edge of node
and all its descendants.
Can optionally be filtered by filter_fn
.
Source code in tact/tree_util.py
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get_age_intervals(node)
Gets the (possibly disjoint) interval that could be generated in the
clade under node
, assuming that grafts to locked edges are restricted.
Source code in tact/tree_util.py
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get_ages(node, include_root=False)
Returns the list of ages of the children of a given node
,
optionally including the node
's age if include_root
is True.
Source code in tact/tree_util.py
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get_birth_death_rates(node, sampfrac, yule=False, include_root=False)
Estimates the birth and death rates for the subtree descending from
node
with sampling fraction sampfrac
. Optionally restrict to a
Yule pure-birth model.
Source code in tact/tree_util.py
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get_min_age(node)
Gets the minimum possible age that could be generated in a clade under node
,
assuming that grafts to locked edges are restricted.
Source code in tact/tree_util.py
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get_monophyletic_node(tree, species)
Returns the node or None that is the MRCA of the species
in tree
.
Source code in tact/tree_util.py
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get_short_branches(node)
Yields an iterator of especially short edges under node
.
Source code in tact/tree_util.py
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get_tip_labels(tree_or_node)
Returns a set
of tip labels for a node or tree.
Source code in tact/tree_util.py
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get_tree(path, namespace=None)
Gets a DendroPy tree from a path and precalculate its node ages and bipartition bitmask.
Source code in tact/tree_util.py
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graft_node(graft_recipient, graft, stem=False)
Grafts a node graft
randomly in the subtree below node
graft_recipient
. The attribute graft.age
must be set so
we know where is the best place to graft the node. The node
graft
can optionally have child nodes, in this case the
edge.length
attribute should be set on all child nodes if
the tree is to remain ultrametric.
We graft things "below" a node by picking one of the children of that node and forcing it to be sister to the grafted node and adjusting the edge lengths accordingly. Therefore, the node above which the graft lives (i.e., the one that will be the child of the new graft) must fulfill the following requirements:
- Must not be the crown node (cannot graft things above crown node)
- Must be younger than the graft node (no negative branches)
- Seed node must be older than graft node (no negative branches)
- Must not be locked (intruding on monophyly)
Source code in tact/tree_util.py
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is_binary(node)
Is the subtree under node
a fully bifurcating tree?
Source code in tact/tree_util.py
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is_fully_locked(node)
Are all the edges below node
locked?
Source code in tact/tree_util.py
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is_ultrametric(tree, tolerance=1e-06)
Is the tree
ultrametric, within a specified tolerance
?
Uses the relative difference between minimum and maximum root-to-tip distances.
Source code in tact/tree_util.py
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lock_clade(node, stem=False)
Locks a clade descending from node
so future grafts will avoid locked edges.
Source code in tact/tree_util.py
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unlock_clade(node, stem=False)
Unlocks a clade descending from node
so new tips can be grafted to its edges.
Source code in tact/tree_util.py
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update_tree_view(tree)
Mutates a DendroPy tree object with updated node ages and bipartition bitmask. We also correct for minor ultrametricity errors.
Returns a list of tip labels.
Source code in tact/tree_util.py
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FastMRCA
Singleton object that helps speed up MRCA lookups.
bitmask(labels)
Gets a bitmask for the taxa in labels
, potentially in parallel.
Source code in tact/fastmrca.py
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fastmrca_getter(tn, x)
Helper function for submitting stuff.
Source code in tact/fastmrca.py
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get(labels)
Pulls a MRCA node out for the taxa in labels
.
Source code in tact/fastmrca.py
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initialize(phy)
Initialize the fastmrca singleton with a tree.
Source code in tact/fastmrca.py
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