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#
# Copyright (c) 2018-2025 Tskit Developers
# Copyright (c) 2015-2018 University of Oxford
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"""
Module responsible for managing trees and tree sequences.
"""
from __future__ import annotations
import base64
import builtins
import collections
import concurrent.futures
import functools
import io
import itertools
import math
import numbers
import warnings
from dataclasses import dataclass
from typing import Any
from typing import NamedTuple
import numpy as np
import _tskit
import tskit
import tskit.combinatorics as combinatorics
import tskit.drawing as drawing
import tskit.metadata as metadata_module
import tskit.tables as tables
import tskit.text_formats as text_formats
import tskit.util as util
import tskit.vcf as vcf
from tskit import NODE_IS_SAMPLE
from tskit import NULL
from tskit import UNKNOWN_TIME
LEGACY_MS_LABELS = "legacy_ms"
class CoalescenceRecord(NamedTuple):
left: float
right: float
node: int
children: np.ndarray
time: float
population: int
[docs]
class Interval(NamedTuple):
"""
A tuple of 2 numbers, ``[left, right)``, defining an interval over the genome.
"""
left: float | int
"""
The left hand end of the interval. By convention this value is included
in the interval
"""
right: float | int
"""
The right hand end of the interval. By convention this value is *not*
included in the interval, i.e., the interval is half-open.
"""
@property
def span(self) -> float | int:
"""
The span of the genome covered by this interval, simply ``right-left``.
"""
return self.right - self.left
@property
def mid(self) -> float | int:
"""
The middle point of this interval, simply ``left+(right-left)/2``.
"""
return self.left + (self.right - self.left) / 2
class EdgeDiff(NamedTuple):
interval: Interval
edges_out: list
edges_in: list
def store_tree_sequence(cls):
wrapped_init = cls.__init__
# Intercept the init to record the tree_sequence
def new_init(self, *args, tree_sequence=None, **kwargs):
builtins.object.__setattr__(self, "_tree_sequence", tree_sequence)
wrapped_init(self, *args, **kwargs)
cls.__init__ = new_init
return cls
[docs]
@store_tree_sequence
@metadata_module.lazy_decode()
@dataclass
class Individual(util.Dataclass):
"""
An :ref:`individual <sec_individual_table_definition>` in a tree sequence.
Since nodes correspond to genomes, individuals are associated with a collection
of nodes (e.g., two nodes per diploid). See :ref:`sec_nodes_or_individuals`
for more discussion of this distinction.
Modifying the attributes in this class will have **no effect** on the
underlying tree sequence data.
"""
__slots__ = [
"id",
"flags",
"location",
"parents",
"nodes",
"metadata",
"_tree_sequence",
]
id: int # noqa A003
"""
The integer ID of this individual. Varies from 0 to
:attr:`TreeSequence.num_individuals` - 1."""
flags: int
"""
The bitwise flags for this individual.
"""
location: np.ndarray
"""
The spatial location of this individual as a numpy array. The location is an empty
array if no spatial location is defined.
"""
parents: np.ndarray
"""
The parent individual ids of this individual as a numpy array. The parents is an
empty array if no parents are defined.
"""
nodes: np.ndarray
"""
The IDs of the nodes that are associated with this individual as
a numpy array (dtype=np.int32). If no nodes are associated with the
individual this array will be empty.
"""
metadata: bytes | dict | None
"""
The :ref:`metadata <sec_metadata_definition>`
for this individual, decoded if a schema applies.
"""
@property
def population(self) -> int:
populations = {self._tree_sequence.node(n).population for n in self.nodes}
if len(populations) > 1:
raise ValueError("Individual has nodes with mis-matched populations")
if len(populations) == 0:
return tskit.NULL
return populations.pop()
@property
def time(self) -> int:
times = {self._tree_sequence.node(n).time for n in self.nodes}
if len(times) > 1:
raise ValueError("Individual has nodes with mis-matched times")
if len(times) == 0:
return tskit.UNKNOWN_TIME
return times.pop()
# Custom eq for the numpy arrays
def __eq__(self, other):
return (
self.id == other.id
and self.flags == other.flags
and np.array_equal(self.location, other.location)
and np.array_equal(self.parents, other.parents)
and np.array_equal(self.nodes, other.nodes)
and self.metadata == other.metadata
)
[docs]
@metadata_module.lazy_decode()
@dataclass
class Node(util.Dataclass):
"""
A :ref:`node <sec_node_table_definition>` in a tree sequence, corresponding
to a single genome. The ``time`` and ``population`` are attributes of the
``Node``, rather than the ``Individual``, as discussed in
:ref:`sec_nodes_or_individuals`.
Modifying the attributes in this class will have **no effect** on the
underlying tree sequence data.
"""
__slots__ = ["id", "flags", "time", "population", "individual", "metadata"]
id: int # noqa A003
"""
The integer ID of this node. Varies from 0 to :attr:`TreeSequence.num_nodes` - 1.
"""
flags: int
"""
The bitwise flags for this node.
"""
time: float
"""
The birth time of this node.
"""
population: int
"""
The integer ID of the population that this node was born in.
"""
individual: int
"""
The integer ID of the individual that this node was a part of.
"""
metadata: bytes | dict | None
"""
The :ref:`metadata <sec_metadata_definition>` for this node, decoded if a schema
applies.
"""
[docs]
def is_sample(self):
"""
Returns True if this node is a sample. This value is derived from the
``flag`` variable.
:rtype: bool
"""
return self.flags & NODE_IS_SAMPLE
[docs]
@metadata_module.lazy_decode(own_init=True)
@dataclass
class Edge(util.Dataclass):
"""
An :ref:`edge <sec_edge_table_definition>` in a tree sequence.
Modifying the attributes in this class will have **no effect** on the
underlying tree sequence data.
"""
__slots__ = ["left", "right", "parent", "child", "metadata", "id"]
left: float
"""
The left coordinate of this edge.
"""
right: float
"""
The right coordinate of this edge.
"""
parent: int
"""
The integer ID of the parent node for this edge.
To obtain further information about a node with a given ID, use
:meth:`TreeSequence.node`.
"""
child: int
"""
The integer ID of the child node for this edge.
To obtain further information about a node with a given ID, use
:meth:`TreeSequence.node`.
"""
metadata: bytes | dict | None
"""
The :ref:`metadata <sec_metadata_definition>` for this edge, decoded if a schema
applies.
"""
id: int # noqa A003
"""
The integer ID of this edge. Varies from 0 to
:attr:`TreeSequence.num_edges` - 1.
"""
# Custom init to define default values with slots
def __init__(
self,
left,
right,
parent,
child,
metadata=b"",
id=None, # noqa A002
metadata_decoder=None,
):
self.id = id
self.left = left
self.right = right
self.parent = parent
self.child = child
self.metadata = metadata
self._metadata_decoder = metadata_decoder
@property
def span(self):
"""
Returns the span of this edge, i.e., the right position minus the left position
:return: The span of this edge.
:rtype: float
"""
return self.right - self.left
@property
def interval(self):
"""
Returns the left and right positions of this edge as an :class:`Interval`
:return: The interval covered by this edge.
:rtype: :class:`Interval`
"""
return Interval(self.left, self.right)
[docs]
@metadata_module.lazy_decode()
@dataclass
class Site(util.Dataclass):
"""
A :ref:`site <sec_site_table_definition>` in a tree sequence.
Modifying the attributes in this class will have **no effect** on the
underlying tree sequence data.
"""
__slots__ = ["id", "position", "ancestral_state", "mutations", "metadata"]
id: int # noqa A003
"""
The integer ID of this site. Varies from 0 to :attr:`TreeSequence.num_sites` - 1.
"""
position: float
"""
The floating point location of this site in genome coordinates.
Ranges from 0 (inclusive) to :attr:`TreeSequence.sequence_length` (exclusive).
"""
ancestral_state: str
"""
The ancestral state at this site (i.e., the state inherited by nodes, unless
mutations occur).
"""
mutations: np.ndarray
"""
The list of mutations at this site. Mutations within a site are returned in the
order they are specified in the underlying :class:`MutationTable`.
"""
metadata: bytes | dict | None
"""
The :ref:`metadata <sec_metadata_definition>` for this site, decoded if a schema
applies.
"""
# We need a custom eq for the numpy arrays
def __eq__(self, other):
return (
isinstance(other, Site)
and self.id == other.id
and self.position == other.position
and self.ancestral_state == other.ancestral_state
and np.array_equal(self.mutations, other.mutations)
and self.metadata == other.metadata
)
@property
def alleles(self) -> set[str]:
"""
Return the set of all the alleles defined at this site
.. note::
This deliberately returns an (unordered) *set* of the possible allelic
states (as defined by the site's ancestral allele and its associated
mutations). If you wish to obtain an (ordered) *list* of alleles, for
example to translate the numeric genotypes at a site into allelic states,
you should instead use ``.alleles`` attribute of the :class:`Variant` class,
which unlike this attribute includes ``None`` as a state when there is
missing data at a site.
"""
return {self.ancestral_state} | {m.derived_state for m in self.mutations}
[docs]
@metadata_module.lazy_decode()
@dataclass
class Mutation(util.Dataclass):
"""
A :ref:`mutation <sec_mutation_table_definition>` in a tree sequence.
Modifying the attributes in this class will have **no effect** on the
underlying tree sequence data.
"""
__slots__ = [
"id",
"site",
"node",
"derived_state",
"parent",
"metadata",
"time",
"edge",
]
id: int # noqa A003
"""
The integer ID of this mutation. Varies from 0 to
:attr:`TreeSequence.num_mutations` - 1.
Modifying the attributes in this class will have **no effect** on the
underlying tree sequence data.
"""
site: int
"""
The integer ID of the site that this mutation occurs at. To obtain
further information about a site with a given ID use :meth:`TreeSequence.site`.
"""
node: int
"""
The integer ID of the first node that inherits this mutation.
To obtain further information about a node with a given ID, use
:meth:`TreeSequence.node`.
"""
derived_state: str
"""
The derived state for this mutation. This is the state
inherited by nodes in the subtree rooted at this mutation's node, unless
another mutation occurs.
"""
parent: int
"""
The integer ID of this mutation's parent mutation. When multiple
mutations occur at a site along a path in the tree, mutations must
record the mutation that is immediately above them. If the mutation does
not have a parent, this is equal to the :data:`NULL` (-1).
To obtain further information about a mutation with a given ID, use
:meth:`TreeSequence.mutation`.
"""
metadata: bytes | dict | None
"""
The :ref:`metadata <sec_metadata_definition>` for this mutation, decoded if a schema
applies.
"""
time: float
"""
The occurrence time of this mutation.
"""
edge: int
"""
The ID of the edge that this mutation is on.
"""
# To get default values on slots we define a custom init
def __init__(
self,
id=NULL, # noqa A003
site=NULL,
node=NULL,
time=UNKNOWN_TIME,
derived_state=None,
parent=NULL,
metadata=b"",
edge=NULL,
):
self.id = id
self.site = site
self.node = node
self.time = time
self.derived_state = derived_state
self.parent = parent
self.metadata = metadata
self.edge = edge
# We need a custom eq to compare unknown times.
def __eq__(self, other):
return (
isinstance(other, Mutation)
and self.id == other.id
and self.site == other.site
and self.node == other.node
and self.derived_state == other.derived_state
and self.parent == other.parent
and self.edge == other.edge
and self.metadata == other.metadata
and (
self.time == other.time
or (
util.is_unknown_time(self.time) and util.is_unknown_time(other.time)
)
)
)
[docs]
@metadata_module.lazy_decode()
@dataclass
class Migration(util.Dataclass):
"""
A :ref:`migration <sec_migration_table_definition>` in a tree sequence.
Modifying the attributes in this class will have **no effect** on the
underlying tree sequence data.
"""
__slots__ = ["left", "right", "node", "source", "dest", "time", "metadata", "id"]
left: float
"""
The left end of the genomic interval covered by this
migration (inclusive).
"""
right: float
"""
The right end of the genomic interval covered by this migration
(exclusive).
"""
node: int
"""
The integer ID of the node involved in this migration event.
To obtain further information about a node with a given ID, use
:meth:`TreeSequence.node`.
"""
source: int
"""
The source population ID.
"""
dest: int
"""
The destination population ID.
"""
time: float
"""
The time at which this migration occurred at.
"""
metadata: bytes | dict | None
"""
The :ref:`metadata <sec_metadata_definition>` for this migration, decoded if a schema
applies.
"""
id: int # noqa A003
"""
The integer ID of this mutation. Varies from 0 to
:attr:`TreeSequence.num_mutations` - 1.
"""
[docs]
@metadata_module.lazy_decode()
@dataclass
class Population(util.Dataclass):
"""
A :ref:`population <sec_population_table_definition>` in a tree sequence.
Modifying the attributes in this class will have **no effect** on the
underlying tree sequence data.
"""
__slots__ = ["id", "metadata"]
id: int # noqa A003
"""
The integer ID of this population. Varies from 0 to
:attr:`TreeSequence.num_populations` - 1.
"""
metadata: bytes | dict | None
"""
The :ref:`metadata <sec_metadata_definition>` for this population, decoded if a
schema applies.
"""
@dataclass
class Edgeset(util.Dataclass):
__slots__ = ["left", "right", "parent", "children"]
left: int
right: int
parent: int
children: np.ndarray
# We need a custom eq for the numpy array
def __eq__(self, other):
return (
isinstance(other, Edgeset)
and self.left == other.left
and self.right == other.right
and self.parent == other.parent
and np.array_equal(self.children, other.children)
)
[docs]
@dataclass
class Provenance(util.Dataclass):
"""
A provenance entry in a tree sequence, detailing how this tree
sequence was generated, or subsequent operations on it (see :ref:`sec_provenance`).
"""
__slots__ = ["id", "timestamp", "record"]
id: int # noqa A003
timestamp: str
"""
The time that this entry was made
"""
record: str
"""
A JSON string giving details of the provenance (see :ref:`sec_provenance_example`
for an example JSON string)
"""
[docs]
class Tree:
"""
A single tree in a :class:`TreeSequence`. Please see the
:ref:`tutorials:sec_processing_trees` section for information
on how efficiently access trees sequentially or obtain a list
of individual trees in a tree sequence.
The ``sample_lists`` parameter controls the features that are enabled
for this tree. If ``sample_lists`` is True a more efficient algorithm is
used in the :meth:`Tree.samples` method.
The ``tracked_samples`` parameter can be used to efficiently count the
number of samples in a given set that exist in a particular subtree
using the :meth:`Tree.num_tracked_samples` method.
The :class:`Tree` class is a state-machine which has a state
corresponding to each of the trees in the parent tree sequence. We
transition between these states by using the seek functions like
:meth:`Tree.first`, :meth:`Tree.last`, :meth:`Tree.seek` and
:meth:`Tree.seek_index`. There is one more state, the so-called "null"
or "cleared" state. This is the state that a :class:`Tree` is in
immediately after initialisation; it has an index of -1, and no edges. We
can also enter the null state by calling :meth:`Tree.next` on the last
tree in a sequence, calling :meth:`Tree.prev` on the first tree in a
sequence or calling calling the :meth:`Tree.clear` method at any time.
The high-level TreeSequence seeking and iterations methods (e.g,
:meth:`TreeSequence.trees`) are built on these low-level state-machine
seek operations. We recommend these higher level operations for most
users.
:param TreeSequence tree_sequence: The parent tree sequence.
:param list tracked_samples: The list of samples to be tracked and
counted using the :meth:`Tree.num_tracked_samples` method.
:param bool sample_lists: If True, provide more efficient access
to the samples beneath a given node using the
:meth:`Tree.samples` method.
:param int root_threshold: The minimum number of samples that a node
must be ancestral to for it to be in the list of roots. By default
this is 1, so that isolated samples (representing missing data)
are roots. To efficiently restrict the roots of the tree to
those subtending meaningful topology, set this to 2. This value
is only relevant when trees have multiple roots.
:param bool sample_counts: Deprecated since 0.2.4.
"""
def __init__(
self,
tree_sequence,
tracked_samples=None,
*,
sample_lists=False,
root_threshold=1,
sample_counts=None,
):
options = 0
if sample_counts is not None:
warnings.warn(
"The sample_counts option is not supported since 0.2.4 "
"and is ignored",
RuntimeWarning,
stacklevel=4,
)
if sample_lists:
options |= _tskit.SAMPLE_LISTS
kwargs = {"options": options}
if root_threshold <= 0:
raise ValueError("Root threshold must be greater than 0")
if tracked_samples is not None:
# TODO remove this when we allow numpy arrays in the low-level API.
kwargs["tracked_samples"] = list(tracked_samples)
self._tree_sequence = tree_sequence
self._ll_tree = _tskit.Tree(tree_sequence.ll_tree_sequence, **kwargs)
self._ll_tree.set_root_threshold(root_threshold)
self._make_arrays()
[docs]
def copy(self):
"""
Returns a deep copy of this tree. The returned tree will have identical state
to this tree.
:return: A copy of this tree.
:rtype: Tree
"""
copy = type(self).__new__(type(self))
copy._tree_sequence = self._tree_sequence
copy._ll_tree = self._ll_tree.copy()
copy._make_arrays()
return copy
# TODO make this method public and document it.
# Note that this probably does not cover all corner cases correctly
# https://github.com/tskit-dev/tskit/issues/1908
def _has_isolated_samples(self):
# TODO Is this definition correct for a single-node tree sequence?
for root in self.roots:
# If the root has no children then it must be a sample
if self.left_child(root) == NULL:
return True
return False
def _make_arrays(self):
# Store the low-level arrays for efficiency. There's no real overhead
# in this, because the refer to the same underlying memory as the
# tree object.
self._parent_array = self._ll_tree.parent_array
self._left_child_array = self._ll_tree.left_child_array
self._right_child_array = self._ll_tree.right_child_array
self._left_sib_array = self._ll_tree.left_sib_array
self._right_sib_array = self._ll_tree.right_sib_array
self._num_children_array = self._ll_tree.num_children_array
self._edge_array = self._ll_tree.edge_array
@property
def tree_sequence(self):
"""
Returns the tree sequence that this tree is from.
:return: The parent tree sequence for this tree.
:rtype: :class:`TreeSequence`
"""
return self._tree_sequence
@property
def root_threshold(self):
"""
Returns the minimum number of samples that a node must be an ancestor
of to be considered a potential root. This can be set, for example, when
calling the :meth:`TreeSequence.trees` iterator.
:return: The root threshold.
:rtype: :class:`TreeSequence`
"""
return self._ll_tree.get_root_threshold()
def __eq__(self, other):
ret = False
if type(other) is type(self):
ret = bool(self._ll_tree.equals(other._ll_tree))
return ret
def __ne__(self, other):
return not self.__eq__(other)
[docs]
def first(self):
"""
Seeks to the first tree in the sequence. This can be called whether
the tree is in the null state or not.
"""
self._ll_tree.first()
[docs]
def last(self):
"""
Seeks to the last tree in the sequence. This can be called whether
the tree is in the null state or not.
"""
self._ll_tree.last()
[docs]
def next(self): # noqa A002
"""
Seeks to the next tree in the sequence. If the tree is in the initial
null state we seek to the first tree (equivalent to calling :meth:`~Tree.first`).
Calling ``next`` on the last tree in the sequence results in the tree
being cleared back into the null initial state (equivalent to calling
:meth:`~Tree.clear`). The return value of the function indicates whether the
tree is in a non-null state, and can be used to loop over the trees::
# Iterate over the trees from left-to-right
tree = tskit.Tree(tree_sequence)
while tree.next()
# Do something with the tree.
print(tree.index)
# tree is now back in the null state.
:return: True if the tree has been transformed into one of the trees
in the sequence; False if the tree has been transformed into the
null state.
:rtype: bool
"""
return bool(self._ll_tree.next())
[docs]
def prev(self):
"""
Seeks to the previous tree in the sequence. If the tree is in the initial
null state we seek to the last tree (equivalent to calling :meth:`~Tree.last`).
Calling ``prev`` on the first tree in the sequence results in the tree
being cleared back into the null initial state (equivalent to calling
:meth:`~Tree.clear`). The return value of the function indicates whether the
tree is in a non-null state, and can be used to loop over the trees::
# Iterate over the trees from right-to-left
tree = tskit.Tree(tree_sequence)
while tree.prev()
# Do something with the tree.
print(tree.index)
# tree is now back in the null state.
:return: True if the tree has been transformed into one of the trees
in the sequence; False if the tree has been transformed into the
null state.
:rtype: bool
"""
return bool(self._ll_tree.prev())
[docs]
def clear(self):
"""
Resets this tree back to the initial null state. Calling this method
on a tree already in the null state has no effect.
"""
self._ll_tree.clear()
[docs]
def seek_index(self, index):
"""
Sets the state to represent the tree at the specified
index in the parent tree sequence. Negative indexes following the
standard Python conventions are allowed, i.e., ``index=-1`` will
seek to the last tree in the sequence.
.. include:: substitutions/linear_traversal_warning.rst
:param int index: The tree index to seek to.
:raises IndexError: If an index outside the acceptable range is provided.
"""
num_trees = self.tree_sequence.num_trees
if index < 0:
index += num_trees
if index < 0 or index >= num_trees:
raise IndexError("Index out of bounds")
self._ll_tree.seek_index(index)
[docs]
def seek(self, position):
"""
Sets the state to represent the tree that covers the specified
position in the parent tree sequence. After a successful return
of this method we have ``tree.interval.left`` <= ``position``
< ``tree.interval.right``.
.. include:: substitutions/linear_traversal_warning.rst
:param float position: The position along the sequence length to
seek to.
:raises ValueError: If 0 < position or position >=
:attr:`TreeSequence.sequence_length`.
"""
if position < 0 or position >= self.tree_sequence.sequence_length:
raise ValueError("Position out of bounds")
self._ll_tree.seek(position)
[docs]
def rank(self) -> tskit.Rank:
"""
Produce the rank of this tree in the enumeration of all leaf-labelled
trees of n leaves. See the :ref:`sec_tree_ranks` section for
details on ranking and unranking trees.
:raises ValueError: If the tree has multiple roots.
"""
return combinatorics.RankTree.from_tsk_tree(self).rank()
[docs]
@staticmethod
def unrank(num_leaves, rank, *, span=1, branch_length=1) -> Tree:
"""
Reconstruct the tree of the given ``rank``
(see :meth:`tskit.Tree.rank`) with ``num_leaves`` leaves.
The labels and times of internal nodes are assigned by a postorder
traversal of the nodes, such that the time of each internal node
is the maximum time of its children plus the specified ``branch_length``.
The time of each leaf is 0.
See the :ref:`sec_tree_ranks` section for details on ranking and
unranking trees and what constitutes valid ranks.
:param int num_leaves: The number of leaves of the tree to generate.
:param tuple(int) rank: The rank of the tree to generate.
:param float span: The genomic span of the returned tree. The tree will cover
the interval :math:`[0, \\text{span})` and the :attr:`~Tree.tree_sequence`
from which the tree is taken will have its
:attr:`~tskit.TreeSequence.sequence_length` equal to ``span``.
:param: float branch_length: The minimum length of a branch in this tree.
:raises ValueError: If the given rank is out of bounds for trees
with ``num_leaves`` leaves.
"""
rank_tree = combinatorics.RankTree.unrank(num_leaves, rank)
return rank_tree.to_tsk_tree(span=span, branch_length=branch_length)
[docs]
def count_topologies(self, sample_sets=None) -> tskit.TopologyCounter:
"""
Calculates the distribution of embedded topologies for every combination
of the sample sets in ``sample_sets``. ``sample_sets`` defaults to all
samples in the tree grouped by population.
``sample_sets`` need not include all samples but must be pairwise disjoint.
The returned object is a :class:`tskit.TopologyCounter` that contains
counts of topologies per combination of sample sets. For example,
>>> topology_counter = tree.count_topologies()
>>> rank, count = topology_counter[0, 1, 2].most_common(1)[0]
produces the most common tree topology, with populations 0, 1
and 2 as its tips, according to the genealogies of those
populations' samples in this tree.
The counts for each topology in the :class:`tskit.TopologyCounter`
are absolute counts that we would get if we were to select all
combinations of samples from the relevant sample sets.
For sample sets :math:`[s_0, s_1, ..., s_n]`, the total number of
topologies for those sample sets is equal to
:math:`|s_0| * |s_1| * ... * |s_n|`, so the counts in the counter
``topology_counter[0, 1, ..., n]`` should sum to
:math:`|s_0| * |s_1| * ... * |s_n|`.
To convert the topology counts to probabilities, divide by the total
possible number of sample combinations from the sample sets in question::
>>> set_sizes = [len(sample_set) for sample_set in sample_sets]
>>> p = count / (set_sizes[0] * set_sizes[1] * set_sizes[2])
.. warning:: The interface for this method is preliminary and may be subject to
backwards incompatible changes in the near future.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with.
Defaults to all samples grouped by population.
:raises ValueError: If nodes in ``sample_sets`` are invalid or are
internal samples.
"""
if sample_sets is None:
sample_sets = [
self.tree_sequence.samples(population=pop.id)
for pop in self.tree_sequence.populations()
]
return combinatorics.tree_count_topologies(self, sample_sets)
def get_branch_length(self, u):
# Deprecated alias for branch_length
return self.branch_length(u)
[docs]
def branch_length(self, u):
"""
Returns the length of the branch (in units of time) joining the
specified node to its parent. This is equivalent to
>>> tree.time(tree.parent(u)) - tree.time(u)
The branch length for a node that has no parent (e.g., a root) is
defined as zero.
Note that this is not related to the property `.length` which
is a deprecated alias for the genomic :attr:`~Tree.span` covered by a tree.
:param int u: The node of interest.
:return: The branch length from u to its parent.
:rtype: float
"""
ret = 0
parent = self.parent(u)
if parent != NULL:
ret = self.time(parent) - self.time(u)
return ret
def get_total_branch_length(self):
# Deprecated alias for total_branch_length
return self.total_branch_length
@property
def total_branch_length(self):
"""
Returns the sum of all the branch lengths in this tree (in
units of time). This is equivalent to
>>> sum(tree.branch_length(u) for u in tree.nodes())
Note that the branch lengths for root nodes are defined as zero.
As this is defined by a traversal of the tree, technically we
return the sum of all branch lengths that are reachable from
roots. Thus, this is the total length of all branches that are connected
to at least one sample. This distinction is only important
in tree sequences that contain 'dead branches', i.e., those
that define topology that is not connected to a tree root
(see :ref:`sec_data_model_tree_dead_leaves_and_branches`)
:return: The sum of lengths of branches in this tree.
:rtype: float
"""
return self._ll_tree.get_total_branch_length()
def get_mrca(self, u, v):
# Deprecated alias for mrca
return self.mrca(u, v)
[docs]
def mrca(self, *args):
"""
Returns the most recent common ancestor of the specified nodes.
:param int `*args`: input node IDs, at least 2 arguments are required.
:return: The node ID of the most recent common ancestor of the
input nodes, or :data:`tskit.NULL` if the nodes do not share
a common ancestor in the tree.
:rtype: int
"""
if len(args) < 2:
raise ValueError("Must supply at least two arguments")
mrca = args[0]
for node in args[1:]:
mrca = self._ll_tree.get_mrca(mrca, node)
if mrca == tskit.NULL:
break
return mrca
def get_tmrca(self, u, v):
# Deprecated alias for tmrca
return self.tmrca(u, v)
[docs]
def tmrca(self, *args):
"""
Returns the time of the most recent common ancestor of the specified
nodes. This is equivalent to::
>>> tree.time(tree.mrca(*args))
.. note::
If you are using this method to calculate average tmrca values along the
genome between pairs of sample nodes, for efficiency reasons you should
instead consider the ``mode="branch"`` option of the
:meth:`TreeSequence.divergence` or :meth:`TreeSequence.diversity` methods.
Since these calculate the average branch length between pairs of sample
nodes, for samples at time 0 the resulting statistics will be exactly
twice the tmrca value.
:param `*args`: input node IDs, at least 2 arguments are required.
:return: The time of the most recent common ancestor of all the nodes.
:rtype: float
:raises ValueError: If the nodes do not share a single common ancestor in this
tree (i.e., if ``tree.mrca(*args) == tskit.NULL``)
"""
mrca = self.mrca(*args)
if mrca == tskit.NULL:
raise ValueError(f"Nodes {args} do not share a common ancestor in the tree")
return self.get_time(mrca)
def get_parent(self, u):
# Deprecated alias for parent
return self.parent(u)
[docs]
def parent(self, u):
"""
Returns the parent of the specified node. Returns
:data:`tskit.NULL` if u is a root or is not a node in
the current tree.
:param int u: The node of interest.
:return: The parent of u.
:rtype: int
"""
return self._ll_tree.get_parent(u)
@property
def parent_array(self):
"""
A numpy array (dtype=np.int32) encoding the parent of each node
in this tree, such that ``tree.parent_array[u] == tree.parent(u)``
for all ``0 <= u <= ts.num_nodes``. See the :meth:`~.parent`
method for details on the semantics of tree parents and the
:ref:`sec_data_model_tree_structure` section for information on the
quintuply linked tree encoding.
.. include:: substitutions/virtual_root_array_note.rst
.. include:: substitutions/tree_array_warning.rst
"""
return self._parent_array
[docs]
def ancestors(self, u):
"""
Returns an iterator over the ancestors of node ``u`` in this tree
(i.e. the chain of parents from ``u`` to the root).
"""
u = self.parent(u)
while u != -1:
yield u
u = self.parent(u)
# Quintuply linked tree structure.
[docs]
def left_child(self, u):
"""
Returns the leftmost child of the specified node. Returns
:data:`tskit.NULL` if u is a leaf or is not a node in
the current tree. The left-to-right ordering of children
is arbitrary and should not be depended on; see the
:ref:`data model <sec_data_model_tree_structure>` section
for details.
This is a low-level method giving access to the quintuply linked
tree structure in memory; the :meth:`.children` method is a more
convenient way to obtain the children of a given node.
:param int u: The node of interest.
:return: The leftmost child of u.
:rtype: int
"""
return self._ll_tree.get_left_child(u)
@property
def left_child_array(self):
"""
A numpy array (dtype=np.int32) encoding the left child of each node
in this tree, such that ``tree.left_child_array[u] == tree.left_child(u)``
for all ``0 <= u <= ts.num_nodes``. See the :meth:`~.left_child`
method for details on the semantics of tree left_child and the
:ref:`sec_data_model_tree_structure` section for information on the
quintuply linked tree encoding.
.. include:: substitutions/virtual_root_array_note.rst
.. include:: substitutions/tree_array_warning.rst
"""
return self._left_child_array
[docs]
def right_child(self, u):
"""
Returns the rightmost child of the specified node. Returns
:data:`tskit.NULL` if u is a leaf or is not a node in
the current tree. The left-to-right ordering of children
is arbitrary and should not be depended on; see the
:ref:`data model <sec_data_model_tree_structure>` section
for details.
This is a low-level method giving access to the quintuply linked
tree structure in memory; the :meth:`.children` method is a more
convenient way to obtain the children of a given node.
:param int u: The node of interest.
:return: The rightmost child of u.
:rtype: int
"""
return self._ll_tree.get_right_child(u)
@property
def right_child_array(self):
"""
A numpy array (dtype=np.int32) encoding the right child of each node
in this tree, such that ``tree.right_child_array[u] == tree.right_child(u)``
for all ``0 <= u <= ts.num_nodes``. See the :meth:`~.right_child`
method for details on the semantics of tree right_child and the
:ref:`sec_data_model_tree_structure` section for information on the
quintuply linked tree encoding.
.. include:: substitutions/virtual_root_array_note.rst
.. include:: substitutions/tree_array_warning.rst
"""
return self._right_child_array
[docs]
def left_sib(self, u):
"""
Returns the sibling node to the left of u, or :data:`tskit.NULL`
if u does not have a left sibling.
The left-to-right ordering of children
is arbitrary and should not be depended on; see the
:ref:`data model <sec_data_model_tree_structure>` section
for details.
:param int u: The node of interest.
:return: The sibling node to the left of u.
:rtype: int
"""
return self._ll_tree.get_left_sib(u)
@property
def left_sib_array(self):
"""
A numpy array (dtype=np.int32) encoding the left sib of each node
in this tree, such that ``tree.left_sib_array[u] == tree.left_sib(u)``
for all ``0 <= u <= ts.num_nodes``. See the :meth:`~.left_sib`
method for details on the semantics of tree left_sib and the
:ref:`sec_data_model_tree_structure` section for information on the
quintuply linked tree encoding.
.. include:: substitutions/virtual_root_array_note.rst
.. include:: substitutions/tree_array_warning.rst
"""
return self._left_sib_array
[docs]
def right_sib(self, u):
"""
Returns the sibling node to the right of u, or :data:`tskit.NULL`
if u does not have a right sibling.
The left-to-right ordering of children
is arbitrary and should not be depended on; see the
:ref:`data model <sec_data_model_tree_structure>` section
for details.
:param int u: The node of interest.
:return: The sibling node to the right of u.
:rtype: int
"""
return self._ll_tree.get_right_sib(u)
@property
def right_sib_array(self):
"""
A numpy array (dtype=np.int32) encoding the right sib of each node
in this tree, such that ``tree.right_sib_array[u] == tree.right_sib(u)``
for all ``0 <= u <= ts.num_nodes``. See the :meth:`~.right_sib`
method for details on the semantics of tree right_sib and the
:ref:`sec_data_model_tree_structure` section for information on the
quintuply linked tree encoding.
.. include:: substitutions/virtual_root_array_note.rst
.. include:: substitutions/tree_array_warning.rst
"""
return self._right_sib_array
[docs]
def siblings(self, u):
"""
Returns the sibling(s) of the specified node ``u`` as a tuple of integer
node IDs. If ``u`` has no siblings or is not a node in the current tree,
returns an empty tuple. If ``u`` is the root of a single-root tree,
returns an empty tuple; if ``u`` is the root of a multi-root tree,
returns the other roots (note all the roots are related by the virtual root).
If ``u`` is the virtual root (which has no siblings), returns an empty tuple.
If ``u`` is an isolated node, whether it has siblings or not depends on
whether it is a sample or non-sample node; if it is a sample node,
returns the root(s) of the tree, otherwise, returns an empty tuple.
The ordering of siblings is arbitrary and should not be depended on;
see the :ref:`data model <sec_data_model_tree_structure>` section for details.
:param int u: The node of interest.
:return: The siblings of ``u``.
:rtype: tuple(int)
"""
if u == self.virtual_root:
return tuple()
parent = self.parent(u)
if self.is_root(u):
parent = self.virtual_root
if parent != tskit.NULL:
return tuple(v for v in self.children(parent) if u != v)
return tuple()
@property
def num_children_array(self):
"""
A numpy array (dtype=np.int32) encoding the number of children of
each node in this tree, such that
``tree.num_children_array[u] == tree.num_children(u)`` for all
``0 <= u <= ts.num_nodes``. See the :meth:`~.num_children`
method for details on the semantics of tree num_children and the
:ref:`sec_data_model_tree_structure` section for information on the
quintuply linked tree encoding.
.. include:: substitutions/virtual_root_array_note.rst
.. include:: substitutions/tree_array_warning.rst
"""
return self._num_children_array
[docs]
def edge(self, u):
"""
Returns the id of the edge encoding the relationship between ``u``
and its parent, or :data:`tskit.NULL` if ``u`` is a root, virtual root
or is not a node in the current tree.
:param int u: The node of interest.
:return: Id of edge connecting u to its parent.
:rtype: int
"""
return self._ll_tree.get_edge(u)
@property
def edge_array(self):
"""
A numpy array (dtype=np.int32) of edge ids encoding the relationship
between the child node ``u`` and its parent, such that
``tree.edge_array[u] == tree.edge(u)`` for all
``0 <= u <= ts.num_nodes``. See the :meth:`~.edge`
method for details on the semantics of tree edge and the
:ref:`sec_data_model_tree_structure` section for information on the
quintuply linked tree encoding.
.. include:: substitutions/virtual_root_array_note.rst
.. include:: substitutions/tree_array_warning.rst
"""
return self._edge_array
# Sample list.
def left_sample(self, u):
return self._ll_tree.get_left_sample(u)
def right_sample(self, u):
return self._ll_tree.get_right_sample(u)
def next_sample(self, u):
return self._ll_tree.get_next_sample(u)
@property
def virtual_root(self):
"""
The ID of the virtual root in this tree. This is equal to
:attr:`TreeSequence.num_nodes`.
Please see the :ref:`tree roots <sec_data_model_tree_roots>`
section for more details.
"""
return self._ll_tree.get_virtual_root()
@property
def num_edges(self):
"""
The total number of edges in this tree. This is equal to the
number of tree sequence edges that intersect with this tree's
genomic interval.
Note that this may be greater than the number of branches that
are reachable from the tree's roots, since we can have topology
that is not associated with any samples.
"""
return self._ll_tree.get_num_edges()
@property
def left_root(self):
"""
The leftmost root in this tree. If there are multiple roots
in this tree, they are siblings of this node, and so we can
use :meth:`.right_sib` to iterate over all roots:
.. code-block:: python
u = tree.left_root
while u != tskit.NULL:
print("Root:", u)
u = tree.right_sib(u)
The left-to-right ordering of roots is arbitrary and should
not be depended on; see the
:ref:`data model <sec_data_model_tree_structure>`
section for details.
This is a low-level method giving access to the quintuply linked
tree structure in memory; the :attr:`~Tree.roots` attribute is a more
convenient way to obtain the roots of a tree. If you are assuming
that there is a single root in the tree you should use the
:attr:`~Tree.root` property.
.. warning:: Do not use this property if you are assuming that there
is a single root in trees that are being processed. The
:attr:`~Tree.root` property should be used in this case, as it will
raise an error when multiple roots exists.
:rtype: int
"""
return self.left_child(self.virtual_root)
@property
def right_root(self):
return self.right_child(self.virtual_root)
def get_children(self, u):
# Deprecated alias for self.children
return self.children(u)
[docs]
def children(self, u):
"""
Returns the children of the specified node ``u`` as a tuple of integer node IDs.
If ``u`` is a leaf, return the empty tuple. The ordering of children
is arbitrary and should not be depended on; see the
:ref:`data model <sec_data_model_tree_structure>` section
for details.
:param int u: The node of interest.
:return: The children of ``u`` as a tuple of integers
:rtype: tuple(int)
"""
return self._ll_tree.get_children(u)
def get_time(self, u):
# Deprecated alias for self.time
return self.time(u)
[docs]
def time(self, u):
"""
Returns the time of the specified node. This is equivalently
to ``tree.tree_sequence.node(u).time`` except for the special
case of the tree's :ref:`virtual root <sec_data_model_tree_roots>`,
which is defined as positive infinity.
:param int u: The node of interest.
:return: The time of u.
:rtype: float
"""
return self._ll_tree.get_time(u)
[docs]
def depth(self, u):
"""
Returns the number of nodes on the path from ``u`` to a
root, not including ``u``. Thus, the depth of a root is
zero.
As a special case, the depth of the :ref:`virtual root
<sec_data_model_tree_roots>` is defined as -1.
:param int u: The node of interest.
:return: The depth of u.
:rtype: int
"""
return self._ll_tree.depth(u)
def get_population(self, u):
# Deprecated alias for self.population
return self.population(u)
[docs]
def population(self, u):
"""
Returns the population associated with the specified node.
Equivalent to ``tree.tree_sequence.node(u).population``.
:param int u: The node of interest.
:return: The ID of the population associated with node u.
:rtype: int
"""
return self._ll_tree.get_population(u)
[docs]
def is_internal(self, u):
"""
Returns True if the specified node is not a leaf. A node is internal
if it has one or more children in the current tree.
:param int u: The node of interest.
:return: True if u is not a leaf node.
:rtype: bool
"""
return not self.is_leaf(u)
[docs]
def is_leaf(self, u):
"""
Returns True if the specified node is a leaf. A node :math:`u` is a
leaf if it has zero children.
.. note::
:math:`u` can be any node in the entire tree sequence, including ones
which are not connected via branches to a root node of the tree (and which
are therefore not conventionally considered part of the tree). Indeed, if
there are many trees in the tree sequence, it is common for the majority of
non-sample nodes to be :meth:`isolated<is_isolated>` in any one
tree. By the definition above, this method will return ``True`` for such
a tree when a node of this sort is specified. Such nodes can be thought of
as "dead leaves", see :ref:`sec_data_model_tree_dead_leaves_and_branches`.
:param int u: The node of interest.
:return: True if u is a leaf node.
:rtype: bool
"""
return len(self.children(u)) == 0
[docs]
def is_isolated(self, u):
"""
Returns True if the specified node is isolated in this tree: that is
it has no parents and no children (note that all isolated nodes in the tree
are therefore also :meth:`leaves<Tree.is_leaf>`). Sample nodes that are isolated
and have no mutations above them are used to represent
:ref:`missing data<sec_data_model_missing_data>`.
:param int u: The node of interest.
:return: True if u is an isolated node.
:rtype: bool
"""
return self.num_children(u) == 0 and self.parent(u) == NULL
[docs]
def is_sample(self, u):
"""
Returns True if the specified node is a sample. A node :math:`u` is a
sample if it has been marked as a sample in the parent tree sequence.
:param int u: The node of interest.
:return: True if u is a sample.
:rtype: bool
"""
return bool(self._ll_tree.is_sample(u))
[docs]
def is_descendant(self, u, v):
"""
Returns True if the specified node u is a descendant of node v and False
otherwise. A node :math:`u` is a descendant of another node :math:`v` if
:math:`v` is on the path from :math:`u` to root. A node is considered
to be a descendant of itself, so ``tree.is_descendant(u, u)`` will be
True for any valid node.
:param int u: The descendant node.
:param int v: The ancestral node.
:return: True if u is a descendant of v.
:rtype: bool
:raises ValueError: If u or v are not valid node IDs.
"""
return bool(self._ll_tree.is_descendant(u, v))
@property
def num_nodes(self):
"""
Returns the number of nodes in the :class:`TreeSequence` this tree is in.
Equivalent to ``tree.tree_sequence.num_nodes``.
.. deprecated:: 0.4
Use :attr:`Tree.tree_sequence.num_nodes<TreeSequence.num_nodes>` if you want
the number of nodes in the entire tree sequence, or
``len(tree.preorder())`` to find the number of nodes that are
reachable from all roots in this tree.
:rtype: int
"""
warnings.warn(
"This property is a deprecated alias for Tree.tree_sequence.num_nodes "
"and will be removed in the future. To obtain the number of nodes "
"in the topology of the current tree (i.e. reachable from the roots) "
"use len(tree.preorder()).",
FutureWarning,
stacklevel=4,
)
return self.tree_sequence.num_nodes
@property
def num_roots(self):
"""
The number of roots in this tree, as defined in the :attr:`~Tree.roots`
attribute.
Only requires O(number of roots) time.
:rtype: int
"""
return self._ll_tree.get_num_roots()
@property
def has_single_root(self):
"""
``True`` if this tree has a single root, ``False`` otherwise.
Equivalent to tree.num_roots == 1. This is a O(1) operation.
:rtype: bool
"""
root = self.left_root
if root != NULL and self.right_sib(root) == NULL:
return True
return False
@property
def has_multiple_roots(self):
"""
``True`` if this tree has more than one root, ``False`` otherwise.
Equivalent to tree.num_roots > 1. This is a O(1) operation.
:rtype: bool
"""
root = self.left_root
if root != NULL and self.right_sib(root) != NULL:
return True
return False
@property
def roots(self):
"""
The list of roots in this tree. A root is defined as a unique endpoint of the
paths starting at samples, subject to the condition that it is connected to at
least :attr:`root_threshold` samples. We can define the set of roots as follows:
.. code-block:: python
roots = set()
for u in tree_sequence.samples():
while tree.parent(u) != tskit.NULL:
u = tree.parent(u)
if tree.num_samples(u) >= tree.root_threshold:
roots.add(u)
# roots is now the set of all roots in this tree.
assert sorted(roots) == sorted(tree.roots)
The roots of the tree are returned in a list, in no particular order.
Only requires O(number of roots) time.
.. note::
In trees with large amounts of :ref:`sec_data_model_missing_data`,
for example where a region of the genome lacks any ancestral information,
there can be a very large number of roots, potentially all the samples
in the tree sequence.
:return: The list of roots in this tree.
:rtype: list
"""
roots = []
u = self.left_root
while u != NULL:
roots.append(u)
u = self.right_sib(u)
return roots
def get_root(self):
# Deprecated alias for self.root
return self.root
@property
def root(self):
"""
The root of this tree. If the tree contains multiple roots, a ValueError is
raised indicating that the :attr:`~Tree.roots` attribute should be used instead.
:return: The root node.
:rtype: int
:raises ValueError: if this tree contains more than one root.
"""
if self.has_multiple_roots:
raise ValueError("More than one root exists. Use tree.roots instead")
return self.left_root
[docs]
def is_root(self, u) -> bool:
"""
Returns ``True`` if the specified node is a root in this tree (see
:attr:`~Tree.roots` for the definition of a root). This is exactly equivalent to
finding the node ID in :attr:`~Tree.roots`, but is more efficient for trees
with large numbers of roots, such as in regions with extensive
:ref:`sec_data_model_missing_data`. Note that ``False`` is returned for all
other nodes, including :ref:`isolated<sec_data_model_tree_isolated_nodes>`
non-sample nodes which are not found in the topology of the current tree.
:param int u: The node of interest.
:return: ``True`` if u is a root.
"""
return (
self.num_samples(u) >= self.root_threshold and self.parent(u) == tskit.NULL
)
def get_index(self):
# Deprecated alias for self.index
return self.index
@property
def index(self):
"""
Returns the index this tree occupies in the parent tree sequence.
This index is zero based, so the first tree in the sequence has index 0.
:return: The index of this tree.
:rtype: int
"""
return self._ll_tree.get_index()
def get_interval(self):
# Deprecated alias for self.interval
return self.interval
@property
def interval(self):
"""
Returns the coordinates of the genomic interval that this tree
represents the history of. The interval is returned as a tuple
:math:`(l, r)` and is a half-open interval such that the left
coordinate is inclusive and the right coordinate is exclusive. This
tree therefore applies to all genomic locations :math:`x` such that
:math:`l \\leq x < r`.
:return: A named tuple (l, r) representing the left-most (inclusive)
and right-most (exclusive) coordinates of the genomic region
covered by this tree. The coordinates can be accessed by index
(``0`` or ``1``) or equivalently by name (``.left`` or ``.right``)
:rtype: Interval
"""
return Interval(self._ll_tree.get_left(), self._ll_tree.get_right())
def get_length(self):
# Deprecated alias for self.span
return self.length
@property
def length(self):
# Deprecated alias for self.span
return self.span
@property
def span(self):
"""
Returns the genomic distance that this tree spans.
This is defined as :math:`r - l`, where :math:`(l, r)` is the genomic
interval returned by :attr:`~Tree.interval`.
:return: The genomic distance covered by this tree.
:rtype: float
"""
return self.interval.span
@property
def mid(self):
"""
Returns the midpoint of the genomic interval that this tree represents
the history of. This is defined as :math:`(l + (r - l) / 2)`, where
:math:`(l, r)` is the genomic interval returned by
:attr:`~Tree.interval`.
:return: The genomic distance covered by this tree.
:rtype: float
"""
return self.interval.mid
def get_sample_size(self):
# Deprecated alias for self.sample_size
return self.sample_size
@property
def sample_size(self):
# Deliberately undocumented but kept for backwards compatibility.
# The proper way to access this is via tree.tree_sequence.num_samples
return self._ll_tree.get_sample_size()
[docs]
def draw_text(
self,
orientation=None,
*,
node_labels=None,
max_time=None,
use_ascii=False,
order=None,
):
"""
Create a text representation of a tree.
:param str orientation: one of ``"top"``, ``"left"``, ``"bottom"``, or
``"right"``, specifying the margin on which the root is placed. Specifying
``"left"`` or ``"right"`` will lead to time being shown on the x axis (i.e.
a "horizontal" tree. If ``None`` (default) use the standard coalescent
arrangement of a vertical tree with recent nodes at the bottom of the plot
and older nodes above.
:param dict node_labels: If specified, show custom labels for the nodes
that are present in the map. Any nodes not specified in the map will
not have a node label.
:param str max_time: If equal to ``"tree"`` (the default), the maximum time
is set to be that of the oldest root in the tree. If equal to ``"ts"`` the
maximum time is set to be the time of the oldest root in the tree
sequence; this is useful when drawing trees from the same tree sequence as it
ensures that node heights are consistent.
:param bool use_ascii: If ``False`` (default) then use unicode
`box drawing characters \
<https://en.wikipedia.org/wiki/Box-drawing_character>`_
to render the tree. If ``True``, use plain ascii characters, which look
cruder but are less susceptible to misalignment or font substitution.
Alternatively, if you are having alignment problems with Unicode, you can try
out the solution documented `here \
<https://github.com/tskit-dev/tskit/issues/189#issuecomment-499114811>`_.
:param str order: The left-to-right ordering of child nodes in the drawn tree.
This can be either: ``"minlex"``, which minimises the differences
between adjacent trees (see also the ``"minlex_postorder"`` traversal
order for the :meth:`.nodes` method); or ``"tree"`` which draws trees
in the left-to-right order defined by the
:ref:`quintuply linked tree structure <sec_data_model_tree_structure>`.
If not specified or None, this defaults to ``"minlex"``.
:return: A text representation of a tree.
:rtype: str
"""
orientation = drawing.check_orientation(orientation)
if orientation in (drawing.LEFT, drawing.RIGHT):
text_tree = drawing.HorizontalTextTree(
self,
orientation=orientation,
node_labels=node_labels,
max_time=max_time,
use_ascii=use_ascii,
order=order,
)
else:
text_tree = drawing.VerticalTextTree(
self,
orientation=orientation,
node_labels=node_labels,
max_time=max_time,
use_ascii=use_ascii,
order=order,
)
return str(text_tree)
[docs]
def draw_svg(
self,
path=None,
*,
size=None,
time_scale=None,
tree_height_scale=None,
title=None,
max_time=None,
min_time=None,
max_tree_height=None,
node_labels=None,
mutation_labels=None,
node_titles=None,
mutation_titles=None,
root_svg_attributes=None,
style=None,
order=None,
force_root_branch=None,
symbol_size=None,
x_axis=None,
x_label=None,
x_regions=None,
y_axis=None,
y_label=None,
y_ticks=None,
y_gridlines=None,
all_edge_mutations=None,
omit_sites=None,
canvas_size=None,
**kwargs,
):
"""
Return an SVG representation of a single tree. By default, numeric
labels are drawn beside nodes and mutations: these can be altered using the
``node_labels`` and ``mutation_labels`` parameters. See the
:ref:`visualization tutorial<tutorials:sec_tskit_viz>` for more details.
:param str path: The path to the file to write the output. If None, do not
write to file.
:param tuple(int, int) size: A tuple of (width, height) specifying a target
drawing size in abstract user units (usually interpreted as pixels on
initial display). Components of the drawing will be scaled so that the total
plot including labels etc. normally fits onto a canvas of this size (see
``canvas_size`` below). If ``None``, pick a size appropriate for a tree
with a reasonably small number (i.e. tens) of samples. Default: ``None``
:type size:
:param str time_scale: Control how height values for nodes are computed.
If this is equal to ``"time"`` (the default), node heights are proportional
to their time values. If this is equal to ``"log_time"``, node heights are
proportional to their log(time) values. If it is equal to ``"rank"``, node
heights are spaced equally according to their ranked times.
:param str tree_height_scale: Deprecated alias for time_scale. (Deprecated in
0.3.6)
:param str title: A title string to be included in the SVG output. If ``None``
(default) no title is shown, which gives more vertical space for the tree.
:param str,float max_time: The maximum plotted time value in the current
scaling system (see ``time_scale``). Can be either a string or a
numeric value. If equal to ``"tree"`` (the default), the maximum time
is set to be that of the oldest root in the tree. If equal to ``"ts"`` the
maximum time is set to be the time of the oldest root in the tree
sequence; this is useful when drawing trees from the same tree sequence as it
ensures that node heights are consistent. If a numeric value, this is used as
the maximum plotted time by which to scale other nodes.
:param str,float min_time: The minimum plotted time value in the current
scaling system (see ``time_scale``). Can be either a string or a
numeric value. If equal to ``"tree"`` (the default), the minimum time
is set to be that of the youngest node in the tree. If equal to ``"ts"`` the
minimum time is set to be the time of the youngest node in the tree
sequence; this is useful when drawing trees from the same tree sequence as it
ensures that node heights are consistent. If a numeric value, this is used as
the minimum plotted time.
:param str,float max_tree_height: Deprecated alias for max_time. (Deprecated in
0.3.6)
:param node_labels: If specified, show custom labels for the nodes
(specified by ID) that are present in this map; any nodes not present will
not have a label.
:type node_labels: dict(int, str)
:param mutation_labels: If specified, show custom labels for the
mutations (specified by ID) that are present in the map; any mutations
not present will not have a label.
:type mutation_labels: dict(int, str)
:param dict(int, str) node_titles: If specified, add a ``<title>`` string to
symbols for each node (specified by ID) present in this map. SVG visualizers
such as web browsers will commonly display this string on mousing over the
node symbol.
:param dict(int, str) mutation_titles: If specified, add a ``<title>`` string to
symbols for each mutation (specified by ID) present in this map. SVG
visualizers such as web browsers will commonly display this string on
mousing over the mutation symbol in the tree and (if show) on the x axis.
:param dict root_svg_attributes: Additional attributes, such as an id, that will
be embedded in the root ``<svg>`` tag of the generated drawing.
:param str style: A
`css style string <https://www.w3.org/TR/CSS22/syndata.html>`_ that will be
included in the ``<style>`` tag of the generated svg.
:param str order: The left-to-right ordering of child nodes in the drawn tree.
This can be either: ``"minlex"``, which minimises the differences
between adjacent trees (see also the ``"minlex_postorder"`` traversal
order for the :meth:`.nodes` method); or ``"tree"`` which draws trees
in the left-to-right order defined by the
:ref:`quintuply linked tree structure <sec_data_model_tree_structure>`.
If not specified or None, this defaults to ``"minlex"``.
:param bool force_root_branch: If ``True`` always plot a branch (edge) above the
root(s) in the tree. If ``None`` (default) then only plot such root branches
if there is a mutation above a root of the tree.
:param float symbol_size: Change the default size of the node and mutation
plotting symbols. If ``None`` (default) use a standard size.
:param bool x_axis: Should the plot have an X axis line, showing the start and
end position of this tree along the genome. If ``None`` (default) do not
plot an X axis.
:param str x_label: Place a label under the plot. If ``None`` (default) and
there is an X axis, create and place an appropriate label.
:param dict x_regions: A dictionary mapping (left, right) tuples to names. This
draws a box, labelled with the name, on the X axis between the left and
right positions, and can be used for annotating genomic regions (e.g.
genes) on the X axis. If ``None`` (default) do not plot any regions.
:param bool y_axis: Should the plot have an Y axis line, showing time (or
ranked node time if ``time_scale="rank"``). If ``None`` (default)
do not plot a Y axis.
:param str y_label: Place a label to the left of the plot. If ``None`` (default)
and there is a Y axis, create and place an appropriate label.
:param Union[list, dict] y_ticks: A list of Y values at which to plot
tickmarks, or a dictionary mapping Y values to labels (``[]`` gives no
tickmarks). If ``None`` (default), plot one tickmark for each unique node
value. Note that if ``time_scale="rank"``, the Y values refer to the
zero-based rank of the plotted nodes, rather than the node time itself.
:param bool y_gridlines: Whether to plot horizontal lines behind the tree
at each y tickmark.
:param bool all_edge_mutations: The edge on which a mutation occurs may span
multiple trees. If ``False`` or ``None`` (default) mutations are only drawn
on an edge if their site position exists within the genomic interval covered
by this tree. If ``True``, all mutations on each edge of the tree are drawn,
even if their genomic position is to the left or right of the tree
itself. Note that this means that independent drawings of different trees
from the same tree sequence may share some plotted mutations.
:param bool omit_sites: If True, omit sites and mutations from the drawing.
Default: False
:param tuple(int, int) canvas_size: The (width, height) of the SVG canvas.
This will change the SVG width and height without rescaling graphical
elements, allowing extra room e.g. for unusually long labels. If ``None``
take the canvas size to be the same as the target drawing size (see
``size``, above). Default: None
:return: An SVG representation of a tree.
:rtype: SVGString
"""
draw = drawing.SvgTree(
self,
size,
time_scale=time_scale,
tree_height_scale=tree_height_scale,
title=title,
max_time=max_time,
min_time=min_time,
max_tree_height=max_tree_height,
node_labels=node_labels,
mutation_labels=mutation_labels,
node_titles=node_titles,
mutation_titles=mutation_titles,
root_svg_attributes=root_svg_attributes,
style=style,
order=order, # NB undocumented: Tree.draw_svg can also take an iterable here
force_root_branch=force_root_branch,
symbol_size=symbol_size,
x_axis=x_axis,
x_label=x_label,
x_regions=x_regions,
y_axis=y_axis,
y_label=y_label,
y_ticks=y_ticks,
y_gridlines=y_gridlines,
all_edge_mutations=all_edge_mutations,
omit_sites=omit_sites,
canvas_size=canvas_size,
**kwargs,
)
output = draw.drawing.tostring()
if path is not None:
# TODO: removed the pretty here when this is stable.
draw.drawing.saveas(path, pretty=True)
return drawing.SVGString(output)
[docs]
def draw(
self,
path=None,
width=None,
height=None,
node_labels=None,
node_colours=None,
mutation_labels=None,
mutation_colours=None,
format=None, # noqa A002
edge_colours=None,
time_scale=None,
tree_height_scale=None,
max_time=None,
min_time=None,
max_tree_height=None,
order=None,
omit_sites=None,
):
"""
Returns a drawing of this tree.
When working in a Jupyter notebook, use the ``IPython.display.SVG``
function to display the SVG output from this function inline in the notebook::
>>> SVG(tree.draw())
The unicode format uses unicode `box drawing characters
<https://en.wikipedia.org/wiki/Box-drawing_character>`_ to render the tree.
This allows rendered trees to be printed out to the terminal::
>>> print(tree.draw(format="unicode"))
6
┏━┻━┓
┃ 5
┃ ┏━┻┓
┃ ┃ 4
┃ ┃ ┏┻┓
3 0 1 2
The ``node_labels`` argument allows the user to specify custom labels
for nodes, or no labels at all::
>>> print(tree.draw(format="unicode", node_labels={}))
┃
┏━┻━┓
┃ ┃
┃ ┏━┻┓
┃ ┃ ┃
┃ ┃ ┏┻┓
┃ ┃ ┃ ┃
Note: in some environments such as Jupyter notebooks with Windows or Mac,
users have observed that the Unicode box drawings can be misaligned. In
these cases, we recommend using the SVG or ASCII display formats instead.
If you have a strong preference for aligned Unicode, you can try out the
solution documented
`here <https://github.com/tskit-dev/tskit/issues/189#issuecomment-499114811>`_.
:param str path: The path to the file to write the output. If None, do not
write to file.
:param int width: The width of the image in pixels. If not specified, either
defaults to the minimum size required to depict the tree (text formats)
or 200 pixels.
:param int height: The height of the image in pixels. If not specified, either
defaults to the minimum size required to depict the tree (text formats)
or 200 pixels.
:param dict node_labels: If specified, show custom labels for the nodes
that are present in the map. Any nodes not specified in the map will
not have a node label.
:param dict node_colours: If specified, show custom colours for the nodes
given in the map. Any nodes not specified in the map will take the default
colour; a value of ``None`` is treated as transparent and hence the node
symbol is not plotted. (Only supported in the SVG format.)
:param dict mutation_labels: If specified, show custom labels for the mutations
(specified by ID) that are present in the map. Any mutations not in the map
will not have a label. (Showing mutations is currently only supported in the
SVG format)
:param dict mutation_colours: If specified, show custom colours for the mutations
given in the map (specified by ID). As for ``node_colours``, mutations not
present in the map take the default colour, and those mapping to ``None``
are not drawn. (Only supported in the SVG format.)
:param str format: The format of the returned image. Currently supported
are 'svg', 'ascii' and 'unicode'. Note that the :meth:`Tree.draw_svg`
method provides more comprehensive functionality for creating SVGs.
:param dict edge_colours: If specified, show custom colours for the edge
joining each node in the map to its parent. As for ``node_colours``,
unspecified edges take the default colour, and ``None`` values result in the
edge being omitted. (Only supported in the SVG format.)
:param str time_scale: Control how height values for nodes are computed.
If this is equal to ``"time"``, node heights are proportional to their time
values. If this is equal to ``"log_time"``, node heights are proportional to
their log(time) values. If it is equal to ``"rank"``, node heights are spaced
equally according to their ranked times. For SVG output the default is
'time'-scale whereas for text output the default is 'rank'-scale.
Time scaling is not currently supported for text output.
:param str tree_height_scale: Deprecated alias for time_scale. (Deprecated in
0.3.6)
:param str,float max_time: The maximum time value in the current
scaling system (see ``time_scale``). Can be either a string or a
numeric value. If equal to ``"tree"``, the maximum time is set to be
that of the oldest root in the tree. If equal to ``"ts"`` the maximum
time is set to be the time of the oldest root in the tree sequence;
this is useful when drawing trees from the same tree sequence as it ensures
that node heights are consistent. If a numeric value, this is used as the
maximum time by which to scale other nodes. This parameter
is not currently supported for text output.
:param str,float min_time: The minimum time value in the current
scaling system (see ``time_scale``). Can be either a string or a
numeric value. If equal to ``"tree"``, the minimum time is set to be
that of the youngest node in the tree. If equal to ``"ts"`` the minimum
time is set to be the time of the youngest node in the tree sequence;
this is useful when drawing trees from the same tree sequence as it ensures
that node heights are consistent. If a numeric value, this is used as the
minimum time to display. This parameter is not currently supported for text
output.
:param str max_tree_height: Deprecated alias for max_time. (Deprecated in
0.3.6)
:param str order: The left-to-right ordering of child nodes in the drawn tree.
This can be either: ``"minlex"``, which minimises the differences
between adjacent trees (see also the ``"minlex_postorder"`` traversal
order for the :meth:`.nodes` method); or ``"tree"`` which draws trees
in the left-to-right order defined by the
:ref:`quintuply linked tree structure <sec_data_model_tree_structure>`.
If not specified or None, this defaults to ``"minlex"``.
:param bool omit_sites: If True, omit sites and mutations from the drawing
(only relevant to the SVG format). Default: False
:return: A representation of this tree in the requested format.
:rtype: str
"""
output = drawing.draw_tree(
self,
format=format,
width=width,
height=height,
node_labels=node_labels,
node_colours=node_colours,
mutation_labels=mutation_labels,
mutation_colours=mutation_colours,
edge_colours=edge_colours,
time_scale=time_scale,
tree_height_scale=tree_height_scale,
max_time=max_time,
min_time=min_time,
max_tree_height=max_tree_height,
order=order,
omit_sites=omit_sites,
)
if path is not None:
with open(path, "w") as f:
f.write(output)
return output
def get_num_mutations(self):
return self.num_mutations
@property
def num_mutations(self):
"""
Returns the total number of mutations across all sites on this tree.
:return: The total number of mutations over all sites on this tree.
:rtype: int
"""
return sum(len(site.mutations) for site in self.sites())
@property
def num_sites(self):
"""
Returns the number of sites on this tree.
:return: The number of sites on this tree.
:rtype: int
"""
return self._ll_tree.get_num_sites()
[docs]
def sites(self):
"""
Returns an iterator over all the :ref:`sites <sec_site_table_definition>`
in this tree. Sites are returned in order of increasing ID
(and also position). See the :class:`Site` class for details on
the available fields for each site.
:return: An iterator over all sites in this tree.
"""
# TODO change the low-level API to just return the IDs of the sites.
for ll_site in self._ll_tree.get_sites():
_, _, _, id_, _ = ll_site
yield self.tree_sequence.site(id_)
[docs]
def mutations(self):
"""
Returns an iterator over all the
:ref:`mutations <sec_mutation_table_definition>` in this tree.
Mutations are returned in their
:ref:`order in the mutations table<sec_mutation_requirements>`,
that is, by nondecreasing site ID, and within a site, by decreasing
mutation time with parent mutations before their children.
See the :class:`Mutation` class for details on the available fields for
each mutation.
The returned iterator is equivalent to iterating over all sites
and all mutations in each site, i.e.::
>>> for site in tree.sites():
>>> for mutation in site.mutations:
>>> yield mutation
:return: An iterator over all :class:`Mutation` objects in this tree.
:rtype: iter(:class:`Mutation`)
"""
for site in self.sites():
yield from site.mutations
def get_leaves(self, u):
# Deprecated alias for samples. See the discussion in the get_num_leaves
# method for why this method is here and why it is semantically incorrect.
# The 'leaves' iterator below correctly returns the leaves below a given
# node.
return self.samples(u)
[docs]
def leaves(self, u=None):
"""
Returns an iterator over all the leaves in this tree that descend from
the specified node. If :math:`u` is not specified, return all leaves on
the tree (i.e. all leaves reachable from the tree root(s), see note below).
.. note::
:math:`u` can be any node in the entire tree sequence, including ones
which are not connected via branches to a root node of the tree. If
called on such a node, the iterator will return "dead" leaves
(see :ref:`sec_data_model_tree_dead_leaves_and_branches`) which cannot
be reached from a root of this tree. However, dead leaves will never be
returned if :math:`u` is left unspecified.
:param int u: The node of interest.
:return: An iterator over all leaves in the subtree rooted at u.
:rtype: collections.abc.Iterable
"""
roots = [u]
if u is None:
roots = self.roots
for root in roots:
for v in self.nodes(root):
if self.is_leaf(v):
yield v
def _sample_generator(self, u):
if self._ll_tree.get_options() & _tskit.SAMPLE_LISTS:
samples = self.tree_sequence.samples()
index = self.left_sample(u)
if index != NULL:
stop = self.right_sample(u)
while True:
yield samples[index]
if index == stop:
break
index = self.next_sample(index)
else:
# Fall back on iterating over all nodes in the tree, yielding
# samples as we see them.
for v in self.nodes(u):
if self.is_sample(v):
yield v
[docs]
def samples(self, u=None):
"""
Returns an iterator over the numerical IDs of all the sample nodes in
this tree that are underneath the node with ID ``u``. If ``u`` is a sample,
it is included in the returned iterator. If ``u`` is not a sample, it is
possible for the returned iterator to be empty, for example if ``u`` is an
:meth:`isolated<Tree.is_isolated>` node that is not part of the the current
topology. If u is not specified, return all sample node IDs in the tree
(equivalent to all the sample node IDs in the tree sequence).
If the :meth:`TreeSequence.trees` method is called with
``sample_lists=True``, this method uses an efficient algorithm to find
the sample nodes. If not, a simple traversal based method is used.
.. note::
The iterator is *not* guaranteed to return the sample node IDs in
numerical or any other particular order.
:param int u: The node of interest.
:return: An iterator over all sample node IDs in the subtree rooted at u.
:rtype: collections.abc.Iterable
"""
roots = [u]
if u is None:
roots = self.roots
for root in roots:
yield from self._sample_generator(root)
[docs]
def num_children(self, u):
"""
Returns the number of children of the specified
node (i.e., ``len(tree.children(u))``)
:param int u: The node of interest.
:return: The number of immediate children of the node u in this tree.
:rtype: int
"""
return self._ll_tree.get_num_children(u)
def get_num_leaves(self, u):
# Deprecated alias for num_samples. The method name is inaccurate
# as this will count the number of tracked _samples_. This is only provided to
# avoid breaking existing code and should not be used in new code. We could
# change this method to be semantically correct and just count the
# number of leaves we hit in the leaves() iterator. However, this would
# have the undesirable effect of making code that depends on the constant
# time performance of get_num_leaves many times slower. So, the best option
# is to leave this method as is, and to slowly deprecate it out. Once this
# has been removed, we might add in a ``num_leaves`` method that returns the
# length of the leaves() iterator as one would expect.
return self.num_samples(u)
def get_num_samples(self, u=None):
# Deprecated alias for num_samples.
return self.num_samples(u)
[docs]
def num_samples(self, u=None):
"""
Returns the number of sample nodes in this tree underneath the specified
node (including the node itself). If u is not specified return
the total number of samples in the tree.
This is a constant time operation.
:param int u: The node of interest.
:return: The number of samples in the subtree rooted at u.
:rtype: int
"""
u = self.virtual_root if u is None else u
return self._ll_tree.get_num_samples(u)
def get_num_tracked_leaves(self, u):
# Deprecated alias for num_tracked_samples. The method name is inaccurate
# as this will count the number of tracked _samples_. This is only provided to
# avoid breaking existing code and should not be used in new code.
return self.num_tracked_samples(u)
def get_num_tracked_samples(self, u=None):
# Deprecated alias for num_tracked_samples
return self.num_tracked_samples(u)
[docs]
def num_tracked_samples(self, u=None):
"""
Returns the number of samples in the set specified in the
``tracked_samples`` parameter of the :meth:`TreeSequence.trees` method
underneath the specified node. If the input node is not specified,
return the total number of tracked samples in the tree.
This is a constant time operation.
:param int u: The node of interest.
:return: The number of samples within the set of tracked samples in
the subtree rooted at u.
:rtype: int
"""
u = self.virtual_root if u is None else u
return self._ll_tree.get_num_tracked_samples(u)
[docs]
def preorder(self, u=NULL):
"""
Returns a numpy array of node ids in `preorder
<https://en.wikipedia.org/wiki/Tree_traversal#Pre-order_(NLR)>`_. If the node u
is specified the traversal is rooted at this node (and it will be the first
element in the returned array). Otherwise, all nodes reachable from the tree
roots will be returned. See :ref:`tutorials:sec_analysing_trees_traversals` for
examples.
:param int u: If specified, return all nodes in the subtree rooted at u
(including u) in traversal order.
:return: Array of node ids
:rtype: numpy.ndarray (dtype=np.int32)
"""
return self._ll_tree.get_preorder(u)
[docs]
def postorder(self, u=NULL):
"""
Returns a numpy array of node ids in `postorder
<https://en.wikipedia.org/wiki/Tree_traversal##Post-order_(LRN)>`_. If the node u
is specified the traversal is rooted at this node (and it will be the last
element in the returned array). Otherwise, all nodes reachable from the tree
roots will be returned. See :ref:`tutorials:sec_analysing_trees_traversals` for
examples.
:param int u: If specified, return all nodes in the subtree rooted at u
(including u) in traversal order.
:return: Array of node ids
:rtype: numpy.ndarray (dtype=np.int32)
"""
return self._ll_tree.get_postorder(u)
[docs]
def timeasc(self, u=NULL):
"""
Returns a numpy array of node ids. Starting at `u`, returns the reachable
descendant nodes in order of increasing time (most recent first), falling back
to increasing ID if times are equal. Also see
:ref:`tutorials:sec_analysing_trees_traversals` for examples of how to use
traversals.
:param int u: If specified, return all nodes in the subtree rooted at u
(including u) in traversal order.
:return: Array of node ids
:rtype: numpy.ndarray (dtype=np.int32)
"""
nodes = self.preorder(u)
is_virtual_root = u == self.virtual_root
time = self.tree_sequence.nodes_time
if is_virtual_root:
# We could avoid creating this array if we wanted to, but
# it's not that often people will be using this with the
# virtual_root as an argument, so doesn't seem worth
# the complexity
time = np.append(time, [np.inf])
order = np.lexsort([nodes, time[nodes]])
return nodes[order]
[docs]
def timedesc(self, u=NULL):
"""
Returns a numpy array of node ids. Starting at `u`, returns the reachable
descendant nodes in order of decreasing time (least recent first), falling back
to decreasing ID if times are equal. Also see
:ref:`tutorials:sec_analysing_trees_traversals` for examples of how to use
traversals.
:param int u: If specified, return all nodes in the subtree rooted at u
(including u) in traversal order.
:return: Array of node ids
:rtype: numpy.ndarray (dtype=np.int32)
"""
return self.timeasc(u)[::-1]
def _preorder_traversal(self, root):
# Return Python integers for compatibility
return map(int, self.preorder(root))
def _postorder_traversal(self, root):
# Return Python integers for compatibility
return map(int, self.postorder(root))
def _inorder_traversal(self, root):
# TODO add a nonrecursive version of the inorder traversal.
def traverse(u):
children = self.get_children(u)
mid = len(children) // 2
for c in children[:mid]:
yield from traverse(c)
yield u
for c in children[mid:]:
yield from traverse(c)
roots = self.roots if root == NULL else [root]
for root in roots:
yield from traverse(root)
def _levelorder_traversal(self, root):
roots = self.roots if root == NULL else [root]
queue = collections.deque(roots)
# For perf we store these to avoid lookups in the tight loop
pop = queue.popleft
extend = queue.extend
children = self.children
# Note: the usual style is to be explicit about what we're testing
# and use while len(queue) > 0, but this form is slightly faster.
while queue:
v = pop()
extend(children(v))
yield v
def _timeasc_traversal(self, root):
"""
Sorts by increasing time but falls back to increasing ID for equal times.
"""
return map(int, self.timeasc(root))
def _timedesc_traversal(self, root):
"""
The reverse of timeasc.
"""
return map(int, self.timedesc(root))
def _minlex_postorder_traversal(self, root):
"""
Postorder traversal that visits leaves in minimum lexicographic order.
Minlex stands for minimum lexicographic. We wish to visit a tree in such
a way that the leaves visited, when their IDs are listed out, have
minimum lexicographic order. This is a useful ordering for drawing
multiple Trees of a TreeSequence, as it leads to more consistency
between adjacent Trees.
"""
# We compute a dictionary mapping from internal node ID to min leaf ID
# under the node, using a first postorder traversal
min_leaf = {}
for u in self.nodes(root, order="postorder"):
if self.is_leaf(u):
min_leaf[u] = u
else:
min_leaf[u] = min(min_leaf[v] for v in self.children(u))
# If we deliberately specify the virtual root, it should also be returned
is_virtual_root = root == self.virtual_root
if root == -1:
root = self.virtual_root
stack = [(root, False)]
while len(stack) > 0:
u, visited = stack.pop()
if visited:
if u != self.virtual_root or is_virtual_root:
yield u
else:
stack.append((u, True)) # Reappend, marking visited
stack.extend(
sorted(
((c, False) for c in self.children(u)),
key=lambda v: min_leaf[v[0]],
reverse=True,
)
)
[docs]
def nodes(self, root=None, order="preorder"):
"""
Returns an iterator over the node IDs reachable from the specified node in this
tree in the specified traversal order.
.. note::
Unlike the :meth:`TreeSequence.nodes` method, this iterator produces
integer node IDs, not :class:`Node` objects.
If the ``root`` parameter is not provided or ``None``, iterate over all
nodes reachable from the roots (see :attr:`Tree.roots` for details
on which nodes are considered roots). If the ``root`` parameter
is provided, only the nodes in the subtree rooted at this node
(including the specified node) will be iterated over. If the
:attr:`.virtual_root` is specified as the traversal root, it will
be included in the traversed nodes in the appropriate position
for the given ordering. (See the
:ref:`tree roots <sec_data_model_tree_virtual_root>` section for more
information on the virtual root.)
The ``order`` parameter defines the order in which tree nodes are visited
in the iteration (also see the :ref:`sec_analysing_trees_traversals` section
in the `tutorials <https://tskit.dev/tutorials>`__). The available orders are:
- 'preorder': starting at root, yield the current node, then recurse
and do a preorder on each child of the current node. See also `Wikipedia
<https://en.wikipedia.org/wiki/Tree_traversal#Pre-order_(NLR)>`__.
- 'inorder': starting at root, assuming binary trees, recurse and do
an inorder on the first child, then yield the current node, then
recurse and do an inorder on the second child. In the case of ``n``
child nodes (not necessarily 2), the first ``n // 2`` children are
visited in the first stage, and the remaining ``n - n // 2`` children
are visited in the second stage. See also `Wikipedia
<https://en.wikipedia.org/wiki/Tree_traversal#In-order_(LNR)>`__.
- 'postorder': starting at root, recurse and do a postorder on each
child of the current node, then yield the current node. See also
`Wikipedia
<https://en.wikipedia.org/wiki/Tree_traversal#Post-order_(LRN)>`__.
- 'levelorder' ('breadthfirst'): visit the nodes under root (including
the root) in increasing order of their depth from root. See also
`Wikipedia
<https://en.wikipedia.org/wiki/Tree_traversal\
#Breadth-first_search_/_level_order>`__.
- 'timeasc': visits the nodes in order of increasing time, falling back to
increasing ID if times are equal.
- 'timedesc': visits the nodes in order of decreasing time, falling back to
decreasing ID if times are equal.
- 'minlex_postorder': a usual postorder has ambiguity in the order in
which children of a node are visited. We constrain this by outputting
a postorder such that the leaves visited, when their IDs are
listed out, have minimum `lexicographic order
<https://en.wikipedia.org/wiki/Lexicographical_order>`__ out of all valid
traversals. This traversal is useful for drawing multiple trees of
a ``TreeSequence``, as it leads to more consistency between adjacent
trees. Note that internal non-leaf nodes are not counted in
assessing the lexicographic order.
:param int root: The root of the subtree we are traversing.
:param str order: The traversal ordering. Currently 'preorder',
'inorder', 'postorder', 'levelorder' ('breadthfirst'), 'timeasc' and
'timedesc' and 'minlex_postorder' are supported.
:return: An iterator over the node IDs in the tree in some traversal order.
:rtype: collections.abc.Iterable, int
"""
methods = {
"preorder": self._preorder_traversal,
"inorder": self._inorder_traversal,
"postorder": self._postorder_traversal,
"levelorder": self._levelorder_traversal,
"breadthfirst": self._levelorder_traversal,
"timeasc": self._timeasc_traversal,
"timedesc": self._timedesc_traversal,
"minlex_postorder": self._minlex_postorder_traversal,
}
try:
iterator = methods[order]
except KeyError:
raise ValueError(f"Traversal ordering '{order}' not supported")
root = -1 if root is None else root
return iterator(root)
def _as_newick_fast(self, *, root, precision, legacy_ms_labels):
"""
Call into the fast but limited C implementation of the newick conversion.
"""
root_time = max(1, self.time(root))
max_label_size = math.ceil(math.log10(self.tree_sequence.num_nodes))
single_node_size = (
5 + max_label_size + math.ceil(math.log10(root_time)) + precision
)
buffer_size = 1 + single_node_size * self.tree_sequence.num_nodes
return self._ll_tree.get_newick(
precision=precision,
root=root,
buffer_size=buffer_size,
legacy_ms_labels=legacy_ms_labels,
)
[docs]
def as_newick(
self,
*,
root=None,
precision=None,
node_labels=None,
include_branch_lengths=None,
):
"""
Returns a `newick encoding
<https://en.wikipedia.org/wiki/Newick_format>`_ of this tree.
For example, a binary tree with 3 leaves generated by
:meth:`Tree.generate_balanced(3)<Tree.generate_balanced>`
encodes as::
(n0:2,(n1:1,n2:1):1);
By default :ref:`sample nodes<sec_data_model_definitions>` are
labelled using the form ``f"n{node_id}"``, i.e. the sample node's
ID prefixed with the string ``"n"``. Node labels can be specified
explicitly using the ``node_labels`` argument, which is a mapping from
integer node IDs to the corresponding string label. If a node is not
present in the mapping, no label is associated with the node in
output.
.. warning:: Node labels are **not** Newick escaped, so care must be taken
to provide labels that will not break the encoding.
.. note:: Specifying a ``node_labels`` dictionary or setting
``include_branch_lengths=False`` results in a less efficient
method being used to generate the newick output. The performance
difference can be substantial for large trees.
By default, branch lengths are printed out with sufficient precision
for them to be recovered exactly in double precision (although note
that this does not necessarily mean that we can precisely recover the
corresponding node times, since branch lengths are obtained by
subtraction). If all times on the tree sequence are discrete, then
branch lengths are printed as integers. Otherwise, branch lengths are
printed with 17 digits of precision (i.e., ``"%.17f"`` in
printf-notation).
The precision for branch lengths can be specified using the ``precision``
argument. Branch lengths can be omitted entirely by setting
``include_branch_lengths=False``.
If the ``root`` argument is specified, we return the newick encoding of
the specified subtree, otherwise the full tree is returned. If the tree
has :ref:`multiple roots <sec_data_model_tree_roots>` and a root node
is not explicitly specified, we raise a ``ValueError``. This is because
most libraries and downstream software consider a newick string that
contains multiple disconnected subtrees an error, and it is therefore
best to consider how such topologies should be interchanged on a
case-by-base basis. A list of the newick strings for each root can be
obtained by ``[tree.as_newick(root=root) for root in tree.roots]``.
:param int precision: The numerical precision with which branch lengths are
printed. If not specified or None default to 0 if the tree sequence
contains only integer node times, or 17 otherwise.
:param int root: If specified, return the tree rooted at this node.
:param dict node_labels: If specified, show custom labels for the nodes
that are present in the map. Any nodes not specified in the map will
not have a node label.
:param include_branch_lengths: If True (default), output branch lengths in the
Newick string. If False, only output the topology, without branch lengths.
:return: A newick representation of this tree.
:rtype: str
"""
if root is None:
if not self.has_single_root:
raise ValueError(
"Cannot get newick unless a tree has a single root. Try "
"[t.as_newick(root) for root in t.roots] to get a list of "
"newick trees, one for each root."
)
root = self.root
if precision is None:
# 17 decimal digits provides the full precision of an IEEE double,
# as defined by DBL_DECIMAL_DIG macro. If we have discrete time
# then write out integer branch lengths.
precision = 0 if self.tree_sequence.discrete_time else 17
include_branch_lengths = (
True if include_branch_lengths is None else include_branch_lengths
)
# Can we run this through the fast path?
if include_branch_lengths and node_labels in [LEGACY_MS_LABELS, None]:
# Note the LEGACY_MS_LABELS code path is not part of the documented
# interface and should not be depended on by client code.
return self._as_newick_fast(
root=root,
precision=precision,
legacy_ms_labels=node_labels == LEGACY_MS_LABELS,
)
# No, we have to use the slower Python code.
if node_labels is None:
node_labels = {u: f"n{u}" for u in self.tree_sequence.samples()}
elif node_labels == LEGACY_MS_LABELS:
# NOTE in the ms format it's the *leaf* nodes we label not
# necessarily the samples. We keep this behaviour to avoid
# breaking legacy code that may depend on it.
node_labels = {u: f"{u + 1}" for u in self.leaves()}
return text_formats.build_newick(
self,
root=root,
precision=precision,
node_labels=node_labels,
include_branch_lengths=include_branch_lengths,
)
[docs]
def newick(
self,
precision=14,
*,
root=None,
node_labels=None,
include_branch_lengths=True,
):
"""
.. warning:: This method is deprecated and may be removed in future
versions of tskit. Please use the :meth:`.as_newick` method
in new code.
This method is a deprecated version of the :meth:`.as_newick` method.
Functionality is equivalent, except for the default node labels.
By default, *leaf* nodes are labelled with their numerical ID + 1,
and internal nodes are not labelled. This default strategy was originally
used to mimic the output of the ``ms`` simulator. However, the choice
of labelling leaf nodes rather than samples is problematic, and this
behaviour is only retained to avoid breaking existing code which may
rely on it.
Other parameters behave as documented in the :meth:`.as_newick` method.
:param int precision: The numerical precision with which branch lengths are
printed. Defaults to 14.
:param int root: If specified, return the tree rooted at this node.
:param dict node_labels: If specified, show custom labels for the nodes
that are present in the map. Any nodes not specified in the map will
not have a node label.
:param include_branch_lengths: If True (default), output branch lengths in the
Newick string. If False, only output the topology, without branch lengths.
:return: A newick representation of this tree.
:rtype: str
"""
node_labels = LEGACY_MS_LABELS if node_labels is None else node_labels
return self.as_newick(
root=root,
precision=precision,
node_labels=node_labels,
include_branch_lengths=include_branch_lengths,
)
[docs]
def as_dict_of_dicts(self):
"""
Convert tree to dict of dicts for conversion to a
`networkx graph <https://networkx.github.io/documentation/stable/
reference/classes/digraph.html>`_.
For example::
>>> import networkx as nx
>>> nx.DiGraph(tree.as_dict_of_dicts())
>>> # undirected graphs work as well
>>> nx.Graph(tree.as_dict_of_dicts())
:return: Dictionary of dictionaries of dictionaries where the first key
is the source, the second key is the target of an edge, and the
third key is an edge annotation. At this point the only annotation
is "branch_length", the length of the branch (in units of time).
"""
dod = {}
for parent in self.nodes():
dod[parent] = {}
for child in self.children(parent):
dod[parent][child] = {"branch_length": self.branch_length(child)}
return dod
@property
def parent_dict(self):
return self.get_parent_dict()
def get_parent_dict(self):
pi = {
u: self.parent(u)
for u in range(self.tree_sequence.num_nodes)
if self.parent(u) != NULL
}
return pi
[docs]
def __str__(self):
"""
Return a plain text summary of a tree in a tree sequence
"""
tree_rows = [
["Index", f"{self.index:,}"],
[
"Interval",
f"{self.interval.left:,.8g}-{self.interval.right:,.8g}"
f"({self.span:,.8g})",
],
["Roots", f"{self.num_roots:,}"],
["Nodes", f"{len(self.preorder()):,}"],
["Sites", f"{self.num_sites:,}"],
["Mutations", f"{self.num_mutations:,}"],
["Total Branch Length", f"{self.total_branch_length:,.8g}"],
]
return util.unicode_table(tree_rows, title="Tree")
[docs]
def _repr_html_(self):
"""
Return an html summary of a tree in a tree sequence. Called by jupyter
notebooks to render trees
"""
return util.tree_html(self)
[docs]
def map_mutations(self, genotypes, alleles, ancestral_state=None):
"""
Given observations for the samples in this tree described by the specified
set of genotypes and alleles, return a parsimonious set of state transitions
explaining these observations. The genotypes array is interpreted as indexes
into the alleles list in the same manner as described in the
:meth:`TreeSequence.variants` method. Thus, if sample ``j`` carries the
allele at index ``k``, then we have ``genotypes[j] = k``.
Missing observations can be specified for a sample using the value
``tskit.MISSING_DATA`` (-1), in which case the state at this sample does not
influence the ancestral state or the position of mutations returned. At least
one non-missing observation must be provided. A maximum of 64 alleles are
supported.
The current implementation uses the Hartigan parsimony algorithm to determine
the minimum number of state transitions required to explain the data. In this
model, transitions between any of the non-missing states is equally likely.
The returned values correspond directly to the data model for describing
variation at sites using mutations. See the :ref:`sec_site_table_definition`
and :ref:`sec_mutation_table_definition` definitions for details and background.
The state reconstruction is returned as two-tuple, ``(ancestral_state,
mutations)``, where ``ancestral_state`` is the allele assigned to the
tree root(s) and ``mutations`` is a list of :class:`Mutation` objects,
ordered as :ref:`required in a mutation table<sec_mutation_requirements>`.
For each mutation, ``derived_state`` is the new state after this mutation and
``node`` is the tree node immediately beneath the mutation (if there are unary
nodes between two branch points, hence multiple nodes above which the
mutation could be parsimoniously placed, the oldest node is used). The
``parent`` property contains the index in the returned list of the previous
mutation on the path to root, or ``tskit.NULL``
if there are no previous mutations (see the :ref:`sec_mutation_table_definition`
for more information on the concept of mutation parents). All other attributes
of the :class:`Mutation` object are undefined and should not be used.
.. note::
Sample states observed as missing in the input ``genotypes`` need
not correspond to samples whose nodes are actually "missing" (i.e.,
:ref:`isolated<sec_data_model_missing_data>`) in the tree. In this
case, mapping the mutations returned by this method onto the tree
will result in these missing observations being imputed to the
most parsimonious state.
Because the ``parent`` in the returned list of mutations refers to the index
in that list, if you are adding mutations to an existing tree sequence, you
will need to maintain a map of list IDs to the newly added mutations, for
instance::
last_tree = ts.last()
anc_state, parsimonious_muts = last_tree.map_mutations([0, 1, 0], ("A", "T"))
# Edit the tree sequence, see the "Tables and Editing" tutorial
tables = ts.dump_tables()
# add a new site at the end of ts, assumes there isn't one there already
site_id = tables.sites.add_row(ts.sequence_length - 1, anc_state)
mut_id_map = {tskit.NULL: tskit.NULL} # don't change if parent id is -1
for list_id, mutation in enumerate(parsimonious_muts):
mut_id_map[list_id] = tables.mutations.append(
mutation.replace(site=site_id, parent=mut_id_map[mutation.parent]))
tables.sort() # Redundant here, but needed if the site is not the last one
new_ts = tables.tree_sequence()
See the :ref:`tutorials:sec_analysing_trees_parsimony` section in the tutorial
for further examples of how to use this method.
:param array_like genotypes: The input observations for the samples in this tree.
:param tuple(str) alleles: The alleles for the specified ``genotypes``. Each
positive value in the ``genotypes`` array is treated as an index into this
list of alleles.
:param ancestral_state: A fixed ancestral state, specified either as a
non-negative integer less than the number of alleles, or a string which
must be one of the ``alleles`` provided above. If ``None`` (default) then
an ancestral state is chosen arbitrarily from among those that provide
the most parsimonious placement of mutations. Note that if the ancestral
state is specified, the placement of mutations may not be as parsimonious
as that which could be achieved by leaving the ancestral state unspecified;
additionally it may lead to mutations being placed above the root node(s) of
the tree (for example if all the samples have a genotype of 1 but the
ancestral state is fixed to be 0).
:type ancestral_state: Union[int, str]
:return: The inferred ancestral state and list of mutations on this tree
that encode the specified observations.
:rtype: (str, list(tskit.Mutation))
"""
genotypes = util.safe_np_int_cast(genotypes, np.int8)
max_alleles = np.max(genotypes)
if ancestral_state is not None:
if isinstance(ancestral_state, str):
# Will raise a ValueError if not in the list
ancestral_state = alleles.index(ancestral_state)
if ancestral_state < 0 or ancestral_state >= len(alleles):
raise ValueError("ancestral_state not between 0 and (num_alleles-1)")
max_alleles = max(ancestral_state, max_alleles)
if max_alleles >= 64:
raise ValueError("A maximum of 64 states is supported")
ancestral_state, transitions = self._ll_tree.map_mutations(
genotypes, ancestral_state
)
# Translate back into string alleles
ancestral_state = alleles[ancestral_state]
mutations = [
Mutation(
node=node,
derived_state=alleles[derived_state],
parent=parent,
metadata=self.tree_sequence.table_metadata_schemas.mutation.empty_value,
)
for node, parent, derived_state in transitions
]
return ancestral_state, mutations
[docs]
def kc_distance(self, other, lambda_=0.0):
"""
Returns the Kendall-Colijn distance between the specified pair of trees.
The ``lambda_`` parameter determines the relative weight of topology
vs branch lengths in calculating the distance. If ``lambda_`` is 0
(the default) we only consider topology, and if it is 1 we only
consider branch lengths. See `Kendall & Colijn (2016)
<https://academic.oup.com/mbe/article/33/10/2735/2925548>`_ for details.
The trees we are comparing to must have identical lists of sample
nodes (i.e., the same IDs in the same order). The metric operates on
samples, not leaves, so internal samples are treated identically to
sample tips. Subtrees with no samples do not contribute to the metric.
:param Tree other: The other tree to compare to.
:param float lambda_: The KC metric lambda parameter determining the
relative weight of topology and branch length.
:return: The computed KC distance between this tree and other.
:rtype: float
"""
return self._ll_tree.get_kc_distance(other._ll_tree, lambda_)
def _get_sample_sets(self):
ret = {}
for u in self.nodes(order="postorder"):
u_sample_set = set()
if self.is_sample(u):
u_sample_set.add(u)
for v in self.children(u):
u_sample_set |= ret[v]
ret[u] = frozenset(u_sample_set)
return ret
[docs]
def rf_distance(self, other):
"""
Returns the (unweighted) Robinson-Foulds distance between the specified pair
of trees, where corresponding samples between the two trees are identified by
node ID. The Robinson-Foulds distance (also known as the symmetric difference)
is defined as the number of bipartitions that are present in one tree but
not the other (see
`Robinson & Foulds (1981) <https://doi.org/10.1016/0025-5564(81)90043-2>`_).
This method returns the unnormalised RF distance: if the
trees are strictly bifurcating, i.e. binary, the value can be
normalised by dividing by the maximum, which is $2n-4$ for two rooted
trees of $n$ samples (however, if the trees contain polytomies, the maximum
RF distance is less easily defined).
.. note::
The RF distance can be sensitive to small changes in topology: in some
cases, changing the position of a single leaf can result in the maximum
RF distance. Therefore even if adjacent trees in a tree sequence differ
by a single subtree-prune-and-regraft operation, the RF distance
between them can be large.
:param Tree other: The other tree to compare to. Trees are treated as rooted.
:return: The unweighted Robinson-Foulds distance between this tree and ``other``.
:rtype: int
:raises ValueError: If either tree has multiple roots, or the trees have
different sample nodes.
"""
if self.num_roots != 1 or other.num_roots != 1:
raise ValueError("Trees must have a single root")
s1 = set(self._get_sample_sets().values())
s2 = set(other._get_sample_sets().values())
return len(s1.symmetric_difference(s2))
[docs]
def path_length(self, u, v):
"""
Returns the number of edges on the path in this tree between the two nodes.
If the two nodes have a most recent common ancestor, then this is defined as
``tree.depth(u) + tree.depth(v) - 2 * tree.depth(tree.mrca(u, v))``. If the nodes
do not have an MRCA (i.e., they are in disconnected subtrees) the path length
is infinity.
.. note:: This counts the number of "hops" between two nodes. To find the branch
length distance between them, in units of time (i.e. the sum of edge lengths
that separate two nodes) use the :meth:`.distance_between` method instead.
.. seealso:: See also the :meth:`.depth` method
:param int u: The first node for path length computation.
:param int v: The second node for path length computation.
:return: The number of edges between the two nodes.
:rtype: int
"""
mrca = self.mrca(u, v)
if mrca == -1:
return math.inf
return self.depth(u) + self.depth(v) - 2 * self.depth(mrca)
[docs]
def distance_between(self, u, v):
"""
Returns the total distance between two nodes in the tree, expressed as
the sum of "branch lengths" from both nodes to their most recent common ancestor.
:param int u: The first node for path length computation.
:param int v: The second node for path length computation.
:return: The distance between the two nodes, the sum of "branch lengths" .
:rtype: float
"""
tmrca = self.tmrca(u, v)
return tmrca - self.time(u) + tmrca - self.time(v)
[docs]
def b1_index(self):
"""
Returns the
`B1 balance index <https://treebalance.wordpress.com/b₁-index/>`_
for this tree. This is defined as the inverse of the sum of all
longest paths to leaves for each node besides roots.
.. seealso:: See `Shao and Sokal (1990)
<https://www.jstor.org/stable/2992186>`_ for details.
:return: The B1 balance index.
:rtype: float
"""
return self._ll_tree.get_b1_index()
[docs]
def b2_index(self, base=10):
"""
Returns the
`B2 balance index <https://treebalance.wordpress.com/b₂-index/>`_
this tree.
This is defined as the Shannon entropy of the probability
distribution to reach leaves assuming a random walk
from a root. The default base is 10, following Shao and Sokal (1990).
.. seealso:: See `Shao and Sokal (1990)
<https://www.jstor.org/stable/2992186>`_ for details.
:param int base: The base used for the logarithm in the
Shannon entropy computation.
:return: The B2 balance index.
:rtype: float
"""
# Let Python decide if the base is acceptable
math.log(10, base)
return self._ll_tree.get_b2_index(base)
[docs]
def colless_index(self):
"""
Returns the
`Colless imbalance index <https://treebalance.wordpress.com/colless-index/>`_
for this tree. This is defined as the sum of all differences between
number of leaves subtended by the left and right child of each node.
The Colless index is undefined for non-binary trees and trees with
multiple roots. This method will raise a LibraryError if the tree is
not singly-rooted and binary.
.. seealso:: See `Shao and Sokal (1990)
<https://www.jstor.org/stable/2992186>`_ for details.
:return: The Colless imbalance index.
:rtype: int
"""
return self._ll_tree.get_colless_index()
[docs]
def sackin_index(self):
"""
Returns the
`Sackin imbalance index <https://treebalance.wordpress.com/sackin-index/>`_
for this tree. This is defined as the sum of the depths of all leaves
in the tree. Equivalent to ``sum(tree.depth(u) for u in
tree.leaves())``
.. seealso:: See `Shao and Sokal (1990)
<https://www.jstor.org/stable/2992186>`_ for details.
:return: The Sackin imbalance index.
:rtype: int
"""
return self._ll_tree.get_sackin_index()
[docs]
def num_lineages(self, t):
"""
Returns the number of lineages present in this tree at time ``t``. This
is defined as the number of branches in this tree (reachable from the
samples) that intersect with ``t``. Thus, ``tree.num_lineages(t)``
is equal to 0 for any ``t`` greater than or equal to the time of
the root in a singly-rooted tree.
.. note:: Note that this definition means that if a (non root) node
with three children has time ``t``, then it will count as one lineage,
not three.
:param int t: The time to count lineages at.
:return: The number of lineages in the tree at time t.
:rtype: int
"""
return self._ll_tree.get_num_lineages(t)
[docs]
def split_polytomies(
self,
*,
epsilon=None,
method=None,
record_provenance=True,
random_seed=None,
**kwargs,
):
"""
Return a new :class:`.Tree` where extra nodes and edges have been inserted
so that any any node ``u`` with greater than 2 children --- a multifurcation
or "polytomy" --- is resolved into successive bifurcations. New nodes are
inserted at times fractionally less than than the time of node ``u``.
Times are allocated to different levels of the tree, such that any newly
inserted sibling nodes will have the same time.
By default, the times of the newly generated children of a particular
node are the minimum representable distance in floating point arithmetic
from their parents (using the `nextafter
<https://numpy.org/doc/stable/reference/generated/numpy.nextafter.html>`_
function). Thus, the generated branches have the shortest possible nonzero
length. A fixed branch length between inserted nodes and their parents
can also be specified by using the ``epsilon`` parameter.
.. note::
A tree sequence :ref:`requires<sec_valid_tree_sequence_requirements>` that
parents be older than children and that mutations are younger than the
parent of the edge on which they lie. If a fixed ``epsilon`` is specifed
and is not small enough compared to the distance between a polytomy and
its oldest child (or oldest child mutation) these requirements may not
be met. In this case an error will be raised.
If the ``method`` is ``"random"`` (currently the only option, and the default
when no method is specified), then for a node with :math:`n` children, the
:math:`(2n - 3)! / (2^(n - 2) (n - 2!))` possible binary trees with equal
probability.
The returned :class:`.Tree` will have the same genomic span as this tree,
and node IDs will be conserved (that is, node ``u`` in this tree will
be the same node in the returned tree). The returned tree is derived from a
tree sequence that contains only one non-degenerate tree, that is, where
edges cover only the interval spanned by this tree.
:param epsilon: If specified, the fixed branch length between inserted
nodes and their parents. If None (the default), the minimal possible
nonzero branch length is generated for each node.
:param str method: The method used to break polytomies. Currently only "random"
is supported, which can also be specified by ``method=None``
(Default: ``None``).
:param bool record_provenance: If True, add details of this operation to the
provenance information of the returned tree sequence. (Default: True).
:param int random_seed: The random seed. If this is None, a random seed will
be automatically generated. Valid random seeds must be between 1 and
:math:`2^32 − 1`.
:param \\**kwargs: Further arguments used as parameters when constructing the
returned :class:`Tree`. For example
``tree.split_polytomies(sample_lists=True)`` will
return a :class:`Tree` created with ``sample_lists=True``.
:return: A new tree with polytomies split into random bifurcations.
:rtype: tskit.Tree
"""
return combinatorics.split_polytomies(
self,
epsilon=epsilon,
method=method,
record_provenance=record_provenance,
random_seed=random_seed,
**kwargs,
)
[docs]
@staticmethod
def generate_star(
num_leaves, *, span=1, branch_length=1, record_provenance=True, **kwargs
):
"""
Generate a :class:`Tree` whose leaf nodes all have the same parent (i.e.,
a "star" tree). The leaf nodes are all at time 0 and are marked as sample nodes.
The tree produced by this method is identical to
``tskit.Tree.unrank(n, (0, 0))``, but generated more efficiently for large ``n``.
:param int num_leaves: The number of leaf nodes in the returned tree (must be
2 or greater).
:param float span: The span of the tree, and therefore the
:attr:`~TreeSequence.sequence_length` of the :attr:`.tree_sequence`
property of the returned :class:`Tree`.
:param float branch_length: The length of every branch in the tree (equivalent
to the time of the root node).
:param bool record_provenance: If True, add details of this operation to the
provenance information of the returned tree sequence. (Default: True).
:param \\**kwargs: Further arguments used as parameters when constructing the
returned :class:`Tree`. For example
``tskit.Tree.generate_star(sample_lists=True)`` will
return a :class:`Tree` created with ``sample_lists=True``.
:return: A star-shaped tree. Its corresponding :class:`TreeSequence` is available
via the :attr:`.tree_sequence` attribute.
:rtype: Tree
"""
return combinatorics.generate_star(
num_leaves,
span=span,
branch_length=branch_length,
record_provenance=record_provenance,
**kwargs,
)
[docs]
@staticmethod
def generate_balanced(
num_leaves,
*,
arity=2,
span=1,
branch_length=1,
record_provenance=True,
**kwargs,
):
"""
Generate a :class:`Tree` with the specified number of leaves that is maximally
balanced. By default, the tree returned is binary, such that for each
node that subtends :math:`n` leaves, the left child will subtend
:math:`\\lfloor{n / 2}\\rfloor` leaves and the right child the
remainder. Balanced trees with higher arity can also generated using the
``arity`` parameter, where the leaves subtending a node are distributed
among its children analogously.
In the returned tree, the leaf nodes are all at time 0, marked as samples,
and labelled 0 to n from left-to-right. Internal node IDs are assigned
sequentially from n in a postorder traversal, and the time of an internal
node is the maximum time of its children plus the specified ``branch_length``.
:param int num_leaves: The number of leaf nodes in the returned tree (must be
be 2 or greater).
:param int arity: The maximum number of children a node can have in the returned
tree.
:param float span: The span of the tree, and therefore the
:attr:`~TreeSequence.sequence_length` of the :attr:`.tree_sequence`
property of the returned :class:`Tree`.
:param float branch_length: The minimum length of a branch in the tree (see
above for details on how internal node times are assigned).
:param bool record_provenance: If True, add details of this operation to the
provenance information of the returned tree sequence. (Default: True).
:param \\**kwargs: Further arguments used as parameters when constructing the
returned :class:`Tree`. For example
``tskit.Tree.generate_balanced(sample_lists=True)`` will
return a :class:`Tree` created with ``sample_lists=True``.
:return: A balanced tree. Its corresponding :class:`TreeSequence` is available
via the :attr:`.tree_sequence` attribute.
:rtype: Tree
"""
return combinatorics.generate_balanced(
num_leaves,
arity=arity,
span=span,
branch_length=branch_length,
record_provenance=record_provenance,
**kwargs,
)
[docs]
@staticmethod
def generate_comb(
num_leaves, *, span=1, branch_length=1, record_provenance=True, **kwargs
):
"""
Generate a :class:`Tree` in which all internal nodes have two children
and the left child is a leaf. This is a "comb", "ladder" or "pectinate"
phylogeny, and also known as a `caterpillar tree
<https://en.wikipedia.org/wiki/Caterpillar_tree>`_.
The leaf nodes are all at time 0, marked as samples,
and labelled 0 to n from left-to-right. Internal node IDs are assigned
sequentially from n as we ascend the tree, and the time of an internal
node is the maximum time of its children plus the specified ``branch_length``.
:param int num_leaves: The number of leaf nodes in the returned tree (must be
2 or greater).
:param float span: The span of the tree, and therefore the
:attr:`~TreeSequence.sequence_length` of the :attr:`.tree_sequence`
property of the returned :class:`Tree`.
:param float branch_length: The branch length between each internal node; the
root node is therefore placed at time ``branch_length * (num_leaves - 1)``.
:param bool record_provenance: If True, add details of this operation to the
provenance information of the returned tree sequence. (Default: True).
:param \\**kwargs: Further arguments used as parameters when constructing the
returned :class:`Tree`. For example
``tskit.Tree.generate_comb(sample_lists=True)`` will
return a :class:`Tree` created with ``sample_lists=True``.
:return: A comb-shaped bifurcating tree. Its corresponding :class:`TreeSequence`
is available via the :attr:`.tree_sequence` attribute.
:rtype: Tree
"""
return combinatorics.generate_comb(
num_leaves,
span=span,
branch_length=branch_length,
record_provenance=record_provenance,
**kwargs,
)
[docs]
@staticmethod
def generate_random_binary(
num_leaves,
*,
span=1,
branch_length=1,
random_seed=None,
record_provenance=True,
**kwargs,
):
"""
Generate a random binary :class:`Tree` with :math:`n` = ``num_leaves``
leaves with an equal probability of returning any topology and
leaf label permutation among the :math:`(2n - 3)! / (2^{n - 2} (n - 2)!)`
leaf-labelled binary trees.
The leaf nodes are marked as samples, labelled 0 to n, and placed at
time 0. Internal node IDs are assigned sequentially from n as we ascend
the tree, and the time of an internal node is the maximum time of its
children plus the specified ``branch_length``.
.. note::
The returned tree has not been created under any explicit model of
evolution. In order to simulate such trees, additional software
such as `msprime <https://github.com/tskit-dev/msprime>`` is required.
:param int num_leaves: The number of leaf nodes in the returned tree (must
be 2 or greater).
:param float span: The span of the tree, and therefore the
:attr:`~TreeSequence.sequence_length` of the :attr:`.tree_sequence`
property of the returned :class:`Tree`.
:param float branch_length: The minimum time between parent and child nodes.
:param int random_seed: The random seed. If this is None, a random seed will
be automatically generated. Valid random seeds must be between 1 and
:math:`2^32 − 1`.
:param bool record_provenance: If True, add details of this operation to the
provenance information of the returned tree sequence. (Default: True).
:param \\**kwargs: Further arguments used as parameters when constructing the
returned :class:`Tree`. For example
``tskit.Tree.generate_comb(sample_lists=True)`` will
return a :class:`Tree` created with ``sample_lists=True``.
:return: A random binary tree. Its corresponding :class:`TreeSequence` is
available via the :attr:`.tree_sequence` attribute.
:rtype: Tree
"""
return combinatorics.generate_random_binary(
num_leaves,
span=span,
branch_length=branch_length,
random_seed=random_seed,
record_provenance=record_provenance,
**kwargs,
)
[docs]
def load(file, *, skip_tables=False, skip_reference_sequence=False):
"""
Return a :class:`TreeSequence` instance loaded from the specified file object or
path. The file must be in the
:ref:`tree sequence file format <sec_tree_sequence_file_format>`
produced by the :meth:`TreeSequence.dump` method.
.. warning:: With any of the ``skip_tables`` or ``skip_reference_sequence``
options set, it is not possible to load data from a non-seekable stream
(e.g. a socket or STDIN) of multiple tree sequences using consecutive
calls to :meth:`tskit.load`.
:param str file: The file object or path of the ``.trees`` file containing the
tree sequence we wish to load.
:param bool skip_tables: If True, no tables are read from the ``.trees``
file and only the top-level information is populated in the tree
sequence object.
:param bool skip_reference_sequence: If True, the tree sequence is read
without loading its reference sequence.
:return: The tree sequence object containing the information
stored in the specified file path.
:rtype: :class:`tskit.TreeSequence`
"""
return TreeSequence.load(
file, skip_tables=skip_tables, skip_reference_sequence=skip_reference_sequence
)
[docs]
def parse_individuals(
source, strict=True, encoding="utf8", base64_metadata=True, table=None
):
"""
Parse the specified file-like object containing a whitespace delimited
description of an individual table and returns the corresponding
:class:`IndividualTable` instance. See the :ref:`individual text format
<sec_individual_text_format>` section for the details of the required
format and the :ref:`individual table definition
<sec_individual_table_definition>` section for the required properties of
the contents.
See :func:`tskit.load_text` for a detailed explanation of the ``strict``
parameter.
:param io.TextIOBase source: The file-like object containing the text.
:param bool strict: If True, require strict tab delimiting (default). If
False, a relaxed whitespace splitting algorithm is used.
:param str encoding: Encoding used for text representation.
:param bool base64_metadata: If True, metadata is encoded using Base64
encoding; otherwise, as plain text.
:param IndividualTable table: If specified write into this table. If not,
create a new :class:`IndividualTable` instance.
"""
sep = None
if strict:
sep = "\t"
if table is None:
table = tables.IndividualTable()
# Read the header and find the indexes of the required fields.
header = source.readline().rstrip("\n").split(sep)
flags_index = header.index("flags")
location_index = None
parents_index = None
metadata_index = None
try:
location_index = header.index("location")
except ValueError:
pass
try:
parents_index = header.index("parents")
except ValueError:
pass
try:
metadata_index = header.index("metadata")
except ValueError:
pass
for line in source:
tokens = line.rstrip("\n").split(sep)
if len(tokens) >= 1:
flags = int(tokens[flags_index])
location = ()
if location_index is not None:
location_string = tokens[location_index]
if len(location_string) > 0:
location = tuple(map(float, location_string.split(",")))
parents = ()
if parents_index is not None:
parents_string = tokens[parents_index]
if len(parents_string) > 0:
parents = tuple(map(int, parents_string.split(",")))
metadata = b""
if metadata_index is not None and metadata_index < len(tokens):
metadata = tokens[metadata_index].encode(encoding)
if base64_metadata:
metadata = base64.b64decode(metadata)
table.add_row(
flags=flags, location=location, parents=parents, metadata=metadata
)
return table
[docs]
def parse_nodes(source, strict=True, encoding="utf8", base64_metadata=True, table=None):
"""
Parse the specified file-like object containing a whitespace delimited
description of a node table and returns the corresponding :class:`NodeTable`
instance. See the :ref:`node text format <sec_node_text_format>` section
for the details of the required format and the
:ref:`node table definition <sec_node_table_definition>` section for the
required properties of the contents.
See :func:`tskit.load_text` for a detailed explanation of the ``strict``
parameter.
:param io.TextIOBase source: The file-like object containing the text.
:param bool strict: If True, require strict tab delimiting (default). If
False, a relaxed whitespace splitting algorithm is used.
:param str encoding: Encoding used for text representation.
:param bool base64_metadata: If True, metadata is encoded using Base64
encoding; otherwise, as plain text.
:param NodeTable table: If specified write into this table. If not,
create a new :class:`NodeTable` instance.
"""
sep = None
if strict:
sep = "\t"
if table is None:
table = tables.NodeTable()
# Read the header and find the indexes of the required fields.
header = source.readline().rstrip("\n").split(sep)
is_sample_index = header.index("is_sample")
time_index = header.index("time")
population_index = None
individual_index = None
metadata_index = None
try:
population_index = header.index("population")
except ValueError:
pass
try:
individual_index = header.index("individual")
except ValueError:
pass
try:
metadata_index = header.index("metadata")
except ValueError:
pass
for line in source:
tokens = line.rstrip("\n").split(sep)
if len(tokens) >= 2:
is_sample = int(tokens[is_sample_index])
time = float(tokens[time_index])
flags = 0
if is_sample != 0:
flags |= NODE_IS_SAMPLE
population = NULL
if population_index is not None:
population = int(tokens[population_index])
individual = NULL
if individual_index is not None:
individual = int(tokens[individual_index])
metadata = b""
if metadata_index is not None and metadata_index < len(tokens):
metadata = tokens[metadata_index].encode(encoding)
if base64_metadata:
metadata = base64.b64decode(metadata)
table.add_row(
flags=flags,
time=time,
population=population,
individual=individual,
metadata=metadata,
)
return table
[docs]
def parse_edges(source, strict=True, table=None):
"""
Parse the specified file-like object containing a whitespace delimited
description of a edge table and returns the corresponding :class:`EdgeTable`
instance. See the :ref:`edge text format <sec_edge_text_format>` section
for the details of the required format and the
:ref:`edge table definition <sec_edge_table_definition>` section for the
required properties of the contents.
See :func:`tskit.load_text` for a detailed explanation of the ``strict`` parameter.
:param io.TextIOBase source: The file-like object containing the text.
:param bool strict: If True, require strict tab delimiting (default). If
False, a relaxed whitespace splitting algorithm is used.
:param EdgeTable table: If specified, write the edges into this table. If
not, create a new :class:`EdgeTable` instance and return.
"""
sep = None
if strict:
sep = "\t"
if table is None:
table = tables.EdgeTable()
header = source.readline().rstrip("\n").split(sep)
left_index = header.index("left")
right_index = header.index("right")
parent_index = header.index("parent")
children_index = header.index("child")
for line in source:
tokens = line.rstrip("\n").split(sep)
if len(tokens) >= 4:
left = float(tokens[left_index])
right = float(tokens[right_index])
parent = int(tokens[parent_index])
children = tuple(map(int, tokens[children_index].split(",")))
for child in children:
table.add_row(left=left, right=right, parent=parent, child=child)
return table
[docs]
def parse_sites(source, strict=True, encoding="utf8", base64_metadata=True, table=None):
"""
Parse the specified file-like object containing a whitespace delimited
description of a site table and returns the corresponding :class:`SiteTable`
instance. See the :ref:`site text format <sec_site_text_format>` section
for the details of the required format and the
:ref:`site table definition <sec_site_table_definition>` section for the
required properties of the contents.
See :func:`tskit.load_text` for a detailed explanation of the ``strict``
parameter.
:param io.TextIOBase source: The file-like object containing the text.
:param bool strict: If True, require strict tab delimiting (default). If
False, a relaxed whitespace splitting algorithm is used.
:param str encoding: Encoding used for text representation.
:param bool base64_metadata: If True, metadata is encoded using Base64
encoding; otherwise, as plain text.
:param SiteTable table: If specified write site into this table. If not,
create a new :class:`SiteTable` instance.
"""
sep = None
if strict:
sep = "\t"
if table is None:
table = tables.SiteTable()
header = source.readline().rstrip("\n").split(sep)
position_index = header.index("position")
ancestral_state_index = header.index("ancestral_state")
metadata_index = None
try:
metadata_index = header.index("metadata")
except ValueError:
pass
for line in source:
tokens = line.rstrip("\n").split(sep)
if len(tokens) >= 2:
position = float(tokens[position_index])
ancestral_state = tokens[ancestral_state_index]
metadata = b""
if metadata_index is not None and metadata_index < len(tokens):
metadata = tokens[metadata_index].encode(encoding)
if base64_metadata:
metadata = base64.b64decode(metadata)
table.add_row(
position=position, ancestral_state=ancestral_state, metadata=metadata
)
return table
[docs]
def parse_mutations(
source, strict=True, encoding="utf8", base64_metadata=True, table=None
):
"""
Parse the specified file-like object containing a whitespace delimited
description of a mutation table and returns the corresponding :class:`MutationTable`
instance. See the :ref:`mutation text format <sec_mutation_text_format>` section
for the details of the required format and the
:ref:`mutation table definition <sec_mutation_table_definition>` section for the
required properties of the contents. Note that if the ``time`` column is missing its
entries are filled with ``UNKNOWN_TIME``.
See :func:`tskit.load_text` for a detailed explanation of the ``strict``
parameter.
:param io.TextIOBase source: The file-like object containing the text.
:param bool strict: If True, require strict tab delimiting (default). If
False, a relaxed whitespace splitting algorithm is used.
:param str encoding: Encoding used for text representation.
:param bool base64_metadata: If True, metadata is encoded using Base64
encoding; otherwise, as plain text.
:param MutationTable table: If specified, write mutations into this table.
If not, create a new :class:`MutationTable` instance.
"""
sep = None
if strict:
sep = "\t"
if table is None:
table = tables.MutationTable()
header = source.readline().rstrip("\n").split(sep)
site_index = header.index("site")
node_index = header.index("node")
try:
time_index = header.index("time")
except ValueError:
time_index = None
derived_state_index = header.index("derived_state")
parent_index = None
parent = NULL
try:
parent_index = header.index("parent")
except ValueError:
pass
metadata_index = None
try:
metadata_index = header.index("metadata")
except ValueError:
pass
for line in source:
tokens = line.rstrip("\n").split(sep)
if len(tokens) >= 3:
site = int(tokens[site_index])
node = int(tokens[node_index])
if time_index is None or tokens[time_index] == tskit.TIME_UNITS_UNKNOWN:
time = UNKNOWN_TIME
else:
time = float(tokens[time_index])
derived_state = tokens[derived_state_index]
if parent_index is not None:
parent = int(tokens[parent_index])
metadata = b""
if metadata_index is not None and metadata_index < len(tokens):
metadata = tokens[metadata_index].encode(encoding)
if base64_metadata:
metadata = base64.b64decode(metadata)
table.add_row(
site=site,
node=node,
time=time,
derived_state=derived_state,
parent=parent,
metadata=metadata,
)
return table
[docs]
def parse_populations(
source, strict=True, encoding="utf8", base64_metadata=True, table=None
):
"""
Parse the specified file-like object containing a whitespace delimited
description of a population table and returns the corresponding
:class:`PopulationTable` instance. See the :ref:`population text format
<sec_population_text_format>` section for the details of the required
format and the :ref:`population table definition
<sec_population_table_definition>` section for the required properties of
the contents.
See :func:`tskit.load_text` for a detailed explanation of the ``strict``
parameter.
:param io.TextIOBase source: The file-like object containing the text.
:param bool strict: If True, require strict tab delimiting (default). If
False, a relaxed whitespace splitting algorithm is used.
:param str encoding: Encoding used for text representation.
:param bool base64_metadata: If True, metadata is encoded using Base64
encoding; otherwise, as plain text.
:param PopulationTable table: If specified write into this table. If not,
create a new :class:`PopulationTable` instance.
"""
sep = None
if strict:
sep = "\t"
if table is None:
table = tables.PopulationTable()
# Read the header and find the indexes of the required fields.
header = source.readline().rstrip("\n").split(sep)
metadata_index = header.index("metadata")
for line in source:
tokens = line.rstrip("\n").split(sep)
if len(tokens) >= 1:
metadata = tokens[metadata_index].encode(encoding)
if base64_metadata:
metadata = base64.b64decode(metadata)
table.add_row(metadata=metadata)
return table
[docs]
def parse_migrations(
source, strict=True, encoding="utf8", base64_metadata=True, table=None
):
"""
Parse the specified file-like object containing a whitespace delimited
description of a migration table and returns the corresponding
:class:`MigrationTable` instance.
See the :ref:`migration text format <sec_migration_text_format>` section
for the details of the required format and the
:ref:`migration table definition <sec_migration_table_definition>` section
for the required properties of the contents. Note that if the ``time`` column
is missing its entries are filled with :data:`UNKNOWN_TIME`.
See :func:`tskit.load_text` for a detailed explanation of the ``strict``
parameter.
:param io.TextIOBase source: The file-like object containing the text.
:param bool strict: If True, require strict tab delimiting (default). If
False, a relaxed whitespace splitting algorithm is used.
:param str encoding: Encoding used for text representation.
:param bool base64_metadata: If True, metadata is encoded using Base64
encoding; otherwise, as plain text.
:param MigrationTable table: If specified, write migrations into this table.
If not, create a new :class:`MigrationTable` instance.
"""
sep = None
if strict:
sep = "\t"
if table is None:
table = tables.MigrationTable()
header = source.readline().rstrip("\n").split(sep)
left_index = header.index("left")
right_index = header.index("right")
node_index = header.index("node")
source_index = header.index("source")
dest_index = header.index("dest")
time_index = header.index("time")
metadata_index = None
try:
metadata_index = header.index("metadata")
except ValueError:
pass
for line in source:
tokens = line.rstrip("\n").split(sep)
if len(tokens) >= 6:
left = float(tokens[left_index])
right = float(tokens[right_index])
node = int(tokens[node_index])
source = int(tokens[source_index])
dest = int(tokens[dest_index])
time = float(tokens[time_index])
metadata = b""
if metadata_index is not None and metadata_index < len(tokens):
metadata = tokens[metadata_index].encode(encoding)
if base64_metadata:
metadata = base64.b64decode(metadata)
table.add_row(
left=left,
right=right,
node=node,
source=source,
dest=dest,
time=time,
metadata=metadata,
)
return table
[docs]
def load_text(
nodes,
edges,
sites=None,
mutations=None,
individuals=None,
populations=None,
migrations=None,
sequence_length=0,
strict=True,
encoding="utf8",
base64_metadata=True,
):
"""
Return a :class:`TreeSequence` instance parsed from tabulated text data
contained in the specified file-like objects. The format
for these files is documented in the :ref:`sec_text_file_format` section,
and is produced by the :meth:`TreeSequence.dump_text` method. Further
properties required for an input tree sequence are described in the
:ref:`sec_valid_tree_sequence_requirements` section. This method is intended as a
convenient interface for importing external data into tskit; the binary
file format using by :meth:`tskit.load` is many times more efficient than
this text format.
The ``nodes`` and ``edges`` parameters are mandatory and must be file-like
objects containing text with whitespace delimited columns, parsable by
:func:`parse_nodes` and :func:`parse_edges`, respectively. ``sites``,
``individuals``, ``populations``, ``mutations``, and ``migrations`` are optional,
and must be parsable by :func:`parse_sites`, :func:`parse_individuals`,
:func:`parse_populations`, :func:`parse_mutations`, and :func:`parse_migrations`,
respectively. For convenience, if the node table refers to populations,
but the ``populations`` parameter is not provided, a minimal set of rows are
added to the population table, so that a valid tree sequence can be returned.
The ``sequence_length`` parameter determines the
:attr:`TreeSequence.sequence_length` of the returned tree sequence. If it
is 0 or not specified, the value is taken to be the maximum right
coordinate of the input edges. This parameter is useful in degenerate
situations (such as when there are zero edges), but can usually be ignored.
The ``strict`` parameter controls the field delimiting algorithm that
is used. If ``strict`` is True (the default), we require exactly one
tab character separating each field. If ``strict`` is False, a more relaxed
whitespace delimiting algorithm is used, such that any run of whitespace
is regarded as a field separator. In most situations, ``strict=False``
is more convenient, but it can lead to error in certain situations. For
example, if a deletion is encoded in the mutation table this will not
be parseable when ``strict=False``.
After parsing the tables, :meth:`TableCollection.sort` is called to ensure that
the loaded tables satisfy the tree sequence :ref:`ordering requirements
<sec_valid_tree_sequence_requirements>`. Note that this may result in the
IDs of various entities changing from their positions in the input file.
:param io.TextIOBase nodes: The file-like object containing text describing a
:class:`NodeTable`.
:param io.TextIOBase edges: The file-like object containing text
describing an :class:`EdgeTable`.
:param io.TextIOBase sites: The file-like object containing text describing a
:class:`SiteTable`.
:param io.TextIOBase mutations: The file-like object containing text
describing a :class:`MutationTable`.
:param io.TextIOBase individuals: The file-like object containing text
describing a :class:`IndividualTable`.
:param io.TextIOBase populations: The file-like object containing text
describing a :class:`PopulationTable`.
:param io.TextIOBase migrations: The file-like object containing text
describing a :class:`MigrationTable`.
:param float sequence_length: The sequence length of the returned tree sequence. If
not supplied or zero this will be inferred from the set of edges.
:param bool strict: If True, require strict tab delimiting (default). If
False, a relaxed whitespace splitting algorithm is used.
:param str encoding: Encoding used for text representation.
:param bool base64_metadata: If True, metadata is encoded using Base64
encoding; otherwise, as plain text.
:return: The tree sequence object containing the information
stored in the specified file paths.
:rtype: :class:`tskit.TreeSequence`
"""
# We need to parse the edges so we can figure out the sequence length, and
# TableCollection.sequence_length is immutable so we need to create a temporary
# edge table.
edge_table = parse_edges(edges, strict=strict)
if sequence_length == 0 and len(edge_table) > 0:
sequence_length = edge_table.right.max()
tc = tables.TableCollection(sequence_length)
tc.edges.set_columns(
left=edge_table.left,
right=edge_table.right,
parent=edge_table.parent,
child=edge_table.child,
)
parse_nodes(
nodes,
strict=strict,
encoding=encoding,
base64_metadata=base64_metadata,
table=tc.nodes,
)
if sites is not None:
parse_sites(
sites,
strict=strict,
encoding=encoding,
base64_metadata=base64_metadata,
table=tc.sites,
)
if mutations is not None:
parse_mutations(
mutations,
strict=strict,
encoding=encoding,
base64_metadata=base64_metadata,
table=tc.mutations,
)
if individuals is not None:
parse_individuals(
individuals,
strict=strict,
encoding=encoding,
base64_metadata=base64_metadata,
table=tc.individuals,
)
if populations is None:
# As a convenience we add any populations referenced in the node table.
if len(tc.nodes) > 0:
max_population = tc.nodes.population.max()
if max_population != NULL:
for _ in range(max_population + 1):
tc.populations.add_row()
else:
parse_populations(
populations,
strict=strict,
encoding=encoding,
base64_metadata=base64_metadata,
table=tc.populations,
)
if migrations is not None:
parse_migrations(
migrations,
strict=strict,
encoding=encoding,
base64_metadata=base64_metadata,
table=tc.migrations,
)
tc.sort()
return tc.tree_sequence()
class TreeIterator:
"""
Simple class providing forward and backward iteration over a tree sequence.
"""
def __init__(self, tree):
self.tree = tree
self.more_trees = True
self.forward = True
def __iter__(self):
return self
def __reversed__(self):
self.forward = False
return self
def __next__(self):
if self.forward:
self.more_trees = self.more_trees and self.tree.next()
else:
self.more_trees = self.more_trees and self.tree.prev()
if not self.more_trees:
raise StopIteration()
return self.tree
def __len__(self):
return self.tree.tree_sequence.num_trees
class SimpleContainerSequence:
"""
Simple wrapper to allow arrays of SimpleContainers (e.g. edges, nodes) that have a
function allowing access by index (e.g. ts.edge(i), ts.node(i)) to be treated as a
python sequence, allowing forward and reverse iteration.
To generate a sequence of items in a different order, the ``order`` parameter allows
an array of indexes to be passed in, such as returned from np.argsort or np.lexsort.
"""
def __init__(self, getter, length, order=None):
if order is None:
self.getter = getter
else:
self.getter = lambda index: getter(order[index])
self.length = length
def __len__(self):
return self.length
def __getitem__(self, index):
return self.getter(index)
[docs]
class TreeSequence:
"""
A single tree sequence, as defined by the :ref:`data model <sec_data_model>`.
A TreeSequence instance can be created from a set of
:ref:`tables <sec_table_definitions>` using
:meth:`TableCollection.tree_sequence`, or loaded from a set of text files
using :func:`tskit.load_text`, or loaded from a native binary file using
:func:`tskit.load`.
TreeSequences are immutable. To change the data held in a particular
tree sequence, first get the table information as a :class:`TableCollection`
instance (using :meth:`.dump_tables`), edit those tables using the
:ref:`tables api <sec_tables_api>`, and create a new tree sequence using
:meth:`TableCollection.tree_sequence`.
The :meth:`.trees` method iterates over all trees in a tree sequence, and
the :meth:`.variants` method iterates over all sites and their genotypes.
"""
def __init__(self, ll_tree_sequence):
self._ll_tree_sequence = ll_tree_sequence
metadata_schema_strings = self._ll_tree_sequence.get_table_metadata_schemas()
metadata_schema_instances = {
name: metadata_module.parse_metadata_schema(
getattr(metadata_schema_strings, name)
)
for name in vars(TableMetadataSchemas)
if not name.startswith("_")
}
self._table_metadata_schemas = TableMetadataSchemas(**metadata_schema_instances)
self._individuals_time = None
self._individuals_population = None
self._individuals_location = None
# NOTE: when we've implemented read-only access via the underlying
# tables we can replace these arrays with reference to the read-only
# tables here (and remove the low-level boilerplate).
llts = self._ll_tree_sequence
self._individuals_flags = llts.individuals_flags
self._nodes_time = llts.nodes_time
self._nodes_flags = llts.nodes_flags
self._nodes_population = llts.nodes_population
self._nodes_individual = llts.nodes_individual
self._edges_left = llts.edges_left
self._edges_right = llts.edges_right
self._edges_parent = llts.edges_parent
self._edges_child = llts.edges_child
self._sites_position = llts.sites_position
self._mutations_site = llts.mutations_site
self._mutations_node = llts.mutations_node
self._mutations_parent = llts.mutations_parent
self._mutations_time = llts.mutations_time
self._migrations_left = llts.migrations_left
self._migrations_right = llts.migrations_right
self._migrations_right = llts.migrations_right
self._migrations_node = llts.migrations_node
self._migrations_source = llts.migrations_source
self._migrations_dest = llts.migrations_dest
self._migrations_time = llts.migrations_time
self._indexes_edge_insertion_order = llts.indexes_edge_insertion_order
self._indexes_edge_removal_order = llts.indexes_edge_removal_order
# Implement the pickle protocol for TreeSequence
def __getstate__(self):
return self.dump_tables()
def __setstate__(self, tc):
self.__init__(tc.tree_sequence().ll_tree_sequence)
def __eq__(self, other):
return self.tables == other.tables
[docs]
def equals(
self,
other,
*,
ignore_metadata=False,
ignore_ts_metadata=False,
ignore_provenance=False,
ignore_timestamps=False,
ignore_tables=False,
ignore_reference_sequence=False,
):
"""
Returns True if `self` and `other` are equal. Uses the underlying table
equality, see :meth:`TableCollection.equals` for details and options.
"""
return self.tables.equals(
other.tables,
ignore_metadata=ignore_metadata,
ignore_ts_metadata=ignore_ts_metadata,
ignore_provenance=ignore_provenance,
ignore_timestamps=ignore_timestamps,
ignore_tables=ignore_tables,
ignore_reference_sequence=ignore_reference_sequence,
)
@property
def ll_tree_sequence(self):
return self.get_ll_tree_sequence()
def get_ll_tree_sequence(self):
return self._ll_tree_sequence
[docs]
def aslist(self, **kwargs):
"""
Returns the trees in this tree sequence as a list. Each tree is
represented by a different instance of :class:`Tree`. As such, this
method is inefficient and may use a large amount of memory, and should
not be used when performance is a consideration. The :meth:`.trees`
method is the recommended way to efficiently iterate over the trees
in a tree sequence.
:param \\**kwargs: Further arguments used as parameters when constructing the
returned trees. For example ``ts.aslist(sample_lists=True)`` will result
in a list of :class:`Tree` instances created with ``sample_lists=True``.
:return: A list of the trees in this tree sequence.
:rtype: list
"""
return [tree.copy() for tree in self.trees(**kwargs)]
@classmethod
def load(cls, file_or_path, *, skip_tables=False, skip_reference_sequence=False):
file, local_file = util.convert_file_like_to_open_file(file_or_path, "rb")
try:
ts = _tskit.TreeSequence()
ts.load(
file,
skip_tables=skip_tables,
skip_reference_sequence=skip_reference_sequence,
)
return TreeSequence(ts)
except tskit.FileFormatError as e:
util.raise_known_file_format_errors(file, e)
finally:
if local_file:
file.close()
@classmethod
def load_tables(cls, tables, *, build_indexes=False):
ts = _tskit.TreeSequence()
ts.load_tables(tables._ll_tables, build_indexes=build_indexes)
return TreeSequence(ts)
[docs]
def dump(self, file_or_path, zlib_compression=False):
"""
Writes the tree sequence to the specified path or file object.
:param str file_or_path: The file object or path to write the TreeSequence to.
:param bool zlib_compression: This parameter is deprecated and ignored.
"""
if zlib_compression:
# Note: the msprime CLI before version 1.0 uses this option, so we need
# to keep it indefinitely.
warnings.warn(
"The zlib_compression option is no longer supported and is ignored",
RuntimeWarning,
stacklevel=4,
)
file, local_file = util.convert_file_like_to_open_file(file_or_path, "wb")
try:
self._ll_tree_sequence.dump(file)
finally:
if local_file:
file.close()
@property
def reference_sequence(self):
"""
The :class:`.ReferenceSequence` associated with this :class:`.TreeSequence`
if one is defined (see :meth:`.TreeSequence.has_reference_sequence`),
or None otherwise.
"""
if self.has_reference_sequence():
return tables.ReferenceSequence(self._ll_tree_sequence.reference_sequence)
return None
[docs]
def has_reference_sequence(self):
"""
Returns True if this :class:`.TreeSequence` has an associated
:ref:`reference sequence<sec_data_model_reference_sequence>`.
"""
return bool(self._ll_tree_sequence.has_reference_sequence())
@property
def tables_dict(self):
"""
Returns a dictionary mapping names to tables in the
underlying :class:`.TableCollection`. Equivalent to calling
``ts.tables.table_name_map``.
"""
return self.tables.table_name_map
@property
def tables(self):
"""
Returns the :class:`tables<TableCollection>` underlying this tree
sequence, intended for read-only access. See :meth:`.dump_tables` if you wish
to modify the tables.
.. warning:: This property currently returns a copy of the tables
underlying a tree sequence but it may return a read-only
**view** in the future. Thus, if the tables will subsequently be
updated, please use the :meth:`.dump_tables` method instead as
this will always return a new copy of the TableCollection.
:return: A :class:`TableCollection` containing all a copy of the
tables underlying this tree sequence.
:rtype: TableCollection
"""
return self.dump_tables()
@property
def nbytes(self):
"""
Returns the total number of bytes required to store the data
in this tree sequence. Note that this may not be equal to
the actual memory footprint.
"""
return self.tables.nbytes
[docs]
def dump_tables(self):
"""
Returns a modifiable copy of the :class:`tables<TableCollection>` defining
this tree sequence.
:return: A :class:`TableCollection` containing all tables underlying
the tree sequence.
:rtype: TableCollection
"""
ll_tables = _tskit.TableCollection(self.sequence_length)
self._ll_tree_sequence.dump_tables(ll_tables)
return tables.TableCollection(ll_tables=ll_tables)
[docs]
def dump_text(
self,
nodes=None,
edges=None,
sites=None,
mutations=None,
individuals=None,
populations=None,
migrations=None,
provenances=None,
precision=6,
encoding="utf8",
base64_metadata=True,
):
"""
Writes a text representation of the tables underlying the tree sequence
to the specified connections.
If Base64 encoding is not used, then metadata will be saved directly, possibly
resulting in errors reading the tables back in if metadata includes whitespace.
:param io.TextIOBase nodes: The file-like object (having a .write() method) to
write the NodeTable to.
:param io.TextIOBase edges: The file-like object to write the EdgeTable to.
:param io.TextIOBase sites: The file-like object to write the SiteTable to.
:param io.TextIOBase mutations: The file-like object to write the
MutationTable to.
:param io.TextIOBase individuals: The file-like object to write the
IndividualTable to.
:param io.TextIOBase populations: The file-like object to write the
PopulationTable to.
:param io.TextIOBase migrations: The file-like object to write the
MigrationTable to.
:param io.TextIOBase provenances: The file-like object to write the
ProvenanceTable to.
:param int precision: The number of digits of precision.
:param str encoding: Encoding used for text representation.
:param bool base64_metadata: Only used if a schema is not present on each table
being dumped. If True, metadata is encoded using Base64
encoding; otherwise, as plain text.
"""
text_formats.dump_text(
self,
nodes=nodes,
edges=edges,
sites=sites,
mutations=mutations,
individuals=individuals,
populations=populations,
migrations=migrations,
provenances=provenances,
precision=precision,
encoding=encoding,
base64_metadata=base64_metadata,
)
[docs]
def __str__(self):
"""
Return a plain text summary of the contents of a tree sequence
"""
ts_rows = [
["Trees", str(self.num_trees)],
[
"Sequence Length",
str(
int(self.sequence_length)
if self.discrete_genome
else self.sequence_length
),
],
["Time Units", self.time_units],
["Sample Nodes", str(self.num_samples)],
["Total Size", util.naturalsize(self.nbytes)],
]
header = ["Table", "Rows", "Size", "Has Metadata"]
table_rows = []
for name, table in self.tables.table_name_map.items():
table_rows.append(
[
name.capitalize(),
f"{table.num_rows:,}",
util.naturalsize(table.nbytes),
(
"Yes"
if hasattr(table, "metadata") and len(table.metadata) > 0
else "No"
),
]
)
return util.unicode_table(ts_rows, title="TreeSequence") + util.unicode_table(
table_rows, header=header
)
[docs]
def _repr_html_(self):
"""
Return an html summary of a tree sequence. Called by jupyter notebooks
to render a TreeSequence.
"""
return util.tree_sequence_html(self)
# num_samples was originally called sample_size, and so we must keep sample_size
# around as a deprecated alias.
@property
def num_samples(self):
"""
Returns the number of sample nodes in this tree sequence. This is also the
number of sample nodes in each tree.
:return: The number of sample nodes in this tree sequence.
:rtype: int
"""
return self._ll_tree_sequence.get_num_samples()
@property
def table_metadata_schemas(self) -> TableMetadataSchemas:
"""
The set of metadata schemas for the tables in this tree sequence.
"""
return self._table_metadata_schemas
@property
def sample_size(self):
# Deprecated alias for num_samples
return self.num_samples
def get_sample_size(self):
# Deprecated alias for num_samples
return self.num_samples
@property
def file_uuid(self):
return self._ll_tree_sequence.get_file_uuid()
@property
def discrete_genome(self):
"""
Returns True if all genome coordinates in this TreeSequence are
discrete integer values. This is true iff all the following are true:
- The sequence length is discrete
- All site positions are discrete
- All left and right edge coordinates are discrete
- All migration left and right coordinates are discrete
:return: True if this TreeSequence uses discrete genome coordinates.
:rtype: bool
"""
return bool(self._ll_tree_sequence.get_discrete_genome())
@property
def discrete_time(self):
"""
Returns True if all time coordinates in this TreeSequence are
discrete integer values. This is true iff all the following are true:
- All node times are discrete
- All mutation times are discrete
- All migration times are discrete
Note that ``tskit.UNKNOWN_TIME`` counts as discrete.
:return: True if this TreeSequence uses discrete time coordinates.
:rtype: bool
"""
return bool(self._ll_tree_sequence.get_discrete_time())
@property
def min_time(self):
"""
Returns the min time in this tree sequence. This is the minimum
of the node times and mutation times.
Note that mutation times with the value ``tskit.UNKNOWN_TIME``
are ignored.
:return: The min time of the nodes and mutations in this tree sequence.
:rtype: float
"""
return self._ll_tree_sequence.get_min_time()
@property
def max_time(self):
"""
Returns the max time in this tree sequence. This is the maximum
of the node times and mutation times.
Note that mutation times with the value ``tskit.UNKNOWN_TIME``
are ignored.
:return: The max time of the nodes and mutations in this tree sequence.
:rtype: float
"""
return self._ll_tree_sequence.get_max_time()
@property
def sequence_length(self):
"""
Returns the sequence length in this tree sequence. This defines the
genomic scale over which tree coordinates are defined. Given a
tree sequence with a sequence length :math:`L`, the constituent
trees will be defined over the half-closed interval
:math:`[0, L)`. Each tree then covers some subset of this
interval --- see :attr:`tskit.Tree.interval` for details.
:return: The length of the sequence in this tree sequence in bases.
:rtype: float
"""
return self.get_sequence_length()
def get_sequence_length(self):
return self._ll_tree_sequence.get_sequence_length()
@property
def metadata(self) -> Any:
"""
The decoded metadata for this TreeSequence.
"""
return self.metadata_schema.decode_row(self._ll_tree_sequence.get_metadata())
@property
def metadata_schema(self) -> metadata_module.MetadataSchema:
"""
The :class:`tskit.MetadataSchema` for this TreeSequence.
"""
return metadata_module.parse_metadata_schema(
self._ll_tree_sequence.get_metadata_schema()
)
@property
def time_units(self) -> str:
"""
String describing the units of the time dimension for this TreeSequence.
"""
return self._ll_tree_sequence.get_time_units()
@property
def num_edges(self):
"""
Returns the number of :ref:`edges <sec_edge_table_definition>` in this
tree sequence.
:return: The number of edges in this tree sequence.
:rtype: int
"""
return self._ll_tree_sequence.get_num_edges()
def get_num_trees(self):
# Deprecated alias for self.num_trees
return self.num_trees
@property
def num_trees(self):
"""
Returns the number of distinct trees in this tree sequence. This
is equal to the number of trees returned by the :meth:`.trees`
method.
:return: The number of trees in this tree sequence.
:rtype: int
"""
return self._ll_tree_sequence.get_num_trees()
def get_num_sites(self):
# Deprecated alias for self.num_sites
return self._ll_tree_sequence.get_num_sites()
@property
def num_sites(self):
"""
Returns the number of :ref:`sites <sec_site_table_definition>` in
this tree sequence.
:return: The number of sites in this tree sequence.
:rtype: int
"""
return self.get_num_sites()
def get_num_mutations(self):
# Deprecated alias for self.num_mutations
return self.num_mutations
@property
def num_mutations(self):
"""
Returns the number of :ref:`mutations <sec_mutation_table_definition>`
in this tree sequence.
:return: The number of mutations in this tree sequence.
:rtype: int
"""
return self._ll_tree_sequence.get_num_mutations()
def get_num_nodes(self):
# Deprecated alias for self.num_nodes
return self.num_nodes
@property
def num_individuals(self):
"""
Returns the number of :ref:`individuals <sec_individual_table_definition>` in
this tree sequence.
:return: The number of individuals in this tree sequence.
:rtype: int
"""
return self._ll_tree_sequence.get_num_individuals()
@property
def num_nodes(self):
"""
Returns the number of :ref:`nodes <sec_node_table_definition>` in
this tree sequence.
:return: The number of nodes in this tree sequence.
:rtype: int
"""
return self._ll_tree_sequence.get_num_nodes()
@property
def num_provenances(self):
"""
Returns the number of :ref:`provenances <sec_provenance_table_definition>`
in this tree sequence.
:return: The number of provenances in this tree sequence.
:rtype: int
"""
return self._ll_tree_sequence.get_num_provenances()
@property
def num_populations(self):
"""
Returns the number of :ref:`populations <sec_population_table_definition>`
in this tree sequence.
:return: The number of populations in this tree sequence.
:rtype: int
"""
return self._ll_tree_sequence.get_num_populations()
@property
def num_migrations(self):
"""
Returns the number of :ref:`migrations <sec_migration_table_definition>`
in this tree sequence.
:return: The number of migrations in this tree sequence.
:rtype: int
"""
return self._ll_tree_sequence.get_num_migrations()
@property
def max_root_time(self):
"""
Returns the time of the oldest root in any of the trees in this tree sequence.
This is usually equal to ``np.max(ts.tables.nodes.time)`` but may not be
since there can be non-sample nodes that are not present in any tree. Note that
isolated samples are also defined as roots (so there can be a max_root_time
even in a tree sequence with no edges).
:return: The maximum time of a root in this tree sequence.
:rtype: float
:raises ValueError: If there are no samples in the tree, and hence no roots (as
roots are defined by the ends of the upward paths from the set of samples).
"""
if self.num_samples == 0:
raise ValueError(
"max_root_time is not defined in a tree sequence with 0 samples"
)
ret = max(self.nodes_time[u] for u in self.samples())
if self.num_edges > 0:
# Edges are guaranteed to be listed in parent-time order, so we can get the
# last one to get the oldest root
edge = self.edge(self.num_edges - 1)
# However, we can have situations where there is a sample older than a
# 'proper' root
ret = max(ret, self.nodes_time[edge.parent])
return ret
[docs]
def migrations(self):
"""
Returns an iterable sequence of all the
:ref:`migrations <sec_migration_table_definition>` in this tree sequence.
Migrations are returned in nondecreasing order of the ``time`` value.
:return: An iterable sequence of all migrations.
:rtype: Sequence(:class:`.Migration`)
"""
return SimpleContainerSequence(self.migration, self.num_migrations)
[docs]
def individuals(self):
"""
Returns an iterable sequence of all the
:ref:`individuals <sec_individual_table_definition>` in this tree sequence.
:return: An iterable sequence of all individuals.
:rtype: Sequence(:class:`.Individual`)
"""
return SimpleContainerSequence(self.individual, self.num_individuals)
[docs]
def nodes(self, *, order=None):
"""
Returns an iterable sequence of all the :ref:`nodes <sec_node_table_definition>`
in this tree sequence.
.. note:: Although node ids are commonly ordered by node time, this is not a
formal tree sequence requirement. If you wish to iterate over nodes in
time order, you should therefore use ``order="timeasc"`` (and wrap the
resulting sequence in the standard Python :func:`python:reversed` function
if you wish to iterate over older nodes before younger ones)
:param str order: The order in which the nodes should be returned: must be
one of "id" (default) or "timeasc" (ascending order of time, then by
ascending node id, matching the first two ordering requirements of
parent nodes in a :meth:`sorted <TableCollection.sort>` edge table).
:return: An iterable sequence of all nodes.
:rtype: Sequence(:class:`.Node`)
"""
order = "id" if order is None else order
if order not in ["id", "timeasc"]:
raise ValueError('order must be "id" or "timeasc"')
odr = None
if order == "timeasc":
odr = np.lexsort((np.arange(self.num_nodes), self.nodes_time))
return SimpleContainerSequence(self.node, self.num_nodes, order=odr)
[docs]
def edges(self):
"""
Returns an iterable sequence of all the :ref:`edges <sec_edge_table_definition>`
in this tree sequence. Edges are returned in the order required
for a :ref:`valid tree sequence <sec_valid_tree_sequence_requirements>`. So,
edges are guaranteed to be ordered such that (a) all parents with a
given ID are contiguous; (b) edges are returned in non-decreasing
order of parent time ago; (c) within the edges for a given parent, edges
are sorted first by child ID and then by left coordinate.
:return: An iterable sequence of all edges.
:rtype: Sequence(:class:`.Edge`)
"""
return SimpleContainerSequence(self.edge, self.num_edges)
def edgesets(self):
# TODO the order that these records are returned in is not well specified.
# Hopefully this does not matter, and we can just state that the ordering
# should not be depended on.
children = collections.defaultdict(set)
active_edgesets = {}
for (left, right), edges_out, edges_in in self.edge_diffs():
# Complete and return any edgesets that are affected by this tree
# transition
parents = iter(edge.parent for edge in itertools.chain(edges_out, edges_in))
for parent in parents:
if parent in active_edgesets:
edgeset = active_edgesets.pop(parent)
edgeset.right = left
edgeset.children = sorted(children[parent])
yield edgeset
for edge in edges_out:
children[edge.parent].remove(edge.child)
for edge in edges_in:
children[edge.parent].add(edge.child)
# Update the active edgesets
for edge in itertools.chain(edges_out, edges_in):
if (
len(children[edge.parent]) > 0
and edge.parent not in active_edgesets
):
active_edgesets[edge.parent] = Edgeset(left, right, edge.parent, [])
for parent in active_edgesets.keys():
edgeset = active_edgesets[parent]
edgeset.right = self.sequence_length
edgeset.children = sorted(children[edgeset.parent])
yield edgeset
def _edge_diffs_forward(self, include_terminal=False):
metadata_decoder = self.table_metadata_schemas.edge.decode_row
edge_left = self.edges_left
edge_right = self.edges_right
sequence_length = self.sequence_length
in_order = self.indexes_edge_insertion_order
out_order = self.indexes_edge_removal_order
M = self.num_edges
j = 0
k = 0
left = 0.0
while j < M or left < sequence_length:
edges_out = []
edges_in = []
while k < M and edge_right[out_order[k]] == left:
edges_out.append(
Edge(
*self._ll_tree_sequence.get_edge(out_order[k]),
id=out_order[k],
metadata_decoder=metadata_decoder,
)
)
k += 1
while j < M and edge_left[in_order[j]] == left:
edges_in.append(
Edge(
*self._ll_tree_sequence.get_edge(in_order[j]),
id=in_order[j],
metadata_decoder=metadata_decoder,
)
)
j += 1
right = sequence_length
if j < M:
right = min(right, edge_left[in_order[j]])
if k < M:
right = min(right, edge_right[out_order[k]])
yield EdgeDiff(Interval(left, right), edges_out, edges_in)
left = right
if include_terminal:
edges_out = []
while k < M:
edges_out.append(
Edge(
*self._ll_tree_sequence.get_edge(out_order[k]),
id=out_order[k],
metadata_decoder=metadata_decoder,
)
)
k += 1
yield EdgeDiff(Interval(left, right), edges_out, [])
def _edge_diffs_reverse(self, include_terminal=False):
metadata_decoder = self.table_metadata_schemas.edge.decode_row
edge_left = self.edges_left
edge_right = self.edges_right
sequence_length = self.sequence_length
in_order = self.indexes_edge_removal_order
out_order = self.indexes_edge_insertion_order
M = self.num_edges
j = M - 1
k = M - 1
right = sequence_length
while j >= 0 or right > 0:
edges_out = []
edges_in = []
while k >= 0 and edge_left[out_order[k]] == right:
edges_out.append(
Edge(
*self._ll_tree_sequence.get_edge(out_order[k]),
id=out_order[k],
metadata_decoder=metadata_decoder,
)
)
k -= 1
while j >= 0 and edge_right[in_order[j]] == right:
edges_in.append(
Edge(
*self._ll_tree_sequence.get_edge(in_order[j]),
id=in_order[j],
metadata_decoder=metadata_decoder,
)
)
j -= 1
left = 0
if j >= 0:
left = max(left, edge_right[in_order[j]])
if k >= 0:
left = max(left, edge_left[out_order[k]])
yield EdgeDiff(Interval(left, right), edges_out, edges_in)
right = left
if include_terminal:
edges_out = []
while k >= 0:
edges_out.append(
Edge(
*self._ll_tree_sequence.get_edge(out_order[k]),
id=out_order[k],
metadata_decoder=metadata_decoder,
)
)
k -= 1
yield EdgeDiff(Interval(left, right), edges_out, [])
[docs]
def edge_diffs(self, include_terminal=False, *, direction=tskit.FORWARD):
"""
Returns an iterator over all the :ref:`edges <sec_edge_table_definition>` that
are inserted and removed to build the trees as we move from left-to-right along
the tree sequence. Each iteration yields a named tuple consisting of 3 values,
``(interval, edges_out, edges_in)``. The first value, ``interval``, is the
genomic interval ``(left, right)`` covered by the incoming tree
(see :attr:`Tree.interval`). The second, ``edges_out`` is a list of the edges
that were just-removed to create the tree covering the interval
(hence ``edges_out`` will always be empty for the first tree). The last value,
``edges_in``, is a list of edges that were just
inserted to construct the tree covering the current interval.
The edges returned within each ``edges_in`` list are ordered by ascending
time of the parent node, then ascending parent id, then ascending child id.
The edges within each ``edges_out`` list are the reverse order (e.g.
descending parent time, parent id, then child_id). This means that within
each list, edges with the same parent appear consecutively.
The ``direction`` argument can be used to control whether diffs are produced
in the forward (left-to-right, increasing genome coordinate value)
or reverse (right-to-left, decreasing genome coordinate value) direction.
:param bool include_terminal: If False (default), the iterator terminates
after the final interval in the tree sequence (i.e., it does not
report a final removal of all remaining edges), and the number
of iterations will be equal to the number of trees in the tree
sequence. If True, an additional iteration takes place, with the last
``edges_out`` value reporting all the edges contained in the final
tree (with both ``left`` and ``right`` equal to the sequence length).
:param int direction: The direction of travel along the sequence for
diffs. Must be one of :data:`.FORWARD` or :data:`.REVERSE`.
(Default: :data:`.FORWARD`).
:return: An iterator over the (interval, edges_out, edges_in) tuples. This
is a named tuple, so the 3 values can be accessed by position
(e.g. ``returned_tuple[0]``) or name (e.g. ``returned_tuple.interval``).
:rtype: :class:`collections.abc.Iterable`
"""
if direction == _tskit.FORWARD:
return self._edge_diffs_forward(include_terminal=include_terminal)
elif direction == _tskit.REVERSE:
return self._edge_diffs_reverse(include_terminal=include_terminal)
else:
raise ValueError("direction must be either tskit.FORWARD or tskit.REVERSE")
[docs]
def sites(self):
"""
Returns an iterable sequence of all the :ref:`sites <sec_site_table_definition>`
in this tree sequence. Sites are returned in order of increasing ID
(and also position). See the :class:`Site` class for details on
the available fields for each site.
:return: An iterable sequence of all sites.
:rtype: Sequence(:class:`.Site`)
"""
return SimpleContainerSequence(self.site, self.num_sites)
[docs]
def mutations(self):
"""
Returns an iterator over all the
:ref:`mutations <sec_mutation_table_definition>` in this tree sequence.
Mutations are returned in order of nondecreasing site ID.
See the :class:`Mutation` class for details on the available fields for
each mutation.
The returned iterator is equivalent to iterating over all sites
and all mutations in each site, i.e.::
>>> for site in tree_sequence.sites():
>>> for mutation in site.mutations:
>>> yield mutation
:return: An iterator over all mutations in this tree sequence.
:rtype: iter(:class:`Mutation`)
"""
for site in self.sites():
yield from site.mutations
[docs]
def populations(self):
"""
Returns an iterable sequence of all the
:ref:`populations <sec_population_table_definition>` in this tree sequence.
:return: An iterable sequence of all populations.
:rtype: Sequence(:class:`.Population`)
"""
return SimpleContainerSequence(self.population, self.num_populations)
[docs]
def provenances(self):
"""
Returns an iterable sequence of all the
:ref:`provenances <sec_provenance_table_definition>` in this tree sequence.
:return: An iterable sequence of all provenances.
:rtype: Sequence(:class:`.Provenance`)
"""
return SimpleContainerSequence(self.provenance, self.num_provenances)
[docs]
def breakpoints(self, as_array=False):
"""
Returns the breakpoints that separate trees along the chromosome, including the
two extreme points 0 and L. This is equivalent to
>>> iter([0] + [t.interval.right for t in self.trees()])
By default we return an iterator over the breakpoints as Python float objects;
if ``as_array`` is True we return them as a numpy array.
Note that the ``as_array`` form will be more efficient and convenient in most
cases; the default iterator behaviour is mainly kept to ensure compatibility
with existing code.
:param bool as_array: If True, return the breakpoints as a numpy array.
:return: The breakpoints defined by the tree intervals along the sequence.
:rtype: collections.abc.Iterable or numpy.ndarray
"""
breakpoints = self.ll_tree_sequence.get_breakpoints()
if not as_array:
# Convert to Python floats for backward compatibility.
breakpoints = map(float, breakpoints)
return breakpoints
[docs]
def at(self, position, **kwargs):
"""
Returns the tree covering the specified genomic location. The returned tree
will have ``tree.interval.left`` <= ``position`` < ``tree.interval.right``.
See also :meth:`Tree.seek`.
.. include:: substitutions/linear_traversal_warning.rst
:param float position: A genomic location.
:param \\**kwargs: Further arguments used as parameters when constructing the
returned :class:`Tree`. For example ``ts.at(2.5, sample_lists=True)`` will
result in a :class:`Tree` created with ``sample_lists=True``.
:return: A new instance of :class:`Tree` positioned to cover the specified
genomic location.
:rtype: Tree
"""
tree = Tree(self, **kwargs)
tree.seek(position)
return tree
[docs]
def at_index(self, index, **kwargs):
"""
Returns the tree at the specified index. See also :meth:`Tree.seek_index`.
.. include:: substitutions/linear_traversal_warning.rst
:param int index: The index of the required tree.
:param \\**kwargs: Further arguments used as parameters when constructing the
returned :class:`Tree`. For example ``ts.at_index(4, sample_lists=True)``
will result in a :class:`Tree` created with ``sample_lists=True``.
:return: A new instance of :class:`Tree` positioned at the specified index.
:rtype: Tree
"""
tree = Tree(self, **kwargs)
tree.seek_index(index)
return tree
[docs]
def first(self, **kwargs):
"""
Returns the first tree in this :class:`TreeSequence`. To iterate over all
trees in the sequence, use the :meth:`.trees` method.
:param \\**kwargs: Further arguments used as parameters when constructing the
returned :class:`Tree`. For example ``ts.first(sample_lists=True)`` will
result in a :class:`Tree` created with ``sample_lists=True``.
:return: The first tree in this tree sequence.
:rtype: :class:`Tree`.
"""
tree = Tree(self, **kwargs)
tree.first()
return tree
[docs]
def last(self, **kwargs):
"""
Returns the last tree in this :class:`TreeSequence`. To iterate over all
trees in the sequence, use the :meth:`.trees` method.
:param \\**kwargs: Further arguments used as parameters when constructing the
returned :class:`Tree`. For example ``ts.first(sample_lists=True)`` will
result in a :class:`Tree` created with ``sample_lists=True``.
:return: The last tree in this tree sequence.
:rtype: :class:`Tree`.
"""
tree = Tree(self, **kwargs)
tree.last()
return tree
[docs]
def trees(
self,
tracked_samples=None,
*,
sample_lists=False,
root_threshold=1,
sample_counts=None,
tracked_leaves=None,
leaf_counts=None,
leaf_lists=None,
):
"""
Returns an iterator over the trees in this tree sequence. Each value
returned in this iterator is an instance of :class:`Tree`. Upon
successful termination of the iterator, the tree will be in the
"cleared" null state.
The ``sample_lists`` and ``tracked_samples`` parameters are passed
to the :class:`Tree` constructor, and control
the options that are set in the returned tree instance.
.. warning:: Do not store the results of this iterator in a list!
For performance reasons, the same underlying object is used
for every tree returned which will most likely lead to unexpected
behaviour. If you wish to obtain a list of trees in a tree sequence
please use ``ts.aslist()`` instead.
:param list tracked_samples: The list of samples to be tracked and
counted using the :meth:`Tree.num_tracked_samples` method.
:param bool sample_lists: If True, provide more efficient access
to the samples beneath a given node using the
:meth:`Tree.samples` method.
:param int root_threshold: The minimum number of samples that a node
must be ancestral to for it to be in the list of roots. By default
this is 1, so that isolated samples (representing missing data)
are roots. To efficiently restrict the roots of the tree to
those subtending meaningful topology, set this to 2. This value
is only relevant when trees have multiple roots.
:param bool sample_counts: Deprecated since 0.2.4.
:return: An iterator over the Trees in this tree sequence.
:rtype: collections.abc.Iterable, :class:`Tree`
"""
# tracked_leaves, leaf_counts and leaf_lists are deprecated aliases
# for tracked_samples, sample_counts and sample_lists respectively.
# These are left over from an older version of the API when leaves
# and samples were synonymous.
if tracked_leaves is not None:
tracked_samples = tracked_leaves
if leaf_counts is not None:
sample_counts = leaf_counts
if leaf_lists is not None:
sample_lists = leaf_lists
tree = Tree(
self,
tracked_samples=tracked_samples,
sample_lists=sample_lists,
root_threshold=root_threshold,
sample_counts=sample_counts,
)
return TreeIterator(tree)
[docs]
def coiterate(self, other, **kwargs):
"""
Returns an iterator over the pairs of trees for each distinct
interval in the specified pair of tree sequences.
:param TreeSequence other: The other tree sequence from which to take trees. The
sequence length must be the same as the current tree sequence.
:param \\**kwargs: Further named arguments that will be passed to the
:meth:`.trees` method when constructing the returned trees.
:return: An iterator returning successive tuples of the form
``(interval, tree_self, tree_other)``. For example, the first item returned
will consist of an tuple of the initial interval, the first tree of the
current tree sequence, and the first tree of the ``other`` tree sequence;
the ``.left`` attribute of the initial interval will be 0 and the ``.right``
attribute will be the smallest non-zero breakpoint of the 2 tree sequences.
:rtype: iter(:class:`Interval`, :class:`Tree`, :class:`Tree`)
"""
if self.sequence_length != other.sequence_length:
raise ValueError("Tree sequences must be of equal sequence length.")
L = self.sequence_length
trees1 = self.trees(**kwargs)
trees2 = other.trees(**kwargs)
tree1 = next(trees1)
tree2 = next(trees2)
right = 0
while right != L:
left = right
right = min(tree1.interval.right, tree2.interval.right)
yield Interval(left, right), tree1, tree2
# Advance
if tree1.interval.right == right:
tree1 = next(trees1, None)
if tree2.interval.right == right:
tree2 = next(trees2, None)
def _check_genomic_range(self, left, right, ensure_integer=False):
if left is None:
left = 0
if right is None:
right = self.sequence_length
if np.isnan(left) or left < 0 or left >= self.sequence_length:
raise ValueError(
"`left` not between zero (inclusive) and sequence length (exclusive)"
)
if np.isnan(right) or right <= 0 or right > self.sequence_length:
raise ValueError(
"`right` not between zero (exclusive) and sequence length (inclusive)"
)
if left >= right:
raise ValueError("`left` must be less than `right`")
if ensure_integer:
if left != int(left) or right != int(right):
raise ValueError("`left` and `right` must be integers")
return Interval(int(left), int(right))
return Interval(left, right)
def _haplotypes_array(
self,
*,
interval,
isolated_as_missing=None,
missing_data_character=None,
samples=None,
):
# return an array of haplotypes and the first and last site positions
if missing_data_character is None:
missing_data_character = "N"
start_site, stop_site = np.searchsorted(self.sites_position, interval)
H = np.empty(
(
self.num_samples if samples is None else len(samples),
stop_site - start_site,
),
dtype=np.int8,
)
missing_int8 = ord(missing_data_character.encode("ascii"))
for var in self.variants(
samples=samples,
isolated_as_missing=isolated_as_missing,
left=interval.left,
right=interval.right,
):
alleles = np.full(len(var.alleles), missing_int8, dtype=np.int8)
for i, allele in enumerate(var.alleles):
if allele is not None:
if len(allele) != 1:
raise TypeError(
"Multi-letter allele or deletion detected at site {}".format(
var.site.id
)
)
try:
ascii_allele = allele.encode("ascii")
except UnicodeEncodeError:
raise TypeError(
"Non-ascii character in allele at site {}".format(
var.site.id
)
)
allele_int8 = ord(ascii_allele)
if allele_int8 == missing_int8:
raise ValueError(
"The missing data character '{}' clashes with an "
"existing allele at site {}".format(
missing_data_character, var.site.id
)
)
alleles[i] = allele_int8
H[:, var.site.id - start_site] = alleles[var.genotypes]
return H, (start_site, stop_site - 1)
[docs]
def haplotypes(
self,
*,
isolated_as_missing=None,
missing_data_character=None,
samples=None,
left=None,
right=None,
impute_missing_data=None,
):
"""
Returns an iterator over the strings of haplotypes that result from
the trees and mutations in this tree sequence. Each haplotype string
is guaranteed to be of the same length. A tree sequence with
:math:`n` samples and with :math:`s` sites lying between ``left`` and
``right`` will return a total of :math:`n`
strings of :math:`s` alleles concatenated together, where an allele
consists of a single ascii character (tree sequences that include alleles
which are not a single character in length, or where the character is
non-ascii, will raise an error). The first string returned is the
haplotype for the first requested sample, and so on.
The alleles at each site must be represented by single byte characters,
(i.e., variants must be single nucleotide polymorphisms, or SNPs), hence
the strings returned will all be of length :math:`s`. If the ``left``
position is less than or equal to the position of the first site, for a
haplotype ``h``, the value of ``h[j]`` will therefore be the observed
allelic state at site ``j``.
If ``isolated_as_missing`` is True (the default), isolated samples without
mutations directly above them will be treated as
:ref:`missing data<sec_data_model_missing_data>` and will be
represented in the string by the ``missing_data_character``. If
instead it is set to False, missing data will be assigned the ancestral state
(unless they have mutations directly above them, in which case they will take
the most recent derived mutational state for that node). This was the default
behaviour in versions prior to 0.2.0. Prior to 0.3.0 the `impute_missing_data`
argument controlled this behaviour.
See also the :meth:`.variants` iterator for site-centric access
to sample genotypes.
.. warning::
For large datasets, this method can consume a **very large** amount of
memory! To output all the sample data, it is more efficient to iterate
over sites rather than over samples.
:return: An iterator over the haplotype strings for the samples in
this tree sequence.
:param bool isolated_as_missing: If True, the allele assigned to
missing samples (i.e., isolated samples without mutations) is
the ``missing_data_character``. If False,
missing samples will be assigned the ancestral state.
Default: True.
:param str missing_data_character: A single ascii character that will
be used to represent missing data.
If any normal allele contains this character, an error is raised.
Default: 'N'.
:param list[int] samples: The samples for which to output haplotypes. If
``None`` (default), return haplotypes for all the samples in the tree
sequence, in the order given by the :meth:`.samples` method.
:param int left: Haplotype strings will start with the first site at or after
this genomic position. If ``None`` (default) start at the first site.
:param int right: Haplotype strings will end with the last site before this
position. If ``None`` (default) assume ``right`` is the sequence length
(i.e. the last character in the string will be the last site in the tree
sequence).
:param bool impute_missing_data:
*Deprecated in 0.3.0. Use ``isolated_as_missing``, but inverting value.
Will be removed in a future version*
:rtype: collections.abc.Iterable
:raises TypeError: if the ``missing_data_character`` or any of the alleles
at a site are not a single ascii character.
:raises ValueError: if the ``missing_data_character`` exists in one of the
alleles
"""
if impute_missing_data is not None:
warnings.warn(
"The impute_missing_data parameter was deprecated in 0.3.0 and will"
" be removed. Use ``isolated_as_missing=False`` instead of"
"``impute_missing_data=True``.",
FutureWarning,
stacklevel=4,
)
# Only use impute_missing_data if isolated_as_missing has the default value
if isolated_as_missing is None:
isolated_as_missing = not impute_missing_data
interval = self._check_genomic_range(left, right)
H, _ = self._haplotypes_array(
interval=interval,
isolated_as_missing=isolated_as_missing,
missing_data_character=missing_data_character,
samples=samples,
)
for h in H:
yield h.tobytes().decode("ascii")
[docs]
def variants(
self,
*,
samples=None,
isolated_as_missing=None,
alleles=None,
impute_missing_data=None,
copy=None,
left=None,
right=None,
):
"""
Returns an iterator over the variants between the ``left`` (inclusive)
and ``right`` (exclusive) genomic positions in this tree sequence. Each
returned :class:`Variant` object has a site, a list of possible allelic
states and an array of genotypes for the specified ``samples``. The
``genotypes`` value is a numpy array containing indexes into the
``alleles`` list. By default, this list is generated automatically for
each site such that the first entry, ``alleles[0]``, is the ancestral
state and subsequent alleles are listed in no
particular order. This means that the encoding of alleles in
terms of genotype values can vary from site-to-site, which is
sometimes inconvenient. It is possible to specify a fixed mapping
from allele strings to genotype values using the ``alleles``
parameter. For example, if we set ``alleles=("A", "C", "G", "T")``,
this will map allele "A" to 0, "C" to 1 and so on (the
:data:`ALLELES_ACGT` constant provides a shortcut for this
common mapping).
By default, genotypes are generated for all samples. The ``samples``
parameter allows us to specify the nodes for which genotypes are
generated; output order of genotypes in the returned variants
corresponds to the order of the samples in this list. It is also
possible to provide **non-sample** nodes as an argument here, if you
wish to generate genotypes for (e.g.) internal nodes. However,
``isolated_as_missing`` must be False in this case, as it is not
possible to detect missing data for non-sample nodes.
If isolated samples are present at a given site without mutations above them,
they are interpreted by default as
:ref:`missing data<sec_data_model_missing_data>`, and the genotypes array
will contain a special value :data:`MISSING_DATA` (-1) to identify them
while the ``alleles`` tuple will end with the value ``None`` (note that this
will be the case whether or not we specify a fixed mapping using the
``alleles`` parameter; see the :class:`Variant` class for more details).
Alternatively, if ``isolated_as_missing`` is set to to False, such isolated
samples will not be treated as missing, and instead assigned the ancestral
state (this was the default behaviour in versions prior to 0.2.0). Prior to
0.3.0 the `impute_missing_data` argument controlled this behaviour.
:param array_like samples: An array of node IDs for which to generate
genotypes, or None for all sample nodes. Default: None.
:param bool isolated_as_missing: If True, the genotype value assigned to
missing samples (i.e., isolated samples without mutations) is
:data:`.MISSING_DATA` (-1). If False, missing samples will be
assigned the allele index for the ancestral state.
Default: True.
:param tuple alleles: A tuple of strings defining the encoding of
alleles as integer genotype values. At least one allele must be provided.
If duplicate alleles are provided, output genotypes will always be
encoded as the first occurrence of the allele. If None (the default),
the alleles are encoded as they are encountered during genotype
generation.
:param bool impute_missing_data:
*Deprecated in 0.3.0. Use ``isolated_as_missing``, but inverting value.
Will be removed in a future version*
:param bool copy:
If False re-use the same Variant object for each site such that any
references held to it are overwritten when the next site is visited.
If True return a fresh :class:`Variant` for each site. Default: True.
:param int left: Start with the first site at or after
this genomic position. If ``None`` (default) start at the first site.
:param int right: End with the last site before this position. If ``None``
(default) assume ``right`` is the sequence length, so that the last
variant corresponds to the last site in the tree sequence.
:return: An iterator over all variants in this tree sequence.
:rtype: iter(:class:`Variant`)
"""
interval = self._check_genomic_range(left, right)
if impute_missing_data is not None:
warnings.warn(
"The impute_missing_data parameter was deprecated in 0.3.0 and will"
" be removed. Use ``isolated_as_missing=False`` instead of"
"``impute_missing_data=True``.",
FutureWarning,
stacklevel=4,
)
# Only use impute_missing_data if isolated_as_missing has the default value
if isolated_as_missing is None:
isolated_as_missing = not impute_missing_data
if copy is None:
copy = True
# See comments for the Variant type for discussion on why the
# present form was chosen.
variant = tskit.Variant(
self,
samples=samples,
isolated_as_missing=isolated_as_missing,
alleles=alleles,
)
if left == 0 and right == self.sequence_length:
start = 0
stop = self.num_sites
else:
start, stop = np.searchsorted(self.sites_position, interval)
if copy:
for site_id in range(start, stop):
variant.decode(site_id)
yield variant.copy()
else:
for site_id in range(start, stop):
variant.decode(site_id)
yield variant
[docs]
def genotype_matrix(
self,
*,
samples=None,
isolated_as_missing=None,
alleles=None,
impute_missing_data=None,
):
"""
Returns an :math:`m \\times n` numpy array of the genotypes in this
tree sequence, where :math:`m` is the number of sites and :math:`n`
the number of samples. The genotypes are the indexes into the array
of ``alleles``, as described for the :class:`Variant` class.
If isolated samples are present at a given site without mutations above them,
they will be interpreted as :ref:`missing data<sec_data_model_missing_data>`
the genotypes array will contain a special value :data:`MISSING_DATA`
(-1) to identify these missing samples.
Such samples are treated as missing data by default, but if
``isolated_as_missing`` is set to to False, they will not be treated as missing,
and so assigned the ancestral state. This was the default behaviour in
versions prior to 0.2.0. Prior to 0.3.0 the `impute_missing_data`
argument controlled this behaviour.
.. warning::
This method can consume a **very large** amount of memory! If
all genotypes are not needed at once, it is usually better to
access them sequentially using the :meth:`.variants` iterator.
:param array_like samples: An array of node IDs for which to generate
genotypes, or None for all sample nodes. Default: None.
:param bool isolated_as_missing: If True, the genotype value assigned to
missing samples (i.e., isolated samples without mutations) is
:data:`.MISSING_DATA` (-1). If False, missing samples will be
assigned the allele index for the ancestral state.
Default: True.
:param tuple alleles: A tuple of strings describing the encoding of
alleles to genotype values. At least one allele must be provided.
If duplicate alleles are provided, output genotypes will always be
encoded as the first occurrence of the allele. If None (the default),
the alleles are encoded as they are encountered during genotype
generation.
:param bool impute_missing_data:
*Deprecated in 0.3.0. Use ``isolated_as_missing``, but inverting value.
Will be removed in a future version*
:return: The full matrix of genotypes.
:rtype: numpy.ndarray (dtype=np.int32)
"""
if impute_missing_data is not None:
warnings.warn(
"The impute_missing_data parameter was deprecated in 0.3.0 and will"
" be removed. Use ``isolated_as_missing=False`` instead of"
"``impute_missing_data=True``.",
FutureWarning,
stacklevel=4,
)
# Only use impute_missing_data if isolated_as_missing has the default value
if isolated_as_missing is None:
isolated_as_missing = not impute_missing_data
variant = tskit.Variant(
self,
samples=samples,
isolated_as_missing=isolated_as_missing,
alleles=alleles,
)
num_samples = self.num_samples if samples is None else len(samples)
ret = np.zeros(shape=(self.num_sites, num_samples), dtype=np.int32)
for site_id in range(self.num_sites):
variant.decode(site_id)
ret[site_id, :] = variant.genotypes
return ret
[docs]
def alignments(
self,
*,
reference_sequence=None,
missing_data_character=None,
samples=None,
left=None,
right=None,
):
"""
Returns an iterator over the full sequence alignments for the defined samples
in this tree sequence. Each alignment ``a`` is a string of length ``L`` where
the first character is the genomic sequence at the ``start`` position in the
genome (defaulting to 0) and the last character is the genomic sequence one
position before the ``stop`` value (defaulting to the :attr:`.sequence_length`
of this tree sequence, which must have :attr:`.discrete_genome` equal to True).
By default ``L`` is therefore equal to the :attr:`.sequence_length`,
and ``a[j]`` is the nucleotide value at genomic position ``j``.
.. note:: This is inherently a **zero-based** representation of the sequence
coordinate space. Care will be needed when interacting with other
libraries and upstream coordinate spaces.
The :ref:`sites<sec_data_model_definitions_site>` in a tree sequence will
usually only define the variation for a subset of the ``L`` nucleotide
positions along the genome, and the remaining positions are filled using
a :ref:`reference sequence <sec_data_model_reference_sequence>`.
The reference sequence data is defined either via the
``reference_sequence`` parameter to this method, or embedded within
with the tree sequence itself via the :attr:`.TreeSequence.reference_sequence`.
Site information from the tree sequence takes precedence over the reference
sequence so that, for example, at a site with no mutations all samples
will have the site's ancestral state.
The reference sequence bases are determined in the following way:
- If the ``reference_sequence`` parameter is supplied this will be
used, regardless of whether the tree sequence has an embedded
reference sequence.
- Otherwise, if the tree sequence has an embedded reference sequence,
this will be used.
- If the ``reference_sequence`` parameter is not specified and
there is no embedded reference sequence, ``L`` copies of the
``missing_data_character`` (which defaults to 'N') are used
instead.
.. warning:: The :class:`.ReferenceSequence` API is preliminary and
some behaviours may change in the future. In particular, a
tree sequence is currently regarded as having an embedded reference
sequence even if it only has some metadata defined. In this case
the ``reference_sequence`` parameter will need to be explicitly set.
.. note::
Two common options for setting a reference sequence are:
- Mark them as missing data, by setting
``reference_sequence="N" * int(ts.sequence_length)``
- Fill the gaps with random nucleotides, by setting
``reference_sequence=tskit.random_nucleotides(ts.sequence_length)``.
See the :func:`.random_nucleotides` function for more information.
.. warning:: Insertions and deletions are not currently supported and
the alleles at each site must be represented by
single byte characters, (i.e., variants must be single nucleotide
polymorphisms, or SNPs).
.. warning:: :ref:`Missing data<sec_data_model_missing_data>` is not
currently supported by this method and it will raise a ValueError
if called on tree sequences containing isolated samples.
See https://github.com/tskit-dev/tskit/issues/1896 for more
information.
See also the :meth:`.variants` iterator for site-centric access
to sample genotypes and :meth:`.haplotypes` for access to sample sequences
at just the sites in the tree sequence.
:param str reference_sequence: The reference sequence to fill in
gaps between sites in the alignments.
:param str missing_data_character: A single ascii character that will
be used to represent missing data.
If any normal allele contains this character, an error is raised.
Default: 'N'.
:param list[int] samples: The samples for which to output alignments. If
``None`` (default), return alignments for all the samples in the tree
sequence, in the order given by the :meth:`.samples` method.
:param int left: Alignments will start at this genomic position. If ``None``
(default) alignments start at 0.
:param int right: Alignments will stop before this genomic position. If ``None``
(default) alignments will continue until the end of the tree sequence.
:return: An iterator over the alignment strings for specified samples in
this tree sequence, in the order given in ``samples``.
:rtype: collections.abc.Iterable
:raises ValueError: if any genome coordinate in this tree sequence is not
discrete, or if the ``reference_sequence`` is not of the correct length.
:raises TypeError: if any of the alleles at a site are not a
single ascii character.
"""
if not self.discrete_genome:
raise ValueError("sequence alignments only defined for discrete genomes")
interval = self._check_genomic_range(left, right, ensure_integer=True)
missing_data_character = (
"N" if missing_data_character is None else missing_data_character
)
L = interval.span
a = np.empty(L, dtype=np.int8)
if reference_sequence is None:
if self.has_reference_sequence():
# This may be inefficient - see #1989. However, since we're
# n copies of the reference sequence anyway, this is a relatively
# minor tweak. We may also want to recode the below not to use direct
# access to the .data attribute, e.g. if we allow reference sequences
# to start at non-zero positions
reference_sequence = self.reference_sequence.data[
interval.left : interval.right
]
else:
reference_sequence = missing_data_character * L
if len(reference_sequence) != L:
if interval.right == int(self.sequence_length):
raise ValueError(
"The reference sequence is shorter than the tree sequence length"
)
else:
raise ValueError(
"The reference sequence ends before the requested stop position"
)
ref_bytes = reference_sequence.encode("ascii")
a[:] = np.frombuffer(ref_bytes, dtype=np.int8)
# To do this properly we'll have to detect the missing data as
# part of a full implementation of alignments in C. The current
# definition might not be calling some degenerate cases correctly;
# see https://github.com/tskit-dev/tskit/issues/1908
#
# Note also that this will call the presence of missing data
# incorrectly if have a sample isolated over the region (a, b],
# and if we have sites at each position from a to b, and at
# each site there is a mutation over the isolated sample.
if any(tree._has_isolated_samples() for tree in self.trees()):
raise ValueError(
"Missing data not currently supported in alignments; see "
"https://github.com/tskit-dev/tskit/issues/1896 for details."
"The current implementation may also incorrectly identify an "
"input tree sequence has having missing data."
)
H, (first_site_id, last_site_id) = self._haplotypes_array(
interval=interval,
missing_data_character=missing_data_character,
samples=samples,
)
site_pos = self.sites_position.astype(np.int64)[
first_site_id : last_site_id + 1
]
for h in H:
a[site_pos - interval.left] = h
yield a.tobytes().decode("ascii")
@property
def individuals_population(self):
"""
Returns the length-``num_individuals`` array containing, for each
individual, the ``population`` attribute of their nodes, or
``tskit.NULL`` for individuals with no nodes. Errors if any individual
has nodes with inconsistent non-NULL populations.
"""
if self._individuals_population is None:
self._individuals_population = (
self._ll_tree_sequence.get_individuals_population()
)
return self._individuals_population
@property
def individual_populations(self):
# Undocumented alias for individuals_population to avoid breaking
# pre-1.0 pyslim code
return self.individuals_population
@property
def individuals_time(self):
"""
Returns the length-``num_individuals`` array containing, for each
individual, the ``time`` attribute of their nodes or ``np.nan`` for
individuals with no nodes. Errors if any individual has nodes with
inconsistent times.
"""
if self._individuals_time is None:
self._individuals_time = self._ll_tree_sequence.get_individuals_time()
return self._individuals_time
@property
def individual_times(self):
# Undocumented alias for individuals_time to avoid breaking
# pre-1.0 pyslim code
return self.individuals_time
@property
def individuals_location(self):
"""
Convenience method returning the ``num_individuals x n`` array
whose row k-th row contains the ``location`` property of the k-th
individual. The method only works if all individuals' locations
have the same length (which is ``n``), and errors otherwise.
"""
if self._individuals_location is None:
individuals = self.tables.individuals
n = 0
lens = np.unique(np.diff(individuals.location_offset))
if len(lens) > 1:
raise ValueError("Individual locations are not all the same length.")
if len(lens) > 0:
n = lens[0]
self._individuals_location = individuals.location.reshape(
(self.num_individuals, n)
)
return self._individuals_location
@property
def individual_locations(self):
# Undocumented alias for individuals_time to avoid breaking
# pre-1.0 pyslim code
return self.individuals_location
@property
def individuals_flags(self):
"""
Efficient access to the bitwise ``flags`` column in the
:ref:`sec_individual_table_definition` as a numpy array (dtype=np.uint32).
Equivalent to ``ts.tables.individuals.flags`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._individuals_flags
@property
def nodes_time(self):
"""
Efficient access to the ``time`` column in the
:ref:`sec_node_table_definition` as a numpy array (dtype=np.float64).
Equivalent to ``ts.tables.nodes.time`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._nodes_time
@property
def nodes_flags(self):
"""
Efficient access to the bitwise ``flags`` column in the
:ref:`sec_node_table_definition` as a numpy array (dtype=np.uint32).
Equivalent to ``ts.tables.nodes.flags`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._nodes_flags
@property
def nodes_population(self):
"""
Efficient access to the ``population`` column in the
:ref:`sec_node_table_definition` as a numpy array (dtype=np.int32).
Equivalent to ``ts.tables.nodes.population`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._nodes_population
@property
def nodes_individual(self):
"""
Efficient access to the ``individual`` column in the
:ref:`sec_node_table_definition` as a numpy array (dtype=np.int32).
Equivalent to ``ts.tables.nodes.individual`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._nodes_individual
@property
def edges_left(self):
"""
Efficient access to the ``left`` column in the
:ref:`sec_edge_table_definition` as a numpy array (dtype=np.float64).
Equivalent to ``ts.tables.edges.left`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._edges_left
@property
def edges_right(self):
"""
Efficient access to the ``right`` column in the
:ref:`sec_edge_table_definition` as a numpy array (dtype=np.float64).
Equivalent to ``ts.tables.edges.right`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._edges_right
@property
def edges_parent(self):
"""
Efficient access to the ``parent`` column in the
:ref:`sec_edge_table_definition` as a numpy array (dtype=np.int32).
Equivalent to ``ts.tables.edges.parent`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._edges_parent
@property
def edges_child(self):
"""
Efficient access to the ``child`` column in the
:ref:`sec_edge_table_definition` as a numpy array (dtype=np.int32).
Equivalent to ``ts.tables.edges.child`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._edges_child
@property
def sites_position(self):
"""
Efficient access to the ``position`` column in the
:ref:`sec_site_table_definition` as a numpy array (dtype=np.float64).
Equivalent to ``ts.tables.sites.position`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._sites_position
@property
def mutations_site(self):
"""
Efficient access to the ``site`` column in the
:ref:`sec_mutation_table_definition` as a numpy array (dtype=np.int32).
Equivalent to ``ts.tables.mutations.site`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._mutations_site
@property
def mutations_node(self):
"""
Efficient access to the ``node`` column in the
:ref:`sec_mutation_table_definition` as a numpy array (dtype=np.int32).
Equivalent to ``ts.tables.mutations.node`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._mutations_node
@property
def mutations_parent(self):
"""
Efficient access to the ``parent`` column in the
:ref:`sec_mutation_table_definition` as a numpy array (dtype=np.int32).
Equivalent to ``ts.tables.mutations.parent`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._mutations_parent
@property
def mutations_time(self):
"""
Efficient access to the ``time`` column in the
:ref:`sec_mutation_table_definition` as a numpy array (dtype=np.float64).
Equivalent to ``ts.tables.mutations.time`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._mutations_time
@property
def migrations_left(self):
"""
Efficient access to the ``left`` column in the
:ref:`sec_migration_table_definition` as a numpy array (dtype=np.float64).
Equivalent to ``ts.tables.migrations.left`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._migrations_left
@property
def migrations_right(self):
"""
Efficient access to the ``right`` column in the
:ref:`sec_migration_table_definition` as a numpy array (dtype=np.float64).
Equivalent to ``ts.tables.migrations.right`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._migrations_right
@property
def migrations_node(self):
"""
Efficient access to the ``node`` column in the
:ref:`sec_migration_table_definition` as a numpy array (dtype=np.int32).
Equivalent to ``ts.tables.migrations.node`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._migrations_node
@property
def migrations_source(self):
"""
Efficient access to the ``source`` column in the
:ref:`sec_migration_table_definition` as a numpy array (dtype=np.int32).
Equivalent to ``ts.tables.migrations.source`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._migrations_source
@property
def migrations_dest(self):
"""
Efficient access to the ``dest`` column in the
:ref:`sec_migration_table_definition` as a numpy array (dtype=np.int32).
Equivalent to ``ts.tables.migrations.dest`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._migrations_dest
@property
def migrations_time(self):
"""
Efficient access to the ``time`` column in the
:ref:`sec_migration_table_definition` as a numpy array (dtype=np.float64).
Equivalent to ``ts.tables.migrations.time`` (but avoiding the full copy
of the table data that accessing ``ts.tables`` currently entails).
"""
return self._migrations_time
@property
def indexes_edge_insertion_order(self):
"""
Efficient access to the ``edge_insertion_order`` column in the
:ref:`sec_table_indexes` as a numpy array (dtype=np.int32).
Equivalent to ``ts.tables.indexes.edge_insertion_order`` (but avoiding
the full copy of the table data that accessing ``ts.tables``
currently entails).
"""
return self._indexes_edge_insertion_order
@property
def indexes_edge_removal_order(self):
"""
Efficient access to the ``edge_removal_order`` column in the
:ref:`sec_table_indexes` as a numpy array (dtype=np.int32).
Equivalent to ``ts.tables.indexes.edge_removal_order`` (but avoiding
the full copy of the table data that accessing ``ts.tables``
currently entails).
"""
return self._indexes_edge_removal_order
[docs]
def individual(self, id_):
"""
Returns the :ref:`individual <sec_individual_table_definition>`
in this tree sequence with the specified ID. As with python lists, negative
IDs can be used to index backwards from the last individual.
:rtype: :class:`Individual`
"""
id_ = self.check_index(id_, self.num_individuals)
(
flags,
location,
parents,
metadata,
nodes,
) = self._ll_tree_sequence.get_individual(id_)
ind = Individual(
id=id_,
flags=flags,
location=location,
parents=parents,
metadata=metadata,
nodes=nodes,
metadata_decoder=self.table_metadata_schemas.individual.decode_row,
tree_sequence=self,
)
return ind
[docs]
def node(self, id_):
"""
Returns the :ref:`node <sec_node_table_definition>` in this tree sequence
with the specified ID. As with python lists, negative IDs can be used to
index backwards from the last node.
:rtype: :class:`Node`
"""
id_ = self.check_index(id_, self.num_nodes)
(
flags,
time,
population,
individual,
metadata,
) = self._ll_tree_sequence.get_node(id_)
return Node(
id=id_,
flags=flags,
time=time,
population=population,
individual=individual,
metadata=metadata,
metadata_decoder=self.table_metadata_schemas.node.decode_row,
)
@staticmethod
def check_index(index, length):
if not isinstance(index, numbers.Integral):
raise TypeError(
f"Index must be of integer type, not '{type(index).__name__}'"
)
if index < 0:
index += length
if index < 0 or index >= length:
raise IndexError("Index out of bounds")
return index
[docs]
def edge(self, id_):
"""
Returns the :ref:`edge <sec_edge_table_definition>` in this tree sequence
with the specified ID. As with python lists, negative IDs can be used to
index backwards from the last edge.
:rtype: :class:`Edge`
"""
id_ = self.check_index(id_, self.num_edges)
left, right, parent, child, metadata = self._ll_tree_sequence.get_edge(id_)
return Edge(
id=id_,
left=left,
right=right,
parent=parent,
child=child,
metadata=metadata,
metadata_decoder=self.table_metadata_schemas.edge.decode_row,
)
[docs]
def migration(self, id_):
"""
Returns the :ref:`migration <sec_migration_table_definition>` in this tree
sequence with the specified ID. As with python lists, negative IDs can be
used to index backwards from the last migration.
:rtype: :class:`.Migration`
"""
id_ = self.check_index(id_, self.num_migrations)
(
left,
right,
node,
source,
dest,
time,
metadata,
) = self._ll_tree_sequence.get_migration(id_)
return Migration(
id=id_,
left=left,
right=right,
node=node,
source=source,
dest=dest,
time=time,
metadata=metadata,
metadata_decoder=self.table_metadata_schemas.migration.decode_row,
)
[docs]
def mutation(self, id_):
"""
Returns the :ref:`mutation <sec_mutation_table_definition>` in this tree sequence
with the specified ID. As with python lists, negative IDs can be used to
index backwards from the last mutation.
:rtype: :class:`Mutation`
"""
id_ = self.check_index(id_, self.num_mutations)
(
site,
node,
derived_state,
parent,
metadata,
time,
edge,
) = self._ll_tree_sequence.get_mutation(id_)
return Mutation(
id=id_,
site=site,
node=node,
derived_state=derived_state,
parent=parent,
metadata=metadata,
time=time,
edge=edge,
metadata_decoder=self.table_metadata_schemas.mutation.decode_row,
)
[docs]
def site(self, id_=None, *, position=None):
"""
Returns the :ref:`site <sec_site_table_definition>` in this tree sequence
with either the specified ID or position. As with python lists, negative IDs
can be used to index backwards from the last site.
When position is specified instead of site ID, a binary search is done
on the list of positions of the sites to try to find a site
with the user-specified position.
:rtype: :class:`Site`
"""
if id_ is None and position is None:
raise TypeError("Site id or position must be provided.")
elif id_ is not None and position is not None:
raise TypeError("Only one of site id or position needs to be provided.")
elif id_ is None:
position = np.array(position)
if len(position.shape) > 0:
raise ValueError("Position must be provided as a scalar value.")
if position < 0 or position >= self.sequence_length:
raise ValueError(
"Position is beyond the coordinates defined by sequence length."
)
site_pos = self.sites_position
id_ = site_pos.searchsorted(position)
if id_ >= len(site_pos) or site_pos[id_] != position:
raise ValueError(f"There is no site at position {position}.")
else:
id_ = self.check_index(id_, self.num_sites)
ll_site = self._ll_tree_sequence.get_site(id_)
pos, ancestral_state, ll_mutations, _, metadata = ll_site
mutations = [self.mutation(mut_id) for mut_id in ll_mutations]
return Site(
id=id_,
position=pos,
ancestral_state=ancestral_state,
mutations=mutations,
metadata=metadata,
metadata_decoder=self.table_metadata_schemas.site.decode_row,
)
[docs]
def population(self, id_):
"""
Returns the :ref:`population <sec_population_table_definition>`
in this tree sequence with the specified ID. As with python lists, negative
IDs can be used to index backwards from the last population.
:rtype: :class:`Population`
"""
id_ = self.check_index(id_, self.num_populations)
(metadata,) = self._ll_tree_sequence.get_population(id_)
return Population(
id=id_,
metadata=metadata,
metadata_decoder=self.table_metadata_schemas.population.decode_row,
)
[docs]
def provenance(self, id_):
"""
Returns the :ref:`provenance <sec_provenance_table_definition>`
in this tree sequence with the specified ID. As with python lists,
negative IDs can be used to index backwards from the last provenance.
"""
id_ = self.check_index(id_, self.num_provenances)
timestamp, record = self._ll_tree_sequence.get_provenance(id_)
return Provenance(id=id_, timestamp=timestamp, record=record)
def get_samples(self, population_id=None):
# Deprecated alias for samples()
return self.samples(population_id)
[docs]
def samples(self, population=None, *, population_id=None, time=None):
"""
Returns an array of the sample node IDs in this tree sequence. If
`population` is specified, only return sample IDs from that population.
It is also possible to restrict samples by time using the parameter
`time`. If `time` is a numeric value, only return sample IDs whose node
time is approximately equal to the specified time. If `time` is a pair
of values of the form `(min_time, max_time)`, only return sample IDs
whose node time `t` is in this interval such that `min_time <= t < max_time`.
:param int population: The population of interest. If None, do not
filter samples by population.
:param int population_id: Deprecated alias for ``population``.
:param float,tuple time: The time or time interval of interest. If
None, do not filter samples by time.
:return: A numpy array of the node IDs for the samples of interest,
listed in numerical order.
:rtype: numpy.ndarray (dtype=np.int32)
"""
if population is not None and population_id is not None:
raise ValueError(
"population_id and population are aliases. Cannot specify both"
)
if population_id is not None:
population = population_id
samples = self._ll_tree_sequence.get_samples()
keep = np.full(shape=samples.shape, fill_value=True)
if population is not None:
sample_population = self.nodes_population[samples]
keep = np.logical_and(keep, sample_population == population)
if time is not None:
# ndmin is set so that scalars are converted into 1d arrays
time = np.array(time, ndmin=1, dtype=float)
sample_times = self.nodes_time[samples]
if time.shape == (1,):
keep = np.logical_and(keep, np.isclose(sample_times, time))
elif time.shape == (2,):
if time[1] <= time[0]:
raise ValueError("time_interval max is less than or equal to min.")
keep = np.logical_and(keep, sample_times >= time[0])
keep = np.logical_and(keep, sample_times < time[1])
else:
raise ValueError(
"time must be either a single value or a pair of values "
"(min_time, max_time)."
)
return samples[keep]
[docs]
def as_vcf(self, *args, **kwargs):
"""
Return the result of :meth:`.write_vcf` as a string.
Keyword parameters are as defined in :meth:`.write_vcf`.
:return: A VCF encoding of the variants in this tree sequence as a string.
:rtype: str
"""
buff = io.StringIO()
self.write_vcf(buff, *args, **kwargs)
return buff.getvalue()
[docs]
def write_vcf(
self,
output,
ploidy=None,
*,
contig_id="1",
individuals=None,
individual_names=None,
position_transform=None,
site_mask=None,
sample_mask=None,
isolated_as_missing=None,
allow_position_zero=None,
):
"""
Convert the genetic variation data in this tree sequence to Variant
Call Format and write to the specified file-like object.
.. seealso: See the :ref:`sec_export_vcf` section for examples
and explanations of how we map VCF to the tskit data model.
Multiploid samples in the output VCF are generated either using
individual information in the data model (see
:ref:`sec_individual_table_definition`), or by combining genotypes for
adjacent sample nodes using the ``ploidy`` argument. See the
:ref:`sec_export_vcf_constructing_gt` section for more details
and examples.
If individuals that are associated with sample nodes are defined in the
data model (see :ref:`sec_individual_table_definition`), the genotypes
for each of the individual's samples are combined into a phased
multiploid values at each site. By default, all individuals associated
with sample nodes are included in increasing order of individual ID.
Subsets or permutations of the sample individuals may be specified
using the ``individuals`` argument. It is an error to specify any
individuals that are not associated with any nodes, or whose
nodes are not all samples.
Mixed-sample individuals (e.g., those associated with one node
that is a sample and another that is not) in the data model will
result in an error by default. However, such individuals can be
excluded using the ``individuals`` argument.
If there are no individuals in the tree sequence,
synthetic individuals are created by combining adjacent samples, and
the number of samples combined is equal to the ``ploidy`` value (1 by
default). For example, if we have a ``ploidy`` of 2 and 6 sample nodes,
then we will have 3 diploid samples in the VCF, consisting of the
combined genotypes for samples [0, 1], [2, 3] and [4, 5]. If we had
genotypes 011110 at a particular variant, then we would output the
diploid genotypes 0|1, 1|1 and 1|0 in VCF.
Each individual in the output is identified by a string; these are the
VCF "sample" names. By default, these are of the form ``tsk_0``,
``tsk_1`` etc, up to the number of individuals, but can be manually
specified using the ``individual_names`` argument. We do not check
for duplicates in this array, or perform any checks to ensure that
the output VCF is well-formed.
.. note::
The default individual names (VCF sample IDs) are always of
the form ``tsk_0``, ``tsk_1``, ..., ``tsk_{N - 1}``, where
N is the number of individuals we output. These numbers
are **not** necessarily the individual IDs.
The REF value in the output VCF is the ancestral allele for a site
and ALT values are the remaining alleles. It is important to note,
therefore, that for real data this means that the REF value for a given
site **may not** be equal to the reference allele. We also do not
check that the alleles result in a valid VCF---for example, it is possible
to use the tab character as an allele, leading to a broken VCF.
The ID value in the output VCF file is the integer ID of the
corresponding :ref:`site <sec_site_table_definition>` (``site.id``).
These ID values can be utilized to match the contents of the VCF file
to the sites in the tree sequence object.
.. note::
Older code often uses the ``ploidy=2`` argument, because old
versions of msprime did not output individual data. Specifying
individuals in the tree sequence is more robust, and since tree
sequences now typically contain individuals (e.g., as produced by
``msprime.sim_ancestry( )``), this is not necessary, and the
``ploidy`` argument can safely be removed as part of the process
of updating from the msprime 0.x legacy API.
:param io.IOBase output: The file-like object to write the VCF output.
:param int ploidy: The ploidy of the individuals to be written to
VCF. This sample size must be evenly divisible by ploidy. Cannot be
used if there is individual data in the tree sequence.
:param str contig_id: The value of the CHROM column in the output VCF.
:param list(int) individuals: A list containing the individual IDs to
corresponding to the VCF samples. Defaults to all individuals
associated with sample nodes in the tree sequence.
See the {ref}`sec_export_vcf_constructing_gt` section for more
details and examples.
:param list(str) individual_names: A list of string names to identify
individual columns in the VCF. In VCF nomenclature, these are the
sample IDs. If specified, this must be a list of strings of
length equal to the number of individuals to be output. Note that
we do not check the form of these strings in any way, so that is
is possible to output malformed VCF (for example, by embedding a
tab character within on of the names). The default is to output
``tsk_j`` for the jth individual.
See the :ref:`sec_export_vcf_individual_names` for examples
and more information.
:param position_transform: A callable that transforms the
site position values into integer valued coordinates suitable for
VCF. The function takes a single positional parameter x and must
return an integer numpy array the same dimension as x. By default,
this is set to ``numpy.round()`` which will round values to the
nearest integer. If the string "legacy" is provided here, the
pre 0.2.0 legacy behaviour of rounding values to the nearest integer
(starting from 1) and avoiding the output of identical positions
by incrementing is used.
See the :ref:`sec_export_vcf_modifying_coordinates` for examples
and more information.
:param site_mask: A numpy boolean array (or something convertable to
a numpy boolean array) with num_sites elements, used to mask out
sites in the output. If ``site_mask[j]`` is True, then this
site (i.e., the line in the VCF file) will be omitted.
See the :ref:`sec_export_vcf_masking_output` for examples
and more information.
:param sample_mask: A numpy boolean array (or something convertable to
a numpy boolean array) with num_samples elements, or a callable
that returns such an array, such that if
``sample_mask[j]`` is True, then the genotype for sample ``j``
will be marked as missing using a ".". If ``sample_mask`` is a
callable, it must take a single argument and return a boolean
numpy array. This function will be called for each (unmasked) site
with the corresponding :class:`.Variant` object, allowing
for dynamic masks to be generated.
See the :ref:`sec_export_vcf_masking_output` for examples
and more information.
:param bool isolated_as_missing: If True, the genotype value assigned to
missing samples (i.e., isolated samples without mutations) is "."
If False, missing samples will be assigned the ancestral allele.
See :meth:`.variants` for more information. Default: True.
:param bool allow_position_zero: If True allow sites with position zero to be
output to the VCF, otherwise if one is present an error will be raised.
The VCF spec does not allow for sites at position 0. However, in practise
many tools will be fine with this. Default: False.
"""
if allow_position_zero is None:
allow_position_zero = False
writer = vcf.VcfWriter(
self,
ploidy=ploidy,
contig_id=contig_id,
individuals=individuals,
individual_names=individual_names,
position_transform=position_transform,
site_mask=site_mask,
sample_mask=sample_mask,
isolated_as_missing=isolated_as_missing,
allow_position_zero=allow_position_zero,
)
writer.write(output)
[docs]
def write_fasta(
self,
file_or_path,
*,
wrap_width=60,
reference_sequence=None,
missing_data_character=None,
):
"""
Writes the :meth:`.alignments` for this tree sequence to file in
`FASTA <https://en.wikipedia.org/wiki/FASTA_format>`__ format.
Please see the :meth:`.alignments` method for details on how
reference sequences are handled.
Alignments are returned for the
:ref:`sample nodes<sec_data_model_definitions>` in this tree
sequence, and a sample with node id ``u`` is given the label
``f"n{u}"``, following the same convention as the
:meth:`.write_nexus` and :meth:`Tree.as_newick` methods.
The ``wrap_width`` parameter controls the maximum width of lines
of sequence data in the output. By default this is 60
characters in accordance with fasta standard outputs. To turn off
line-wrapping of sequences, set ``wrap_width`` = 0.
Example usage:
.. code-block:: python
ts.write_fasta("output.fa")
.. warning:: :ref:`Missing data<sec_data_model_missing_data>` is not
currently supported by this method and it will raise a ValueError
if called on tree sequences containing isolated samples.
See https://github.com/tskit-dev/tskit/issues/1896 for more
information.
:param file_or_path: The file object or path to write the output.
Paths can be either strings or :class:`python:pathlib.Path` objects.
:param int wrap_width: The number of sequence
characters to include on each line in the fasta file, before wrapping
to the next line for each sequence, or 0 to turn off line wrapping.
(Default=60).
:param str reference_sequence: As for the :meth:`.alignments` method.
:param str missing_data_character: As for the :meth:`.alignments` method.
"""
text_formats.write_fasta(
self,
file_or_path,
wrap_width=wrap_width,
reference_sequence=reference_sequence,
missing_data_character=missing_data_character,
)
[docs]
def as_fasta(self, **kwargs):
"""
Return the result of :meth:`.write_fasta` as a string.
Keyword parameters are as defined in :meth:`.write_fasta`.
:return: A FASTA encoding of the alignments in this tree sequence as a string.
:rtype: str
"""
buff = io.StringIO()
self.write_fasta(buff, **kwargs)
return buff.getvalue()
[docs]
def write_nexus(
self,
file_or_path,
*,
precision=None,
include_trees=None,
include_alignments=None,
reference_sequence=None,
missing_data_character=None,
):
"""
Returns a `nexus encoding <https://en.wikipedia.org/wiki/Nexus_file>`_
of this tree sequence. By default, tree topologies are included
in the output, and sequence data alignments are included by default
if this tree sequence has discrete genome coordinates and one or
more sites. Inclusion of these sections can be controlled manually
using the ``include_trees`` and ``include_alignments`` parameters.
Tree topologies and branch lengths are listed
sequentially in the TREES block and the spatial location of each tree
encoded within the tree name labels. Specifically, a tree spanning
the interval :math:`[x, y)`` is given the name ``f"t{x}^{y}"``
(See below for a description of the precision at which these spatial
coordinates are printed out).
The :ref:`sample nodes<sec_data_model_definitions>` in this tree
sequence are regarded as taxa, and a sample with node id ``u``
is given the label ``f"n{u}"``, following the same convention
as the :meth:`Tree.as_newick` method.
By default, genome positions are printed out with with sufficient
precision for them to be recovered exactly in double precision.
If the tree sequence is defined on a :attr:`.discrete_genome`,
then positions are written out as integers. Otherwise, 17 digits
of precision is used. Branch length precision defaults are handled
in the same way as :meth:`.Tree.as_newick`.
If the ``precision`` argument is provided, genome positions and
branch lengths are printed out with this many digits of precision.
For example, here is the nexus encoding of a simple tree sequence
with integer times and genome coordinates with three samples
and two trees::
#NEXUS
BEGIN TAXA;
DIMENSIONS NTAX=3;
TAXLABELS n0 n1 n2;
END;
BEGIN TREES;
TREE t0^2 = [&R] (n0:3,(n1:2,n2:2):1);
TREE t2^10 = [&R] (n1:2,(n0:1,n2:1):1);
END;
If sequence data :meth:`.alignments` are defined for this tree sequence
and there is at least one site present, sequence alignment data will also
be included by default (this can be suppressed by setting
``include_alignments=False``). For example, this tree sequence has
a sequence length of 10, two variable sites and no
:ref:`reference sequence<sec_data_model_reference_sequence>`::
#NEXUS
BEGIN TAXA;
DIMENSIONS NTAX=3;
TAXLABELS n0 n1 n2;
END;
BEGIN DATA;
DIMENSIONS NCHAR=10;
FORMAT DATATYPE=DNA MISSING=?;
MATRIX
n0 ??G??????T
n1 ??A??????C
n2 ??A??????C
;
END;
BEGIN TREES;
TREE t0^10 = [&R] (n0:2,(n1:1,n2:1):1);
END;
Please see the :meth:`.alignments` method for details on how
reference sequences are handled.
.. note:: Note the default ``missing_data_character`` for this method
is "?" rather then "N", in keeping with common conventions for
nexus data. This can be changed using the ``missing_data_character``
parameter.
.. warning:: :ref:`Missing data<sec_data_model_missing_data>`
is not supported for encoding tree topology information
as our convention of using trees with multiple roots
is not often supported by newick parsers. Thus, the method
will raise a ValueError if we try to output trees with
multiple roots. Additionally, missing data
is not currently supported for alignment data.
See https://github.com/tskit-dev/tskit/issues/1896 for more
information.
.. seealso: See also the :meth:`.as_nexus` method which will
return this nexus representation as a string.
:param int precision: The numerical precision with which branch lengths
and tree positions are printed.
:param bool include_trees: True if the tree topology information should
be included; False otherwise (default=True).
:param bool include_alignments: True if the sequence data alignment information
should be included; False otherwise (default=True if sequence alignments
are well-defined and the tree sequence contains at least one site).
:param str reference_sequence: As for the :meth:`.alignments` method.
:param str missing_data_character: As for the :meth:`.alignments` method,
but defaults to "?".
:return: A nexus representation of this :class:`TreeSequence`
:rtype: str
"""
text_formats.write_nexus(
self,
file_or_path,
precision=precision,
include_trees=include_trees,
include_alignments=include_alignments,
reference_sequence=reference_sequence,
missing_data_character=missing_data_character,
)
[docs]
def as_nexus(self, **kwargs):
"""
Return the result of :meth:`.write_nexus` as a string.
Keyword parameters are as defined in :meth:`.write_nexus`.
:return: A nexus encoding of the alignments in this tree sequence as a string.
:rtype: str
"""
buff = io.StringIO()
self.write_nexus(buff, **kwargs)
return buff.getvalue()
# TODO
# (1) Move the definition to text_formats.py
# (2) Rename to as_macs and keep to_macs as a deprecated synonym
[docs]
def to_macs(self):
"""
Return a `macs encoding <https://github.com/gchen98/macs>`_
of this tree sequence.
:return: The macs representation of this TreeSequence as a string.
:rtype: str
"""
n = self.get_sample_size()
m = self.get_sequence_length()
output = [f"COMMAND:\tnot_macs {n} {m}"]
output.append("SEED:\tASEED")
for variant in self.variants(copy=False):
if any(len(allele) > 1 for allele in variant.alleles):
raise ValueError("macs output only supports single letter alleles")
bytes_genotypes = np.empty(self.num_samples, dtype=np.uint8)
lookup = np.array([ord(a[0]) for a in variant.alleles], dtype=np.uint8)
bytes_genotypes[:] = lookup[variant.genotypes]
genotypes = bytes_genotypes.tobytes().decode()
output.append(
f"SITE:\t{variant.index}\t{variant.position / m}\t0.0\t" f"{genotypes}"
)
return "\n".join(output) + "\n"
[docs]
def simplify(
self,
samples=None,
*,
map_nodes=False,
reduce_to_site_topology=False,
filter_populations=None,
filter_individuals=None,
filter_sites=None,
filter_nodes=None,
update_sample_flags=None,
keep_unary=False,
keep_unary_in_individuals=None,
keep_input_roots=False,
record_provenance=True,
filter_zero_mutation_sites=None, # Deprecated alias for filter_sites
):
"""
Returns a simplified tree sequence that retains only the history of
the nodes given in the list ``samples``. If ``map_nodes`` is true,
also return a numpy array whose ``u``-th element is the ID of the node
in the simplified tree sequence that corresponds to node ``u`` in the
original tree sequence, or :data:`tskit.NULL` (-1) if ``u`` is no longer
present in the simplified tree sequence.
.. note::
If you wish to simplify a set of tables that do not satisfy all
requirements for building a TreeSequence, then use
:meth:`TableCollection.simplify`.
If the ``reduce_to_site_topology`` parameter is True, the returned tree
sequence will contain only topological information that is necessary to
represent the trees that contain sites. If there are zero sites in this
tree sequence, this will result in an output tree sequence with zero edges.
When the number of sites is greater than zero, every tree in the output
tree sequence will contain at least one site. For a given site, the
topology of the tree containing that site will be identical
(up to node ID remapping) to the topology of the corresponding tree
in the input tree sequence.
If ``filter_populations``, ``filter_individuals``, ``filter_sites``, or
``filter_nodes`` is True, any of the corresponding objects that are not
referenced elsewhere are filtered out. As this is the default behaviour,
it is important to realise IDs for these objects may change through
simplification. By setting these parameters to False, however, the
corresponding tables can be preserved without changes.
If ``filter_nodes`` is False, then the output node table will be
unchanged except for updating the sample status of nodes and any ID
remappings caused by filtering individuals and populations (if the
``filter_individuals`` and ``filter_populations`` options are enabled).
Nodes that are in the specified list of ``samples`` will be marked as
samples in the output, and nodes that are currently marked as samples
in the node table but not in the specified list of ``samples`` will
have their :data:`tskit.NODE_IS_SAMPLE` flag cleared. Note also that
the order of the ``samples`` list is not meaningful when
``filter_nodes`` is False. In this case, the returned node mapping is
always the identity mapping, such that ``a[u] == u`` for all nodes.
Setting the ``update_sample_flags`` parameter to False disables the
automatic sample status update of nodes (described above) from
occuring, making it the responsibility of calling code to keep track of
the ultimate sample status of nodes. This is an advanced option, mostly
of use when combined with the ``filter_nodes=False``,
``filter_populations=False`` and ``filter_individuals=False`` options,
which then guarantees that the node table will not be altered by
simplification.
:param list[int] samples: A list of node IDs to retain as samples. They
need not be nodes marked as samples in the original tree sequence, but
will constitute the entire set of samples in the returned tree sequence.
If not specified or None, use all nodes marked with the IS_SAMPLE flag.
The list may be provided as a numpy array (or array-like) object
(dtype=np.int32).
:param bool map_nodes: If True, return a tuple containing the resulting
tree sequence and a numpy array mapping node IDs in the current tree
sequence to their corresponding node IDs in the returned tree sequence.
If False (the default), return only the tree sequence object itself.
:param bool reduce_to_site_topology: Whether to reduce the topology down
to the trees that are present at sites. (Default: False)
:param bool filter_populations: If True, remove any populations that are
not referenced by nodes after simplification; new population IDs are
allocated sequentially from zero. If False, the population table will
not be altered in any way. (Default: None, treated as True)
:param bool filter_individuals: If True, remove any individuals that are
not referenced by nodes after simplification; new individual IDs are
allocated sequentially from zero. If False, the individual table will
not be altered in any way. (Default: None, treated as True)
:param bool filter_sites: If True, remove any sites that are
not referenced by mutations after simplification; new site IDs are
allocated sequentially from zero. If False, the site table will not
be altered in any way. (Default: None, treated as True)
:param bool filter_nodes: If True, remove any nodes that are
not referenced by edges after simplification. If False, the only
potential change to the node table may be to change the node flags
(if ``samples`` is specified and different from the existing samples).
(Default: None, treated as True)
:param bool update_sample_flags: If True, update node flags to so that
nodes in the specified list of samples have the NODE_IS_SAMPLE
flag after simplification, and nodes that are not in this list
do not. (Default: None, treated as True)
:param bool keep_unary: If True, preserve unary nodes (i.e., nodes with
exactly one child) that exist on the path from samples to root.
(Default: False)
:param bool keep_unary_in_individuals: If True, preserve unary nodes
that exist on the path from samples to root, but only if they are
associated with an individual in the individuals table. Cannot be
specified at the same time as ``keep_unary``. (Default: ``None``,
equivalent to False)
:param bool keep_input_roots: Whether to retain history ancestral to the
MRCA of the samples. If ``False``, no topology older than the MRCAs of the
samples will be included. If ``True`` the roots of all trees in the returned
tree sequence will be the same roots as in the original tree sequence.
(Default: False)
:param bool record_provenance: If True, record details of this call to
simplify in the returned tree sequence's provenance information
(Default: True).
:param bool filter_zero_mutation_sites: Deprecated alias for ``filter_sites``.
:return: The simplified tree sequence, or (if ``map_nodes`` is True)
a tuple consisting of the simplified tree sequence and a numpy array
mapping source node IDs to their corresponding IDs in the new tree
sequence.
:rtype: tskit.TreeSequence or (tskit.TreeSequence, numpy.ndarray)
"""
tables = self.dump_tables()
assert tables.sequence_length == self.sequence_length
node_map = tables.simplify(
samples=samples,
reduce_to_site_topology=reduce_to_site_topology,
filter_populations=filter_populations,
filter_individuals=filter_individuals,
filter_sites=filter_sites,
filter_nodes=filter_nodes,
update_sample_flags=update_sample_flags,
keep_unary=keep_unary,
keep_unary_in_individuals=keep_unary_in_individuals,
keep_input_roots=keep_input_roots,
record_provenance=record_provenance,
filter_zero_mutation_sites=filter_zero_mutation_sites,
)
new_ts = tables.tree_sequence()
assert new_ts.sequence_length == self.sequence_length
if map_nodes:
return new_ts, node_map
else:
return new_ts
[docs]
def delete_sites(self, site_ids, record_provenance=True):
"""
Returns a copy of this tree sequence with the specified sites (and their
associated mutations) entirely removed. The site IDs do not need to be in any
particular order, and specifying the same ID multiple times does not have any
effect (i.e., calling ``tree_sequence.delete_sites([0, 1, 1])`` has the same
effect as calling ``tree_sequence.delete_sites([0, 1])``.
.. note::
To remove only the mutations associated with a site, but keep the site
itself, use the :meth:`MutationTable.keep_rows` method.
:param list[int] site_ids: A list of site IDs specifying the sites to remove.
:param bool record_provenance: If ``True``, add details of this operation to the
provenance information of the returned tree sequence. (Default: ``True``).
"""
tables = self.dump_tables()
tables.delete_sites(site_ids, record_provenance)
return tables.tree_sequence()
[docs]
def delete_intervals(self, intervals, simplify=True, record_provenance=True):
"""
Returns a copy of this tree sequence for which information in the
specified list of genomic intervals has been deleted. Edges spanning these
intervals are truncated or deleted, and sites and mutations falling within
them are discarded. Note that it is the information in the intervals that
is deleted, not the intervals themselves, so in particular, all samples
will be isolated in the deleted intervals.
Note that node IDs may change as a result of this operation,
as by default :meth:`.simplify` is called on the returned tree sequence
to remove redundant nodes. If you wish to map node IDs onto the same
nodes before and after this method has been called, specify ``simplify=False``.
See also :meth:`.keep_intervals`, :meth:`.ltrim`, :meth:`.rtrim`, and
:ref:`missing data<sec_data_model_missing_data>`.
:param array_like intervals: A list (start, end) pairs describing the
genomic intervals to delete. Intervals must be non-overlapping and
in increasing order. The list of intervals must be interpretable as a
2D numpy array with shape (N, 2), where N is the number of intervals.
:param bool simplify: If True, return a simplified tree sequence where nodes
no longer used are discarded. (Default: True).
:param bool record_provenance: If ``True``, add details of this operation to the
provenance information of the returned tree sequence. (Default: ``True``).
:rtype: tskit.TreeSequence
"""
tables = self.dump_tables()
tables.delete_intervals(intervals, simplify, record_provenance)
return tables.tree_sequence()
[docs]
def keep_intervals(self, intervals, simplify=True, record_provenance=True):
"""
Returns a copy of this tree sequence which includes only information in
the specified list of genomic intervals. Edges are truncated to lie within
these intervals, and sites and mutations falling outside these intervals
are discarded. Note that it is the information outside the intervals that
is deleted, not the intervals themselves, so in particular, all samples
will be isolated outside of the retained intervals.
Note that node IDs may change as a result of this operation,
as by default :meth:`.simplify` is called on the returned tree sequence
to remove redundant nodes. If you wish to map node IDs onto the same
nodes before and after this method has been called, specify ``simplify=False``.
See also :meth:`.keep_intervals`, :meth:`.ltrim`, :meth:`.rtrim`, and
:ref:`missing data<sec_data_model_missing_data>`.
:param array_like intervals: A list (start, end) pairs describing the
genomic intervals to keep. Intervals must be non-overlapping and
in increasing order. The list of intervals must be interpretable as a
2D numpy array with shape (N, 2), where N is the number of intervals.
:param bool simplify: If True, return a simplified tree sequence where nodes
no longer used are discarded. (Default: True).
:param bool record_provenance: If True, add details of this operation to the
provenance information of the returned tree sequence.
(Default: True).
:rtype: tskit.TreeSequence
"""
tables = self.dump_tables()
tables.keep_intervals(intervals, simplify, record_provenance)
return tables.tree_sequence()
[docs]
def ltrim(self, record_provenance=True):
"""
Returns a copy of this tree sequence with a potentially changed coordinate
system, such that empty regions (i.e., those not covered by any edge) at the
start of the tree sequence are trimmed away, and the leftmost edge starts at
position 0. This affects the reported position of sites and
edges. Additionally, sites and their associated mutations to the left of
the new zero point are thrown away.
:param bool record_provenance: If True, add details of this operation to the
provenance information of the returned tree sequence. (Default: True).
"""
tables = self.dump_tables()
tables.ltrim(record_provenance)
return tables.tree_sequence()
[docs]
def rtrim(self, record_provenance=True):
"""
Returns a copy of this tree sequence with the ``sequence_length`` property reset
so that the sequence ends at the end of the rightmost edge. Additionally, sites
and their associated mutations at positions greater than the new
``sequence_length`` are thrown away.
:param bool record_provenance: If True, add details of this operation to the
provenance information of the returned tree sequence. (Default: True).
"""
tables = self.dump_tables()
tables.rtrim(record_provenance)
return tables.tree_sequence()
[docs]
def trim(self, record_provenance=True):
"""
Returns a copy of this tree sequence with any empty regions (i.e., those not
covered by any edge) on the right and left trimmed away. This may reset both the
coordinate system and the ``sequence_length`` property. It is functionally
equivalent to :meth:`.rtrim` followed by :meth:`.ltrim`. Sites and their
associated mutations in the empty regions are thrown away.
:param bool record_provenance: If True, add details of this operation to the
provenance information of the returned tree sequence. (Default: True).
"""
tables = self.dump_tables()
tables.trim(record_provenance)
return tables.tree_sequence()
[docs]
def split_edges(self, time, *, flags=None, population=None, metadata=None):
"""
Returns a copy of this tree sequence in which we replace any
edge ``(left, right, parent, child)`` in which
``node_time[child] < time < node_time[parent]`` with two edges
``(left, right, parent, u)`` and ``(left, right, u, child)``,
where ``u`` is a newly added node for each intersecting edge.
If ``metadata``, ``flags``, or ``population`` are specified, newly
added nodes will be assigned these values. Otherwise, default values
will be used. The default metadata is an empty dictionary if a metadata
schema is defined for the node table, and is an empty byte string
otherwise. The default population for the new node is
:data:`tskit.NULL`. Newly added have a default ``flags`` value of 0.
Any metadata associated with a split edge will be copied to the new edge.
.. warning:: This method currently does not support migrations
and a error will be raised if the migration table is not
empty. Future versions may take migrations that intersect with the
edge into account when determining the default population
assignments for new nodes.
Any mutations lying on the edge whose time is >= ``time`` will have
their node value set to ``u``. Note that the time of the mutation is
defined as the time of the child node if the mutation's time is
unknown.
:param float time: The cutoff time.
:param int flags: The flags value for newly-inserted nodes. (Default = 0)
:param int population: The population value for newly inserted nodes.
Defaults to ``tskit.NULL`` if not specified.
:param metadata: The metadata for any newly inserted nodes. See
:meth:`.NodeTable.add_row` for details on how default metadata
is produced for a given schema (or none).
:return: A copy of this tree sequence with edges split at the specified time.
:rtype: tskit.TreeSequence
"""
population = tskit.NULL if population is None else population
flags = 0 if flags is None else flags
schema = self.table_metadata_schemas.node
if metadata is None:
metadata = schema.empty_value
metadata = schema.validate_and_encode_row(metadata)
ll_ts = self._ll_tree_sequence.split_edges(
time=time,
flags=flags,
population=population,
metadata=metadata,
)
return TreeSequence(ll_ts)
[docs]
def decapitate(self, time, *, flags=None, population=None, metadata=None):
"""
Delete all edge topology and mutational information at least as old
as the specified time from this tree sequence.
Removes all edges in which the time of the child is >= the specified
time ``t``, and breaks edges that intersect with ``t``. For each edge
intersecting with ``t`` we create a new node with time equal to ``t``,
and set the parent of the edge to this new node. The node table
is not altered in any other way. Newly added nodes have values
for ``flags``, ``population`` and ``metadata`` controlled by parameters
to this function in the same way as :meth:`.split_edges`.
.. note::
Note that each edge is treated independently, so that even if two
edges that are broken by this operation share the same parent and
child nodes, there will be two different new parent nodes inserted.
Any mutation whose time is >= ``t`` will be removed. A mutation's time
is its associated ``time`` value, or the time of its node if the
mutation's time was marked as unknown (:data:`UNKNOWN_TIME`).
Migrations are not supported, and a LibraryError will be raise if
called on a tree sequence containing migration information.
.. seealso:: This method is implemented using the :meth:`.split_edges`
and :meth:`TableCollection.delete_older` functions.
:param float time: The cutoff time.
:param int flags: The flags value for newly-inserted nodes. (Default = 0)
:param int population: The population value for newly inserted nodes.
Defaults to ``tskit.NULL`` if not specified.
:param metadata: The metadata for any newly inserted nodes. See
:meth:`.NodeTable.add_row` for details on how default metadata
is produced for a given schema (or none).
:return: A copy of this tree sequence with edges split at the specified time.
:rtype: tskit.TreeSequence
"""
split_ts = self.split_edges(
time, flags=flags, population=population, metadata=metadata
)
tables = split_ts.dump_tables()
del split_ts
tables.delete_older(time)
return tables.tree_sequence()
[docs]
def extend_haplotypes(self, max_iter=10):
"""
Returns a new tree sequence in which the span covered by ancestral nodes
is "extended" to regions of the genome according to the following rule:
If an ancestral segment corresponding to node `n` has ancestor `p` and
descendant `c` on some portion of the genome, and on an adjacent segment of
genome `p` is still an ancestor of `c`, then `n` is inserted into the
path from `p` to `c`. For instance, if `p` is the parent of `n` and `n`
is the parent of `c`, then the span of the edges from `p` to `n` and
`n` to `c` are extended, and the span of the edge from `p` to `c` is
reduced. Thus, the ancestral haplotype represented by `n` is extended
to a longer span of the genome. However, any edges whose child node is
a sample are not modified.
Since some edges may be removed entirely, this process usually reduces
the number of edges in the tree sequence.
The method works by iterating over the genome to look for paths that can
be extended in this way; the maximum number of such iterations is
controlled by ``max_iter``.
The rationale is that we know that `n` carries a portion of the segment
of ancestral genome inherited by `c` from `p`, and so likely carries
the *entire* inherited segment (since the implication otherwise would
be that distinct recombined segments were passed down separately from
`p` to `c`).
In the example above, if there was a mutation on the node above `c`
older than the time of `n` in the span into which `n` was extended,
then the mutation will now occur above `n`. So, this operation may change
mutations' nodes (but will not affect genotypes). This is only
unambiguous if the mutation's time is known, so the method requires
known mutation times. See :meth:`.impute_unknown_mutations_time` if
mutation times are not known.
The method will not affect the marginal trees (so, if the original tree
sequence was simplified, then following up with `simplify` will recover
the original tree sequence, possibly with edges in a different order).
It will also not affect the genotype matrix, or any of the tables other
than the edge table or the node column in the mutation table.
:param int max_iters: The maximum number of iterations over the tree
sequence. Defaults to 10.
:return: A new tree sequence with unary nodes extended.
:rtype: tskit.TreeSequence
"""
max_iter = int(max_iter)
ll_ts = self._ll_tree_sequence.extend_haplotypes(max_iter)
return TreeSequence(ll_ts)
[docs]
def subset(
self,
nodes,
record_provenance=True,
reorder_populations=True,
remove_unreferenced=True,
):
"""
Returns a tree sequence containing only information directly
referencing the provided list of nodes to retain. The result will
retain only the nodes whose IDs are listed in ``nodes``, only edges for
which both parent and child are in ``nodes```, only mutations whose
node is in ``nodes``, and only individuals that are referred to by one
of the retained nodes. Note that this does *not* retain
the ancestry of these nodes - for that, see :meth:`.simplify`.
This has the side effect that it may change the order of the nodes,
individuals, populations, and migrations in the tree sequence: the nodes
in the new tree sequence will be in the order provided in ``nodes``, and
both individuals and populations will be ordered by the earliest retained
node that refers to them. (However, ``reorder_populations`` may be set to
False to keep the population table unchanged.)
By default, the method removes all individuals and populations not
referenced by any nodes, and all sites not referenced by any mutations.
To retain these unreferenced individuals, populations, and sites, pass
``remove_unreferenced=False``. If this is done, the site table will
remain unchanged, unreferenced individuals will appear at the end of
the individuals table (and in their original order), and unreferenced
populations will appear at the end of the population table (unless
``reorder_populations=False``).
.. seealso::
:meth:`.keep_intervals` for subsetting a given portion of the genome;
:meth:`.simplify` for retaining the ancestry of a subset of nodes.
:param list nodes: The list of nodes for which to retain information. This
may be a numpy array (or array-like) object (dtype=np.int32).
:param bool record_provenance: Whether to record a provenance entry
in the provenance table for this operation.
:param bool reorder_populations: Whether to reorder populations
(default: True). If False, the population table will not be altered in
any way.
:param bool remove_unreferenced: Whether sites, individuals, and populations
that are not referred to by any retained entries in the tables should
be removed (default: True). See the description for details.
:rtype: tskit.TreeSequence
"""
tables = self.dump_tables()
tables.subset(
nodes,
record_provenance=record_provenance,
reorder_populations=reorder_populations,
remove_unreferenced=remove_unreferenced,
)
return tables.tree_sequence()
[docs]
def union(
self,
other,
node_mapping,
check_shared_equality=True,
add_populations=True,
record_provenance=True,
):
"""
Returns an expanded tree sequence which contains the node-wise union of
``self`` and ``other``, obtained by adding the non-shared portions of
``other`` onto ``self``. The "shared" portions are specified using a
map that specifies which nodes in ``other`` are equivalent to those in
``self``: the ``node_mapping`` argument should be an array of length
equal to the number of nodes in ``other`` and whose entries are the ID
of the matching node in ``self``, or ``tskit.NULL`` if there is no
matching node. Those nodes in ``other`` that map to ``tskit.NULL`` will
be added to ``self``, along with:
1. Individuals whose nodes are new to ``self``.
2. Edges whose parent or child are new to ``self``.
3. Mutations whose nodes are new to ``self``.
4. Sites which were not present in ``self``, if the site contains a newly
added mutation.
By default, populations of newly added nodes are assumed to be new
populations, and added to the population table as well.
Note that this operation also sorts the resulting tables, so the
resulting tree sequence may not be equal to ``self`` even if nothing
new was added (although it would differ only in ordering of the tables).
:param TableCollection other: Another table collection.
:param list node_mapping: An array of node IDs that relate nodes in
``other`` to nodes in ``self``.
:param bool check_shared_equality: If True, the shared portions of the
tree sequences will be checked for equality. It does so by
subsetting both ``self`` and ``other`` on the equivalent nodes
specified in ``node_mapping``, and then checking for equality of
the subsets.
:param bool add_populations: If True, nodes new to ``self`` will be
assigned new population IDs.
:param bool record_provenance: Whether to record a provenance entry
in the provenance table for this operation.
"""
tables = self.dump_tables()
other_tables = other.dump_tables()
tables.union(
other_tables,
node_mapping,
check_shared_equality=check_shared_equality,
add_populations=add_populations,
record_provenance=record_provenance,
)
return tables.tree_sequence()
[docs]
def draw_svg(
self,
path=None,
*,
size=None,
x_scale=None,
time_scale=None,
tree_height_scale=None,
title=None,
node_labels=None,
mutation_labels=None,
node_titles=None,
mutation_titles=None,
root_svg_attributes=None,
style=None,
order=None,
force_root_branch=None,
symbol_size=None,
x_axis=None,
x_label=None,
x_lim=None,
x_regions=None,
y_axis=None,
y_label=None,
y_ticks=None,
y_gridlines=None,
omit_sites=None,
canvas_size=None,
max_num_trees=None,
**kwargs,
):
"""
Return an SVG representation of a tree sequence. See the
:ref:`visualization tutorial<tutorials:sec_tskit_viz>` for more details.
:param str path: The path to the file to write the output. If None, do not write
to file.
:param tuple(int, int) size: A tuple of (width, height) specifying a target
drawing size in abstract user units (usually interpreted as pixels on
initial display). Components of the drawing will be scaled so that the total
plot including labels etc. normally fits onto a canvas of this size (see
``canvas_size`` below). If ``None``, chose values such that each tree is
drawn at a size appropriate for a reasonably small set of samples (this will
nevertheless result in a very wide drawing if there are many trees to
display). Default: ``None``
:param str x_scale: Control how the X axis is drawn. If "physical" (the default)
the axis scales linearly with physical distance along the sequence,
background shading is used to indicate the position of the trees along the
X axis, and sites (with associated mutations) are marked at the
appropriate physical position on axis line. If "treewise", each axis tick
corresponds to a tree boundary, which are positioned evenly along the axis,
so that the X axis is of variable scale, no background scaling is required,
and site positions are not marked on the axis.
:param str time_scale: Control how height values for nodes are computed.
If this is equal to ``"time"``, node heights are proportional to their time
values (this is the default). If this is equal to ``"log_time"``, node
heights are proportional to their log(time) values. If it is equal to
``"rank"``, node heights are spaced equally according to their ranked times.
:param str tree_height_scale: Deprecated alias for time_scale. (Deprecated in
0.3.6)
:param str title: A title string to be included in the SVG output. If ``None``
(default) no title is shown, which gives more vertical space for the tree.
:param node_labels: If specified, show custom labels for the nodes
(specified by ID) that are present in this map; any nodes not present will
not have a label.
:type node_labels: dict(int, str)
:param mutation_labels: If specified, show custom labels for the
mutations (specified by ID) that are present in the map; any mutations
not present will not have a label.
:param dict(int, str) node_titles: If specified, add a ``<title>`` string to
symbols for each node (specified by ID) present in this map. SVG visualizers
such as web browsers will commonly display this string on mousing over
node symbol.
:param dict(int, str) mutation_titles: If specified, add a ``<title>`` string to
symbols for each mutation (specified by ID) present in this map. SVG
visualizers such as web browsers will commonly display this string on
mousing over the mutation symbol in the tree and (if show) on the x axis.
:param dict root_svg_attributes: Additional attributes, such as an id, that will
be embedded in the root ``<svg>`` tag of the generated drawing.
:param str style: A `css string <https://www.w3.org/TR/CSS21/syndata.htm>`_
that will be included in the ``<style>`` tag of the generated svg.
:param str order: The left-to-right ordering of child nodes in each drawn tree.
This can be either: ``"minlex"``, which minimises the differences
between adjacent trees (see also the ``"minlex_postorder"`` traversal
order for the :meth:`.Tree.nodes` method); or ``"tree"`` which draws trees
in the left-to-right order defined by the
:ref:`quintuply linked tree structure <sec_data_model_tree_structure>`.
If not specified or None, this defaults to ``"minlex"``.
:param bool force_root_branch: If ``True`` plot a branch (edge) above every tree
root in the tree sequence. If ``None`` (default) then only plot such
root branches if any root in the tree sequence has a mutation above it.
:param float symbol_size: Change the default size of the node and mutation
plotting symbols. If ``None`` (default) use a standard size.
:param bool x_axis: Should the plot have an X axis line, showing the positions
of trees along the genome. The scale used is determined by the ``x_scale``
parameter. If ``None`` (default) plot an X axis.
:param str x_label: Place a label under the plot. If ``None`` (default) and
there is an X axis, create and place an appropriate label.
:param list x_lim: A list of size two giving the genomic positions between which
trees should be plotted. If the first is ``None``, then plot from the first
non-empty region of the tree sequence. If the second is ``None``, then plot
up to the end of the last non-empty region of the tree sequence. The default
value ``x_lim=None`` is shorthand for the list [``None``, ``None``]. If
numerical values are given, then regions outside the interval have all
information discarded: this means that mutations outside the interval will
not be shown. To force display of the entire tree sequence, including empty
flanking regions, specify ``x_lim=[0, ts.sequence_length]``.
:param dict x_regions: A dictionary mapping (left, right) tuples to names. This
draws a box, labelled with the name, on the X axis between the left and
right positions, and can be used for annotating genomic regions (e.g.
genes) on the X axis. If ``None`` (default) do not plot any regions.
:param bool y_axis: Should the plot have an Y axis line, showing time (or
ranked node time if ``time_scale="rank"``. If ``None`` (default)
do not plot a Y axis.
:param str y_label: Place a label to the left of the plot. If ``None`` (default)
and there is a Y axis, create and place an appropriate label.
:param Union[list, dict] y_ticks: A list of Y values at which to plot
tickmarks, or a dictionary mapping Y values to labels (``[]`` gives no
tickmarks). If ``None`` (default), plot one tickmark for each unique node
value. Note that if ``time_scale="rank"``, the Y values refer to the
zero-based rank of the plotted nodes, rather than the node time itself.
:param bool y_gridlines: Whether to plot horizontal lines behind the tree
at each y tickmark.
:param bool omit_sites: If True, omit sites and mutations from the drawing.
Default: False
:param tuple(int, int) canvas_size: The (width, height) of the SVG canvas.
This will change the SVG width and height without rescaling graphical
elements, allowing extra room e.g. for unusually long labels. If ``None``
take the canvas size to be the same as the target drawing size (see
``size``, above). Default: None
:param int max_num_trees: The maximum number of trees to plot. If there are
more trees than this in the tree sequence, the middle trees will be skipped
from the plot and a message "XX trees skipped" displayed in their place.
If ``None``, all the trees will be plotted: this can produce a very wide
plot if there are many trees in the tree sequence. Default: None
:return: An SVG representation of a tree sequence.
:rtype: SVGString
.. note::
Technically, x_lim[0] specifies a *minimum* value for the start of the X
axis, and x_lim[1] specifies a *maximum* value for the end. This is only
relevant if the tree sequence contains "empty" regions with no edges or
mutations. In this case if x_lim[0] lies strictly within an empty region
(i.e., ``empty_tree.interval.left < x_lim[0] < empty_tree.interval.right``)
then that tree will not be plotted on the left hand side, and the X axis
will start at ``empty_tree.interval.right``. Similarly, if x_lim[1] lies
strictly within an empty region then that tree will not be plotted on the
right hand side, and the X axis will end at ``empty_tree.interval.left``
"""
draw = drawing.SvgTreeSequence(
self,
size,
x_scale=x_scale,
time_scale=time_scale,
tree_height_scale=tree_height_scale,
title=title,
node_labels=node_labels,
mutation_labels=mutation_labels,
node_titles=node_titles,
mutation_titles=mutation_titles,
root_svg_attributes=root_svg_attributes,
style=style,
order=order,
force_root_branch=force_root_branch,
symbol_size=symbol_size,
x_axis=x_axis,
x_label=x_label,
x_lim=x_lim,
x_regions=x_regions,
y_axis=y_axis,
y_label=y_label,
y_ticks=y_ticks,
y_gridlines=y_gridlines,
omit_sites=omit_sites,
canvas_size=canvas_size,
max_num_trees=max_num_trees,
**kwargs,
)
output = draw.drawing.tostring()
if path is not None:
# TODO remove the 'pretty' when we are done debugging this.
draw.drawing.saveas(path, pretty=True)
return drawing.SVGString(output)
[docs]
def draw_text(
self,
*,
node_labels=None,
use_ascii=False,
time_label_format=None,
position_label_format=None,
order=None,
**kwargs,
):
"""
Create a text representation of a tree sequence.
:param dict node_labels: If specified, show custom labels for the nodes
that are present in the map. Any nodes not specified in the map will
not have a node label.
:param bool use_ascii: If ``False`` (default) then use unicode
`box drawing characters \
<https://en.wikipedia.org/wiki/Box-drawing_character>`_
to render the tree. If ``True``, use plain ascii characters, which look
cruder but are less susceptible to misalignment or font substitution.
Alternatively, if you are having alignment problems with Unicode, you can try
out the solution documented `here \
<https://github.com/tskit-dev/tskit/issues/189#issuecomment-499114811>`_.
:param str time_label_format: A python format string specifying the format (e.g.
number of decimal places or significant figures) used to print the numerical
time values on the time axis. If ``None``, this defaults to ``"{:.2f}"``.
:param str position_label_format: A python format string specifying the format
(e.g. number of decimal places or significant figures) used to print genomic
positions. If ``None``, this defaults to ``"{:.2f}"``.
:param str order: The left-to-right ordering of child nodes in the drawn tree.
This can be either: ``"minlex"``, which minimises the differences
between adjacent trees (see also the ``"minlex_postorder"`` traversal
order for the :meth:`.Tree.nodes` method); or ``"tree"`` which draws trees
in the left-to-right order defined by the
:ref:`quintuply linked tree structure <sec_data_model_tree_structure>`.
If not specified or None, this defaults to ``"minlex"``.
:return: A text representation of a tree sequence.
:rtype: str
"""
return str(
drawing.TextTreeSequence(
self,
node_labels=node_labels,
use_ascii=use_ascii,
time_label_format=time_label_format,
position_label_format=position_label_format,
order=order,
)
)
############################################
#
# Statistics computation
#
############################################
[docs]
def general_stat(
self,
W,
f,
output_dim,
windows=None,
polarised=False,
mode=None,
span_normalise=True,
strict=True,
):
"""
Compute a windowed statistic from weights and a summary function.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
On each tree, this
propagates the weights ``W`` up the tree, so that the "weight" of each
node is the sum of the weights of all samples at or below the node.
Then the summary function ``f`` is applied to the weights, giving a
summary for each node in each tree. How this is then aggregated depends
on ``mode``:
"site"
Adds together the total summary value across all alleles in each window.
"branch"
Adds together the summary value for each node, multiplied by the
length of the branch above the node and the span of the tree.
"node"
Returns each node's summary value added across trees and multiplied
by the span of the tree.
Both the weights and the summary can be multidimensional: if ``W`` has ``k``
columns, and ``f`` takes a ``k``-vector and returns an ``m``-vector,
then the output will be ``m``-dimensional for each node or window (depending
on "mode").
.. note::
The summary function ``f`` should return zero when given both 0 and
the total weight (i.e., ``f(0) = 0`` and ``f(np.sum(W, axis=0)) = 0``),
unless ``strict=False``. This is necessary for the statistic to be
unaffected by parts of the tree sequence ancestral to none or all
of the samples, respectively.
:param numpy.ndarray W: An array of values with one row for each sample and one
column for each weight.
:param f: A function that takes a one-dimensional array of length
equal to the number of columns of ``W`` and returns a one-dimensional
array.
:param int output_dim: The length of ``f``'s return value.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param bool polarised: Whether to leave the ancestral state out of computations:
see :ref:`sec_stats` for more details.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:param bool strict: Whether to check that f(0) and f(total weight) are zero.
:return: A ndarray with shape equal to (num windows, num statistics).
"""
if mode is None:
mode = "site"
if strict:
total_weights = np.sum(W, axis=0)
for x in [total_weights, total_weights * 0.0]:
with np.errstate(invalid="ignore", divide="ignore"):
fx = np.array(f(x))
fx[np.isnan(fx)] = 0.0
if not np.allclose(fx, np.zeros((output_dim,))):
raise ValueError(
"Summary function does not return zero for both "
"zero weight and total weight."
)
return self.__run_windowed_stat(
windows,
self.ll_tree_sequence.general_stat,
W,
f,
output_dim,
polarised=polarised,
span_normalise=span_normalise,
mode=mode,
)
[docs]
def sample_count_stat(
self,
sample_sets,
f,
output_dim,
windows=None,
polarised=False,
mode=None,
span_normalise=True,
strict=True,
):
"""
Compute a windowed statistic from sample counts and a summary function.
This is a wrapper around :meth:`.general_stat` for the common case in
which the weights are all either 1 or 0, i.e., functions of the joint
allele frequency spectrum.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`sample sets <sec_stats_sample_sets>`,
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
If ``sample_sets`` is a list of ``k`` sets of samples, then
``f`` should be a function that takes an argument of length ``k`` and
returns a one-dimensional array. The ``j``-th element of the argument
to ``f`` will be the number of samples in ``sample_sets[j]`` that lie
below the node that ``f`` is being evaluated for. See
:meth:`.general_stat` for more details.
Here is a contrived example: suppose that ``A`` and ``B`` are two sets
of samples with ``nA`` and ``nB`` elements, respectively. Passing these
as sample sets will give ``f`` an argument of length two, giving the number
of samples in ``A`` and ``B`` below the node in question. So, if we define
.. code-block:: python
def f(x):
pA = x[0] / nA
pB = x[1] / nB
return np.array([pA * pB])
then if all sites are biallelic,
.. code-block:: python
ts.sample_count_stat([A, B], f, 1, windows="sites", polarised=False, mode="site")
would compute, for each site, the product of the derived allele
frequencies in the two sample sets, in a (num sites, 1) array. If
instead ``f`` returns ``np.array([pA, pB, pA * pB])``, then the
output would be a (num sites, 3) array, with the first two columns
giving the allele frequencies in ``A`` and ``B``, respectively.
.. note::
The summary function ``f`` should return zero when given both 0 and
the sample size (i.e., ``f(0) = 0`` and
``f(np.array([len(x) for x in sample_sets])) = 0``). This is
necessary for the statistic to be unaffected by parts of the tree
sequence ancestral to none or all of the samples, respectively.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with.
:param f: A function that takes a one-dimensional array of length
equal to the number of sample sets and returns a one-dimensional array.
:param int output_dim: The length of ``f``'s return value.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param bool polarised: Whether to leave the ancestral state out of computations:
see :ref:`sec_stats` for more details.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:param bool strict: Whether to check that f(0) and f(total weight) are zero.
:return: A ndarray with shape equal to (num windows, num statistics).
""" # noqa: B950
# helper function for common case where weights are indicators of sample sets
for U in sample_sets:
if len(U) != len(set(U)):
raise ValueError(
"Elements of sample_sets must be lists without repeated elements."
)
if len(U) == 0:
raise ValueError("Elements of sample_sets cannot be empty.")
for u in U:
if not self.node(u).is_sample():
raise ValueError("Not all elements of sample_sets are samples.")
W = np.array([[float(u in A) for A in sample_sets] for u in self.samples()])
return self.general_stat(
W,
f,
output_dim,
windows=windows,
polarised=polarised,
mode=mode,
span_normalise=span_normalise,
strict=strict,
)
def parse_windows(self, windows):
# Note: need to make sure windows is a string or we try to compare the
# target with a numpy array elementwise.
if windows is None:
windows = [0.0, self.sequence_length]
elif isinstance(windows, str):
if windows == "trees":
windows = self.breakpoints(as_array=True)
elif windows == "sites":
# breakpoints are at 0.0 and at the sites and at the end
windows = np.concatenate(
[
[] if self.num_sites > 0 else [0.0],
self.sites_position,
[self.sequence_length],
]
)
windows[0] = 0.0
else:
raise ValueError(
f"Unrecognized window specification {windows}:",
"the only allowed strings are 'sites' or 'trees'",
)
return np.array(windows)
def __run_windowed_stat(self, windows, method, *args, **kwargs):
strip_dim = windows is None
windows = self.parse_windows(windows)
stat = method(*args, **kwargs, windows=windows)
if strip_dim:
stat = stat[0]
return stat
def __one_way_sample_set_stat(
self,
ll_method,
sample_sets,
windows=None,
mode=None,
span_normalise=True,
polarised=False,
):
if sample_sets is None:
sample_sets = self.samples()
# First try to convert to a 1D numpy array. If it is, then we strip off
# the corresponding dimension from the output.
drop_dimension = False
try:
sample_sets = np.array(sample_sets, dtype=np.uint64)
except ValueError:
pass
else:
# If we've successfully converted sample_sets to a 1D numpy array
# of integers then drop the dimension
if len(sample_sets.shape) == 1:
sample_sets = [sample_sets]
drop_dimension = True
sample_set_sizes = np.array(
[len(sample_set) for sample_set in sample_sets], dtype=np.uint32
)
if np.any(sample_set_sizes == 0):
raise ValueError("Sample sets must contain at least one element")
flattened = util.safe_np_int_cast(np.hstack(sample_sets), np.int32)
stat = self.__run_windowed_stat(
windows,
ll_method,
sample_set_sizes,
flattened,
mode=mode,
span_normalise=span_normalise,
polarised=polarised,
)
if drop_dimension:
stat = stat.reshape(stat.shape[:-1])
if stat.shape == () and windows is None:
stat = stat[()]
return stat
def parse_sites(self, sites):
row_sites, col_sites = None, None
if sites is not None:
if any(not hasattr(a, "__getitem__") or isinstance(a, str) for a in sites):
raise ValueError("Sites must be a list of lists, tuples, or ndarrays")
if len(sites) == 2:
row_sites, col_sites = sites
elif len(sites) == 1:
row_sites = col_sites = sites[0]
else:
raise ValueError(
f"Sites must be a length 1 or 2 list, got a length {len(sites)} list"
)
return row_sites, col_sites
def parse_positions(self, positions):
row_positions, col_positions = None, None
if positions is not None:
if any(
not hasattr(a, "__getitem__") or isinstance(a, str) for a in positions
):
raise ValueError(
"Positions must be a list of lists, tuples, or ndarrays"
)
if len(positions) == 2:
row_positions, col_positions = positions
elif len(positions) == 1:
row_positions = col_positions = positions[0]
else:
raise ValueError(
"Positions must be a length 1 or 2 list, "
f"got a length {len(positions)} list"
)
return row_positions, col_positions
def __two_locus_sample_set_stat(
self,
ll_method,
sample_sets,
sites=None,
positions=None,
mode=None,
):
if sample_sets is None:
sample_sets = self.samples()
row_sites, col_sites = self.parse_sites(sites)
row_positions, col_positions = self.parse_positions(positions)
# First try to convert to a 1D numpy array. If we succeed, then we strip off
# the corresponding dimension from the output.
drop_dimension = False
try:
sample_sets = np.array(sample_sets, dtype=np.uint64)
except ValueError:
pass
else:
# If we've successfully converted sample_sets to a 1D numpy array
# of integers then drop the dimension
if len(sample_sets.shape) == 1:
sample_sets = [sample_sets]
drop_dimension = True
sample_set_sizes = np.array(
[len(sample_set) for sample_set in sample_sets], dtype=np.uint32
)
if np.any(sample_set_sizes == 0):
raise ValueError("Sample sets must contain at least one element")
flattened = util.safe_np_int_cast(np.hstack(sample_sets), np.int32)
result = ll_method(
sample_set_sizes,
flattened,
row_sites,
col_sites,
row_positions,
col_positions,
mode,
)
if drop_dimension:
result = result.reshape(result.shape[:2])
else:
# Orient the data so that the first dimension is the sample set.
# With this orientation, we get one LD matrix per sample set.
result = result.swapaxes(0, 2).swapaxes(1, 2)
return result
def __k_way_sample_set_stat(
self,
ll_method,
k,
sample_sets,
indexes=None,
windows=None,
mode=None,
span_normalise=True,
polarised=False,
centre=True,
):
sample_set_sizes = np.array(
[len(sample_set) for sample_set in sample_sets], dtype=np.uint32
)
if np.any(sample_set_sizes == 0):
raise ValueError("Sample sets must contain at least one element")
flattened = util.safe_np_int_cast(np.hstack(sample_sets), np.int32)
drop_based_on_index = False
if indexes is None:
drop_based_on_index = True
if len(sample_sets) != k:
raise ValueError(
"Must specify indexes if there are not exactly {} sample "
"sets.".format(k)
)
indexes = np.arange(k, dtype=np.int32)
drop_dimension = False
indexes = util.safe_np_int_cast(indexes, np.int32)
if len(indexes.shape) == 1:
indexes = indexes.reshape((1, indexes.shape[0]))
drop_dimension = True
if len(indexes.shape) != 2 or indexes.shape[1] != k:
raise ValueError(
"Indexes must be convertable to a 2D numpy array with {} "
"columns".format(k)
)
stat = self.__run_windowed_stat(
windows,
ll_method,
sample_set_sizes,
flattened,
indexes,
mode=mode,
span_normalise=span_normalise,
polarised=polarised,
centre=centre,
)
if drop_dimension:
stat = stat.reshape(stat.shape[:-1])
if stat.shape == () and windows is None and drop_based_on_index:
stat = stat[()]
return stat
def __k_way_weighted_stat(
self,
ll_method,
k,
W,
indexes=None,
windows=None,
mode=None,
span_normalise=True,
polarised=False,
centre=True,
):
W = np.asarray(W)
if indexes is None:
if W.shape[1] != k:
raise ValueError(
"Must specify indexes if there are not exactly {} columns "
"in W.".format(k)
)
indexes = np.arange(k, dtype=np.int32)
drop_dimension = False
indexes = util.safe_np_int_cast(indexes, np.int32)
if len(indexes.shape) == 1:
indexes = indexes.reshape((1, indexes.shape[0]))
drop_dimension = True
if len(indexes.shape) != 2 or indexes.shape[1] != k:
raise ValueError(
"Indexes must be convertable to a 2D numpy array with {} "
"columns".format(k)
)
stat = self.__run_windowed_stat(
windows,
ll_method,
W,
indexes,
mode=mode,
span_normalise=span_normalise,
polarised=polarised,
centre=centre,
)
if drop_dimension:
stat = stat.reshape(stat.shape[:-1])
return stat
def __weighted_vector_stat(
self,
ll_method,
W,
windows=None,
mode=None,
span_normalise=True,
centre=True,
nodes=None,
):
W = np.asarray(W)
if len(W.shape) == 1:
W = W.reshape(W.shape[0], 1)
if nodes is None:
nodes = list(self.samples())
else:
if centre:
raise ValueError("If `nodes` is provided, must have centre=False.")
try:
nodes = util.safe_np_int_cast(nodes, np.int32)
except Exception:
raise ValueError("Could not interpret `nodes` as a list of node IDs.")
stat = self.__run_windowed_stat(
windows,
ll_method,
W,
mode=mode,
span_normalise=span_normalise,
centre=centre,
nodes=nodes,
)
return stat
############################################
# Statistics definitions
############################################
[docs]
def diversity(
self, sample_sets=None, windows=None, mode="site", span_normalise=True
):
"""
Computes mean genetic diversity (also known as "pi") in each of the
sets of nodes from ``sample_sets``. The statistic is also known as
"sample heterozygosity"; a common citation for the definition is
`Nei and Li (1979) <https://doi.org/10.1073/pnas.76.10.5269>`_
(equation 22), so it is sometimes called called "Nei's pi"
(but also sometimes "Tajima's pi").
Please see the :ref:`one-way statistics <sec_stats_sample_sets_one_way>`
section for details on how the ``sample_sets`` argument is interpreted
and how it interacts with the dimensions of the output array.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
Note that this quantity can also be computed by the
:meth:`divergence <.TreeSequence.divergence>` method.
What is computed depends on ``mode``:
"site"
Mean pairwise genetic diversity: the average over all n choose 2 pairs of
sample nodes, of the density of sites at
which the two carry different alleles, per unit of chromosome length.
"branch"
Mean distance in the tree: the average across over all n choose 2 pairs of
sample nodes and locations in the window, of the mean distance in
the tree between the two samples (in units of time).
"node"
For each node, the proportion of genome on which the node is an ancestor to
only one of a pair of sample nodes from the sample set, averaged
over over all n choose 2 pairs of sample nodes.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes for which the statistic is computed. If any of the
sample sets contain only a single node, the returned diversity will be
NaN. If ``None`` (default), average over all n choose 2 pairs of distinct
sample nodes in the tree sequence.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A numpy array whose length is equal to the number of sample sets.
If there is one sample set and windows=None, a numpy scalar is returned.
"""
return self.__one_way_sample_set_stat(
self._ll_tree_sequence.diversity,
sample_sets,
windows=windows,
mode=mode,
span_normalise=span_normalise,
)
[docs]
def divergence(
self, sample_sets, indexes=None, windows=None, mode="site", span_normalise=True
):
r"""
Computes mean genetic divergence between (and within) pairs of
sets of nodes from ``sample_sets``.
This is the "average number of differences", usually referred to as "dxy";
a common citation for this definition is Nei and Li (1979), who called it
:math:`\pi_{XY}`. Note that the mean pairwise nucleotide diversity of a
sample set to itself (computed by passing an index of the form (j,j))
is its :meth:`diversity <.TreeSequence.diversity>` (see the note below).
Operates on ``k = 2`` sample sets at a time; please see the
:ref:`multi-way statistics <sec_stats_sample_sets_multi_way>`
section for details on how the ``sample_sets`` and ``indexes`` arguments are
interpreted and how they interact with the dimensions of the output array.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
.. note::
To avoid unexpected results, sample sets should be nonoverlapping,
since comparisons of individuals to themselves are not removed when computing
divergence between distinct sample sets. (However, specifying an index
``(j, j)`` computes the :meth:`diversity <.TreeSequence.diversity>`
of ``sample_set[j]``, which removes self comparisons to provide
an unbiased estimate.)
What is computed depends on ``mode``:
"site"
Mean pairwise genetic divergence: the average across every possible pair of
chromosomes (one from each sample set), of the density of sites at which
the two carry different alleles, per unit of chromosome length.
"branch"
Mean distance in the tree: the average across every possible pair of
chromosomes (one from each sample set) and locations in the window, of
the mean distance in the tree between the two samples (in units of time).
"node"
For each node, the proportion of genome on which the node is an ancestor to
only one of a pair of chromosomes from the sample set, averaged
over all possible pairs.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with.
:param list indexes: A list of 2-tuples, or None.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A ndarray with shape equal to (num windows, num statistics).
If there is one pair of sample sets and windows=None, a numpy scalar is
returned.
"""
return self.__k_way_sample_set_stat(
self._ll_tree_sequence.divergence,
2,
sample_sets,
indexes=indexes,
windows=windows,
mode=mode,
span_normalise=span_normalise,
)
############################################
# Pairwise sample x sample statistics
############################################
def _chunk_sequence_by_tree(self, num_chunks):
"""
Return list of (left, right) genome interval tuples that contain
approximately equal numbers of trees as a 2D numpy array. A
maximum of self.num_trees single-tree intervals can be returned.
"""
if num_chunks <= 0 or int(num_chunks) != num_chunks:
raise ValueError("Number of chunks must be an integer > 0")
num_chunks = min(self.num_trees, num_chunks)
breakpoints = self.breakpoints(as_array=True)[:-1]
splits = np.array_split(breakpoints, num_chunks)
chunks = []
for j in range(num_chunks - 1):
chunks.append((splits[j][0], splits[j + 1][0]))
chunks.append((splits[-1][0], self.sequence_length))
return chunks
@staticmethod
def _chunk_windows(windows, num_chunks):
"""
Returns a list of (at most) num_chunks windows, which represent splitting
up the specified list of windows into roughly equal work.
Currently this is implemented by just splitting up into roughly equal
numbers of windows in each chunk.
"""
if num_chunks <= 0 or int(num_chunks) != num_chunks:
raise ValueError("Number of chunks must be an integer > 0")
num_chunks = min(len(windows) - 1, num_chunks)
splits = np.array_split(windows[:-1], num_chunks)
chunks = []
for j in range(num_chunks - 1):
chunk = np.append(splits[j], splits[j + 1][0])
chunks.append(chunk)
chunk = np.append(splits[-1], windows[-1])
chunks.append(chunk)
return chunks
def _parallelise_divmat_by_tree(self, num_threads, span_normalise, **kwargs):
"""
No windows were specified, so we can chunk up the whole genome by
tree, and do a simple sum of the results. This means that we have to
handle span_normalise specially, though.
"""
def worker(interval):
return self._ll_tree_sequence.divergence_matrix(interval, **kwargs)
work = self._chunk_sequence_by_tree(num_threads)
with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as pool:
results = pool.map(worker, work)
total = sum(results)
if span_normalise:
total /= self.sequence_length
return total
def _parallelise_divmat_by_window(self, windows, num_threads, **kwargs):
"""
We assume we have a number of windows that's >= to the number
of threads available, and let each thread have a chunk of the
windows. There will definitely cases where this leads to
pathological behaviour, so we may need a more sophisticated
strategy at some point.
"""
def worker(sub_windows):
return self._ll_tree_sequence.divergence_matrix(sub_windows, **kwargs)
work = self._chunk_windows(windows, num_threads)
with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
futures = [executor.submit(worker, sub_windows) for sub_windows in work]
concurrent.futures.wait(futures)
return np.vstack([future.result() for future in futures])
@staticmethod
def _parse_stat_matrix_sample_sets(ids):
"""
Returns a flattened list of sets of IDs. If ids is a 1D list,
interpret as n one-element sets. Otherwise, it must be a sequence
of ID lists.
"""
id_dtype = np.int32
size_dtype = np.uint64
# Exclude some types that could be specified accidentally, and
# we may want to reserve for future use.
if isinstance(ids, (str, bytes, collections.abc.Mapping, numbers.Number)):
raise TypeError(f"ID specification cannot be a {type(ids)}")
if len(ids) == 0:
return np.array([], dtype=id_dtype), np.array([], dtype=size_dtype)
if isinstance(ids[0], numbers.Number):
# Interpret as a 1D array
flat = util.safe_np_int_cast(ids, id_dtype)
sizes = np.ones(len(flat), dtype=size_dtype)
else:
set_lists = []
sizes = []
for id_list in ids:
a = util.safe_np_int_cast(id_list, id_dtype)
if len(a.shape) != 1:
raise ValueError("ID sets must be 1D integer arrays")
set_lists.append(a)
sizes.append(len(a))
flat = np.hstack(set_lists)
sizes = np.array(sizes, dtype=size_dtype)
return flat, sizes
# def divergence_matrix(self, sample_sets, windows=None, mode="site"):
# """
# Finds the mean divergence between pairs of samples from each set of
# samples and in each window. Returns a numpy array indexed by (window,
# sample_set, sample_set). Diagonal entries are corrected so that the
# value gives the mean divergence for *distinct* samples, but it is not
# checked whether the sample_sets are disjoint (so offdiagonals are not
# corrected). For this reason, if an element of `sample_sets` has only
# one element, the corresponding diagonal will be NaN.
# The mean divergence between two samples is defined to be the mean: (as
# a TreeStat) length of all edges separating them in the tree, or (as a
# SiteStat) density of segregating sites, at a uniformly chosen position
# on the genome.
# :param list sample_sets: A list of sets of IDs of samples.
# :param iterable windows: The breakpoints of the windows (including start
# and end, so has one more entry than number of windows).
# :return: A list of the upper triangle of mean TMRCA values in row-major
# order, including the diagonal.
# """
# ns = len(sample_sets)
# indexes = [(i, j) for i in range(ns) for j in range(i, ns)]
# x = self.divergence(sample_sets, indexes, windows, mode=mode)
# nw = len(windows) - 1
# A = np.ones((nw, ns, ns), dtype=float)
# for w in range(nw):
# k = 0
# for i in range(ns):
# for j in range(i, ns):
# A[w, i, j] = A[w, j, i] = x[w][k]
# k += 1
# return A
# NOTE: see older definition of divmat here, which may be useful when documenting
# this function. See https://github.com/tskit-dev/tskit/issues/2781
# NOTE for documentation of sample_sets. We *must* use samples currently because
# the normalisation for non-sample nodes is tricky. Do we normalise by the
# total span of the ts where the node is 'present' in the tree? We avoid this
# by insisting on sample nodes.
# NOTE for documentation of num_threads. Need to explain that the
# its best to think of as the number of background *worker* threads.
# default is to run without any worker threads. If you want to run
# with all the cores on the machine, use num_threads=os.cpu_count().
def divergence_matrix(
self,
sample_sets=None,
*,
windows=None,
num_threads=0,
mode=None,
span_normalise=True,
):
windows_specified = windows is not None
windows = self.parse_windows(windows)
mode = "site" if mode is None else mode
if sample_sets is None:
sample_sets = self.samples()
flattened_samples = self.samples()
sample_set_sizes = np.ones(len(sample_sets), dtype=np.uint32)
else:
flattened_samples, sample_set_sizes = self._parse_stat_matrix_sample_sets(
sample_sets
)
# FIXME this logic should be merged into __run_windowed_stat if
# we generalise the num_threads argument to all stats.
if num_threads <= 0:
D = self._ll_tree_sequence.divergence_matrix(
windows,
sample_sets=flattened_samples,
sample_set_sizes=sample_set_sizes,
mode=mode,
span_normalise=span_normalise,
)
else:
if windows_specified:
D = self._parallelise_divmat_by_window(
windows,
num_threads,
sample_sets=flattened_samples,
sample_set_sizes=sample_set_sizes,
mode=mode,
span_normalise=span_normalise,
)
else:
D = self._parallelise_divmat_by_tree(
num_threads,
span_normalise=span_normalise,
sample_sets=flattened_samples,
sample_set_sizes=sample_set_sizes,
mode=mode,
)
if not windows_specified:
# Drop the windows dimension
D = D[0]
return D
def _expand_indices(self, x: np.ndarray, indices: np.ndarray) -> np.ndarray:
y = np.zeros((self.num_samples, x.shape[1]))
y[indices] = x
return y
def _genetic_relatedness_vector_node(
self,
arr: np.ndarray,
indices: np.ndarray,
mode: str,
centre: bool = True,
windows=None,
) -> np.ndarray:
x = arr - arr.mean(axis=0) if centre else arr
x = self._expand_indices(x, indices)
x = self.genetic_relatedness_vector(
W=x,
windows=windows,
mode=mode,
centre=False,
nodes=indices,
)[0]
x = x - x.mean(axis=0) if centre else x
return x
def _genetic_relatedness_vector_individual(
self,
arr: np.ndarray,
indices: np.ndarray,
mode: str,
centre: bool = True,
windows=None,
) -> np.ndarray:
ij = np.vstack(
[[n, k] for k, i in enumerate(indices) for n in self.individual(i).nodes]
)
samples, sample_individuals = (
ij[:, 0],
ij[:, 1],
)
ploidy = np.bincount(sample_individuals)
x = arr - arr.mean(axis=0) if centre else arr
x = x[sample_individuals] / ploidy[sample_individuals, np.newaxis]
x = self._expand_indices(x, samples)
x = self.genetic_relatedness_vector(
W=x,
windows=windows,
mode=mode,
centre=False,
nodes=samples,
)[0]
def bincount_fn(w):
return np.bincount(sample_individuals, w) / ploidy
x = np.apply_along_axis(bincount_fn, axis=0, arr=x)
x = x - x.mean(axis=0) if centre else x # centering within index in cols
return x
[docs]
def pca(
self,
num_components: int,
windows: list = None,
samples: np.ndarray = None,
individuals: np.ndarray = None,
time_windows: np.ndarray = None,
mode: str = "branch",
centre: bool = True,
iterated_power: int = 5,
num_oversamples: int = None,
random_seed: int = None,
range_sketch: np.ndarray = None,
) -> (np.ndarray, np.ndarray, np.ndarray):
"""
Performs principal component analysis (PCA) for a given set of samples or
individuals. The principal components are the eigenvectors of the genetic
relatedness matrix, which are obtained by a randomized singular value
decomposition (rSVD) algorithm.
Concretely, if :math:`M` is the matrix of genetic relatedness values, with
:math:`M_{ij}` the output of
:meth:`genetic_relatedness <.TreeSequence.genetic_relatedness>`
between sample :math:`i` and sample :math:`j`, then by default this returns
the top `num_components` eigenvectors of :math:`M`, so that
`output.factor[i,k]` is the position of sample `i` on the `k`th PC.
If `samples` or `individuals` are provided, then this does the same thing,
except with :math:`M_{ij}` either the relatedness between `samples[i]`
and `samples[j]` or the nodes of `individuals[i]` and `individuals[j]`,
respectively.
The parameters `centre` and `mode` are passed to
:meth:`genetic_relatedness <.TreeSequence.genetic_relatedness>`;
if `windows` are provided then PCA is carried out separately in each window.
If `time_window` is provided, then genetic relatedness is measured using only
ancestral material within the given time window (see
:meth:`decapitate <.TreeSequence.decapitate>` for how this is defined).
So that the method scales to large tree sequences, the underlying method
relies on a randomized SVD algorithm. Larger values of `iterated_power` and
`num_oversamples` should produce better approximations to the true eigenvalues,
at the cost of greater compute times and/or memory usage. The method relies on
constructing `range_sketch`, a low-dimensional approximation to the range
of :math:`M`, so that the result of a previous call to `pca()` may be passed in.
To check for convergence, compare
`pc1 = ts.pca()` and `pc2 = ts.pca(range_sketch=pc1.range_sketch)`; the
difference between `pc1.factors` and `pc2.factors` provides a
diagnostic of the convergence of the algorithm (i.e., if they are close
then it has likely converged). Algorithms are based on Algorithms 8
and 9 in Martinsson and Tropp, https://arxiv.org/pdf/2002.01387 .
:param int num_components: Number of principal components to return.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in (default: the entire genome).
:param numpy.ndarray samples: Samples to perform PCA with (default: all samples).
:param numpy.ndarray individuals: Individuals to perform PCA with. Cannot specify
both `samples` and `individuals`.
:param numpy.ndarray time_windows: The time interval on which to apply PCA:
currently, this must be either None (default, covers all time)
or a single interval.
:param str mode: A string giving the "type" of relatedness to be computed
(defaults to "branch"; see
:meth:`genetic_relatedness_vector
<.TreeSequence.genetic_relatedness_vector>`).
:param bool centre: Centre the genetic relatedness matrix.
:param int iterated_power: Number of power iterations of range finder.
:param int num_oversamples: Number of additional test vectors (default: 10).
Cannot specify along with range_sketch.
:param int random_seed: The random seed. If this is None, a random seed will
be automatically generated. Valid random seeds are between 1 and
:math:`2^32 − 1`.
:param numpy.ndarray range_sketch: Sketch matrix for each window. Default is
randomly generated; cannot specify along with num_oversamples.
:return: A :class:`PCAResult` object, containing estimated principal components,
eigenvalues, and other information:
the principal component loadings are in PCAResult.factors
and the principal values are in PCAResult.eigenvalues.
"""
if (not isinstance(iterated_power, int)) or iterated_power < 1:
raise ValueError("iterated_power should be a positive integer.")
if samples is None and individuals is None:
samples = self.samples()
if samples is not None and individuals is not None:
raise ValueError("Samples and individuals cannot be used at the same time")
elif samples is not None:
output_type = "node"
dim = len(samples)
else:
assert individuals is not None
output_type = "individual"
dim = len(individuals)
if time_windows is None:
tree_sequence_low, tree_sequence_high = None, self
else:
assert (
time_windows[0] < time_windows[1]
), "The second argument should be larger."
tree_sequence_low, tree_sequence_high = (
self.decapitate(time_windows[0]),
self.decapitate(time_windows[1]),
)
drop_windows = windows is None
windows = self.parse_windows(windows)
num_windows = len(windows) - 1
if num_windows < 1:
raise ValueError("Must have at least one window.")
if num_components > dim:
raise ValueError(
"Number of components must be less than or equal to "
"the number of samples (or individuals, if specified)."
)
if num_oversamples is not None and range_sketch is not None:
raise ValueError("Cannot specify both num_oversamples and range_sketch.")
if range_sketch is None:
if num_oversamples is None:
num_oversamples = min(10, dim - num_components)
rng = np.random.default_rng(random_seed)
range_sketch = rng.normal(
size=(num_windows, dim, num_components + num_oversamples)
)
else:
if drop_windows:
range_sketch = np.expand_dims(range_sketch, 0)
if range_sketch.shape[-1] < num_components:
raise ValueError(
"range_sketch must have at least as many columns as num_components"
)
num_oversamples = range_sketch.shape[-1] - num_components
num_vectors = num_components + num_oversamples
if num_vectors > dim:
raise ValueError(
"Number of columns in range_sketch "
"(num_components + num_oversamples) must be less"
" than or equal to the number of samples"
" (or individuals, if specified)."
)
rs_exp_dims = (num_windows, dim, num_vectors)
rs_obs_dims = range_sketch.shape
if rs_obs_dims != rs_exp_dims:
if drop_windows:
rs_obs_dims = rs_obs_dims[1:]
rs_exp_dims = rs_exp_dims[1:]
raise ValueError(
"Incorrect shape of range_sketch:"
f" expected {rs_exp_dims}; got {rs_obs_dims}."
)
def _rand_pow_range_finder(
operator,
operator_dim: int,
rank: int,
depth: int,
num_vectors: int,
Q: np.ndarray,
) -> np.ndarray:
"""
Algorithm 9 in https://arxiv.org/pdf/2002.01387
"""
assert (
num_vectors >= rank > 0
), "num_vectors should not be smaller than rank"
for _ in range(depth):
Q = np.linalg.qr(Q).Q
Q = operator(Q)
Q = np.linalg.qr(Q).Q
return Q
def _rand_svd(
operator,
operator_dim: int,
rank: int,
depth: int,
num_vectors: int,
range_sketch: np.ndarray,
) -> (np.ndarray, np.ndarray, np.ndarray, float):
"""
Algorithm 8 in https://arxiv.org/pdf/2002.01387
"""
assert num_vectors >= rank > 0
Q = _rand_pow_range_finder(
operator,
operator_dim,
rank=num_vectors,
depth=depth,
num_vectors=num_vectors,
Q=range_sketch,
)
C = operator(Q).T
U_hat, D, _ = np.linalg.svd(C, full_matrices=False)
U = Q @ U_hat
error_factor = np.power(
1 + 4 * np.sqrt(2 * operator_dim / max(1, (rank - 1))),
1 / (2 * depth + 1),
)
error_bound = D[rank] * (1 + error_factor)
return U[:, :rank], D[:rank], Q, error_bound
_f_high = (
tree_sequence_high._genetic_relatedness_vector_node
if output_type == "node"
else tree_sequence_high._genetic_relatedness_vector_individual
)
if time_windows is not None:
_f_low = (
tree_sequence_low._genetic_relatedness_vector_node
if output_type == "node"
else tree_sequence_low._genetic_relatedness_vector_individual
)
indices = samples if output_type == "node" else individuals
U = np.empty((num_windows, dim, num_components))
D = np.empty((num_windows, num_components))
Q = np.empty((num_windows, dim, num_vectors))
E = np.empty(num_windows)
for i in range(num_windows):
def _G(x, i=i):
high = _f_high(
arr=x,
indices=indices,
mode=mode,
centre=centre,
windows=windows[i : i + 2],
)
if time_windows is None:
return high
else:
low = _f_low(
arr=x,
indices=indices,
mode=mode,
centre=centre,
windows=windows[i : i + 2],
)
return high - low
U[i], D[i], Q[i], E[i] = _rand_svd(
operator=_G,
operator_dim=dim,
rank=num_components,
depth=iterated_power,
num_vectors=num_vectors,
range_sketch=range_sketch[i],
)
if drop_windows:
U, D, Q, E = U[0], D[0], Q[0], E[0]
pca_result = PCAResult(factors=U, eigenvalues=D, range_sketch=Q, error_bound=E)
return pca_result
[docs]
def trait_covariance(self, W, windows=None, mode="site", span_normalise=True):
"""
Computes the mean squared covariances between each of the columns of ``W``
(the "phenotypes") and inheritance along the tree sequence.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
Operates on all samples in the tree sequence.
Concretely, if `g` is a binary vector that indicates inheritance from an allele,
branch, or node and `w` is a column of W, normalised to have mean zero,
then the covariance of `g` and `w` is :math:`\\sum_i g_i w_i`, the sum of the
weights corresponding to entries of `g` that are `1`. Since weights sum to
zero, this is also equal to the sum of weights whose entries of `g` are 0.
So, :math:`cov(g,w)^2 = ((\\sum_i g_i w_i)^2 + (\\sum_i (1-g_i) w_i)^2)/2`.
What is computed depends on ``mode``:
"site"
The sum of squared covariances between presence/absence of each allele and
phenotypes, divided by length of the window (if ``span_normalise=True``).
This is computed as sum_a (sum(w[a])^2 / 2), where
w is a column of W with the average subtracted off,
and w[a] is the sum of all entries of w corresponding to samples
carrying allele "a", and the first sum is over all alleles.
"branch"
The sum of squared covariances between the split induced by each branch and
phenotypes, multiplied by branch length, averaged across trees in
the window. This is computed as above: a branch with total weight
w[b] below b contributes (branch length) * w[b]^2 to the total
value for a tree. (Since the sum of w is zero, the total weight
below b and not below b are equal, canceling the factor of 2
above.)
"node"
For each node, the squared covariance between the property of
inheriting from this node and phenotypes, computed as in "branch".
:param numpy.ndarray W: An array of values with one row for each sample and one
column for each "phenotype".
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A ndarray with shape equal to (num windows, num statistics).
If windows=None and W is a single column, a numpy scalar is returned.
"""
if W.shape[0] != self.num_samples:
raise ValueError(
"First trait dimension must be equal to number of samples."
)
return self.__run_windowed_stat(
windows,
self._ll_tree_sequence.trait_covariance,
W,
mode=mode,
span_normalise=span_normalise,
)
[docs]
def trait_correlation(self, W, windows=None, mode="site", span_normalise=True):
"""
Computes the mean squared correlations between each of the columns of ``W``
(the "phenotypes") and inheritance along the tree sequence.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
Operates on all samples in the tree sequence.
This is computed as squared covariance in
:meth:`trait_covariance <.TreeSequence.trait_covariance>`,
but divided by :math:`p (1-p)`, where `p` is the proportion of samples
inheriting from the allele, branch, or node in question.
What is computed depends on ``mode``:
"site"
The sum of squared correlations between presence/absence of each allele and
phenotypes, divided by length of the window (if ``span_normalise=True``).
This is computed as the
:meth:`trait_covariance <.TreeSequence.trait_covariance>`
divided by the variance of the relevant column of W
and by :math:`p * (1 - p)`, where :math:`p` is the allele frequency.
"branch"
The sum of squared correlations between the split induced by each branch and
phenotypes, multiplied by branch length, averaged across trees in
the window. This is computed as the
:meth:`trait_covariance <.TreeSequence.trait_covariance>`,
divided by the variance of the column of w
and by :math:`p * (1 - p)`, where :math:`p` is the proportion of
the samples lying below the branch.
"node"
For each node, the squared correlation between the property of
inheriting from this node and phenotypes, computed as in "branch".
Note that above we divide by the **sample** variance, which for a
vector x of length n is ``np.var(x) * n / (n-1)``.
:param numpy.ndarray W: An array of values with one row for each sample and one
column for each "phenotype". Each column must have positive standard
deviation.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A ndarray with shape equal to (num windows, num statistics).
If windows=None and W is a single column, a numpy scalar is returned.
"""
if W.shape[0] != self.num_samples:
raise ValueError(
"First trait dimension must be equal to number of samples."
)
sds = np.std(W, axis=0)
if np.any(sds == 0):
raise ValueError(
"Weight columns must have positive variance", "to compute correlation."
)
return self.__run_windowed_stat(
windows,
self._ll_tree_sequence.trait_correlation,
W,
mode=mode,
span_normalise=span_normalise,
)
[docs]
def trait_regression(self, *args, **kwargs):
"""
Deprecated synonym for
:meth:`trait_linear_model <.TreeSequence.trait_linear_model>`.
"""
warnings.warn(
"This is deprecated: please use trait_linear_model( ) instead.",
FutureWarning,
stacklevel=4,
)
return self.trait_linear_model(*args, **kwargs)
[docs]
def trait_linear_model(
self, W, Z=None, windows=None, mode="site", span_normalise=True
):
"""
Finds the relationship between trait and genotype after accounting for
covariates. Concretely, for each trait w (i.e., each column of W),
this does a least-squares fit of the linear model :math:`w \\sim g + Z`,
where :math:`g` is inheritance in the tree sequence (e.g., genotype)
and the columns of :math:`Z` are covariates, and returns the squared
coefficient of :math:`g` in this linear model.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
Operates on all samples in the tree sequence.
To do this, if `g` is a binary vector that indicates inheritance from an allele,
branch, or node and `w` is a column of W, there are :math:`k` columns of
:math:`Z`, and the :math:`k+2`-vector :math:`b` minimises
:math:`\\sum_i (w_i - b_0 - b_1 g_i - b_2 z_{2,i} - ... b_{k+2} z_{k+2,i})^2`
then this returns the number :math:`b_1^2`. If :math:`g` lies in the linear span
of the columns of :math:`Z`, then :math:`b_1` is set to 0. To fit the
linear model without covariates (only the intercept), set `Z = None`.
What is computed depends on ``mode``:
"site"
Computes the sum of :math:`b_1^2/2` for each allele in the window,
as above with :math:`g` indicating presence/absence of the allele,
then divided by the length of the window if ``span_normalise=True``.
(For biallelic loci, this number is the same for both alleles, and so summing
over each cancels the factor of two.)
"branch"
The squared coefficient :math:`b_1^2`, computed for the split induced by each
branch (i.e., with :math:`g` indicating inheritance from that branch),
multiplied by branch length and tree span, summed over all trees
in the window, and divided by the length of the window if
``span_normalise=True``.
"node"
For each node, the squared coefficient :math:`b_1^2`, computed for
the property of inheriting from this node, as in "branch".
:param numpy.ndarray W: An array of values with one row for each sample and one
column for each "phenotype".
:param numpy.ndarray Z: An array of values with one row for each sample and one
column for each "covariate", or `None`. Columns of `Z` must be linearly
independent.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A ndarray with shape equal to (num windows, num statistics).
If windows=None and W is a single column, a numpy scalar is returned.
"""
if W.shape[0] != self.num_samples:
raise ValueError(
"First trait dimension must be equal to number of samples."
)
if Z is None:
Z = np.ones((self.num_samples, 1))
else:
tZ = np.column_stack([Z, np.ones((Z.shape[0], 1))])
if np.linalg.matrix_rank(tZ) == tZ.shape[1]:
Z = tZ
if Z.shape[0] != self.num_samples:
raise ValueError("First dimension of Z must equal the number of samples.")
if np.linalg.matrix_rank(Z) < Z.shape[1]:
raise ValueError("Matrix of covariates is computationally singular.")
# numpy returns a lower-triangular cholesky
K = np.linalg.cholesky(np.matmul(Z.T, Z)).T
Z = np.matmul(Z, np.linalg.inv(K))
return self.__run_windowed_stat(
windows,
self._ll_tree_sequence.trait_linear_model,
W,
Z,
mode=mode,
span_normalise=span_normalise,
)
[docs]
def segregating_sites(
self, sample_sets=None, windows=None, mode="site", span_normalise=True
):
"""
Computes the density of segregating sites for each of the sets of nodes
from ``sample_sets``, and related quantities.
Please see the :ref:`one-way statistics <sec_stats_sample_sets_one_way>`
section for details on how the ``sample_sets`` argument is interpreted
and how it interacts with the dimensions of the output array.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`, :ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
What is computed depends on ``mode``. For a sample set ``A``, computes:
"site"
The sum over sites of the number of alleles found in ``A`` at each site
minus one, per unit of chromosome length.
If all sites have at most two alleles, this is the density of sites
that are polymorphic in ``A``. To get the **number** of segregating minor
alleles per window, pass ``span_normalise=False``.
"branch"
The total length of all branches in the tree subtended by the samples in
``A``, averaged across the window.
"node"
The proportion of the window on which the node is ancestral to some,
but not all, of the samples in ``A``.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A ndarray with shape equal to (num windows, num statistics).
If there is one sample set and windows=None, a numpy scalar is returned.
"""
return self.__one_way_sample_set_stat(
self._ll_tree_sequence.segregating_sites,
sample_sets,
windows=windows,
mode=mode,
span_normalise=span_normalise,
)
[docs]
def allele_frequency_spectrum(
self,
sample_sets=None,
windows=None,
mode="site",
span_normalise=True,
polarised=False,
):
"""
Computes the allele frequency spectrum (AFS) in windows across the genome for
with respect to the specified ``sample_sets``.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`sample sets <sec_stats_sample_sets>`,
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
:ref:`polarised <sec_stats_polarisation>`,
and :ref:`return value <sec_stats_output_format>`.
and see :ref:`sec_tutorial_afs` for examples of how to use this method.
Similar to other windowed stats, the first dimension in the returned array
corresponds to windows, such that ``result[i]`` is the AFS in the ith
window. The AFS in each window is a k-dimensional numpy array, where k is
the number of input sample sets, such that ``result[i, j0, j1, ...]`` is the
value associated with frequency ``j0`` in ``sample_sets[0]``, ``j1`` in
``sample_sets[1]``, etc, in window ``i``. From here, we will assume that
``afs`` corresponds to the result in a single window, i.e.,
``afs = result[i]``.
If a single sample set is specified, the allele frequency spectrum within
this set is returned, such that ``afs[j]`` is the value associated with
frequency ``j``. Thus, singletons are counted in ``afs[1]``, doubletons in
``afs[2]``, and so on. The zeroth entry counts alleles or branches not
seen in the samples but that are polymorphic among the rest of the samples
of the tree sequence; likewise, the last entry counts alleles fixed in
the sample set but polymorphic in the entire set of samples. Please see
the :ref:`sec_tutorial_afs_zeroth_entry` for an illustration.
.. warning:: Please note that singletons are **not** counted in the initial
entry in each AFS array (i.e., ``afs[0]``), but in ``afs[1]``.
If ``sample_sets`` is None (the default), the allele frequency spectrum
for all samples in the tree sequence is returned.
If more than one sample set is specified, the **joint** allele frequency
spectrum within windows is returned. For example, if we set
``sample_sets = [S0, S1]``, then afs[1, 2] counts the number of sites that
are singletons within S0 and doubletons in S1. The dimensions of the
output array will be ``[num_windows] + [1 + len(S) for S in sample_sets]``.
If ``polarised`` is False (the default) the AFS will be *folded*, so that
the counts do not depend on knowing which allele is ancestral. If folded,
the frequency spectrum for a single sample set ``S`` has ``afs[j] = 0`` for
all ``j > len(S) / 2``, so that alleles at frequency ``j`` and ``len(S) - j``
both add to the same entry. If there is more than one sample set, the
returned array is "lower triangular" in a similar way. For more details,
especially about handling of multiallelic sites, see :ref:`sec_stats_notes_afs`.
What is computed depends on ``mode``:
"site"
The number of alleles at a given frequency within the specified sample
sets for each window, per unit of sequence length. To obtain the total
number of alleles, set ``span_normalise`` to False.
"branch"
The total length of branches in the trees subtended by subsets of the
specified sample sets, per unit of sequence length. To obtain the
total, set ``span_normalise`` to False.
"node"
Not supported for this method (raises a ValueError).
For example, suppose that `S0` is a list of 5 sample IDs, and `S1` is
a list of 3 other sample IDs. Then `afs = ts.allele_frequency_spectrum([S0, S1],
mode="site", span_normalise=False)` will be a 5x3 numpy array, and if
there are six alleles that are present in only one sample of `S0` but
two samples of `S1`, then `afs[1,2]` will be equal to 6. Similarly,
`branch_afs = ts.allele_frequency_spectrum([S0, S1], mode="branch",
span_normalise=False)` will also be a 5x3 array, and `branch_afs[1,2]`
will be the total area (i.e., length times span) of all branches that
are above exactly one sample of `S0` and two samples of `S1`.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of samples to compute the joint allele frequency
:param list windows: An increasing list of breakpoints between windows
along the genome.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A (k + 1) dimensional numpy array, where k is the number of sample
sets specified.
If there is one sample set and windows=None, a 1 dimensional array is
returned.
"""
if sample_sets is None:
sample_sets = [self.samples()]
return self.__one_way_sample_set_stat(
self._ll_tree_sequence.allele_frequency_spectrum,
sample_sets,
windows=windows,
mode=mode,
span_normalise=span_normalise,
polarised=polarised,
)
[docs]
def Tajimas_D(self, sample_sets=None, windows=None, mode="site"):
"""
Computes Tajima's D of sets of nodes from ``sample_sets`` in windows.
Please see the :ref:`one-way statistics <sec_stats_sample_sets_one_way>`
section for details on how the ``sample_sets`` argument is interpreted
and how it interacts with the dimensions of the output array.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`, :ref:`mode <sec_stats_mode>`,
and :ref:`return value <sec_stats_output_format>`.
Operates on ``k = 1`` sample sets at a
time. For a sample set ``X`` of ``n`` nodes, if and ``T`` is the mean
number of pairwise differing sites in ``X`` and ``S`` is the number of
sites segregating in ``X`` (computed with :meth:`diversity
<.TreeSequence.diversity>` and :meth:`segregating sites
<.TreeSequence.segregating_sites>`, respectively, both not span
normalised), then Tajima's D is
.. code-block:: python
D = (T - S / h) / sqrt(a * S + (b / c) * S * (S - 1))
h = 1 + 1 / 2 + ... + 1 / (n - 1)
g = 1 + 1 / 2**2 + ... + 1 / (n - 1) ** 2
a = (n + 1) / (3 * (n - 1) * h) - 1 / h**2
b = 2 * (n**2 + n + 3) / (9 * n * (n - 1)) - (n + 2) / (h * n) + g / h**2
c = h**2 + g
What is computed for diversity and divergence depends on ``mode``;
see those functions for more details.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with.
:param list indexes: A list of 2-tuples, or None.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:return: A ndarray with shape equal to (num windows, num statistics).
If there is one sample set and windows=None, a numpy scalar is returned.
"""
# TODO this should be done in C as we'll want to support this method there.
def tjd_func(sample_set_sizes, flattened, **kwargs):
n = sample_set_sizes
T = self.ll_tree_sequence.diversity(n, flattened, **kwargs)
S = self.ll_tree_sequence.segregating_sites(n, flattened, **kwargs)
h = np.array([np.sum(1 / np.arange(1, nn)) for nn in n])
g = np.array([np.sum(1 / np.arange(1, nn) ** 2) for nn in n])
with np.errstate(invalid="ignore", divide="ignore"):
a = (n + 1) / (3 * (n - 1) * h) - 1 / h**2
b = (
2 * (n**2 + n + 3) / (9 * n * (n - 1))
- (n + 2) / (h * n)
+ g / h**2
)
D = (T - S / h) / np.sqrt(a * S + (b / (h**2 + g)) * S * (S - 1))
return D
return self.__one_way_sample_set_stat(
tjd_func, sample_sets, windows=windows, mode=mode, span_normalise=False
)
[docs]
def Fst(
self, sample_sets, indexes=None, windows=None, mode="site", span_normalise=True
):
"""
Computes "windowed" Fst between pairs of sets of nodes from ``sample_sets``.
Operates on ``k = 2`` sample sets at a time; please see the
:ref:`multi-way statistics <sec_stats_sample_sets_multi_way>`
section for details on how the ``sample_sets`` and ``indexes`` arguments are
interpreted and how they interact with the dimensions of the output array.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
For sample sets ``X`` and ``Y``, if ``d(X, Y)`` is the
:meth:`divergence <.TreeSequence.divergence>`
between ``X`` and ``Y``, and ``d(X)`` is the
:meth:`diversity <.TreeSequence.diversity>` of ``X``, then what is
computed is
.. code-block:: python
Fst = 1 - 2 * (d(X) + d(Y)) / (d(X) + 2 * d(X, Y) + d(Y))
What is computed for diversity and divergence depends on ``mode``;
see those functions for more details.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with.
:param list indexes: A list of 2-tuples.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A ndarray with shape equal to (num windows, num statistics).
If there is one pair of sample sets and windows=None, a numpy scalar is
returned.
"""
# TODO this should really be implemented in C (presumably C programmers will want
# to compute Fst too), but in the mean time implementing using the low-level
# calls has two advantages: (a) we automatically change dimensions like the other
# two-way stats and (b) it's a bit more efficient because we're not messing
# around with indexes and samples sets twice.
def fst_func(
sample_set_sizes,
flattened,
indexes,
windows,
mode,
span_normalise,
polarised,
centre,
):
# note: this is kinda hacky - polarised and centre are not used here -
# but this seems necessary to use our __k_way_sample_set_stat framework
divergences = self._ll_tree_sequence.divergence(
sample_set_sizes,
flattened,
indexes=indexes,
windows=windows,
mode=mode,
span_normalise=span_normalise,
polarised=polarised,
centre=centre,
)
diversities = self._ll_tree_sequence.diversity(
sample_set_sizes,
flattened,
windows=windows,
mode=mode,
span_normalise=span_normalise,
polarised=polarised,
)
orig_shape = divergences.shape
# "node" statistics produce a 3D array
if len(divergences.shape) == 2:
divergences.shape = (divergences.shape[0], 1, divergences.shape[1])
diversities.shape = (diversities.shape[0], 1, diversities.shape[1])
fst = np.repeat(1.0, np.prod(divergences.shape))
fst.shape = divergences.shape
for i, (u, v) in enumerate(indexes):
denom = (
diversities[:, :, u]
+ diversities[:, :, v]
+ 2 * divergences[:, :, i]
)
with np.errstate(divide="ignore", invalid="ignore"):
fst[:, :, i] -= (
2 * (diversities[:, :, u] + diversities[:, :, v]) / denom
)
fst.shape = orig_shape
return fst
return self.__k_way_sample_set_stat(
fst_func,
2,
sample_sets,
indexes=indexes,
windows=windows,
mode=mode,
span_normalise=span_normalise,
)
[docs]
def Y3(
self, sample_sets, indexes=None, windows=None, mode="site", span_normalise=True
):
"""
Computes the 'Y' statistic between triples of sets of nodes from ``sample_sets``.
Operates on ``k = 3`` sample sets at a time; please see the
:ref:`multi-way statistics <sec_stats_sample_sets_multi_way>`
section for details on how the ``sample_sets`` and ``indexes`` arguments are
interpreted and how they interact with the dimensions of the output array.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
What is computed depends on ``mode``. Each is an average across every
combination of trios of samples ``(a, b, c)``, one chosen from each sample set:
"site"
The average density of sites at which ``a`` differs from ``b`` and
``c``, per unit of chromosome length.
"branch"
The average length of all branches that separate ``a`` from ``b``
and ``c`` (in units of time).
"node"
For each node, the average proportion of the window on which ``a``
inherits from that node but ``b`` and ``c`` do not, or vice-versa.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with.
:param list indexes: A list of 3-tuples, or None.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A ndarray with shape equal to (num windows, num statistics).
If there is one triple of sample sets and windows=None, a numpy scalar is
returned.
"""
return self.__k_way_sample_set_stat(
self._ll_tree_sequence.Y3,
3,
sample_sets,
indexes=indexes,
windows=windows,
mode=mode,
span_normalise=span_normalise,
)
[docs]
def Y2(
self, sample_sets, indexes=None, windows=None, mode="site", span_normalise=True
):
"""
Computes the 'Y2' statistic between pairs of sets of nodes from ``sample_sets``.
Operates on ``k = 2`` sample sets at a time; please see the
:ref:`multi-way statistics <sec_stats_sample_sets_multi_way>`
section for details on how the ``sample_sets`` and ``indexes`` arguments are
interpreted and how they interact with the dimensions of the output array.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
What is computed depends on ``mode``. Each is computed exactly as
``Y3``, except that the average is across every possible trio of samples
``(a, b1, b2)``, where ``a`` is chosen from the first sample set, and
``b1, b2`` are chosen (without replacement) from the second sample set.
See :meth:`Y3 <.TreeSequence.Y3>` for more details.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with.
:param list indexes: A list of 2-tuples, or None.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A ndarray with shape equal to (num windows, num statistics).
If there is one pair of sample sets and windows=None, a numpy scalar is
returned.
"""
return self.__k_way_sample_set_stat(
self._ll_tree_sequence.Y2,
2,
sample_sets,
indexes=indexes,
windows=windows,
mode=mode,
span_normalise=span_normalise,
)
[docs]
def Y1(self, sample_sets, windows=None, mode="site", span_normalise=True):
"""
Computes the 'Y1' statistic within each of the sets of nodes given by
``sample_sets``.
Please see the :ref:`one-way statistics <sec_stats_sample_sets_one_way>`
section for details on how the ``sample_sets`` argument is interpreted
and how it interacts with the dimensions of the output array.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`, :ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
Operates on ``k = 1`` sample set at a time.
What is computed depends on ``mode``. Each is computed exactly as
``Y3``, except that the average is across every possible trio of samples
samples ``(a1, a2, a3)`` all chosen without replacement from the same
sample set. See :meth:`Y3 <.TreeSequence.Y3>` for more details.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A ndarray with shape equal to (num windows, num statistics).
If there is one sample set and windows=None, a numpy scalar is returned.
"""
return self.__one_way_sample_set_stat(
self._ll_tree_sequence.Y1,
sample_sets,
windows=windows,
mode=mode,
span_normalise=span_normalise,
)
[docs]
def f4(
self, sample_sets, indexes=None, windows=None, mode="site", span_normalise=True
):
"""
Computes Patterson's f4 statistic between four groups of nodes from
``sample_sets``.
Operates on ``k = 4`` sample sets at a time; please see the
:ref:`multi-way statistics <sec_stats_sample_sets_multi_way>`
section for details on how the ``sample_sets`` and ``indexes`` arguments are
interpreted and how they interact with the dimensions of the output array.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
What is computed depends on ``mode``. Each is an average across every possible
combination of four samples ``(a, b; c, d)``, one chosen from each sample set:
"site"
The average density of sites at which ``a`` and ``c`` agree but
differs from ``b`` and ``d``, minus the average density of sites at
which ``a`` and ``d`` agree but differs from ``b`` and ``c``, per
unit of chromosome length.
"branch"
The average length of all branches that separate ``a`` and ``c``
from ``b`` and ``d``, minus the average length of all branches that
separate ``a`` and ``d`` from ``b`` and ``c`` (in units of time).
"node"
For each node, the average proportion of the window on which ``a`` and ``c``
inherit from that node but ``b`` and ``d`` do not, or vice-versa,
minus the average proportion of the window on which ``a`` and ``d``
inherit from that node but ``b`` and ``c`` do not, or vice-versa.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with.
:param list indexes: A list of 4-tuples, or None.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A ndarray with shape equal to (num windows, num statistics).
If there are four sample sets and windows=None, a numpy scalar is returned.
"""
return self.__k_way_sample_set_stat(
self._ll_tree_sequence.f4,
4,
sample_sets,
indexes=indexes,
windows=windows,
mode=mode,
span_normalise=span_normalise,
)
[docs]
def f3(
self, sample_sets, indexes=None, windows=None, mode="site", span_normalise=True
):
r"""
Computes Patterson's f3 statistic between three groups of nodes from
``sample_sets``.
Note that the order of the arguments of f3 differs across the literature:
here, ``f3([A, B, C])`` for sample sets ``A``, ``B``, and ``C``
will estimate
:math:`f_3(A; B, C) = \mathbb{E}[(p_A - p_B) (p_A - p_C)]`,
where :math:`p_A` is the allele frequency in ``A``.
When used as a test for admixture, the putatively admixed population
is usually placed as population ``A`` (see
`Peter (2016) <https://doi.org/10.1534/genetics.115.183913>`_
for more discussion).
Operates on ``k = 3`` sample sets at a time; please see the
:ref:`multi-way statistics <sec_stats_sample_sets_multi_way>`
section for details on how the ``sample_sets`` and ``indexes`` arguments are
interpreted and how they interact with the dimensions of the output array.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
What is computed depends on ``mode``. Each works exactly as
:meth:`f4 <.TreeSequence.f4>`, except the average is across every possible
combination of four samples ``(a1, b; a2, c)`` where `a1` and `a2` have both
been chosen (without replacement) from the first sample set. See
:meth:`f4 <.TreeSequence.f4>` for more details.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with.
:param list indexes: A list of 3-tuples, or None.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A ndarray with shape equal to (num windows, num statistics).
If there are three sample sets and windows=None, a numpy scalar is returned.
"""
return self.__k_way_sample_set_stat(
self._ll_tree_sequence.f3,
3,
sample_sets,
indexes=indexes,
windows=windows,
mode=mode,
span_normalise=span_normalise,
)
[docs]
def f2(
self, sample_sets, indexes=None, windows=None, mode="site", span_normalise=True
):
"""
Computes Patterson's f2 statistic between two groups of nodes from
``sample_sets``.
Operates on ``k = 2`` sample sets at a time; please see the
:ref:`multi-way statistics <sec_stats_sample_sets_multi_way>`
section for details on how the ``sample_sets`` and ``indexes`` arguments are
interpreted and how they interact with the dimensions of the output array.
See the :ref:`statistics interface <sec_stats_interface>` section for details on
:ref:`windows <sec_stats_windows>`,
:ref:`mode <sec_stats_mode>`,
:ref:`span normalise <sec_stats_span_normalise>`,
and :ref:`return value <sec_stats_output_format>`.
What is computed depends on ``mode``. Each works exactly as
:meth:`f4 <.TreeSequence.f4>`, except the average is across every possible
combination of four samples ``(a1, b1; a2, b2)`` where `a1` and `a2` have
both been chosen (without replacement) from the first sample set, and ``b1``
and ``b2`` have both been chosen (without replacement) from the second
sample set. See :meth:`f4 <.TreeSequence.f4>` for more details.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with.
:param list indexes: A list of 2-tuples, or None.
:param list windows: An increasing list of breakpoints between the windows
to compute the statistic in.
:param str mode: A string giving the "type" of the statistic to be computed
(defaults to "site").
:param bool span_normalise: Whether to divide the result by the span of the
window (defaults to True).
:return: A ndarray with shape equal to (num windows, num statistics).
If there is one pair of sample sets and windows=None, a numpy scalar is
returned.
"""
return self.__k_way_sample_set_stat(
self._ll_tree_sequence.f2,
2,
sample_sets,
indexes=indexes,
windows=windows,
mode=mode,
span_normalise=span_normalise,
)
[docs]
def mean_descendants(self, sample_sets):
"""
Computes for every node the mean number of samples in each of the
`sample_sets` that descend from that node, averaged over the
portions of the genome for which the node is ancestral to *any* sample.
The output is an array, `C[node, j]`, which reports the total span of
all genomes in `sample_sets[j]` that inherit from `node`, divided by
the total span of the genome on which `node` is an ancestor to any
sample in the tree sequence.
.. warning:: The interface for this method is preliminary and may be subject to
backwards incompatible changes in the near future. The long-term stable
API for this method will be consistent with other :ref:`sec_stats`.
In particular, the normalization by proportion of the genome that `node`
is an ancestor to anyone may not be the default behaviour in the future.
:param list sample_sets: A list of lists of node IDs.
:return: An array with dimensions (number of nodes in the tree sequence,
number of reference sets)
"""
return self._ll_tree_sequence.mean_descendants(sample_sets)
[docs]
def genealogical_nearest_neighbours(self, focal, sample_sets, num_threads=0):
"""
Return the genealogical nearest neighbours (GNN) proportions for the given
focal nodes, with reference to two or more sets of interest, averaged over all
trees in the tree sequence.
The GNN proportions for a focal node in a single tree are given by first finding
the most recent common ancestral node :math:`a` between the focal node and any
other node present in the reference sets. The GNN proportion for a specific
reference set, :math:`S` is the number of nodes in :math:`S` that descend from
:math:`a`, as a proportion of the total number of descendant nodes in any of the
reference sets.
For example, consider a case with 2 sample sets, :math:`S_1` and :math:`S_2`.
For a given tree, :math:`a` is the node that includes at least one descendant in
:math:`S_1` or :math:`S_2` (not including the focal node). If the descendants of
:math:`a` include some nodes in :math:`S_1` but no nodes in :math:`S_2`, then the
GNN proportions for that tree will be 100% :math:`S_1` and 0% :math:`S_2`, or
:math:`[1.0, 0.0]`.
For a given focal node, the GNN proportions returned by this function are an
average of the GNNs for each tree, weighted by the genomic distance spanned by
that tree.
For an precise mathematical definition of GNN, see https://doi.org/10.1101/458067
.. note:: The reference sets need not include all the samples, hence the most
recent common ancestral node of the reference sets, :math:`a`, need not be
the immediate ancestor of the focal node. If the reference sets only comprise
sequences from relatively distant individuals, the GNN statistic may end up
as a measure of comparatively distant ancestry, even for tree sequences that
contain many closely related individuals.
.. warning:: The interface for this method is preliminary and may be subject to
backwards incompatible changes in the near future. The long-term stable
API for this method will be consistent with other :ref:`sec_stats`.
:param list focal: A list of :math:`n` nodes whose GNNs should be calculated.
:param list sample_sets: A list of :math:`m` lists of node IDs.
:return: An :math:`n` by :math:`m` array of focal nodes by GNN proportions.
Every focal node corresponds to a row. The numbers in each
row corresponding to the GNN proportion for each of the passed-in reference
sets. Rows therefore sum to one.
:rtype: numpy.ndarray
"""
# TODO add windows=None option: https://github.com/tskit-dev/tskit/issues/193
if num_threads <= 0:
return self._ll_tree_sequence.genealogical_nearest_neighbours(
focal, sample_sets
)
else:
worker = functools.partial(
self._ll_tree_sequence.genealogical_nearest_neighbours,
reference_sets=sample_sets,
)
focal = util.safe_np_int_cast(focal, np.int32)
splits = np.array_split(focal, num_threads)
with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as pool:
arrays = pool.map(worker, splits)
return np.vstack(list(arrays))
[docs]
def kc_distance(self, other, lambda_=0.0):
"""
Returns the average :meth:`Tree.kc_distance` between pairs of trees along
the sequence whose intervals overlap. The average is weighted by the
fraction of the sequence on which each pair of trees overlap.
:param TreeSequence other: The other tree sequence to compare to.
:param float lambda_: The KC metric lambda parameter determining the
relative weight of topology and branch length.
:return: The computed KC distance between this tree sequence and other.
:rtype: float
"""
return self._ll_tree_sequence.get_kc_distance(other._ll_tree_sequence, lambda_)
[docs]
def count_topologies(self, sample_sets=None):
"""
Returns a generator that produces the same distribution of topologies as
:meth:`Tree.count_topologies` but sequentially for every tree in a tree
sequence. For use on a tree sequence this method is much faster than
computing the result independently per tree.
.. warning:: The interface for this method is preliminary and may be subject to
backwards incompatible changes in the near future.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with.
:rtype: iter(:class:`tskit.TopologyCounter`)
:raises ValueError: If nodes in ``sample_sets`` are invalid or are
internal samples.
"""
if sample_sets is None:
sample_sets = [
self.samples(population=pop.id) for pop in self.populations()
]
yield from combinatorics.treeseq_count_topologies(self, sample_sets)
[docs]
def ibd_segments(
self,
*,
within=None,
between=None,
max_time=None,
min_span=None,
store_pairs=None,
store_segments=None,
):
"""
Finds pairs of samples that are identical by descent (IBD) and returns
the result as an :class:`.IdentitySegments` instance. The information
stored in this object is controlled by the ``store_pairs`` and
``store_segments`` parameters. By default only total counts and other
statistics of the IBD segments are stored (i.e.,
``store_pairs=False``), since storing pairs and segments has a
substantial CPU and memory overhead. Please see the
:ref:`sec_identity` section for more details on how to access the
information stored in the :class:`.IdentitySegments`.
If ``within`` is specified, only IBD segments for pairs of nodes within
that set will be recorded. If ``between`` is specified, only IBD
segments from pairs that are in one or other of the specified sample
sets will be reported. Note that ``within`` and ``between`` are
mutually exclusive.
A pair of nodes ``(u, v)`` has an IBD segment with a left and right
coordinate ``[left, right)`` and ancestral node ``a`` iff the most
recent common ancestor of the segment ``[left, right)`` in nodes ``u``
and ``v`` is ``a``, and the segment has been inherited along the same
genealogical path (ie. it has not been broken by recombination). The
segments returned are the longest possible ones.
Note that this definition is purely genealogical --- allelic states
*are not* considered here. If used without time or length thresholds, the
segments returned for a given pair will partition the span of the contig
represented by the tree sequence.
:param list within: A list of node IDs defining set of nodes that
we finding IBD segments for. If not specified, this defaults to
all samples in the tree sequence.
:param list[list] between: A list of lists of sample node IDs. Given
two sample sets A and B, only IBD segments will be returned such
that one of the samples is an element of A and the other is
an element of B. Cannot be specified with ``within``.
:param float max_time: Only segments inherited from common
ancestors whose node times are more recent than the specified time
will be returned. Specifying a maximum time is strongly recommended when
working with large tree sequences.
:param float min_span: Only segments in which the difference between
the right and left genome coordinates (i.e., the span of the
segment) is greater than this value will be included. (Default=0)
:param bool store_pairs: If True store information separately for each
pair of samples ``(a, b)`` that are found to be IBD. Otherwise
store summary information about all sample apirs. (Default=False)
:param bool store_segments: If True store each IBD segment
``(left, right, c)`` and associate it with the corresponding
sample pair ``(a, b)``. If True, implies ``store_pairs``.
(Default=False).
:return: An :class:`.IdentitySegments` object containing the recorded
IBD information.
:rtype: IdentitySegments
"""
return self.tables.ibd_segments(
within=within,
between=between,
max_time=max_time,
min_span=min_span,
store_segments=store_segments,
store_pairs=store_pairs,
)
[docs]
def pair_coalescence_counts(
self,
sample_sets=None,
indexes=None,
windows=None,
span_normalise=True,
pair_normalise=False,
time_windows="nodes",
):
"""
Calculate the number of coalescing sample pairs per node, summed over
trees and weighted by tree span.
The number of coalescing pairs may be calculated within or between the
non-overlapping lists of samples contained in `sample_sets`. In the
latter case, pairs are counted if they have exactly one member in each
of two sample sets. If `sample_sets` is omitted, a single set
containing all samples is assumed.
The argument `indexes` may be used to specify which pairs of sample
sets to compute the statistic between, and in what order. If
`indexes=None`, then `indexes` is assumed to equal `[(0,0)]` for a
single sample set and `[(0,1)]` for two sample sets. For more than two
sample sets, `indexes` must be explicitly passed.
The argument `time_windows` may be used to count coalescence
events within time intervals (if an array of breakpoints is supplied)
rather than for individual nodes (the default).
The output array has dimension `(windows, indexes, nodes)` with
dimensions dropped when the corresponding argument is set to None.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with, or None.
:param list indexes: A list of 2-tuples, or None.
:param list windows: An increasing list of breakpoints between the
sequence windows to compute the statistic in, or None.
:param bool span_normalise: Whether to divide the result by the span of
non-missing sequence in the window (defaults to True).
:param bool pair_normalise: Whether to divide the result by the total
number of pairs for a given index (defaults to False).
:param time_windows: Either a string "nodes" or an increasing
list of breakpoints between time intervals.
"""
if sample_sets is None:
sample_sets = [list(self.samples())]
drop_middle_dimension = False
if indexes is None:
drop_middle_dimension = True
if len(sample_sets) == 1:
indexes = [(0, 0)]
elif len(sample_sets) == 2:
indexes = [(0, 1)]
else:
raise ValueError(
"Must specify indexes if there are more than two sample sets"
)
num_indexes = len(indexes)
drop_left_dimension = False
if windows is None:
drop_left_dimension = True
windows = np.array([0.0, self.sequence_length])
num_windows = len(windows) - 1
if isinstance(time_windows, str) and time_windows == "nodes":
num_time_windows = self.num_nodes
node_bin_map = np.arange(num_time_windows, dtype=np.int32)
else:
if self.time_units == tskit.TIME_UNITS_UNCALIBRATED:
raise ValueError("Time windows require calibrated node times")
num_time_windows = len(time_windows) - 1
node_bin_map = np.digitize(self.nodes_time, time_windows) - 1
node_bin_map[node_bin_map == num_time_windows] = tskit.NULL
node_bin_map = node_bin_map.astype(np.int32)
num_bins = node_bin_map.max() + 1
sample_set_sizes = np.array([len(s) for s in sample_sets], dtype=np.uint32)
sample_sets = util.safe_np_int_cast(np.hstack(sample_sets), np.int32)
coalescing_pairs = np.zeros((num_windows, num_indexes, num_time_windows))
coalescing_pairs[..., :num_bins] = (
self.ll_tree_sequence.pair_coalescence_counts(
sample_sets=sample_sets,
sample_set_sizes=sample_set_sizes,
windows=windows,
indexes=indexes,
node_bin_map=node_bin_map,
span_normalise=span_normalise,
pair_normalise=pair_normalise,
)
)
if drop_middle_dimension:
coalescing_pairs = np.squeeze(coalescing_pairs, axis=1)
if drop_left_dimension:
coalescing_pairs = np.squeeze(coalescing_pairs, axis=0)
return coalescing_pairs
[docs]
def pair_coalescence_quantiles(
self,
quantiles,
sample_sets=None,
indexes=None,
windows=None,
):
"""
Estimate quantiles of pair coalescence times by inverting the empirical
CDF. This is equivalent to the "inverted_cdf" method of
`numpy.quantile` applied to node times, with weights proportional to
the number of coalescing pairs per node (averaged over trees, see
`TreeSequence.pair_coalescence_counts`).
Quantiles of pair coalescence times may be calculated within or
between the non-overlapping lists of samples contained in `sample_sets`. In
the latter case, pairs are counted if they have exactly one member in each
of two sample sets. If `sample_sets` is omitted, a single set containing
all samples is assumed.
The argument `indexes` may be used to specify which pairs of sample sets to
compute coalescences between, and in what order. If `indexes=None`, then
`indexes` is assumed to equal `[(0,0)]` for a single sample set and
`[(0,1)]` for two sample sets. For more than two sample sets, `indexes`
must be explicitly passed.
The output array has dimension `(windows, indexes, quantiles)` with
dimensions dropped when the corresponding argument is set to None.
:param quantiles: A list of increasing breakpoints between [0, 1].
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with, or None.
:param list indexes: A list of 2-tuples, or None.
:param list windows: An increasing list of breakpoints between the
sequence windows to compute the statistic in, or None.
"""
if sample_sets is None:
sample_sets = [list(self.samples())]
drop_middle_dimension = False
if indexes is None:
drop_middle_dimension = True
if len(sample_sets) == 1:
indexes = [(0, 0)]
elif len(sample_sets) == 2:
indexes = [(0, 1)]
else:
raise ValueError(
"Must specify indexes if there are more than two sample sets"
)
if self.time_units == tskit.TIME_UNITS_UNCALIBRATED:
raise ValueError("Pair coalescence quantiles require calibrated node times")
drop_left_dimension = False
if windows is None:
drop_left_dimension = True
windows = np.array([0.0, self.sequence_length])
sample_set_sizes = np.array([len(s) for s in sample_sets], dtype=np.uint32)
sample_sets = util.safe_np_int_cast(np.hstack(sample_sets), np.int32)
_, node_bin_map = np.unique(self.nodes_time, return_inverse=True)
node_bin_map = util.safe_np_int_cast(node_bin_map, np.int32)
coalescence_times = self.ll_tree_sequence.pair_coalescence_quantiles(
sample_sets=sample_sets,
sample_set_sizes=sample_set_sizes,
windows=windows,
indexes=indexes,
node_bin_map=node_bin_map,
quantiles=quantiles,
)
if drop_middle_dimension:
coalescence_times = np.squeeze(coalescence_times, axis=1)
if drop_left_dimension:
coalescence_times = np.squeeze(coalescence_times, axis=0)
return coalescence_times
[docs]
def pair_coalescence_rates(
self,
time_windows,
sample_sets=None,
indexes=None,
windows=None,
):
"""
Estimate the rate at which pairs of samples coalesce within time
windows, using the empirical cumulative distribution function (ecdf) of
pair coalescence times. Assuming that pair coalescence events follow a
nonhomogeneous Poisson process, the empirical rate for a time window
:math:`[a, b)` where :math:`ecdf(b) < 1` is,
..math:
log(1 - \\frac{ecdf(b) - ecdf(a)}{1 - ecdf(a)}) / (a - b)
If the last coalescence event is within :math:`[a, b)`, so that
:math:`ecdf(b) = 1`, then an estimate of the empirical rate is
..math:
(\\mathbb{E}[t | t > a] - a)^{-1}
where :math:`\\mathbb{E}[t | t < a]` is the average pair coalescence time
conditional on coalescence after the start of the last epoch.
The first breakpoint in `time_windows` must start at the age of the
samples, and the last must end at infinity. In the output array, any
time windows where all pairs have coalesced by start of the window will
contain `NaN` values.
Pair coalescence rates may be calculated within or between the
non-overlapping lists of samples contained in `sample_sets`. In the
latter case, pairs are counted if they have exactly one member in each
of two sample sets. If `sample_sets` is omitted, a single group
containing all samples is assumed.
The argument `indexes` may be used to specify which pairs of sample
sets to compute the statistic between, and in what order. If
`indexes=None`, then `indexes` is assumed to equal `[(0,0)]` for a
single sample set and `[(0,1)]` for two sample sets. For more than two
sample sets, `indexes` must be explicitly passed.
The output array has dimension `(windows, indexes, time_windows)` with
dimensions dropped when the corresponding argument is set to None.
:param time_windows: An increasing list of breakpoints between time
intervals, starting at the age of the samples and ending at
infinity.
:param list sample_sets: A list of lists of Node IDs, specifying the
groups of nodes to compute the statistic with, or None.
:param list indexes: A list of 2-tuples, or None.
:param list windows: An increasing list of breakpoints between the
sequence windows to compute the statistic in, or None.
"""
if sample_sets is None:
sample_sets = [list(self.samples())]
drop_middle_dimension = False
if indexes is None:
drop_middle_dimension = True
if len(sample_sets) == 1:
indexes = [(0, 0)]
elif len(sample_sets) == 2:
indexes = [(0, 1)]
else:
raise ValueError(
"Must specify indexes if there are more than two sample sets"
)
if self.time_units == tskit.TIME_UNITS_UNCALIBRATED:
raise ValueError("Pair coalescence rates require calibrated node times")
drop_left_dimension = False
if windows is None:
drop_left_dimension = True
windows = np.array([0.0, self.sequence_length])
sample_set_sizes = np.array([len(s) for s in sample_sets], dtype=np.uint32)
sample_sets = util.safe_np_int_cast(np.hstack(sample_sets), np.int32)
node_bin_map = np.digitize(self.nodes_time, time_windows) - 1
node_bin_map[node_bin_map == time_windows.size - 1] = tskit.NULL
node_bin_map = node_bin_map.astype(np.int32)
coalescence_rates = self.ll_tree_sequence.pair_coalescence_rates(
sample_sets=sample_sets,
sample_set_sizes=sample_set_sizes,
windows=windows,
indexes=indexes,
node_bin_map=node_bin_map,
time_windows=time_windows,
)
if drop_middle_dimension:
coalescence_rates = np.squeeze(coalescence_rates, axis=1)
if drop_left_dimension:
coalescence_rates = np.squeeze(coalescence_rates, axis=0)
return coalescence_rates
[docs]
def impute_unknown_mutations_time(
self,
method=None,
):
"""
Returns an array of mutation times, where any unknown times are
imputed from the times of associated nodes. Not to be confused with
:meth:`TableCollection.compute_mutation_times`, which modifies the
``time`` column of the mutations table in place.
:param str method: The method used to impute the unknown mutation times.
Currently only "min" is supported, which uses the time of the node
below the mutation as the mutation time. The "min" method can also
be specified by ``method=None`` (Default: ``None``).
:return: An array of length equal to the number of mutations in the
tree sequence.
"""
allowed_methods = ["min"]
if method is None:
method = "min"
if method not in allowed_methods:
raise ValueError(
f"Mutations time imputation method must be chosen from {allowed_methods}"
)
if method == "min":
mutations_time = self.mutations_time.copy()
unknown = tskit.is_unknown_time(mutations_time)
mutations_time[unknown] = self.nodes_time[self.mutations_node[unknown]]
return mutations_time
def ld_matrix(
self, sample_sets=None, sites=None, positions=None, mode="site", stat="r2"
):
stats = {
"D": self._ll_tree_sequence.D_matrix,
"D2": self._ll_tree_sequence.D2_matrix,
"r2": self._ll_tree_sequence.r2_matrix,
"D_prime": self._ll_tree_sequence.D_prime_matrix,
"r": self._ll_tree_sequence.r_matrix,
"Dz": self._ll_tree_sequence.Dz_matrix,
"pi2": self._ll_tree_sequence.pi2_matrix,
"Dz_unbiased": self._ll_tree_sequence.Dz_unbiased_matrix,
"D2_unbiased": self._ll_tree_sequence.D2_unbiased_matrix,
"pi2_unbiased": self._ll_tree_sequence.pi2_unbiased_matrix,
}
try:
two_locus_stat = stats[stat]
except KeyError:
raise ValueError(
f"Unknown two-locus statistic '{stat}', we support: {list(stats.keys())}"
)
return self.__two_locus_sample_set_stat(
two_locus_stat,
sample_sets,
sites=sites,
positions=positions,
mode=mode,
)
############################################
#
# Deprecated APIs. These are either already unsupported, or will be unsupported in a
# later release.
#
############################################
def get_pairwise_diversity(self, samples=None):
# Deprecated alias for self.pairwise_diversity
return self.pairwise_diversity(samples)
[docs]
def pairwise_diversity(self, samples=None):
"""
Returns the pairwise nucleotide site diversity, the average number of sites
that differ between a every possible pair of distinct samples. If `samples` is
specified, calculate the diversity within this set.
.. deprecated:: 0.2.0
please use :meth:`.diversity` instead. Since version 0.2.0 the error
semantics have also changed slightly. It is no longer an error
when there is one sample and a tskit.LibraryError is raised
when non-sample IDs are provided rather than a ValueError. It is
also no longer an error to compute pairwise diversity at sites
with multiple mutations.
:param list samples: The set of samples within which we calculate
the diversity. If None, calculate diversity within the entire sample.
:return: The pairwise nucleotide site diversity.
:rtype: float
"""
if samples is None:
samples = self.samples()
return float(
self.diversity(
[samples], windows=[0, self.sequence_length], span_normalise=False
)[0][0]
)
def get_time(self, u):
# Deprecated. Use ts.node(u).time
if u < 0 or u >= self.get_num_nodes():
raise ValueError("ID out of bounds")
node = self.node(u)
return node.time
def get_population(self, u):
# Deprecated. Use ts.node(u).population
if u < 0 or u >= self.get_num_nodes():
raise ValueError("ID out of bounds")
node = self.node(u)
return node.population
def records(self):
# Deprecated. Use either ts.edges() or ts.edgesets().
t = [node.time for node in self.nodes()]
pop = [node.population for node in self.nodes()]
for e in self.edgesets():
yield CoalescenceRecord(
e.left, e.right, e.parent, e.children, t[e.parent], pop[e.parent]
)
# Unsupported old methods.
def get_num_records(self):
raise NotImplementedError(
"This method is no longer supported. Please use the "
"TreeSequence.num_edges if possible to work with edges rather "
"than coalescence records. If not, please use len(list(ts.edgesets())) "
"which should return the number of coalescence records, as previously "
"defined. Please open an issue on GitHub if this is "
"important for your workflow."
)
def diffs(self):
raise NotImplementedError(
"This method is no longer supported. Please use the "
"TreeSequence.edge_diffs() method instead"
)
def newick_trees(self, precision=3, breakpoints=None, Ne=1):
raise NotImplementedError(
"This method is no longer supported. Please use the Tree.newick"
" method instead"
)
def to_nexus(self, precision=14):
raise NotImplementedError(
"This method is no longer supported since 0.4.0. Please use the as_nexus "
"or write_nexus methods instead"
)
# TODO move to "text_formats.py"
def write_ms(
tree_sequence,
output,
print_trees=False,
precision=4,
num_replicates=1,
write_header=True,
):
"""
Write ``ms`` formatted output from the genotypes of a tree sequence
or an iterator over tree sequences. Usage:
.. code-block:: python
import tskit as ts
tree_sequence = msprime.simulate(
sample_size=sample_size,
Ne=Ne,
length=length,
mutation_rate=mutation_rate,
recombination_rate=recombination_rate,
random_seed=random_seed,
num_replicates=num_replicates,
)
with open("output.ms", "w") as ms_file:
ts.write_ms(tree_sequence, ms_file)
:param ts tree_sequence: The tree sequence (or iterator over tree sequences) to
write to ms file
:param io.IOBase output: The file-like object to write the ms-style output
:param bool print_trees: Boolean parameter to write out newick format trees
to output [optional]
:param int precision: Numerical precision with which to write the ms
output [optional]
:param bool write_header: Boolean parameter to write out the header. [optional]
:param int num_replicates: Number of replicates simulated [required if
num_replicates used in simulation]
The first line of this ms-style output file written has two arguments which
are sample size and number of replicates. The second line has a 0 as a substitute
for the random seed.
"""
if not isinstance(tree_sequence, collections.abc.Iterable):
tree_sequence = [tree_sequence]
i = 0
for tree_seq in tree_sequence:
if i > 0:
write_header = False
i = i + 1
if write_header is True:
print(
f"ms {tree_seq.sample_size} {num_replicates}",
file=output,
)
print("0", file=output)
print(file=output)
print("//", file=output)
if print_trees is True:
"""
Print out the trees in ms-format from the specified tree sequence.
"""
if len(tree_seq.trees()) == 1:
tree = next(tree_seq.trees())
newick = tree.newick(precision=precision)
print(newick, file=output)
else:
for tree in tree_seq.trees():
newick = tree.newick(precision=precision)
print(f"[{tree.span:.{precision}f}]", newick, file=output)
else:
s = tree_seq.get_num_sites()
print("segsites:", s, file=output)
if s != 0:
print("positions: ", end="", file=output)
positions = [
variant.position / (tree_seq.sequence_length)
for variant in tree_seq.variants()
]
for position in positions:
print(
f"{position:.{precision}f}",
end=" ",
file=output,
)
print(file=output)
genotypes = tree_seq.genotype_matrix()
for k in range(tree_seq.num_samples):
tmp_str = "".join(map(str, genotypes[:, k]))
if set(tmp_str).issubset({"0", "1", "-"}):
print(tmp_str, file=output)
else:
raise ValueError(
"This tree sequence contains non-biallelic"
"SNPs and is incompatible with the ms format!"
)
else:
print(file=output)
[docs]
@dataclass
class PCAResult:
"""
The result of a call to TreeSequence.pca() capturing the output values
and algorithm convergence details.
"""
factors: np.ndarray
"""
The principal component factors (or scores).
Columns are orthogonal, with one entry per sample
or individual (see :meth:`pca <.TreeSequence.pca>`).
This is the same as the loadings because the GRM is symmetric.
"""
eigenvalues: np.ndarray
"""
Eigenvalues of the genetic relatedness matrix.
"""
range_sketch: np.ndarray
"""
Range sketch matrix. Can be used as an input for
:meth:`pca <.TreeSequence.pca>` option to further improve precision.
"""
error_bound: np.ndarray
"""
An estimate of the error bounds for the eigenvalues (experimental).
This quantifies the average reconstruction error E|G-USVt| where G is the
genetic relatedness matrix, U is the PC factors (scores),
S is the diagonal eigenvalue matrix, and V is the loadings.
The norm is the matrix operator norm.
"""
