#
# MIT License
#
# Copyright (c) 2018-2020 Tskit Developers
# Copyright (c) 2015-2018 University of Oxford
#
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"""
Module responsible for managing trees and tree sequences.
"""
import base64
import collections
import concurrent.futures
import copy
import functools
import itertools
import math
import textwrap
import warnings
from typing import Any
import attr
import numpy as np
import _tskit
import tskit.combinatorics as combinatorics
import tskit.drawing as drawing
import tskit.exceptions as exceptions
import tskit.formats as formats
import tskit.metadata as metadata_module
import tskit.tables as tables
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
CoalescenceRecord = collections.namedtuple(
"CoalescenceRecord", ["left", "right", "node", "children", "time", "population"]
)
BaseInterval = collections.namedtuple("BaseInterval", ["left", "right"])
[docs]class Interval(BaseInterval):
"""
A tuple of 2 numbers, ``[left, right)``, defining an interval over the genome.
:ivar left: The left hand end of the interval. By convention this value is included
in the interval.
:vartype left: float
:ivar right: The right hand end of the iterval. By convention this value is *not*
included in the interval, i.e. the interval is half-open.
:vartype right: float
:ivar span: The span of the genome covered by this interval, simply ``right-left``.
:vartype span: float
"""
@property
def span(self):
return self.right - self.left
# TODO this interface is rubbish. Should have much better printing options.
# TODO we should be use __slots__ here probably.
class SimpleContainer:
def __eq__(self, other):
return self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
def __repr__(self):
return repr(self.__dict__)
class SimpleContainerWithMetadata(SimpleContainer):
"""
This class allows metadata to be lazily decoded and cached
"""
class CachedMetadata:
"""
If we had python>=3.8 we could just use @functools.cached_property here. We
don't so we implement it similarly using a descriptor
"""
def __get__(self, container: "SimpleContainerWithMetadata", owner: type):
decoded = container._metadata_decoder(container._encoded_metadata)
container.__dict__["metadata"] = decoded
return decoded
metadata: Any = CachedMetadata()
def __eq__(self, other: SimpleContainer) -> bool:
# We need to remove metadata and the decoder so we are just comparing
# the encoded metadata, along with the other attributes
other = {**other.__dict__}
other["metadata"] = None
other["_metadata_decoder"] = None
self_ = {**self.__dict__}
self_["metadata"] = None
self_["_metadata_decoder"] = None
return self_ == other
def __repr__(self) -> str:
# Make sure we have a decoded metadata
_ = self.metadata
out = {**self.__dict__}
del out["_encoded_metadata"]
del out["_metadata_decoder"]
return repr(out)
[docs]class Individual(SimpleContainerWithMetadata):
"""
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.
:ivar id: The integer ID of this individual. Varies from 0 to
:attr:`TreeSequence.num_individuals` - 1.
:vartype id: int
:ivar flags: The bitwise flags for this individual.
:vartype flags: int
:ivar location: The spatial location of this individual as a numpy array. The
location is an empty array if no spatial location is defined.
:vartype location: numpy.ndarray
:ivar nodes: 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.
:vartype location: numpy.ndarray
:ivar metadata: The decoded :ref:`metadata <sec_metadata_definition>`
for this individual.
:vartype metadata: object
"""
def __init__(
self,
id_=None,
flags=0,
location=None,
nodes=None,
encoded_metadata=b"",
metadata_decoder=lambda metadata: metadata,
):
self.id = id_
self.flags = flags
self.location = location
self._encoded_metadata = encoded_metadata
self._metadata_decoder = metadata_decoder
self.nodes = nodes
def __eq__(self, other):
return (
self.id == other.id
and self.flags == other.flags
and self._encoded_metadata == other._encoded_metadata
and np.array_equal(self.nodes, other.nodes)
and np.array_equal(self.location, other.location)
)
[docs]class Node(SimpleContainerWithMetadata):
"""
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.
:ivar id: The integer ID of this node. Varies from 0 to
:attr:`TreeSequence.num_nodes` - 1.
:vartype id: int
:ivar flags: The bitwise flags for this node.
:vartype flags: int
:ivar time: The birth time of this node.
:vartype time: float
:ivar population: The integer ID of the population that this node was born in.
:vartype population: int
:ivar individual: The integer ID of the individual that this node was a part of.
:vartype individual: int
:ivar metadata: The decoded :ref:`metadata <sec_metadata_definition>` for this node.
:vartype metadata: object
"""
def __init__(
self,
id_=None,
flags=0,
time=0,
population=NULL,
individual=NULL,
encoded_metadata=b"",
metadata_decoder=lambda metadata: metadata,
):
self.id = id_
self.time = time
self.population = population
self.individual = individual
self._encoded_metadata = encoded_metadata
self._metadata_decoder = metadata_decoder
self.flags = flags
[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]class Edge(SimpleContainerWithMetadata):
"""
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.
:ivar left: The left coordinate of this edge.
:vartype left: float
:ivar right: The right coordinate of this edge.
:vartype right: float
:ivar parent: 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`.
:vartype parent: int
:ivar child: 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`.
:vartype child: int
:ivar id: The integer ID of this edge. Varies from 0 to
:attr:`TreeSequence.num_edges` - 1.
:vartype id: int
:ivar metadata: The decoded :ref:`metadata <sec_metadata_definition>` for this edge.
:vartype metadata: object
"""
def __init__(
self,
left,
right,
parent,
child,
encoded_metadata=b"",
id_=None,
metadata_decoder=lambda metadata: metadata,
):
self.id = id_
self.left = left
self.right = right
self.parent = parent
self.child = child
self._encoded_metadata = encoded_metadata
self._metadata_decoder = metadata_decoder
def __repr__(self):
return (
"{{left={:.3f}, right={:.3f}, parent={}, child={}, id={}, "
"metadata={}}}".format(
self.left, self.right, self.parent, self.child, self.id, self.metadata
)
)
@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
[docs]class Site(SimpleContainerWithMetadata):
"""
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.
:ivar id: The integer ID of this site. Varies from 0 to
:attr:`TreeSequence.num_sites` - 1.
:vartype id: int
:ivar position: The floating point location of this site in genome coordinates.
Ranges from 0 (inclusive) to :attr:`TreeSequence.sequence_length`
(exclusive).
:vartype position: float
:ivar ancestral_state: The ancestral state at this site (i.e., the state
inherited by nodes, unless mutations occur).
:vartype ancestral_state: str
:ivar metadata: The decoded :ref:`metadata <sec_metadata_definition>` for this site.
:vartype metadata: object
:ivar mutations: The list of mutations at this site. Mutations
within a site are returned in the order they are specified in the
underlying :class:`MutationTable`.
:vartype mutations: list[:class:`Mutation`]
"""
def __init__(
self,
id_,
position,
ancestral_state,
mutations,
encoded_metadata=b"",
metadata_decoder=lambda metadata: metadata,
):
self.id = id_
self.position = position
self.ancestral_state = ancestral_state
self.mutations = mutations
self._encoded_metadata = encoded_metadata
self._metadata_decoder = metadata_decoder
[docs]class Mutation(SimpleContainerWithMetadata):
"""
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.
:ivar id: The integer ID of this mutation. Varies from 0 to
:attr:`TreeSequence.num_mutations` - 1.
:vartype id: int
:ivar site: 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`.
:vartype site: int
:ivar node: 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`.
:vartype node: int
:ivar time: The occurrence time of this mutation.
:vartype time: float
:ivar derived_state: 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.
:vartype derived_state: str
:ivar parent: 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`.
:vartype parent: int
:ivar metadata: The decoded :ref:`metadata <sec_metadata_definition>` for this
mutation.
:vartype metadata: object
"""
def __init__(
self,
id_=NULL,
site=NULL,
node=NULL,
time=UNKNOWN_TIME,
derived_state=None,
parent=NULL,
encoded_metadata=b"",
metadata_decoder=lambda metadata: metadata,
):
self.id = id_
self.site = site
self.node = node
self.time = time
self.derived_state = derived_state
self.parent = parent
self._encoded_metadata = encoded_metadata
self._metadata_decoder = metadata_decoder
def __eq__(self, other):
# We need to remove metadata and the decoder so we are just comparing
# the encoded metadata, along with the other attributes.
# We also need to remove time as we have to compare to unknown time.
other_ = copy.copy(other.__dict__)
other_["metadata"] = None
other_["_metadata_decoder"] = None
other_["time"] = None
self_ = copy.copy(self.__dict__)
self_["metadata"] = None
self_["_metadata_decoder"] = None
self_["time"] = None
return self_ == other_ and (
self.time == other.time
# We need to special case unknown times as they are a nan value.
or (util.is_unknown_time(self.time) and util.is_unknown_time(other.time))
)
[docs]class Migration(SimpleContainerWithMetadata):
"""
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.
:ivar left: The left end of the genomic interval covered by this
migration (inclusive).
:vartype left: float
:ivar right: The right end of the genomic interval covered by this migration
(exclusive).
:vartype right: float
:ivar node: 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`.
:vartype node: int
:ivar source: The source population ID.
:vartype source: int
:ivar dest: The destination population ID.
:vartype dest: int
:ivar time: The time at which this migration occured at.
:vartype time: float
:ivar metadata: The decoded :ref:`metadata <sec_metadata_definition>` for this
migration.
:vartype metadata: object
"""
def __init__(
self,
left,
right,
node,
source,
dest,
time,
encoded_metadata=b"",
metadata_decoder=lambda metadata: metadata,
id_=None,
):
self.id = id_
self.left = left
self.right = right
self.node = node
self.source = source
self.dest = dest
self.time = time
self._encoded_metadata = encoded_metadata
self._metadata_decoder = metadata_decoder
def __repr__(self):
return (
"{{left={:.3f}, right={:.3f}, node={}, source={}, dest={} time={:.3f}"
" id={}, metadata={}}}".format(
self.left,
self.right,
self.node,
self.source,
self.dest,
self.time,
self.id,
self.metadata,
)
)
[docs]class Population(SimpleContainerWithMetadata):
"""
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.
:ivar id: The integer ID of this population. Varies from 0 to
:attr:`TreeSequence.num_populations` - 1.
:vartype id: int
:ivar metadata: The decoded :ref:`metadata <sec_metadata_definition>`
for this population.
:vartype metadata: object
"""
def __init__(
self, id_, encoded_metadata=b"", metadata_decoder=lambda metadata: metadata
):
self.id = id_
self._encoded_metadata = encoded_metadata
self._metadata_decoder = metadata_decoder
[docs]class Variant(SimpleContainer):
"""
A variant represents the observed variation among samples
for a given site. A variant consists (a) of a reference to the
:class:`Site` instance in question; (b) the **alleles** that may be
observed at the samples for this site; and (c) the **genotypes**
mapping sample IDs to the observed alleles.
Each element in the ``alleles`` tuple is a string, representing the
actual observed state for a given sample. The ``alleles`` tuple is
generated in one of two ways. The first (and default) way is for
``tskit`` to generate the encoding on the fly as alleles are encountered
while generating genotypes. In this case, the first element of this
tuple is guaranteed to be the same as the site's ``ancestral_state`` value
and the list of alleles is also guaranteed not to contain any duplicates.
Note that allelic values may be listed that are not referred to by any
samples. For example, if we have a site that is fixed for the derived state
(i.e., we have a mutation over the tree root), all genotypes will be 1, but
the alleles list will be equal to ``('0', '1')``. Other than the
ancestral state being the first allele, the alleles are listed in
no particular order, and the ordering should not be relied upon
(but see the notes on missing data below).
The second way is for the user to define the mapping between
genotype values and allelic state strings using the
``alleles`` parameter to the :meth:`TreeSequence.variants` method.
In this case, there is no indication of which allele is the ancestral state,
as the ordering is determined by the user.
The ``genotypes`` represent the observed allelic states for each sample,
such that ``var.alleles[var.genotypes[j]]`` gives the string allele
for sample ID ``j``. Thus, the elements of the genotypes array are
indexes into the ``alleles`` list. The genotypes are provided in this
way via a numpy array to enable efficient calculations.
When :ref:`missing data<sec_data_model_missing_data>` is present at a given
site boolean flag ``has_missing_data`` will be True, at least one element
of the ``genotypes`` array will be equal to ``tskit.MISSING_DATA``, and the
last element of the ``alleles`` array will be ``None``. Note that in this
case ``variant.num_alleles`` will **not** be equal to
``len(variant.alleles)``. The rationale for adding ``None`` to the end of
the ``alleles`` list is to help code that does not handle missing data
correctly fail early rather than introducing subtle and hard-to-find bugs.
As ``tskit.MISSING_DATA`` is equal to -1, code that decodes genotypes into
allelic values without taking missing data into account would otherwise
output the last allele in the list rather missing data.
Modifying the attributes in this class will have **no effect** on the
underlying tree sequence data.
:ivar site: The site object for this variant.
:vartype site: :class:`Site`
:ivar alleles: A tuple of the allelic values that may be observed at the
samples at the current site. The first element of this tuple is always
the site's ancestral state.
:vartype alleles: tuple(str)
:ivar genotypes: An array of indexes into the list ``alleles``, giving the
state of each sample at the current site.
:ivar has_missing_data: True if there is missing data for any of the
samples at the current site.
:vartype has_missing_data: bool
:ivar num_alleles: The number of distinct alleles at this site. Note that
this may be greater than the number of distinct values in the genotypes
array.
:vartype num_alleles: int
:vartype genotypes: numpy.ndarray
"""
def __init__(self, site, alleles, genotypes):
self.site = site
self.alleles = alleles
self.has_missing_data = alleles[-1] is None
self.num_alleles = len(alleles) - self.has_missing_data
self.genotypes = genotypes
# Deprecated aliases to avoid breaking existing code.
self.position = site.position
self.index = site.id
def __eq__(self, other):
return (
self.site == other.site
and self.alleles == other.alleles
and np.array_equal(self.genotypes, other.genotypes)
)
class Edgeset(SimpleContainer):
def __init__(self, left, right, parent, children):
self.left = left
self.right = right
self.parent = parent
self.children = children
def __repr__(self):
return "{{left={:.3f}, right={:.3f}, parent={}, children={}}}".format(
self.left, self.right, self.parent, self.children
)
[docs]class Provenance(SimpleContainer):
def __init__(self, id_=None, timestamp=None, record=None):
self.id = id_
self.timestamp = timestamp
self.record = record
def add_deprecated_mutation_attrs(site, mutation):
"""
Add in attributes for the older deprecated way of defining
mutations. These attributes will be removed in future releases
and are deliberately undocumented in tskit v0.2.2.
"""
mutation.position = site.position
mutation.index = site.id
return mutation
[docs]class Tree:
"""
A single tree in a :class:`TreeSequence`. Please see the
:ref:`sec_tutorial_moving_along_a_tree_sequence` 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 give 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,
)
if sample_lists:
options |= _tskit.SAMPLE_LISTS
kwargs = {"options": options}
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)
[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()
return copy
@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.
: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:`.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:`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:`.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:`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.
: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")
# This should be implemented in C efficiently using the indexes.
# No point in complicating the current implementation by trying
# to seek from the correct direction.
self.first()
while self.index != index:
self.next()
[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``.
: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")
# This should be implemented in C efficiently using the indexes.
# No point in complicating the current implementation by trying
# to seek from the correct direction.
self.first()
while self.interval.right <= position:
self.next()
[docs] def rank(self):
"""
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.
:rtype: tuple(int)
: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):
"""
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.
:rtype: 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):
"""
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.
:rtype: tskit.TopologyCounter
: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 generations) 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 generations). 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 sum of all branches that are ancestral
to at least one sample. This distinction is only important
in tree sequences that contain 'dead branches', i.e., those
that define topology not ancestral to any samples.
:return: The sum of lengths of branches in this tree.
:rtype: float
"""
return sum(self.branch_length(u) for u in self.nodes())
def get_mrca(self, u, v):
# Deprecated alias for mrca
return self.mrca(u, v)
[docs] def mrca(self, u, v):
"""
Returns the most recent common ancestor of the specified nodes.
:param int u: The first node.
:param int v: The second node.
:return: The most recent common ancestor of u and v.
:rtype: int
"""
return self._ll_tree.get_mrca(u, v)
def get_tmrca(self, u, v):
# Deprecated alias for tmrca
return self.tmrca(u, v)
[docs] def tmrca(self, u, v):
"""
Returns the time of the most recent common ancestor of the specified
nodes. This is equivalent to::
>>> tree.time(tree.mrca(u, v))
:param int u: The first node.
:param int v: The second node.
:return: The time of the most recent common ancestor of u and v.
:rtype: float
"""
return self.get_time(self.get_mrca(u, v))
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)
# 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)
[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)
[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)
[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)
# 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)
# TODO do we also have right_root?
@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._ll_tree.get_left_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 in generations.
Equivalent to ``tree.tree_sequence.node(u).time``.
: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.
: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.
: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. 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``. To find the number of
nodes that are reachable from all roots use ``len(list(tree.nodes()))``.
:rtype: int
"""
return self._ll_tree.get_num_nodes()
@property
def num_roots(self):
"""
The number of roots in this tree, as defined in the :attr:`~Tree.roots`
attribute.
Requires O(number of roots) time.
:rtype: int
"""
return self._ll_tree.get_num_roots()
@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. 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)
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.
Requires O(number of roots) time.
: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: :class:`ValueError` if this tree contains more than one root.
"""
root = self.left_root
if root != NULL and self.right_sib(root) != NULL:
raise ValueError("More than one root exists. Use tree.roots instead")
return root
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: tuple
"""
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
"""
left, right = self.get_interval()
return right - left
# The sample_size (or num_samples) is really a property of the tree sequence,
# and so we should provide access to this via a tree.tree_sequence.num_samples
# property access. However, we can't just remove the method as a lot of code
# may depend on it. To complicate things a bit more, sample_size has been
# changed to num_samples elsewhere for consistency. We can't do this here
# because there is already a num_samples method which returns the number of
# samples below a particular node. The best thing to do is probably to
# undocument the sample_size property, but keep it around for ever.
def get_sample_size(self):
# Deprecated alias for self.sample_size
return self.sample_size
@property
def sample_size(self):
"""
Returns the sample size for this tree. This is the number of sample
nodes in the tree.
:return: The number of sample nodes in the tree.
:rtype: int
"""
return self._ll_tree.get_sample_size()
def draw_text(self, orientation=None, **kwargs):
orientation = drawing.check_orientation(orientation)
if orientation in (drawing.LEFT, drawing.RIGHT):
text_tree = drawing.HorizontalTextTree(
self, orientation=orientation, **kwargs
)
else:
text_tree = drawing.VerticalTextTree(
self, orientation=orientation, **kwargs
)
return str(text_tree)
[docs] def draw_svg(
self,
path=None,
*,
size=None,
tree_height_scale=None,
max_tree_height=None,
node_labels=None,
mutation_labels=None,
root_svg_attributes=None,
style=None,
order=None,
force_root_branch=None,
**kwargs,
):
"""
Return an SVG representation of a single 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_svg())
The elements in the tree are grouped according to the structure of the tree,
using `SVG groups <https://www.w3.org/TR/SVG2/struct.html#Groups>`_. This allows
easy styling and manipulation of elements and subtrees. Elements in the SVG file
are marked with SVG classes so that they can be targetted, allowing
different components of the drawing to be hidden, styled, or otherwise
manipulated. For example, when drawing (say) the first tree from a tree
sequence, all the SVG components will be placed in a group of class ``tree``.
The group will have the additional class ``t0``, indicating that this tree
has index 0 in the tree sequence. The general SVG structure is as follows:
The tree is contained in a group of class ``tree``. Additionally, this group
has a class ``tN`` where `N` is the tree index.
Within the ``tree`` group there is a nested hierarchy of groups corresponding
to the tree structure. Any particular node in the tree will have a corresponding
group containing child groups (if any) followed by the edge above that node, a
node symbol, and (potentially) text containing the node label. For example, a
simple two tip tree, with tip node ids 0 and 1, and a root node id of 2 will have
a structure similar to the following:
.. code-block::
<g class="tree t0">
<g class="node n2 root">
<g class="node n1 a2 i1 p1 sample leaf">
<path class="edge" ... />
<circle class="sym" ... />
<text class="lab" ...>Node 1</text>
</g>
<g class="node n0 a2 i2 p1 sample leaf">
<path class="edge" ... />
<circle class="sym" .../>
<text class="lab" ...>Node 0</text>
</g>
<path class="edge" ... />
<circle />
<text class="lab">Root (Node 2)</text>
</g>
</g>
The classes can be used to manipulate the element, e.g. by using
`stylesheets <https://www.w3.org/TR/SVG2/styling.html>`_. Style strings can
be embedded in the svg by using the ``style`` parameter, or added to html
pages which contain the raw SVG (e.g. within a Jupyter notebook by using the
IPython ``HTML()`` function). As a simple example, passing the following
string as the ``style`` parameter will hide all labels:
.. code-block:: css
.tree .lab {visibility: hidden}
You can also change the format of various items: in SVG2-compatible viewers,
the following styles will rotate the leaf nodes labels by 90 degrees, colour
the leaf node symbols blue, and
hide the non-sample node labels. Note that SVG1.1 does not recognize the
``transform`` style, so in some SVG viewers, the labels will not appear rotated:
a workaround is to convert the SVG to PDF first, using e.g. the programmable
chromium engine: ``chromium --headless --print-to-pdf=out.pdf in.svg``)
.. code-block:: css
.tree .node.leaf > .lab {
transform: translateY(0.5em) rotate(90deg); text-anchor: start}
.tree .node.leaf > .sym {fill: blue}
.tree .node:not(.sample) > .lab {visibility: hidden}
Nodes contain classes that allow them to be targetted by node id (``nX``),
ancestor (parent) id (``aX`` or ``root`` if this node has no parent), and
(if defined) the id of the individual (``iX``) and population (``pX``) to
which this node belongs. Hence the following style will display
a large symbol for node 10, coloured red with a black border, and will also use
thick red lines for all the edges that have it as a direct or indirect parent
(note that, as with the ``transform`` style, changing the geometrical size of
symbols is only possible in SVG2 and above and therefore not all SVG viewers
will render such symbol size changes correctly).
.. code-block:: css
.tree .node.n10 > .sym {fill: red; stroke: black; r: 8px}
.tree .node.a10 .edge {stroke: red; stroke-width: 2px}
.. note::
A feature of SVG style commands is that they apply not just to the contents
within the <svg> container, but to the entire file. Thus if an SVG file is
embedded in a larger document, such as an HTML file (e.g. when an SVG
is displayed inline in a Jupyter notebook), the style will apply to all SVG
drawings in the notebook. To avoid this, you can tag the SVG with a unique
SVG using ``root_svg_attributes={'id':'MY_UID'}``, and prepend this to the
style string, as in ``#MY_UID .tree .edges {stroke: gray}``.
:param str path: The path to the file to write the output. If None, do not
write to file.
:param size: A tuple of (width, height) giving the width and height of the
produced SVG drawing in abstract user units (usually interpreted as pixels on
initial display).
:type size: tuple(int, int)
:param str tree_height_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,float max_tree_height: The maximum tree height value in the current
scaling system (see ``tree_height_scale``). Can be either a string or a
numeric value. If equal to ``"tree"`` (the default), the maximum tree height
is set to be that of the oldest root in the tree. If equal to ``"ts"`` the
maximum height is set to be the height 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 tree height by which to scale other nodes.
: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 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. Note that certain
styles, in particular transformations and changes in geometrical properties
of objects, will only be recognised by SVG2-compatible viewers.
:param str order: A string specifying the traversal type used to order the tips
in the tree, as detailed in :meth:`Tree.nodes`. If None (default), use
the default order as described in that method.
: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.
:return: An SVG representation of a tree.
:rtype: str
"""
draw = drawing.SvgTree(
self,
size,
tree_height_scale=tree_height_scale,
max_tree_height=max_tree_height,
node_labels=node_labels,
mutation_labels=mutation_labels,
root_svg_attributes=root_svg_attributes,
style=style,
order=order,
force_root_branch=force_root_branch,
**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 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,
tree_height_scale=None,
max_tree_height=None,
order=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 tree_height_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,float max_tree_height: The maximum tree height value in the current
scaling system (see ``tree_height_scale``). Can be either a string or a
numeric value. If equal to ``"tree"``, the maximum tree height is set to be
that of the oldest root in the tree. If equal to ``"ts"`` the maximum
height is set to be the height 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 tree height by which to scale other nodes. This parameter
is not currently supported for text output.
: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 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,
tree_height_scale=tree_height_scale,
max_tree_height=max_tree_height,
order=order,
)
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():
for mutation in site.mutations:
yield add_deprecated_mutation_attrs(site, mutation)
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 are
underneath the specified node. If u is not specified, return all leaves
in the tree.
: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 all the samples in this tree that are
underneath the specified node. If u is a sample, it is included in the
returned iterator. If u is not specified, return all samples in the tree.
If the :meth:`TreeSequence.trees` method is called with
``sample_lists=True``, this method uses an efficient algorithm to find
the samples. If not, a simple traversal based method is used.
:param int u: The node of interest.
:return: An iterator over all samples 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 samples 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
"""
if u is None:
return sum(self._ll_tree.get_num_samples(u) for u in self.roots)
else:
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
"""
roots = [u]
if u is None:
roots = self.roots
return sum(self._ll_tree.get_num_tracked_samples(root) for root in roots)
def _preorder_traversal(self, u):
stack = collections.deque([u])
# For perf we store these to avoid lookups in the tight loop
pop = stack.pop
extend = stack.extend
get_children = self.children
# Note: the usual style is to be explicit about what we're testing
# and use while len(stack) > 0, but this form is slightly faster.
while stack:
v = pop()
extend(reversed(get_children(v)))
yield v
def _postorder_traversal(self, u):
stack = collections.deque([u])
parent = NULL
# For perf we store these to avoid lookups in the tight loop
pop = stack.pop
extend = stack.extend
get_children = self.children
get_parent = self.get_parent
# Note: the usual style is to be explicit about what we're testing
# and use while len(stack) > 0, but this form is slightly faster.
while stack:
v = stack[-1]
children = [] if v == parent else get_children(v)
if children:
extend(reversed(children))
else:
parent = get_parent(v)
yield pop()
def _inorder_traversal(self, u):
# TODO add a nonrecursive version of the inorder traversal.
children = self.get_children(u)
mid = len(children) // 2
for c in children[:mid]:
yield from self._inorder_traversal(c)
yield u
for c in children[mid:]:
yield from self._inorder_traversal(c)
def _levelorder_traversal(self, u):
queue = collections.deque([u])
# 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, u):
"""
Sorts by increasing time but falls back to increasing ID for equal times.
"""
yield from sorted(
self.nodes(u, order="levelorder"), key=lambda u: (self.time(u), u)
)
def _timedesc_traversal(self, u):
"""
Sorts by decreasing time but falls back to decreasing ID for equal times.
"""
yield from sorted(
self.nodes(u, order="levelorder"),
key=lambda u: (self.time(u), u),
reverse=True,
)
def _minlex_postorder_traversal(self, u):
"""
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 skip perf optimisations here (compared to _preorder_traversal and
# _postorder_traversal) as this ordering is unlikely to be used in perf
# sensitive applications
stack = collections.deque([u])
parent = NULL
# We compute a dictionary mapping from internal node ID to min leaf ID
# under the node, using a first postorder traversal
min_leaf_dict = {}
while len(stack) > 0:
v = stack[-1]
children = [] if v == parent else self.children(v)
if children:
# The first time visiting a node, we push its children onto the stack.
# reversed is not strictly necessary, but it gives the postorder
# we would intuitively expect.
stack.extend(reversed(children))
else:
# The second time visiting a node, we record its min leaf ID
# underneath, pop it, and update the parent variable
if v != parent:
# at a leaf node
min_leaf_dict[v] = v
else:
# at a parent after finishing all its children
min_leaf_dict[v] = min([min_leaf_dict[c] for c in self.children(v)])
parent = self.get_parent(v)
stack.pop()
# Now we do a second postorder traversal
stack.clear()
stack.extend([u])
parent = NULL
while len(stack) > 0:
v = stack[-1]
children = [] if v == parent else self.children(v)
if children:
# The first time visiting a node, we push onto the stack its children
# in order of reverse min leaf ID under each child. This guarantees
# that the earlier children visited have smaller min leaf ID,
# which is equivalent to the minlex condition.
stack.extend(
sorted(children, key=lambda u: min_leaf_dict[u], reverse=True)
)
else:
# The second time visiting a node, we pop and yield it, and
# we update the parent variable
parent = self.get_parent(v)
yield stack.pop()
[docs] def nodes(self, root=None, order="preorder"):
"""
Returns an iterator over the node IDs in this tree. If the root parameter
is provided, iterate over the node IDs in the subtree rooted at this
node. If this is None, iterate over all node IDs. If the order parameter
is provided, iterate over the nodes in required tree traversal order.
.. note::
Unlike the :meth:`TreeSequence.nodes` method, this iterator produces
integer node IDs, not :class:`Node` objects.
The currently implemented traversal 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")
roots = [root]
if root is None:
roots = self.roots
if order == "minlex_postorder" and len(roots) > 1:
# we need to visit the roots in minlex order as well
# we first visit all the roots and then sort by the min value
root_values = []
for u in roots:
root_minlex_postorder = list(iterator(u))
min_value = root_minlex_postorder[0]
root_values.append([min_value, root_minlex_postorder])
root_values.sort()
for _, nodes_for_root in root_values:
yield from nodes_for_root
else:
for u in roots:
yield from iterator(u)
# TODO make this a bit less embarrassing by using an iterative method.
def __build_newick(self, *, node, precision, node_labels, include_branch_lengths):
"""
Simple recursive version of the newick generator used when non-default
node labels are needed, or when branch lengths are omitted
"""
label = node_labels.get(node, "")
if self.is_leaf(node):
s = f"{label}"
else:
s = "("
for child in self.children(node):
branch_length = self.branch_length(child)
subtree = self.__build_newick(
node=child,
precision=precision,
node_labels=node_labels,
include_branch_lengths=include_branch_lengths,
)
if include_branch_lengths:
subtree += ":{0:.{1}f}".format(branch_length, precision)
s += subtree + ","
s = s[:-1] + f"){label}"
return s
[docs] def newick(
self,
precision=14, # Should probably be keyword only, left positional for legacy use
*,
root=None,
node_labels=None,
include_branch_lengths=True,
):
"""
Returns a `newick encoding <https://en.wikipedia.org/wiki/Newick_format>`_
of this tree. If the ``root`` argument is specified, return a representation
of the specified subtree, otherwise the full tree is returned. If the tree
has multiple roots then seperate newick strings for each rooted subtree
must be found (i.e., we do not attempt to concatenate the different trees).
By default, leaf nodes are labelled with their numerical ID + 1,
and internal nodes are not labelled. Arbitrary node labels can be specified
using the ``node_labels`` argument, which maps node IDs to the desired
labels.
.. warning:: Node labels are **not** Newick escaped, so care must be taken
to provide labels that will not break the encoding.
:param int precision: The numerical precision with which branch lengths are
printed.
: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 self.num_roots > 1:
raise ValueError(
"Cannot get newick for multiroot trees. Try "
"[t.newick(root) for root in t.roots] to get a list of "
"newick trees, one for each root."
)
root = self.root
if not include_branch_lengths and node_labels is None:
# C code always puts branch lengths: force Py code by setting default labels
node_labels = {i: str(i + 1) for i in self.leaves()}
if node_labels is None:
root_time = max(1, self.time(root))
max_label_size = math.ceil(math.log10(self.tree_sequence.num_nodes))
single_node_size = (
4 + max_label_size + math.ceil(math.log10(root_time)) + precision
)
buffer_size = 1 + single_node_size * self.num_nodes
s = self._ll_tree.get_newick(
precision=precision, root=root, buffer_size=buffer_size
)
s = s.decode()
else:
s = (
self.__build_newick(
node=root,
precision=precision,
node_labels=node_labels,
include_branch_lengths=include_branch_lengths,
)
+ ";"
)
return s
[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 generations).
"""
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.num_nodes) if self.parent(u) != NULL
}
return pi
def __str__(self):
return str(self.get_parent_dict())
[docs] def map_mutations(self, genotypes, alleles):
"""
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 Fitch 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, ``node`` is the tree node at the bottom of the branch
on which the transition occurred, and ``derived_state`` is the new state
after this mutation. 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.
See the :ref:`sec_tutorial_parsimony` section in the tutorial for 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.
: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)
if np.max(genotypes) >= 64:
raise ValueError("A maximum of 64 states is supported")
ancestral_state, transitions = self._ll_tree.map_mutations(genotypes)
# Translate back into string alleles
ancestral_state = alleles[ancestral_state]
mutations = [
Mutation(node=node, derived_state=alleles[derived_state], parent=parent)
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_)
[docs] def split_polytomies(
self,
*,
epsilon=None,
method=None,
record_provenance=True,
random_seed=None,
):
"""
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``
(controlled by the ``epsilon`` parameter).
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.
.. 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 ``epsilon`` 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 is
raised, recommending a smaller epsilon value be used.
:param epsilon: A small time period used to separate each newly inserted node.
For a given polytomy of degree :math:`n`, the :math:`n-2` extra nodes are
inserted with the oldest at time ``epsilon`` less than the original parent,
``u``, and successive nodes at time ``epsilon`` from each other. Times
are allocated to different levels of the tree, such that any newly
inserted sibling nodes will have the same time. (Default :math:`1e-10`).
: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`.
: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,
)
[docs] @staticmethod
def generate_star(num_leaves, *, span=1, branch_length=1, record_provenance=True):
"""
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
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).
: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,
)
[docs] @staticmethod
def generate_balanced(
num_leaves, *, arity=2, span=1, branch_length=1, record_provenance=True
):
"""
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:`\\floor{n / 2}` 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).
: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,
)
[docs] @staticmethod
def generate_comb(num_leaves, *, span=1, branch_length=1, record_provenance=True):
"""
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 `caterpiller 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
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).
:return: A star-shaped 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,
)
[docs] @staticmethod
def generate_random(
num_leaves,
*,
arity=2,
span=1,
branch_length=1,
random_seed=None,
record_provenance=True,
):
"""
Generate a random :class:<Tree> with :math:`n` = ``num_leaves``
leaves with an equal probability of returning any valid topology.
The leaf nodes are marked as samples, labelled 0 to n, and placed at time 0.
The root node is placed at time ``(num_leaves - 1) * branch_length``, and
the other non-leaf nodes placed at a time of ``branch_length`` from each
other and from the root.
.. 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 int arity: The number of children of each internal node. If this is
2 (the default) then a strictly bifurcating (binary) tree is generated
chosen at random from the :math:`(2n - 3)! / (2^(n - 2) (n - 2!))`
possible topologies.
: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 time separating successive non-leaf nodes
from each other.
:return: A random tree. Its corresponding :class:`TreeSequence` is available
via the :attr:`.tree_sequence` attribute.
:rtype: Tree
"""
return combinatorics.generate_random(
num_leaves,
arity=arity,
span=span,
branch_length=branch_length,
random_seed=random_seed,
record_provenance=record_provenance,
)
[docs]def load(file):
"""
Loads a tree sequence 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.
:param str file: The file object or path of the ``.trees`` file containing the
tree sequence we wish to load.
:return: The tree sequence object containing the information
stored in the specified file path.
:rtype: :class:`tskit.TreeSequence`
"""
return TreeSequence.load(file)
[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().strip("\n").split(sep)
flags_index = header.index("flags")
location_index = None
metadata_index = None
try:
location_index = header.index("location")
except ValueError:
pass
try:
metadata_index = header.index("metadata")
except ValueError:
pass
for line in source:
tokens = line.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(",")))
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, 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().strip("\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.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().strip("\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.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().strip("\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.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().strip("\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.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] == "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().strip("\n").split(sep)
metadata_index = header.index("metadata")
for line in source:
tokens = line.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 load_text(
nodes,
edges,
sites=None,
mutations=None,
individuals=None,
populations=None,
sequence_length=0,
strict=True,
encoding="utf8",
base64_metadata=True,
):
"""
Parses the tree sequence data from the specified file-like objects, and
returns the resulting :class:`TreeSequence` object. 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``,
``mutations``, ``individuals`` and ``populations`` are optional, and must
be parsable by :func:`parse_sites`, :func:`parse_individuals`,
:func:`parse_populations`, and :func:`parse_mutations`, respectively.
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 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,
)
# We need to add populations any 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()
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 not None:
parse_populations(
populations,
strict=strict,
encoding=encoding,
base64_metadata=base64_metadata,
table=tc.populations,
)
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.
"""
def __init__(self, getter, length):
self.getter = getter
self.length = length
def __len__(self):
return self.length
def __getitem__(self, index):
if index < 0:
index += len(self)
if index < 0 or index >= len(self):
raise IndexError("Index out of bounds")
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.
"""
@attr.s(slots=True, frozen=True, kw_only=True, auto_attribs=True)
class _TableMetadataSchemas:
"""
Convenience class for returning schemas
"""
node: metadata_module.MetadataSchema
edge: metadata_module.MetadataSchema
site: metadata_module.MetadataSchema
mutation: metadata_module.MetadataSchema
migration: metadata_module.MetadataSchema
individual: metadata_module.MetadataSchema
population: metadata_module.MetadataSchema
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(self._TableMetadataSchemas)
if not name.startswith("_")
}
self._table_metadata_schemas = self._TableMetadataSchemas(
**metadata_schema_instances
)
# Implement the pickle protocol for TreeSequence
def __getstate__(self):
return self.dump_tables()
def __setstate__(self, tc):
self._ll_tree_sequence = 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,
):
"""
Returns True if `self` and `other` are equal. Uses the underlying table equlity,
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,
)
@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):
file, local_file = util.convert_file_like_to_open_file(file_or_path, "rb")
try:
ts = _tskit.TreeSequence()
ts.load(file)
return TreeSequence(ts)
except exceptions.FileFormatError as e:
# TODO Fix this for new file semantics
formats.raise_hdf5_format_error(file_or_path, 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):
"""
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.
"""
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 tables_dict(self):
"""
Returns a dictionary mapping names to tables in the
underlying :class:`.TableCollection`. Equivalent to calling
``ts.tables.name_map``.
"""
return self.tables.name_map
@property
def tables(self):
"""
A copy of the tables underlying this tree sequence. See also
:meth:`.dump_tables`.
.. warning:: This propery 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):
"""
A copy of the tables defining this tree sequence.
:return: A :class:`TableCollection` containing all tables underlying
the tree sequence.
:rtype: TableCollection
"""
t = tables.TableCollection(sequence_length=self.sequence_length)
self._ll_tree_sequence.dump_tables(t._ll_tables)
return t
[docs] def dump_text(
self,
nodes=None,
edges=None,
sites=None,
mutations=None,
individuals=None,
populations=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 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: If True, metadata is encoded using Base64
encoding; otherwise, as plain text.
"""
if nodes is not None:
print(
"id",
"is_sample",
"time",
"population",
"individual",
"metadata",
sep="\t",
file=nodes,
)
for node in self.nodes():
metadata = node.metadata
if base64_metadata:
metadata = base64.b64encode(metadata).decode(encoding)
row = (
"{id:d}\t"
"{is_sample:d}\t"
"{time:.{precision}f}\t"
"{population:d}\t"
"{individual:d}\t"
"{metadata}"
).format(
precision=precision,
id=node.id,
is_sample=node.is_sample(),
time=node.time,
population=node.population,
individual=node.individual,
metadata=metadata,
)
print(row, file=nodes)
if edges is not None:
print("left", "right", "parent", "child", sep="\t", file=edges)
for edge in self.edges():
row = (
"{left:.{precision}f}\t"
"{right:.{precision}f}\t"
"{parent:d}\t"
"{child:d}"
).format(
precision=precision,
left=edge.left,
right=edge.right,
parent=edge.parent,
child=edge.child,
)
print(row, file=edges)
if sites is not None:
print("position", "ancestral_state", "metadata", sep="\t", file=sites)
for site in self.sites():
metadata = site.metadata
if base64_metadata:
metadata = base64.b64encode(metadata).decode(encoding)
row = (
"{position:.{precision}f}\t" "{ancestral_state}\t" "{metadata}"
).format(
precision=precision,
position=site.position,
ancestral_state=site.ancestral_state,
metadata=metadata,
)
print(row, file=sites)
if mutations is not None:
print(
"site",
"node",
"time",
"derived_state",
"parent",
"metadata",
sep="\t",
file=mutations,
)
for site in self.sites():
for mutation in site.mutations:
metadata = mutation.metadata
if base64_metadata:
metadata = base64.b64encode(metadata).decode(encoding)
row = (
"{site}\t"
"{node}\t"
"{time}\t"
"{derived_state}\t"
"{parent}\t"
"{metadata}"
).format(
site=mutation.site,
node=mutation.node,
time="unknown"
if util.is_unknown_time(mutation.time)
else mutation.time,
derived_state=mutation.derived_state,
parent=mutation.parent,
metadata=metadata,
)
print(row, file=mutations)
if individuals is not None:
print("id", "flags", "location", "metadata", sep="\t", file=individuals)
for individual in self.individuals():
metadata = individual.metadata
if base64_metadata:
metadata = base64.b64encode(metadata).decode(encoding)
location = ",".join(map(str, individual.location))
row = ("{id}\t" "{flags}\t" "{location}\t" "{metadata}").format(
id=individual.id,
flags=individual.flags,
location=location,
metadata=metadata,
)
print(row, file=individuals)
if populations is not None:
print("id", "metadata", sep="\t", file=populations)
for population in self.populations():
metadata = population.metadata
if base64_metadata:
metadata = base64.b64encode(metadata).decode(encoding)
row = ("{id}\t" "{metadata}").format(
id=population.id, metadata=metadata
)
print(row, file=populations)
if provenances is not None:
print("id", "timestamp", "record", sep="\t", file=provenances)
for provenance in self.provenances():
row = ("{id}\t" "{timestamp}\t" "{record}\t").format(
id=provenance.id,
timestamp=provenance.timestamp,
record=provenance.record,
)
print(row, file=provenances)
def __repr__(self):
ts_rows = [
["Trees", str(self.num_trees)],
["Sequence Length", str(self.sequence_length)],
["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.name_map.items():
table_rows.append(
[
str(s)
for s in [
name.capitalize(),
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
)
def _repr_html_(self):
"""
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 samples in this tree sequence. This is 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 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 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 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 nodes that are not present in any tree. Consistent
with the definition of tree roots, if there are no edges in the tree
sequence we return the time of the oldest sample.
:return: The maximum time of a root in this tree sequence.
:rtype: float
"""
ret = max(self.node(u).time 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.node(edge.parent).time)
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):
"""
Returns an iterable sequence of all the :ref:`nodes <sec_node_table_definition>`
in this tree sequence.
:return: An iterable sequence of all nodes.
:rtype: Sequence(:class:`.Node`)
"""
return SimpleContainerSequence(self.node, self.num_nodes)
[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-descreasing
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
[docs] def edge_diffs(self, include_terminal=False):
"""
Returns an iterator over all the edges that are inserted and removed to
build the trees as we move from left-to-right along the tree sequence.
The iterator yields a sequence of 3-tuples, ``(interval, edges_out,
edges_in)``. The ``interval`` is a pair ``(left, right)`` representing
the genomic interval (see :attr:`Tree.interval`). The ``edges_out``
value 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 ``edges_in`` value 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.
: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).
:return: An iterator over the (interval, edges_out, edges_in) tuples.
:rtype: :class:`collections.abc.Iterable`
"""
iterator = _tskit.TreeDiffIterator(self._ll_tree_sequence, include_terminal)
metadata_decoder = self.table_metadata_schemas.edge.decode_row
for interval, edge_tuples_out, edge_tuples_in in iterator:
edges_out = [Edge(*(e + (metadata_decoder,))) for e in edge_tuples_out]
edges_in = [Edge(*(e + (metadata_decoder,))) for e in edge_tuples_in]
yield Interval(*interval), edges_out, edges_in
[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():
for mutation in site.mutations:
yield add_deprecated_mutation_attrs(site, mutation)
[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 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 compatability
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`.
: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`.
: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 give 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[1], tree2.interval[1])
yield Interval(left, right), tree1, tree2
# Advance
if tree1.interval[1] == right:
tree1 = next(trees1, None)
if tree2.interval[1] == right:
tree2 = next(trees2, None)
[docs] def haplotypes(
self,
*,
isolated_as_missing=None,
missing_data_character="-",
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 :math:`s` sites 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 sample ``0``, 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`, and for a haplotype
``h``, the value of ``h[j]`` will 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. If you have a large dataset but only
want to output the haplotypes for a subset of samples, it may be worth
calling :meth:`.simplify` to reduce tree sequence down to the required
samples before outputting haplotypes.
: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: '-'.
: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 or the 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,
)
# 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
H = np.empty((self.num_samples, self.num_sites), dtype=np.int8)
missing_int8 = ord(missing_data_character.encode("ascii"))
for var in self.variants(isolated_as_missing=isolated_as_missing):
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] = alleles[var.genotypes]
for h in H:
yield h.tobytes().decode("ascii")
[docs] def variants(
self,
*,
as_bytes=False,
samples=None,
isolated_as_missing=None,
alleles=None,
impute_missing_data=None,
):
"""
Returns an iterator over the variants in this tree sequence. See the
:class:`Variant` class for details on the fields of each returned
object. The ``genotypes`` for the variants are numpy arrays,
corresponding to indexes into the ``alleles`` attribute in the
:class:`Variant` object. By default, the ``alleles`` for each
site are generated automatically, such that the ancestral state
is at the zeroth index 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 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, and the ``alleles`` tuple will
end with the value ``None`` (note that this is true whether we specify
a fixed mapping using the ``alleles`` parameter or not).
See the :class:`Variant` class for more details on how missing data is
reported.
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.
.. note::
The ``as_bytes`` parameter is kept as a compatibility
option for older code. It is not the recommended way of
accessing variant data, and will be deprecated in a later
release.
:param bool as_bytes: If True, the genotype values will be returned
as a Python bytes object. Legacy use only.
:param array_like samples: An array of sample IDs for which to generate
genotypes, or None for all samples. Default: None.
: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 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 occurance 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: An iterator of all variants this tree sequence.
:rtype: iter(:class:`Variant`)
"""
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,
)
# 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
# See comments for the Variant type for discussion on why the
# present form was chosen.
iterator = _tskit.VariantGenerator(
self._ll_tree_sequence,
samples=samples,
isolated_as_missing=isolated_as_missing,
alleles=alleles,
)
for site_id, genotypes, alleles in iterator:
site = self.site(site_id)
if as_bytes:
if any(len(allele) > 1 for allele in alleles):
raise ValueError(
"as_bytes only supported for single-letter alleles"
)
bytes_genotypes = np.empty(self.num_samples, dtype=np.uint8)
lookup = np.array([ord(a[0]) for a in alleles], dtype=np.uint8)
bytes_genotypes[:] = lookup[genotypes]
genotypes = bytes_genotypes.tobytes()
yield Variant(site, alleles, genotypes)
[docs] def genotype_matrix(
self, *, 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 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 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 occurance 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.int8)
"""
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,
)
# 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
return self._ll_tree_sequence.get_genotype_matrix(
isolated_as_missing=isolated_as_missing, alleles=alleles
)
[docs] def individual(self, id_):
"""
Returns the :ref:`individual <sec_individual_table_definition>`
in this tree sequence with the specified ID.
:rtype: :class:`Individual`
"""
flags, location, metadata, nodes = self._ll_tree_sequence.get_individual(id_)
return Individual(
id_=id_,
flags=flags,
location=location,
encoded_metadata=metadata,
metadata_decoder=self.table_metadata_schemas.individual.decode_row,
nodes=nodes,
)
[docs] def node(self, id_):
"""
Returns the :ref:`node <sec_node_table_definition>` in this tree sequence
with the specified ID.
:rtype: :class:`Node`
"""
(
flags,
time,
population,
individual,
metadata,
) = self._ll_tree_sequence.get_node(id_)
return Node(
id_=id_,
flags=flags,
time=time,
population=population,
individual=individual,
encoded_metadata=metadata,
metadata_decoder=self.table_metadata_schemas.node.decode_row,
)
[docs] def edge(self, id_):
"""
Returns the :ref:`edge <sec_edge_table_definition>` in this tree sequence
with the specified ID.
:rtype: :class:`Edge`
"""
left, right, parent, child, metadata = self._ll_tree_sequence.get_edge(id_)
return Edge(
id_=id_,
left=left,
right=right,
parent=parent,
child=child,
encoded_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.
:rtype: :class:`.Migration`
"""
(
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,
encoded_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.
:rtype: :class:`Mutation`
"""
(
site,
node,
derived_state,
parent,
metadata,
time,
) = self._ll_tree_sequence.get_mutation(id_)
return Mutation(
id_=id_,
site=site,
node=node,
derived_state=derived_state,
parent=parent,
encoded_metadata=metadata,
metadata_decoder=self.table_metadata_schemas.mutation.decode_row,
time=time,
)
[docs] def site(self, id_):
"""
Returns the :ref:`site <sec_site_table_definition>` in this tree sequence
with the specified ID.
:rtype: :class:`Site`
"""
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,
encoded_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.
:rtype: :class:`Population`
"""
(metadata,) = self._ll_tree_sequence.get_population(id_)
return Population(
id_=id_,
encoded_metadata=metadata,
metadata_decoder=self.table_metadata_schemas.population.decode_row,
)
def provenance(self, id_):
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):
"""
Returns an array of the sample node IDs in this tree sequence. If the
``population`` parameter is specified, only return sample IDs from this
population.
:param int population: The population of interest. If None,
return all samples.
:param int population_id: Deprecated alias for ``population``.
:return: A numpy array of the node IDs for the samples of interest.
: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()
if population is not None:
sample_population = self.tables.nodes.population[samples]
samples = samples[sample_population == population]
return samples
def write_fasta(self, output, sequence_ids=None, wrap_width=60):
""
# suppress fasta visibility pending https://github.com/tskit-dev/tskit/issues/353
"""
Writes haplotype data for samples in FASTA format to the
specified file-like object.
Default `sequence_ids` (i.e. the text immediately following ">") are
"tsk_{sample_number}" e.g. "tsk_0", "tsk_1" etc. They can be set by providing
a list of strings to the `sequence_ids` argument, which must equal the length
of the number of samples. Please ensure that these are unique and compatible with
fasta standards, since we do not check this.
Default `wrap_width` for sequences is 60 characters in accordance with fasta
standard outputs, but this can be specified. In order to avoid any line-wrapping
of sequences, set `wrap_width = 0`.
Example usage:
.. code-block:: python
with open("output.fasta", "w") as fasta_file:
ts.write_fasta(fasta_file)
This can also be achieved on the command line use the ``tskit fasta`` command,
e.g.:
.. code-block:: bash
$ tskit fasta example.trees > example.fasta
:param io.IOBase output: The file-like object to write the fasta output.
:param list(str) sequence_ids: A list of string names to uniquely identify
each of the sequences in the fasta file. If specified, this must be a
list of strings of length equal to the number of samples which are output.
Note that we do not check the form of these strings in any way, so that it
is possible to output bad fasta IDs (for example, by including spaces
before the unique identifying part of the string).
The default is to output ``tsk_j`` for the jth individual.
:param int wrap_width: This parameter specifies the number of sequence
characters to include on each line in the fasta file, before wrapping
to the next line for each sequence. Defaults to 60 characters in
accordance with fasta standard outputs. To avoid any line-wrapping of
sequences, set `wrap_width = 0`. Otherwise, supply any positive integer.
"""
# if not specified, IDs default to sample index
if sequence_ids is None:
sequence_ids = [f"tsk_{j}" for j in self.samples()]
if len(sequence_ids) != self.num_samples:
raise ValueError(
"sequence_ids must have length equal to the number of samples."
)
wrap_width = int(wrap_width)
if wrap_width < 0:
raise ValueError(
"wrap_width must be a non-negative integer. "
"You may specify `wrap_width=0` "
"if you do not want any wrapping."
)
for j, hap in enumerate(self.haplotypes()):
print(">", sequence_ids[j], sep="", file=output)
if wrap_width == 0:
print(hap, file=output)
else:
for hap_wrap in textwrap.wrap(hap, wrap_width):
print(hap_wrap, file=output)
[docs] def write_vcf(
self,
output,
ploidy=None,
contig_id="1",
individuals=None,
individual_names=None,
position_transform=None,
):
"""
Writes a VCF formatted file to the specified file-like object.
If there is individual information present in the tree sequence
(see :ref:`sec_individual_table_definition`), the values for
sample nodes associated with these individuals are combined
into phased multiploid individuals and output.
If there is no individual data present in the tree sequence, synthetic
individuals are created by combining adjacent samples, and the number
of samples combined is equal to the specified ploidy value (1 by
default). For example, if we have a ploidy of 2 and a sample of size 6,
then we will have 3 diploid samples in the output, 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::
Warning to ``plink`` users:
As the default first individual name is ``tsk_0``, ``plink`` will
throw this error when loading the VCF:
``Error: Sample ID ends with "_0", which induces an invalid IID of '0'.``
This can be fixed by using the ``individual_names`` argument
to set the names to anything where the first name doesn't end with ``_0``.
An example implementation for diploid individuals is:
.. code-block:: python
n_dip_indv = int(ts.num_samples / 2)
indv_names = [f"tsk_{str(i)}indv" for i in range(n_dip_indv)]
with open("output.vcf", "w") as vcf_file:
ts.write_vcf(vcf_file, ploidy=2, individual_names=indv_names)
Adding a second ``_`` (eg: ``tsk_0_indv``) is not recommended as
``plink`` uses ``_`` as the default separator for separating family
id and individual id, and two ``_`` will throw an error.
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 ``position_transform`` argument provides a way to flexibly translate
the genomic location of sites in tskit to the appropriate value in VCF.
There are two fundamental differences in the way that tskit and VCF define
genomic coordinates. The first is that tskit uses floating point values
to encode positions, whereas VCF uses integers. Thus, if the tree sequence
contains positions at non-integral locations there is an information loss
incurred by translating to VCF. By default, we round the site positions
to the nearest integer, such that there may be several sites with the
same integer position in the output. The second difference between VCF
and tskit is that VCF is defined to be a 1-based coordinate system, whereas
tskit uses 0-based. However, how coordinates are transformed depends
on the VCF parser, and so we do **not** account for this change in
coordinate system by default.
Example usage:
.. code-block:: python
with open("output.vcf", "w") as vcf_file:
tree_sequence.write_vcf(vcf_file, ploidy=2)
The VCF output can also be compressed using the :mod:`gzip` module, if you wish:
.. code-block:: python
import gzip
with gzip.open("output.vcf.gz", "wt") as f:
ts.write_vcf(f)
However, this gzipped VCF may not be fully compatible with downstream tools
such as tabix, which may require the VCF use the specialised bgzip format.
A general way to convert VCF data to various formats is to pipe the text
produced by ``tskit`` into ``bcftools``, as done here:
.. code-block:: python
import os
import subprocess
read_fd, write_fd = os.pipe()
write_pipe = os.fdopen(write_fd, "w")
with open("output.bcf", "w") as bcf_file:
proc = subprocess.Popen(
["bcftools", "view", "-O", "b"], stdin=read_fd, stdout=bcf_file
)
ts.write_vcf(write_pipe)
write_pipe.close()
os.close(read_fd)
proc.wait()
if proc.returncode != 0:
raise RuntimeError("bcftools failed with status:", proc.returncode)
This can also be achieved on the command line use the ``tskit vcf`` command,
e.g.:
.. code-block:: bash
$ tskit vcf example.trees | bcftools view -O b > example.bcf
: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.
: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
write out to VCF. Defaults to all individuals in the tree sequence.
: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.
: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.
"""
writer = vcf.VcfWriter(
self,
ploidy=ploidy,
contig_id=contig_id,
individuals=individuals,
individual_names=individual_names,
position_transform=position_transform,
)
writer.write(output)
[docs] def to_nexus(self, precision=14):
"""
Returns a `nexus encoding <https://en.wikipedia.org/wiki/Nexus_file>`_
of this tree sequence. Trees along the sequence are listed sequentially in
the TREES block. The tree spanning the interval :math:`[x, y)`` is
given the name "tree_x_y". Spatial positions are written at the
specified precision.
Nodes in the tree sequence are identified by the taxon labels of the
form ``f"tsk_{node.id}_{node.flags}"``, such that a node with ``id=5``
and ``flags=1`` will have the label ``"tsk_5_1"`` (please see the
:ref:`data model <sec_node_table_definition>` section for details
on the interpretation of node ID and flags values). These labels are
listed for all nodes in the tree sequence in the ``TAXLABELS`` block.
:param int precision: The numerical precision with which branch lengths
and tree positions are printed.
:return: A nexus representation of this TreeSequence.
:rtype: str
"""
node_labels = {node.id: f"tsk_{node.id}_{node.flags}" for node in self.nodes()}
s = "#NEXUS\n"
s += "BEGIN TAXA;\n"
s += "TAXLABELS "
s += ",".join(node_labels[node.id] for node in self.nodes()) + ";\n"
s += "END;\n"
s += "BEGIN TREES;\n"
for tree in self.trees():
start_interval = "{0:.{1}f}".format(tree.interval.left, precision)
end_interval = "{0:.{1}f}".format(tree.interval.right, precision)
newick = tree.newick(precision=precision, node_labels=node_labels)
s += f"\tTREE tree{start_interval}_{end_interval} = {newick}\n"
s += "END;\n"
return s
[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(as_bytes=True):
output.append(
f"SITE:\t{variant.index}\t{variant.position / m}\t0.0\t"
f"{variant.genotypes.decode()}"
)
return "\n".join(output) + "\n"
[docs] def simplify(
self,
samples=None,
*,
map_nodes=False,
reduce_to_site_topology=False,
filter_populations=True,
filter_individuals=True,
filter_sites=True,
keep_unary=False,
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.
In the returned tree sequence, the node with ID ``0`` corresponds to
``samples[0]``, node ``1`` corresponds to ``samples[1]``, and so on.
Besides the samples, node IDs in the returned tree sequence are then
allocated sequentially in time order.
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`` or ``filter_sites`` 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.
:param list samples: The list of nodes for which to retain information. This
may be 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: 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: 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: True)
:param bool keep_unary: If True, any unary nodes (i.e. nodes with exactly
one child) that exist on the path from samples to root will be preserved
in the output. (Default: False)
:param bool keep_input_roots: If True, insert edges from the MRCAs of the
samples to the roots in the input trees. If False, no topology older
than the MRCAs of the samples will be included. (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: .TreeSequence or (.TreeSequence, numpy.ndarray)
"""
tables = self.dump_tables()
if samples is None:
samples = self.get_samples()
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,
keep_unary=keep_unary,
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])``.
: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: .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: .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 subset(self, nodes, record_provenance=True):
"""
Returns a tree sequence modified to contain only the entries referring to
the provided list of nodes, with nodes reordered according to the order
they appear in the ``nodes`` argument. Specifically, this subsets and reorders
each of the tables as follows:
1. Nodes: if in the list of nodes, and in the order provided.
2. Individuals and Populations: if referred to by a retained node,
and in the order first seen when traversing the list of retained nodes.
3. Edges: if both parent and child are retained nodes.
4. Mutations: if the mutation's node is a retained node.
5. Sites: if any mutations remain at the site after removing mutations.
Retained edges, mutations, and sites appear in the same
order as in the original tables.
If ``nodes`` is the entire list of nodes in the tables, then the
resulting tables will be identical to the original tables, but with
nodes (and individuals and populations) reordered.
To instead subset the tables to a given portion of the *genome*, see
:meth:`.keep_intervals`.
**Note:** This is quite different from :meth:`.simplify`: the resulting
tables contain only the nodes given, not ancestral ones as well, and
does not simplify the relationships in any way.
: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: If True, add details of this operation to the
provenance information of the returned tree sequence. (Default: True).
:rtype: .TreeSequence
"""
tables = self.dump_tables()
tables.subset(nodes, record_provenance)
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,
tree_height_scale=None,
node_labels=None,
mutation_labels=None,
root_svg_attributes=None,
style=None,
order=None,
force_root_branch=None,
**kwargs,
):
"""
Return an SVG representation of a tree sequence.
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_svg())
The visual elements in the svg are
`grouped <https://www.w3.org/TR/SVG2/struct.html#Groups>`_
for easy styling and manipulation. The entire visualization with trees and X
axis is contained within a group of class ``tree-sequence``. Each tree in
the displayed tree sequence is contained in a group of class ``tree``, as
described in :meth:`Tree.draw_svg`, so that visual elements pertaining to one
or more trees targetted as documented in that method. For instance, the
following style will change the colour of all the edges of the *initial*
tree in the sequence and hide the non-sample node labels in *all* the trees
.. code-block:: css
.tree.t0 .edge {stroke: blue}
.tree .node:not(.sample) > text {visibility: hidden}
See :meth:`Tree.draw_svg` for further details.
:param str path: The path to the file to write the output. If None, do not write
to file.
:param size: A tuple of (width, height) giving the width and height of the
produced SVG drawing in abstract user units (usually interpreted as pixels on
display).
:type size: tuple(int, int)
: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, and
background shading is used to indicate the position of the trees along the
sequence. 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
and no background scaling is required.
:param str tree_height_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 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 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: A string specifying the traversal type used to order the tips
in each tree, as detailed in :meth:`Tree.nodes`. If None (default), use
the default order as described in that method.
: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.
:return: An SVG representation of a tree.
:rtype: str
"""
draw = drawing.SvgTreeSequence(
self,
size,
x_scale=x_scale,
tree_height_scale=tree_height_scale,
node_labels=node_labels,
mutation_labels=mutation_labels,
root_svg_attributes=root_svg_attributes,
style=style,
order=order,
force_root_branch=force_root_branch,
**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 output
def draw_text(self, **kwargs):
# TODO document this method.
return str(drawing.TextTreeSequence(self, **kwargs))
############################################
#
# 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, windows="site", 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).
"""
# 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.tables.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.int32)
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])
return stat
def __k_way_sample_set_stat(
self,
ll_method,
k,
sample_sets,
indexes=None,
windows=None,
mode=None,
span_normalise=True,
polarised=False,
):
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)
if indexes is None:
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,
)
if drop_dimension:
stat = stat.reshape(stat.shape[:-1])
return stat
############################################
# Statistics definitions
############################################
[docs] def diversity(
self, sample_sets=None, windows=None, mode="site", span_normalise=True
):
"""
Computes mean genetic diversity (also knowns as "Tajima's pi") in each of the
sets of nodes from ``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>`.
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 across distinct,
randomly chosen pairs of chromosomes, 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 distinct, randomly chosen pairs
of chromsomes 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 random pair from the sample set, averaged over choices of pair.
: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 numpy array.
"""
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
):
"""
Computes mean genetic divergence between (and within) 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>`.
As a special case, an index ``(j, j)`` will compute the
:meth:`diversity <.TreeSequence.diversity>` of ``sample_set[j]``.
What is computed depends on ``mode``:
"site"
Mean pairwise genetic divergence: the average across distinct,
randomly chosen pairs 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 distinct, randomly
chosen pairs of chromsomes (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 random pair (one from each sample set), averaged over
choices of pair.
: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).
"""
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,
)
# JK: commenting this out for now to get the other methods well tested.
# Issue: https://github.com/tskit-dev/tskit/issues/201
# 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
[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 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 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,
)
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 `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 `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 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).
"""
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_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.
"""
# TODO should we allow a single sample_set to be specified here as a 1D array?
# This won't change the output dimensions like the other stats.
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).
"""
# 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).
"""
# 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, **kwargs):
diversities = self._ll_tree_sequence.diversity(
sample_set_sizes, flattened, **kwargs
)
divergences = self._ll_tree_sequence.divergence(
sample_set_sizes, flattened, indexes, **kwargs
)
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.product(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
randomly chosen trios of samples ``(a, b, c)``, one 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).
"""
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 across randomly chosen trios 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).
"""
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 a randomly chosen trio of
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).
"""
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
randomly chosen set of four samples ``(a, b; c, d)``, one 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`` anc ``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).
"""
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
):
"""
Computes Patterson's f3 statistic between three groups 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 works exactly as
:meth:`f4 <.TreeSequence.f4>`, except the average is across randomly
chosen set of four samples ``(a1, b; a2, c)``, with `a1` and `a2` both
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).
"""
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 f3 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 randomly
chosen set of four samples ``(a1, b1; a2, b2)``, with `a1` and `a2`
both chosen (without replacement) from the first sample set and ``b1``
and ``b2`` chosen randomly 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).
"""
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)
############################################
#
# 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 randomly chosen pair of 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]
)
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 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)