Source code for tskit.trees

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"""
Module responsible for managing trees and tree sequences.
"""
import base64
import collections
import concurrent.futures
import functools
import itertools
import math
import textwrap
import warnings
from dataclasses import dataclass
from typing import Any
from typing import NamedTuple
from typing import Optional
from typing import Union

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


class CoalescenceRecord(NamedTuple):
    left: float
    right: float
    node: int
    children: np.ndarray
    time: float
    population: int


[docs]class Interval(NamedTuple): """ A tuple of 2 numbers, ``[left, right)``, defining an interval over the genome. :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 interval. 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 """ left: float right: float @property def span(self) -> float: return self.right - self.left
class EdgeDiff(NamedTuple): interval: Interval edges_out: list edges_in: list
[docs]@metadata_module.lazy_decode @dataclass class Individual(util.Dataclass): """ An :ref:`individual <sec_individual_table_definition>` in a tree sequence. Since nodes correspond to genomes, individuals are associated with a collection of nodes (e.g., two nodes per diploid). See :ref:`sec_nodes_or_individuals` for more discussion of this distinction. Modifying the attributes in this class will have **no effect** on the underlying tree sequence data. """ __slots__ = ["id", "flags", "location", "parents", "nodes", "metadata"] id: int # noqa A003 """ The integer ID of this individual. Varies from 0 to :attr:`TreeSequence.num_individuals` - 1.""" flags: int """ The bitwise flags for this individual. """ location: np.ndarray """ The spatial location of this individual as a numpy array. The location is an empty array if no spatial location is defined. """ parents: np.ndarray """ The parent individual ids of this individual as a numpy array. The parents is an empty array if no parents are defined. """ nodes: np.ndarray """ The IDs of the nodes that are associated with this individual as a numpy array (dtype=np.int32). If no nodes are associated with the individual this array will be empty. """ metadata: Optional[Union[bytes, dict]] """ The :ref:`metadata <sec_metadata_definition>` for this individual, decoded if a schema applies. """ # Custom eq for the numpy arrays def __eq__(self, other): return ( self.id == other.id and self.flags == other.flags and np.array_equal(self.location, other.location) and np.array_equal(self.parents, other.parents) and np.array_equal(self.nodes, other.nodes) and self.metadata == other.metadata )
[docs]@metadata_module.lazy_decode @dataclass class Node(util.Dataclass): """ A :ref:`node <sec_node_table_definition>` in a tree sequence, corresponding to a single genome. The ``time`` and ``population`` are attributes of the ``Node``, rather than the ``Individual``, as discussed in :ref:`sec_nodes_or_individuals`. Modifying the attributes in this class will have **no effect** on the underlying tree sequence data. """ __slots__ = ["id", "flags", "time", "population", "individual", "metadata"] id: int # noqa A003 """ The integer ID of this node. Varies from 0 to :attr:`TreeSequence.num_nodes` - 1. """ flags: int """ The bitwise flags for this node. """ time: float """ The birth time of this node. """ population: int """ The integer ID of the population that this node was born in. """ individual: int """ The integer ID of the individual that this node was a part of. """ metadata: Optional[Union[bytes, dict]] """ The :ref:`metadata <sec_metadata_definition>` for this node, decoded if a schema applies. """
[docs] def is_sample(self): """ Returns True if this node is a sample. This value is derived from the ``flag`` variable. :rtype: bool """ return self.flags & NODE_IS_SAMPLE
[docs]@metadata_module.lazy_decode @dataclass class Edge(util.Dataclass): """ An :ref:`edge <sec_edge_table_definition>` in a tree sequence. Modifying the attributes in this class will have **no effect** on the underlying tree sequence data. """ __slots__ = ["left", "right", "parent", "child", "metadata", "id"] left: float """ The left coordinate of this edge. """ right: float """ The right coordinate of this edge. """ parent: int """ The integer ID of the parent node for this edge. To obtain further information about a node with a given ID, use :meth:`TreeSequence.node`. """ child: int """ The integer ID of the child node for this edge. To obtain further information about a node with a given ID, use :meth:`TreeSequence.node`. """ metadata: Optional[Union[bytes, dict]] """ The :ref:`metadata <sec_metadata_definition>` for this edge, decoded if a schema applies. """ id: int # noqa A003 """ The integer ID of this edge. Varies from 0 to :attr:`TreeSequence.num_edges` - 1. """ # Custom init to define default values with slots def __init__(self, left, right, parent, child, metadata=b"", id=None): # noqa A003 self.id = id self.left = left self.right = right self.parent = parent self.child = child self.metadata = 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]@metadata_module.lazy_decode @dataclass class Site(util.Dataclass): """ A :ref:`site <sec_site_table_definition>` in a tree sequence. Modifying the attributes in this class will have **no effect** on the underlying tree sequence data. """ __slots__ = ["id", "position", "ancestral_state", "mutations", "metadata"] id: int # noqa A003 """ The integer ID of this site. Varies from 0 to :attr:`TreeSequence.num_sites` - 1. """ position: float """ The floating point location of this site in genome coordinates. Ranges from 0 (inclusive) to :attr:`TreeSequence.sequence_length` (exclusive). """ ancestral_state: str """ The ancestral state at this site (i.e., the state inherited by nodes, unless mutations occur). """ mutations: np.ndarray """ The list of mutations at this site. Mutations within a site are returned in the order they are specified in the underlying :class:`MutationTable`. """ metadata: Optional[Union[bytes, dict]] """ The :ref:`metadata <sec_metadata_definition>` for this site, decoded if a schema applies. """ # We need a custom eq for the numpy arrays def __eq__(self, other): return ( isinstance(other, Site) and self.id == other.id and self.position == other.position and self.ancestral_state == other.ancestral_state and np.array_equal(self.mutations, other.mutations) and self.metadata == other.metadata )
[docs]@metadata_module.lazy_decode @dataclass class Mutation(util.Dataclass): """ A :ref:`mutation <sec_mutation_table_definition>` in a tree sequence. Modifying the attributes in this class will have **no effect** on the underlying tree sequence data. """ __slots__ = ["id", "site", "node", "derived_state", "parent", "metadata", "time"] id: int # noqa A003 """ The integer ID of this mutation. Varies from 0 to :attr:`TreeSequence.num_mutations` - 1. Modifying the attributes in this class will have **no effect** on the underlying tree sequence data. """ site: int """ The integer ID of the site that this mutation occurs at. To obtain further information about a site with a given ID use :meth:`TreeSequence.site`. """ node: int """ The integer ID of the first node that inherits this mutation. To obtain further information about a node with a given ID, use :meth:`TreeSequence.node`. """ derived_state: str """ The derived state for this mutation. This is the state inherited by nodes in the subtree rooted at this mutation's node, unless another mutation occurs. """ parent: int """ The integer ID of this mutation's parent mutation. When multiple mutations occur at a site along a path in the tree, mutations must record the mutation that is immediately above them. If the mutation does not have a parent, this is equal to the :data:`NULL` (-1). To obtain further information about a mutation with a given ID, use :meth:`TreeSequence.mutation`. """ metadata: Optional[Union[bytes, dict]] """ The :ref:`metadata <sec_metadata_definition>` for this mutation, decoded if a schema applies. """ time: float """ The occurrence time of this mutation. """ # To get default values on slots we define a custom init def __init__( self, id=NULL, # noqa A003 site=NULL, node=NULL, time=UNKNOWN_TIME, derived_state=None, parent=NULL, metadata=b"", ): self.id = id self.site = site self.node = node self.time = time self.derived_state = derived_state self.parent = parent self.metadata = metadata # We need a custom eq to compare unknown times. def __eq__(self, other): return ( isinstance(other, Mutation) and self.id == other.id and self.site == other.site and self.node == other.node and self.derived_state == other.derived_state and self.parent == other.parent and self.metadata == other.metadata and ( self.time == other.time or ( util.is_unknown_time(self.time) and util.is_unknown_time(other.time) ) ) )
[docs]@metadata_module.lazy_decode @dataclass class Migration(util.Dataclass): """ A :ref:`migration <sec_migration_table_definition>` in a tree sequence. Modifying the attributes in this class will have **no effect** on the underlying tree sequence data. """ __slots__ = ["left", "right", "node", "source", "dest", "time", "metadata", "id"] left: float """ The left end of the genomic interval covered by this migration (inclusive). """ right: float """ The right end of the genomic interval covered by this migration (exclusive). """ node: int """ The integer ID of the node involved in this migration event. To obtain further information about a node with a given ID, use :meth:`TreeSequence.node`. """ source: int """ The source population ID. """ dest: int """ The destination population ID. """ time: float """ The time at which this migration occurred at. """ metadata: Optional[Union[bytes, dict]] """ The :ref:`metadata <sec_metadata_definition>` for this migration, decoded if a schema applies. """ id: int # noqa A003 """ The integer ID of this mutation. Varies from 0 to :attr:`TreeSequence.num_mutations` - 1. """
[docs]@metadata_module.lazy_decode @dataclass class Population(util.Dataclass): """ A :ref:`population <sec_population_table_definition>` in a tree sequence. Modifying the attributes in this class will have **no effect** on the underlying tree sequence data. """ __slots__ = ["id", "metadata"] id: int # noqa A003 """ The integer ID of this population. Varies from 0 to :attr:`TreeSequence.num_populations` - 1. """ metadata: Optional[Union[bytes, dict]] """ The :ref:`metadata <sec_metadata_definition>` for this population, decoded if a schema applies. """
[docs]@dataclass class Variant(util.Dataclass): """ 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, the property ``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 incorrectly output the last allele in the list. Modifying the attributes in this class will have **no effect** on the underlying tree sequence data. """ __slots__ = ["site", "alleles", "genotypes"] site: Site """ The site object for this variant. """ alleles: tuple """ 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. """ genotypes: np.ndarray """ An array of indexes into the list ``alleles``, giving the state of each sample at the current site. """ @property def has_missing_data(self): """ True if there is missing data for any of the samples at the current site. """ return self.alleles[-1] is None @property def num_alleles(self): """ The number of distinct alleles at this site. Note that this may be greater than the number of distinct values in the genotypes array. """ return len(self.alleles) - self.has_missing_data # Deprecated alias to avoid breaking existing code. @property def position(self): return self.site.position # Deprecated alias to avoid breaking existing code. @property def index(self): return self.site.id # We need a custom eq for the numpy array def __eq__(self, other): return ( isinstance(other, Variant) and self.site == other.site and self.alleles == other.alleles and np.array_equal(self.genotypes, other.genotypes) )
@dataclass class Edgeset(util.Dataclass): __slots__ = ["left", "right", "parent", "children"] left: int right: int parent: int children: np.ndarray # We need a custom eq for the numpy array def __eq__(self, other): return ( isinstance(other, Edgeset) and self.left == other.left and self.right == other.right and self.parent == other.parent and np.array_equal(self.children, other.children) )
[docs]@dataclass class Provenance(util.Dataclass): __slots__ = ["id", "timestamp", "record"] id: int # noqa A003 timestamp: str record: str
[docs]class Tree: """ A single tree in a :class:`TreeSequence`. Please see the :ref:`tutorials:sec_processing_trees` section for information on how efficiently access trees sequentially or obtain a list of individual trees in a tree sequence. The ``sample_lists`` parameter controls the features that are enabled for this tree. If ``sample_lists`` is True a more efficient algorithm is used in the :meth:`Tree.samples` method. The ``tracked_samples`` parameter can be used to efficiently count the number of samples in a given set that exist in a particular subtree using the :meth:`Tree.num_tracked_samples` method. The :class:`Tree` class is a state-machine which has a state corresponding to each of the trees in the parent tree sequence. We transition between these states by using the seek functions like :meth:`Tree.first`, :meth:`Tree.last`, :meth:`Tree.seek` and :meth:`Tree.seek_index`. There is one more state, the so-called "null" or "cleared" state. This is the state that a :class:`Tree` is in immediately after initialisation; it has an index of -1, and no edges. We can also enter the null state by calling :meth:`Tree.next` on the last tree in a sequence, calling :meth:`Tree.prev` on the first tree in a sequence or calling calling the :meth:`Tree.clear` method at any time. The high-level TreeSequence seeking and iterations methods (e.g, :meth:`TreeSequence.trees`) are built on these low-level state-machine seek operations. We recommend these higher level operations for most users. :param TreeSequence tree_sequence: The parent tree sequence. :param list tracked_samples: The list of samples to be tracked and counted using the :meth:`Tree.num_tracked_samples` method. :param bool sample_lists: If True, provide more efficient access to the samples beneath a 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) self._make_arrays()
[docs] def copy(self): """ Returns a deep copy of this tree. The returned tree will have identical state to this tree. :return: A copy of this tree. :rtype: Tree """ copy = type(self).__new__(type(self)) copy._tree_sequence = self._tree_sequence copy._ll_tree = self._ll_tree.copy() copy._make_arrays() return copy
def _make_arrays(self): # Store the low-level arrays for efficiency. There's no real overhead # in this, because the refer to the same underlying memory as the # tree object. self._parent_array = self._ll_tree.parent_array self._left_child_array = self._ll_tree.left_child_array self._right_child_array = self._ll_tree.right_child_array self._left_sib_array = self._ll_tree.left_sib_array self._right_sib_array = self._ll_tree.right_sib_array @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 units of time) joining the specified node to its parent. This is equivalent to >>> tree.time(tree.parent(u)) - tree.time(u) The branch length for a node that has no parent (e.g., a root) is defined as zero. Note that this is not related to the property `.length` which is a deprecated alias for the genomic :attr:`~Tree.span` covered by a tree. :param int u: The node of interest. :return: The branch length from u to its parent. :rtype: float """ ret = 0 parent = self.parent(u) if parent != NULL: ret = self.time(parent) - self.time(u) return ret
def get_total_branch_length(self): # Deprecated alias for total_branch_length return self.total_branch_length @property def total_branch_length(self): """ Returns the sum of all the branch lengths in this tree (in units of time). This is equivalent to >>> sum(tree.branch_length(u) for u in tree.nodes()) Note that the branch lengths for root nodes are defined as zero. As this is defined by a traversal of the tree, technically we return the sum of all branch lengths that are reachable from roots. Thus, this is the 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)
@property def parent_array(self): """ A numpy array (dtype=np.int32) encoding the parent of each node in this tree, such that ``tree.parent_array[u] == tree.parent(u)`` for all ``0 <= u < ts.num_nodes``. See the :meth:`~.parent` method for details on the semantics of tree parents and the :ref:`sec_data_model_tree_structure` section for information on the quintuply linked tree encoding. .. warning:: |tree_array_warning| """ return self._parent_array # Quintuply linked tree structure.
[docs] def left_child(self, u): """ Returns the leftmost child of the specified node. Returns :data:`tskit.NULL` if u is a leaf or is not a node in the current tree. The left-to-right ordering of children is arbitrary and should not be depended on; see the :ref:`data model <sec_data_model_tree_structure>` section for details. This is a low-level method giving access to the quintuply linked tree structure in memory; the :meth:`.children` method is a more convenient way to obtain the children of a given node. :param int u: The node of interest. :return: The leftmost child of u. :rtype: int """ return self._ll_tree.get_left_child(u)
@property def left_child_array(self): """ A numpy array (dtype=np.int32) encoding the left child of each node in this tree, such that ``tree.left_child_array[u] == tree.left_child(u)`` for all ``0 <= u < ts.num_nodes``. See the :meth:`~.left_child` method for details on the semantics of tree left_child and the :ref:`sec_data_model_tree_structure` section for information on the quintuply linked tree encoding. .. warning:: |tree_array_warning| """ return self._left_child_array
[docs] def right_child(self, u): """ Returns the rightmost child of the specified node. Returns :data:`tskit.NULL` if u is a leaf or is not a node in the current tree. The left-to-right ordering of children is arbitrary and should not be depended on; see the :ref:`data model <sec_data_model_tree_structure>` section for details. This is a low-level method giving access to the quintuply linked tree structure in memory; the :meth:`.children` method is a more convenient way to obtain the children of a given node. :param int u: The node of interest. :return: The rightmost child of u. :rtype: int """ return self._ll_tree.get_right_child(u)
@property def right_child_array(self): """ A numpy array (dtype=np.int32) encoding the right child of each node in this tree, such that ``tree.right_child_array[u] == tree.right_child(u)`` for all ``0 <= u < ts.num_nodes``. See the :meth:`~.right_child` method for details on the semantics of tree right_child and the :ref:`sec_data_model_tree_structure` section for information on the quintuply linked tree encoding. .. warning:: |tree_array_warning| """ return self._right_child_array
[docs] def left_sib(self, u): """ Returns the sibling node to the left of u, or :data:`tskit.NULL` if u does not have a left sibling. The left-to-right ordering of children is arbitrary and should not be depended on; see the :ref:`data model <sec_data_model_tree_structure>` section for details. :param int u: The node of interest. :return: The sibling node to the left of u. :rtype: int """ return self._ll_tree.get_left_sib(u)
@property def left_sib_array(self): """ A numpy array (dtype=np.int32) encoding the left sib of each node in this tree, such that ``tree.left_sib_array[u] == tree.left_sib(u)`` for all ``0 <= u < ts.num_nodes``. See the :meth:`~.left_sib` method for details on the semantics of tree left_sib and the :ref:`sec_data_model_tree_structure` section for information on the quintuply linked tree encoding. .. warning:: |tree_array_warning| """ return self._left_sib_array
[docs] def right_sib(self, u): """ Returns the sibling node to the right of u, or :data:`tskit.NULL` if u does not have a right sibling. The left-to-right ordering of children is arbitrary and should not be depended on; see the :ref:`data model <sec_data_model_tree_structure>` section for details. :param int u: The node of interest. :return: The sibling node to the right of u. :rtype: int """ return self._ll_tree.get_right_sib(u)
@property def right_sib_array(self): """ A numpy array (dtype=np.int32) encoding the right sib of each node in this tree, such that ``tree.right_sib_array[u] == tree.right_sib(u)`` for all ``0 <= u < ts.num_nodes``. See the :meth:`~.right_sib` method for details on the semantics of tree right_sib and the :ref:`sec_data_model_tree_structure` section for information on the quintuply linked tree encoding. .. warning:: |tree_array_warning| """ return self._right_sib_array # Sample list. def left_sample(self, u): return self._ll_tree.get_left_sample(u) def right_sample(self, u): return self._ll_tree.get_right_sample(u) def next_sample(self, u): return self._ll_tree.get_next_sample(u) # 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. 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. Only requires O(number of roots) time. :rtype: int """ return self._ll_tree.get_num_roots() @property def has_single_root(self): """ ``True`` if this tree has a single root, ``False`` otherwise. Equivalent to tree.num_roots == 1. This is a O(1) operation. :rtype: bool """ root = self.left_root if root != NULL and self.right_sib(root) == NULL: return True return False @property def has_multiple_roots(self): """ ``True`` if this tree has more than one root, ``False`` otherwise. Equivalent to tree.num_roots > 1. This is a O(1) operation. :rtype: bool """ root = self.left_root if root != NULL and self.right_sib(root) != NULL: return True return False @property def roots(self): """ The list of roots in this tree. A root is defined as a unique endpoint of the paths starting at samples. 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. Only 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. """ if self.has_multiple_roots: raise ValueError("More than one root exists. Use tree.roots instead") return self.left_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 """ return self.interval.span # 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()
[docs] def draw_text( self, orientation=None, *, node_labels=None, max_time=None, use_ascii=False, order=None, ): """ Create a text representation of a tree. :param str orientation: one of ``"top"``, ``"left"``, ``"bottom"``, or ``"right"``, specifying the margin on which the root is placed. Specifying ``"left"`` or ``"right"`` will lead to time being shown on the x axis (i.e. a "horizontal" tree. If ``None`` (default) use the standard coalescent arrangement of a vertical tree with recent nodes at the bottom of the plot and older nodes above. :param dict node_labels: If specified, show custom labels for the nodes that are present in the map. Any nodes not specified in the map will not have a node label. :param str max_time: If equal to ``"tree"`` (the default), the maximum time is set to be that of the oldest root in the tree. If equal to ``"ts"`` the maximum time is set to be the time of the oldest root in the tree sequence; this is useful when drawing trees from the same tree sequence as it ensures that node heights are consistent. :param bool use_ascii: If ``False`` (default) then use unicode `box drawing characters \ <https://en.wikipedia.org/wiki/Box-drawing_character>`_ to render the tree. If ``True``, use plain ascii characters, which look cruder but are less susceptible to misalignment or font substitution. Alternatively, if you are having alignment problems with Unicode, you can try out the solution documented `here \ <https://github.com/tskit-dev/tskit/issues/189#issuecomment-499114811>`_. :param str order: The left-to-right ordering of child nodes in the drawn tree. This can be either: ``"minlex"``, which minimises the differences between adjacent trees (see also the ``"minlex_postorder"`` traversal order for the :meth:`.nodes` method); or ``"tree"`` which draws trees in the left-to-right order defined by the :ref:`quintuply linked tree structure <sec_data_model_tree_structure>`. If not specified or None, this defaults to ``"minlex"``. :return: A text representation of a tree. :rtype: str """ orientation = drawing.check_orientation(orientation) if orientation in (drawing.LEFT, drawing.RIGHT): text_tree = drawing.HorizontalTextTree( self, orientation=orientation, node_labels=node_labels, max_time=max_time, use_ascii=use_ascii, order=order, ) else: text_tree = drawing.VerticalTextTree( self, orientation=orientation, node_labels=node_labels, max_time=max_time, use_ascii=use_ascii, order=order, ) return str(text_tree)
[docs] def draw_svg( self, path=None, *, size=None, time_scale=None, tree_height_scale=None, max_time=None, max_tree_height=None, node_labels=None, mutation_labels=None, root_svg_attributes=None, style=None, order=None, force_root_branch=None, symbol_size=None, x_axis=None, x_label=None, y_axis=None, y_label=None, y_ticks=None, y_gridlines=None, all_edge_mutations=None, **kwargs, ): """ Return an SVG representation of a single tree. By default, numeric labels are drawn beside nodes and mutations: these can be altered using the ``node_labels`` and ``mutation_labels`` parameters. See the :ref:`visualization tutorial<tutorials:sec_tskit_viz>` for more details. :param str path: The path to the file to write the output. If None, do not write to file. :param 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 time_scale: Control how height values for nodes are computed. If this is equal to ``"time"`` (the default), node heights are proportional to their time values. If this is equal to ``"log_time"``, node heights are proportional to their log(time) values. If it is equal to ``"rank"``, node heights are spaced equally according to their ranked times. :param str tree_height_scale: Deprecated alias for time_scale. (Deprecated in 0.3.6) :param str,float max_time: The maximum time value in the current scaling system (see ``time_scale``). Can be either a string or a numeric value. If equal to ``"tree"`` (the default), the maximum time is set to be that of the oldest root in the tree. If equal to ``"ts"`` the maximum time is set to be the time of the oldest root in the tree sequence; this is useful when drawing trees from the same tree sequence as it ensures that node heights are consistent. If a numeric value, this is used as the maximum time by which to scale other nodes. :param str,float max_tree_height: Deprecated alias for max_time. (Deprecated in 0.3.6) :param node_labels: If specified, show custom labels for the nodes (specified by ID) that are present in this map; any nodes not present will not have a label. :type node_labels: dict(int, str) :param mutation_labels: If specified, show custom labels for the mutations (specified by ID) that are present in the map; any mutations not present will not have a label. :type mutation_labels: dict(int, str) :param dict root_svg_attributes: Additional attributes, such as an id, that will be embedded in the root ``<svg>`` tag of the generated drawing. :param str style: A `css style string <https://www.w3.org/TR/CSS22/syndata.html>`_ that will be included in the ``<style>`` tag of the generated svg. :param str order: The left-to-right ordering of child nodes in the drawn tree. This can be either: ``"minlex"``, which minimises the differences between adjacent trees (see also the ``"minlex_postorder"`` traversal order for the :meth:`.nodes` method); or ``"tree"`` which draws trees in the left-to-right order defined by the :ref:`quintuply linked tree structure <sec_data_model_tree_structure>`. If not specified or None, this defaults to ``"minlex"``. :param bool force_root_branch: If ``True`` always plot a branch (edge) above the root(s) in the tree. If ``None`` (default) then only plot such root branches if there is a mutation above a root of the tree. :param float symbol_size: Change the default size of the node and mutation plotting symbols. If ``None`` (default) use a standard size. :param bool x_axis: Should the plot have an X axis line, showing the start and end position of this tree along the genome. If ``None`` (default) do not plot an X axis. :param str x_label: Place a label under the plot. If ``None`` (default) and there is an X axis, create and place an appropriate label. :param bool y_axis: Should the plot have an Y axis line, showing time (or ranked node time if ``time_scale="rank"``). If ``None`` (default) do not plot a Y axis. :param str y_label: Place a label to the left of the plot. If ``None`` (default) and there is a Y axis, create and place an appropriate label. :param list y_ticks: A list of Y values at which to plot tickmarks (``[]`` gives no tickmarks). If ``None``, plot one tickmark for each unique node value. :param bool y_gridlines: Whether to plot horizontal lines behind the tree at each y tickmark. :param bool all_edge_mutations: The edge on which a mutation occurs may span multiple trees. If ``False`` or ``None`` (default) mutations are only drawn on an edge if their site position exists within the genomic interval covered by this tree. If ``True``, all mutations on each edge of the tree are drawn, even if the their genomic position is to the left or right of the tree itself. Note that this means that independent drawings of different trees from the same tree sequence may share some plotted mutations. :return: An SVG representation of a tree. :rtype: str """ draw = drawing.SvgTree( self, size, time_scale=time_scale, tree_height_scale=tree_height_scale, max_time=max_time, 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, symbol_size=symbol_size, x_axis=x_axis, x_label=x_label, y_axis=y_axis, y_label=y_label, y_ticks=y_ticks, y_gridlines=y_gridlines, all_edge_mutations=all_edge_mutations, **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, time_scale=None, tree_height_scale=None, max_time=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 time_scale: Control how height values for nodes are computed. If this is equal to ``"time"``, node heights are proportional to their time values. If this is equal to ``"log_time"``, node heights are proportional to their log(time) values. If it is equal to ``"rank"``, node heights are spaced equally according to their ranked times. For SVG output the default is 'time'-scale whereas for text output the default is 'rank'-scale. Time scaling is not currently supported for text output. :param str tree_height_scale: Deprecated alias for time_scale. (Deprecated in 0.3.6) :param str,float max_time: The maximum time value in the current scaling system (see ``time_scale``). Can be either a string or a numeric value. If equal to ``"tree"``, the maximum time is set to be that of the oldest root in the tree. If equal to ``"ts"`` the maximum time is set to be the time of the oldest root in the tree sequence; this is useful when drawing trees from the same tree sequence as it ensures that node heights are consistent. If a numeric value, this is used as the maximum time by which to scale other nodes. This parameter is not currently supported for text output. :param str max_tree_height: Deprecated alias for max_time. (Deprecated in 0.3.6) :param str order: The left-to-right ordering of child nodes in the drawn tree. This can be either: ``"minlex"``, which minimises the differences between adjacent trees (see also the ``"minlex_postorder"`` traversal order for the :meth:`.nodes` method); or ``"tree"`` which draws trees in the left-to-right order defined by the :ref:`quintuply linked tree structure <sec_data_model_tree_structure>`. If not specified or None, this defaults to ``"minlex"``. :return: A representation of this tree in the requested format. :rtype: str """ output = drawing.draw_tree( self, format=format, width=width, height=height, node_labels=node_labels, node_colours=node_colours, mutation_labels=mutation_labels, mutation_colours=mutation_colours, edge_colours=edge_colours, time_scale=time_scale, tree_height_scale=tree_height_scale, max_time=max_time, 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(): yield from site.mutations
def get_leaves(self, u): # Deprecated alias for samples. See the discussion in the get_num_leaves # method for why this method is here and why it is semantically incorrect. # The 'leaves' iterator below correctly returns the leaves below a given # node. return self.samples(u)
[docs] def leaves(self, u=None): """ Returns an iterator over all the leaves in this tree that 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 the numerical IDs of all the sample nodes in this tree that are underneath node ``u``. If ``u`` is a sample, it is included in the returned iterator. If u is not specified, return all sample node IDs in the tree. If the :meth:`TreeSequence.trees` method is called with ``sample_lists=True``, this method uses an efficient algorithm to find the sample nodes. If not, a simple traversal based method is used. .. note:: The iterator is *not* guaranteed to return the sample node IDs in numerical or any other particular order. :param int u: The node of interest. :return: An iterator over all sample node IDs in the subtree rooted at u. :rtype: collections.abc.Iterable """ roots = [u] if u is None: roots = self.roots for root in roots: yield from self._sample_generator(root)
[docs] def num_children(self, u): """ Returns the number of children of the specified node (i.e., ``len(tree.children(u))``) :param int u: The node of interest. :return: The number of immediate children of the node u in this tree. :rtype: int """ return self._ll_tree.get_num_children(u)
def get_num_leaves(self, u): # Deprecated alias for num_samples. The method name is inaccurate # as this will count the number of tracked _samples_. This is only provided to # avoid breaking existing code and should not be used in new code. We could # change this method to be semantically correct and just count the # number of leaves we hit in the leaves() iterator. However, this would # have the undesirable effect of making code that depends on the constant # time performance of get_num_leaves many times slower. So, the best option # is to leave this method as is, and to slowly deprecate it out. Once this # has been removed, we might add in a ``num_leaves`` method that returns the # length of the leaves() iterator as one would expect. return self.num_samples(u) def get_num_samples(self, u=None): # Deprecated alias for num_samples. return self.num_samples(u)
[docs] def num_samples(self, u=None): """ Returns the number of 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 reachable from the root(s) in this tree. See :meth:`Tree.roots` for which nodes are considered roots. If the root parameter is provided, only the subtree rooted at this single root node will be iterated over. If this parameter is None, iterate over the node IDs in the subtrees rooted at each root node in turn. If the order parameter is provided, iterate over the nodes in each subtree in the specified 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)
def _node_edges(self): """ Return a numpy array mapping the node IDs in this tree to the ID of the edge above them. This is in lieu of a tree.edge(u) function, currently implemented using the non-optimised Python TreeSequence._tree_node_edges() generator """ for index, node_edge_map in enumerate( # pragma: no branch self.tree_sequence._tree_node_edges() ): if index == self.index: return node_edge_map # 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 separate 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 not self.has_single_root: raise ValueError( "Cannot get newick unless a tree has a single root. 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 units of time). """ dod = {} for parent in self.nodes(): dod[parent] = {} for child in self.children(parent): dod[parent][child] = {"branch_length": self.branch_length(child)} return dod
@property def parent_dict(self): return self.get_parent_dict() def get_parent_dict(self): pi = { u: self.parent(u) for u in range(self.num_nodes) if self.parent(u) != NULL } return pi def __str__(self): tree_rows = [ ["Index", str(self.index)], [ "Interval", f"{self.interval.left:.8g}-{self.interval.right:.8g} ({self.span:.8g})", ], ["Roots", str(self.num_roots)], ["Nodes", str(self.num_nodes)], ["Sites", str(self.num_sites)], ["Mutations", str(self.num_mutations)], ["Total Branch Length", f"{self.total_branch_length:.8g}"], ] return util.unicode_table(tree_rows, title="Tree") def _repr_html_(self): """ Called by jupyter notebooks to render tables """ return util.tree_html(self)
[docs] def map_mutations(self, genotypes, alleles, ancestral_state=None): """ Given observations for the samples in this tree described by the specified set of genotypes and alleles, return a parsimonious set of state transitions explaining these observations. The genotypes array is interpreted as indexes into the alleles list in the same manner as described in the :meth:`TreeSequence.variants` method. Thus, if sample ``j`` carries the allele at index ``k``, then we have ``genotypes[j] = k``. Missing observations can be specified for a sample using the value ``tskit.MISSING_DATA`` (-1), in which case the state at this sample does not influence the ancestral state or the position of mutations returned. At least one non-missing observation must be provided. A maximum of 64 alleles are supported. The current implementation uses the Hartigan parsimony algorithm to determine the minimum number of state transitions required to explain the data. In this model, transitions between any of the non-missing states is equally likely. The returned values correspond directly to the data model for describing variation at sites using mutations. See the :ref:`sec_site_table_definition` and :ref:`sec_mutation_table_definition` definitions for details and background. The state reconstruction is returned as two-tuple, ``(ancestral_state, mutations)``, where ``ancestral_state`` is the allele assigned to the tree root(s) and ``mutations`` is a list of :class:`Mutation` objects, ordered as :ref:`required in a mutation table<sec_mutation_requirements>`. For each mutation, ``derived_state`` is the new state after this mutation and ``node`` is the tree node immediately beneath the mutation (if there are unary nodes between two branch points, hence multiple nodes above which the mutation could be parsimoniously placed, the oldest node is used). The ``parent`` property contains the index in the returned list of the previous mutation on the path to root, or ``tskit.NULL`` if there are no previous mutations (see the :ref:`sec_mutation_table_definition` for more information on the concept of mutation parents). All other attributes of the :class:`Mutation` object are undefined and should not be used. .. note:: Sample states observed as missing in the input ``genotypes`` need not correspond to samples whose nodes are actually "missing" (i.e., :ref:`isolated<sec_data_model_missing_data>`) in the tree. In this case, mapping the mutations returned by this method onto the tree will result in these missing observations being imputed to the most parsimonious state. See the :ref:`tutorials:sec_analysing_trees_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. :param ancestral_state: A fixed ancestral state, specified either as a non-negative integer less than the number of alleles, or a string which must be one of the ``alleles`` provided above. If ``None`` (default) then an ancestral state is chosen arbitrarily from among those that provide the most parsimonious placement of mutations. Note that if the ancestral state is specified, the placement of mutations may not be as parsimonious as that which could be achieved by leaving the ancestral state unspecified; additionally it may lead to mutations being placed above the root node(s) of the tree (for example if all the samples have a genotype of 1 but the ancestral state is fixed to be 0). :type ancestral_state: Union[int, str] :return: The inferred ancestral state and list of mutations on this tree that encode the specified observations. :rtype: (str, list(tskit.Mutation)) """ genotypes = util.safe_np_int_cast(genotypes, np.int8) max_alleles = np.max(genotypes) if ancestral_state is not None: if isinstance(ancestral_state, str): # Will raise a ValueError if not in the list ancestral_state = alleles.index(ancestral_state) if ancestral_state < 0 or ancestral_state >= len(alleles): raise ValueError("ancestral_state not between 0 and (num_alleles-1)") max_alleles = max(ancestral_state, max_alleles) if max_alleles >= 64: raise ValueError("A maximum of 64 states is supported") ancestral_state, transitions = self._ll_tree.map_mutations( genotypes, ancestral_state ) # Translate back into string alleles ancestral_state = alleles[ancestral_state] mutations = [ Mutation( node=node, derived_state=alleles[derived_state], parent=parent, metadata=self.tree_sequence.table_metadata_schemas.mutation.empty_value, ) for node, parent, derived_state in transitions ] return ancestral_state, mutations
[docs] def kc_distance(self, other, lambda_=0.0): """ Returns the Kendall-Colijn distance between the specified pair of trees. The ``lambda_`` parameter determines the relative weight of topology vs branch lengths in calculating the distance. If ``lambda_`` is 0 (the default) we only consider topology, and if it is 1 we only consider branch lengths. See `Kendall & Colijn (2016) <https://academic.oup.com/mbe/article/33/10/2735/2925548>`_ for details. The trees we are comparing to must have identical lists of sample nodes (i.e., the same IDs in the same order). The metric operates on samples, not leaves, so internal samples are treated identically to sample tips. Subtrees with no samples do not contribute to the metric. :param Tree other: The other tree to compare to. :param float lambda_: The KC metric lambda parameter determining the relative weight of topology and branch length. :return: The computed KC distance between this tree and other. :rtype: float """ return self._ll_tree.get_kc_distance(other._ll_tree, lambda_)
[docs] def split_polytomies( self, *, epsilon=None, method=None, record_provenance=True, random_seed=None, **kwargs, ): """ Return a new :class:`.Tree` where extra nodes and edges have been inserted so that any any node ``u`` with greater than 2 children --- a multifurcation or "polytomy" --- is resolved into successive bifurcations. New nodes are inserted at times fractionally less than than the time of node ``u``. Times are allocated to different levels of the tree, such that any newly inserted sibling nodes will have the same time. By default, the times of the newly generated children of a particular node are the minimum representable distance in floating point arithmetic from their parents (using the `nextafter <https://numpy.org/doc/stable/reference/generated/numpy.nextafter.html>`_ function). Thus, the generated branches have the shortest possible nonzero length. A fixed branch length between inserted nodes and their parents can also be specified by using the ``epsilon`` parameter. .. note:: A tree sequence :ref:`requires<sec_valid_tree_sequence_requirements>` that parents be older than children and that mutations are younger than the parent of the edge on which they lie. If a fixed ``epsilon`` is specifed and is not small enough compared to the distance between a polytomy and its oldest child (or oldest child mutation) these requirements may not be met. In this case an error will be raised. If the ``method`` is ``"random"`` (currently the only option, and the default when no method is specified), then for a node with :math:`n` children, the :math:`(2n - 3)! / (2^(n - 2) (n - 2!))` possible binary trees with equal probability. The returned :class`.Tree` will have the same genomic span as this tree, and node IDs will be conserved (that is, node ``u`` in this tree will be the same node in the returned tree). The returned tree is derived from a tree sequence that contains only one non-degenerate tree, that is, where edges cover only the interval spanned by this tree. :param epsilon: If specified, the fixed branch length between inserted nodes and their parents. If None (the default), the minimal possible nonzero branch length is generated for each node. :param str method: The method used to break polytomies. Currently only "random" is supported, which can also be specified by ``method=None`` (Default: ``None``). :param bool record_provenance: If True, add details of this operation to the provenance information of the returned tree sequence. (Default: True). :param int random_seed: The random seed. If this is None, a random seed will be automatically generated. Valid random seeds must be between 1 and :math:`2^32 − 1`. :param \\**kwargs: Further arguments used as parameters when constructing the returned :class:`Tree`. For example ``tree.split_polytomies(sample_lists=True)`` will return a :class:`Tree` created with ``sample_lists=True``. :return: A new tree with polytomies split into random bifurcations. :rtype: tskit.Tree """ return combinatorics.split_polytomies( self, epsilon=epsilon, method=method, record_provenance=record_provenance, random_seed=random_seed, **kwargs, )
[docs] @staticmethod def generate_star( num_leaves, *, span=1, branch_length=1, record_provenance=True, **kwargs ): """ Generate a :class:`Tree` whose leaf nodes all have the same parent (i.e., a "star" tree). The leaf nodes are all at time 0 and are marked as sample nodes. The tree produced by this method is identical to ``tskit.Tree.unrank(n, (0, 0))``, but generated more efficiently for large ``n``. :param int num_leaves: The number of leaf nodes in the returned tree (must be 2 or greater). :param float span: The span of the tree, and therefore the :attr:`~TreeSequence.sequence_length` of the :attr:`.tree_sequence` property of the returned :class:`Tree`. :param float branch_length: The length of every branch in the tree (equivalent to the time of the root node). :param bool record_provenance: If True, add details of this operation to the provenance information of the returned tree sequence. (Default: True). :param \\**kwargs: Further arguments used as parameters when constructing the returned :class:`Tree`. For example ``tskit.Tree.generate_star(sample_lists=True)`` will return a :class:`Tree` created with ``sample_lists=True``. :return: A star-shaped tree. Its corresponding :class:`TreeSequence` is available via the :attr:`.tree_sequence` attribute. :rtype: Tree """ return combinatorics.generate_star( num_leaves, span=span, branch_length=branch_length, record_provenance=record_provenance, **kwargs, )
[docs] @staticmethod def generate_balanced( num_leaves, *, arity=2, span=1, branch_length=1, record_provenance=True, **kwargs, ): """ Generate a :class:`Tree` with the specified number of leaves that is maximally balanced. By default, the tree returned is binary, such that for each node that subtends :math:`n` leaves, the left child will subtend :math:`\\lfloor{n / 2}\\rfloor` leaves and the right child the remainder. Balanced trees with higher arity can also generated using the ``arity`` parameter, where the leaves subtending a node are distributed among its children analogously. In the returned tree, the leaf nodes are all at time 0, marked as samples, and labelled 0 to n from left-to-right. Internal node IDs are assigned sequentially from n in a postorder traversal, and the time of an internal node is the maximum time of its children plus the specified ``branch_length``. :param int num_leaves: The number of leaf nodes in the returned tree (must be be 2 or greater). :param int arity: The maximum number of children a node can have in the returned tree. :param float span: The span of the tree, and therefore the :attr:`~TreeSequence.sequence_length` of the :attr:`.tree_sequence` property of the returned :class:`Tree`. :param float branch_length: The minimum length of a branch in the tree (see above for details on how internal node times are assigned). :param bool record_provenance: If True, add details of this operation to the provenance information of the returned tree sequence. (Default: True). :param \\**kwargs: Further arguments used as parameters when constructing the returned :class:`Tree`. For example ``tskit.Tree.generate_balanced(sample_lists=True)`` will return a :class:`Tree` created with ``sample_lists=True``. :return: A balanced tree. Its corresponding :class:`TreeSequence` is available via the :attr:`.tree_sequence` attribute. :rtype: Tree """ return combinatorics.generate_balanced( num_leaves, arity=arity, span=span, branch_length=branch_length, record_provenance=record_provenance, **kwargs, )
[docs] @staticmethod def generate_comb( num_leaves, *, span=1, branch_length=1, record_provenance=True, **kwargs ): """ Generate a :class:`Tree` in which all internal nodes have two children and the left child is a leaf. This is a "comb", "ladder" or "pectinate" phylogeny, and also known as a `caterpillar tree <https://en.wikipedia.org/wiki/Caterpillar_tree>`_. The leaf nodes are all at time 0, marked as samples, and labelled 0 to n from left-to-right. Internal node IDs are assigned sequentially from n as we ascend the tree, and the time of an internal node is the maximum time of its children plus the specified ``branch_length``. :param int num_leaves: The number of leaf nodes in the returned tree (must be 2 or greater). :param float span: The span of the tree, and therefore the :attr:`~TreeSequence.sequence_length` of the :attr:`.tree_sequence` property of the returned :class:`Tree`. :param float branch_length: The branch length between each internal node; the root node is therefore placed at time ``branch_length * (num_leaves - 1)``. :param bool record_provenance: If True, add details of this operation to the provenance information of the returned tree sequence. (Default: True). :param \\**kwargs: Further arguments used as parameters when constructing the returned :class:`Tree`. For example ``tskit.Tree.generate_comb(sample_lists=True)`` will return a :class:`Tree` created with ``sample_lists=True``. :return: A comb-shaped bifurcating tree. Its corresponding :class:`TreeSequence` is available via the :attr:`.tree_sequence` attribute. :rtype: Tree """ return combinatorics.generate_comb( num_leaves, span=span, branch_length=branch_length, record_provenance=record_provenance, **kwargs, )
[docs] @staticmethod def generate_random_binary( num_leaves, *, span=1, branch_length=1, random_seed=None, record_provenance=True, **kwargs, ): """ Generate a random binary :class:`Tree` with :math:`n` = ``num_leaves`` leaves with an equal probability of returning any topology and leaf label permutation among the :math:`(2n - 3)! / (2^{n - 2} (n - 2)!)` leaf-labelled binary trees. The leaf nodes are marked as samples, labelled 0 to n, and placed at time 0. Internal node IDs are assigned sequentially from n as we ascend the tree, and the time of an internal node is the maximum time of its children plus the specified ``branch_length``. .. note:: The returned tree has not been created under any explicit model of evolution. In order to simulate such trees, additional software such as `msprime <https://github.com/tskit-dev/msprime>`` is required. :param int num_leaves: The number of leaf nodes in the returned tree (must be 2 or greater). :param float span: The span of the tree, and therefore the :attr:`~TreeSequence.sequence_length` of the :attr:`.tree_sequence` property of the returned :class:`Tree`. :param float branch_length: The minimum time between parent and child nodes. :param int random_seed: The random seed. If this is None, a random seed will be automatically generated. Valid random seeds must be between 1 and :math:`2^32 − 1`. :param bool record_provenance: If True, add details of this operation to the provenance information of the returned tree sequence. (Default: True). :param \\**kwargs: Further arguments used as parameters when constructing the returned :class:`Tree`. For example ``tskit.Tree.generate_comb(sample_lists=True)`` will return a :class:`Tree` created with ``sample_lists=True``. :return: A random binary tree. Its corresponding :class:`TreeSequence` is available via the :attr:`.tree_sequence` attribute. :rtype: Tree """ return combinatorics.generate_random_binary( num_leaves, span=span, branch_length=branch_length, random_seed=random_seed, record_provenance=record_provenance, **kwargs, )
[docs]def load(file): """ 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 parents_index = None metadata_index = None try: location_index = header.index("location") except ValueError: pass try: parents_index = header.index("parents") except ValueError: pass try: metadata_index = header.index("metadata") except ValueError: pass for line in source: tokens = line.split(sep) if len(tokens) >= 1: flags = int(tokens[flags_index]) location = () if location_index is not None: location_string = tokens[location_index] if len(location_string) > 0: location = tuple(map(float, location_string.split(","))) parents = () if parents_index is not None: parents_string = tokens[parents_index] if len(parents_string) > 0: parents = tuple(map(int, parents_string.split(","))) metadata = b"" if metadata_index is not None and metadata_index < len(tokens): metadata = tokens[metadata_index].encode(encoding) if base64_metadata: metadata = base64.b64decode(metadata) table.add_row( flags=flags, location=location, parents=parents, metadata=metadata ) return table
[docs]def parse_nodes(source, strict=True, encoding="utf8", base64_metadata=True, table=None): """ Parse the specified file-like object containing a whitespace delimited description of a node table and returns the corresponding :class:`NodeTable` instance. See the :ref:`node text format <sec_node_text_format>` section for the details of the required format and the :ref:`node table definition <sec_node_table_definition>` section for the required properties of the contents. See :func:`tskit.load_text` for a detailed explanation of the ``strict`` parameter. :param io.TextIOBase source: The file-like object containing the text. :param bool strict: If True, require strict tab delimiting (default). If False, a relaxed whitespace splitting algorithm is used. :param str encoding: Encoding used for text representation. :param bool base64_metadata: If True, metadata is encoded using Base64 encoding; otherwise, as plain text. :param NodeTable table: If specified write into this table. If not, create a new :class:`NodeTable` instance. """ sep = None if strict: sep = "\t" if table is None: table = tables.NodeTable() # Read the header and find the indexes of the required fields. header = source.readline().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. """ @dataclass(frozen=True) class _TableMetadataSchemas: """ Convenience class for returning schemas """ node: metadata_module.MetadataSchema = None edge: metadata_module.MetadataSchema = None site: metadata_module.MetadataSchema = None mutation: metadata_module.MetadataSchema = None migration: metadata_module.MetadataSchema = None individual: metadata_module.MetadataSchema = None population: metadata_module.MetadataSchema = None 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.__init__(tc.tree_sequence().ll_tree_sequence) def __eq__(self, other): return self.tables == other.tables
[docs] def equals( self, other, *, ignore_metadata=False, ignore_ts_metadata=False, ignore_provenance=False, ignore_timestamps=False, ): """ Returns True if `self` and `other` are equal. Uses the underlying table equality, see :meth:`TableCollection.equals` for details and options. """ return self.tables.equals( other.tables, ignore_metadata=ignore_metadata, ignore_ts_metadata=ignore_ts_metadata, ignore_provenance=ignore_provenance, ignore_timestamps=ignore_timestamps, )
@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, zlib_compression=False): """ Writes the tree sequence to the specified path or file object. :param str file_or_path: The file object or path to write the TreeSequence to. :param bool zlib_compression: This parameter is deprecated and ignored. """ if zlib_compression: # Note: the msprime CLI before version 1.0 uses this option, so we need # to keep it indefinitely. warnings.warn( "The zlib_compression option is no longer supported and is ignored", RuntimeWarning, ) 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 property currently returns a copy of the tables underlying a tree sequence but it may return a read-only **view** in the future. Thus, if the tables will subsequently be updated, please use the :meth:`.dump_tables` method instead as this will always return a new copy of the TableCollection. :return: A :class:`TableCollection` containing all a copy of the tables underlying this tree sequence. :rtype: TableCollection """ return self.dump_tables() @property def nbytes(self): """ Returns the total number of bytes required to store the data in this tree sequence. Note that this may not be equal to the actual memory footprint. """ return self.tables.nbytes
[docs] def dump_tables(self): """ 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 __str__(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 the time of the oldest root in any of the trees in this tree sequence. This is usually equal to ``np.max(ts.tables.nodes.time)`` but may not be since there can be non-sample nodes that are not present in any tree. Note that isolated samples are also defined as roots (so there can be a max_root_time even in a tree sequence with no edges). :return: The maximum time of a root in this tree sequence. :rtype: float :raises ValueError: If there are no samples in the tree, and hence no roots (as roots are defined by the ends of the upward paths from the set of samples). """ if self.num_samples == 0: raise ValueError( "max_root_time is not defined in a tree sequence with 0 samples" ) ret = max(self.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-decreasing order of parent time ago; (c) within the edges for a given parent, edges are sorted first by child ID and then by left coordinate. :return: An iterable sequence of all edges. :rtype: Sequence(:class:`.Edge`) """ return SimpleContainerSequence(self.edge, self.num_edges)
def edgesets(self): # TODO the order that these records are returned in is not well specified. # Hopefully this does not matter, and we can just state that the ordering # should not be depended on. children = collections.defaultdict(set) active_edgesets = {} for (left, right), edges_out, edges_in in self.edge_diffs(): # Complete and return any edgesets that are affected by this tree # transition parents = iter(edge.parent for edge in itertools.chain(edges_out, edges_in)) for parent in parents: if parent in active_edgesets: edgeset = active_edgesets.pop(parent) edgeset.right = left edgeset.children = sorted(children[parent]) yield edgeset for edge in edges_out: children[edge.parent].remove(edge.child) for edge in edges_in: children[edge.parent].add(edge.child) # Update the active edgesets for edge in itertools.chain(edges_out, edges_in): if ( len(children[edge.parent]) > 0 and edge.parent not in active_edgesets ): active_edgesets[edge.parent] = Edgeset(left, right, edge.parent, []) for parent in active_edgesets.keys(): edgeset = active_edgesets[parent] edgeset.right = self.sequence_length edgeset.children = sorted(children[edgeset.parent]) yield edgeset
[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. Each iteration yields a named tuple consisting of 3 values, ``(interval, edges_out, edges_in)``. The first value, ``interval``, is the genomic interval ``(left, right)`` covered by the incoming tree (see :attr:`Tree.interval`). The second, ``edges_out`` is a list of the edges that were just-removed to create the tree covering the interval (hence ``edges_out`` will always be empty for the first tree). The last value, ``edges_in``, is a list of edges that were just inserted to construct the tree covering the current interval. The edges returned within each ``edges_in`` list are ordered by ascending time of the parent node, then ascending parent id, then ascending child id. The edges within each ``edges_out`` list are the reverse order (e.g. descending parent time, parent id, then child_id). This means that within each list, edges with the same parent appear consecutively. :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. This is a named tuple, so the 3 values can be accessed by position (e.g. ``returned_tuple[0]``) or name (e.g. ``returned_tuple.interval``). :rtype: :class:`collections.abc.Iterable` """ 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=metadata_decoder) for e in edge_tuples_out ] edges_in = [ Edge(*e, metadata_decoder=metadata_decoder) for e in edge_tuples_in ] yield EdgeDiff(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(): yield from site.mutations
[docs] def populations(self): """ Returns an iterable sequence of all the :ref:`populations <sec_population_table_definition>` in this tree sequence. :return: An iterable sequence of all populations. :rtype: Sequence(:class:`.Population`) """ return SimpleContainerSequence(self.population, self.num_populations)
[docs] def provenances(self): """ Returns an iterable sequence of all the :ref:`provenances <sec_provenance_table_definition>` in this tree sequence. :return: An iterable sequence of all provenances. :rtype: Sequence(:class:`.Provenance`) """ return SimpleContainerSequence(self.provenance, self.num_provenances)
[docs] def breakpoints(self, as_array=False): """ Returns the breakpoints along the chromosome, including the two extreme points 0 and L. This is equivalent to >>> iter([0] + [t.interval.right for t in self.trees()]) By default we return an iterator over the breakpoints as Python float objects; if ``as_array`` is True we return them as a numpy array. Note that the ``as_array`` form will be more efficient and convenient in most cases; the default iterator behaviour is mainly kept to ensure compatibility with existing code. :param bool as_array: If True, return the breakpoints as a numpy array. :return: The breakpoints defined by the tree intervals along the sequence. :rtype: collections.abc.Iterable or numpy.ndarray """ breakpoints = self.ll_tree_sequence.get_breakpoints() if not as_array: # Convert to Python floats for backward compatibility. breakpoints = map(float, breakpoints) return breakpoints
[docs] def at(self, position, **kwargs): """ Returns the tree covering the specified genomic location. The returned tree will have ``tree.interval.left`` <= ``position`` < ``tree.interval.right``. See also :meth:`Tree.seek`. :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 given node using the :meth:`Tree.samples` method. :param int root_threshold: The minimum number of samples that a node must be ancestral to for it to be in the list of roots. By default this is 1, so that isolated samples (representing missing data) are roots. To efficiently restrict the roots of the tree to those subtending meaningful topology, set this to 2. This value is only relevant when trees have multiple roots. :param bool sample_counts: Deprecated since 0.2.4. :return: An iterator over the Trees in this tree sequence. :rtype: collections.abc.Iterable, :class:`Tree` """ # tracked_leaves, leaf_counts and leaf_lists are deprecated aliases # for tracked_samples, sample_counts and sample_lists respectively. # These are left over from an older version of the API when leaves # and samples were synonymous. if tracked_leaves is not None: tracked_samples = tracked_leaves if leaf_counts is not None: sample_counts = leaf_counts if leaf_lists is not None: sample_lists = leaf_lists tree = Tree( self, tracked_samples=tracked_samples, sample_lists=sample_lists, root_threshold=root_threshold, sample_counts=sample_counts, ) return TreeIterator(tree)
[docs] def coiterate(self, other, **kwargs): """ Returns an iterator over the pairs of trees for each distinct interval in the specified pair of tree sequences. :param TreeSequence other: The other tree sequence from which to take trees. The sequence length must be the same as the current tree sequence. :param \\**kwargs: Further named arguments that will be passed to the :meth:`.trees` method when constructing the returned trees. :return: An iterator returning successive tuples of the form ``(interval, tree_self, tree_other)``. For example, the first item returned will consist of an tuple of the initial interval, the first tree of the current tree sequence, and the first tree of the ``other`` tree sequence; the ``.left`` attribute of the initial interval will be 0 and the ``.right`` attribute will be the smallest non-zero breakpoint of the 2 tree sequences. :rtype: iter(:class:`Interval`, :class:`Tree`, :class:`Tree`) """ if self.sequence_length != other.sequence_length: raise ValueError("Tree sequences must be of equal sequence length.") L = self.sequence_length trees1 = self.trees(**kwargs) trees2 = other.trees(**kwargs) tree1 = next(trees1) tree2 = next(trees2) right = 0 while right != L: left = right right = min(tree1.interval.right, tree2.interval.right) yield Interval(left, right), tree1, tree2 # Advance if tree1.interval.right == right: tree1 = next(trees1, None) if tree2.interval.right == right: tree2 = next(trees2, None)
[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 are interpreted by default as :ref:`missing data<sec_data_model_missing_data>`, and the genotypes array will contain a special value :data:`MISSING_DATA` (-1) to identify them while the ``alleles`` tuple will end with the value ``None`` (note that this will be the case whether or not we specify a fixed mapping using the ``alleles`` parameter; see the :class:`Variant` class for more details). Alternatively, if ``isolated_as_missing`` is set to to False, such isolated samples will not be treated as missing, and instead assigned the ancestral state (this was the default behaviour in versions prior to 0.2.0). Prior to 0.3.0 the `impute_missing_data` argument controlled this behaviour. .. 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 node IDs for which to generate genotypes, or None for all sample nodes. 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 occurrence of the allele. If None (the default), the alleles are encoded as they are encountered during genotype generation. :param bool impute_missing_data: *Deprecated in 0.3.0. Use ``isolated_as_missing``, but inverting value. Will be removed in a future version* :return: 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 occurrence of the allele. If None (the default), the alleles are encoded as they are encountered during genotype generation. :param bool impute_missing_data: *Deprecated in 0.3.0. Use ``isolated_as_missing``, but inverting value. Will be removed in a future version* :return: The full matrix of genotypes. :rtype: numpy.ndarray (dtype=np.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, parents, metadata, nodes, ) = self._ll_tree_sequence.get_individual(id_) return Individual( id=id_, flags=flags, location=location, parents=parents, metadata=metadata, nodes=nodes, metadata_decoder=self.table_metadata_schemas.individual.decode_row, )
[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, 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, metadata=metadata, metadata_decoder=self.table_metadata_schemas.edge.decode_row, )
def _tree_node_edges(self): """ Return a generator over the trees in the tree sequence, yielding a numpy array that maps the node IDs in the tree to the ID of the edge above them. Currently this is a private, non-optimised Python implementation. """ node_edges = np.full(self.num_nodes, NULL, dtype=np.int32) for _, edges_out, edges_in in self.edge_diffs(): for e in edges_out: node_edges[e.child] = NULL for e in edges_in: node_edges[e.child] = e.id yield node_edges
[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, 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, metadata=metadata, time=time, metadata_decoder=self.table_metadata_schemas.mutation.decode_row, )
[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, 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_, 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 that 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, listed in numerical order. :rtype: numpy.ndarray (dtype=np.int32) """ if population is not None and population_id is not None: raise ValueError( "population_id and population are aliases. Cannot specify both" ) if population_id is not None: population = population_id samples = self._ll_tree_sequence.get_samples() 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_unary_in_individuals=None, keep_input_roots=False, record_provenance=True, filter_zero_mutation_sites=None, # Deprecated alias for filter_sites ): """ Returns a simplified tree sequence that retains only the history of the nodes given in the list ``samples``. If ``map_nodes`` is true, also return a numpy array whose ``u``th element is the ID of the node in the simplified tree sequence that corresponds to node ``u`` in the original tree sequence, or :data:`tskit.NULL` (-1) if ``u`` is no longer present in the simplified tree sequence. In the returned tree sequence, the node with ID ``0`` corresponds to ``samples[0]``, node ``1`` corresponds to ``samples[1]`` etc., and all the passed-in nodes are flagged as samples. The remaining node IDs in the returned tree sequence are allocated sequentially in time order and are not flagged as samples. 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[int] samples: A list of node IDs to retain as samples. They need not be nodes marked as samples in the original tree sequence, but will constitute the entire set of samples in the returned tree sequence. If not specified or None, use all nodes marked with the IS_SAMPLE flag. The list may be provided as a numpy array (or array-like) object (dtype=np.int32). :param bool map_nodes: If True, return a tuple containing the resulting tree sequence and a numpy array mapping node IDs in the current tree sequence to their corresponding node IDs in the returned tree sequence. If False (the default), return only the tree sequence object itself. :param bool reduce_to_site_topology: Whether to reduce the topology down to the trees that are present at sites. (Default: False) :param bool filter_populations: If True, remove any populations that are not referenced by nodes after simplification; new population IDs are allocated sequentially from zero. If False, the population table will not be altered in any way. (Default: 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, preserve unary nodes (i.e., nodes with exactly one child) that exist on the path from samples to root. (Default: False) :param bool keep_unary_in_individuals: If True, preserve unary nodes that exist on the path from samples to root, but only if they are associated with an individual in the individuals table. Cannot be specified at the same time as ``keep_unary``. (Default: ``None``, equivalent to False) :param bool keep_input_roots: Whether to retain history ancestral to the MRCA of the samples. If ``False``, no topology older than the MRCAs of the samples will be included. If ``True`` the roots of all trees in the returned tree sequence will be the same roots as in the original tree sequence. (Default: False) :param bool record_provenance: If True, record details of this call to simplify in the returned tree sequence's provenance information (Default: True). :param bool filter_zero_mutation_sites: Deprecated alias for ``filter_sites``. :return: The simplified tree sequence, or (if ``map_nodes`` is True) a tuple consisting of the simplified tree sequence and a numpy array mapping source node IDs to their corresponding IDs in the new tree sequence. :rtype: .TreeSequence or (.TreeSequence, numpy.ndarray) """ tables = self.dump_tables() assert tables.sequence_length == self.sequence_length node_map = tables.simplify( samples=samples, reduce_to_site_topology=reduce_to_site_topology, filter_populations=filter_populations, filter_individuals=filter_individuals, filter_sites=filter_sites, keep_unary=keep_unary, keep_unary_in_individuals=keep_unary_in_individuals, keep_input_roots=keep_input_roots, record_provenance=record_provenance, filter_zero_mutation_sites=filter_zero_mutation_sites, ) new_ts = tables.tree_sequence() assert new_ts.sequence_length == self.sequence_length if map_nodes: return new_ts, node_map else: return new_ts
[docs] def delete_sites(self, site_ids, record_provenance=True): """ Returns a copy of this tree sequence with the specified sites (and their associated mutations) entirely removed. The site IDs do not need to be in any particular order, and specifying the same ID multiple times does not have any effect (i.e., calling ``tree_sequence.delete_sites([0, 1, 1])`` has the same effect as calling ``tree_sequence.delete_sites([0, 1])``. :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, reorder_populations=True, remove_unreferenced=True, ): """ Returns a tree sequence containing only information directly referencing the provided list of nodes to retain. The result will retain only the nodes whose IDs are listed in ``nodes``, only edges for which both parent and child are in ``nodes```, only mutations whose node is in ``nodes``, and only individuals that are referred to by one of the retained nodes. Note that this does *not* retain the ancestry of these nodes - for that, see :meth:`.simplify`. This has the side effect of reordering the nodes, individuals, and populations in the tree sequence: the nodes in the new tree sequence will be in the order provided in ``nodes``, and both individuals and populations will be ordered by the earliest retained node that refers to them. (However, ``reorder_populations`` may be set to False to keep the population table unchanged.) By default, the method removes all individuals and populations not referenced by any nodes, and all sites not referenced by any mutations. To retain these unreferenced individuals, populations, and sites, pass ``remove_unreferenced=False``. If this is done, the site table will remain unchanged, unreferenced individuals will appear at the end of the individuals table (and in their original order), and unreferenced populations will appear at the end of the population table (unless ``reorder_populations=False``). .. seealso:: :meth:`.keep_intervals` for subsetting a given portion of the genome; :meth:`.simplify` for retaining the ancestry of a subset of nodes. :param list nodes: The list of nodes for which to retain information. This may be a numpy array (or array-like) object (dtype=np.int32). :param bool record_provenance: Whether to record a provenance entry in the provenance table for this operation. :param bool reorder_populations: Whether to reorder populations (default: True). If False, the population table will not be altered in any way. :param bool remove_unreferenced: Whether sites, individuals, and populations that are not referred to by any retained entries in the tables should be removed (default: True). See the description for details. :rtype: .TreeSequence """ tables = self.dump_tables() tables.subset( nodes, record_provenance=record_provenance, reorder_populations=reorder_populations, remove_unreferenced=remove_unreferenced, ) return tables.tree_sequence()
[docs] def union( self, other, node_mapping, check_shared_equality=True, add_populations=True, record_provenance=True, ): """ Returns an expanded tree sequence which contains the node-wise union of ``self`` and ``other``, obtained by adding the non-shared portions of ``other`` onto ``self``. The "shared" portions are specified using a map that specifies which nodes in ``other`` are equivalent to those in ``self``: the ``node_mapping`` argument should be an array of length equal to the number of nodes in ``other`` and whose entries are the ID of the matching node in ``self``, or ``tskit.NULL`` if there is no matching node. Those nodes in ``other`` that map to ``tskit.NULL`` will be added to ``self``, along with: 1. Individuals whose nodes are new to ``self``. 2. Edges whose parent or child are new to ``self``. 3. Mutations whose nodes are new to ``self``. 4. Sites which were not present in ``self``, if the site contains a newly added mutation. By default, populations of newly added nodes are assumed to be new populations, and added to the population table as well. Note that this operation also sorts the resulting tables, so the resulting tree sequence may not be equal to ``self`` even if nothing new was added (although it would differ only in ordering of the tables). :param TableCollection other: Another table collection. :param list node_mapping: An array of node IDs that relate nodes in ``other`` to nodes in ``self``. :param bool check_shared_equality: If True, the shared portions of the tree sequences will be checked for equality. It does so by subsetting both ``self`` and ``other`` on the equivalent nodes specified in ``node_mapping``, and then checking for equality of the subsets. :param bool add_populations: If True, nodes new to ``self`` will be assigned new population IDs. :param bool record_provenance: Whether to record a provenance entry in the provenance table for this operation. """ tables = self.dump_tables() other_tables = other.dump_tables() tables.union( other_tables, node_mapping, check_shared_equality=check_shared_equality, add_populations=add_populations, record_provenance=record_provenance, ) return tables.tree_sequence()
[docs] def draw_svg( self, path=None, *, size=None, x_scale=None, time_scale=None, tree_height_scale=None, node_labels=None, mutation_labels=None, root_svg_attributes=None, style=None, order=None, force_root_branch=None, symbol_size=None, x_axis=None, x_label=None, x_lim=None, y_axis=None, y_label=None, y_ticks=None, y_gridlines=None, **kwargs, ): """ Return an SVG representation of a tree sequence. See the :ref:`visualization tutorial<tutorials:sec_tskit_viz>` for more details. :param str path: The path to the file to write the output. If None, do not write to file. :param 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, background shading is used to indicate the position of the trees along the X axis, and sites (with associated mutations) are marked at the appropriate physical position on axis line. If "treewise", each axis tick corresponds to a tree boundary, which are positioned evenly along the axis, so that the X axis is of variable scale, no background scaling is required, and site positions are not marked on the axis. :param str time_scale: Control how height values for nodes are computed. If this is equal to ``"time"``, node heights are proportional to their time values (this is the default). If this is equal to ``"log_time"``, node heights are proportional to their log(time) values. If it is equal to ``"rank"``, node heights are spaced equally according to their ranked times. :param str tree_height_scale: Deprecated alias for time_scale. (Deprecated in 0.3.6) :param 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: The left-to-right ordering of child nodes in each drawn tree. This can be either: ``"minlex"``, which minimises the differences between adjacent trees (see also the ``"minlex_postorder"`` traversal order for the :meth:`.nodes` method); or ``"tree"`` which draws trees in the left-to-right order defined by the :ref:`quintuply linked tree structure <sec_data_model_tree_structure>`. If not specified or None, this defaults to ``"minlex"``. :param bool force_root_branch: If ``True`` plot a branch (edge) above every tree root in the tree sequence. If ``None`` (default) then only plot such root branches if any root in the tree sequence has a mutation above it. :param float symbol_size: Change the default size of the node and mutation plotting symbols. If ``None`` (default) use a standard size. :param bool x_axis: Should the plot have an X axis line, showing the positions of trees along the genome. The scale used is determined by the ``x_scale`` parameter. If ``None`` (default) plot an X axis. :param str x_label: Place a label under the plot. If ``None`` (default) and there is an X axis, create and place an appropriate label. :param list x_lim: A list of size two giving the genomic positions between which trees should be plotted. If the first is ``None``, then plot from the first non-empty region of the tree sequence. If the second is ``None``, then plot up to the end of the last non-empty region of the tree sequence. The default value ``x_lim=None`` is shorthand for the list [``None``, ``None``]. If numerical values are given, then regions outside the interval have all information discarded: this means that mutations outside the interval will not be shown. To force display of the entire tree sequence, including empty flanking regions, specify ``x_lim=[0, ts.sequence_length]``. :param bool y_axis: Should the plot have an Y axis line, showing time (or ranked node time if ``time_scale="rank"``. If ``None`` (default) do not plot a Y axis. :param str y_label: Place a label to the left of the plot. If ``None`` (default) and there is a Y axis, create and place an appropriate label. :param list y_ticks: A list of Y values at which to plot tickmarks (``[]`` gives no tickmarks). If ``None``, plot one tickmark for each unique node value. :param bool y_gridlines: Whether to plot horizontal lines behind the tree at each y tickmark. :return: An SVG representation of a tree sequence. :rtype: str .. note:: Technically, x_lim[0] specifies a *minimum* value for the start of the X axis, and x_lim[1] specifies a *maximum* value for the end. This is only relevant if the tree sequence contains "empty" regions with no edges or mutations. In this case if x_lim[0] lies strictly within an empty region (i.e., ``empty_tree.interval.left < x_lim[0] < empty_tree.interval.right``) then that tree will not be plotted on the left hand side, and the X axis will start at ``empty_tree.interval.right``. Similarly, if x_lim[1] lies strictly within an empty region then that tree will not be plotted on the right hand side, and the X axis will end at ``empty_tree.interval.left`` """ draw = drawing.SvgTreeSequence( self, size, x_scale=x_scale, time_scale=time_scale, tree_height_scale=tree_height_scale, node_labels=node_labels, mutation_labels=mutation_labels, root_svg_attributes=root_svg_attributes, style=style, order=order, force_root_branch=force_root_branch, symbol_size=symbol_size, x_axis=x_axis, x_label=x_label, x_lim=x_lim, y_axis=y_axis, y_label=y_label, y_ticks=y_ticks, y_gridlines=y_gridlines, **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
[docs] def draw_text( self, *, node_labels=None, use_ascii=False, time_label_format=None, position_label_format=None, order=None, **kwargs, ): """ Create a text representation of a tree sequence. :param dict node_labels: If specified, show custom labels for the nodes that are present in the map. Any nodes not specified in the map will not have a node label. :param bool use_ascii: If ``False`` (default) then use unicode `box drawing characters \ <https://en.wikipedia.org/wiki/Box-drawing_character>`_ to render the tree. If ``True``, use plain ascii characters, which look cruder but are less susceptible to misalignment or font substitution. Alternatively, if you are having alignment problems with Unicode, you can try out the solution documented `here \ <https://github.com/tskit-dev/tskit/issues/189#issuecomment-499114811>`_. :param str time_label_format: A python format string specifying the format (e.g. number of decimal places or significant figures) used to print the numerical time values on the time axis. If ``None``, this defaults to ``"{:.2f}"``. :param str position_label_format: A python format string specifying the format (e.g. number of decimal places or significant figures) used to print genomic positions. If ``None``, this defaults to ``"{:.2f}"``. :param str order: The left-to-right ordering of child nodes in the drawn tree. This can be either: ``"minlex"``, which minimises the differences between adjacent trees (see also the ``"minlex_postorder"`` traversal order for the :meth:`.nodes` method); or ``"tree"`` which draws trees in the left-to-right order defined by the :ref:`quintuply linked tree structure <sec_data_model_tree_structure>`. If not specified or None, this defaults to ``"minlex"``. :return: A text representation of a tree sequence. :rtype: str """ return str( drawing.TextTreeSequence( self, node_labels=node_labels, use_ascii=use_ascii, time_label_format=time_label_format, position_label_format=position_label_format, order=order, ) )
############################################ # # Statistics computation # ############################################
[docs] def general_stat( self, W, f, output_dim, windows=None, polarised=False, mode=None, span_normalise=True, strict=True, ): """ Compute a windowed statistic from weights and a summary function. See the :ref:`statistics interface <sec_stats_interface>` section for details on :ref:`windows <sec_stats_windows>`, :ref:`mode <sec_stats_mode>`, :ref:`span normalise <sec_stats_span_normalise>`, and :ref:`return value <sec_stats_output_format>`. On each tree, this propagates the weights ``W`` up the tree, so that the "weight" of each node is the sum of the weights of all samples at or below the node. Then the summary function ``f`` is applied to the weights, giving a summary for each node in each tree. How this is then aggregated depends on ``mode``: "site" Adds together the total summary value across all alleles in each window. "branch" Adds together the summary value for each node, multiplied by the length of the branch above the node and the span of the tree. "node" Returns each node's summary value added across trees and multiplied by the span of the tree. Both the weights and the summary can be multidimensional: if ``W`` has ``k`` columns, and ``f`` takes a ``k``-vector and returns an ``m``-vector, then the output will be ``m``-dimensional for each node or window (depending on "mode"). .. note:: The summary function ``f`` should return zero when given both 0 and the total weight (i.e., ``f(0) = 0`` and ``f(np.sum(W, axis=0)) = 0``), unless ``strict=False``. This is necessary for the statistic to be unaffected by parts of the tree sequence ancestral to none or all of the samples, respectively. :param numpy.ndarray W: An array of values with one row for each sample and one column for each weight. :param f: A function that takes a one-dimensional array of length equal to the number of columns of ``W`` and returns a one-dimensional array. :param int output_dim: The length of ``f``'s return value. :param list windows: An increasing list of breakpoints between the windows to compute the statistic in. :param bool polarised: Whether to leave the ancestral state out of computations: see :ref:`sec_stats` for more details. :param str mode: A string giving the "type" of the statistic to be computed (defaults to "site"). :param bool span_normalise: Whether to divide the result by the span of the window (defaults to True). :param bool strict: Whether to check that f(0) and f(total weight) are zero. :return: A ndarray with shape equal to (num windows, num statistics). """ if mode is None: mode = "site" if strict: total_weights = np.sum(W, axis=0) for x in [total_weights, total_weights * 0.0]: with np.errstate(invalid="ignore", divide="ignore"): fx = np.array(f(x)) fx[np.isnan(fx)] = 0.0 if not np.allclose(fx, np.zeros((output_dim,))): raise ValueError( "Summary function does not return zero for both " "zero weight and total weight." ) return self.__run_windowed_stat( windows, self.ll_tree_sequence.general_stat, W, f, output_dim, polarised=polarised, span_normalise=span_normalise, mode=mode, )
[docs] def sample_count_stat( self, sample_sets, f, output_dim, windows=None, polarised=False, mode=None, span_normalise=True, strict=True, ): """ Compute a windowed statistic from sample counts and a summary function. This is a wrapper around :meth:`.general_stat` for the common case in which the weights are all either 1 or 0, i.e., functions of the joint allele frequency spectrum. See the :ref:`statistics interface <sec_stats_interface>` section for details on :ref:`sample sets <sec_stats_sample_sets>`, :ref:`windows <sec_stats_windows>`, :ref:`mode <sec_stats_mode>`, :ref:`span normalise <sec_stats_span_normalise>`, and :ref:`return value <sec_stats_output_format>`. If ``sample_sets`` is a list of ``k`` sets of samples, then ``f`` should be a function that takes an argument of length ``k`` and returns a one-dimensional array. The ``j``-th element of the argument to ``f`` will be the number of samples in ``sample_sets[j]`` that lie below the node that ``f`` is being evaluated for. See :meth:`.general_stat` for more details. Here is a contrived example: suppose that ``A`` and ``B`` are two sets of samples with ``nA`` and ``nB`` elements, respectively. Passing these as sample sets will give ``f`` an argument of length two, giving the number of samples in ``A`` and ``B`` below the node in question. So, if we define .. code-block:: python def f(x): pA = x[0] / nA pB = x[1] / nB return np.array([pA * pB]) then if all sites are biallelic, .. code-block:: python ts.sample_count_stat([A, B], f, 1, windows="sites", polarised=False, mode="site") would compute, for each site, the product of the derived allele frequencies in the two sample sets, in a (num sites, 1) array. If instead ``f`` returns ``np.array([pA, pB, pA * pB])``, then the output would be a (num sites, 3) array, with the first two columns giving the allele frequencies in ``A`` and ``B``, respectively. .. note:: The summary function ``f`` should return zero when given both 0 and the sample size (i.e., ``f(0) = 0`` and ``f(np.array([len(x) for x in sample_sets])) = 0``). This is necessary for the statistic to be unaffected by parts of the tree sequence ancestral to none or all of the samples, respectively. :param list sample_sets: A list of lists of Node IDs, specifying the groups of nodes to compute the statistic with. :param f: A function that takes a one-dimensional array of length equal to the number of sample sets and returns a one-dimensional array. :param int output_dim: The length of ``f``'s return value. :param list windows: An increasing list of breakpoints between the windows to compute the statistic in. :param bool polarised: Whether to leave the ancestral state out of computations: see :ref:`sec_stats` for more details. :param str mode: A string giving the "type" of the statistic to be computed (defaults to "site"). :param bool span_normalise: Whether to divide the result by the span of the window (defaults to True). :param bool strict: Whether to check that f(0) and f(total weight) are zero. :return: A ndarray with shape equal to (num windows, num statistics). """ # noqa: B950 # helper function for common case where weights are indicators of sample sets for U in sample_sets: if len(U) != len(set(U)): raise ValueError( "Elements of sample_sets must be lists without repeated elements." ) if len(U) == 0: raise ValueError("Elements of sample_sets cannot be empty.") for u in U: if not self.node(u).is_sample(): raise ValueError("Not all elements of sample_sets are samples.") W = np.array([[float(u in A) for A in sample_sets] for u in self.samples()]) return self.general_stat( W, f, output_dim, windows=windows, polarised=polarised, mode=mode, span_normalise=span_normalise, strict=strict, )
def parse_windows(self, windows): # Note: need to make sure windows is a string or we try to compare the # target with a numpy array elementwise. if windows is None: windows = [0.0, self.sequence_length] elif isinstance(windows, str): if windows == "trees": windows = self.breakpoints(as_array=True) elif windows == "sites": # breakpoints are at 0.0 and at the sites and at the end windows = np.concatenate( [ [] if self.num_sites > 0 else [0.0], self.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 chromosomes 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 chromosomes (one from each sample set) and locations in the window, of the mean distance in the tree between the two samples (in units of time). "node" For each node, the proportion of genome on which the node is an ancestor to only one of a 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 genetic_relatedness( self, sample_sets, indexes=None, windows=None, mode="site", span_normalise=True, polarised=False, proportion=True, ): """ Computes genetic relatedness 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>`, :ref:`polarised <sec_stats_polarisation>`, and :ref:`return value <sec_stats_output_format>`. What is computed depends on ``mode``: "site" Number of pairwise allelic matches in the window between two sample sets relative to the rest of the sample sets. To be precise, let `m(u,v)` denote the total number of alleles shared between nodes `u` and `v`, and let `m(I,J)` be the sum of `m(u,v)` over all nodes `u` in sample set `I` and `v` in sample set `J`. Let `S` and `T` be independently chosen sample sets. Then, for sample sets `I` and `J`, this computes `E[m(I,J) - m(I,S) - m(J,T) + m(S,T)]`. This can also be seen as the covariance of a quantitative trait determined by additive contributions from the genomes in each sample set. Let each allele be associated with an effect drawn from a `N(0,1/2)` distribution, and let the trait value of a sample set be the sum of its allele effects. Then, this computes the covariance between the trait values of two sample sets. For example, to compute covariance between the traits of diploid individuals, each sample set would be the pair of genomes of each individual; if ``proportion=True``, this then corresponds to :math:`K_{c0}` in `Speed & Balding (2014) <https://www.nature.com/articles/nrg3821>`_. "branch" Total area of branches in the window ancestral to pairs of samples in two sample sets relative to the rest of the sample sets. To be precise, let `B(u,v)` denote the total area of all branches ancestral to nodes `u` and `v`, and let `B(I,J)` be the sum of `B(u,v)` over all nodes `u` in sample set `I` and `v` in sample set `J`. Let `S` and `T` be two independently chosen sample sets. Then for sample sets `I` and `J`, this computes `E[B(I,J) - B(I,S) - B(J,T) + B(S,T)]`. "node" For each node, the proportion of the window over which pairs of samples in two sample sets are descendants, relative to the rest of the sample sets. To be precise, for each node `n`, let `N(u,v)` denote the proportion of the window over which samples `u` and `v` are descendants of `n`, and let and let `N(I,J)` be the sum of `N(u,v)` over all nodes `u` in sample set `I` and `v` in sample set `J`. Let `S` and `T` be two independently chosen sample sets. Then for sample sets `I` and `J`, this computes `E[N(I,J) - N(I,S) - N(J,T) + N(S,T)]`. :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). :param bool proportion: Whether to divide the result by :meth:`.segregating_sites`, called with the same ``windows`` and ``mode`` (defaults to True). Note that this counts sites that are segregating between *any* of the samples of *any* of the sample sets (rather than segregating between all of the samples of the tree sequence). :return: A ndarray with shape equal to (num windows, num statistics). """ if proportion: # TODO this should be done in C also all_samples = list({u for s in sample_sets for u in s}) denominator = self.segregating_sites( sample_sets=[all_samples], windows=windows, mode=mode, span_normalise=span_normalise, ) else: denominator = 1 numerator = self.__k_way_sample_set_stat( self._ll_tree_sequence.genetic_relatedness, 2, sample_sets, indexes=indexes, windows=windows, mode=mode, span_normalise=span_normalise, polarised=polarised, ) with np.errstate(divide="ignore", invalid="ignore"): out = numerator / denominator return out
[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`` and ``d`` inherit from that node but ``b`` and ``c`` do not, or vice-versa. :param list sample_sets: A list of lists of Node IDs, specifying the groups of nodes to compute the statistic with. :param list indexes: A list of 4-tuples, or None. :param list windows: An increasing list of breakpoints between the windows to compute the statistic in. :param str mode: A string giving the "type" of the statistic to be computed (defaults to "site"). :param bool span_normalise: Whether to divide the result by the span of the window (defaults to True). :return: A ndarray with shape equal to (num windows, num statistics). """ 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)