Source code for msprime.ancestry

# Copyright (C) 2015-2020 University of Oxford
# This file is part of msprime.
# msprime is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# msprime is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with msprime.  If not, see <>.
Module responsible for defining and running ancestry simulations.
import bisect
import collections
import copy
import gzip
import inspect
import logging
import warnings

import attr
import numpy as np
import tskit

import _msprime
from . import core
from . import demography as demog
from . import mutations
from . import provenance

logger = logging.getLogger(__name__)

def model_factory(model):
    Returns a simulation model corresponding to the specified model.
    - If model is None, the default simulation model is returned.
    - If model is a string, return the corresponding model instance.
    - If model is an instance of SimulationModel, return a copy of it.
    - Otherwise raise a type error.
    model_map = {
        "hudson": StandardCoalescent(),
        "smc": SmcApproxCoalescent(),
        "smc_prime": SmcPrimeApproxCoalescent(),
        "dtwf": DiscreteTimeWrightFisher(),
        "wf_ped": WrightFisherPedigree(),
    if model is None:
        model_instance = StandardCoalescent()
    elif isinstance(model, str):
        lower_model = model.lower()
        if lower_model not in model_map:
            raise ValueError(
                "Model '{}' unknown. Choose from {}".format(
                    model, list(model_map.keys())
        model_instance = model_map[lower_model]
    elif not isinstance(model, SimulationModel):
        raise TypeError(
            "Simulation model must be a string or an instance of SimulationModel"
        model_instance = model
    return model_instance

def parse_model_change_events(events):
    Parses the specified list of events provided in model_arg[1:] into
    SimulationModelChange events. There are two different forms supported,
    and model descriptions are anything supported by model_factory.
    err = (
        "Simulation model change events must be either a two-tuple "
        "(time, model), describing the time of the model change and "
        "the new model or be an instance of SimulationModelChange."
    model_change_events = []
    for event in events:
        if isinstance(event, (tuple, list)):
            if len(event) != 2:
                raise ValueError(err)
            t = event[0]
            if t is not None:
                    t = float(t)
                except (TypeError, ValueError):
                    raise ValueError(
                        "Model change times must be either a floating point "
                        "value or None"
            event = SimulationModelChange(t, model_factory(event[1]))
        elif isinstance(event, SimulationModelChange):
            # We don't want to modify our inputs, so take a deep copy.
            event = copy.copy(event)
            event.model = model_factory(event.model)
            raise TypeError(err)
    return model_change_events

def parse_model_arg(model_arg):
    Parses the specified model argument from the simulate function,
    returning the initial model and any model change events.
    err = (
        "The model argument must be either (a) a value that can be "
        "interpreted as a simulation model or (b) a list in which "
        "the first element is a model description and the remaining "
        "elements are model change events. These can either be described "
        "by a (time, model) tuple or SimulationModelChange instances."
    if isinstance(model_arg, (list, tuple)):
        if len(model_arg) < 1:
            raise ValueError(err)
        model = model_factory(model_arg[0])
        model_change_events = parse_model_change_events(model_arg[1:])
        model = model_factory(model_arg)
        model_change_events = []
    return model, model_change_events

def filter_events(demographic_events):
    Returns a tuple (demographic_events, model_change_events) which separates
    out the SimulationModelChange events from the list. This is to support the
    pre-1.0 syntax for model changes, where they were included in the
    demographic_events parameter.
    filtered_events = []
    model_change_events = []
    for event in demographic_events:
        if isinstance(event, SimulationModelChange):
    # Make sure any model references are resolved.
    model_change_events = parse_model_change_events(model_change_events)
    return filtered_events, model_change_events

def _check_population_configurations(population_configurations):
    err = (
        "Population configurations must be a list of PopulationConfiguration instances"
    for config in population_configurations:
        if not isinstance(config, demog.PopulationConfiguration):
            raise TypeError(err)
        if config.initial_size is not None and config.initial_size <= 0:
            raise ValueError("Population size must be > 0")
        if config.sample_size is not None and config.sample_size < 0:
            raise ValueError("Sample size must be >= 0")

def _replicate_generator(
    sim, mutation_generator, num_replicates, provenance_dict, end_time
    Generator function for the many-replicates case of the simulate
    encoded_provenance = None
    # The JSON is modified for each replicate to insert the replicate number.
    # To avoid repeatedly encoding the same JSON (which can take milliseconds)
    # we insert a replaceable string.
    placeholder = "@@_MSPRIME_REPLICATE_INDEX_@@"
    if provenance_dict is not None:
        provenance_dict["parameters"]["replicate_index"] = placeholder
        encoded_provenance = provenance.json_encode_provenance(
            provenance_dict, num_replicates

    for j in range(num_replicates):
        replicate_provenance = None
        if encoded_provenance is not None:
            replicate_provenance = encoded_provenance.replace(
                f'"{placeholder}"', str(j)
        tree_sequence = sim.get_tree_sequence(mutation_generator, replicate_provenance)
        yield tree_sequence

def samples_factory(sample_size, samples, pedigree, population_configurations):
    Returns a list of Sample objects, given the specified inputs.
    the_samples = []
    if sample_size is not None:
        if samples is not None:
            raise ValueError("Cannot specify sample size and samples simultaneously.")
        if population_configurations is not None:
            raise ValueError(
                "Cannot specify sample size and population_configurations "
        s = demog.Sample(population=0, time=0.0)
        # NOTE: we need to review this before finalising the interface
        # and see if this gels with the idea of having ploidy and
        # individuals.
        # In pedigrees samples are diploid individuals
        if pedigree is not None:
            sample_size *= 2  # TODO: Update for different ploidy
        the_samples = [s for _ in range(sample_size)]
    # If we have population configurations we may have embedded sample_size
    # values telling us how many samples to take from each population.
    if population_configurations is not None:
        if samples is None:
            the_samples = []
            for j, conf in enumerate(population_configurations):
                if conf.sample_size is not None:
                    the_samples += [demog.Sample(j, 0) for _ in range(conf.sample_size)]
            for conf in population_configurations:
                if conf.sample_size is not None:
                    raise ValueError(
                        "Cannot specify population configuration sample size"
                        " and samples simultaneously"
            the_samples = samples
    elif samples is not None:
        the_samples = samples
    return the_samples

def demography_factory(
    Ne, demography, population_configurations, migration_matrix, demographic_events
    if demography is not None:
        if population_configurations is not None:
            raise ValueError(
                "The demography and population_configurations options "
                "cannot be used together"
        if migration_matrix is not None:
            raise ValueError(
                "The demography and migration_matrix options cannot be used together"
        if demographic_events is not None:
            raise ValueError(
                "The demography and demographic_events options cannot be used together"
        # Take a copy so that we don't modify the input parameters when
        # resolving defaults
        demography = copy.deepcopy(demography)
        demography = demog.Demography.from_old_style(
            population_configurations, migration_matrix, demographic_events

    # For any populations in which the initial size is None set it to Ne
    for pop in demography.populations:
        if pop.initial_size is None:
            pop.initial_size = Ne

    return demography

def simulator_factory(
    Convenience method to create a simulator instance using the same
    parameters as the `simulate` function.
    if Ne <= 0:
        raise ValueError("Population size must be positive")

    samples_specified = (
        sample_size is None
        and population_configurations is None
        and samples is None
        and from_ts is None
    if samples_specified:
        # TODO remove the population_configurations message here if we're
        # deprecating it?
        raise ValueError(
            "Either sample_size, samples, population_configurations or from_ts must "
            "be specified"
    samples = samples_factory(sample_size, samples, pedigree, population_configurations)

    model, model_change_events = parse_model_arg(model)
    if demographic_events is not None:
        demographic_events, old_style_model_change_events = filter_events(
        if len(old_style_model_change_events) > 0:
            if len(model_change_events) > 0:
                raise ValueError(
                    "Cannot specify SimulationModelChange events using both new-style "
                    "and pre 1.0 syntax"
            model_change_events = old_style_model_change_events

    demography = demography_factory(
        Ne, demography, population_configurations, migration_matrix, demographic_events

    # The logic for checking from_ts and recombination map is bound together
    # in a complicated way, so we can factor them out into separate functions.
    if from_ts is None:
        if len(samples) < 2:
            raise ValueError("Sample size must be >= 2")
        if len(samples) > 0:
            raise ValueError("Cannot specify samples with from_ts")
        if not isinstance(from_ts, tskit.TreeSequence):
            raise TypeError("from_ts must be a TreeSequence instance.")
        if demography.num_populations != from_ts.num_populations:
            raise ValueError(
                "Mismatch in the number of populations in from_ts and simulation "
                "parameters. The number of populations in the simulation must be "
                "equal to the number of populations in from_ts"

    if recombination_map is None:
        # Default to 1 if no from_ts; otherwise default to the sequence length
        # of from_ts
        if from_ts is None:
            the_length = 1 if length is None else length
            the_length = from_ts.sequence_length if length is None else length
        the_rate = 0 if recombination_rate is None else recombination_rate
        if the_length <= 0:
            raise ValueError("Cannot provide non-positive sequence length")
        if the_rate < 0:
            raise ValueError("Cannot provide negative recombination rate")
        recombination_map = RecombinationMap.uniform_map(
            the_length, the_rate, discrete=False
        if not isinstance(recombination_map, RecombinationMap):
            raise TypeError("RecombinationMap instance required")
        if length is not None or recombination_rate is not None:
            raise ValueError(
                "Cannot specify length/recombination_rate along with "
                "a recombination map"
    if from_ts is not None:
        if recombination_map.get_length() != from_ts.sequence_length:
            raise ValueError(
                "Recombination map and from_ts must have identical " "sequence_length"

    if start_time is not None and start_time < 0:
        raise ValueError("start_time cannot be negative")

    if num_labels is not None and num_labels < 1:
        raise ValueError("Must have at least one structured coalescent label")

    # FIXME check the valid inputs for GC. Should we allow it when we
    # have a non-trivial genetic map?
    if gene_conversion_rate is None:
        gene_conversion_rate = 0
        if not recombination_map.discrete:
            raise ValueError(
                "Cannot specify gene_conversion_rate along with "
                "a nondiscrete recombination map"
    if gene_conversion_track_length is None:
        gene_conversion_track_length = 1

    if random_generator is None:
        # For the simulate code-path the rng will already be set, but
        # for convenience we allow it to be null to help with writing
        # tests.
        random_generator = _msprime.RandomGenerator(core.get_random_seed())

    sim = Simulator(
    return sim

[docs]def simulate( sample_size=None, Ne=1, length=None, recombination_rate=None, recombination_map=None, mutation_rate=None, population_configurations=None, pedigree=None, migration_matrix=None, demographic_events=None, samples=None, model=None, record_migrations=False, random_seed=None, mutation_generator=None, num_replicates=None, replicate_index=None, from_ts=None, start_time=None, end_time=None, record_full_arg=False, num_labels=None, record_provenance=True, # FIXME add documentation for these. gene_conversion_rate=None, gene_conversion_track_length=None, demography=None, ): """ Simulates the coalescent with recombination under the specified model parameters and returns the resulting :class:`tskit.TreeSequence`. Note that Ne is the effective diploid population size (so the effective number of genomes in the population is 2*Ne), but ``sample_size`` is the number of (monoploid) genomes sampled. :param int sample_size: The number of sampled monoploid genomes. If not specified or None, this defaults to the sum of the subpopulation sample sizes. Either ``sample_size``, ``population_configurations`` or ``samples`` must be specified. :param float Ne: The effective (diploid) population size for the reference population. This defaults to 1 if not specified. Please see the :ref:`sec_api_simulation_models` section for more details on specifying simulations models. :param float length: The length of the simulated region in bases. This parameter cannot be used along with ``recombination_map``. Defaults to 1 if not specified. :param float recombination_rate: The rate of recombination per base per generation. This parameter cannot be used along with ``recombination_map``. Defaults to 0 if not specified. :param recombination_map: The map describing the changing rates of recombination along the simulated chromosome. This parameter cannot be used along with the ``recombination_rate`` or ``length`` parameters, as these values are encoded within the map. Defaults to a uniform rate as described in the ``recombination_rate`` parameter if not specified. :type recombination_map: :class:`.RecombinationMap` :param float mutation_rate: The rate of infinite sites mutations per unit of sequence length per generation. If not specified, no mutations are generated. This option only allows for infinite sites mutations with a binary (i.e., 0/1) alphabet. For more control over the mutational process, please use the :func:`.mutate` function. :param list population_configurations: The list of :class:`.PopulationConfiguration` instances describing the sampling configuration, relative sizes and growth rates of the populations to be simulated. If this is not specified, a single population with a sample of size ``sample_size`` is assumed. :type population_configurations: list or None. :param list migration_matrix: The matrix describing the rates of migration between all pairs of populations. If :math:`N` populations are defined in the ``population_configurations`` parameter, then the migration matrix must be an :math:`N \\times N` matrix with 0 on the diagonal, consisting of :math:`N` lists of length :math:`N` or an :math:`N \\times N` numpy array, with the [j, k]th element giving the fraction of population j that consists of migrants from population k in each generation. :param list demographic_events: The list of demographic events to simulate. Demographic events describe changes to the populations in the past. Events should be supplied in non-decreasing order of time in the past. Events with the same time value will be applied sequentially in the order that they were supplied before the simulation algorithm continues with the next time step. :param list samples: The list specifying the location and time of all samples. This parameter may be used to specify historical samples, and cannot be used in conjunction with the ``sample_size`` parameter. Each sample is a (``population``, ``time``) pair such that the sample in position ``j`` in the list of samples is drawn in the specified population at the specfied time. Time is measured in generations ago, as elsewhere. :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 int num_replicates: The number of replicates of the specified parameters to simulate. If this is not specified or None, no replication is performed and a :class:`tskit.TreeSequence` object returned. If :obj:`num_replicates` is provided, the specified number of replicates is performed, and an iterator over the resulting :class:`tskit.TreeSequence` objects returned. :param int replicate_index: Return only a specific tree sequence from the set of replicates. This is used to recreate a specific tree sequence from e.g. provenance. This argument only makes sense when used with `random seed`, and is not compatible with `num_replicates`. Note also that msprime will have to create and discard all the tree sequences up to this index. :param tskit.TreeSequence from_ts: If specified, initialise the simulation from the root segments of this tree sequence and return the completed tree sequence. Please see :ref:`here <sec_api_simulate_from>` for details on the required properties of this tree sequence and its interactions with other parameters. (Default: None). :param float start_time: If specified, set the initial time that the simulation starts to this value. If not specified, the start time is zero if performing a simulation of a set of samples, or is the time of the oldest node if simulating from an existing tree sequence (see the ``from_ts`` parameter). :param float end_time: If specified, terminate the simulation at the specified time. In the returned tree sequence, all rootward paths from samples with time < end_time will end in a node with one child with time equal to end_time. Sample nodes with time >= end_time will also be present in the output tree sequence. If not specified or ``None``, run the simulation until all samples have an MRCA at all positions in the genome. :param bool record_full_arg: If True, record all intermediate nodes arising from common ancestor and recombination events in the output tree sequence. This will result in unary nodes (i.e., nodes in marginal trees that have only one child). Defaults to False. :param model: The simulation model to use. This can either be a string (e.g., ``"smc_prime"``) or an instance of a simulation model class (e.g, ``msprime.DiscreteTimeWrightFisher(100)``. Please see the :ref:`sec_api_simulation_models` section for more details on specifying simulations models. :type model: str or simulation model instance :param bool record_provenance: If True, record all configuration and parameters required to recreate the tree sequence. These can be accessed via ``TreeSequence.provenances()``). :return: The :class:`tskit.TreeSequence` object representing the results of the simulation if no replication is performed, or an iterator over the independent replicates simulated if the :obj:`num_replicates` parameter has been used. :rtype: :class:`tskit.TreeSequence` or an iterator over :class:`tskit.TreeSequence` replicates. :warning: If using replication, do not store the results of the iterator in a list! For performance reasons, the same underlying object may be used for every TreeSequence returned which will most likely lead to unexpected behaviour. """ seed = random_seed if random_seed is None: seed = core.get_random_seed() seed = int(seed) rng = _msprime.RandomGenerator(seed) provenance_dict = None if record_provenance: argspec = inspect.getargvalues(inspect.currentframe()) # num_replicates is excluded as provenance is per replicate # replicate index is excluded as it is inserted for each replicate parameters = { "command": "simulate", **{ arg: argspec.locals[arg] for arg in argspec.args if arg not in ["num_replicates", "replicate_index"] }, } parameters["random_seed"] = seed provenance_dict = provenance.get_provenance_dict(parameters) sim = simulator_factory( sample_size=sample_size, random_generator=rng, Ne=Ne, length=length, recombination_rate=recombination_rate, recombination_map=recombination_map, population_configurations=population_configurations, pedigree=pedigree, migration_matrix=migration_matrix, demographic_events=demographic_events, samples=samples, model=model, record_migrations=record_migrations, from_ts=from_ts, start_time=start_time, record_full_arg=record_full_arg, num_labels=num_labels, gene_conversion_rate=gene_conversion_rate, gene_conversion_track_length=gene_conversion_track_length, demography=demography, ) if mutation_generator is not None: # This error was added in version 0.6.1. raise ValueError( "mutation_generator is not longer supported. Please use " "msprime.mutate instead" ) if mutation_rate is not None: # There is ambiguity in how we should throw mutations onto partially # built tree sequences: on the whole thing, or must the newly added # topology? Before or after start_time? We avoid this complexity by # asking the user to use mutate(), which should have the required # flexibility. if from_ts is not None: raise ValueError( "Cannot specify mutation rate combined with from_ts. Please use " "msprime.mutate on the final tree sequence instead" ) # There is ambiguity in how the start_time argument should interact with # the mutation generator: should we throw mutations down on the whole # tree or just the (partial) edges after start_time? To avoid complicating # things here, make the user use mutate() which should have the flexibility # to do whatever is needed. if start_time is not None and start_time > 0: raise ValueError( "Cannot specify mutation rate combined with a non-zero " "start_time. Please use msprime.mutate on the returned " "tree sequence instead" ) # TODO when the ``discrete`` parameter is added here, pass it through # to make it a property of the mutation generator. mutation_generator = mutations._simple_mutation_generator( mutation_rate, sim.sequence_length, sim.random_generator ) if replicate_index is not None and random_seed is None: raise ValueError( "Cannot specify replicate_index without random_seed as this " "has the same effect as not specifying replicate_index i.e. a " "random tree sequence" ) if replicate_index is not None and num_replicates is not None: raise ValueError( "Cannot specify replicate_index with num_replicates as only " "the replicate_index specified will be returned." ) if num_replicates is None and replicate_index is None: replicate_index = 0 if replicate_index is not None: iterator = _replicate_generator( sim, mutation_generator, replicate_index + 1, provenance_dict, end_time ) # Return the last element of the iterator deque = collections.deque(iterator, maxlen=1) return deque.pop() else: return _replicate_generator( sim, mutation_generator, num_replicates, provenance_dict, end_time )
class Simulator(_msprime.Simulator): """ Class to simulate trees under a variety of population models. """ def __init__( self, samples, recombination_map, Ne, random_generator, demography, model_change_events, pedigree=None, model=None, from_ts=None, store_migrations=False, store_full_arg=False, start_time=None, num_labels=None, gene_conversion_rate=0, gene_conversion_track_length=1, ): # We always need at least n segments, so no point in making # allocation any smaller than this. num_samples = len(samples) if samples is not None else from_ts.num_samples block_size = 64 * 1024 segment_block_size = max(block_size, num_samples) avl_node_block_size = block_size node_mapping_block_size = block_size if num_labels is None: num_labels = self._choose_num_labels(model, model_change_events) # Now, convert the high-level values into their low-level # counterparts. ll_pedigree = None if pedigree is not None: # TODO see notes on the WrightFisherPedigree; the pedigree should # be a parameter of the *model*. pedigree = self._check_pedigree( pedigree, samples, model, demography, model_change_events ) ll_pedigree = pedigree.get_ll_representation() ll_simulation_model = model.get_ll_representation() ll_population_configuration = [pop.asdict() for pop in demography.populations] ll_demographic_events = [ event.get_ll_representation() for event in ] ll_recomb_map = recombination_map.get_ll_recombination_map() ll_tables = _msprime.LightweightTableCollection( recombination_map.get_sequence_length() ) if from_ts is not None: from_ts_tables = from_ts.tables.asdict() ll_tables.fromdict(from_ts_tables) start_time = -1 if start_time is None else start_time super().__init__( samples=samples, recombination_map=ll_recomb_map, tables=ll_tables, start_time=start_time, random_generator=random_generator, model=ll_simulation_model, migration_matrix=demography.migration_matrix, population_configuration=ll_population_configuration, pedigree=ll_pedigree, demographic_events=ll_demographic_events, store_migrations=store_migrations, store_full_arg=store_full_arg, num_labels=num_labels, segment_block_size=segment_block_size, avl_node_block_size=avl_node_block_size, node_mapping_block_size=node_mapping_block_size, gene_conversion_rate=gene_conversion_rate, gene_conversion_track_length=gene_conversion_track_length, ) # attributes that are internal to the highlevel Simulator class self._hl_from_ts = from_ts # highlevel attributes used externally that have no lowlevel equivalent self.model_change_events = model_change_events self.demography = demography self.recombination_map = recombination_map @property def tables(self): # convert lowlevel tables to highlevel tables return tskit.TableCollection.fromdict(super().tables.asdict()) @property def sample_configuration(self): """ Returns the number of samples from each popuation. """ ret = [0 for _ in range(self.num_populations)] for ll_sample in self.samples: sample = demog.Sample(*ll_sample) ret[sample.population] += 1 return ret def _check_pedigree( self, pedigree, samples, model, demography, model_change_events ): # TODO this functionality is preliminary and undocumented, so this # code should be expected to change. if demography.num_populations != 1: raise ValueError( "Cannot yet specify population structure " "and pedigrees simultaneously" ) if not isinstance(model, WrightFisherPedigree): raise ValueError("Pedigree can only be specified for wf_ped model") if len(samples) % 2 != 0: raise ValueError( "In (diploid) pedigrees, must specify two lineages per individual." ) if pedigree.is_sample is None: pedigree.set_samples(num_samples=len(samples) // 2) if sum(pedigree.is_sample) * 2 != len(samples): raise ValueError( "{} sample lineages to be simulated, but {} in pedigree".format( len(samples), pedigree.num_samples * 2 ) ) pedigree_max_time = np.max(pedigree.time) if len( > 0: de_min_time = min([x.time for x in]) if de_min_time <= pedigree_max_time: raise NotImplementedError( "Demographic events must be older than oldest pedigree founder." ) if len(model_change_events) > 0: mc_min_time = min([x.time for x in model_change_events]) if mc_min_time < pedigree_max_time: raise NotImplementedError( "Model change events earlier than founders of pedigree unsupported." ) return pedigree def _choose_num_labels(self, model, model_change_events): """ Choose the number of labels appropriately, given the simulation models that will be simulated. """ num_labels = 1 models = [model] + [event.model for event in model_change_events] for model in models: if isinstance(model, SweepGenicSelection): num_labels = 2 return num_labels def _run_until(self, end_time, event_chunk=None): # This is a pretty big default event chunk so that we don't spend # too much time going back and forth into Python. We could imagine # doing something a bit more sophisticated where we try to tune the # number of events so that we end up with roughly 10 second slices # (say). if event_chunk is None: event_chunk = 10 ** 4 if event_chunk <= 0: raise ValueError("Must have at least 1 event per chunk")"Running model %s until max time: %f", self.model, end_time) while super().run(end_time, event_chunk) == _msprime.EXIT_MAX_EVENTS: logger.debug("time=%g ancestors=%d", self.time, self.num_ancestors) def run(self, end_time=None, event_chunk=None): """ Runs the simulation until complete coalescence has occurred. """ for event in self.model_change_events: # If the event time is a callable, we compute the end_time # as a function of the current simulation time. current_time = self.time model_start_time = event.time if callable(event.time): model_start_time = event.time(current_time) # If model_start_time is None, we run until the current # model completes. Note that when event.time is a callable # it can also return None for this behaviour. if model_start_time is None: model_start_time = np.inf if model_start_time < current_time: raise ValueError( "Model start times out of order or not computed correctly. " f"current time = {current_time}; start_time = {model_start_time}" ) self._run_until(model_start_time, event_chunk) ll_new_model = event.model.get_ll_representation() self.model = ll_new_model end_time = np.inf if end_time is None else end_time self._run_until(end_time, event_chunk) self.finalise_tables() "Completed at time=%g nodes=%d edges=%d", self.time, self.num_nodes, self.num_edges, ) def get_tree_sequence(self, mutation_generator=None, provenance_record=None): """ Returns a TreeSequence representing the state of the simulation. """ if mutation_generator is not None: mutation_generator.generate(super().tables) tables = self.tables if provenance_record is not None: tables.provenances.add_row(provenance_record) if self._hl_from_ts is None: # Add the populations with metadata assert len(tables.populations) == self.demography.num_populations tables.populations.clear() for population in self.demography.populations: tables.populations.add_row( metadata=population.temporary_hack_for_encoding_old_style_metadata() ) return tables.tree_sequence()
[docs]class RecombinationMap: """ A RecombinationMap represents the changing rates of recombination along a chromosome. This is defined via two lists of numbers: ``positions`` and ``rates``, which must be of the same length. Given an index j in these lists, the rate of recombination per base per generation is ``rates[j]`` over the interval ``positions[j]`` to ``positions[j + 1]``. Consequently, the first position must be zero, and by convention the last rate value is also required to be zero (although it is not used). .. warning:: The ``num_loci`` parameter is deprecated. To set a discrete number of possible recombination sites along the sequence, scale ``positions`` to the desired number of sites and set ``discrete=True`` to ensure recombination occurs only at integer values. :param list positions: The positions (in bases) denoting the distinct intervals where recombination rates change. These can be floating point values. :param list rates: The list of rates corresponding to the supplied ``positions``. Recombination rates are specified per base, per generation. :param int num_loci: **This parameter is deprecated**. The maximum number of non-recombining loci in the underlying simulation. By default this is set to the largest possible value, allowing the maximum resolution in the recombination process. However, for a finite sites model this can be set to smaller values. :param bool discrete: Whether recombination can occur only at integer positions. When ``False``, recombination sites can take continuous values. To simulate a fixed number of loci, set this parameter to ``True`` and scale ``positions`` to span the desired number of loci. """ def __init__(self, positions, rates, num_loci=None, discrete=False, map_start=0): if num_loci is not None: if num_loci == positions[-1]: warnings.warn("num_loci is no longer supported and should not be used.") else: raise ValueError( "num_loci does not match sequence length. " "To set a discrete number of recombination sites, " "scale positions to span the desired number of loci " "and set discrete=True" ) self._ll_recombination_map = _msprime.RecombinationMap( positions, rates, discrete ) self.map_start = map_start
[docs] @classmethod def uniform_map(cls, length, rate, num_loci=None, discrete=False): """ Returns a :class:`.RecombinationMap` instance in which the recombination rate is constant over a chromosome of the specified length. The optional ``discrete`` controls whether recombination sites can occur only on integer positions or can take continuous values. The legacy ``num_loci`` option is no longer supported and should not be used. The following map can be used to simulate a true finite locus model with a fixed number of loci ``m``:: >>> recomb_map = RecombinationMap.uniform_map(m, rate, discrete=True) :param float length: The length of the chromosome. :param float rate: The rate of recombination per unit of sequence length along this chromosome. :param int num_loci: This parameter is no longer supported. :param bool discrete: Whether recombination can occur only at integer positions. When ``False``, recombination sites can take continuous values. To simulate a fixed number of loci, set this parameter to ``True`` and set ``length`` to the desired number of loci. """ return cls([0, length], [rate, 0], num_loci=num_loci, discrete=discrete)
[docs] @classmethod def read_hapmap(cls, filename): """ Parses the specified file in HapMap format. These files must be white-space-delimited, and contain a single header line (which is ignored), and then each subsequent line contains the starting position and recombination rate for the segment from that position (inclusive) to the starting position on the next line (exclusive). Starting positions of each segment are given in units of bases, and recombination rates in centimorgans/Megabase. The first column in this file is ignored, as are additional columns after the third (Position is assumed to be the second column, and Rate is assumed to be the third). If the first starting position is not equal to zero, then a zero-recombination region is inserted at the start of the chromosome. A sample of this format is as follows:: Chromosome Position(bp) Rate(cM/Mb) Map(cM) chr1 55550 2.981822 0.000000 chr1 82571 2.082414 0.080572 chr1 88169 2.081358 0.092229 chr1 254996 3.354927 0.439456 chr1 564598 2.887498 1.478148 ... chr1 182973428 2.512769 122.832331 chr1 183630013 0.000000 124.482178 :param str filename: The name of the file to be parsed. This may be in plain text or gzipped plain text. """ positions = [] rates = [] if filename.endswith(".gz"): f = else: f = open(filename) try: # Skip the header line f.readline() for j, line in enumerate(f): pos, rate, = map(float, line.split()[1:3]) if j == 0: map_start = pos if pos != 0: positions.append(0) rates.append(0) positions.append(pos) # Rate is expressed in centimorgans per megabase, which # we convert to per-base rates rates.append(rate * 1e-8) if rate != 0: raise ValueError( "The last rate provided in the recombination map must be zero" ) finally: f.close() return cls(positions, rates, map_start=map_start)
@property def mean_recombination_rate(self): """ Return the weighted mean recombination rate across all windows of the entire recombination map. """ chrom_length = self._ll_recombination_map.get_sequence_length() positions = self._ll_recombination_map.get_positions() positions_diff = self._ll_recombination_map.get_positions()[1:] positions_diff = np.append(positions_diff, chrom_length) window_sizes = positions_diff - positions weights = window_sizes / chrom_length if self.map_start != 0: weights[0] = 0 rates = self._ll_recombination_map.get_rates() return np.average(rates, weights=weights)
[docs] def slice(self, start=None, end=None, trim=False): # noqa: A003 """ Returns a subset of this recombination map between the specified end points. If start is None, it defaults to 0. If end is None, it defaults to the end of the map. If trim is True, remove the flanking zero recombination rate regions such that the sequence length of the new recombination map is end - start. """ positions = self.get_positions() rates = self.get_rates() if start is None: i = 0 start = 0 if end is None: end = positions[-1] j = len(positions) if ( start < 0 or end < 0 or start > positions[-1] or end > positions[-1] or start > end ): raise IndexError(f"Invalid subset: start={start}, end={end}") if start != 0: i = bisect.bisect_left(positions, start) if start < positions[i]: i -= 1 if end != positions[-1]: j = bisect.bisect_right(positions, end, lo=i) new_positions = list(positions[i:j]) new_rates = list(rates[i:j]) new_positions[0] = start if end > new_positions[-1]: new_positions.append(end) new_rates.append(0) else: new_rates[-1] = 0 if trim: new_positions = [pos - start for pos in new_positions] else: if new_positions[0] != 0: if new_rates[0] == 0: new_positions[0] = 0 else: new_positions.insert(0, 0) new_rates.insert(0, 0.0) if new_positions[-1] != positions[-1]: new_positions.append(positions[-1]) new_rates.append(0) return self.__class__(new_positions, new_rates, discrete=self.discrete)
def __getitem__(self, key): """ Use slice syntax for obtaining a recombination map subset. E.g. >>> recomb_map_4m_to_5m = recomb_map[4e6:5e6] """ if not isinstance(key, slice) or key.step is not None: raise TypeError("Only interval slicing is supported") start, end = key.start, key.stop if start is not None and start < 0: start += self.get_sequence_length() if end is not None and end < 0: end += self.get_sequence_length() return self.slice(start=start, end=end, trim=True) def get_ll_recombination_map(self): return self._ll_recombination_map def physical_to_genetic(self, physical_x): return self._ll_recombination_map.position_to_mass(physical_x) def physical_to_discrete_genetic(self, physical_x): raise ValueError("Discrete genetic space is no longer supported") def genetic_to_physical(self, genetic_x): return self._ll_recombination_map.mass_to_position(genetic_x) def get_total_recombination_rate(self): return self._ll_recombination_map.get_total_recombination_rate() def get_per_locus_recombination_rate(self): raise ValueError("Genetic loci are no longer supported") def get_size(self): return self._ll_recombination_map.get_size() def get_num_loci(self): raise ValueError("num_loci is no longer supported") def get_positions(self): # For compatability with existing code we convert to a list return list(self._ll_recombination_map.get_positions()) def get_rates(self): # For compatability with existing code we convert to a list return list(self._ll_recombination_map.get_rates()) def get_sequence_length(self): return self._ll_recombination_map.get_sequence_length() def get_length(self): # Deprecated: use sequence_length instead return self.get_sequence_length() @property def discrete(self): return self._ll_recombination_map.get_discrete() def asdict(self): return { "positions": self.get_positions(), "rates": self.get_rates(), "discrete": self.discrete, "map_start": self.map_start, }
# TODO update the documentation here to state that using this class is # deprecated, and users should use the model=[...] notation instead.
[docs]@attr.s class SimulationModelChange: """ An event representing a change of underlying :ref:`simulation model <sec_api_simulation_models>`. :param float time: The time at which the simulation model changes to the new model, in generations. After this time, all internal tree nodes, edges and migrations are the result of the new model. If time is set to None (the default), the model change will occur immediately after the previous model has completed. If time is a callable, the time at which the simulation model changes is the result of calling this function with the time that the previous model started with as a parameter. :param model: The new simulation model to use. This can either be a string (e.g., ``"smc_prime"``) or an instance of a simulation model class (e.g, ``msprime.DiscreteTimeWrightFisher(100)``. Please see the :ref:`sec_api_simulation_models` section for more details on specifying simulations models. If the argument is a string, the reference population size is set from the top level ``Ne`` parameter to :func:`.simulate`. If this is None (the default) the model is changed to the standard coalescent with a reference_size of Ne (if model was not specified). :type model: str or simulation model instance """ time = attr.ib(default=None) model = attr.ib(default=None) def asdict(self): return attr.asdict(self)
@attr.s class SimulationModel: """ Abstract superclass of all simulation models. """ name = None def get_ll_representation(self): return {"name":} def asdict(self): return attr.asdict(self)
[docs]class StandardCoalescent(SimulationModel): """ The classical coalescent with recombination model (i.e., Hudson's algorithm). The string ``"hudson"`` can be used to refer to this model. This is the default simulation model. """ name = "hudson"
[docs]class SmcApproxCoalescent(SimulationModel): """ The original SMC model defined by McVean and Cardin. This model is implemented using a naive rejection sampling approach and so it may not be any more efficient to simulate than the standard Hudson model. The string ``"smc"`` can be used to refer to this model. """ name = "smc"
[docs]class SmcPrimeApproxCoalescent(SimulationModel): """ The SMC' model defined by Marjoram and Wall as an improvement on the original SMC. model is implemented using a naive rejection sampling approach and so it may not be any more efficient to simulate than the standard Hudson model. The string ``"smc_prime"`` can be used to refer to this model. """ name = "smc_prime"
[docs]class DiscreteTimeWrightFisher(SimulationModel): """ A discrete backwards-time Wright-Fisher model, with diploid back-and-forth recombination. The string ``"dtwf"`` can be used to refer to this model. Wright-Fisher simulations are performed very similarly to coalescent simulations, with all parameters denoting the same quantities in both models. Because events occur at discrete times however, the order in which they occur matters. Each generation consists of the following ordered events: - Migration events. As in the Hudson coalescent, these move single extant lineages between populations. Because migration events occur before lineages choose parents, migrant lineages choose parents from their new population in the same generation. - Demographic events. All events with `previous_generation < event_time <= current_generation` are carried out here. - Lineages draw parents. Each (monoploid) extant lineage draws a parent from their current population. - Diploid recombination. Each parent is diploid, so all child lineages recombine back-and-forth into the same two parental genome copies. These become two independent lineages in the next generation. - Historical sampling events. All historical samples with `previous_generation < sample_time <= current_generation` are inserted. """ name = "dtwf"
class WrightFisherPedigree(SimulationModel): # TODO Complete documentation. # TODO Since the pedigree is a necessary parameter for this simulation # model and it cannot be used with any other model we should make it a # parametric model where the parameter is the pedigree. This would # streamline a bunch of logic. """ Backwards-time simulations through a pre-specified pedigree, with diploid individuals and back-and-forth recombination. The string ``"wf_ped"`` can be used to refer to this model. """ name = "wf_ped" class ParametricSimulationModel(SimulationModel): """ The superclass of simulation models that require extra parameters. """ def get_ll_representation(self): d = super().get_ll_representation() d.update(self.__dict__) return d
[docs]@attr.s class BetaCoalescent(ParametricSimulationModel): """ A diploid Xi-coalescent with up to four simultaneous multiple mergers and crossover recombination. There are two main differences between the Beta-Xi-coalescent and the standard coalescent. Firstly, the number of lineages that take part in each common ancestor event is random, with distribution determined by moments of the :math:`Beta(2 - \\alpha, \\alpha)`-distribution. In particular, when there are :math:`n` lineages, each set of :math:`k \\leq n` of them participates in a common ancestor event at rate .. math:: \\frac{8}{B(2 - \\alpha, \\alpha)} \\int_0^1 x^{k - \\alpha - 1} (1 - x)^{n - k + \\alpha - 1} dx, where :math:`B(2 - \\alpha, \\alpha)` is the Beta-function. In a common ancestor event, all participating lineages are randomly split into four groups, corresponding to the four parental chromosomes in a diploid, bi-parental reproduction event. All lineages within each group merge simultaneously. .. warning:: The prefactor of 8 in the common ancestor event rate arises as the product of two terms. A factor of 4 compensates for the fact that only one quarter of binary common ancestor events result in a merger due to diploidy. A further factor of 2 is included for consistency with the implementation of the Hudson model, in which :math:`n` lineages undergo binary mergers at rate :math:`n (n - 1)`. Secondly, the time scale predicted by the Beta-Xi-coalescent is proportional to :math:`N_e^{\\alpha - 1}` generations, where :math:`N_e` is the effective population size. Specifically, one unit of coalescent time corresponds to a number of generations given by .. math:: \\frac{m^{\\alpha} N_e^{\\alpha - 1}}{\\alpha B(2 - \\alpha, \\alpha)}, where .. math:: m = 2 + \\frac{2^{\\alpha}}{3^{\\alpha - 1} (\\alpha - 1)}. Note that the time scale depends both on the effective population size :math:`N_e` and :math:`\\alpha`, and can be dramatically shorter than the timescale of the standard coalescent. Thus, effective population sizes must often be many orders of magnitude larger than census population sizes. The per-generation recombination rate is rescaled similarly to obtain the population-rescaled recombination rate. See `Schweinsberg (2003) <>`_ for the derivation of the common ancestor event rate, as well as the time scaling. Note however that the model of Schweinsberg (2003) is haploid, so that all participating lineages merge in a common ancestor event without splitting into four groups. :param float alpha: Determines the degree of skewness in the family size distribution, and must satisfy :math:`1 < \\alpha < 2`. Smaller values of :math:`\\alpha` correspond to greater skewness, and :math:`\\alpha = 2` would coincide with the standard coalescent. :param float truncation_point: Determines the maximum fraction of the population replaced by offspring in one reproduction event, and must satisfy :math:`0 < \\tau \\leq 1`, where :math:`\\tau` is the truncation point. The default is :math:`\\tau = 1`, which corresponds to the standard Beta-Xi-coalescent. When :math:`\\tau < 1`, the number of lineages participating in a common ancestor event is determined by moments of the :math:`Beta(2 - \\alpha, \\alpha)` distribution conditioned on not exceeding :math:`\\tau`, and the Beta-function in the expression for the time scale is also replaced by the incomplete Beta function :math:`Beta(\\tau; 2 - \\alpha, \\alpha)`. """ name = "beta" alpha = attr.ib(default=None) truncation_point = attr.ib(default=1)
[docs]@attr.s class DiracCoalescent(ParametricSimulationModel): """ A diploid Xi-coalescent with up to four simultaneous multiple mergers and crossover recombination. The Dirac-Xi-coalescent is an implementation of the model of `Blath et al. (2013) <>`_ The simulation proceeds similarly to the standard coalescent. In addition to binary common ancestor events at rate :math:`n (n - 1)` when there are :math:`n` lineages, potential multiple merger events take place at rate :math:`2 c > 0`. Each lineage participates in each multiple merger event independently with probability :math:`0 < \\psi \\leq 1`. All participating lineages are randomly split into four groups, corresponding to the four parental chromosomes present in a diploid, bi-parental reproduction event, and the lineages within each group merge simultaneously. .. warning:: The Dirac-Xi-coalescent is obtained as a scaling limit of Moran models, rather than Wright-Fisher models. As a consequence, one unit of coalescent time is proportional to :math:`N_e^2` generations, rather than :math:`N_e` generations as in the standard coalescent. However, the coalescent recombination rate is obtained from the per-generation recombination probability by rescaling with :math:`N_e`. See :ref:`sec_tutorial_multiple_mergers` for an illustration of how this affects simulation output in practice. :param float c: Determines the rate of potential multiple merger events. We require :math:`c > 0`. :param float psi: Determines the fraction of the population replaced by offspring in one large reproduction event, i.e. one reproduction event giving rise to potential multiple mergers when viewed backwards in time. We require :math:`0 < \\psi \\leq 1`. """ name = "dirac" psi = attr.ib(default=None) c = attr.ib(default=None)
@attr.s class SweepGenicSelection(ParametricSimulationModel): # TODO document and finalise the API name = "sweep_genic_selection" position = attr.ib(default=None) start_frequency = attr.ib(default=None) end_frequency = attr.ib(default=None) alpha = attr.ib(default=None) dt = attr.ib(default=None)