Metadata

Overview

SLiM puts SLiM-specific information into the metadata for the tree sequence, as well as for each populations, individuals, nodes and mutations. Here is a quick reference to what information is available: see the SLiM manual for the more technical writeup. A good way to get a generic metadata example is with default_slim_metadata().

Top-level: If ts is your tree sequence, then ts.metadata is a dict, and ts.metadata["SLiM"] contains information about the simulation:

  • file_version: the version of the SLiM tree sequence file format

  • generation: the value of sim.generation within SLiM when the file was written out

  • model_type: either "WF" or "nonWF"

  • nucleotide_based: whether this is a nucleotide-based simulation

  • separate_sexes: whether the simulation has separate sexes or not

  • spatial_dimensionality: for instance, "" or "x" or "xy" (etcetera)

  • spatial_periodicity: whether space wraps around in some directions (same format as dimensionality)

  • stage: the stage of the life cycle at which the file was written out (either "first", "early", or "late")

Populations: Information about each SLiM-produced population is written to metatadata. The format uses JSON and is extensible, so other keys may be present and some keys may be missing (for instance, there are no spatial bounds in a nonspatial simulation). The metadata may be None for populations that SLiM did not use. The keys that SLiM uses are:

  • slim_id: the ID of this population in SLiM

  • name: the name of the population (by default, p0, p1, etcetera)

  • description: a string describing the population

  • selfing_fraction, female_cloning_fraction, male_cloning_fraction, and sex_ratio: only present when applicable (e.g., in WF simulations)

  • bounds_x0, bounds_x1, bounds_y0, bounds_y1, bounds_z0, and bounds_z1: the spatial bounds, when applicable

  • migration_records: A list of entries decribing migration between populations in a WF model.

Individuals: Each individual produced by SLiM contains the following metadata:

  • pedigree_id: the “pedigree ID”, unique within the SLiM simulation

  • pedigree_p1, pedigree_p2: the pedigree IDs of the individuals’ two parents (they may be equal in the case of selfing, or -1 to indicate no parent, in the case of the initial generation or for cloning)

  • age: the .age property within SLiM at the time the file was written out

  • subpopulation: the subpopulation within SLiM the individual was in at the time the file was written out

  • sex: the sex of the individual (either INDIVIDUAL_TYPE_FEMALE, INDIVIDUAL_TYPE_MALE, or INDIVIDUAL_TYPE_HERMAPHRODITE)

  • flags: additional information; currently only recording whether the individual was a “migrant” or not (see the SLiM manual)

Nodes: Each “node” produced by SLiM (i.e., “genome” within SLiM) has:

  • ‘slim_id’: the unique ID associated with the genome by SLiM

  • ‘is_null’: whether the genome is a “null” genome (in which case it isn’t really there, so shouldn’t have any mutations or relationships in the tree sequence!)

  • ‘genome_type’: the ‘type’ of this genome (0 for autosome, 1 for X, 2 for Y)

Mutations: Each mutation’s metadata is a dictionary with a single key, "mutation_list", whose entry is a list of metadata dictionaries corresponding to the mutations that are “stacked”, i.e., all present, in all genomes inheriting from this (tskit) mutation. So, ts.mutation(12).metadata["mutation_list"] is a list, each of whose entries contains:

  • mutation_type: the numeric ID of the MutationType within SLiM

  • selection_coeff: the selection coefficient

  • subpopulation: the numeric ID of the subpopulation the mutation occurred in

  • slim_time: the value of sim.generation when the mutation occurred

  • nucleotide: either -1 if there is no associated nucleotide, or the numeric code for the nucleotide (see NUCLEOTIDES)

Metadata tools

The dictionaries describing the schema for these metadata entries are available in slim_metadata_schemas. Furthermore, this method may be useful in working with metadata:

pyslim.default_slim_metadata(name)[source]

Returns default metadata of type name, where name is one of “tree_sequence”, “edge”, “site”, “mutation”, “mutation_list_entry”, “node”, “individual”, or “population”.

Parameters

name (str) – The type of metadata requested.

Rtype dict

Converting from SLiM time to tskit time

Note

This is a nitpicky, document-the-details section. Hopefully, you don’t have to deal with the specifics of converting between tskit and SLiM time, but this page is here for you if you do.

SLiM is a forwards simulator, while the tree sequence format thinks about things retrospectively, and so works with times in units of time ago. Mostly, you don’t have to convert between the two, unless you want to match up information in a tree sequence with information written out by SLiM itself. In other words, SLiM’s time counter measures the number of time steps (“generations”) since the start of the simulation, and times in the tree sequence record how long before the end of the simulation. However, there are Some Details, and off-by-one errors are easy to make, so we’ll spell it out in detail.

SLiM’s time counter is called the “generation” (although a “year” or “life cycle” would be a more appropriate name for a nonWF model). The SLiM generation starts at 1, and records which round of the life cycle the simulation is in. However, the order of the life cycle differs between WF and nonWF models: in a WF model, it is “early \(\to\) birth \(\to\) late”, while in a nonWF model, it is “birth \(\to\) early \(\to\) late”. Usually, the first set of individuals are created in the early() phase of generation 1, and so in a WF model reproduce immediately, in the same generation they were “born”. Parents and offspring cannot have the same birth time in the tree sequence, and so some clever bookkeeping was required. You’ll want to refer to the tables below to see what’s going on. “Time” in a tree sequence is actually time ago, or time before the tree sequence was recorded. To obtain this number, and ensure that offspring cannot have the same birth time-ago in the tree sequence as their parents, SLiM also keeps track of “how many birth phases of the life cycle have happened so far” (the column “# births” in the tables). As the simulation goes along, tskit time ago is recorded as minus one times the number of birth phases so far. When the tree sequence is output, the current cumulative number of birth phases is added to this, so “tskit time ago” is, equivalently, “how many birth phases happened since this time”. In a nonWF model, the two counters (“generation” and “number of birth phases”) are always in sync; but in a WF they are not (during early). The extra wrinkle this introduces is that the correspondence between “tskit time ago” and “SLiM time” depends on which phase the tree sequence was recorded in, but only for WF models.

To help keep all this straight, here are schematics for WF and nonWF models. (To see the nonWF model, click on the tab.)

For a WF model, the SLiM generation (first column) can be obtained by subtracting the tskit time ago from the SLiM generation at time of output only during the same stage that output occured in.

generation

stage

# births

tskit time ago, early output

tskit time ago, late output

1

early

0

\(\leftarrow\) add subpops

n-1

n

1

birth

1

n-2

n-1

1

late

1

n-2

n-1

2

early

1

n-2

n-1

2

birth

2

n-3

n-2

2

late

2

n-3

n-2

3

early

2

n-3

n-2

3

birth

3

n-4

n-3

3

late

3

n-4

n-3

\(\downarrow\)

\(\cdots\)

\(\downarrow\)

\(\uparrow\)

\(\uparrow\)

n-2

early

n-3

2

2

n-2

birth

n-2

1

2

n-2

late

n-2

1

2

n-1

early

n-2

1

2

n-1

birth

n-1

0

1

n-1

late

n-1

0

1

n

early

n-1

treeSeqOutput \(\to\)

0

1

n

birth

n

0

n

late

n

treeSeqOutput \(\to\)

0

Note that for nonWF models the SLiM generation (first column) can always be obtained by subtracting the tskit time ago from the SLiM generation at time of output.

generation

stage

# births

tskit time ago, early output

tskit time ago, late output

1

birth

1

n-1

n-1

1

early

1

\(\leftarrow\) add subpops

n-1

n-1

1

late

1

n-1

n-1

2

birth

2

n-2

n-2

2

early

2

n-2

n-2

2

late

2

n-2

n-2

3

birth

3

n-3

n-3

3

early

3

n-3

n-3

3

late

3

n-3

n-3

\(\downarrow\)

\(\cdots\)

\(\downarrow\)

\(\uparrow\)

\(\uparrow\)

n-2

birth

n-2

2

2

n-2

early

n-2

2

2

n-2

late

n-2

2

2

n-1

birth

n-1

1

1

n-1

early

n-1

1

1

n-1

late

n-1

1

1

n

birth

n

0

0

n

early

n

treeSeqOutput \(\to\)

0

0

n

late

n

treeSeqOutput \(\to\)

0

When the tree sequence is written out, SLiM records the value of its current generation, which can be found in the metadata: ts.metadata['SLiM']['generation'] (or, the ts.slim_generation attribute). In most cases, the “SLiM time” referred to by a time in the tree sequence (i.e., the value that would be reported by sim.generation within SLiM at the point in time thus referenced) can be obtained by subtracting time from ts.slim_generation. However, in WF models, birth happens between the “early()” and “late()” stages, so if the tree sequence was written out using sim.treeSeqOutput() during “early()” in a WF model, the tree sequence’s times measure time before the last set of individuals are born, i.e., before SLiM time step ts.slim_generation - 1. The stage that the tree sequence was saved is recorded in the metadata of the tree sequence, as ts.metadata['SLiM']['stage']. Using this, we can convert from the times of a tree sequence ts to SLiM time as follows:

def slim_time(ts, time, stage):
  slim_time = ts.metadata["SLiM"]["generation"] - time
  if ts.metadata['SLiM']['model_type'] == "WF":
    if (ts.metadata['SLiM']['stage'] == "early"
        and stage == "late"):
        slim_time -= 1
    if (ts.metadata['SLiM']['stage'] == "late"
        and stage == "early"):
        slim_time += 1
  return slim_time

This is what is computed by the SlimTreeSequence.slim_time() method (which also has a stage argument).

Some of the other methods in pyslim – those that depend on SlimTreeSequence.individuals_alive_at() – need you to tell them during which stage the tree sequence was saved with sim.treeSeqOutput, and need this to be the same as the stage that any individuals were saved with sim.treeSeqRememberIndividuals. This argument, remembered_stage, defaults to “late()”; we recommend that you also default to always Remembering individuals, and saving out the tree sequence, during “late()” as well, unless you have good reason not to. (This means you must specify the stage of the block in your SLiM script, since the stage defaults to “early()”!)

Modifying SLiM metadata

For more on working with metadata, see tskit’s metadata documentation.

Top-level metadata

The entries of the top-level metadata dict are read-only. So, you might think that tables.metadata["SLiM"]["model_type"] = "nonWF" would switch the model type, but this in fact (silently) does nothing. To modify the top-level metadata, we must (a) work with tables (as tree sequences are immutable, and (b) extract the metadata dict, modify the dict, and copy it back in. Instead, you should do

md = tables.metadata
md["SLiM"]["model_type"] = "nonWF"
tables.metadata = md

Modifying the top-level metadata could be used to set spatial bounds on an annotated msprime simulation, for instance. (This is recorded in the population metadata.)

Modifying SLiM metadata in tables

To modify the metadata that pyslim has introduced into the tree sequence produced by a coalescent simulation, or the metadata in a SLiM-produced tree sequence, we need to edit the TableCollection that forms the editable data behind the tree sequence. For instance, to set the ages of the individuals in the tree sequence to random numbers between 1 and 4, we will extract a copy of the underlying tables, clear it, and then iterate over the individuals in the tree sequence, as we go re-inserting them into the tables after replacing their metadata with a modified version:

tables = ts.dump_tables()
tables.individuals.clear()
for ind in ts.individuals():
    md = ind.metadata
    md["age"] = random.choice([1,2,3,4])
    _ = tables.individuals.append(
        ind.replace(metadata=md)
    )

mod_ts = tables.tree_sequence()

# check that it worked:
print("First ten ages:", [mod_ts.individual(i).metadata["age"] for i in range(10)])
for ind in mod_ts.individuals():
    assert ind.metadata['age'] in [1, 2, 3, 4]

# save out the tree sequence
mod_ts.dump("modified_ts.trees")
First ten ages: [3, 1, 1, 3, 4, 4, 3, 2, 2, 3]

Technical details

Metadata entries

SLiM records additional information in the metadata columns of Individual, Node, and Mutation tables, in a binary format using the python struct module. See tskit’s metadata documentation for details on how this works. Nothing besides this binary information can be stored in the metadata of these tables if the tree sequence is to be used by SLiM, and so when pyslim annotates an existing tree sequence, anything in those columns is overwritten. Population metadata is stored as JSON, however, which is more flexible. For more detailed documentation on the contents and format of the metadata, see the SLiM manual.

Of particular note is that nodes and populations may have empty metadata. SLiM will not use the metadata of nodes that are not associated with alive individuals, so this can safely be omitted (and makes recapitation easier). And, populations not used by SLiM will have empty metadata. All remaining metadata are required (besides edges and sites, whose metadata is not used at all).