Migrating from previous versions of pyslim¶
A number of features that were first introduced in pyslim have been made part of core
tskit functionality. For instance, reference sequence support was provided (although
loosely) inpyslim to support SLiM’s nucleotide models, but is now part of a standard
tskit tskit.TreeSequence. Similarly, metadata processing in tskit made
code to do this within pyslim obsolete; this “legacy metadata” code has been removed
and instructions for how to migrate your code are below.
In fact, we are now at the (very good) place where we don’t really need
the pyslim.SlimTreeSequence class any longer,
and it will soon be deprecated.
So, pyslim is migrating to be purely functional: instead of providing the SlimTreeSequence
class with specialized methods, all methods will be functions of TreeSequences,
that take in a tree sequence and return something
(a modified tree sequence or some summary of it).
Backwards compatibility will be maintained for some time, but we request that you
switch over sooner, as your code will be cleaner and faster.
To migrate, you should:
Replace
ts.slim_generationwithts.metadata['SLiM']['generation'], andts.model_typewithts.metadata['SLiM']['model_type'].Replace
ts.reference_sequencewithts.reference_sequence.data.Replace calls to
ts.recapitate(...)withpyslim.recapitate(ts, ...), and similarly with other SlimTreeSequence methods.
If you encounter difficulties, please post an issue or discussion on github.
Legacy metadata¶
In previous versions of pyslim,
SLiM-specific metadata was provided as customized objects:
for instance, for a node n provided by a SlimTreeSequence,
we’d have n.metadata as a NodeMetadata object,
with attributes n.metadata.slim_id and n.metadata.is_null and n.metadata.genome_type.
However, with tskit 0.3,
the capacity to deal with structured metadata
was implemented in tskit itself,
and so pyslim shifted to using the tskit-native metadata tools.
As a result, parsed metadata is provided as a dictionary instead of an object,
so that now n.metadata would be a dict,
with entries n.metadata["slim_id"] and n.metadata["is_null"] and n.metadata["genome_type"].
Annotation should be done with tskit methods (e.g., packset_metadata).
.. note::
Until pyslim version 0.600, the old-style metadata was still available,
but this functionality has been removed.
Here are more detailed notes on how to migrate a script from the legacy metadata handling. If you run into issues, please ask (open a discussion on github).
1. Use top-level metadata instead of slim_provenance:
previously, information about the model type and the time counter (generation)
in SLiM was provided in the Provenances table, made available through
the ts.slim_provenance object. This is still available but deprecated,
and should be obtained from the top-level metadata object, ts.metadata["SLiM"].
So, in your scripts ts.slim_provenance.model_type should be replaced with
ts.metadata["SLiM"]["model_type"],
and (although it’s not deprecated), probably ts.slim_generation should
probably be replaced with
ts.metadata["SLiM"]["generation"].
2. Switch metadata objects to dicts:
if md is the metadata property of a population, individual, or node,
this means replacing md.X with md["X"].
The migration_records property of population metadata is similarly
a list of dicts rather than a list of objects, so instead of
ts.population(1).metadata.migration_records[0].source_subpop
we would write
ts.population(1).metadata["migration_records"][0]["source_subpop"].
Mutations were previously a bit different - if mut is a mutation
(e.g., mut = ts.mutation(0))
then mut.metadata was previously a list of MutationMetadata objects.
Now, mut.metadata is a dict, with a single entry:
mut.metadata["mutation_list"] is a list of dicts, each containing the information
that was previously in the MutationMetadata objects.
So, for instance, instead of mut.metadata[0].selection_coeff
we would write mut.metadata["mutation_list"][0]["selection_coeff"].
3. The decode_X and encode_X methods are now deprecated,
as this is handled by tskit itself.
For instance, encode_node would take a NodeMetadata object
and produce the raw bytes necessary to encode it in a Node table,
and decode_node would do the inverse operation.
This is now handled by the relevant MetadataSchema object:
for nodes one can obtain this as nms = ts.tables.nodes.metadata_schema,
which has the methods nms.validate_and_encode_row and nms.decode_row.
Decoding is for the most part not necessary,
since the metadata is automatically decoded,
but pyslim.decode_node(raw_md) could be replaced by nms.decode_row(raw_md).
Encoding is necessary to modify tables,
and pyslim.encode_node(md) can be replaced by nms.validate_and_encode_row(md)
(where furthermore md should now be a dict rather than a NodeMetadata object).
4. The annotate_X_metadata methods are deprecated,
as again tskit has tools to do this.
These methods would set the metadata column of a table -
for instance, if metadata is a list of NodeMetadata objects, then
annotate_node_metadata(tables, metadata) would modify tables.nodes in place
to contain the (encoded) metadata in the list metadata.
Now, this could be done as follows (where now metadata is a list of metadata dicts):
metadata = [ {'slim_id': k, 'is_null': False, 'genome_type': 0}
for k in range(tables.nodes.num_rows) ]
nms = tables.nodes.metadata_schema
tables.nodes.packset_metadata(
[nms.validate_and_encode_row(r) for r in metadata]
)
If speed is an issue, then encode_row can be substituted for validate_and_encode_row,
but at the risk of missing errors in metadata.
5. the extract_X_metadata methods are not necessary,
since the metadata in the tables of a TableCollection are automatically decoded.
For instance, [ind.metadata["sex"] for ind in tables.individuals] will obtain
a list of sexes of the individuals in the IndividualTable.
Warning
It is our intention to remain backwards-compatible for a time. However, the legacy code will disappear at some point in the future, so please migrate over scripts you intend to rely on.
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483184a (deprecation start)