lamindb.Transform

class lamindb.Transform(key: str | None = None, type: TransformType | None = None, version: str | None = None, description: str | None = None, reference: str | None = None, reference_type: str | None = None, source_code: str | None = None, revises: Transform | None = None)

Bases: SQLRecord, IsVersioned

Data transformations such as scripts, notebooks, functions, or pipelines.

A transform can be a function, a script, a notebook, or a pipeline. If you execute a transform, you generate a run (Run). A run has inputs and outputs.

Pipelines are typically created with a workflow tool (Nextflow, Snakemake, Prefect, Flyte, Dagster, redun, Airflow, …).

Transforms are versioned so that a given transform version maps on a given source code version.

Can I sync transforms to git?

If you switch on sync_git_repo a script-like transform is synched to its hashed state in a git repository upon calling ln.track():

ln.settings.sync_git_repo = "https://github.com/laminlabs/lamindb"
ln.track()

Alternatively, you create transforms that map pipelines via Transform.from_git().

The definition of transforms and runs is consistent with the OpenLineage specification where a transform would be called a “job” and a run a “run”.

Parameters:

  • key – str | None = None A short name or path-like semantic key.

  • type – TransformType | None = "pipeline" See TransformType.

  • version – str | None = None A version string.

  • description – str | None = None A description.

  • reference – str | None = None A reference, e.g., a URL.

  • reference_type – str | None = None A reference type, e.g., ‘url’.

  • source_code – str | None = None Source code of the transform.

  • revises – Transform | None = None An old version of the transform.

See also

track()

Track a script or notebook run.

Run

Executions of transforms.

Notes

Examples

Create a transform for a pipeline:

transform = ln.Transform(key="Cell Ranger", version="7.2.0", type="pipeline").save()

Create a transform from a notebook:

ln.track()

Attributes

property latest_run: Run

The latest run of this transform.

property stem_uid: str

Universal id characterizing the version family.

The full uid of a record is obtained via concatenating the stem uid and version information:

stem_uid = random_base62(n_char)  # a random base62 sequence of length 12 (transform) or 16 (artifact, collection)
version_uid = "0000"  # an auto-incrementing 4-digit base62 number
uid = f"{stem_uid}{version_uid}"  # concatenate the stem_uid & version_uid
property versions: QuerySet

Lists all records of the same version family.

>>> new_artifact = ln.Artifact(df2, revises=artifact).save()
>>> new_artifact.versions()

Simple fields

uid: str

Universal id.

key: str | None

A name or “/”-separated path-like string.

All transforms with the same key are part of the same version family.

description: str | None

A description.

type: TransformType

TransformType (default "pipeline").

source_code: str | None

Source code of the transform.

hash: str | None

Hash of the source code.

reference: str | None

Reference for the transform, e.g., a URL.

reference_type: str | None

Reference type of the transform, e.g., ‘url’.

created_at: datetime

Time of creation of record.

updated_at: datetime

Time of last update to record.

version: str | None

Version (default None).

Defines version of a family of records characterized by the same stem_uid.

Consider using semantic versioning with Python versioning.

is_latest: bool

Boolean flag that indicates whether a record is the latest in its version family.

is_locked: bool

Whether the record is locked for edits.

Relational fields

branch: Branch

Whether record is on a branch or in another “special state”.

space: Space

The space in which the record lives.

created_by: User

Creator of record.

ulabels: ULabel

ULabel annotations of this transform.

predecessors: Transform

Preceding transforms.

Allows to _manually_ define predecessors. Is typically not necessary as data lineage is automatically tracked via runs whenever an artifact or collection serves as an input for a run.

runs: Run

Runs of this transform.

successors: Transform

Subsequent transforms.

See predecessors.

references: Reference

Linked references.

projects: Project

Linked projects.

blocks: TransformBlock

Blocks that annotate this artifact.

Class methods

classmethod from_git(url, path, key=None, version=None, entrypoint=None, branch=None)

Create a transform from a path in a git repository.

Parameters:

  • url (str) – URL of the git repository.

  • path (str) – Path to the file within the repository.

  • key (str | None, default: None) – Optional key for the transform.

  • version (str | None, default: None) – Optional version tag to checkout in the repository.

  • entrypoint (str | None, default: None) – Optional entrypoint for the transform.

  • branch (str | None, default: None) – Optional branch to checkout.

Return type:

Transform

Examples

Create from a Nextflow repo and auto-infer the commit hash from its latest version:

transform = ln.Transform.from_git(
    url="https://github.com/openproblems-bio/task_batch_integration",
    path="main.nf"
).save()

Create from a Nextflow repo and checkout a specific version:

transform = ln.Transform.from_git(
    url="https://github.com/openproblems-bio/task_batch_integration",
    path="main.nf",
    version="v2.0.0"
).save()
assert transform.version == "v2.0.0"

Create a sliding transform from a Nextflow repo’s dev branch. Unlike a regular transform, a sliding transform doesn’t pin a specific source code state, but adapts to whatever the referenced state on the branch is:

transform = ln.Transform.from_git(
    url="https://github.com/openproblems-bio/task_batch_integration",
    path="main.nf",
    branch="dev",
    version="dev",
).save()

Notes

A regular transform pins a specific source code state through its commit hash:

transform.source_code
#> repo: https://github.com/openproblems-bio/task_batch_integration
#> path: main.nf
#> commit: 68eb2ecc52990617dbb6d1bb5c7158d9893796bb

A sliding transform infers the source code state from a branch:

transform.source_code
#> repo: https://github.com/openproblems-bio/task_batch_integration
#> path: main.nf
#> branch: dev

If an entrypoint is provided, it is added to the source code below the path, e.g.:

transform.source_code
#> repo: https://github.com/openproblems-bio/task_batch_integration
#> path: main.nf
#> entrypoint: myentrypoint
#> commit: 68eb2ecc52990617dbb6d1bb5c7158d9893796bb
classmethod filter(*queries, **expressions)

Query records.

Parameters:

  • queries – One or multiple Q objects.

  • expressions – Fields and values passed as Django query expressions.

Return type:

QuerySet

See also

Examples

>>> ln.Project(name="my label").save()
>>> ln.Project.filter(name__startswith="my").to_dataframe()
classmethod get(idlike=None, **expressions)

Get a single record.

Parameters:

  • idlike (int | str | None, default: None) – Either a uid stub, uid or an integer id.

  • expressions – Fields and values passed as Django query expressions.

Raises:

lamindb.errors.DoesNotExist – In case no matching record is found.

Return type:

SQLRecord

See also

Examples

ulabel = ln.ULabel.get("FvtpPJLJ")
ulabel = ln.ULabel.get(name="my-label")
classmethod to_dataframe(include=None, features=False, limit=100)

Evaluate and convert to pd.DataFrame.

By default, maps simple fields and foreign keys onto DataFrame columns.

Guide: Query & search registries

Parameters:

  • include (str | list[str] | None, default: None) – Related data to include as columns. Takes strings of form "records__name", "cell_types__name", etc. or a list of such strings. For Artifact, Record, and Run, can also pass "features" to include features with data types pointing to entities in the core schema. If "privates", includes private fields (fields starting with _).

  • features (bool | list[str], default: False) – Configure the features to include. Can be a feature name or a list of such names. If "queryset", infers the features used within the current queryset. Only available for Artifact, Record, and Run.

  • limit (int, default: 100) – Maximum number of rows to display. If None, includes all results.

  • order_by – Field name to order the records by. Prefix with ‘-’ for descending order. Defaults to ‘-id’ to get the most recent records. This argument is ignored if the queryset is already ordered or if the specified field does not exist.

Return type:

DataFrame

Examples

Include the name of the creator:

ln.Record.to_dataframe(include="created_by__name"])

Include features:

ln.Artifact.to_dataframe(include="features")

Include selected features:

ln.Artifact.to_dataframe(features=["cell_type_by_expert", "cell_type_by_model"])
classmethod search(string, *, field=None, limit=20, case_sensitive=False)

Search.

Parameters:

  • string (str) – The input string to match against the field ontology values.

  • field (str | DeferredAttribute | None, default: None) – The field or fields to search. Search all string fields by default.

  • limit (int | None, default: 20) – Maximum amount of top results to return.

  • case_sensitive (bool, default: False) – Whether the match is case sensitive.

Return type:

QuerySet

Returns:

A sorted DataFrame of search results with a score in column score. If return_queryset is True. QuerySet.

See also

filter() lookup()

Examples

>>> ulabels = ln.ULabel.from_values(["ULabel1", "ULabel2", "ULabel3"], field="name")
>>> ln.save(ulabels)
>>> ln.ULabel.search("ULabel2")
classmethod lookup(field=None, return_field=None)

Return an auto-complete object for a field.

Parameters:

  • field (str | DeferredAttribute | None, default: None) – The field to look up the values for. Defaults to first string field.

  • return_field (str | DeferredAttribute | None, default: None) – The field to return. If None, returns the whole record.

  • keep – When multiple records are found for a lookup, how to return the records. - "first": return the first record. - "last": return the last record. - False: return all records.

Return type:

NamedTuple

Returns:

A NamedTuple of lookup information of the field values with a dictionary converter.

See also

search()

Examples

>>> import bionty as bt
>>> bt.settings.organism = "human"
>>> bt.Gene.from_source(symbol="ADGB-DT").save()
>>> lookup = bt.Gene.lookup()
>>> lookup.adgb_dt
>>> lookup_dict = lookup.dict()
>>> lookup_dict['ADGB-DT']
>>> lookup_by_ensembl_id = bt.Gene.lookup(field="ensembl_gene_id")
>>> genes.ensg00000002745
>>> lookup_return_symbols = bt.Gene.lookup(field="ensembl_gene_id", return_field="symbol")
classmethod using(instance)

Use a non-default LaminDB instance.

Parameters:

instance (str | None) – An instance identifier of form “account_handle/instance_name”.

Return type:

QuerySet

Examples

>>> ln.ULabel.using("account_handle/instance_name").search("ULabel7", field="name")
            uid    score
name
ULabel7  g7Hk9b2v  100.0
ULabel5  t4Jm6s0q   75.0
ULabel6  r2Xw8p1z   75.0

Methods

describe(return_str=False)

Describe record including relations.

Parameters:

return_str (bool, default: False) – Return a string instead of printing.

Return type:

None | str

view_lineage(with_successors=False, distance=5)

View lineage of transforms.

Note that this only accounts for manually defined predecessors and successors.

Auto-generate lineage through inputs and outputs of runs is not included.

restore()

Restore from trash onto the main branch.

Return type:

None

delete(permanent=None, **kwargs)

Delete record.

Parameters:

permanent (bool | None, default: None) – Whether to permanently delete the record (skips trash). If None, performs soft delete if the record is not already in the trash.

Return type:

None

Examples

For any SQLRecord object record, call:

>>> record.delete()
save(*args, **kwargs)

Save.

Always saves to the default database.

Return type:

TypeVar(T, bound= SQLRecord)