kaptive.db.models¶

Data models and custom exception classes for Kaptive database representation.

This module provides data models, structured metadata schemas, and exception classes used by Database and DatabaseManager to encapsulate surface antigen reference database specifications, phenotype definitions, and vectorized phenotype matching containers.

Classes:

DatabaseError –

Custom exception for database loading, validation, and format errors.
DatabaseMetadata –

Strict schema for reference database metadata with attributes and validation.
Phenotype –

Single phenotype rule mapping loci and gene requirements to a serotype identifier.
Phenotypes –

Structure-of-Arrays (SoA) batch container for vectorized phenotype evaluation.

DatabaseError ¶


              flowchart TD
              kaptive.db.models.DatabaseError[DatabaseError]

              

              click kaptive.db.models.DatabaseError href "" "kaptive.db.models.DatabaseError"

Exception raised for database loading, metadata validation, or format errors.

This exception is raised when database metadata is invalid, required files are missing, or reference database files fail validation in DatabaseMetadata, Database, or DatabaseManager.

DatabaseMetadata `dataclass` ¶

Python

DatabaseMetadata(name: str, keyword: str, genbank: str, organism: str, taxon: int, antigen: str, pathway: str, version: str, id_threshold: float, doi: list[str], owner: str, repo: str, branch: str, contact: dict, phenotype_logic: dict, antigenic_units: dict)

Strict schema for Database metadata with dependency-free validation and ergonomic attribute access.

Represents the metadata associated with a Kaptive reference database, including organism details, locus pathway classifications, repository location, curator contact details, and phenotype logic rules. Used by Database and DatabaseManager.

Attributes:

name (str) –

The name of the database, e.g. 'Klebsiella pneumoniae Species Complex K'.
keyword (str) –

The database keyword, e.g. 'kpsc_k'.
genbank (str) –

The name of the main database file, e.g. 'Klebsiella_pneumoniae_Species_Complex_K.gbk'.
organism (str) –

The name of the database organism, e.g. 'Klebsiella pneumoniae Species Complex'.
taxon (int) –

The NCBI Taxonomy ID of the database organism, e.g. 3390273.
antigen (str) –

The name of the database antigen, e.g. 'Capsular polysaccharide'.
pathway (str) –

The name of the database antigen synthesis pathway, e.g. 'Wzx/Wzy-dependent'.
version (str) –

The version of the database, e.g. '3.2.1'.
id_threshold (float) –

The identity threshold of the database, e.g. 82.5.
doi (list[str]) –

A list of DOIs associated with the database, e.g. ['TBD'].
owner (str) –

The owner of the database Github repo, e.g. 'klebgenomics'.
repo (str) –

The name of the database Github repo, e.g. 'KpSC_surface_antigen_loci'.
branch (str) –

The branch of the database Github repo, e.g. 'main'.
contact (dict) –

The details of the database curators, e.g. {'Kelly Wyres': 'kaptive.typing@gmail.com'}.
phenotype_logic (dict) –

Phenotype logic rules defining required loci and genes.
antigenic_units (dict) –

Antigenic unit mappings.

Methods:

from_dict –

Instantiates a DatabaseMetadata object from a dictionary.

parsed_version `property` ¶

Python

parsed_version: tuple[int, ...]

Parses the semantic version string into a tuple of integers for numeric comparison.

Extracts numeric digit sequences from the DatabaseMetadata attribute and converts them into a tuple of integers (e.g., '3.2.1' becomes (3, 2, 1)).

Returns:

tuple[int, ...] –

tuple[int, ...]: A tuple of extracted integer components representing the database version.

from_dict `classmethod` ¶

Python

from_dict(data: dict) -> DatabaseMetadata

Instantiates a DatabaseMetadata object from a dictionary.

Validates required fields, casts numeric types, and sets default fallback dictionaries for phenotype logic and antigenic units.

Parameters:

data ¶
(dict) –

Dictionary containing metadata fields (e.g., parsed from JSON or TOML).

Returns:

DatabaseMetadata ( DatabaseMetadata ) –

Validated DatabaseMetadata instance.

Raises:

DatabaseError –

If data is not a dict, missing required keys, or contains invalid attribute types.

Source code in src/kaptive/db/models.py

Python
@classmethod
def from_dict(cls, data: dict) -> "DatabaseMetadata":  # type: ignore
    r"""Instantiates a `DatabaseMetadata` object from a dictionary.

    Validates required fields, casts numeric types, and sets default fallback dictionaries for
    phenotype logic and antigenic units.

    Args:
        data (dict): Dictionary containing metadata fields (e.g., parsed from JSON or TOML).

    Returns:
        DatabaseMetadata: Validated [`DatabaseMetadata`][kaptive.db.models.DatabaseMetadata] instance.

    Raises:
        DatabaseError: If `data` is not a dict, missing required keys, or contains invalid attribute types.
    """
    if not isinstance(data, dict):
        raise DatabaseError("Metadata must be a dictionary.")

    try:
        meta = cls(
            name=data["name"],
            keyword=data["keyword"],
            genbank=data["genbank"],
            organism=data["organism"],
            taxon=int(data["taxon"]),
            antigen=data["antigen"],
            pathway=data["pathway"],
            version=data["version"],
            id_threshold=float(data["id_threshold"]),
            doi=data["doi"],
            owner=data["owner"],
            repo=data["repo"],
            branch=data["branch"],
            contact=data["contact"],
            phenotype_logic=data.get("phenotype_logic", data.get("logic", {})),
            antigenic_units=data.get("antigenic_units", data.get("units", {})),
        )
    except KeyError as e:
        raise DatabaseError(f"Metadata is missing required field: {e.args[0]!r}")
    except ValueError as e:
        raise DatabaseError(f"Metadata has an invalid value type: {e}")

    return meta

Phenotype `dataclass` ¶

Python

Phenotype(id: str, loci: set[str], extra_genes: set[str], inactive_genes: set[str], priority: int = 50, as_suffix: bool = False)

Single locus phenotype rule mapping loci and gene requirements to a serotype identifier.

Defines criteria for assigning a specific phenotype (e.g., K-type or O-type serotype) based on identified reference loci, required extra genes, and forbidden inactive genes. Processed by Database into vectorized Phenotypes batches.

Attributes:

id (str) –

Unique phenotype or serotype identifier string.
loci (set[str]) –

Locus names in the database to which this phenotype applies.
extra_genes (set[str]) –

Set of gene cluster names that must all be present for this phenotype match.
inactive_genes (set[str]) –

Set of gene cluster names that must not be inactivated for this phenotype match.
priority (int) –

Sorting priority when resolving multiple matching phenotypes. Defaults to 50.
as_suffix (bool) –

Whether to append this phenotype identifier as a suffix to matching phenotypes. Defaults to False.

Phenotypes `dataclass` ¶

Python

Phenotypes(ids: NDArray[bytes_], locus_masks: NDArray[bool_], extra_masks: NDArray[int8], inactive_masks: NDArray[int8], extra_counts: NDArray[int8], priorities: NDArray[int8], as_suffix: NDArray[bool_])


              flowchart TD
              kaptive.db.models.Phenotypes[Phenotypes]
              kaptive.core.collections.BatchedContainer[BatchedContainer]

                              kaptive.core.collections.BatchedContainer --> kaptive.db.models.Phenotypes
                


              click kaptive.db.models.Phenotypes href "" "kaptive.db.models.Phenotypes"
              click kaptive.core.collections.BatchedContainer href "" "kaptive.core.collections.BatchedContainer"

Structure-of-Arrays (SoA) container for vectorized phenotype evaluation.

Encapsulates boolean matrix masks and priority arrays across a batch of Phenotype definitions for high-performance vectorized evaluation during serotyping. Inherits from BatchedContainer.

Attributes:

ids (NDArray[bytes_]) –

1D byte string array (e.g. S32) of phenotype identifier strings.
locus_masks (NDArray[bool_]) –

2D boolean array of shape (N, num_loci) indicating locus requirements.
extra_masks (NDArray[int8]) –

2D integer array of shape (N, num_extra_genes) for required extra genes.
inactive_masks (NDArray[int8]) –

2D integer array of shape (N, num_inactive_genes) for forbidden inactive genes.
extra_counts (NDArray[int8]) –

1D integer array storing the sum of extra required genes per phenotype.
priorities (NDArray[int8]) –

1D integer array of shape (N,) indicating resolution priority values.
as_suffix (NDArray[bool_]) –

1D boolean array of shape (N,) indicating if phenotype is used as a suffix.

Methods:

__getitem__ –

Slices or masks the Phenotypes container batch along the primary dimension.
__len__ –

Returns the number of phenotype records in the container batch.
concat –

Concatenates multiple Phenotypes batch containers into a single Phenotypes instance.
empty –

Constructs an empty Phenotypes batch container.
from_dict –

Reconstructs a Phenotypes batch container from a dictionary of array data.
to_dict –

Converts the Phenotypes container attributes into a dictionary representation.

getitem ¶

Python

__getitem__(item: int | slice | NDArray[Any] | list[int]) -> Any | Phenotypes

Slices or masks the Phenotypes container batch along the primary dimension.

Parameters:

item ¶
(int | slice | NDArray | list) –

Slice object, boolean mask array, or list of indices to select.

Returns:

Any | Phenotypes –

Any | Phenotypes: A new Phenotypes container containing the selected subset of records.

Raises:

NotImplementedError –

If a single integer index is provided, as scalar indexing is not supported on SoA containers.

Source code in src/kaptive/db/models.py

Python
def __getitem__(self, item: int | slice | npt.NDArray[Any] | list[int]) -> "Any | Phenotypes":
    r"""Slices or masks the `Phenotypes` container batch along the primary dimension.

    Args:
        item (int | slice | npt.NDArray | list): Slice object, boolean mask array, or list of indices to select.

    Returns:
        Any | Phenotypes: A new [`Phenotypes`][kaptive.db.models.Phenotypes] container containing
            the selected subset of records.

    Raises:
        NotImplementedError: If a single integer index is provided, as scalar indexing is not supported
            on SoA containers.
    """
    if isinstance(item, (int, np.integer)):
        raise NotImplementedError("Single item access not implemented for Phenotypes")
    return Phenotypes(
        ids=self.ids[item],
        locus_masks=self.locus_masks[item],
        extra_masks=self.extra_masks[item],
        inactive_masks=self.inactive_masks[item],
        extra_counts=self.extra_counts[item],
        priorities=self.priorities[item],
        as_suffix=self.as_suffix[item],
    )

len ¶

Python

__len__() -> int

Returns the number of phenotype records in the container batch.

Returns:

int ( int ) –

Number of phenotype records.

Source code in src/kaptive/db/models.py

Python
def __len__(self) -> int:
    r"""Returns the number of phenotype records in the container batch.

    Returns:
        int: Number of phenotype records.
    """
    return len(self.ids)

concat `classmethod` ¶

Python

concat(batches: Iterable[Self]) -> Self

Concatenates multiple Phenotypes batch containers into a single Phenotypes instance.

Parameters:

batches ¶
(Iterable[Phenotypes]) –

An iterable of Phenotypes container batches to concatenate.

Returns:

Phenotypes ( Self ) –

A single combined Phenotypes batch container. Returns an empty container if batches is empty.

Source code in src/kaptive/db/models.py

Python
@classmethod
def concat(cls, batches: Iterable[Self]) -> Self:  # type: ignore
    r"""Concatenates multiple `Phenotypes` batch containers into a single `Phenotypes` instance.

    Args:
        batches (Iterable[Phenotypes]): An iterable of [`Phenotypes`][kaptive.db.models.Phenotypes]
            container batches to concatenate.

    Returns:
        Phenotypes: A single combined [`Phenotypes`][kaptive.db.models.Phenotypes] batch container.
            Returns an empty container if `batches` is empty.
    """
    batches = list(batches)
    if not batches:
        return cls.empty()  # type: ignore
    return cls(
        ids=np.concatenate([b.ids for b in batches]),
        locus_masks=np.concatenate([b.locus_masks for b in batches]),
        extra_masks=np.concatenate([b.extra_masks for b in batches]),
        inactive_masks=np.concatenate([b.inactive_masks for b in batches]),
        extra_counts=np.concatenate([b.extra_counts for b in batches]),
        priorities=np.concatenate([b.priorities for b in batches]),
        as_suffix=np.concatenate([b.as_suffix for b in batches]),
    )

empty `classmethod` ¶

Python

empty() -> Phenotypes

Constructs an empty Phenotypes batch container.

Returns:

Phenotypes ( Phenotypes ) –

An empty Phenotypes instance with 0 elements and 2D empty arrays.

Source code in src/kaptive/db/models.py

Python
@classmethod
def empty(cls) -> "Phenotypes":
    r"""Constructs an empty `Phenotypes` batch container.

    Returns:
        Phenotypes: An empty [`Phenotypes`][kaptive.db.models.Phenotypes] instance with 0 elements
            and 2D empty arrays.
    """
    return cls(
        ids=np.empty(0, dtype="S32"),
        locus_masks=np.empty((0, 0), dtype=bool),
        extra_masks=np.empty((0, 0), dtype=np.int8),
        inactive_masks=np.empty((0, 0), dtype=np.int8),
        extra_counts=np.empty(0, dtype=np.int8),
        priorities=np.empty(0, dtype=np.int8),
        as_suffix=np.empty(0, dtype=bool),
    )

from_dict `classmethod` ¶

Python

from_dict(data: dict) -> Phenotypes

Reconstructs a Phenotypes batch container from a dictionary of array data.

Parameters:

data ¶
(dict) –

Dictionary containing array and tuple entries corresponding to Phenotypes attributes.

Returns:

Phenotypes ( Phenotypes ) –

Reconstructed Phenotypes container instance.

Source code in src/kaptive/db/models.py

Python
@classmethod
def from_dict(cls, data: dict) -> "Phenotypes":  # type: ignore
    r"""Reconstructs a `Phenotypes` batch container from a dictionary of array data.

    Args:
        data (dict): Dictionary containing array and tuple entries corresponding to `Phenotypes` attributes.

    Returns:
        Phenotypes: Reconstructed [`Phenotypes`][kaptive.db.models.Phenotypes] container instance.
    """
    return cls(  # type: ignore
        ids=np.array([p.encode("utf-8") for p in data["ids"]], dtype="S32"),
        locus_masks=np.array(data["locus_masks"], dtype=bool),
        extra_masks=np.array(data["extra_masks"], dtype=bool),
        inactive_masks=np.array(data["inactive_masks"], dtype=bool),
        priorities=np.array(data["priorities"], dtype=np.int8),
        as_suffix=np.array(data["as_suffix"], dtype=bool),
    )

to_dict ¶

Python

to_dict() -> dict

Converts the Phenotypes container attributes into a dictionary representation.

Returns:

dict ( dict ) –

Dictionary mapping attribute names (ids, locus_masks, extra_masks, inactive_masks, priorities, as_suffix) to their stored values/arrays.

Source code in src/kaptive/db/models.py

Python
def to_dict(self) -> dict:  # type: ignore
    r"""Converts the `Phenotypes` container attributes into a dictionary representation.

    Returns:
        dict: Dictionary mapping attribute names (`ids`, `locus_masks`, `extra_masks`,
            `inactive_masks`, `priorities`, `as_suffix`) to their stored values/arrays.
    """
    return {
        "ids": np.char.decode(self.ids, "utf-8").tolist(),
        "locus_masks": self.locus_masks,
        "extra_masks": self.extra_masks,
        "inactive_masks": self.inactive_masks,
        "extra_counts": self.extra_counts,
        "priorities": self.priorities,
        "as_suffix": self.as_suffix,
    }

kaptive.db.models¶

DatabaseError ¶

DatabaseMetadata `dataclass` ¶

parsed_version `property` ¶

from_dict `classmethod` ¶

`data` ¶

Phenotype `dataclass` ¶

Phenotypes `dataclass` ¶

getitem ¶

`item` ¶

len ¶

concat `classmethod` ¶

`batches` ¶

empty `classmethod` ¶

from_dict `classmethod` ¶

`data` ¶

to_dict ¶

kaptive.db.models¶

DatabaseError ¶

DatabaseMetadata dataclass ¶

parsed_version property ¶

from_dict classmethod ¶

data ¶

Phenotype dataclass ¶

Phenotypes dataclass ¶

__getitem__ ¶

item ¶

__len__ ¶

concat classmethod ¶

batches ¶

empty classmethod ¶

from_dict classmethod ¶

data ¶

to_dict ¶

DatabaseMetadata `dataclass` ¶

parsed_version `property` ¶

from_dict `classmethod` ¶

`data` ¶

Phenotype `dataclass` ¶

Phenotypes `dataclass` ¶

getitem ¶

`item` ¶

len ¶

concat `classmethod` ¶

`batches` ¶

empty `classmethod` ¶

from_dict `classmethod` ¶

`data` ¶