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Dataset reference

Bases: Generic[_FormatT]

File or object-store resource that Beacon can scan directly.

The class acts as a light-weight descriptor containing the user's original file_path plus convenience methods to inspect schema information and kick off JSON query builders.

Source code in beacon_api/dataset.py 
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class Dataset(Generic[_FormatT]):
    """File or object-store resource that Beacon can scan directly.

    The class acts as a light-weight descriptor containing the user's
    original ``file_path`` plus convenience methods to inspect schema
    information and kick off JSON query builders.
    """

    def __init__(self, http_session: BaseBeaconSession, file_path: str, file_format: _FormatT):
        """Create a dataset descriptor.

        Args:
            http_session: Session that knows how to communicate with the Beacon Node.
            file_path: Absolute/relative path or URI that Beacon can read.
            file_format: File format string supported by Beacon (e.g. ``parquet``).
        """

        self.session = http_session
        self.file_path = file_path
        self.file_format = file_format

    def get_file_path(self) -> str:
        """Return the original path/URI provided when constructing the dataset."""

        return self.file_path

    def get_file_format(self) -> str:
        """Return the declared file format string (case preserved)."""

        return self.file_format

    def get_file_name(self) -> str:
        """Return the basename portion of the dataset path/URI."""

        return os.path.basename(self.file_path.rstrip("/\\"))

    def get_schema(self) -> SchemaType:
        """Fetch the dataset schema by calling the Beacon Node.

        Returns:
            SchemaType: JSON-compatible schema description mirroring the
                server's ``/api/dataset-schema`` payload.

        Raises:
            RuntimeError: If the HTTP request fails.
            ValueError: When the response body is not valid JSON or the
                decoded value is not a JSON object.
            Exception: For unsupported field types surfaced by Beacon.
        """

        response = self.session.get("/api/dataset-schema", params={"file": self.file_path})
        if response.status_code != 200:
            raise RuntimeError(f"Failed to get dataset schema: {response.text}")

        try:
            schema_data = response.json()
        except ValueError as exc:
            raise ValueError("Dataset schema response was not valid JSON") from exc

        if not isinstance(schema_data, dict):
            raise ValueError("Dataset schema response must be a JSON object")

        fields = []

        for field in schema_data['fields']:
            field_type = field['data_type']

            if isinstance(field_type, str):
                fields.append(pa.field(field['name'], field_type))

            elif isinstance(field_type, dict) and field_type.get("Timestamp") == ["Second", None]:
                fields.append(pa.field(field['name'], pa.timestamp('s')))
            elif isinstance(field_type, dict) and field_type.get("Timestamp") == ["Millisecond", None]:
                fields.append(pa.field(field['name'], pa.timestamp('ms')))
            elif isinstance(field_type, dict) and field_type.get("Timestamp") == ["Microsecond", None]:
                fields.append(pa.field(field['name'], pa.timestamp('us')))
            elif isinstance(field_type, dict) and field_type.get("Timestamp") == ["Nanosecond", None]:
                fields.append(pa.field(field['name'], pa.timestamp('ns')))

            else:
                raise Exception(f"Unsupported data type for field {field['name']}: {field_type}")

        return pa.schema(fields)

    def get_file_extension(self) -> str:
        """Return the lowercase file extension without the leading dot."""

        file_name = self.get_file_name()
        _, extension = os.path.splitext(file_name)
        return extension.lstrip(".")

    def __str__(self) -> str:
        return self.file_path

    def __repr__(self) -> str:
        return f"Dataset(file_path={self.file_path})"

    @overload
    def query(self: "Dataset[Literal['csv']]", *, delimiter: str | None = None, **kwargs: Any) -> JSONQuery:
        ...

    @overload
    def query(self: "Dataset[Literal['zarr']]", *, statistics_columns: Sequence[str] | None = None, **kwargs: Any) -> JSONQuery:
        ...

    @overload
    def query(self, **kwargs: Any) -> JSONQuery:
        ...

    def query(
        self,
        *,
        delimiter: str | None = None,
        statistics_columns: Sequence[str] | None = None,
        **kwargs: Any,
    ) -> JSONQuery:
        """Build a :class:`~beacon_api.query.JSONQuery` starting from this dataset.

        Args:
            delimiter: Optional CSV delimiter override (only valid for CSV datasets).
            statistics_columns: Optional Zarr statistics column names (only valid for Zarr datasets).
            **kwargs: Additional format-specific options forwarded to the query builder.

        Returns:
            JSONQuery: Query builder tied to this dataset source.

        Raises:
            ValueError: If a format-specific option is passed to the wrong
                dataset type or the format is not supported.
        """

        file_format_str = self.get_file_format().lower()
        builder = _DATASET_FROM_FACTORIES.get(file_format_str)

        if builder is None:
            supported = ", ".join(sorted(_DATASET_FROM_FACTORIES))
            raise ValueError(f"Unsupported dataset format '{file_format_str}'. Supported formats: {supported}")

        builder_options: dict[str, Any] = dict(kwargs)

        if delimiter is not None:
            if file_format_str != "csv":
                raise ValueError("The 'delimiter' option is only supported for CSV datasets.")
            builder_options["delimiter"] = delimiter

        if statistics_columns is not None:
            if file_format_str != "zarr":
                raise ValueError("The 'statistics_columns' option is only supported for Zarr datasets.")
            builder_options["statistics_columns"] = list(statistics_columns)

        _from = builder(self.file_path, builder_options)
        return JSONQuery(http_session=self.session, _from=_from)

__init__(http_session, file_path, file_format)

Create a dataset descriptor.

Args: http_session: Session that knows how to communicate with the Beacon Node. file_path: Absolute/relative path or URI that Beacon can read. file_format: File format string supported by Beacon (e.g. parquet).

Source code in beacon_api/dataset.py 
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def __init__(self, http_session: BaseBeaconSession, file_path: str, file_format: _FormatT):
    """Create a dataset descriptor.

    Args:
        http_session: Session that knows how to communicate with the Beacon Node.
        file_path: Absolute/relative path or URI that Beacon can read.
        file_format: File format string supported by Beacon (e.g. ``parquet``).
    """

    self.session = http_session
    self.file_path = file_path
    self.file_format = file_format

get_file_extension()

Return the lowercase file extension without the leading dot.

Source code in beacon_api/dataset.py 
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def get_file_extension(self) -> str:
    """Return the lowercase file extension without the leading dot."""

    file_name = self.get_file_name()
    _, extension = os.path.splitext(file_name)
    return extension.lstrip(".")

get_file_format()

Return the declared file format string (case preserved).

Source code in beacon_api/dataset.py 
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def get_file_format(self) -> str:
    """Return the declared file format string (case preserved)."""

    return self.file_format

get_file_name()

Return the basename portion of the dataset path/URI.

Source code in beacon_api/dataset.py 
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def get_file_name(self) -> str:
    """Return the basename portion of the dataset path/URI."""

    return os.path.basename(self.file_path.rstrip("/\\"))

get_file_path()

Return the original path/URI provided when constructing the dataset.

Source code in beacon_api/dataset.py 
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def get_file_path(self) -> str:
    """Return the original path/URI provided when constructing the dataset."""

    return self.file_path

get_schema()

Fetch the dataset schema by calling the Beacon Node.

Returns: SchemaType: JSON-compatible schema description mirroring the server's /api/dataset-schema payload.

Raises: RuntimeError: If the HTTP request fails. ValueError: When the response body is not valid JSON or the decoded value is not a JSON object. Exception: For unsupported field types surfaced by Beacon.

Source code in beacon_api/dataset.py 
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def get_schema(self) -> SchemaType:
    """Fetch the dataset schema by calling the Beacon Node.

    Returns:
        SchemaType: JSON-compatible schema description mirroring the
            server's ``/api/dataset-schema`` payload.

    Raises:
        RuntimeError: If the HTTP request fails.
        ValueError: When the response body is not valid JSON or the
            decoded value is not a JSON object.
        Exception: For unsupported field types surfaced by Beacon.
    """

    response = self.session.get("/api/dataset-schema", params={"file": self.file_path})
    if response.status_code != 200:
        raise RuntimeError(f"Failed to get dataset schema: {response.text}")

    try:
        schema_data = response.json()
    except ValueError as exc:
        raise ValueError("Dataset schema response was not valid JSON") from exc

    if not isinstance(schema_data, dict):
        raise ValueError("Dataset schema response must be a JSON object")

    fields = []

    for field in schema_data['fields']:
        field_type = field['data_type']

        if isinstance(field_type, str):
            fields.append(pa.field(field['name'], field_type))

        elif isinstance(field_type, dict) and field_type.get("Timestamp") == ["Second", None]:
            fields.append(pa.field(field['name'], pa.timestamp('s')))
        elif isinstance(field_type, dict) and field_type.get("Timestamp") == ["Millisecond", None]:
            fields.append(pa.field(field['name'], pa.timestamp('ms')))
        elif isinstance(field_type, dict) and field_type.get("Timestamp") == ["Microsecond", None]:
            fields.append(pa.field(field['name'], pa.timestamp('us')))
        elif isinstance(field_type, dict) and field_type.get("Timestamp") == ["Nanosecond", None]:
            fields.append(pa.field(field['name'], pa.timestamp('ns')))

        else:
            raise Exception(f"Unsupported data type for field {field['name']}: {field_type}")

    return pa.schema(fields)

query(*, delimiter=None, statistics_columns=None, **kwargs) 

query(*, delimiter: str | None = None, **kwargs: Any) -> JSONQuery

query(*, statistics_columns: Sequence[str] | None = None, **kwargs: Any) -> JSONQuery

query(**kwargs: Any) -> JSONQuery

Build a :class:~beacon_api.query.JSONQuery starting from this dataset.

Args: delimiter: Optional CSV delimiter override (only valid for CSV datasets). statistics_columns: Optional Zarr statistics column names (only valid for Zarr datasets). **kwargs: Additional format-specific options forwarded to the query builder.

Returns: JSONQuery: Query builder tied to this dataset source.

Raises: ValueError: If a format-specific option is passed to the wrong dataset type or the format is not supported.

Source code in beacon_api/dataset.py 
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def query(
    self,
    *,
    delimiter: str | None = None,
    statistics_columns: Sequence[str] | None = None,
    **kwargs: Any,
) -> JSONQuery:
    """Build a :class:`~beacon_api.query.JSONQuery` starting from this dataset.

    Args:
        delimiter: Optional CSV delimiter override (only valid for CSV datasets).
        statistics_columns: Optional Zarr statistics column names (only valid for Zarr datasets).
        **kwargs: Additional format-specific options forwarded to the query builder.

    Returns:
        JSONQuery: Query builder tied to this dataset source.

    Raises:
        ValueError: If a format-specific option is passed to the wrong
            dataset type or the format is not supported.
    """

    file_format_str = self.get_file_format().lower()
    builder = _DATASET_FROM_FACTORIES.get(file_format_str)

    if builder is None:
        supported = ", ".join(sorted(_DATASET_FROM_FACTORIES))
        raise ValueError(f"Unsupported dataset format '{file_format_str}'. Supported formats: {supported}")

    builder_options: dict[str, Any] = dict(kwargs)

    if delimiter is not None:
        if file_format_str != "csv":
            raise ValueError("The 'delimiter' option is only supported for CSV datasets.")
        builder_options["delimiter"] = delimiter

    if statistics_columns is not None:
        if file_format_str != "zarr":
            raise ValueError("The 'statistics_columns' option is only supported for Zarr datasets.")
        builder_options["statistics_columns"] = list(statistics_columns)

    _from = builder(self.file_path, builder_options)
    return JSONQuery(http_session=self.session, _from=_from)