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Output Writing

Writer Retrieval

Return the writer class based on the selection in the config file.

dbms: the database management system; for options, see DBMS_TO_CLASS.
translator: the Translator object.
deduplicator: the Deduplicator object.
output_directory: the directory to output.write the output files to.
strict_mode: whether to use strict mode.

instance: an instance of the selected writer class.
Source code in biocypher/output/write/_get_writer.py 
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def get_writer(
    dbms: str,
    translator: "Translator",
    deduplicator: "Deduplicator",
    output_directory: str,
    strict_mode: bool,
) -> _BatchWriter | None:
    """Return the writer class based on the selection in the config file.

    Args:
    ----
        dbms: the database management system; for options, see DBMS_TO_CLASS.
        translator: the Translator object.
        deduplicator: the Deduplicator object.
        output_directory: the directory to output.write the output files to.
        strict_mode: whether to use strict mode.

    Returns:
    -------
        instance: an instance of the selected writer class.

    """
    dbms_config = _config(dbms) or {}

    writer = DBMS_TO_CLASS[dbms]

    if "rdf_format" in dbms_config:
        logger.warning("The 'rdf_format' config option is deprecated, use 'file_format' instead.")
        if "file_format" not in dbms_config:
            format = dbms_config["rdf_format"]
            logger.warning(f"I will set 'file_format: {format}' for you.")
            dbms_config["file_format"] = format
            dbms_config.pop("rdf_format")
        logger.warning("NOTE: this warning will become an error in next versions.")

    if not writer:
        msg = f"Unknown dbms: {dbms}"
        raise ValueError(msg)

    if writer is not None:
        return writer(
            translator=translator,
            deduplicator=deduplicator,
            delimiter=dbms_config.get("delimiter"),
            array_delimiter=dbms_config.get("array_delimiter"),
            quote=dbms_config.get("quote_character"),
            output_directory=output_directory,
            db_name=dbms_config.get("database_name"),
            import_call_bin_prefix=dbms_config.get("import_call_bin_prefix"),
            import_call_file_prefix=dbms_config.get("import_call_file_prefix"),
            wipe=dbms_config.get("wipe"),
            strict_mode=strict_mode,
            skip_bad_relationships=dbms_config.get("skip_bad_relationships"),  # neo4j
            skip_duplicate_nodes=dbms_config.get("skip_duplicate_nodes"),  # neo4j
            db_user=dbms_config.get("user"),  # psql
            db_password=dbms_config.get("password"),  # psql
            db_port=dbms_config.get("port"),  # psql
            file_format=dbms_config.get("file_format"),  # rdf, owl
            rdf_namespaces=dbms_config.get("rdf_namespaces"),  # rdf, owl
            edge_model=dbms_config.get("edge_model"),  # owl
        )
    return None

Writer Base Class

Bases: ABC

Abstract class for writing node and edge representations to disk. Specifics of the different writers (e.g. neo4j, postgresql, csv, etc.) are implemented in the child classes. Any concrete writer needs to implement at least: - _write_node_data - _write_edge_data - _construct_import_call - _get_import_script_name

translator (Translator): Instance of :py:class:`Translator` to enable translation of
    nodes and manipulation of properties.
deduplicator (Deduplicator): Instance of :py:class:`Deduplicator` to enable deduplication
    of nodes and edges.
output_directory (str, optional): Path for exporting CSV files. Defaults to None.
strict_mode (bool, optional): Whether to enforce source, version, and license properties. Defaults to False.

NotImplementedError: Writer implementation must override '_write_node_data'
NotImplementedError: Writer implementation must override '_write_edge_data'
NotImplementedError: Writer implementation must override '_construct_import_call'
NotImplementedError: Writer implementation must override '_get_import_script_name'
Source code in biocypher/output/write/_writer.py 
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class _Writer(ABC):
    """Abstract class for writing node and edge representations to disk.
    Specifics of the different writers (e.g. neo4j, postgresql, csv, etc.)
    are implemented in the child classes. Any concrete writer needs to
    implement at least:
    - _write_node_data
    - _write_edge_data
    - _construct_import_call
    - _get_import_script_name

    Args:
    ----
        translator (Translator): Instance of :py:class:`Translator` to enable translation of
            nodes and manipulation of properties.
        deduplicator (Deduplicator): Instance of :py:class:`Deduplicator` to enable deduplication
            of nodes and edges.
        output_directory (str, optional): Path for exporting CSV files. Defaults to None.
        strict_mode (bool, optional): Whether to enforce source, version, and license properties. Defaults to False.

    Raises:
    ------
        NotImplementedError: Writer implementation must override '_write_node_data'
        NotImplementedError: Writer implementation must override '_write_edge_data'
        NotImplementedError: Writer implementation must override '_construct_import_call'
        NotImplementedError: Writer implementation must override '_get_import_script_name'

    """

    def __init__(
        self,
        translator: Translator,
        deduplicator: Deduplicator,
        output_directory: str | None = None,
        strict_mode: bool = False,
        *args,
        **kwargs,
    ):
        """Abstract class for writing node and edge representations to disk.

        Args:
        ----
            translator (Translator): Instance of :py:class:`Translator` to enable translation of
                nodes and manipulation of properties.
            deduplicator (Deduplicator): Instance of :py:class:`Deduplicator` to enable deduplication
                of nodes and edges.
            output_directory (str, optional): Path for exporting CSV files. Defaults to None.
            strict_mode (bool, optional): Whether to enforce source, version, and license properties. Defaults to False.

        """
        self.translator = translator
        self.deduplicator = deduplicator
        self.strict_mode = strict_mode
        self.output_directory = output_directory

        if os.path.exists(self.output_directory):
            if kwargs.get("write_to_file", True):
                logger.warning(
                    f"Output directory `{self.output_directory}` already exists. "
                    "If this is not planned, file consistency may be compromised.",
                )
        else:
            logger.info(f"Creating output directory `{self.output_directory}`.")
            os.makedirs(self.output_directory)

    @abstractmethod
    def _write_node_data(
        self,
        nodes: Iterable[BioCypherNode | BioCypherEdge | BioCypherRelAsNode],
    ) -> bool:
        """Implement how to output.write nodes to disk.

        Args:
        ----
            nodes (Iterable): An iterable of BioCypherNode / BioCypherEdge / BioCypherRelAsNode objects.

        Returns:
        -------
            bool: The return value. True for success, False otherwise.

        """
        raise NotImplementedError("Writer implementation must override 'write_nodes'")

    @abstractmethod
    def _write_edge_data(
        self,
        edges: Iterable[BioCypherNode | BioCypherEdge | BioCypherRelAsNode],
    ) -> bool:
        """Implement how to output.write edges to disk.

        Args:
        ----
            edges (Iterable): An iterable of BioCypherNode / BioCypherEdge / BioCypherRelAsNode objects.

        Returns:
        -------
            bool: The return value. True for success, False otherwise.

        """
        raise NotImplementedError("Writer implementation must override 'write_edges'")

    @abstractmethod
    def _construct_import_call(self) -> str:
        """Function to construct the import call detailing folder and
        individual node and edge headers and data files, as well as
        delimiters and database name. Built after all data has been
        processed to ensure that nodes are called before any edges.

        Returns
        -------
            str: command for importing the output files into a DBMS.

        """
        raise NotImplementedError("Writer implementation must override '_construct_import_call'")

    @abstractmethod
    def _get_import_script_name(self) -> str:
        """Returns the name of the import script.

        Returns
        -------
            str: The name of the import script (ending in .sh)

        """
        raise NotImplementedError("Writer implementation must override '_get_import_script_name'")

    def write_nodes(self, nodes, batch_size: int = int(1e6), force: bool = False):
        """Wrapper for writing nodes.

        Args:
        ----
            nodes (BioCypherNode): a list or generator of nodes in
                :py:class:`BioCypherNode` format
            batch_size (int): The batch size for writing nodes.
            force (bool): Whether to force writing nodes even if their type is
                not present in the schema.

        Returns:
        -------
            bool: The return value. True for success, False otherwise.

        """
        passed = self._write_node_data(nodes)
        if not passed:
            logger.error("Error while writing node data.")
            return False
        return True

    def write_edges(self, edges, batch_size: int = int(1e6), force: bool = False):
        """Wrapper for writing edges.

        Args:
        ----
            nodes (BioCypherNode): a list or generator of nodes in
                :py:class:`BioCypherNode` format
            batch_size (int): The batch size for writing nodes.
            force (bool): Whether to force writing nodes even if their type is
                not present in the schema.

        Returns:
        -------
            bool: The return value. True for success, False otherwise.

        """
        passed = self._write_edge_data(edges)
        if not passed:
            logger.error("Error while writing edge data.")
            return False
        return True

    def write_import_call(self):
        """Function to output.write the import call detailing folder and
        individual node and edge headers and data files, as well as
        delimiters and database name, to the export folder as txt.

        Returns
        -------
            str: The path of the file holding the import call.

        """
        file_path = os.path.join(self.output_directory, self._get_import_script_name())
        logger.info(f"Writing {self.__class__.__name__} import call to `{file_path}`.")

        with open(file_path, "w", encoding="utf-8") as f:
            f.write(self._construct_import_call())

        return file_path

__init__(translator, deduplicator, output_directory=None, strict_mode=False, *args, **kwargs)

Abstract class for writing node and edge representations to disk.

translator (Translator): Instance of :py:class:`Translator` to enable translation of
    nodes and manipulation of properties.
deduplicator (Deduplicator): Instance of :py:class:`Deduplicator` to enable deduplication
    of nodes and edges.
output_directory (str, optional): Path for exporting CSV files. Defaults to None.
strict_mode (bool, optional): Whether to enforce source, version, and license properties. Defaults to False.
Source code in biocypher/output/write/_writer.py 
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def __init__(
    self,
    translator: Translator,
    deduplicator: Deduplicator,
    output_directory: str | None = None,
    strict_mode: bool = False,
    *args,
    **kwargs,
):
    """Abstract class for writing node and edge representations to disk.

    Args:
    ----
        translator (Translator): Instance of :py:class:`Translator` to enable translation of
            nodes and manipulation of properties.
        deduplicator (Deduplicator): Instance of :py:class:`Deduplicator` to enable deduplication
            of nodes and edges.
        output_directory (str, optional): Path for exporting CSV files. Defaults to None.
        strict_mode (bool, optional): Whether to enforce source, version, and license properties. Defaults to False.

    """
    self.translator = translator
    self.deduplicator = deduplicator
    self.strict_mode = strict_mode
    self.output_directory = output_directory

    if os.path.exists(self.output_directory):
        if kwargs.get("write_to_file", True):
            logger.warning(
                f"Output directory `{self.output_directory}` already exists. "
                "If this is not planned, file consistency may be compromised.",
            )
    else:
        logger.info(f"Creating output directory `{self.output_directory}`.")
        os.makedirs(self.output_directory)

write_edges(edges, batch_size=int(1000000.0), force=False)

Wrapper for writing edges.

nodes (BioCypherNode): a list or generator of nodes in
    :py:class:`BioCypherNode` format
batch_size (int): The batch size for writing nodes.
force (bool): Whether to force writing nodes even if their type is
    not present in the schema.

bool: The return value. True for success, False otherwise.
Source code in biocypher/output/write/_writer.py 
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def write_edges(self, edges, batch_size: int = int(1e6), force: bool = False):
    """Wrapper for writing edges.

    Args:
    ----
        nodes (BioCypherNode): a list or generator of nodes in
            :py:class:`BioCypherNode` format
        batch_size (int): The batch size for writing nodes.
        force (bool): Whether to force writing nodes even if their type is
            not present in the schema.

    Returns:
    -------
        bool: The return value. True for success, False otherwise.

    """
    passed = self._write_edge_data(edges)
    if not passed:
        logger.error("Error while writing edge data.")
        return False
    return True

write_import_call()

Function to output.write the import call detailing folder and individual node and edge headers and data files, as well as delimiters and database name, to the export folder as txt.

Returns
str: The path of the file holding the import call.
Source code in biocypher/output/write/_writer.py 
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def write_import_call(self):
    """Function to output.write the import call detailing folder and
    individual node and edge headers and data files, as well as
    delimiters and database name, to the export folder as txt.

    Returns
    -------
        str: The path of the file holding the import call.

    """
    file_path = os.path.join(self.output_directory, self._get_import_script_name())
    logger.info(f"Writing {self.__class__.__name__} import call to `{file_path}`.")

    with open(file_path, "w", encoding="utf-8") as f:
        f.write(self._construct_import_call())

    return file_path

write_nodes(nodes, batch_size=int(1000000.0), force=False)

Wrapper for writing nodes.

nodes (BioCypherNode): a list or generator of nodes in
    :py:class:`BioCypherNode` format
batch_size (int): The batch size for writing nodes.
force (bool): Whether to force writing nodes even if their type is
    not present in the schema.

bool: The return value. True for success, False otherwise.
Source code in biocypher/output/write/_writer.py 
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def write_nodes(self, nodes, batch_size: int = int(1e6), force: bool = False):
    """Wrapper for writing nodes.

    Args:
    ----
        nodes (BioCypherNode): a list or generator of nodes in
            :py:class:`BioCypherNode` format
        batch_size (int): The batch size for writing nodes.
        force (bool): Whether to force writing nodes even if their type is
            not present in the schema.

    Returns:
    -------
        bool: The return value. True for success, False otherwise.

    """
    passed = self._write_node_data(nodes)
    if not passed:
        logger.error("Error while writing node data.")
        return False
    return True

Batch Writer Base Class

Bases: _Writer, ABC

Abstract batch writer class.

Source code in biocypher/output/write/_batch_writer.py 
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class _BatchWriter(_Writer, ABC):
    """Abstract batch writer class."""

    @abstractmethod
    def _quote_string(self, value: str) -> str:
        """Quote a string.

        Escaping is handled by the database-specific writer.
        """
        msg = "Database writer must override '_quote_string'"
        logger.error(msg)
        raise NotImplementedError(msg)

    @abstractmethod
    def _get_default_import_call_bin_prefix(self):
        """Provide the default string for the import call bin prefix.

        Returns
        -------
            str: The database-specific string for the path to the import call bin prefix

        """
        msg = "Database writer must override '_get_default_import_call_bin_prefix'"
        logger.error(msg)
        raise NotImplementedError(msg)

    @abstractmethod
    def _write_array_string(self, string_list):
        """Write the string representation of an array into a .csv file.

        Different databases require different formats of array to optimize
        import speed.

        Args:
        ----
            string_list (list): list of ontology strings

        Returns:
        -------
            str: The database-specific string representation of an array

        """
        msg = "Database writer must override '_write_array_string'"
        logger.error(msg)
        raise NotImplementedError(msg)

    @abstractmethod
    def _write_node_headers(self):
        """Write header files for nodes.

        Write header files (node properties) for nodes as per the
        definition in the `schema_config.yaml`.

        Returns
        -------
            bool: The return value. True for success, False otherwise.

        """
        msg = "Database writer must override '_write_node_headers'"
        logger.error(msg)
        raise NotImplementedError(msg)

    @abstractmethod
    def _write_edge_headers(self):
        """Write a database import-file for an edge.

        Write a database import-file for an edge as per the definition in
        the `schema_config.yaml`, containing only the header for this type
        of edge.

        Returns
        -------
            bool: The return value. True for success, False otherwise.

        """
        msg = "Database writer must override '_write_edge_headers'"
        logger.error(msg)
        raise NotImplementedError(msg)

    @abstractmethod
    def _construct_import_call(self) -> str:
        """Construct the import call.

        Construct the import call detailing folder and individual node and
        edge headers and data files, as well as delimiters and database name.
        Built after all data has been processed to ensure that nodes are
        called before any edges.

        Returns
        -------
            str: A bash command for csv import.

        """
        msg = "Database writer must override '_construct_import_call'"
        logger.error(msg)
        raise NotImplementedError(msg)

    @abstractmethod
    def _get_import_script_name(self) -> str:
        """Return the name of the import script.

        The name will be chosen based on the used database.

        Returns
        -------
            str: The name of the import script (ending in .sh)

        """
        msg = "Database writer must override '_get_import_script_name'"
        logger.error(msg)
        raise NotImplementedError(msg)

    def __init__(
        self,
        translator: "Translator",
        deduplicator: "Deduplicator",
        delimiter: str,
        array_delimiter: str = ",",
        quote: str = '"',
        output_directory: str | None = None,
        db_name: str = "neo4j",
        import_call_bin_prefix: str | None = None,
        import_call_file_prefix: str | None = None,
        wipe: bool = True,
        strict_mode: bool = False,
        skip_bad_relationships: bool = False,
        skip_duplicate_nodes: bool = False,
        db_user: str = None,
        db_password: str = None,
        db_host: str = None,
        db_port: str = None,
        file_format: str = None,
        rdf_namespaces: dict = {},
        labels_order: str = "Ascending",
        **kwargs,
    ):
        """Write node and edge representations to disk.

        Abstract parent class for writing node and edge representations to disk
        using the format specified by each database type. The database-specific
        functions are implemented by the respective child-classes. This abstract
        class contains all methods expected by a bach writer instance, some of
        which need to be overwritten by the child classes.

        Each batch writer instance has a fixed representation that needs to be
        passed at instantiation via the :py:attr:`schema` argument. The instance
        also expects an ontology adapter via :py:attr:`ontology_adapter` to be
        able to convert and extend the hierarchy.

        Requires the following methods to be overwritten by database-specific
        writer classes:

            - _write_node_headers
            - _write_edge_headers
            - _construct_import_call
            - _write_array_string
            - _get_import_script_name

        Args:
        ----
            translator:
                Instance of :py:class:`Translator` to enable translation of
                nodes and manipulation of properties.

            deduplicator:
                Instance of :py:class:`Deduplicator` to enable deduplication
                of nodes and edges.

            delimiter:
                The delimiter to use for the CSV files.

            array_delimiter:
                The delimiter to use for array properties.

            quote:
                The quote character to use for the CSV files.

            output_directory:
                Path for exporting CSV files.

            db_name:
                Name of the database that will be used in the generated
                commands.

            import_call_bin_prefix:
                Path prefix for the admin import call binary.

            import_call_file_prefix:
                Path prefix for the data files (headers and parts) in the import
                call.

            wipe:
                Whether to force import (removing existing DB content).
                    (Specific to Neo4j.)

            strict_mode:
                Whether to enforce source, version, and license properties.

            skip_bad_relationships:
                Whether to skip relationships that do not have a valid
                start and end node. (Specific to Neo4j.)

            skip_duplicate_nodes:
                Whether to skip duplicate nodes. (Specific to Neo4j.)

            db_user:
                The database user.

            db_password:
                The database password.

            db_host:
                The database host. Defaults to localhost.

            db_port:
                The database port.

            file_format:
                The format of RDF.

            rdf_namespaces:
                The namespaces for RDF.

            labels_order:
                The order of labels, to reflect the hierarchy (or not).
                Default: "Ascending" (from more specific to more generic).

        """
        super().__init__(
            translator=translator,
            deduplicator=deduplicator,
            output_directory=output_directory,
            strict_mode=strict_mode,
        )
        self.db_name = db_name
        self.db_user = db_user
        self.db_password = db_password
        self.db_host = db_host or "localhost"
        self.db_port = db_port
        self.file_format = file_format
        self.rdf_namespaces = rdf_namespaces

        self.delim, self.escaped_delim = self._process_delimiter(delimiter)
        self.adelim, self.escaped_adelim = self._process_delimiter(array_delimiter)
        self.quote = quote
        self.skip_bad_relationships = skip_bad_relationships
        self.skip_duplicate_nodes = skip_duplicate_nodes

        if import_call_bin_prefix is None:
            self.import_call_bin_prefix = self._get_default_import_call_bin_prefix()
        else:
            self.import_call_bin_prefix = import_call_bin_prefix

        self.wipe = wipe
        self.strict_mode = strict_mode

        self.translator = translator
        self.deduplicator = deduplicator
        self.node_property_dict = {}
        self.edge_property_dict = {}
        self.import_call_nodes = set()
        self.import_call_edges = set()

        self.outdir = output_directory

        self._import_call_file_prefix = import_call_file_prefix

        self.parts = {}  # dict to store the paths of part files for each label

        self._labels_orders = ["Alphabetical", "Ascending", "Descending", "Leaves"]
        if labels_order not in self._labels_orders:
            msg = (
                f"neo4j's 'labels_order' parameter cannot be '{labels_order}',"
                "must be one of: {' ,'.join(self._labels_orders)}",
            )
            raise ValueError(msg)
        self.labels_order = labels_order

        # TODO not memory efficient, but should be fine for most cases; is
        # there a more elegant solution?

    @property
    def import_call_file_prefix(self):
        """Property for output directory path."""
        if self._import_call_file_prefix is None:
            return self.outdir
        else:
            return self._import_call_file_prefix

    def _process_delimiter(self, delimiter: str) -> str:
        """Process a delimited to escape correctly.

        Args:
        ----
            delimiter (str): The delimiter to process.

        Returns:
        -------
            tuple: The delimiter and its escaped representation.

        """
        if delimiter == "\\t":
            return "\t", "\\t"

        else:
            return delimiter, delimiter

    def write_nodes(self, nodes, batch_size: int = int(1e6), force: bool = False):
        """Write nodes and their headers.

        Args:
        ----
            nodes (BioCypherNode): a list or generator of nodes in
                :py:class:`BioCypherNode` format

            batch_size (int): The batch size for writing nodes.

            force (bool): Whether to force writing nodes even if their type is
                not present in the schema.


        Returns:
        -------
            bool: The return value. True for success, False otherwise.

        """
        # TODO check represented_as

        # write node data
        passed = self._write_node_data(nodes, batch_size, force)
        if not passed:
            logger.error("Error while writing node data.")
            return False
        # pass property data to header writer per node type written
        passed = self._write_node_headers()
        if not passed:
            logger.error("Error while writing node headers.")
            return False

        return True

    def write_edges(
        self,
        edges: list | GeneratorType,
        batch_size: int = int(1e6),
    ) -> bool:
        """Write edges and their headers.

        Args:
        ----
            edges (BioCypherEdge): a list or generator of edges in
                :py:class:`BioCypherEdge` or :py:class:`BioCypherRelAsNode`
                format

        Returns:
        -------
            bool: The return value. True for success, False otherwise.

        """
        passed = False
        edges = list(edges)  # force evaluation to handle empty generator
        if edges:
            nodes_flat = []
            edges_flat = []
            for edge in edges:
                if isinstance(edge, BioCypherRelAsNode):
                    # check if relationship has already been written, if so skip
                    if self.deduplicator.rel_as_node_seen(edge):
                        continue

                    nodes_flat.append(edge.get_node())
                    edges_flat.append(edge.get_source_edge())
                    edges_flat.append(edge.get_target_edge())

                else:
                    # check if relationship has already been written, if so skip
                    if self.deduplicator.edge_seen(edge):
                        continue

                    edges_flat.append(edge)

            if nodes_flat and edges_flat:
                passed = self.write_nodes(nodes_flat) and self._write_edge_data(
                    edges_flat,
                    batch_size,
                )
            else:
                passed = self._write_edge_data(edges_flat, batch_size)

        else:
            # is this a problem? if the generator or list is empty, we
            # don't write anything.
            logger.debug(
                "No edges to write, possibly due to no matched Biolink classes.",
            )

        if not passed:
            logger.error("Error while writing edge data.")
            return False
        # pass property data to header writer per edge type written
        passed = self._write_edge_headers()
        if not passed:
            logger.error("Error while writing edge headers.")
            return False

        return True

    def _write_node_data(self, nodes, batch_size, force: bool = False):
        """Write biocypher nodes to CSV.

        Conforms to the headers created with `_write_node_headers()`, and
        is actually required to be run before calling `_write_node_headers()`
        to set the :py:attr:`self.node_property_dict` for passing the node
        properties to the instance. Expects list or generator of nodes from
        the :py:class:`BioCypherNode` class.

        Args:
        ----
            nodes (BioCypherNode): a list or generator of nodes in
                :py:class:`BioCypherNode` format

        Returns:
        -------
            bool: The return value. True for success, False otherwise.

        """
        if isinstance(nodes, GeneratorType | peekable):
            logger.debug("Writing node CSV from generator.")

            bins = defaultdict(list)  # dict to store a list for each
            # label that is passed in
            bin_l = {}  # dict to store the length of each list for
            # batching cutoff
            reference_props = defaultdict(
                dict,
            )  # dict to store a dict of properties
            # for each label to check for consistency and their type
            # for now, relevant for `int`
            labels = {}  # dict to store the additional labels for each
            # primary graph constituent from biolink hierarchy
            for node in nodes:
                # check if node has already been written, if so skip
                if self.deduplicator.node_seen(node):
                    continue

                _id = node.get_id()
                label = node.get_label()

                # check for non-id
                if not _id:
                    logger.warning(f"Node {label} has no id; skipping.")
                    continue

                if label not in bins.keys():
                    # start new list
                    all_labels = None
                    bins[label].append(node)
                    bin_l[label] = 1

                    # get properties from config if present
                    if label in self.translator.ontology.mapping.extended_schema:
                        cprops = self.translator.ontology.mapping.extended_schema.get(label).get(
                            "properties",
                        )
                    else:
                        cprops = None
                    if cprops:
                        d = dict(cprops)

                        # add id and preferred id to properties; these are
                        # created in node creation (`_create.BioCypherNode`)
                        d["id"] = "str"
                        d["preferred_id"] = "str"

                        # add strict mode properties
                        if self.strict_mode:
                            d["source"] = "str"
                            d["version"] = "str"
                            d["licence"] = "str"

                    else:
                        d = dict(node.get_properties())
                        # encode property type
                        for k, v in d.items():
                            if d[k] is not None:
                                d[k] = type(v).__name__
                    # else use first encountered node to define properties for
                    # checking; could later be by checking all nodes but much
                    # more complicated, particularly involving batch writing
                    # (would require "do-overs"). for now, we output a warning
                    # if node properties diverge from reference properties (in
                    # write_single_node_list_to_file) TODO if it occurs, ask
                    # user to select desired properties and restart the process

                    reference_props[label] = d

                    # get label hierarchy
                    # multiple labels:
                    if not force:
                        all_labels = self.translator.ontology.get_ancestors(label)
                    else:
                        all_labels = None

                    if all_labels:
                        # convert to pascal case
                        all_labels = [self.translator.name_sentence_to_pascal(label) for label in all_labels]
                        # remove duplicates
                        all_labels = list(OrderedDict.fromkeys(all_labels))
                        match self.labels_order:
                            case "Ascending":
                                pass  # Default from get_ancestors.
                            case "Alphabetical":
                                all_labels.sort()
                            case "Descending":
                                all_labels.reverse()
                            case "Leaves":
                                if len(all_labels) < 1:
                                    msg = "Labels list cannot be empty when using 'Leaves' order."
                                    raise ValueError(msg)
                                all_labels = [all_labels[0]]
                            case _:
                                # In case someone touched _label_orders after constructor.
                                if self.labels_order not in self._labels_orders:
                                    msg = (
                                        f"Invalid labels_order: {self.labels_order}. "
                                        f"Must be one of {self._labels_orders}"
                                    )
                                    raise ValueError(msg)
                        # concatenate with array delimiter
                        all_labels = self._write_array_string(all_labels)
                    else:
                        all_labels = self.translator.name_sentence_to_pascal(label)

                    labels[label] = all_labels

                else:
                    # add to list
                    bins[label].append(node)
                    bin_l[label] += 1
                    if not bin_l[label] < batch_size:
                        # batch size controlled here
                        passed = self._write_single_node_list_to_file(
                            bins[label],
                            label,
                            reference_props[label],
                            labels[label],
                        )

                        if not passed:
                            return False

                        bins[label] = []
                        bin_l[label] = 0

            # after generator depleted, write remainder of bins
            for label, nl in bins.items():
                passed = self._write_single_node_list_to_file(
                    nl,
                    label,
                    reference_props[label],
                    labels[label],
                )

                if not passed:
                    return False

            # use complete bin list to write header files
            # TODO if a node type has varying properties
            # (ie missingness), we'd need to collect all possible
            # properties in the generator pass

            # save config or first-node properties to instance attribute
            for label in reference_props.keys():
                self.node_property_dict[label] = reference_props[label]

            return True
        elif not isinstance(nodes, list):
            logger.error("Nodes must be passed as list or generator.")
            return False
        else:

            def gen(nodes):
                yield from nodes

            return self._write_node_data(gen(nodes), batch_size=batch_size)

    def _write_single_node_list_to_file(
        self,
        node_list: list,
        label: str,
        prop_dict: dict,
        labels: str,
    ):
        """Write a list of biocypher nodes to a CSV file.

        This function takes one list of biocypher nodes and writes them
        to a Neo4j admin import compatible CSV file.

        Args:
        ----
            node_list (list): list of BioCypherNodes to be written
            label (str): the primary label of the node
            prop_dict (dict): properties of node class passed from parsing
                function and their types
            labels (str): string of one or several concatenated labels
                for the node class

        Returns:
        -------
            bool: The return value. True for success, False otherwise.

        """
        if not all(isinstance(n, BioCypherNode) for n in node_list):
            logger.error("Nodes must be passed as type BioCypherNode.")
            return False

        # from list of nodes to list of strings
        lines = []

        for n in node_list:
            # check for deviations in properties
            # node properties
            n_props = n.get_properties()
            n_keys = list(n_props.keys())
            # reference properties
            ref_props = list(prop_dict.keys())

            # compare lists order invariant
            if set(ref_props) != set(n_keys):
                onode = n.get_id()
                oprop1 = set(ref_props).difference(n_keys)
                oprop2 = set(n_keys).difference(ref_props)
                logger.error(
                    f"At least one node of the class {n.get_label()} "
                    f"has more or fewer properties than another. "
                    f"Offending node: {onode!r}, offending property: "
                    f"{max([oprop1, oprop2])}. "
                    f"All reference properties: {ref_props}, "
                    f"All node properties: {n_keys}.",
                )
                return False

            line = [n.get_id()]

            if ref_props:
                plist = []
                # make all into strings, put actual strings in quotes
                for k, v in prop_dict.items():
                    p = n_props.get(k)
                    if p is None:  # TODO make field empty instead of ""?
                        plist.append("")
                    elif v in [
                        "int",
                        "integer",
                        "long",
                        "float",
                        "double",
                        "dbl",
                        "bool",
                        "boolean",
                    ]:
                        plist.append(str(p))
                    elif isinstance(p, list):
                        plist.append(self._write_array_string(p))
                    else:
                        plist.append(f"{self.quote}{p!s}{self.quote}")

                line.append(self.delim.join(plist))
            line.append(labels)

            lines.append(self.delim.join(line) + "\n")

        # avoid writing empty files
        if lines:
            self._write_next_part(label, lines)

        return True

    def _write_edge_data(self, edges, batch_size):
        """Write biocypher edges to CSV.

        Writes biocypher edges to CSV conforming to the headers created
        with `_write_edge_headers()`, and is actually required to be run
        before calling `_write_node_headers()` to set the
        :py:attr:`self.edge_property_dict` for passing the edge
        properties to the instance. Expects list or generator of edges
        from the :py:class:`BioCypherEdge` class.

        Args:
        ----
            edges (BioCypherEdge): a list or generator of edges in
                :py:class:`BioCypherEdge` format

        Returns:
        -------
            bool: The return value. True for success, False otherwise.

        Todo:
        ----
            - currently works for mixed edges but in practice often is
              called on one iterable containing one type of edge only

        """
        if isinstance(edges, GeneratorType):
            logger.debug("Writing edge CSV from generator.")

            bins = defaultdict(list)  # dict to store a list for each
            # label that is passed in
            bin_l = {}  # dict to store the length of each list for
            # batching cutoff
            reference_props = defaultdict(
                dict,
            )  # dict to store a dict of properties
            # for each label to check for consistency and their type
            # for now, relevant for `int`
            for edge in edges:
                if not (edge.get_source_id() and edge.get_target_id()):
                    logger.error(
                        f"Edge must have source and target node. Caused by: {edge}",
                    )
                    continue

                label = edge.get_label()

                if label not in bins.keys():
                    # start new list
                    bins[label].append(edge)
                    bin_l[label] = 1

                    # get properties from config if present

                    # check whether label is in ontology_adapter.leaves
                    # (may not be if it is an edge that carries the
                    # "label_as_edge" property)
                    cprops = None
                    if label in self.translator.ontology.mapping.extended_schema:
                        cprops = self.translator.ontology.mapping.extended_schema.get(label).get(
                            "properties",
                        )
                    else:
                        # try via "label_as_edge"
                        for (
                            k,
                            v,
                        ) in self.translator.ontology.mapping.extended_schema.items():
                            if isinstance(v, dict):
                                if v.get("label_as_edge") == label:
                                    cprops = v.get("properties")
                                    break
                    if cprops:
                        d = cprops

                        # add strict mode properties
                        if self.strict_mode:
                            d["source"] = "str"
                            d["version"] = "str"
                            d["licence"] = "str"

                    else:
                        d = dict(edge.get_properties())
                        # encode property type
                        for k, v in d.items():
                            if d[k] is not None:
                                d[k] = type(v).__name__
                    # else use first encountered edge to define
                    # properties for checking; could later be by
                    # checking all edges but much more complicated,
                    # particularly involving batch writing (would
                    # require "do-overs"). for now, we output a warning
                    # if edge properties diverge from reference
                    # properties (in write_single_edge_list_to_file)
                    # TODO

                    reference_props[label] = d

                else:
                    # add to list
                    bins[label].append(edge)
                    bin_l[label] += 1
                    if not bin_l[label] < batch_size:
                        # batch size controlled here
                        passed = self._write_single_edge_list_to_file(
                            bins[label],
                            label,
                            reference_props[label],
                        )

                        if not passed:
                            return False

                        bins[label] = []
                        bin_l[label] = 0

            # after generator depleted, write remainder of bins
            for label, nl in bins.items():
                passed = self._write_single_edge_list_to_file(
                    nl,
                    label,
                    reference_props[label],
                )

                if not passed:
                    return False

            # use complete bin list to write header files
            # TODO if a edge type has varying properties
            # (ie missingness), we'd need to collect all possible
            # properties in the generator pass

            # save first-edge properties to instance attribute
            for label in reference_props.keys():
                self.edge_property_dict[label] = reference_props[label]

            return True
        elif not isinstance(edges, list):
            logger.error("Edges must be passed as list or generator.")
            return False
        else:

            def gen(edges):
                yield from edges

            return self._write_edge_data(gen(edges), batch_size=batch_size)

    def _write_single_edge_list_to_file(
        self,
        edge_list: list,
        label: str,
        prop_dict: dict,
    ):
        """Write a list of biocypher edges to a CSV file.

        This function takes one list of biocypher edges and writes them
        to a Neo4j admin import compatible CSV file.

        Args:
        ----
            edge_list (list): list of BioCypherEdges to be written

            label (str): the label (type) of the edge

            prop_dict (dict): properties of node class passed from parsing
                function and their types

        Returns:
        -------
            bool: The return value. True for success, False otherwise.

        """
        if not all(isinstance(n, BioCypherEdge) for n in edge_list):
            logger.error("Edges must be passed as type BioCypherEdge.")
            return False

        # from list of edges to list of strings
        lines = []
        for e in edge_list:
            # check for deviations in properties
            # edge properties
            e_props = e.get_properties()
            e_keys = list(e_props.keys())
            ref_props = list(prop_dict.keys())

            # compare list order invariant
            if set(ref_props) != set(e_keys):
                oedge = f"{e.get_source_id()}-{e.get_target_id()}"
                oprop1 = set(ref_props).difference(e_keys)
                oprop2 = set(e_keys).difference(ref_props)
                logger.error(
                    f"At least one edge of the class {e.get_label()} "
                    f"has more or fewer properties than another. "
                    f"Offending edge: {oedge!r}, offending property: "
                    f"{max([oprop1, oprop2])}. "
                    f"All reference properties: {ref_props}, "
                    f"All edge properties: {e_keys}.",
                )
                return False

            plist = []
            # make all into strings, put actual strings in quotes
            for k, v in prop_dict.items():
                p = e_props.get(k)
                if p is None:  # TODO make field empty instead of ""?
                    plist.append("")
                elif v in [
                    "int",
                    "integer",
                    "long",
                    "float",
                    "double",
                    "dbl",
                    "bool",
                    "boolean",
                ]:
                    plist.append(str(p))
                elif isinstance(p, list):
                    plist.append(self._write_array_string(p))
                else:
                    plist.append(self.quote + str(p) + self.quote)

            entries = [e.get_source_id()]

            skip_id = False
            schema_label = None

            if label in ["IS_SOURCE_OF", "IS_TARGET_OF", "IS_PART_OF"]:
                skip_id = True
            elif not self.translator.ontology.mapping.extended_schema.get(label):
                # find label in schema by label_as_edge
                for (
                    k,
                    v,
                ) in self.translator.ontology.mapping.extended_schema.items():
                    if v.get("label_as_edge") == label:
                        schema_label = k
                        break
            else:
                schema_label = label

            if schema_label:
                if (
                    self.translator.ontology.mapping.extended_schema.get(
                        schema_label,
                    ).get("use_id")
                    == False  # noqa: E712 (seems to not work with 'not')
                ):
                    skip_id = True

            if not skip_id:
                entries.append(e.get_id() or "")

            if ref_props:
                entries.append(self.delim.join(plist))

            entries.append(e.get_target_id())
            entries.append(
                self.translator.name_sentence_to_pascal(
                    e.get_label(),
                ),
            )

            lines.append(
                self.delim.join(entries) + "\n",
            )

        # avoid writing empty files
        if lines:
            self._write_next_part(label, lines)

        return True

    def _write_next_part(self, label: str, lines: list):
        """Write a list of strings to a new part file.

        Args:
        ----
            label (str): the label (type) of the edge; internal
            representation sentence case -> needs to become PascalCase
            for disk representation

            lines (list): list of strings to be written

        Returns:
        -------
            bool: The return value. True for success, False otherwise.

        """
        # translate label to PascalCase
        label_pascal = self.translator.name_sentence_to_pascal(parse_label(label))

        # list files in self.outdir
        files = glob.glob(os.path.join(self.outdir, f"{label_pascal}-part*.csv"))
        # find file with highest part number
        if not files:
            next_part = 0

        else:
            next_part = (
                max(
                    [int(f.split(".")[-2].split("-")[-1].replace("part", "")) for f in files],
                )
                + 1
            )

        # write to file
        padded_part = str(next_part).zfill(3)
        logger.info(
            f"Writing {len(lines)} entries to {label_pascal}-part{padded_part}.csv",
        )

        # store name only in case import_call_file_prefix is set
        part = f"{label_pascal}-part{padded_part}.csv"
        file_path = os.path.join(self.outdir, part)

        with open(file_path, "w", encoding="utf-8") as f:
            # concatenate with delimiter
            f.writelines(lines)

        if not self.parts.get(label):
            self.parts[label] = [part]
        else:
            self.parts[label].append(part)

    def get_import_call(self) -> str:
        """Eeturn the import call.

        Return the import call detailing folder and individual node and
        edge headers and data files, as well as delimiters and database name.

        Returns
        -------
            str: a bash command for the database import

        """
        return self._construct_import_call()

    def write_import_call(self) -> str:
        """Write the import call.

        Function to write the import call detailing folder and
        individual node and edge headers and data files, as well as
        delimiters and database name, to the export folder as txt.

        Returns
        -------
            str: The path of the file holding the import call.

        """
        file_path = os.path.join(self.outdir, self._get_import_script_name())
        logger.info(f"Writing {self.db_name + ' ' if self.db_name else ''}import call to `{file_path}`.")

        with open(file_path, "w", encoding="utf-8") as f:
            f.write(self._construct_import_call())

        return file_path

import_call_file_prefix property

Property for output directory path.

__init__(translator, deduplicator, delimiter, array_delimiter=',', quote='"', output_directory=None, db_name='neo4j', import_call_bin_prefix=None, import_call_file_prefix=None, wipe=True, strict_mode=False, skip_bad_relationships=False, skip_duplicate_nodes=False, db_user=None, db_password=None, db_host=None, db_port=None, file_format=None, rdf_namespaces={}, labels_order='Ascending', **kwargs)

Write node and edge representations to disk.

Abstract parent class for writing node and edge representations to disk using the format specified by each database type. The database-specific functions are implemented by the respective child-classes. This abstract class contains all methods expected by a bach writer instance, some of which need to be overwritten by the child classes.

Each batch writer instance has a fixed representation that needs to be passed at instantiation via the 🇵🇾attr:schema argument. The instance also expects an ontology adapter via 🇵🇾attr:ontology_adapter to be able to convert and extend the hierarchy.

Requires the following methods to be overwritten by database-specific writer classes:

- _write_node_headers
- _write_edge_headers
- _construct_import_call
- _write_array_string
- _get_import_script_name

translator:
    Instance of :py:class:`Translator` to enable translation of
    nodes and manipulation of properties.

deduplicator:
    Instance of :py:class:`Deduplicator` to enable deduplication
    of nodes and edges.

delimiter:
    The delimiter to use for the CSV files.

array_delimiter:
    The delimiter to use for array properties.

quote:
    The quote character to use for the CSV files.

output_directory:
    Path for exporting CSV files.

db_name:
    Name of the database that will be used in the generated
    commands.

import_call_bin_prefix:
    Path prefix for the admin import call binary.

import_call_file_prefix:
    Path prefix for the data files (headers and parts) in the import
    call.

wipe:
    Whether to force import (removing existing DB content).
        (Specific to Neo4j.)

strict_mode:
    Whether to enforce source, version, and license properties.

skip_bad_relationships:
    Whether to skip relationships that do not have a valid
    start and end node. (Specific to Neo4j.)

skip_duplicate_nodes:
    Whether to skip duplicate nodes. (Specific to Neo4j.)

db_user:
    The database user.

db_password:
    The database password.

db_host:
    The database host. Defaults to localhost.

db_port:
    The database port.

file_format:
    The format of RDF.

rdf_namespaces:
    The namespaces for RDF.

labels_order:
    The order of labels, to reflect the hierarchy (or not).
    Default: "Ascending" (from more specific to more generic).
Source code in biocypher/output/write/_batch_writer.py 
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def __init__(
    self,
    translator: "Translator",
    deduplicator: "Deduplicator",
    delimiter: str,
    array_delimiter: str = ",",
    quote: str = '"',
    output_directory: str | None = None,
    db_name: str = "neo4j",
    import_call_bin_prefix: str | None = None,
    import_call_file_prefix: str | None = None,
    wipe: bool = True,
    strict_mode: bool = False,
    skip_bad_relationships: bool = False,
    skip_duplicate_nodes: bool = False,
    db_user: str = None,
    db_password: str = None,
    db_host: str = None,
    db_port: str = None,
    file_format: str = None,
    rdf_namespaces: dict = {},
    labels_order: str = "Ascending",
    **kwargs,
):
    """Write node and edge representations to disk.

    Abstract parent class for writing node and edge representations to disk
    using the format specified by each database type. The database-specific
    functions are implemented by the respective child-classes. This abstract
    class contains all methods expected by a bach writer instance, some of
    which need to be overwritten by the child classes.

    Each batch writer instance has a fixed representation that needs to be
    passed at instantiation via the :py:attr:`schema` argument. The instance
    also expects an ontology adapter via :py:attr:`ontology_adapter` to be
    able to convert and extend the hierarchy.

    Requires the following methods to be overwritten by database-specific
    writer classes:

        - _write_node_headers
        - _write_edge_headers
        - _construct_import_call
        - _write_array_string
        - _get_import_script_name

    Args:
    ----
        translator:
            Instance of :py:class:`Translator` to enable translation of
            nodes and manipulation of properties.

        deduplicator:
            Instance of :py:class:`Deduplicator` to enable deduplication
            of nodes and edges.

        delimiter:
            The delimiter to use for the CSV files.

        array_delimiter:
            The delimiter to use for array properties.

        quote:
            The quote character to use for the CSV files.

        output_directory:
            Path for exporting CSV files.

        db_name:
            Name of the database that will be used in the generated
            commands.

        import_call_bin_prefix:
            Path prefix for the admin import call binary.

        import_call_file_prefix:
            Path prefix for the data files (headers and parts) in the import
            call.

        wipe:
            Whether to force import (removing existing DB content).
                (Specific to Neo4j.)

        strict_mode:
            Whether to enforce source, version, and license properties.

        skip_bad_relationships:
            Whether to skip relationships that do not have a valid
            start and end node. (Specific to Neo4j.)

        skip_duplicate_nodes:
            Whether to skip duplicate nodes. (Specific to Neo4j.)

        db_user:
            The database user.

        db_password:
            The database password.

        db_host:
            The database host. Defaults to localhost.

        db_port:
            The database port.

        file_format:
            The format of RDF.

        rdf_namespaces:
            The namespaces for RDF.

        labels_order:
            The order of labels, to reflect the hierarchy (or not).
            Default: "Ascending" (from more specific to more generic).

    """
    super().__init__(
        translator=translator,
        deduplicator=deduplicator,
        output_directory=output_directory,
        strict_mode=strict_mode,
    )
    self.db_name = db_name
    self.db_user = db_user
    self.db_password = db_password
    self.db_host = db_host or "localhost"
    self.db_port = db_port
    self.file_format = file_format
    self.rdf_namespaces = rdf_namespaces

    self.delim, self.escaped_delim = self._process_delimiter(delimiter)
    self.adelim, self.escaped_adelim = self._process_delimiter(array_delimiter)
    self.quote = quote
    self.skip_bad_relationships = skip_bad_relationships
    self.skip_duplicate_nodes = skip_duplicate_nodes

    if import_call_bin_prefix is None:
        self.import_call_bin_prefix = self._get_default_import_call_bin_prefix()
    else:
        self.import_call_bin_prefix = import_call_bin_prefix

    self.wipe = wipe
    self.strict_mode = strict_mode

    self.translator = translator
    self.deduplicator = deduplicator
    self.node_property_dict = {}
    self.edge_property_dict = {}
    self.import_call_nodes = set()
    self.import_call_edges = set()

    self.outdir = output_directory

    self._import_call_file_prefix = import_call_file_prefix

    self.parts = {}  # dict to store the paths of part files for each label

    self._labels_orders = ["Alphabetical", "Ascending", "Descending", "Leaves"]
    if labels_order not in self._labels_orders:
        msg = (
            f"neo4j's 'labels_order' parameter cannot be '{labels_order}',"
            "must be one of: {' ,'.join(self._labels_orders)}",
        )
        raise ValueError(msg)
    self.labels_order = labels_order

get_import_call()

Eeturn the import call.

Return the import call detailing folder and individual node and edge headers and data files, as well as delimiters and database name.

Returns
str: a bash command for the database import
Source code in biocypher/output/write/_batch_writer.py 
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def get_import_call(self) -> str:
    """Eeturn the import call.

    Return the import call detailing folder and individual node and
    edge headers and data files, as well as delimiters and database name.

    Returns
    -------
        str: a bash command for the database import

    """
    return self._construct_import_call()

write_edges(edges, batch_size=int(1000000.0))

Write edges and their headers.

edges (BioCypherEdge): a list or generator of edges in
    :py:class:`BioCypherEdge` or :py:class:`BioCypherRelAsNode`
    format

bool: The return value. True for success, False otherwise.
Source code in biocypher/output/write/_batch_writer.py 
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def write_edges(
    self,
    edges: list | GeneratorType,
    batch_size: int = int(1e6),
) -> bool:
    """Write edges and their headers.

    Args:
    ----
        edges (BioCypherEdge): a list or generator of edges in
            :py:class:`BioCypherEdge` or :py:class:`BioCypherRelAsNode`
            format

    Returns:
    -------
        bool: The return value. True for success, False otherwise.

    """
    passed = False
    edges = list(edges)  # force evaluation to handle empty generator
    if edges:
        nodes_flat = []
        edges_flat = []
        for edge in edges:
            if isinstance(edge, BioCypherRelAsNode):
                # check if relationship has already been written, if so skip
                if self.deduplicator.rel_as_node_seen(edge):
                    continue

                nodes_flat.append(edge.get_node())
                edges_flat.append(edge.get_source_edge())
                edges_flat.append(edge.get_target_edge())

            else:
                # check if relationship has already been written, if so skip
                if self.deduplicator.edge_seen(edge):
                    continue

                edges_flat.append(edge)

        if nodes_flat and edges_flat:
            passed = self.write_nodes(nodes_flat) and self._write_edge_data(
                edges_flat,
                batch_size,
            )
        else:
            passed = self._write_edge_data(edges_flat, batch_size)

    else:
        # is this a problem? if the generator or list is empty, we
        # don't write anything.
        logger.debug(
            "No edges to write, possibly due to no matched Biolink classes.",
        )

    if not passed:
        logger.error("Error while writing edge data.")
        return False
    # pass property data to header writer per edge type written
    passed = self._write_edge_headers()
    if not passed:
        logger.error("Error while writing edge headers.")
        return False

    return True

write_import_call()

Write the import call.

Function to write the import call detailing folder and individual node and edge headers and data files, as well as delimiters and database name, to the export folder as txt.

Returns
str: The path of the file holding the import call.
Source code in biocypher/output/write/_batch_writer.py 
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def write_import_call(self) -> str:
    """Write the import call.

    Function to write the import call detailing folder and
    individual node and edge headers and data files, as well as
    delimiters and database name, to the export folder as txt.

    Returns
    -------
        str: The path of the file holding the import call.

    """
    file_path = os.path.join(self.outdir, self._get_import_script_name())
    logger.info(f"Writing {self.db_name + ' ' if self.db_name else ''}import call to `{file_path}`.")

    with open(file_path, "w", encoding="utf-8") as f:
        f.write(self._construct_import_call())

    return file_path

write_nodes(nodes, batch_size=int(1000000.0), force=False)

Write nodes and their headers.

nodes (BioCypherNode): a list or generator of nodes in
    :py:class:`BioCypherNode` format

batch_size (int): The batch size for writing nodes.

force (bool): Whether to force writing nodes even if their type is
    not present in the schema.

bool: The return value. True for success, False otherwise.
Source code in biocypher/output/write/_batch_writer.py 
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def write_nodes(self, nodes, batch_size: int = int(1e6), force: bool = False):
    """Write nodes and their headers.

    Args:
    ----
        nodes (BioCypherNode): a list or generator of nodes in
            :py:class:`BioCypherNode` format

        batch_size (int): The batch size for writing nodes.

        force (bool): Whether to force writing nodes even if their type is
            not present in the schema.


    Returns:
    -------
        bool: The return value. True for success, False otherwise.

    """
    # TODO check represented_as

    # write node data
    passed = self._write_node_data(nodes, batch_size, force)
    if not passed:
        logger.error("Error while writing node data.")
        return False
    # pass property data to header writer per node type written
    passed = self._write_node_headers()
    if not passed:
        logger.error("Error while writing node headers.")
        return False

    return True

Neo4j Batch Writer

Bases: _BatchWriter

Class for writing node and edge representations to disk using the format specified by Neo4j for the use of admin import. Each batch writer instance has a fixed representation that needs to be passed at instantiation via the 🇵🇾attr:schema argument. The instance also expects an ontology adapter via 🇵🇾attr:ontology_adapter to be able to convert and extend the hierarchy.

This class inherits from the abstract class "_BatchWriter" and implements the Neo4j-specific methods:

- _write_node_headers
- _write_edge_headers
- _construct_import_call
- _write_array_string
Source code in biocypher/output/write/graph/_neo4j.py 
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class _Neo4jBatchWriter(_BatchWriter):
    """Class for writing node and edge representations to disk using the
    format specified by Neo4j for the use of admin import. Each batch
    writer instance has a fixed representation that needs to be passed
    at instantiation via the :py:attr:`schema` argument. The instance
    also expects an ontology adapter via :py:attr:`ontology_adapter` to be able
    to convert and extend the hierarchy.

    This class inherits from the abstract class "_BatchWriter" and implements the
    Neo4j-specific methods:

        - _write_node_headers
        - _write_edge_headers
        - _construct_import_call
        - _write_array_string
    """

    def __init__(self, *args, **kwargs):
        """Constructor.

        Check the version of Neo4j and adds a command scope if version >= 5.

        Returns
        -------
            _Neo4jBatchWriter: An instance of the writer.

        """
        # Should read the configuration and setup import_call_bin_prefix.
        super().__init__(*args, **kwargs)

    def _get_default_import_call_bin_prefix(self):
        """Method to provide the default string for the import call bin prefix.

        Returns
        -------
            str: The default location for the neo4j admin import location

        """
        return "bin/"

    def _quote_string(self, value: str) -> str:
        """Quote a string. Quote character is escaped by doubling it."""
        return f"{self.quote}{value.replace(self.quote, self.quote * 2)}{self.quote}"

    def _write_array_string(self, string_list):
        """Abstract method to output.write the string representation of an array into a .csv file
        as required by the neo4j admin-import.

        Args:
        ----
            string_list (list): list of ontology strings

        Returns:
        -------
            str: The string representation of an array for the neo4j admin import

        """
        string = self.adelim.join(string_list)
        return self._quote_string(string)

    def _write_node_headers(self):
        """Writes single CSV file for a graph entity that is represented
        as a node as per the definition in the `schema_config.yaml`,
        containing only the header for this type of node.

        Returns
        -------
            bool: The return value. True for success, False otherwise.

        """
        # load headers from data parse
        if not self.node_property_dict:
            logger.error(
                "Header information not found. Was the data parsed first?",
            )
            return False

        for label, props in self.node_property_dict.items():
            _id = ":ID"

            # translate label to PascalCase
            pascal_label = self.translator.name_sentence_to_pascal(parse_label(label))

            header = f"{pascal_label}-header.csv"
            header_path = os.path.join(
                self.outdir,
                header,
            )
            parts = f"{pascal_label}-part.*"

            # check if file already exists
            if os.path.exists(header_path):
                logger.warning(
                    f"Header file `{header_path}` already exists. Overwriting.",
                )

            # concatenate key:value in props
            props_list = []
            for k, v in props.items():
                if v in ["int", "long", "integer"]:
                    props_list.append(f"{k}:long")
                elif v in ["int[]", "long[]", "integer[]"]:
                    props_list.append(f"{k}:long[]")
                elif v in ["float", "double", "dbl"]:
                    props_list.append(f"{k}:double")
                elif v in ["float[]", "double[]"]:
                    props_list.append(f"{k}:double[]")
                elif v in ["bool", "boolean"]:
                    # TODO Neo4j boolean support / spelling?
                    props_list.append(f"{k}:boolean")
                elif v in ["bool[]", "boolean[]"]:
                    props_list.append(f"{k}:boolean[]")
                elif v in ["str[]", "string[]"]:
                    props_list.append(f"{k}:string[]")
                else:
                    props_list.append(f"{k}")

            # create list of lists and flatten
            out_list = [[_id], props_list, [":LABEL"]]
            out_list = [val for sublist in out_list for val in sublist]

            with open(header_path, "w", encoding="utf-8") as f:
                # concatenate with delimiter
                row = self.delim.join(out_list)
                f.write(row)

            # add file path to neo4 admin import statement (import call file
            # path may be different from actual file path)
            import_call_header_path = os.path.join(
                self.import_call_file_prefix,
                header,
            )
            import_call_parts_path = os.path.join(
                self.import_call_file_prefix,
                parts,
            )
            self.import_call_nodes.add((import_call_header_path, import_call_parts_path))

        return True

    def _write_edge_headers(self):
        """Writes single CSV file for a graph entity that is represented
        as an edge as per the definition in the `schema_config.yaml`,
        containing only the header for this type of edge.

        Returns
        -------
            bool: The return value. True for success, False otherwise.

        """
        # load headers from data parse
        if not self.edge_property_dict:
            logger.error(
                "Header information not found. Was the data parsed first?",
            )
            return False

        for label, props in self.edge_property_dict.items():
            # translate label to PascalCase
            pascal_label = self.translator.name_sentence_to_pascal(parse_label(label))

            # paths
            header = f"{pascal_label}-header.csv"
            header_path = os.path.join(
                self.outdir,
                header,
            )
            parts = f"{pascal_label}-part.*"

            # check for file exists
            if os.path.exists(header_path):
                logger.warning(f"File {header_path} already exists. Overwriting.")

            # concatenate key:value in props
            props_list = []
            for k, v in props.items():
                if v in ["int", "long", "integer"]:
                    props_list.append(f"{k}:long")
                elif v in ["int[]", "long[]", "integer[]"]:
                    props_list.append(f"{k}:long[]")
                elif v in ["float", "double"]:
                    props_list.append(f"{k}:double")
                elif v in ["float[]", "double[]"]:
                    props_list.append(f"{k}:double[]")
                elif v in [
                    "bool",
                    "boolean",
                ]:  # TODO does Neo4j support bool?
                    props_list.append(f"{k}:boolean")
                elif v in ["bool[]", "boolean[]"]:
                    props_list.append(f"{k}:boolean[]")
                elif v in ["str[]", "string[]"]:
                    props_list.append(f"{k}:string[]")
                else:
                    props_list.append(f"{k}")

            skip_id = False
            schema_label = None

            if label in ["IS_SOURCE_OF", "IS_TARGET_OF", "IS_PART_OF"]:
                skip_id = True
            elif not self.translator.ontology.mapping.extended_schema.get(label):
                # find label in schema by label_as_edge
                for (
                    k,
                    v,
                ) in self.translator.ontology.mapping.extended_schema.items():
                    if v.get("label_as_edge") == label:
                        schema_label = k
                        break
            else:
                schema_label = label

            out_list = [":START_ID"]

            if schema_label:
                if (
                    self.translator.ontology.mapping.extended_schema.get(  # (seems to not work with 'not')
                        schema_label,
                    ).get("use_id")
                    == False  # noqa: E712 (seems to not work with 'not')
                ):
                    skip_id = True

            if not skip_id:
                out_list.append("id")

            out_list.extend(props_list)
            out_list.extend([":END_ID", ":TYPE"])

            with open(header_path, "w", encoding="utf-8") as f:
                # concatenate with delimiter
                row = self.delim.join(out_list)
                f.write(row)

            # add file path to neo4 admin import statement (import call file
            # path may be different from actual file path)
            import_call_header_path = os.path.join(
                self.import_call_file_prefix,
                header,
            )
            import_call_parts_path = os.path.join(
                self.import_call_file_prefix,
                parts,
            )
            self.import_call_edges.add((import_call_header_path, import_call_parts_path))

        return True

    def _get_import_script_name(self) -> str:
        """Returns the name of the neo4j admin import script

        Returns
        -------
            str: The name of the import script (ending in .sh)

        """
        return "neo4j-admin-import-call.sh"

    def _construct_import_call(self) -> str:
        """Function to construct the import call detailing folder and
        individual node and edge headers and data files, as well as
        delimiters and database name. Built after all data has been
        processed to ensure that nodes are called before any edges.

        Returns
        -------
            str: a bash command for neo4j-admin import

        """
        import_call_neo4j_v4 = self._get_import_call("import", "--database=", "--force=")
        import_call_neo4j_v5 = self._get_import_call("database import full", "", "--overwrite-destination=")
        neo4j_version_check = f"version=$({self.import_call_bin_prefix}neo4j-admin --version | cut -d '.' -f 1)"

        import_script = (
            f"#!/bin/bash\n{neo4j_version_check}\nif [[ $version -ge 5 ]]; "
            f"then\n\t{import_call_neo4j_v5}\nelse\n\t{import_call_neo4j_v4}\nfi"
        )
        return import_script

    def _get_import_call(self, import_cmd: str, database_cmd: str, wipe_cmd: str) -> str:
        """Get parametrized import call for Neo4j 4 or 5+.

        Args:
        ----
            import_cmd (str): The import command to use.
            database_cmd (str): The database command to use.
            wipe_cmd (str): The wipe command to use.

        Returns:
        -------
            str: The import call.

        """
        import_call = f"{self.import_call_bin_prefix}neo4j-admin {import_cmd} "

        import_call += f"{database_cmd}{self.db_name} "

        import_call += f'--delimiter="{self.escaped_delim}" '

        import_call += f'--array-delimiter="{self.escaped_adelim}" '

        if self.quote == "'":
            import_call += f'--quote="{self.quote}" '
        else:
            import_call += f"--quote='{self.quote}' "

        if self.wipe:
            import_call += f"{wipe_cmd}true "
        if self.skip_bad_relationships:
            import_call += "--skip-bad-relationships=true "
        if self.skip_duplicate_nodes:
            import_call += "--skip-duplicate-nodes=true "

        # append node import calls
        for header_path, parts_path in self.import_call_nodes:
            import_call += f'--nodes="{header_path},{parts_path}" '

        # append edge import calls
        for header_path, parts_path in self.import_call_edges:
            import_call += f'--relationships="{header_path},{parts_path}" '

        return import_call

__init__(*args, **kwargs)

Constructor.

Check the version of Neo4j and adds a command scope if version >= 5.

Returns
_Neo4jBatchWriter: An instance of the writer.
Source code in biocypher/output/write/graph/_neo4j.py 
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def __init__(self, *args, **kwargs):
    """Constructor.

    Check the version of Neo4j and adds a command scope if version >= 5.

    Returns
    -------
        _Neo4jBatchWriter: An instance of the writer.

    """
    # Should read the configuration and setup import_call_bin_prefix.
    super().__init__(*args, **kwargs)

ArangoDB Batch Writer

Bases: _Neo4jBatchWriter

Class for writing node and edge representations to disk.

Uses the format specified by ArangoDB for the use of "arangoimport". Output files are similar to Neo4j, but with a different header format.

Source code in biocypher/output/write/graph/_arangodb.py 
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class _ArangoDBBatchWriter(_Neo4jBatchWriter):
    """Class for writing node and edge representations to disk.

    Uses the format specified by ArangoDB for the use of "arangoimport".
    Output files are similar to Neo4j, but with a different header format.
    """

    def _get_default_import_call_bin_prefix(self):
        """Provide the default string for the import call bin prefix.

        Returns
        -------
            str: The default location for the neo4j admin import location

        """
        return ""

    def _get_import_script_name(self) -> str:
        """Return the name of the neo4j admin import script.

        Returns
        -------
            str: The name of the import script (ending in .sh)

        """
        return "arangodb-import-call.sh"

    def _write_node_headers(self):
        """Write single CSV file for a graph entity.

        The graph entity is represented as a node as per the definition
        in the `schema_config.yaml`, containing only the header for this type
        of node.

        Returns
        -------
            bool: The return value. True for success, False otherwise.

        """
        # load headers from data parse
        if not self.node_property_dict:
            logger.error(
                "Header information not found. Was the data parsed first?",
            )
            return False

        for label, props in self.node_property_dict.items():
            # create header CSV with ID, properties, labels

            _id = "_key"

            # translate label to PascalCase
            pascal_label = self.translator.name_sentence_to_pascal(label)

            header = f"{pascal_label}-header.csv"
            header_path = os.path.join(
                self.outdir,
                header,
            )

            # check if file already exists
            if os.path.exists(header_path):
                logger.warning(f"File {header_path} already exists. Overwriting.")

            # concatenate key:value in props
            props_list = []
            for k in props.keys():
                props_list.append(f"{k}")

            # create list of lists and flatten
            # removes need for empty check of property list
            out_list = [[_id], props_list]
            out_list = [val for sublist in out_list for val in sublist]

            with open(header_path, "w", encoding="utf-8") as f:
                # concatenate with delimiter
                row = self.delim.join(out_list)
                f.write(row)

            # add collection from schema config
            collection = self.translator.ontology.mapping.extended_schema[label].get("db_collection_name", None)

            # add file path to neo4 admin import statement
            # do once for each part file
            parts = self.parts.get(label, [])

            if not parts:
                msg = f"No parts found for node label {label}. Check that the data was parsed first."
                logger.error(msg)
                raise ValueError(msg)

            for part in parts:
                import_call_header_path = os.path.join(
                    self.import_call_file_prefix,
                    header,
                )
                import_call_parts_path = os.path.join(
                    self.import_call_file_prefix,
                    part,
                )

                self.import_call_nodes.add(
                    (
                        import_call_header_path,
                        import_call_parts_path,
                        collection,
                    ),
                )

        return True

    def _write_edge_headers(self):
        """Write single CSV file for a graph entity.

        The graph entity is represented as an edge as per the definition
        in the `schema_config.yaml`, containing only the header for this type
        of edge.

        Returns
        -------
            bool: The return value. True for success, False otherwise.

        """
        # load headers from data parse
        if not self.edge_property_dict:
            logger.error(
                "Header information not found. Was the data parsed first?",
            )
            return False

        for label, props in self.edge_property_dict.items():
            # translate label to PascalCase
            pascal_label = self.translator.name_sentence_to_pascal(label)

            # paths
            header = f"{pascal_label}-header.csv"
            header_path = os.path.join(
                self.outdir,
                header,
            )
            parts = f"{pascal_label}-part.*"

            # check for file exists
            if os.path.exists(header_path):
                logger.warning(f"Header file {header_path} already exists. Overwriting.")

            # concatenate key:value in props
            props_list = []
            for k in props.keys():
                props_list.append(f"{k}")

            out_list = ["_from", "_key", *props_list, "_to"]

            with open(header_path, "w", encoding="utf-8") as f:
                # concatenate with delimiter
                row = self.delim.join(out_list)
                f.write(row)

            # add collection from schema config
            if not self.translator.ontology.mapping.extended_schema.get(label):
                for (
                    _,
                    v,
                ) in self.translator.ontology.mapping.extended_schema.items():
                    if v.get("label_as_edge") == label:
                        collection = v.get("db_collection_name", None)
                        break

            else:
                collection = self.translator.ontology.mapping.extended_schema[label].get("db_collection_name", None)

            # add file path to neo4 admin import statement (import call path
            # may be different from actual output path)
            header_import_call_path = os.path.join(
                self.import_call_file_prefix,
                header,
            )
            parts_import_call_path = os.path.join(
                self.import_call_file_prefix,
                parts,
            )
            self.import_call_edges.add(
                (
                    header_import_call_path,
                    parts_import_call_path,
                    collection,
                ),
            )

        return True

    def _construct_import_call(self) -> str:
        """Construct the import call.

        Details folder and individual node and edge headers and data files,
        as well as delimiters and database name. Built after all data has been
        processed to ensure that nodes are called before any edges.

        Returns
        -------
            str: a bash command for arangoimport

        """
        import_call = f"{self.import_call_bin_prefix}arangoimp --type csv " f'--separator="{self.escaped_delim}" '

        if self.quote == "'":
            import_call += f'--quote="{self.quote}" '
        else:
            import_call += f"--quote='{self.quote}' "

        node_lines = ""

        # node import calls: one line per node type
        for header_path, parts_path, collection in self.import_call_nodes:
            line = f"{import_call} --headers-file {header_path} --file= {parts_path} "

            if collection:
                line += f"--create-collection --collection {collection} "

            node_lines += f"{line}\n"

        edge_lines = ""

        # edge import calls: one line per edge type
        for header_path, parts_path, collection in self.import_call_edges:
            import_call += f'--relationships="{header_path},{parts_path}" '

        return node_lines + edge_lines

RDF Writer

Bases: _BatchWriter

Write BioCypher's property graph into an RDF format.

Uses rdflib and all the extensions it supports (RDF/XML, N3, NTriples, N-Quads, Turtle, TriX, Trig and JSON-LD). By default, the conversion is done keeping only the minimum information about node and edges, skipping all properties.

Source code in biocypher/output/write/graph/_rdf.py 
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class _RDFWriter(_BatchWriter):
    """Write BioCypher's property graph into an RDF format.

    Uses `rdflib` and all the extensions it supports (RDF/XML, N3, NTriples,
    N-Quads, Turtle, TriX, Trig and JSON-LD). By default, the conversion
    is done keeping only the minimum information about node and edges,
    skipping all properties.
    """

    def __init__(
        self,
        translator: Translator,
        deduplicator: Deduplicator,
        delimiter: str,
        array_delimiter: str = ",",
        quote: str = '"',
        output_directory: str | None = None,
        db_name: str = "neo4j",
        import_call_bin_prefix: str | None = None,
        import_call_file_prefix: str | None = None,
        wipe: bool = True,
        strict_mode: bool = False,
        skip_bad_relationships: bool = False,
        skip_duplicate_nodes: bool = False,
        db_user: str = None,
        db_password: str = None,
        db_host: str = None,
        db_port: str = None,
        file_format: str = None,
        rdf_namespaces: dict = {},
        labels_order: str = "Ascending",
        **kwargs,
    ):
        super().__init__(
            translator=translator,
            deduplicator=deduplicator,
            delimiter=delimiter,
            array_delimiter=array_delimiter,
            quote=quote,
            output_directory=output_directory,
            db_name=db_name,
            import_call_bin_prefix=import_call_bin_prefix,
            import_call_file_prefix=import_call_file_prefix,
            wipe=wipe,
            strict_mode=strict_mode,
            skip_bad_relationships=skip_bad_relationships,
            skip_duplicate_nodes=skip_duplicate_nodes,
            db_user=db_user,
            db_password=db_password,
            db_host=db_host,
            db_port=db_port,
            file_format=file_format,
            rdf_namespaces=rdf_namespaces,
            labels_order=labels_order,
            **kwargs,
        )
        if not self.rdf_namespaces:
            # For some reason, the config can pass
            # the None object.
            self.rdf_namespaces = {}

        if "rdf_format" in kwargs:
            logger.warning("The 'rdf_format' config option is deprecated, use 'file_format' instead.")
            if not file_format:
                format = kwargs["rdf_format"]
                logger.warning(f"I will set 'file_format: {format}' for you.")
                self.file_format = format
                kwargs.pop("rdf_format")
            logger.warning("NOTE: this warning will become an error in next versions.")

        if not file_format:
            msg = "You need to indicate a 'file_format'."
            logger.error(msg)
            raise RuntimeError(msg)

        self.namespaces = {}

    def _get_import_script_name(self) -> str:
        """Return the name of the RDF admin import script.

        This function is used for RDF export.

        Returns
        -------
            str: The name of the import script (ending in .sh)

        """
        return "rdf-import-call.sh"

    def _get_default_import_call_bin_prefix(self):
        """Provide the default string for the import call bin prefix.

        Returns
        -------
            str: The default location for the RDF admin import location

        """
        return "bin/"

    def _is_rdf_format_supported(self, file_format: str) -> bool:
        """Check if the specified RDF format is supported.

        Args:
        ----
            file_format (str): The RDF format to check.

        Returns:
        -------
            bool: Returns True if rdf format supported, False otherwise.

        """
        supported_formats = [
            "xml",
            "n3",
            "turtle",
            "ttl",
            "nt",
            "pretty-xml",
            "trix",
            "trig",
            "nquads",
            "json-ld",
        ]
        if file_format not in supported_formats:
            logger.error(
                f"Incorrect or unsupported RDF format: '{file_format}',"
                f"use one of the following: {', '.join(supported_formats)}.",
            )
            return False
        else:
            # Set the file extension to match the format
            if self.file_format == "turtle":
                self.extension = "ttl"
            else:
                self.extension = self.file_format
            return True

    def _write_single_edge_list_to_file(
        self,
        edge_list: list,
        label: str,
        prop_dict: dict,
    ):
        """Write a list of BioCypherEdges to an RDF file.

        Args:
        ----
            edge_list (list): list of BioCypherEdges to be written

            label (str): the label (type) of the edge

            prop_dict (dict): properties of node class passed from parsing
                function and their types

        Returns:
        -------
            bool: The return value. True for success, False otherwise.

        """
        # NOTE: prop_dict is not used. Remove in next refactor.

        if not all(isinstance(n, BioCypherEdge) for n in edge_list):
            logger.error("Edges must be passed as type BioCypherEdge.")
            return False

        # translate label to PascalCase
        label_pascal = self.translator.name_sentence_to_pascal(label)

        # create file name
        file_name = os.path.join(self.outdir, f"{label_pascal}.{self.extension}")

        # write data in graph
        graph = Graph()
        self._init_namespaces(graph)

        for edge in edge_list:
            rdf_subject = edge.get_source_id()
            rdf_object = edge.get_target_id()
            rdf_predicate = edge.get_id()
            rdf_properties = edge.get_properties()
            if rdf_predicate is None:
                rdf_predicate = rdf_subject + rdf_object

            edge_label = self.translator.name_sentence_to_pascal(edge.get_label())
            edge_uri = self.as_uri(edge_label, "biocypher")
            graph.add((edge_uri, RDF.type, RDFS.Class))
            graph.add(
                (
                    self.as_uri(rdf_predicate, "biocypher"),
                    RDF.type,
                    edge_uri,
                ),
            )
            graph.add(
                (
                    self.as_uri(rdf_predicate, "biocypher"),
                    self.as_uri("subject", "biocypher"),
                    self.to_uri(rdf_subject),
                ),
            )
            graph.add(
                (
                    self.as_uri(rdf_predicate, "biocypher"),
                    self.as_uri("object", "biocypher"),
                    self.to_uri(rdf_object),
                ),
            )

            # add properties to the transformed edge --> node
            for key, value in rdf_properties.items():
                # only write value if it exists.
                if value:
                    self.add_property_to_graph(graph, rdf_predicate, value, key)

        graph.serialize(destination=file_name, format=self.file_format)

        logger.info(
            f"Writing {len(edge_list)} entries to {label_pascal}.{self.file_format}",
        )

        return True

    def add_property_to_graph(
        self,
        graph: Graph,
        rdf_subject: str,
        rdf_object: str,
        rdf_predicate: str,
    ):
        """Add the properties to an RDF node.

        It takes the graph, the subject, object, and predicate of the RDF
        triple. It checks if the property is a list and adds it to the graph
        accordingly. Otherwise it checks if the string represents a list. If it
        does, it transforms it to a list and adds it to the graph. If not, it
        adds the property to the graph as a literal. If the property is neither
        a list or string, it will also be added as a literal.

        Args:
        ----
            graph (RDFLib.Graph): The RDF graph to add the nodes to.

            rdf_subject (str): The subject of the RDF triple.

            rdf_object (str): The object of the RDF triple.

            rdf_predicate (str): The predicate of the RDF triple.

        Returns:
        -------
            None

        """
        if isinstance(rdf_object, list):
            for obj in rdf_object:
                graph.add(
                    (
                        self.to_uri(rdf_subject),
                        self.property_to_uri(rdf_predicate),
                        Literal(obj),
                    ),
                )
        elif isinstance(rdf_object, str):
            if rdf_object.startswith("[") and rdf_object.endswith("]"):
                self.add_property_to_graph(
                    graph,
                    rdf_subject,
                    self.transform_string_to_list(rdf_object),
                    rdf_predicate,
                )
            else:
                graph.add(
                    (
                        self.to_uri(rdf_subject),
                        self.property_to_uri(rdf_predicate),
                        Literal(rdf_object),
                    ),
                )
        else:
            graph.add(
                (
                    self.to_uri(rdf_subject),
                    self.property_to_uri(rdf_predicate),
                    Literal(rdf_object),
                ),
            )

    def transform_string_to_list(self, string_list: str) -> list:
        """Transform a string representation of a list into a list.

        Args:
        ----
            string_list (str): The string representation of the list.

        Returns:
        -------
            list: The list representation of the input string.

        """
        return string_list.replace("[", "").replace("]", "").replace("'", "").split(", ")

    def _write_single_node_list_to_file(
        self,
        node_list: list,
        label: str,
        prop_dict: dict,
        labels: str,
    ):
        """Write a list of BioCypherNodes to an RDF file.

        Args:
        ----
            node_list (list): A list of BioCypherNodes to be written.

            label (str): The label (type) of the nodes.

            prop_dict (dict): A dictionary of properties and their types for the node class.

            labels (str): string of one or several concatenated labels

        Returns:
        -------
            bool: True if the writing is successful, False otherwise.

        """
        # NOTE: labels and prop_dict are not used.

        if not all(isinstance(n, BioCypherNode) for n in node_list):
            logger.error("Nodes must be passed as type BioCypherNode.")
            return False

        # translate label to PascalCase
        label_pascal = self.translator.name_sentence_to_pascal(label)

        # create file name
        file_name = os.path.join(self.outdir, f"{label_pascal}.{self.extension}")

        # write data in graph
        graph = Graph()
        self._init_namespaces(graph)

        for n in node_list:
            rdf_subject = n.get_id()
            rdf_object = n.get_label()
            properties = n.get_properties()
            class_name = self.translator.name_sentence_to_pascal(rdf_object)
            graph.add(
                (
                    self.as_uri(class_name, "biocypher"),
                    RDF.type,
                    RDFS.Class,
                ),
            )
            graph.add(
                (
                    self.to_uri(rdf_subject),
                    RDF.type,
                    self.as_uri(class_name, "biocypher"),
                ),
            )
            for key, value in properties.items():
                # only write value if it exists.
                if value:
                    self.add_property_to_graph(graph, rdf_subject, value, key)

        graph.serialize(destination=file_name, format=self.file_format)

        logger.info(
            f"Writing {len(node_list)} entries to {label_pascal}.{self.file_format}",
        )

        return True

    def write_nodes(self, nodes, batch_size: int = int(1e6), force: bool = False) -> bool:
        """Write nodes in RDF format.

        Args:
        ----
            nodes (list or generator): A list or generator of nodes in
                BioCypherNode format.
            batch_size (int): The number of nodes to write in each batch.
            force (bool): Flag to force the writing even if the output file
                already exists.

        Returns:
        -------
            bool: True if the writing is successful, False otherwise.

        """
        # check if specified output format is correct
        passed = self._is_rdf_format_supported(self.file_format)
        if not passed:
            logger.error("Error while writing node data, wrong RDF format")
            return False
        # write node data using _write_node_data method
        passed = self._write_node_data(nodes, batch_size, force)
        if not passed:
            logger.error("Error while writing node data.")
            return False
        return True

    def write_edges(
        self,
        edges: list | GeneratorType,
        batch_size: int = int(1e6),
    ) -> bool:
        """Write edges in RDF format.

        Args:
        ----
            edges (BioCypherEdge): a list or generator of edges in
                :py:class:`BioCypherEdge` format
            batch_size (int): The number of edges to write in each batch.

        Returns:
        -------
            bool: The return value. True for success, False otherwise.

        """
        # check if specified output format is correct
        passed = self._is_rdf_format_supported(self.file_format)
        if not passed:
            logger.error("Error while writing edge data, wrong RDF format")
            return False
        # write edge data using _write_edge_data method
        passed = self._write_edge_data(edges, batch_size=batch_size)
        if not passed:
            logger.error("Error while writing edge data.")
            return False

        return True

    def _construct_import_call(self) -> bool:
        """Write the import call.

        This function is not applicable for RDF.

        Returns
        -------
            bool: The return value. True for success, False otherwise.

        """
        return ""

    def _quote_string(self, value: str) -> str:
        """Quote a string."""
        return f"{self.quote}{value}{self.quote}"

    def _write_array_string(self, string_list):
        """Write the string representation of an array into a .csv file.

        This function is not applicable for RDF.

        Args:
        ----
            string_list (list): list of ontology strings

        Returns:
        -------
            str: The string representation of an array for the neo4j admin import

        """
        return True

    def _write_node_headers(self):
        """Import properties of a graph entity.

        This function is not applicable for RDF.

        Returns
        -------
            bool: The return value. True for success, False otherwise.

        """
        return True

    def _write_edge_headers(self):
        """Write a database import-file for a graph entity.

        This function is not applicable for RDF.

        Returns
        -------
            bool: The return value. True for success, False otherwise.

        """
        return True

    def as_uri(self, name: str, namespace: str = "") -> str:
        """Return an RDFlib object with the given namespace as a URI.

        There is often a default for empty namespaces, which would have been
        loaded with the ontology, and put in `self.namespace` by
        `self._init_namespaces`.

        Args:
        ----
            name (str): The name to be transformed.
            namespace (str): The namespace to be used.

        Returns:
        -------
            str: The URI for the given name and namespace.

        """
        if namespace in self.namespaces:
            return URIRef(self.namespaces[namespace][name])
        else:
            assert "biocypher" in self.namespaces
            # If no default empty NS, use the biocypher one,
            # which is always there.
            logger.debug(f"I'll consider '{name}' as part of 'biocypher' namespace.")
            return URIRef(self.namespaces["biocypher"][name])

    def to_uri(self, subject: str) -> str:
        """Extract the namespace from the given subject.

        Split the subject's string on ":". Then convert the subject to a
        proper URI, if the namespace is known. If namespace is unknown,
        defaults to the default prefix of the ontology.

        Args:
        ----
            subject (str): The subject to be converted to a URI.

        Returns:
        -------
            str: The corresponding URI for the subject.

        """
        pref_id = subject.split(":")
        if len(pref_id) == 2:
            pref, id = pref_id
            return self.as_uri(id, pref)
        else:
            return self.as_uri(subject)

    def find_uri(self, regexp: str) -> str:
        query = f'SELECT DISTINCT ?s WHERE {{ ?s ?p ?o . FILTER regex(str(?s), "{regexp}")}}'
        gen = self.graph.query(query)
        uris = list(gen)
        if len(uris) > 1:
            logger.warning(
                f"Found several terms matching `{regexp}`, I will consider only the first one: `{uris[0][0]}`",
            )
            logger.debug("\tothers:")
            for u in uris[1:]:
                logger.debug(f"\t{u[0]}")
        if uris:
            logger.debug(f"Found {len(uris)} terms, returning: `{uris[0][0]}`")
            return uris[0][0]
        else:
            logger.debug(f"Found no term matching: `{query}`")
            return None

    def property_to_uri(self, property_name: str) -> dict[str, str]:
        """Convert a property name to its corresponding URI.

        This function takes a property name and searches for its corresponding
        URI in various namespaces. It first checks the core namespaces for
        rdflib, including owl, rdf, rdfs, xsd, and xml.

        Args:
        ----
            property_name (str): The property name to be converted to a URI.

        Returns:
        -------
            str: The corresponding URI for the input property name.

        """
        # These namespaces are core for rdflib; owl, rdf, rdfs, xsd and xml
        for namespace in _NAMESPACE_PREFIXES_CORE.values():
            if property_name in namespace:
                return namespace[property_name]

        # If the property name is not found in the core namespaces, search in
        # the SKOS, DC, and DCTERMS namespaces
        for namespace in [SKOS, DC, DCTERMS]:
            if property_name in namespace:
                return namespace[property_name]

        # If the property name is still not found, try other namespaces from
        # rdflib.
        for namespace in _NAMESPACE_PREFIXES_RDFLIB.values():
            if property_name in namespace:
                return namespace[property_name]

        # If the property name is "licence", it recursively calls the function
        # with "license" as the input.
        if property_name == "licence":
            return self.property_to_uri("license")

        # TODO: add an option to search trough manually implemented namespaces

        # If the input is not found in any of the namespaces, it returns
        # the corresponding URI from the biocypher namespace.
        # TODO: give a warning and try to prevent this option altogether
        return self.as_uri(property_name, "biocypher")

    def _init_namespaces(self, graph: Graph):
        """Initialise the namespaces for the RDF graph.

        This function adds the biocypher standard namespace to the `namespaces`
        attribute of the class. If `namespaces` is empty, it sets it to the
        biocypher standard namespace. Otherwise, it merges the biocypher
        standard namespace with the namespaces defined in the
        biocypher_config.yaml.

        Args:
        ----
            graph (RDFLib.Graph): The RDF graph to bind the namespaces to.

        Returns:
        -------
            None

        """
        # Bind and keep the biocypher namespace.
        bcns = Namespace("https://biocypher.org/biocypher#")
        bck = "biocypher"
        self.namespaces = {bck: bcns}
        graph.bind(bck, bcns)

        # Keep track of namespaces loaded with the ontologies in the given graph.
        logger.debug("Bind namespaces:")
        for prefix, ns in graph.namespaces():
            if prefix in self.namespaces and str(ns) != str(self.namespaces[prefix]):
                logger.warning(
                    f"Namespace '{prefix}' was already loaded"
                    f"as '{self.namespaces[prefix]}',"
                    f"I will overwrite it with '{ns}'.",
                )
            logger.debug(f"\t'{prefix}'\t=>\t'{ns}'")
            self.namespaces[prefix] = Namespace(ns)

        # Bind and keep the namespaces given in the config.
        for prefix, ns in self.rdf_namespaces.items():
            assert prefix not in self.namespaces
            self.namespaces[prefix] = Namespace(ns)
            logger.debug(f"\t'{prefix}'\t->\t{ns}")
            graph.bind(prefix, self.namespaces[prefix])

add_property_to_graph(graph, rdf_subject, rdf_object, rdf_predicate)

Add the properties to an RDF node.

It takes the graph, the subject, object, and predicate of the RDF triple. It checks if the property is a list and adds it to the graph accordingly. Otherwise it checks if the string represents a list. If it does, it transforms it to a list and adds it to the graph. If not, it adds the property to the graph as a literal. If the property is neither a list or string, it will also be added as a literal.

graph (RDFLib.Graph): The RDF graph to add the nodes to.

rdf_subject (str): The subject of the RDF triple.

rdf_object (str): The object of the RDF triple.

rdf_predicate (str): The predicate of the RDF triple.

None
Source code in biocypher/output/write/graph/_rdf.py 
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def add_property_to_graph(
    self,
    graph: Graph,
    rdf_subject: str,
    rdf_object: str,
    rdf_predicate: str,
):
    """Add the properties to an RDF node.

    It takes the graph, the subject, object, and predicate of the RDF
    triple. It checks if the property is a list and adds it to the graph
    accordingly. Otherwise it checks if the string represents a list. If it
    does, it transforms it to a list and adds it to the graph. If not, it
    adds the property to the graph as a literal. If the property is neither
    a list or string, it will also be added as a literal.

    Args:
    ----
        graph (RDFLib.Graph): The RDF graph to add the nodes to.

        rdf_subject (str): The subject of the RDF triple.

        rdf_object (str): The object of the RDF triple.

        rdf_predicate (str): The predicate of the RDF triple.

    Returns:
    -------
        None

    """
    if isinstance(rdf_object, list):
        for obj in rdf_object:
            graph.add(
                (
                    self.to_uri(rdf_subject),
                    self.property_to_uri(rdf_predicate),
                    Literal(obj),
                ),
            )
    elif isinstance(rdf_object, str):
        if rdf_object.startswith("[") and rdf_object.endswith("]"):
            self.add_property_to_graph(
                graph,
                rdf_subject,
                self.transform_string_to_list(rdf_object),
                rdf_predicate,
            )
        else:
            graph.add(
                (
                    self.to_uri(rdf_subject),
                    self.property_to_uri(rdf_predicate),
                    Literal(rdf_object),
                ),
            )
    else:
        graph.add(
            (
                self.to_uri(rdf_subject),
                self.property_to_uri(rdf_predicate),
                Literal(rdf_object),
            ),
        )

as_uri(name, namespace='')

Return an RDFlib object with the given namespace as a URI.

There is often a default for empty namespaces, which would have been loaded with the ontology, and put in self.namespace by self._init_namespaces.

name (str): The name to be transformed.
namespace (str): The namespace to be used.

str: The URI for the given name and namespace.
Source code in biocypher/output/write/graph/_rdf.py 
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def as_uri(self, name: str, namespace: str = "") -> str:
    """Return an RDFlib object with the given namespace as a URI.

    There is often a default for empty namespaces, which would have been
    loaded with the ontology, and put in `self.namespace` by
    `self._init_namespaces`.

    Args:
    ----
        name (str): The name to be transformed.
        namespace (str): The namespace to be used.

    Returns:
    -------
        str: The URI for the given name and namespace.

    """
    if namespace in self.namespaces:
        return URIRef(self.namespaces[namespace][name])
    else:
        assert "biocypher" in self.namespaces
        # If no default empty NS, use the biocypher one,
        # which is always there.
        logger.debug(f"I'll consider '{name}' as part of 'biocypher' namespace.")
        return URIRef(self.namespaces["biocypher"][name])

property_to_uri(property_name)

Convert a property name to its corresponding URI.

This function takes a property name and searches for its corresponding URI in various namespaces. It first checks the core namespaces for rdflib, including owl, rdf, rdfs, xsd, and xml.

property_name (str): The property name to be converted to a URI.

str: The corresponding URI for the input property name.
Source code in biocypher/output/write/graph/_rdf.py 
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def property_to_uri(self, property_name: str) -> dict[str, str]:
    """Convert a property name to its corresponding URI.

    This function takes a property name and searches for its corresponding
    URI in various namespaces. It first checks the core namespaces for
    rdflib, including owl, rdf, rdfs, xsd, and xml.

    Args:
    ----
        property_name (str): The property name to be converted to a URI.

    Returns:
    -------
        str: The corresponding URI for the input property name.

    """
    # These namespaces are core for rdflib; owl, rdf, rdfs, xsd and xml
    for namespace in _NAMESPACE_PREFIXES_CORE.values():
        if property_name in namespace:
            return namespace[property_name]

    # If the property name is not found in the core namespaces, search in
    # the SKOS, DC, and DCTERMS namespaces
    for namespace in [SKOS, DC, DCTERMS]:
        if property_name in namespace:
            return namespace[property_name]

    # If the property name is still not found, try other namespaces from
    # rdflib.
    for namespace in _NAMESPACE_PREFIXES_RDFLIB.values():
        if property_name in namespace:
            return namespace[property_name]

    # If the property name is "licence", it recursively calls the function
    # with "license" as the input.
    if property_name == "licence":
        return self.property_to_uri("license")

    # TODO: add an option to search trough manually implemented namespaces

    # If the input is not found in any of the namespaces, it returns
    # the corresponding URI from the biocypher namespace.
    # TODO: give a warning and try to prevent this option altogether
    return self.as_uri(property_name, "biocypher")

to_uri(subject)

Extract the namespace from the given subject.

Split the subject's string on ":". Then convert the subject to a proper URI, if the namespace is known. If namespace is unknown, defaults to the default prefix of the ontology.

subject (str): The subject to be converted to a URI.

str: The corresponding URI for the subject.
Source code in biocypher/output/write/graph/_rdf.py 
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def to_uri(self, subject: str) -> str:
    """Extract the namespace from the given subject.

    Split the subject's string on ":". Then convert the subject to a
    proper URI, if the namespace is known. If namespace is unknown,
    defaults to the default prefix of the ontology.

    Args:
    ----
        subject (str): The subject to be converted to a URI.

    Returns:
    -------
        str: The corresponding URI for the subject.

    """
    pref_id = subject.split(":")
    if len(pref_id) == 2:
        pref, id = pref_id
        return self.as_uri(id, pref)
    else:
        return self.as_uri(subject)

transform_string_to_list(string_list)

Transform a string representation of a list into a list.

string_list (str): The string representation of the list.

list: The list representation of the input string.
Source code in biocypher/output/write/graph/_rdf.py 
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def transform_string_to_list(self, string_list: str) -> list:
    """Transform a string representation of a list into a list.

    Args:
    ----
        string_list (str): The string representation of the list.

    Returns:
    -------
        list: The list representation of the input string.

    """
    return string_list.replace("[", "").replace("]", "").replace("'", "").split(", ")

write_edges(edges, batch_size=int(1000000.0))

Write edges in RDF format.

edges (BioCypherEdge): a list or generator of edges in
    :py:class:`BioCypherEdge` format
batch_size (int): The number of edges to write in each batch.

bool: The return value. True for success, False otherwise.
Source code in biocypher/output/write/graph/_rdf.py 
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def write_edges(
    self,
    edges: list | GeneratorType,
    batch_size: int = int(1e6),
) -> bool:
    """Write edges in RDF format.

    Args:
    ----
        edges (BioCypherEdge): a list or generator of edges in
            :py:class:`BioCypherEdge` format
        batch_size (int): The number of edges to write in each batch.

    Returns:
    -------
        bool: The return value. True for success, False otherwise.

    """
    # check if specified output format is correct
    passed = self._is_rdf_format_supported(self.file_format)
    if not passed:
        logger.error("Error while writing edge data, wrong RDF format")
        return False
    # write edge data using _write_edge_data method
    passed = self._write_edge_data(edges, batch_size=batch_size)
    if not passed:
        logger.error("Error while writing edge data.")
        return False

    return True

write_nodes(nodes, batch_size=int(1000000.0), force=False)

Write nodes in RDF format.

nodes (list or generator): A list or generator of nodes in
    BioCypherNode format.
batch_size (int): The number of nodes to write in each batch.
force (bool): Flag to force the writing even if the output file
    already exists.

bool: True if the writing is successful, False otherwise.
Source code in biocypher/output/write/graph/_rdf.py 
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def write_nodes(self, nodes, batch_size: int = int(1e6), force: bool = False) -> bool:
    """Write nodes in RDF format.

    Args:
    ----
        nodes (list or generator): A list or generator of nodes in
            BioCypherNode format.
        batch_size (int): The number of nodes to write in each batch.
        force (bool): Flag to force the writing even if the output file
            already exists.

    Returns:
    -------
        bool: True if the writing is successful, False otherwise.

    """
    # check if specified output format is correct
    passed = self._is_rdf_format_supported(self.file_format)
    if not passed:
        logger.error("Error while writing node data, wrong RDF format")
        return False
    # write node data using _write_node_data method
    passed = self._write_node_data(nodes, batch_size, force)
    if not passed:
        logger.error("Error while writing node data.")
        return False
    return True

NetworkX Writer

Bases: _Writer

Class for writing the in-memory networkx DiGraph to file.

Call _construct_import_call to write the networkx DiGraph to a pickle file and return the Python call to load it.

TODO: this is a non-intuitive name, should be adjusted.

Source code in biocypher/output/write/graph/_networkx.py 
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class _NetworkXWriter(_Writer):
    """
    Class for writing the in-memory networkx DiGraph to file.

    Call `_construct_import_call` to write the networkx DiGraph to a pickle
    file and return the Python call to load it.

    TODO: this is a non-intuitive name, should be adjusted.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.in_memory_networkx_kg = NetworkxKG(
            deduplicator=self.deduplicator,
        )

    def _construct_import_call(self) -> str:
        """Dump networkx graph to a pickle file and return Python call.

        Returns:
            str: Python code to load the networkx graph from a pickle file.
        """
        self.G = self.in_memory_networkx_kg._create_networkx_kg()
        logger.info(f"Writing networkx {self.G} to pickle file networkx_graph.pkl.")
        with open(f"{self.output_directory}/networkx_graph.pkl", "wb") as f:
            pickle.dump(self.G, f)

        import_call = "import pickle\n"
        import_call += "with open('./networkx_graph.pkl', 'rb') as f:\n\tG_loaded = pickle.load(f)"
        return import_call

    def _get_import_script_name(self) -> str:
        """Function to return the name of the import script."""
        return "import_networkx.py"

    def _write_node_data(self, nodes) -> bool:
        """Add nodes to the networkx graph.

        TODO: this is not strictly writing, should be refactored.

        Args:
            nodes (list): List of nodes to add to the networkx graph.

        Returns:
            bool: True if the nodes were added successfully, False otherwise.
        """
        passed = self.in_memory_networkx_kg.add_nodes(nodes)
        return passed

    def _write_edge_data(self, edges) -> bool:
        """Add edges to the networkx graph.

        TODO: this is not strictly writing, should be refactored.

        Args:
            edges (list): List of edges to add to the networkx graph.

        Returns:
            bool: True if the edges were added successfully, False otherwise.
        """
        passed = self.in_memory_networkx_kg.add_edges(edges)
        return passed

PostgreSQL Batch Writer

Bases: _BatchWriter

Write node and edge representations for PostgreSQL.

Class for writing node and edge representations to disk using the format specified by PostgreSQL for the use of "COPY FROM...". Each batch writer instance has a fixed representation that needs to be passed at instantiation via the 🇵🇾attr:schema argument. The instance also expects an ontology adapter via 🇵🇾attr:ontology_adapter to be able to convert and extend the hierarchy.

This class inherits from the abstract class "_BatchWriter" and implements the PostgreSQL-specific methods:

- _write_node_headers
- _write_edge_headers
- _construct_import_call
- _write_array_string
Source code in biocypher/output/write/relational/_postgresql.py 
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class _PostgreSQLBatchWriter(_BatchWriter):
    """Write node and edge representations for PostgreSQL.

    Class for writing node and edge representations to disk using the
    format specified by PostgreSQL for the use of "COPY FROM...". Each batch
    writer instance has a fixed representation that needs to be passed
    at instantiation via the :py:attr:`schema` argument. The instance
    also expects an ontology adapter via :py:attr:`ontology_adapter` to be able
    to convert and extend the hierarchy.

    This class inherits from the abstract class "_BatchWriter" and implements the
    PostgreSQL-specific methods:

        - _write_node_headers
        - _write_edge_headers
        - _construct_import_call
        - _write_array_string
    """

    DATA_TYPE_LOOKUP = {
        "str": "VARCHAR",  # VARCHAR needs limit
        "int": "INTEGER",
        "long": "BIGINT",
        "float": "NUMERIC",
        "double": "NUMERIC",
        "dbl": "NUMERIC",
        "boolean": "BOOLEAN",
        "str[]": "VARCHAR[]",
        "string[]": "VARCHAR[]",
    }

    def __init__(self, *args, **kwargs):
        self._copy_from_csv_commands = set()
        super().__init__(*args, **kwargs)

    def _get_default_import_call_bin_prefix(self) -> str:
        """Provide the default string for the import call bin prefix.

        Returns
        -------
            str: The default location for the psql command

        """
        return ""

    def _get_data_type(self, string) -> str:
        try:
            return self.DATA_TYPE_LOOKUP[string]
        except KeyError:
            logger.info('Could not determine data type {string}. Using default "VARCHAR"')
            return "VARCHAR"

    def _quote_string(self, value: str) -> str:
        """Quote a string."""
        return f"{self.quote}{value}{self.quote}"

    def _write_array_string(self, string_list) -> str:
        """Write the string representation of an array into a .csv file.

        Abstract method to output.write the string representation of an array
        into a .csv file as required by the postgresql COPY command, with
        '{','}' brackets and ',' separation.

        Args:
        ----
            string_list (list): list of ontology strings

        Returns:
        -------
            str: The string representation of an array for postgres COPY

        """
        string = ",".join(string_list)
        string = f'"{{{string}}}"'
        return string

    def _get_import_script_name(self) -> str:
        """Return the name of the psql import script.

        Returns
        -------
            str: The name of the import script (ending in .sh)

        """
        return f"{self.db_name}-import-call.sh"

    def _adjust_pascal_to_psql(self, string):
        string = string.replace(".", "_")
        string = string.lower()
        return string

    def _write_node_headers(self) -> bool:
        """Write node header files for PostgreSQL.

        Writes single CSV file for a graph entity that is represented
        as a node as per the definition in the `schema_config.yaml`,
        containing only the header for this type of node.

        Returns
        -------
            bool: The return value. True for success, False otherwise.

        """
        # load headers from data parse
        if not self.node_property_dict:
            logger.error(
                "Header information not found. Was the data parsed first?",
            )
            return False

        for label, props in self.node_property_dict.items():
            # create header CSV with ID, properties, labels

            # translate label to PascalCase
            pascal_label = self.translator.name_sentence_to_pascal(label)

            parts = f"{pascal_label}-part*.csv"
            parts_paths = os.path.join(self.outdir, parts)
            parts_paths = glob.glob(parts_paths)
            parts_paths.sort()

            # adjust label for import to psql
            pascal_label = self._adjust_pascal_to_psql(pascal_label)
            table_create_command_path = os.path.join(
                self.outdir,
                f"{pascal_label}-create_table.sql",
            )

            # check if file already exists
            if os.path.exists(table_create_command_path):
                logger.warning(
                    f"File {table_create_command_path} already exists. Overwriting.",
                )

            # concatenate key:value in props
            columns = ["_ID VARCHAR"]
            for col_name, col_type in props.items():
                col_type = self._get_data_type(col_type)
                col_name = self._adjust_pascal_to_psql(col_name)
                columns.append(f"{col_name} {col_type}")
            columns.append("_LABEL VARCHAR[]")

            with open(table_create_command_path, "w", encoding="utf-8") as f:
                command = ""
                if self.wipe:
                    command += f"DROP TABLE IF EXISTS {pascal_label};\n"

                # table creation requires comma separation
                command += f"CREATE TABLE {pascal_label}({','.join(columns)});\n"
                f.write(command)

                for parts_path in parts_paths:
                    # if import_call_file_prefix is set, replace actual path
                    # with prefix
                    if self.import_call_file_prefix != self.outdir:
                        parts_path = parts_path.replace(
                            self.outdir,
                            self.import_call_file_prefix,
                        )

                    self._copy_from_csv_commands.add(
                        f"\\copy {pascal_label} FROM '{parts_path}' DELIMITER E'{self.delim}' CSV;",
                    )

            # add file path to import statement
            # if import_call_file_prefix is set, replace actual path
            # with prefix
            if self.import_call_file_prefix != self.outdir:
                table_create_command_path = table_create_command_path.replace(
                    self.outdir,
                    self.import_call_file_prefix,
                )

            self.import_call_nodes.add(table_create_command_path)

        return True

    def _write_edge_headers(self):
        """Writes single CSV file for a graph entity that is represented
        as an edge as per the definition in the `schema_config.yaml`,
        containing only the header for this type of edge.

        Returns
        -------
            bool: The return value. True for success, False otherwise.

        """
        # load headers from data parse
        if not self.edge_property_dict:
            logger.error(
                "Header information not found. Was the data parsed first?",
            )
            return False

        for label, props in self.edge_property_dict.items():
            # translate label to PascalCase
            pascal_label = self.translator.name_sentence_to_pascal(label)

            parts_paths = os.path.join(self.outdir, f"{pascal_label}-part*.csv")
            parts_paths = glob.glob(parts_paths)
            parts_paths.sort()

            # adjust label for import to psql
            pascal_label = self._adjust_pascal_to_psql(pascal_label)
            table_create_command_path = os.path.join(
                self.outdir,
                f"{pascal_label}-create_table.sql",
            )

            # check for file exists
            if os.path.exists(table_create_command_path):
                logger.warning(
                    f"File {table_create_command_path} already exists. Overwriting.",
                )

            # concatenate key:value in props
            columns = []
            for col_name, col_type in props.items():
                col_type = self._get_data_type(col_type)
                col_name = self._adjust_pascal_to_psql(col_name)
                if col_name == "_ID":
                    # should ideally never happen
                    raise ValueError(
                        "Column name '_ID' is reserved for internal use, "
                        "denoting the relationship ID. Please choose a "
                        "different name for your column.",
                    )

                columns.append(f"{col_name} {col_type}")

            # create list of lists and flatten
            # removes need for empty check of property list
            out_list = [
                "_START_ID VARCHAR",
                "_ID VARCHAR",
                *columns,
                "_END_ID VARCHAR",
                "_TYPE VARCHAR",
            ]

            with open(table_create_command_path, "w", encoding="utf-8") as f:
                command = ""
                if self.wipe:
                    command += f"DROP TABLE IF EXISTS {pascal_label};\n"

                # table creation requires comma separation
                command += f"CREATE TABLE {pascal_label}({','.join(out_list)});\n"
                f.write(command)

                for parts_path in parts_paths:
                    # if import_call_file_prefix is set, replace actual path
                    # with prefix
                    if self.import_call_file_prefix != self.outdir:
                        parts_path = parts_path.replace(
                            self.outdir,
                            self.import_call_file_prefix,
                        )

                    self._copy_from_csv_commands.add(
                        f"\\copy {pascal_label} FROM '{parts_path}' DELIMITER E'{self.delim}' CSV;",
                    )

            # add file path to import statement
            # if import_call_file_prefix is set, replace actual path
            # with prefix
            if self.import_call_file_prefix != self.outdir:
                table_create_command_path = table_create_command_path.replace(
                    self.outdir,
                    self.import_call_file_prefix,
                )

            self.import_call_edges.add(table_create_command_path)

        return True

    def _construct_import_call(self) -> str:
        """Function to construct the import call detailing folder and
        individual node and edge headers and data files, as well as
        delimiters and database name. Built after all data has been
        processed to ensure that nodes are called before any edges.

        Returns
        -------
            str: a bash command for postgresql import

        """
        import_call = ""

        # create tables
        # At this point, csv files of nodes and edges do not require differentiation
        for import_file_path in [
            *self.import_call_nodes,
            *self.import_call_edges,
        ]:
            import_call += f'echo "Setup {import_file_path}..."\n'
            if {self.db_password}:
                # set password variable inline
                import_call += f"PGPASSWORD={self.db_password} "
            import_call += f"{self.import_call_bin_prefix}psql -f {import_file_path}"
            import_call += f" --dbname {self.db_name}"
            import_call += f" --host {self.db_host}"
            import_call += f" --port {self.db_port}"
            import_call += f" --user {self.db_user}"
            import_call += '\necho "Done!"\n'
            import_call += "\n"

        # copy data to tables
        for command in self._copy_from_csv_commands:
            table_part = command.split(" ")[3]
            import_call += f'echo "Importing {table_part}..."\n'
            if {self.db_password}:
                # set password variable inline
                import_call += f"PGPASSWORD={self.db_password} "
            import_call += f'{self.import_call_bin_prefix}psql -c "{command}"'
            import_call += f" --dbname {self.db_name}"
            import_call += f" --host {self.db_host}"
            import_call += f" --port {self.db_port}"
            import_call += f" --user {self.db_user}"
            import_call += '\necho "Done!"\n'
            import_call += "\n"

        return import_call

SQLite Batch Writer

Bases: _PostgreSQLBatchWriter

Class for writing node and edge representations to a SQLite database. It uses the _PostgreSQLBatchWriter class under the hood, which already implements the logic to write the nodes/edges to a relational DBMS. Only the import bash script differs between PostgreSQL and SQLite and is therefore implemented in this class.

  • _construct_import_call
Source code in biocypher/output/write/relational/_sqlite.py 
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class _SQLiteBatchWriter(_PostgreSQLBatchWriter):
    """
    Class for writing node and edge representations to a SQLite database.
    It uses the _PostgreSQLBatchWriter class under the hood, which already
    implements the logic to write the nodes/edges to a relational DBMS.
    Only the import bash script differs between PostgreSQL and SQLite
    and is therefore implemented in this class.

    - _construct_import_call
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def _construct_import_call(self) -> str:
        """
        Function to construct the import call detailing folder and
        individual node and edge headers and data files, as well as
        delimiters and database name. Built after all data has been
        processed to ensure that nodes are called before any edges.

        Returns:
            str: a bash command for sqlite import
        """
        import_call = ""

        # create tables
        # At this point, csv files of nodes and edges do not require differentiation
        for import_file_path in [
            *self.import_call_nodes,
            *self.import_call_edges,
        ]:
            import_call += f'echo "Setup {import_file_path}..."\n'
            import_call += f"{self.import_call_bin_prefix}sqlite3 {self.db_name} < {import_file_path}"
            import_call += '\necho "Done!"\n'
            import_call += "\n"

        for command in self._copy_from_csv_commands:
            table_name = command.split(" ")[1]
            table_part = command.split(" ")[3].replace("'", "")
            import_call += f'echo "Importing {table_part}..."\n'
            separator = self.delim
            import_part = f".import {table_part} {table_name}"
            import_call += (
                f"{self.import_call_bin_prefix}sqlite3 -separator $'{separator}' {self.db_name} \"{import_part}\""
            )
            import_call += '\necho "Done!"\n'
            import_call += "\n"

        return import_call

Pandas CSV Writer

Bases: _Writer

Class for writing node and edge representations to CSV files.

Source code in biocypher/output/write/relational/_csv.py 
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class _PandasCSVWriter(_Writer):
    """
    Class for writing node and edge representations to CSV files.
    """

    def __init__(self, *args, write_to_file: bool = True, **kwargs):
        kwargs["write_to_file"] = write_to_file
        super().__init__(*args, **kwargs)
        self.in_memory_dfs = {}
        self.stored_dfs = {}
        self.pandas_in_memory = PandasKG(
            deduplicator=self.deduplicator,
        )
        self.delimiter = kwargs.get("delimiter")
        if not self.delimiter:
            self.delimiter = ","
        self.write_to_file = write_to_file

    def _construct_import_call(self) -> str:
        """Function to construct the Python code to load all node and edge csv files again into Pandas dfs.

        Returns:
            str: Python code to load the csv files into Pandas dfs.
        """
        import_call = "import pandas as pd\n\n"
        for df_name in self.stored_dfs.keys():
            import_call += f"{df_name} = pd.read_csv('./{df_name}.csv', header=0, index_col=0)\n"
        return import_call

    def _get_import_script_name(self) -> str:
        """Function to return the name of the import script."""
        return "import_pandas_csv.py"

    def _write_node_data(self, nodes) -> bool:
        passed = self._write_entities_to_file(nodes)
        return passed

    def _write_edge_data(self, edges) -> bool:
        passed = self._write_entities_to_file(edges)
        return passed

    def _write_entities_to_file(self, entities: iter) -> bool:
        """Function to write the entities to a CSV file.

        Args:
            entities (iterable): An iterable of BioCypherNode / BioCypherEdge / BioCypherRelAsNode objects.
        """
        entities = peekable(entities)
        entity_list = self.pandas_in_memory._separate_entity_types(entities)
        for entity_type, entities in entity_list.items():
            self.in_memory_dfs[entity_type] = self.pandas_in_memory._add_entity_df(entity_type, entities)
        for entity_type in self.in_memory_dfs.keys():
            entity_df = self.in_memory_dfs[entity_type]
            if " " in entity_type or "." in entity_type:
                entity_type = entity_type.replace(" ", "_").replace(".", "_")
            if self.write_to_file:
                logger.info(f"Writing {entity_df.shape[0]} entries to {entity_type}.csv.")
                entity_df.to_csv(
                    f"{self.output_directory}/{entity_type}.csv",
                    sep=self.delimiter,
                )
            self.stored_dfs[entity_type] = entity_df
        self.in_memory_dfs = {}
        return True